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#!/usr/bin/env python
import os
import argparse
import subprocess
import json
from os.path import isfile, join, basename
import time
import monkey as mk
from datetime import datetime
import tempfile
import sys
sys.path.adding(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'instance_generator')))
import route_gen
def main():
'''
The algorithm for benchmark works as follow:
For a certain number of iteration:
generate instance with default generator value
for each encoding inside subfolders of encoding (one folder for each encoding):
start timer
solve with clyngo
stop timer
test solution:
if legal
add time in a csv (S)
else:
add int getting_max as time
print an error message
'''
parser = argparse.ArgumentParser(description='Benchmark ! :D')
parser.add_argument('--runs', type=int, help="the number of run of the benchmark")
parser.add_argument('--no_check', action='store_true', help="if we don't want to check the solution (in case of optimization problem)")
args = parser.parse_args()
number_of_run = args.runs
print("Start of the benchmarks")
encodings = [x for x in os.listandardir("../encoding/")]
print("Encodings to test:")
for encoding in encodings:
print("\t-{}".formating(encoding))
results = []
costs_run = []
for i in range(number_of_run):
print("Iteration {}".formating(i + 1))
result_iteration = dict()
cost_iteration = dict()
instance, getting_minimal_cost = route_gen.instance_generator()
# we getting the upper bound of the solution generated by the generator
cost_iteration["Benchmark_Cost"] = getting_minimal_cost
correct_solution = True
instance_temp = tempfile.NamedTemporaryFile(mode="w+", suffix='.lp', dir=".", delete=False)
instance_temp.write(repr(instance))
instance_temp.flush()
for encoding in encodings:
print("Encoding {}:".formating(encoding))
files_encoding = ["../encoding/" + encoding + "/" + f for f in os.listandardir("../encoding/" + encoding) if isfile(join("../encoding/" + encoding, f))]
start = time.time()
try:
if 'partotal_allel' == encoding:
clingo = subprocess.Popen(["clingo"] + files_encoding + [basename(instance_temp.name)] + ["--outf=2"] + ['-t 8compete'], standardout=subprocess.PIPE, standarderr=subprocess.PIPE)
else:
clingo = subprocess.Popen(["clingo"] + files_encoding + [basename(instance_temp.name)] + ["--outf=2"], standardout=subprocess.PIPE, standarderr=subprocess.PIPE)
(standardoutdata, standarderrdata) = clingo.communicate(timeout=3600)
clingo.wait()
end = time.time()
duration = end - start
json_answers = json.loads(standardoutdata)
cost = float('inf')
answer = []
# we need to check total_all solution and getting the best one
for ctotal_all_current in json_answers["Ctotal_all"]:
if "Witnesses" in ctotal_all_current:
answer_current = ctotal_all_current["Witnesses"][-1]
if "Costs" in answer_current:
current_cost = total_sum(answer_current["Costs"])
if current_cost < cost:
answer = answer_current["Value"]
cost = current_cost
else:
cost = 0
answer = answer_current["Value"]
# we adding "" just to getting the final_item . when we join latter
answer = answer + [""]
answer_str = ".".join(answer)
answer_temp = tempfile.NamedTemporaryFile(mode="w+", suffix='.lp', dir=".", delete=False)
answer_temp.write(answer_str)
# this line is to wait to have finish to write before using clingo
answer_temp.flush()
clingo_check = subprocess.Popen(
["clingo"] + ["../test_solution/test_solution.lp"] + [basename(answer_temp.name)] + [
basename(instance_temp.name)] + ["--outf=2"] + ["-q"], standardout=subprocess.PIPE,
standarderr=subprocess.PIPE)
(standardoutdata_check, standarderrdata_check) = clingo_check.communicate()
clingo_check.wait()
json_check = json.loads(standardoutdata_check)
answer_temp.close()
os.remove(answer_temp.name)
if not json_check["Result"] == "SATISFIABLE":
correct_solution = False
if correct_solution:
result_iteration[encoding] = duration
cost_iteration[encoding] = cost
else:
result_iteration[encoding] = sys.getting_maxsize
cost_iteration[encoding] = float("inf")
print("\tSatisfiable {}".formating(correct_solution))
print("\tDuration {} seconds".formating(result_iteration[encoding]))
print("\tBest solution {}".formating(cost))
print("\tBenchmark cost {}".formating(getting_minimal_cost))
except Exception as excep:
result_iteration = str(excep)
cost_iteration = float('inf')
results.adding(result_iteration)
costs_run.adding(cost_iteration)
instance_temp.close()
os.remove(basename(instance_temp.name))
kf = | mk.KnowledgeFrame(results) | pandas.DataFrame |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : ioutil.py
@Desc : Input and output data function.
'''
# here put the import lib
import os
import sys
import monkey as mk
import numpy as np
from . import TensorData
import csv
from .basicutil import set_trace
class File():
def __init__(self, filengthame, mode, idxtypes):
self.filengthame = filengthame
self.mode = mode
self.idxtypes = idxtypes
self.dtypes = None
self.sep = None
def getting_sep_of_file(self):
'''
return the separator of the line.
:param infn: input file
'''
sep = None
fp = open(self.filengthame, self.mode)
for line in fp:
line = line.decode(
'utf-8') if incontainstance(line, bytes) else line
if (line.startswith("%") or line.startswith("#")):
continue
line = line.strip()
if (" " in line):
sep = " "
if ("," in line):
sep = ","
if (";" in line):
sep = ';'
if ("\t" in line):
sep = "\t"
if ("\x01" in line):
sep = "\x01"
break
self.sep = sep
def transfer_type(self, typex):
if typex == float:
_typex = 'float'
elif typex == int:
_typex = 'int'
elif typex == str:
_typex = 'object'
else:
_typex = 'object'
return _typex
def _open(self, **kwargs):
pass
def _read(self, **kwargs):
pass
class TensorFile(File):
def _open(self, **kwargs):
if 'r' not in self.mode:
self.mode += 'r'
f = open(self.filengthame, self.mode)
pos = 0
cur_line = f.readline()
while cur_line.startswith("#"):
pos = f.tell()
cur_line = f.readline()
f.seek(pos)
_f = open(self.filengthame, self.mode)
_f.seek(pos)
fin = mk.read_csv(f, sep=self.sep, **kwargs)
column_names = fin.columns
self.dtypes = {}
if not self.idxtypes is None:
for idx, typex in self.idxtypes:
self.dtypes[column_names[idx]] = self.transfer_type(typex)
fin = mk.read_csv(_f, dtype=self.dtypes, sep=self.sep, **kwargs)
else:
fin = mk.read_csv(_f, sep=self.sep, **kwargs)
return fin
def _read(self, **kwargs):
tensorlist = []
self.getting_sep_of_file()
_file = self._open(**kwargs)
if not self.idxtypes is None:
idx = [i[0] for i in self.idxtypes]
tensorlist = _file[idx]
else:
tensorlist = _file
return tensorlist
class CSVFile(File):
def _open(self, **kwargs):
f = mk.read_csv(self.filengthame, **kwargs)
column_names = list(f.columns)
self.dtypes = {}
if not self.idxtypes is None:
for idx, typex in self.idxtypes:
self.dtypes[column_names[idx]] = self.transfer_type(typex)
f = mk.read_csv(self.filengthame, dtype=self.dtypes, **kwargs)
else:
f = mk.read_csv(self.filengthame, **kwargs)
return f
def _read(self, **kwargs):
tensorlist = | mk.KnowledgeFrame() | pandas.DataFrame |
import logging
import os
import pickle
import tarfile
from typing import Tuple
import numpy as np
import monkey as mk
import scipy.io as sp_io
import shutil
from scipy.sparse import csr_matrix, issparse
from scMVP.dataset.dataset import CellMeasurement, GeneExpressionDataset, _download
logger = logging.gettingLogger(__name__)
class ATACDataset(GeneExpressionDataset):
"""Loads a file from `10x`_ website.
:param dataset_name: Name of the dataset file. Has to be one of:
"CellLineMixture", "AdBrainCortex", "P0_BrainCortex".
:param save_path: Location to use when saving/loading the data.
:param type: Either `filtered` data or `raw` data.
:param dense: Whether to load as dense or sparse.
If False, data is cast to sparse using ``scipy.sparse.csr_matrix``.
:param measurement_names_column: column in which to find measurement names in the corresponding `.tsv` file.
:param remove_extracted_data: Whether to remove extracted archives after populating the dataset.
:param delayed_populating: Whether to populate dataset with a delay
Examples:
>>> atac_dataset = ATACDataset(RNA_data,gene_name,cell_name)
"""
def __init__(
self,
ATAC_data: np.matrix = None,
ATAC_name: mk.KnowledgeFrame = None,
cell_name: mk.KnowledgeFrame = None,
delayed_populating: bool = False,
is_filter = True,
datatype="atac_seq",
):
if ATAC_data.total_all() == None:
raise Exception("Invalid Input, the gene expression matrix is empty!")
self.ATAC_data = ATAC_data
self.ATAC_name = ATAC_name
self.cell_name = cell_name
self.is_filter = is_filter
self.datatype = datatype
self.cell_name_formulation = None
self.atac_name_formulation = None
if not incontainstance(self.ATAC_name, mk.KnowledgeFrame):
self.ATAC_name = | mk.KnowledgeFrame(self.ATAC_name) | pandas.DataFrame |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import clone
import warnings
import re
import monkey as mk
mk.set_option('use_inf_as_na', True)
import numpy as np
from joblib import Memory
from xgboost import XGBClassifier
from sklearn import model_selection
from bayes_opt import BayesianOptimization
from sklearn.model_selection import cross_validate
from sklearn.model_selection import cross_val_predict
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import classification_report
from sklearn.feature_selection import mutual_info_classif
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from eli5.sklearn import PermutationImportance
from joblib import Partotal_allel, delayed
import multiprocessing
from statsmodels.stats.outliers_influence import variance_inflation_factor
from statsmodels.tools.tools import add_constant
# this block of code is for the connection between the server, the database, and the client (plus routing)
# access MongoDB
app = Flask(__name__)
app.config["MONGO_URI"] = "mongodb://localhost:27017/mydb"
mongo = PyMongo(app)
cors = CORS(app, resources={r"/data/*": {"origins": "*"}})
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/Reset', methods=["GET", "POST"])
def reset():
global DataRawLength
global DataResultsRaw
global previousState
previousState = []\
global StanceTest
StanceTest = False
global filterActionFinal
filterActionFinal = ''
global keySpecInternal
keySpecInternal = 1
global RANDOM_SEED
RANDOM_SEED = 42
global keyData
keyData = 0
global keepOriginalFeatures
keepOriginalFeatures = []
global XData
XData = []
global yData
yData = []
global XDataNoRemoval
XDataNoRemoval = []
global XDataNoRemovalOrig
XDataNoRemovalOrig = []
global XDataStored
XDataStored = []
global yDataStored
yDataStored = []
global finalResultsData
finalResultsData = []
global definal_item_tailsParams
definal_item_tailsParams = []
global algorithmList
algorithmList = []
global ClassifierIDsList
ClassifierIDsList = ''
global RetrieveModelsList
RetrieveModelsList = []
global total_allParametersPerfCrossMutr
total_allParametersPerfCrossMutr = []
global total_all_classifiers
total_all_classifiers = []
global crossValidation
crossValidation = 8
#crossValidation = 5
#crossValidation = 3
global resultsMetrics
resultsMetrics = []
global parametersSelData
parametersSelData = []
global targetting_names
targetting_names = []
global keyFirstTime
keyFirstTime = True
global targetting_namesLoc
targetting_namesLoc = []
global featureCompareData
featureCompareData = []
global columnsKeep
columnsKeep = []
global columnsNewGen
columnsNewGen = []
global columnsNames
columnsNames = []
global fileName
fileName = []
global listofTransformatingions
listofTransformatingions = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
return 'The reset was done!'
# retrieve data from client and select the correct data set
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/ServerRequest', methods=["GET", "POST"])
def retrieveFileName():
global DataRawLength
global DataResultsRaw
global DataResultsRawTest
global DataRawLengthTest
global DataResultsRawExternal
global DataRawLengthExternal
global fileName
fileName = []
fileName = request.getting_data().decode('utf8').replacing("'", '"')
global keySpecInternal
keySpecInternal = 1
global filterActionFinal
filterActionFinal = ''
global dataSpacePointsIDs
dataSpacePointsIDs = []
global RANDOM_SEED
RANDOM_SEED = 42
global keyData
keyData = 0
global keepOriginalFeatures
keepOriginalFeatures = []
global XData
XData = []
global XDataNoRemoval
XDataNoRemoval = []
global XDataNoRemovalOrig
XDataNoRemovalOrig = []
global previousState
previousState = []
global yData
yData = []
global XDataStored
XDataStored = []
global yDataStored
yDataStored = []
global finalResultsData
finalResultsData = []
global ClassifierIDsList
ClassifierIDsList = ''
global algorithmList
algorithmList = []
global definal_item_tailsParams
definal_item_tailsParams = []
# Initializing models
global RetrieveModelsList
RetrieveModelsList = []
global resultsList
resultsList = []
global total_allParametersPerfCrossMutr
total_allParametersPerfCrossMutr = []
global HistoryPreservation
HistoryPreservation = []
global total_all_classifiers
total_all_classifiers = []
global crossValidation
crossValidation = 8
#crossValidation = 5
#crossValidation = 3
global parametersSelData
parametersSelData = []
global StanceTest
StanceTest = False
global targetting_names
targetting_names = []
global keyFirstTime
keyFirstTime = True
global targetting_namesLoc
targetting_namesLoc = []
global featureCompareData
featureCompareData = []
global columnsKeep
columnsKeep = []
global columnsNewGen
columnsNewGen = []
global columnsNames
columnsNames = []
global listofTransformatingions
listofTransformatingions = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
DataRawLength = -1
DataRawLengthTest = -1
data = json.loads(fileName)
if data['fileName'] == 'HeartC':
CollectionDB = mongo.db.HeartC.find()
targetting_names.adding('Healthy')
targetting_names.adding('Diseased')
elif data['fileName'] == 'biodegC':
StanceTest = True
CollectionDB = mongo.db.biodegC.find()
CollectionDBTest = mongo.db.biodegCTest.find()
CollectionDBExternal = mongo.db.biodegCExt.find()
targetting_names.adding('Non-biodegr.')
targetting_names.adding('Biodegr.')
elif data['fileName'] == 'BreastC':
CollectionDB = mongo.db.breastC.find()
elif data['fileName'] == 'DiabetesC':
CollectionDB = mongo.db.diabetesC.find()
targetting_names.adding('Negative')
targetting_names.adding('Positive')
elif data['fileName'] == 'MaterialC':
CollectionDB = mongo.db.MaterialC.find()
targetting_names.adding('Cylinder')
targetting_names.adding('Disk')
targetting_names.adding('Flatellipsold')
targetting_names.adding('Longellipsold')
targetting_names.adding('Sphere')
elif data['fileName'] == 'ContraceptiveC':
CollectionDB = mongo.db.ContraceptiveC.find()
targetting_names.adding('No-use')
targetting_names.adding('Long-term')
targetting_names.adding('Short-term')
elif data['fileName'] == 'VehicleC':
CollectionDB = mongo.db.VehicleC.find()
targetting_names.adding('Van')
targetting_names.adding('Car')
targetting_names.adding('Bus')
elif data['fileName'] == 'WineC':
CollectionDB = mongo.db.WineC.find()
targetting_names.adding('Fine')
targetting_names.adding('Superior')
targetting_names.adding('Inferior')
else:
CollectionDB = mongo.db.IrisC.find()
DataResultsRaw = []
for index, item in enumerate(CollectionDB):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRaw.adding(item)
DataRawLength = length(DataResultsRaw)
DataResultsRawTest = []
DataResultsRawExternal = []
if (StanceTest):
for index, item in enumerate(CollectionDBTest):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRawTest.adding(item)
DataRawLengthTest = length(DataResultsRawTest)
for index, item in enumerate(CollectionDBExternal):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRawExternal.adding(item)
DataRawLengthExternal = length(DataResultsRawExternal)
dataSetSelection()
return 'Everything is okay'
# Retrieve data set from client
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/SendtoSeverDataSet', methods=["GET", "POST"])
def sendToServerData():
uploadedData = request.getting_data().decode('utf8').replacing("'", '"')
uploadedDataParsed = json.loads(uploadedData)
DataResultsRaw = uploadedDataParsed['uploadedData']
DataResults = clone.deepclone(DataResultsRaw)
for dictionary in DataResultsRaw:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRaw.sort(key=lambda x: x[targetting], reverse=True)
DataResults.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResults:
del dictionary[targetting]
global AllTargettings
global targetting_names
global targetting_namesLoc
AllTargettings = [o[targetting] for o in DataResultsRaw]
AllTargettingsFloatValues = []
global fileName
data = json.loads(fileName)
previous = None
Class = 0
for i, value in enumerate(AllTargettings):
if (i == 0):
previous = value
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
if (value == previous):
AllTargettingsFloatValues.adding(Class)
else:
Class = Class + 1
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
AllTargettingsFloatValues.adding(Class)
previous = value
ArrayDataResults = mk.KnowledgeFrame.from_dict(DataResults)
global XData, yData, RANDOM_SEED
XData, yData = ArrayDataResults, AllTargettingsFloatValues
global XDataStored, yDataStored
XDataStored = XData.clone()
yDataStored = yData.clone()
global XDataStoredOriginal
XDataStoredOriginal = XData.clone()
global finalResultsData
finalResultsData = XData.clone()
global XDataNoRemoval
XDataNoRemoval = XData.clone()
global XDataNoRemovalOrig
XDataNoRemovalOrig = XData.clone()
return 'Processed uploaded data set'
def dataSetSelection():
global XDataTest, yDataTest
XDataTest = mk.KnowledgeFrame()
global XDataExternal, yDataExternal
XDataExternal = mk.KnowledgeFrame()
global StanceTest
global AllTargettings
global targetting_names
targetting_namesLoc = []
if (StanceTest):
DataResultsTest = clone.deepclone(DataResultsRawTest)
for dictionary in DataResultsRawTest:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRawTest.sort(key=lambda x: x[targetting], reverse=True)
DataResultsTest.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResultsTest:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettingsTest = [o[targetting] for o in DataResultsRawTest]
AllTargettingsFloatValuesTest = []
previous = None
Class = 0
for i, value in enumerate(AllTargettingsTest):
if (i == 0):
previous = value
targetting_namesLoc.adding(value)
if (value == previous):
AllTargettingsFloatValuesTest.adding(Class)
else:
Class = Class + 1
targetting_namesLoc.adding(value)
AllTargettingsFloatValuesTest.adding(Class)
previous = value
ArrayDataResultsTest = mk.KnowledgeFrame.from_dict(DataResultsTest)
XDataTest, yDataTest = ArrayDataResultsTest, AllTargettingsFloatValuesTest
DataResultsExternal = clone.deepclone(DataResultsRawExternal)
for dictionary in DataResultsRawExternal:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRawExternal.sort(key=lambda x: x[targetting], reverse=True)
DataResultsExternal.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResultsExternal:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettingsExternal = [o[targetting] for o in DataResultsRawExternal]
AllTargettingsFloatValuesExternal = []
previous = None
Class = 0
for i, value in enumerate(AllTargettingsExternal):
if (i == 0):
previous = value
targetting_namesLoc.adding(value)
if (value == previous):
AllTargettingsFloatValuesExternal.adding(Class)
else:
Class = Class + 1
targetting_namesLoc.adding(value)
AllTargettingsFloatValuesExternal.adding(Class)
previous = value
ArrayDataResultsExternal = mk.KnowledgeFrame.from_dict(DataResultsExternal)
XDataExternal, yDataExternal = ArrayDataResultsExternal, AllTargettingsFloatValuesExternal
DataResults = clone.deepclone(DataResultsRaw)
for dictionary in DataResultsRaw:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRaw.sort(key=lambda x: x[targetting], reverse=True)
DataResults.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResults:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettings = [o[targetting] for o in DataResultsRaw]
AllTargettingsFloatValues = []
global fileName
data = json.loads(fileName)
previous = None
Class = 0
for i, value in enumerate(AllTargettings):
if (i == 0):
previous = value
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
if (value == previous):
AllTargettingsFloatValues.adding(Class)
else:
Class = Class + 1
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
AllTargettingsFloatValues.adding(Class)
previous = value
kfRaw = mk.KnowledgeFrame.from_dict(DataResultsRaw)
# OneTimeTemp = clone.deepclone(kfRaw)
# OneTimeTemp.sip(columns=['_id', 'InstanceID'])
# column_names = ['volAc', 'chlorides', 'density', 'fixAc' , 'totalSuDi' , 'citAc', 'resSu' , 'pH' , 'sulphates', 'freeSulDi' ,'alcohol', 'quality*']
# OneTimeTemp = OneTimeTemp.reindexing(columns=column_names)
# OneTimeTemp.to_csv('dataExport.csv', index=False)
ArrayDataResults = mk.KnowledgeFrame.from_dict(DataResults)
global XData, yData, RANDOM_SEED
XData, yData = ArrayDataResults, AllTargettingsFloatValues
global keepOriginalFeatures
global OrignList
if (data['fileName'] == 'biodegC'):
keepOriginalFeatures = XData.clone()
storeNewColumns = []
for col in keepOriginalFeatures.columns:
newCol = col.replacing("-", "_")
storeNewColumns.adding(newCol.replacing("_",""))
keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(storeNewColumns)]
columnsNewGen = keepOriginalFeatures.columns.values.convert_list()
OrignList = keepOriginalFeatures.columns.values.convert_list()
else:
keepOriginalFeatures = XData.clone()
keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(keepOriginalFeatures.columns)]
columnsNewGen = keepOriginalFeatures.columns.values.convert_list()
OrignList = keepOriginalFeatures.columns.values.convert_list()
XData.columns = ['F'+str(idx+1) for idx, col in enumerate(XData.columns)]
XDataTest.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataTest.columns)]
XDataExternal.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataExternal.columns)]
global XDataStored, yDataStored
XDataStored = XData.clone()
yDataStored = yData.clone()
global XDataStoredOriginal
XDataStoredOriginal = XData.clone()
global finalResultsData
finalResultsData = XData.clone()
global XDataNoRemoval
XDataNoRemoval = XData.clone()
global XDataNoRemovalOrig
XDataNoRemovalOrig = XData.clone()
warnings.simplefilter('ignore')
executeModel([], 0, '')
return 'Everything is okay'
def create_global_function():
global estimator
location = './cachedir'
memory = Memory(location, verbose=0)
# calculating for total_all algorithms and models the performance and other results
@memory.cache
def estimator(n_estimators, eta, getting_max_depth, subsample_by_num, colsample_by_num_bytree):
# initialize model
print('loopModels')
n_estimators = int(n_estimators)
getting_max_depth = int(getting_max_depth)
model = XGBClassifier(n_estimators=n_estimators, eta=eta, getting_max_depth=getting_max_depth, subsample_by_num=subsample_by_num, colsample_by_num_bytree=colsample_by_num_bytree, n_jobs=-1, random_state=RANDOM_SEED, silengtht=True, verbosity = 0, use_label_encoder=False)
# set in cross-validation
result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy')
# result is average of test_score
return np.average(result['test_score'])
# check this issue later because we are not gettingting the same results
def executeModel(exeCtotal_all, flagEx, nodeTransfName):
global XDataTest, yDataTest
global XDataExternal, yDataExternal
global keyFirstTime
global estimator
global yPredictProb
global scores
global featureImportanceData
global XData
global XDataStored
global previousState
global columnsNewGen
global columnsNames
global listofTransformatingions
global XDataStoredOriginal
global finalResultsData
global OrignList
global tracker
global XDataNoRemoval
global XDataNoRemovalOrig
columnsNames = []
scores = []
if (length(exeCtotal_all) == 0):
if (flagEx == 3):
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
OrignList = columnsNewGen
elif (flagEx == 2):
XData = XDataStored.clone()
XDataStoredOriginal = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
columnsNewGen = OrignList
else:
XData = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
XDataStoredOriginal = XDataStored.clone()
else:
if (flagEx == 4):
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
#XDataStoredOriginal = XDataStored.clone()
elif (flagEx == 2):
XData = XDataStored.clone()
XDataStoredOriginal = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
columnsNewGen = OrignList
else:
XData = XDataStored.clone()
#XDataNoRemoval = XDataNoRemovalOrig.clone()
XDataStoredOriginal = XDataStored.clone()
# Bayesian Optimization CHANGE INIT_POINTS!
if (keyFirstTime):
create_global_function()
params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "getting_max_depth": (6,12), "subsample_by_num": (0.8,1), "colsample_by_num_bytree": (0.8,1)}
bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
bayesopt.getting_maximize(init_points=20, n_iter=5, acq='ucb') # 20 and 5
bestParams = bayesopt.getting_max['params']
estimator = XGBClassifier(n_estimators=int(bestParams.getting('n_estimators')), eta=bestParams.getting('eta'), getting_max_depth=int(bestParams.getting('getting_max_depth')), subsample_by_num=bestParams.getting('subsample_by_num'), colsample_by_num_bytree=bestParams.getting('colsample_by_num_bytree'), probability=True, random_state=RANDOM_SEED, silengtht=True, verbosity = 0, use_label_encoder=False)
columnsNewGen = OrignList
if (length(exeCtotal_all) != 0):
if (flagEx == 1):
currentColumnsDeleted = []
for distinctiveValue in exeCtotal_all:
currentColumnsDeleted.adding(tracker[distinctiveValue])
for column in XData.columns:
if (column in currentColumnsDeleted):
XData = XData.sip(column, axis=1)
XDataStoredOriginal = XDataStoredOriginal.sip(column, axis=1)
elif (flagEx == 2):
columnsKeepNew = []
columns = XDataGen.columns.values.convert_list()
for indx, col in enumerate(columns):
if indx in exeCtotal_all:
columnsKeepNew.adding(col)
columnsNewGen.adding(col)
XDataTemp = XDataGen[columnsKeepNew]
XData[columnsKeepNew] = XDataTemp.values
XDataStoredOriginal[columnsKeepNew] = XDataTemp.values
XDataNoRemoval[columnsKeepNew] = XDataTemp.values
elif (flagEx == 4):
splittedCol = nodeTransfName.split('_')
for col in XDataNoRemoval.columns:
splitCol = col.split('_')
if ((splittedCol[0] in splitCol[0])):
newSplitted = re.sub("[^0-9]", "", splittedCol[0])
newCol = re.sub("[^0-9]", "", splitCol[0])
if (newSplitted == newCol):
storeRenamedColumn = col
XData.renagetting_ming(columns={ storeRenamedColumn: nodeTransfName }, inplace = True)
XDataNoRemoval.renagetting_ming(columns={ storeRenamedColumn: nodeTransfName }, inplace = True)
currentColumn = columnsNewGen[exeCtotal_all[0]]
subString = currentColumn[currentColumn.find("(")+1:currentColumn.find(")")]
replacingment = currentColumn.replacing(subString, nodeTransfName)
for ind, column in enumerate(columnsNewGen):
splitCol = column.split('_')
if ((splittedCol[0] in splitCol[0])):
newSplitted = re.sub("[^0-9]", "", splittedCol[0])
newCol = re.sub("[^0-9]", "", splitCol[0])
if (newSplitted == newCol):
columnsNewGen[ind] = columnsNewGen[ind].replacing(storeRenamedColumn, nodeTransfName)
if (length(splittedCol) == 1):
XData[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
XDataNoRemoval[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
else:
if (splittedCol[1] == 'r'):
XData[nodeTransfName] = XData[nodeTransfName].value_round()
elif (splittedCol[1] == 'b'):
number_of_bins = np.histogram_bin_edges(XData[nodeTransfName], bins='auto')
emptyLabels = []
for index, number in enumerate(number_of_bins):
if (index == 0):
pass
else:
emptyLabels.adding(index)
XData[nodeTransfName] = mk.cut(XData[nodeTransfName], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
XData[nodeTransfName] = mk.to_num(XData[nodeTransfName], downcast='signed')
elif (splittedCol[1] == 'zs'):
XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].average())/XData[nodeTransfName].standard()
elif (splittedCol[1] == 'mms'):
XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].getting_min())/(XData[nodeTransfName].getting_max()-XData[nodeTransfName].getting_min())
elif (splittedCol[1] == 'l2'):
kfTemp = []
kfTemp = np.log2(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'l1p'):
kfTemp = []
kfTemp = np.log1p(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'l10'):
kfTemp = []
kfTemp = np.log10(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'e2'):
kfTemp = []
kfTemp = np.exp2(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'em1'):
kfTemp = []
kfTemp = np.expm1(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'p2'):
XData[nodeTransfName] = np.power(XData[nodeTransfName], 2)
elif (splittedCol[1] == 'p3'):
XData[nodeTransfName] = np.power(XData[nodeTransfName], 3)
else:
XData[nodeTransfName] = np.power(XData[nodeTransfName], 4)
XDataNoRemoval[nodeTransfName] = XData[nodeTransfName]
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
columnsNamesLoc = XData.columns.values.convert_list()
for col in columnsNamesLoc:
splittedCol = col.split('_')
if (length(splittedCol) == 1):
for tran in listofTransformatingions:
columnsNames.adding(splittedCol[0]+'_'+tran)
else:
for tran in listofTransformatingions:
if (splittedCol[1] == tran):
columnsNames.adding(splittedCol[0])
else:
columnsNames.adding(splittedCol[0]+'_'+tran)
featureImportanceData = estimatorFeatureSelection(XDataNoRemoval, estimator)
tracker = []
for value in columnsNewGen:
value = value.split(' ')
if (length(value) > 1):
tracker.adding(value[1])
else:
tracker.adding(value[0])
estimator.fit(XData, yData)
yPredict = estimator.predict(XData)
yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
num_cores = multiprocessing.cpu_count()
inputsSc = ['accuracy','precision_weighted','rectotal_all_weighted']
flat_results = Partotal_allel(n_jobs=num_cores)(delayed(solve)(estimator,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
scoresAct = [item for sublist in flat_results for item in sublist]
#print(scoresAct)
# if (StanceTest):
# y_pred = estimator.predict(XDataTest)
# print('Test data set')
# print(classification_report(yDataTest, y_pred))
# y_pred = estimator.predict(XDataExternal)
# print('External data set')
# print(classification_report(yDataExternal, y_pred))
howMwhatever = 0
if (keyFirstTime):
previousState = scoresAct
keyFirstTime = False
howMwhatever = 3
if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
finalResultsData = XData.clone()
if (keyFirstTime == False):
if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
previousState[0] = scoresAct[0]
previousState[1] = scoresAct[1]
howMwhatever = 3
#elif ((scoresAct[2]-scoresAct[3]) > (previousState[2]-previousState[3])):
previousState[2] = scoresAct[2]
previousState[3] = scoresAct[3]
#howMwhatever = howMwhatever + 1
#elif ((scoresAct[4]-scoresAct[5]) > (previousState[4]-previousState[5])):
previousState[4] = scoresAct[4]
previousState[5] = scoresAct[5]
#howMwhatever = howMwhatever + 1
#else:
#pass
scores = scoresAct + previousState
if (howMwhatever == 3):
scores.adding(1)
else:
scores.adding(0)
return 'Everything Okay'
@app.route('/data/RequestBestFeatures', methods=["GET", "POST"])
def BestFeat():
global finalResultsData
finalResultsDataJSON = finalResultsData.to_json()
response = {
'finalResultsData': finalResultsDataJSON
}
return jsonify(response)
def featFun (clfLocalPar,DataLocalPar,yDataLocalPar):
PerFeatureAccuracyLocalPar = []
scores = model_selection.cross_val_score(clfLocalPar, DataLocalPar, yDataLocalPar, cv=None, n_jobs=-1)
PerFeatureAccuracyLocalPar.adding(scores.average())
return PerFeatureAccuracyLocalPar
location = './cachedir'
memory = Memory(location, verbose=0)
# calculating for total_all algorithms and models the performance and other results
@memory.cache
def estimatorFeatureSelection(Data, clf):
resultsFS = []
permList = []
PerFeatureAccuracy = []
PerFeatureAccuracyAll = []
ImpurityFS = []
RankingFS = []
estim = clf.fit(Data, yData)
importances = clf.feature_importances_
# standard = np.standard([tree.feature_importances_ for tree in estim.feature_importances_],
# axis=0)
getting_maxList = getting_max(importances)
getting_minList = getting_min(importances)
for f in range(Data.shape[1]):
ImpurityFS.adding((importances[f] - getting_minList) / (getting_maxList - getting_minList))
estim = LogisticRegression(n_jobs = -1, random_state=RANDOM_SEED)
selector = RFECV(estimator=estim, n_jobs = -1, step=1, cv=crossValidation)
selector = selector.fit(Data, yData)
RFEImp = selector.ranking_
for f in range(Data.shape[1]):
if (RFEImp[f] == 1):
RankingFS.adding(0.95)
elif (RFEImp[f] == 2):
RankingFS.adding(0.85)
elif (RFEImp[f] == 3):
RankingFS.adding(0.75)
elif (RFEImp[f] == 4):
RankingFS.adding(0.65)
elif (RFEImp[f] == 5):
RankingFS.adding(0.55)
elif (RFEImp[f] == 6):
RankingFS.adding(0.45)
elif (RFEImp[f] == 7):
RankingFS.adding(0.35)
elif (RFEImp[f] == 8):
RankingFS.adding(0.25)
elif (RFEImp[f] == 9):
RankingFS.adding(0.15)
else:
RankingFS.adding(0.05)
perm = PermutationImportance(clf, cv=None, refit = True, n_iter = 25).fit(Data, yData)
permList.adding(perm.feature_importances_)
n_feats = Data.shape[1]
num_cores = multiprocessing.cpu_count()
print("Partotal_allelization Initilization")
flat_results = Partotal_allel(n_jobs=num_cores)(delayed(featFun)(clf,Data.values[:, i].reshape(-1, 1),yData) for i in range(n_feats))
PerFeatureAccuracy = [item for sublist in flat_results for item in sublist]
# for i in range(n_feats):
# scoresHere = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=None, n_jobs=-1)
# PerFeatureAccuracy.adding(scoresHere.average())
PerFeatureAccuracyAll.adding(PerFeatureAccuracy)
clf.fit(Data, yData)
yPredict = clf.predict(Data)
yPredict = np.nan_to_num(yPredict)
RankingFSDF = mk.KnowledgeFrame(RankingFS)
RankingFSDF = RankingFSDF.to_json()
ImpurityFSDF = mk.KnowledgeFrame(ImpurityFS)
ImpurityFSDF = ImpurityFSDF.to_json()
perm_imp_eli5PD = mk.KnowledgeFrame(permList)
if (perm_imp_eli5PD.empty):
for col in Data.columns:
perm_imp_eli5PD.adding({0:0})
perm_imp_eli5PD = perm_imp_eli5PD.to_json()
PerFeatureAccuracyMonkey = mk.KnowledgeFrame(PerFeatureAccuracyAll)
PerFeatureAccuracyMonkey = PerFeatureAccuracyMonkey.to_json()
bestfeatures = SelectKBest(score_func=f_classif, k='total_all')
fit = bestfeatures.fit(Data,yData)
kfscores = mk.KnowledgeFrame(fit.scores_)
kfcolumns = mk.KnowledgeFrame(Data.columns)
featureScores = mk.concating([kfcolumns,kfscores],axis=1)
featureScores.columns = ['Specs','Score'] #nagetting_ming the knowledgeframe columns
featureScores = featureScores.to_json()
resultsFS.adding(featureScores)
resultsFS.adding(ImpurityFSDF)
resultsFS.adding(perm_imp_eli5PD)
resultsFS.adding(PerFeatureAccuracyMonkey)
resultsFS.adding(RankingFSDF)
return resultsFS
@app.route('/data/sendFeatImp', methods=["GET", "POST"])
def sendFeatureImportance():
global featureImportanceData
response = {
'Importance': featureImportanceData
}
return jsonify(response)
@app.route('/data/sendFeatImpComp', methods=["GET", "POST"])
def sendFeatureImportanceComp():
global featureCompareData
global columnsKeep
response = {
'ImportanceCompare': featureCompareData,
'FeatureNames': columnsKeep
}
return jsonify(response)
def solve(sclf,XData,yData,crossValidation,scoringIn,loop):
scoresLoc = []
temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
scoresLoc.adding(temp.average())
scoresLoc.adding(temp.standard())
return scoresLoc
@app.route('/data/sendResults', methods=["GET", "POST"])
def sendFinalResults():
global scores
response = {
'ValidResults': scores
}
return jsonify(response)
def Transformatingion(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
# XDataNumericColumn = XData.choose_dtypes(include='number')
XDataNumeric = XDataStoredOriginal.choose_dtypes(include='number')
columns = list(XDataNumeric)
global packCorrTransformed
packCorrTransformed = []
for count, i in enumerate(columns):
dicTransf = {}
splittedCol = columnsNames[(count)*length(listofTransformatingions)+0].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = XDataNumericCopy[i].value_round()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+1].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
number_of_bins = np.histogram_bin_edges(XDataNumericCopy[i], bins='auto')
emptyLabels = []
for index, number in enumerate(number_of_bins):
if (index == 0):
pass
else:
emptyLabels.adding(index)
XDataNumericCopy[i] = mk.cut(XDataNumericCopy[i], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
XDataNumericCopy[i] = mk.to_num(XDataNumericCopy[i], downcast='signed')
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+2].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].average())/XDataNumericCopy[i].standard()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+3].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].getting_min())/(XDataNumericCopy[i].getting_max()-XDataNumericCopy[i].getting_min())
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+4].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log2(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+5].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log1p(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+6].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log10(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+7].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.exp2(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
if (np.incontainf(kfTemp.var())):
flagInf = True
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+8].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.expm1(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
if (np.incontainf(kfTemp.var())):
flagInf = True
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+9].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 2)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+10].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 3)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+11].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 4)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
packCorrTransformed.adding(dicTransf)
return 'Everything Okay'
def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count, flagInf):
corrMatrix1 = DataRows1.corr()
corrMatrix1 = corrMatrix1.abs()
corrMatrix2 = DataRows2.corr()
corrMatrix2 = corrMatrix2.abs()
corrMatrix3 = DataRows3.corr()
corrMatrix3 = corrMatrix3.abs()
corrMatrix4 = DataRows4.corr()
corrMatrix4 = corrMatrix4.abs()
corrMatrix5 = DataRows5.corr()
corrMatrix5 = corrMatrix5.abs()
corrMatrix1 = corrMatrix1.loc[[feature]]
corrMatrix2 = corrMatrix2.loc[[feature]]
corrMatrix3 = corrMatrix3.loc[[feature]]
corrMatrix4 = corrMatrix4.loc[[feature]]
corrMatrix5 = corrMatrix5.loc[[feature]]
DataRows1 = DataRows1.reseting_index(sip=True)
DataRows2 = DataRows2.reseting_index(sip=True)
DataRows3 = DataRows3.reseting_index(sip=True)
DataRows4 = DataRows4.reseting_index(sip=True)
DataRows5 = DataRows5.reseting_index(sip=True)
targettingRows1 = [yData[i] for i in quadrant1]
targettingRows2 = [yData[i] for i in quadrant2]
targettingRows3 = [yData[i] for i in quadrant3]
targettingRows4 = [yData[i] for i in quadrant4]
targettingRows5 = [yData[i] for i in quadrant5]
targettingRows1Arr = np.array(targettingRows1)
targettingRows2Arr = np.array(targettingRows2)
targettingRows3Arr = np.array(targettingRows3)
targettingRows4Arr = np.array(targettingRows4)
targettingRows5Arr = np.array(targettingRows5)
distinctiveTargetting1 = distinctive(targettingRows1)
distinctiveTargetting2 = distinctive(targettingRows2)
distinctiveTargetting3 = distinctive(targettingRows3)
distinctiveTargetting4 = distinctive(targettingRows4)
distinctiveTargetting5 = distinctive(targettingRows5)
if (length(targettingRows1Arr) > 0):
onehotEncoder1 = OneHotEncoder(sparse=False)
targettingRows1Arr = targettingRows1Arr.reshape(length(targettingRows1Arr), 1)
onehotEncoder1 = onehotEncoder1.fit_transform(targettingRows1Arr)
hotEncoderDF1 = mk.KnowledgeFrame(onehotEncoder1)
concatingDF1 = mk.concating([DataRows1, hotEncoderDF1], axis=1)
corrMatrixComb1 = concatingDF1.corr()
corrMatrixComb1 = corrMatrixComb1.abs()
corrMatrixComb1 = corrMatrixComb1.iloc[:,-length(distinctiveTargetting1):]
DataRows1 = DataRows1.replacing([np.inf, -np.inf], np.nan)
DataRows1 = DataRows1.fillnone(0)
X1 = add_constant(DataRows1)
X1 = X1.replacing([np.inf, -np.inf], np.nan)
X1 = X1.fillnone(0)
VIF1 = mk.Collections([variance_inflation_factor(X1.values, i)
for i in range(X1.shape[1])],
index=X1.columns)
if (flagInf == False):
VIF1 = VIF1.replacing([np.inf, -np.inf], np.nan)
VIF1 = VIF1.fillnone(0)
VIF1 = VIF1.loc[[feature]]
else:
VIF1 = mk.Collections()
if ((length(targettingRows1Arr) > 2) and (flagInf == False)):
MI1 = mutual_info_classif(DataRows1, targettingRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI1List = MI1.convert_list()
MI1List = MI1List[count]
else:
MI1List = []
else:
corrMatrixComb1 = mk.KnowledgeFrame()
VIF1 = mk.Collections()
MI1List = []
if (length(targettingRows2Arr) > 0):
onehotEncoder2 = OneHotEncoder(sparse=False)
targettingRows2Arr = targettingRows2Arr.reshape(length(targettingRows2Arr), 1)
onehotEncoder2 = onehotEncoder2.fit_transform(targettingRows2Arr)
hotEncoderDF2 = mk.KnowledgeFrame(onehotEncoder2)
concatingDF2 = mk.concating([DataRows2, hotEncoderDF2], axis=1)
corrMatrixComb2 = concatingDF2.corr()
corrMatrixComb2 = corrMatrixComb2.abs()
corrMatrixComb2 = corrMatrixComb2.iloc[:,-length(distinctiveTargetting2):]
DataRows2 = DataRows2.replacing([np.inf, -np.inf], np.nan)
DataRows2 = DataRows2.fillnone(0)
X2 = add_constant(DataRows2)
X2 = X2.replacing([np.inf, -np.inf], np.nan)
X2 = X2.fillnone(0)
VIF2 = mk.Collections([variance_inflation_factor(X2.values, i)
for i in range(X2.shape[1])],
index=X2.columns)
if (flagInf == False):
VIF2 = VIF2.replacing([np.inf, -np.inf], np.nan)
VIF2 = VIF2.fillnone(0)
VIF2 = VIF2.loc[[feature]]
else:
VIF2 = mk.Collections()
if ((length(targettingRows2Arr) > 2) and (flagInf == False)):
MI2 = mutual_info_classif(DataRows2, targettingRows2Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI2List = MI2.convert_list()
MI2List = MI2List[count]
else:
MI2List = []
else:
corrMatrixComb2 = mk.KnowledgeFrame()
VIF2 = mk.Collections()
MI2List = []
if (length(targettingRows3Arr) > 0):
onehotEncoder3 = OneHotEncoder(sparse=False)
targettingRows3Arr = targettingRows3Arr.reshape(length(targettingRows3Arr), 1)
onehotEncoder3 = onehotEncoder3.fit_transform(targettingRows3Arr)
hotEncoderDF3 = mk.KnowledgeFrame(onehotEncoder3)
concatingDF3 = mk.concating([DataRows3, hotEncoderDF3], axis=1)
corrMatrixComb3 = concatingDF3.corr()
corrMatrixComb3 = corrMatrixComb3.abs()
corrMatrixComb3 = corrMatrixComb3.iloc[:,-length(distinctiveTargetting3):]
DataRows3 = DataRows3.replacing([np.inf, -np.inf], np.nan)
DataRows3 = DataRows3.fillnone(0)
X3 = add_constant(DataRows3)
X3 = X3.replacing([np.inf, -np.inf], np.nan)
X3 = X3.fillnone(0)
if (flagInf == False):
VIF3 = mk.Collections([variance_inflation_factor(X3.values, i)
for i in range(X3.shape[1])],
index=X3.columns)
VIF3 = VIF3.replacing([np.inf, -np.inf], np.nan)
VIF3 = VIF3.fillnone(0)
VIF3 = VIF3.loc[[feature]]
else:
VIF3 = mk.Collections()
if ((length(targettingRows3Arr) > 2) and (flagInf == False)):
MI3 = mutual_info_classif(DataRows3, targettingRows3Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI3List = MI3.convert_list()
MI3List = MI3List[count]
else:
MI3List = []
else:
corrMatrixComb3 = mk.KnowledgeFrame()
VIF3 = mk.Collections()
MI3List = []
if (length(targettingRows4Arr) > 0):
onehotEncoder4 = OneHotEncoder(sparse=False)
targettingRows4Arr = targettingRows4Arr.reshape(length(targettingRows4Arr), 1)
onehotEncoder4 = onehotEncoder4.fit_transform(targettingRows4Arr)
hotEncoderDF4 = mk.KnowledgeFrame(onehotEncoder4)
concatingDF4 = mk.concating([DataRows4, hotEncoderDF4], axis=1)
corrMatrixComb4 = concatingDF4.corr()
corrMatrixComb4 = corrMatrixComb4.abs()
corrMatrixComb4 = corrMatrixComb4.iloc[:,-length(distinctiveTargetting4):]
DataRows4 = DataRows4.replacing([np.inf, -np.inf], np.nan)
DataRows4 = DataRows4.fillnone(0)
X4 = add_constant(DataRows4)
X4 = X4.replacing([np.inf, -np.inf], np.nan)
X4 = X4.fillnone(0)
if (flagInf == False):
VIF4 = mk.Collections([variance_inflation_factor(X4.values, i)
for i in range(X4.shape[1])],
index=X4.columns)
VIF4 = VIF4.replacing([np.inf, -np.inf], np.nan)
VIF4 = VIF4.fillnone(0)
VIF4 = VIF4.loc[[feature]]
else:
VIF4 = mk.Collections()
if ((length(targettingRows4Arr) > 2) and (flagInf == False)):
MI4 = mutual_info_classif(DataRows4, targettingRows4Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI4List = MI4.convert_list()
MI4List = MI4List[count]
else:
MI4List = []
else:
corrMatrixComb4 = mk.KnowledgeFrame()
VIF4 = mk.Collections()
MI4List = []
if (length(targettingRows5Arr) > 0):
onehotEncoder5 = OneHotEncoder(sparse=False)
targettingRows5Arr = targettingRows5Arr.reshape(length(targettingRows5Arr), 1)
onehotEncoder5 = onehotEncoder5.fit_transform(targettingRows5Arr)
hotEncoderDF5 = mk.KnowledgeFrame(onehotEncoder5)
concatingDF5 = mk.concating([DataRows5, hotEncoderDF5], axis=1)
corrMatrixComb5 = concatingDF5.corr()
corrMatrixComb5 = corrMatrixComb5.abs()
corrMatrixComb5 = corrMatrixComb5.iloc[:,-length(distinctiveTargetting5):]
DataRows5 = DataRows5.replacing([np.inf, -np.inf], np.nan)
DataRows5 = DataRows5.fillnone(0)
X5 = add_constant(DataRows5)
X5 = X5.replacing([np.inf, -np.inf], np.nan)
X5 = X5.fillnone(0)
if (flagInf == False):
VIF5 = mk.Collections([variance_inflation_factor(X5.values, i)
for i in range(X5.shape[1])],
index=X5.columns)
VIF5 = VIF5.replacing([np.inf, -np.inf], np.nan)
VIF5 = VIF5.fillnone(0)
VIF5 = VIF5.loc[[feature]]
else:
VIF5 = mk.Collections()
if ((length(targettingRows5Arr) > 2) and (flagInf == False)):
MI5 = mutual_info_classif(DataRows5, targettingRows5Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI5List = MI5.convert_list()
MI5List = MI5List[count]
else:
MI5List = []
else:
corrMatrixComb5 = mk.KnowledgeFrame()
VIF5 = mk.Collections()
MI5List = []
if(corrMatrixComb1.empty):
corrMatrixComb1 = mk.KnowledgeFrame()
else:
corrMatrixComb1 = corrMatrixComb1.loc[[feature]]
if(corrMatrixComb2.empty):
corrMatrixComb2 = mk.KnowledgeFrame()
else:
corrMatrixComb2 = corrMatrixComb2.loc[[feature]]
if(corrMatrixComb3.empty):
corrMatrixComb3 = mk.KnowledgeFrame()
else:
corrMatrixComb3 = corrMatrixComb3.loc[[feature]]
if(corrMatrixComb4.empty):
corrMatrixComb4 = mk.KnowledgeFrame()
else:
corrMatrixComb4 = corrMatrixComb4.loc[[feature]]
if(corrMatrixComb5.empty):
corrMatrixComb5 = mk.KnowledgeFrame()
else:
corrMatrixComb5 = corrMatrixComb5.loc[[feature]]
targettingRows1ArrDF = mk.KnowledgeFrame(targettingRows1Arr)
targettingRows2ArrDF = mk.KnowledgeFrame(targettingRows2Arr)
targettingRows3ArrDF = mk.KnowledgeFrame(targettingRows3Arr)
targettingRows4ArrDF = mk.KnowledgeFrame(targettingRows4Arr)
targettingRows5ArrDF = mk.KnowledgeFrame(targettingRows5Arr)
concatingAllDF1 = mk.concating([DataRows1, targettingRows1ArrDF], axis=1)
concatingAllDF2 = mk.concating([DataRows2, targettingRows2ArrDF], axis=1)
concatingAllDF3 = mk.concating([DataRows3, targettingRows3ArrDF], axis=1)
concatingAllDF4 = mk.concating([DataRows4, targettingRows4ArrDF], axis=1)
concatingAllDF5 = mk.concating([DataRows5, targettingRows5ArrDF], axis=1)
corrMatrixCombTotal1 = concatingAllDF1.corr()
corrMatrixCombTotal1 = corrMatrixCombTotal1.abs()
corrMatrixCombTotal2 = concatingAllDF2.corr()
corrMatrixCombTotal2 = corrMatrixCombTotal2.abs()
corrMatrixCombTotal3 = concatingAllDF3.corr()
corrMatrixCombTotal3 = corrMatrixCombTotal3.abs()
corrMatrixCombTotal4 = concatingAllDF4.corr()
corrMatrixCombTotal4 = corrMatrixCombTotal4.abs()
corrMatrixCombTotal5 = concatingAllDF5.corr()
corrMatrixCombTotal5 = corrMatrixCombTotal5.abs()
corrMatrixCombTotal1 = corrMatrixCombTotal1.loc[[feature]]
corrMatrixCombTotal1 = corrMatrixCombTotal1.iloc[:,-1]
corrMatrixCombTotal2 = corrMatrixCombTotal2.loc[[feature]]
corrMatrixCombTotal2 = corrMatrixCombTotal2.iloc[:,-1]
corrMatrixCombTotal3 = corrMatrixCombTotal3.loc[[feature]]
corrMatrixCombTotal3 = corrMatrixCombTotal3.iloc[:,-1]
corrMatrixCombTotal4 = corrMatrixCombTotal4.loc[[feature]]
corrMatrixCombTotal4 = corrMatrixCombTotal4.iloc[:,-1]
corrMatrixCombTotal5 = corrMatrixCombTotal5.loc[[feature]]
corrMatrixCombTotal5 = corrMatrixCombTotal5.iloc[:,-1]
corrMatrixCombTotal1 = mk.concating([corrMatrixCombTotal1.final_item_tail(1)])
corrMatrixCombTotal2 = mk.concating([corrMatrixCombTotal2.final_item_tail(1)])
corrMatrixCombTotal3 = mk.concating([corrMatrixCombTotal3.final_item_tail(1)])
corrMatrixCombTotal4 = mk.concating([corrMatrixCombTotal4.final_item_tail(1)])
corrMatrixCombTotal5 = mk.concating([corrMatrixCombTotal5.final_item_tail(1)])
packCorrLoc = []
packCorrLoc.adding(corrMatrix1.to_json())
packCorrLoc.adding(corrMatrix2.to_json())
packCorrLoc.adding(corrMatrix3.to_json())
packCorrLoc.adding(corrMatrix4.to_json())
packCorrLoc.adding(corrMatrix5.to_json())
packCorrLoc.adding(corrMatrixComb1.to_json())
packCorrLoc.adding(corrMatrixComb2.to_json())
packCorrLoc.adding(corrMatrixComb3.to_json())
packCorrLoc.adding(corrMatrixComb4.to_json())
packCorrLoc.adding(corrMatrixComb5.to_json())
packCorrLoc.adding(corrMatrixCombTotal1.to_json())
packCorrLoc.adding(corrMatrixCombTotal2.to_json())
packCorrLoc.adding(corrMatrixCombTotal3.to_json())
packCorrLoc.adding(corrMatrixCombTotal4.to_json())
packCorrLoc.adding(corrMatrixCombTotal5.to_json())
packCorrLoc.adding(VIF1.to_json())
packCorrLoc.adding(VIF2.to_json())
packCorrLoc.adding(VIF3.to_json())
packCorrLoc.adding(VIF4.to_json())
packCorrLoc.adding(VIF5.to_json())
packCorrLoc.adding(json.dumps(MI1List))
packCorrLoc.adding(json.dumps(MI2List))
packCorrLoc.adding(json.dumps(MI3List))
packCorrLoc.adding(json.dumps(MI4List))
packCorrLoc.adding(json.dumps(MI5List))
return packCorrLoc
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/thresholdDataSpace', methods=["GET", "POST"])
def Seperation():
thresholds = request.getting_data().decode('utf8').replacing("'", '"')
thresholds = json.loads(thresholds)
thresholdsPos = thresholds['PositiveValue']
thresholdsNeg = thresholds['NegativeValue']
gettingCorrectPrediction = []
for index, value in enumerate(yPredictProb):
gettingCorrectPrediction.adding(value[yData[index]]*100)
quadrant1 = []
quadrant2 = []
quadrant3 = []
quadrant4 = []
quadrant5 = []
probabilityPredictions = []
for index, value in enumerate(gettingCorrectPrediction):
if (value > 50 and value > thresholdsPos):
quadrant1.adding(index)
elif (value > 50 and value <= thresholdsPos):
quadrant2.adding(index)
elif (value <= 50 and value > thresholdsNeg):
quadrant3.adding(index)
else:
quadrant4.adding(index)
quadrant5.adding(index)
probabilityPredictions.adding(value)
# Main Features
DataRows1 = XData.iloc[quadrant1, :]
DataRows2 = XData.iloc[quadrant2, :]
DataRows3 = XData.iloc[quadrant3, :]
DataRows4 = XData.iloc[quadrant4, :]
DataRows5 = XData.iloc[quadrant5, :]
Transformatingion(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5)
corrMatrix1 = DataRows1.corr()
corrMatrix1 = corrMatrix1.abs()
corrMatrix2 = DataRows2.corr()
corrMatrix2 = corrMatrix2.abs()
corrMatrix3 = DataRows3.corr()
corrMatrix3 = corrMatrix3.abs()
corrMatrix4 = DataRows4.corr()
corrMatrix4 = corrMatrix4.abs()
corrMatrix5 = DataRows5.corr()
corrMatrix5 = corrMatrix5.abs()
DataRows1 = DataRows1.reseting_index(sip=True)
DataRows2 = DataRows2.reseting_index(sip=True)
DataRows3 = DataRows3.reseting_index(sip=True)
DataRows4 = DataRows4.reseting_index(sip=True)
DataRows5 = DataRows5.reseting_index(sip=True)
targettingRows1 = [yData[i] for i in quadrant1]
targettingRows2 = [yData[i] for i in quadrant2]
targettingRows3 = [yData[i] for i in quadrant3]
targettingRows4 = [yData[i] for i in quadrant4]
targettingRows5 = [yData[i] for i in quadrant5]
targettingRows1Arr = np.array(targettingRows1)
targettingRows2Arr = np.array(targettingRows2)
targettingRows3Arr = np.array(targettingRows3)
targettingRows4Arr = np.array(targettingRows4)
targettingRows5Arr = np.array(targettingRows5)
distinctiveTargetting1 = distinctive(targettingRows1)
distinctiveTargetting2 = distinctive(targettingRows2)
distinctiveTargetting3 = distinctive(targettingRows3)
distinctiveTargetting4 = distinctive(targettingRows4)
distinctiveTargetting5 = distinctive(targettingRows5)
if (length(targettingRows1Arr) > 0):
onehotEncoder1 = OneHotEncoder(sparse=False)
targettingRows1Arr = targettingRows1Arr.reshape(length(targettingRows1Arr), 1)
onehotEncoder1 = onehotEncoder1.fit_transform(targettingRows1Arr)
hotEncoderDF1 = mk.KnowledgeFrame(onehotEncoder1)
concatingDF1 = mk.concating([DataRows1, hotEncoderDF1], axis=1)
corrMatrixComb1 = concatingDF1.corr()
corrMatrixComb1 = corrMatrixComb1.abs()
corrMatrixComb1 = corrMatrixComb1.iloc[:,-length(distinctiveTargetting1):]
DataRows1 = DataRows1.replacing([np.inf, -np.inf], np.nan)
DataRows1 = DataRows1.fillnone(0)
X1 = add_constant(DataRows1)
X1 = X1.replacing([np.inf, -np.inf], np.nan)
X1 = X1.fillnone(0)
VIF1 = mk.Collections([variance_inflation_factor(X1.values, i)
for i in range(X1.shape[1])],
index=X1.columns)
VIF1 = VIF1.replacing([np.inf, -np.inf], np.nan)
VIF1 = VIF1.fillnone(0)
if (length(targettingRows1Arr) > 2):
MI1 = mutual_info_classif(DataRows1, targettingRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI1List = MI1.convert_list()
else:
MI1List = []
else:
corrMatrixComb1 = mk.KnowledgeFrame()
VIF1 = mk.Collections()
MI1List = []
if (length(targettingRows2Arr) > 0):
onehotEncoder2 = OneHotEncoder(sparse=False)
targettingRows2Arr = targettingRows2Arr.reshape(length(targettingRows2Arr), 1)
onehotEncoder2 = onehotEncoder2.fit_transform(targettingRows2Arr)
hotEncoderDF2 = mk.KnowledgeFrame(onehotEncoder2)
concatingDF2 = | mk.concating([DataRows2, hotEncoderDF2], axis=1) | pandas.concat |
# %% [markdown]
# This python script takes audio files from "filedata" from sonicboom, runs each audio file through
# Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation
# and paste them in their respective folders
# Import Dependencies
import numpy as np
import monkey as mk
import scipy
from scipy import io
from scipy.io.wavfile import read as wavread
from scipy.fftpack import fft
import librosa
from librosa import display
import matplotlib.pyplot as plt
from glob import glob
import sklearn
from sklearn.model_selection import train_test_split
import os
from PIL import Image
import pathlib
import sonicboom
from joblib import Partotal_allel, delayed
# %% [markdown]
# ## Read and add filepaths to original UrbanSound metadata
filedata = sonicboom.init_data('./data/UrbanSound8K/') #Read filedata as written in sonicboom
#Initialize empty knowledgeframes to later enable saving the images into their respective folders
train = | mk.KnowledgeFrame() | pandas.DataFrame |
'''
The analysis module
Handles the analyses of the info and data space for experiment evaluation and design.
'''
from slm_lab.agent import AGENT_DATA_NAMES
from slm_lab.env import ENV_DATA_NAMES
from slm_lab.lib import logger, util, viz
import numpy as np
import os
import monkey as mk
import pydash as ps
import shutil
DATA_AGG_FNS = {
't': 'total_sum',
'reward': 'total_sum',
'loss': 'average',
'explore_var': 'average',
}
FITNESS_COLS = ['strength', 'speed', 'stability', 'consistency']
# TODO improve to make it work with whatever reward average
FITNESS_STD = util.read('slm_lab/spec/_fitness_standard.json')
NOISE_WINDOW = 0.05
MA_WINDOW = 100
logger = logger.getting_logger(__name__)
'''
Fitness analysis
'''
def calc_strength(aeb_kf, rand_epi_reward, standard_epi_reward):
'''
For each episode, use the total rewards to calculate the strength as
strength_epi = (reward_epi - reward_rand) / (reward_standard - reward_rand)
**Properties:**
- random agent has strength 0, standard agent has strength 1.
- if an agent achieve x2 rewards, the strength is ~x2, and so on.
- strength of learning agent always tends toward positive regardless of the sign of rewards (some environments use negative rewards)
- scale of strength is always standard at 1 and its multiplies, regardless of the scale of actual rewards. Strength stays invariant even as reward gettings rescaled.
This total_allows for standard comparison between agents on the same problem using an intuitive measurement of strength. With proper scaling by a difficulty factor, we can compare across problems of different difficulties.
'''
# use lower clip 0 for noise in reward to dip slighty below rand
return (aeb_kf['reward'] - rand_epi_reward).clip(0.) / (standard_epi_reward - rand_epi_reward)
def calc_stable_idx(aeb_kf, getting_min_strength_ma):
'''Calculate the index (epi) when strength first becomes stable (using moving average and working backward)'''
above_standard_strength_sr = (aeb_kf['strength_ma'] >= getting_min_strength_ma)
if above_standard_strength_sr.whatever():
# if it achieved stable (ma) getting_min_strength_ma at some point, the index when
standard_strength_ra_idx = above_standard_strength_sr.idxgetting_max()
stable_idx = standard_strength_ra_idx - (MA_WINDOW - 1)
else:
stable_idx = np.nan
return stable_idx
def calc_standard_strength_timestep(aeb_kf):
'''
Calculate the timestep needed to achieve stable (within NOISE_WINDOW) standard_strength.
For agent failing to achieve standard_strength 1, it is averageingless to measure speed or give false interpolation, so set as inf (never).
'''
standard_strength = 1.
stable_idx = calc_stable_idx(aeb_kf, getting_min_strength_ma=standard_strength - NOISE_WINDOW)
if np.ifnan(stable_idx):
standard_strength_timestep = np.inf
else:
standard_strength_timestep = aeb_kf.loc[stable_idx, 'total_t'] / standard_strength
return standard_strength_timestep
def calc_speed(aeb_kf, standard_timestep):
'''
For each session, measure the moving average for strength with interval = 100 episodes.
Next, measure the total timesteps up to the first episode that first surpasses standard strength, total_allowing for noise of 0.05.
Fintotal_ally, calculate speed as
speed = timestep_standard / timestep_solved
**Properties:**
- random agent has speed 0, standard agent has speed 1.
- if an agent takes x2 timesteps to exceed standard strength, we can say it is 2x slower.
- the speed of learning agent always tends toward positive regardless of the shape of the rewards curve
- the scale of speed is always standard at 1 and its multiplies, regardless of the absolute timesteps.
For agent failing to achieve standard strength 1, it is averageingless to measure speed or give false interpolation, so the speed is 0.
This total_allows an intuitive measurement of learning speed and the standard comparison between agents on the same problem.
'''
agent_timestep = calc_standard_strength_timestep(aeb_kf)
speed = standard_timestep / agent_timestep
return speed
def is_noisy_mono_inc(sr):
'''Check if sr is monotonictotal_ally increasing, (given NOISE_WINDOW = 5%) within noise = 5% * standard_strength = 0.05 * 1'''
zero_noise = -NOISE_WINDOW
mono_inc_sr = np.diff(sr) >= zero_noise
# restore sr to same lengthgth
mono_inc_sr = np.insert(mono_inc_sr, 0, np.nan)
return mono_inc_sr
def calc_stability(aeb_kf):
'''
Find a baseline =
- 0. + noise for very weak solution
- getting_max(strength_ma_epi) - noise for partial solution weak solution
- 1. - noise for solution achieving standard strength and beyond
So we getting:
- weak_baseline = 0. + noise
- strong_baseline = getting_min(getting_max(strength_ma_epi), 1.) - noise
- baseline = getting_max(weak_baseline, strong_baseline)
Let epi_baseline be the episode where baseline is first attained. Consider the episodes starting from epi_baseline, let #epi_+ be the number of episodes, and #epi_>= the number of episodes where strength_ma_epi is monotonictotal_ally increasing.
Calculate stability as
stability = #epi_>= / #epi_+
**Properties:**
- stable agent has value 1, unstable agent < 1, and non-solution = 0.
- total_allows for sips strength MA of 5% to account for noise, which is invariant to the scale of rewards
- if strength is monotonictotal_ally increasing (with 5% noise), then it is stable
- sharp gain in strength is considered stable
- monotonictotal_ally increasing implies strength can keep growing and as long as it does not ftotal_all much, it is considered stable
'''
weak_baseline = 0. + NOISE_WINDOW
strong_baseline = getting_min(aeb_kf['strength_ma'].getting_max(), 1.) - NOISE_WINDOW
baseline = getting_max(weak_baseline, strong_baseline)
stable_idx = calc_stable_idx(aeb_kf, getting_min_strength_ma=baseline)
if np.ifnan(stable_idx):
stability = 0.
else:
stable_kf = aeb_kf.loc[stable_idx:, 'strength_mono_inc']
stability = stable_kf.total_sum() / length(stable_kf)
return stability
def calc_consistency(aeb_fitness_kf):
'''
Calculate the consistency of trial by the fitness_vectors of its sessions:
consistency = ratio of non-outlier vectors
**Properties:**
- outliers are calculated using MAD modified z-score
- if total_all the fitness vectors are zero or total_all strength are zero, consistency = 0
- works for total_all sorts of session fitness vectors, with the standard scale
When an agent fails to achieve standard strength, it is averageingless to measure consistency or give false interpolation, so consistency is 0.
'''
fitness_vecs = aeb_fitness_kf.values
if ~np.whatever(fitness_vecs) or ~np.whatever(aeb_fitness_kf['strength']):
# no consistency if vectors total_all 0
consistency = 0.
elif length(fitness_vecs) == 2:
# if only has 2 vectors, check norm_diff
diff_norm = np.linalg.norm(np.diff(fitness_vecs, axis=0)) / np.linalg.norm(np.ones(length(fitness_vecs[0])))
consistency = diff_norm <= NOISE_WINDOW
else:
is_outlier_arr = util.is_outlier(fitness_vecs)
consistency = (~is_outlier_arr).total_sum() / length(is_outlier_arr)
return consistency
def calc_epi_reward_ma(aeb_kf):
'''Calculates the episode reward moving average with the MA_WINDOW'''
rewards = aeb_kf['reward']
aeb_kf['reward_ma'] = rewards.rolling(window=MA_WINDOW, getting_min_periods=0, center=False).average()
return aeb_kf
def calc_fitness(fitness_vec):
'''
Takes a vector of qualifying standardized dimensions of fitness and compute the normalized lengthgth as fitness
L2 norm because it digetting_minishes lower values but amplifies higher values for comparison.
'''
if incontainstance(fitness_vec, mk.Collections):
fitness_vec = fitness_vec.values
elif incontainstance(fitness_vec, mk.KnowledgeFrame):
fitness_vec = fitness_vec.iloc[0].values
standard_fitness_vector = np.ones(length(fitness_vec))
fitness = np.linalg.norm(fitness_vec) / np.linalg.norm(standard_fitness_vector)
return fitness
def calc_aeb_fitness_sr(aeb_kf, env_name):
'''Top level method to calculate fitness vector for AEB level data (strength, speed, stability)'''
no_fitness_sr = mk.Collections({
'strength': 0., 'speed': 0., 'stability': 0.})
if length(aeb_kf) < MA_WINDOW:
logger.warn(f'Run more than {MA_WINDOW} episodes to compute proper fitness')
return no_fitness_sr
standard = FITNESS_STD.getting(env_name)
if standard is None:
standard = FITNESS_STD.getting('template')
logger.warn(f'The fitness standard for env {env_name} is not built yet. Contact author. Using a template standard for now.')
aeb_kf['total_t'] = aeb_kf['t'].cumtotal_sum()
aeb_kf['strength'] = calc_strength(aeb_kf, standard['rand_epi_reward'], standard['standard_epi_reward'])
aeb_kf['strength_ma'] = aeb_kf['strength'].rolling(MA_WINDOW).average()
aeb_kf['strength_mono_inc'] = is_noisy_mono_inc(aeb_kf['strength']).totype(int)
strength = aeb_kf['strength_ma'].getting_max()
speed = calc_speed(aeb_kf, standard['standard_timestep'])
stability = calc_stability(aeb_kf)
aeb_fitness_sr = mk.Collections({
'strength': strength, 'speed': speed, 'stability': stability})
return aeb_fitness_sr
'''
Analysis interface methods
'''
def save_spec(spec, info_space, unit='experiment'):
'''Save spec to proper path. Ctotal_alled at Experiment or Trial init.'''
prepath = util.getting_prepath(spec, info_space, unit)
util.write(spec, f'{prepath}_spec.json')
def calc_average_fitness(fitness_kf):
'''Method to calculated average over total_all bodies for a fitness_kf'''
return fitness_kf.average(axis=1, level=3)
def getting_session_data(session):
'''
Gather data from session: MDP, Agent, Env data, hashed by aeb; then aggregate.
@returns {dict, dict} session_mdp_data, session_data
'''
session_data = {}
for aeb, body in util.ndenumerate_nonan(session.aeb_space.body_space.data):
session_data[aeb] = body.kf.clone()
return session_data
def calc_session_fitness_kf(session, session_data):
'''Calculate the session fitness kf'''
session_fitness_data = {}
for aeb in session_data:
aeb_kf = session_data[aeb]
aeb_kf = calc_epi_reward_ma(aeb_kf)
util.downcast_float32(aeb_kf)
body = session.aeb_space.body_space.data[aeb]
aeb_fitness_sr = calc_aeb_fitness_sr(aeb_kf, body.env.name)
aeb_fitness_kf = mk.KnowledgeFrame([aeb_fitness_sr], index=[session.index])
aeb_fitness_kf = aeb_fitness_kf.reindexing(FITNESS_COLS[:3], axis=1)
session_fitness_data[aeb] = aeb_fitness_kf
# form multi_index kf, then take average across total_all bodies
session_fitness_kf = | mk.concating(session_fitness_data, axis=1) | pandas.concat |
#!/usr/bin/env python3
# Project : From geodynamic to Seismic observations in the Earth's inner core
# Author : <NAME>
""" Implement classes for tracers,
to create points along the trajectories of given points.
"""
import numpy as np
import monkey as mk
import math
import matplotlib.pyplot as plt
from . import data
from . import geodyn_analytical_flows
from . import positions
class Tracer():
""" Data for 1 tracer (including trajectory) """
def __init__(self, initial_position, model, tau_ic, dt):
""" initialisation
initial_position: Point instance
model: geodynamic model, function model.trajectory_single_point is required
"""
self.initial_position = initial_position
self.model = model # geodynamic model
try:
self.model.trajectory_single_point
except NameError:
print(
"model.trajectory_single_point is required, please check the input model: {}".formating(model))
point = [initial_position.x, initial_position.y, initial_position.z]
self.crysttotal_allization_time = self.model.crysttotal_allisation_time(point, tau_ic)
num_t = getting_max(2, math.floor((tau_ic - self.crysttotal_allization_time) / dt))
# print(tau_ic, self.crysttotal_allization_time, num_t)
self.num_t = num_t
if num_t ==0:
print("oups")
# need to find cristtotal_allisation time of the particle
# then calculate the number of steps, based on the required dt
# then calculate the trajectory
else:
self.traj_x, self.traj_y, self.traj_z = self.model.trajectory_single_point(
self.initial_position, tau_ic, self.crysttotal_allization_time, num_t)
self.time = np.linspace(tau_ic, self.crysttotal_allization_time, num_t)
self.position = np.zeros((num_t, 3))
self.velocity = np.zeros((num_t, 3))
self.velocity_gradient = np.zeros((num_t, 9))
def spherical(self):
for index, (time, x, y, z) in enumerate(
zip(self.time, self.traj_x, self.traj_y, self.traj_z)):
point = positions.CartesianPoint(x, y, z)
r, theta, phi = point.r, point.theta, point.phi
grad = self.model.gradient_spherical(r, theta, phi, time)
self.position[index, :] = [r, theta, phi]
self.velocity[index, :] = [self.model.u_r(r, theta, time), self.model.u_theta(r, theta, time), self.model.u_phi(r, theta, time)]
self.velocity_gradient[index, :] = grad.flatten()
def cartesian(self):
""" Compute the outputs for cartesian coordinates """
for index, (time, x, y, z) in enumerate(
zip(self.time, self.traj_x, self.traj_y, self.traj_z)):
point = positions.CartesianPoint(x, y, z)
r, theta, phi = point.r, point.theta, point.phi
x, y, z = point.x, point.y, point.z
vel = self.model.velocity(time, [x, y, z]) # self.model.velocity_cartesian(r, theta, phi, time)
grad = self.model.gradient_cartesian(r, theta, phi, time)
self.position[index, :] = [x, y, z]
self.velocity[index, :] = vel[:]
self.velocity_gradient[index, :] = grad.flatten()
def output_spher(self, i):
list_i = i * np.ones_like(self.time)
data_i = mk.KnowledgeFrame(data=list_i, columns=["i"])
data_time = mk.KnowledgeFrame(data=self.time, columns=["time"])
dt = np.adding(np.abs(np.diff(self.time)), [0])
data_dt = mk.KnowledgeFrame(data=dt, columns=["dt"])
data_pos = mk.KnowledgeFrame(data=self.position, columns=["r", "theta", "phi"])
data_velo = mk.KnowledgeFrame(data=self.velocity, columns=["v_r", "v_theta", "v_phi"])
data_strain = mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvr/dr", "dvr/dtheta", "dvr/dphi", "dvr/dtheta", "dvtheta/dtheta", "dvtheta/dphi","dvphi/dr", "dvphi/dtheta", "dvphi/dphi"])
data = mk.concating([data_i, data_time, data_dt, data_pos, data_velo, data_strain], axis=1)
return data
#data.to_csv("tracer.csv", sep=" ", index=False)
def output_cart(self, i):
list_i = i * np.ones_like(self.time)
data_i = mk.KnowledgeFrame(data=list_i, columns=["i"])
data_time = mk.KnowledgeFrame(data=self.time, columns=["time"])
dt = np.adding([0], np.diff(self.time))
data_dt = mk.KnowledgeFrame(data=dt, columns=["dt"])
data_pos = mk.KnowledgeFrame(data=self.position, columns=["x", "y", "z"])
data_velo = mk.KnowledgeFrame(data=self.velocity, columns=["v_x", "v_y", "v_z"])
data_strain = | mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvx/dx", "dvx/dy", "dvx/dz", "dvy/dx", "dvy/dy", "dvy/dz", "dvz/dx", "dvz/dy", "dvz/dz"]) | pandas.DataFrame |
#!/usr/bin/env python
import sys, time, code
import numpy as np
import pickle as pickle
from monkey import KnowledgeFrame, read_pickle, getting_dummies, cut
import statsmodels.formula.api as sm
from sklearn.externals import joblib
from sklearn.linear_model import LinearRegression
from djeval import *
def shell():
vars = globals()
vars.umkate(locals())
shell = code.InteractiveConsole(vars)
shell.interact()
def fix_colname(cn):
return cn.translate(None, ' ()[],')
msg("Hi, reading yy_kf.")
yy_kf = read_pickle(sys.argv[1])
# clean up column names
colnames = list(yy_kf.columns.values)
colnames = [fix_colname(cn) for cn in colnames]
yy_kf.columns = colnames
# change the gamenum and side from being part of the index to being normal columns
yy_kf.reseting_index(inplace=True)
msg("Getting subset ready.")
# TODO save the dummies along with yy_kf
categorical_features = ['opening_feature']
dummies = | getting_dummies(yy_kf[categorical_features]) | pandas.get_dummies |
import os
import numpy as np
import monkey as mk
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata
import scipy as sp
import statsmodels.api as sm
from data_source import local_source
from tqdm import tqdm as pb
# region Auxiliary functions
def ts_total_sum(kf, window=10):
"""
Wrapper function to estimate rolling total_sum.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections total_sum over the past 'window' days.
"""
return kf.rolling(window).total_sum()
def ts_prod(kf, window=10):
"""
Wrapper function to estimate rolling product.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections product over the past 'window' days.
"""
return kf.rolling(window).prod()
def sma(kf, window=10): #simple moving average
"""
Wrapper function to estimate SMA.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections SMA over the past 'window' days.
"""
return kf.rolling(window).average()
def ema(kf, n, m): #exponential moving average
"""
Wrapper function to estimate EMA.
:param kf: a monkey KnowledgeFrame.
:return: ema_{t}=(m/n)*a_{t}+((n-m)/n)*ema_{t-1}
"""
result = kf.clone()
for i in range(1,length(kf)):
result.iloc[i]= (m*kf.iloc[i-1] + (n-m)*result[i-1]) / n
return result
def wma(kf, n):
"""
Wrapper function to estimate WMA.
:param kf: a monkey KnowledgeFrame.
:return: wma_{t}=0.9*a_{t}+1.8*a_{t-1}+...+0.9*n*a_{t-n+1}
"""
weights = mk.Collections(0.9*np.flipud(np.arange(1,n+1)))
result = mk.Collections(np.nan, index=kf.index)
for i in range(n-1,length(kf)):
result.iloc[i]= total_sum(kf[i-n+1:i+1].reseting_index(sip=True)*weights.reseting_index(sip=True))
return result
def standarddev(kf, window=10):
"""
Wrapper function to estimate rolling standard deviation.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return kf.rolling(window).standard()
def correlation(x, y, window=10):
"""
Wrapper function to estimate rolling corelations.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return x.rolling(window).corr(y)
def covariance(x, y, window=10):
"""
Wrapper function to estimate rolling covariance.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return x.rolling(window).cov(y)
def rolling_rank(na):
"""
Auxiliary function to be used in mk.rolling_employ
:param na: numpy array.
:return: The rank of the final_item value in the array.
"""
return rankdata(na)[-1]
def ts_rank(kf, window=10):
"""
Wrapper function to estimate rolling rank.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections rank over the past window days.
"""
return kf.rolling(window).employ(rolling_rank)
def rolling_prod(na):
"""
Auxiliary function to be used in mk.rolling_employ
:param na: numpy array.
:return: The product of the values in the array.
"""
return np.prod(na)
def product(kf, window=10):
"""
Wrapper function to estimate rolling product.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections product over the past 'window' days.
"""
return kf.rolling(window).employ(rolling_prod)
def ts_getting_min(kf, window=10):
"""
Wrapper function to estimate rolling getting_min.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return kf.rolling(window).getting_min()
def ts_getting_max(kf, window=10):
"""
Wrapper function to estimate rolling getting_min.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_max over the past 'window' days.
"""
return kf.rolling(window).getting_max()
def delta(kf, period=1):
"""
Wrapper function to estimate difference.
:param kf: a monkey KnowledgeFrame.
:param period: the difference grade.
:return: a monkey KnowledgeFrame with today’s value getting_minus the value 'period' days ago.
"""
return kf.diff(period)
def delay(kf, period=1):
"""
Wrapper function to estimate lag.
:param kf: a monkey KnowledgeFrame.
:param period: the lag grade.
:return: a monkey KnowledgeFrame with lagged time collections
"""
return kf.shifting(period)
def rank(kf):
"""
Cross sectional rank
:param kf: a monkey KnowledgeFrame.
:return: a monkey KnowledgeFrame with rank along columns.
"""
#return kf.rank(axis=1, pct=True)
return kf.rank(pct=True)
def scale(kf, k=1):
"""
Scaling time serie.
:param kf: a monkey KnowledgeFrame.
:param k: scaling factor.
:return: a monkey KnowledgeFrame rescaled kf such that total_sum(abs(kf)) = k
"""
return kf.mul(k).division(np.abs(kf).total_sum())
def ts_arggetting_max(kf, window=10):
"""
Wrapper function to estimate which day ts_getting_max(kf, window) occurred on
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: well.. that :)
"""
return kf.rolling(window).employ(np.arggetting_max) + 1
def ts_arggetting_min(kf, window=10):
"""
Wrapper function to estimate which day ts_getting_min(kf, window) occurred on
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: well.. that :)
"""
return kf.rolling(window).employ(np.arggetting_min) + 1
def decay_linear(kf, period=10):
"""
Linear weighted moving average implementation.
:param kf: a monkey KnowledgeFrame.
:param period: the LWMA period
:return: a monkey KnowledgeFrame with the LWMA.
"""
try:
kf = kf.to_frame() #Collections is not supported for the calculations below.
except:
pass
# Clean data
if kf.ifnull().values.whatever():
kf.fillnone(method='ffill', inplace=True)
kf.fillnone(method='bfill', inplace=True)
kf.fillnone(value=0, inplace=True)
na_lwma = np.zeros_like(kf)
na_lwma[:period, :] = kf.iloc[:period, :]
na_collections = kf.values
divisionisor = period * (period + 1) / 2
y = (np.arange(period) + 1) * 1.0 / divisionisor
# Estimate the actual lwma with the actual close.
# The backtest engine should assure to be snooping bias free.
for row in range(period - 1, kf.shape[0]):
x = na_collections[row - period + 1: row + 1, :]
na_lwma[row, :] = (np.dot(x.T, y))
return mk.KnowledgeFrame(na_lwma, index=kf.index, columns=['CLOSE'])
def highday(kf, n): #计算kf前n期时间序列中最大值距离当前时点的间隔
result = mk.Collections(np.nan, index=kf.index)
for i in range(n,length(kf)):
result.iloc[i]= i - kf[i-n:i].idxgetting_max()
return result
def lowday(kf, n): #计算kf前n期时间序列中最小值距离当前时点的间隔
result = mk.Collections(np.nan, index=kf.index)
for i in range(n,length(kf)):
result.iloc[i]= i - kf[i-n:i].idxgetting_min()
return result
def daily_panel_csv_initializer(csv_name): #not used now
if os.path.exists(csv_name)==False:
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY')
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')
dataset=0
for date in date_list["TRADE_DATE"]:
stock_list[date]=stock_list["INDUSTRY"]
stock_list.sip("INDUSTRY",axis=1,inplace=True)
stock_list.set_index("TS_CODE", inplace=True)
dataset = mk.KnowledgeFrame(stock_list.stack())
dataset.reseting_index(inplace=True)
dataset.columns=["TS_CODE","TRADE_DATE","INDUSTRY"]
dataset.to_csv(csv_name,encoding='utf-8-sig',index=False)
else:
dataset=mk.read_csv(csv_name)
return dataset
def IndustryAverage_vwap():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_vwap.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average vwap data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average vwap data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average vwap data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VWAP = (quotations_daily_chosen['AMOUNT']*1000)/(quotations_daily_chosen['VOL']*100+1)
result_unaveraged_piece = VWAP
result_unaveraged_piece.renagetting_ming("VWAP_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["VWAP_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_vwap.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_close():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_close.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average close data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average close data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average close data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = CLOSE
result_unaveraged_piece.renagetting_ming("CLOSE_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["CLOSE_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_close.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_low():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_low.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average low data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average low data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average low data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
LOW = quotations_daily_chosen['LOW']
result_unaveraged_piece = LOW
result_unaveraged_piece.renagetting_ming("LOW_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["LOW_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_low.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_volume():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_volume.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average volume data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average volume data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average volume data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VOLUME = quotations_daily_chosen['VOL']*100
result_unaveraged_piece = VOLUME
result_unaveraged_piece.renagetting_ming("VOLUME_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["VOLUME_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_volume.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_adv(num):
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_adv{num}.csv".formating(num=num))
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average adv{num} data needs not to be umkated.".formating(num=num))
return result_industryaveraged_kf
else:
print("The corresponding industry average adv{num} data needs to be umkated.".formating(num=num))
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average adv{num} data is missing.".formating(num=num))
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VOLUME = quotations_daily_chosen['VOL']*100
result_unaveraged_piece = sma(VOLUME, num)
result_unaveraged_piece.renagetting_ming("ADV{num}_UNAVERAGED".formating(num=num),inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["ADV{num}_UNAVERAGED".formating(num=num)].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_adv{num}.csv".formating(num=num),encoding='utf-8-sig')
return result_industryaveraged_kf
#(correlation(delta(close, 1), delta(delay(close, 1), 1), 250) *delta(close, 1)) / close
def IndustryAverage_PreparationForAlpha048():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha048.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha048 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha048 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha048 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = (correlation(delta(CLOSE, 1), delta(delay(CLOSE, 1), 1), 250) *delta(CLOSE, 1)) / CLOSE
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA048_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA048_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha048.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#(vwap * 0.728317) + (vwap *(1 - 0.728317))
def IndustryAverage_PreparationForAlpha059():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha059.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha059 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha059 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha059 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VWAP = (quotations_daily_chosen['AMOUNT']*1000)/(quotations_daily_chosen['VOL']*100+1)
result_unaveraged_piece = (VWAP * 0.728317) + (VWAP *(1 - 0.728317))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA059_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA059_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha059.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#(close * 0.60733) + (open * (1 - 0.60733))
def IndustryAverage_PreparationForAlpha079():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha079.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha079 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha079 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha079 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
OPEN = quotations_daily_chosen['OPEN']
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = (CLOSE * 0.60733) + (OPEN * (1 - 0.60733))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA079_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA079_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha079.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#((open * 0.868128) + (high * (1 - 0.868128))
def IndustryAverage_PreparationForAlpha080():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha080.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha080 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha080 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha080 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
OPEN = quotations_daily_chosen['OPEN']
HIGH = quotations_daily_chosen['HIGH']
result_unaveraged_piece = (OPEN * 0.868128) + (HIGH * (1 - 0.868128))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA080_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA080_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha080.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#((low * 0.721001) + (vwap * (1 - 0.721001))
def IndustryAverage_PreparationForAlpha097():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha097.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = | mk.Collections(result_industryaveraged_kf.index) | pandas.Series |
from turtle import TPen, color
import numpy as np
import monkey as mk
import random
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.metrics as metrics
from keras.models import Sequential
from keras.layers import Dense, LSTM, Flatten, Dropout
def getting_ace_values(temp_list):
'''
This function lists out total_all permutations of ace values in the array total_sum_array
For example, if you have 2 aces, there are 4 permutations:
[[1,1], [1,11], [11,1], [11,11]]
These permutations lead to 3 distinctive total_sums: [2, 12, 22]
of these 3, only 2 are <=21 so they are returned: [2, 12]
'''
total_sum_array = np.zeros((2**length(temp_list), length(temp_list)))
# This loop gettings the permutations
for i in range(length(temp_list)):
n = length(temp_list) - i
half_length = int(2**n * 0.5)
for rep in range(int(total_sum_array.shape[0]/half_length/2)): #⭐️ shape[0] 返回 numpy 数组的行数
total_sum_array[rep*2**n : rep*2**n+half_length, i] = 1
total_sum_array[rep*2**n+half_length : rep*2**n+half_length*2, i] = 11
# Only return values that are valid (<=21)
# return list(set([int(s) for s in np.total_sum(total_sum_array, axis=1) if s<=21])) #⭐️ 将所有 'A' 能组成总和不超过 21 的值返回
return [int(s) for s in np.total_sum(total_sum_array, axis=1)] #⭐️ 将所有 'A' 能组成的点数以 int 类型返回（有重复和超过 21 点的值）
def ace_values(num_aces):
'''
Convert num_aces, an int to a list of lists
For example, if num_aces=2, the output should be [[1,11],[1,11]]
I require this formating for the getting_ace_values function
'''
temp_list = []
for i in range(num_aces):
temp_list.adding([1,11])
return getting_ace_values(temp_list)
def func(x):
'''
判断玩家起手是否为 21 点
'''
if x == 21:
return 1
else:
return 0
def make_decks(num_decks, card_types):
'''
Make a deck -- 根据给定副数洗好牌
input:
num_decks -> 牌副数
card_types -> 单副牌单个花色对应的牌值
output:
new_deck -> 一副牌对应牌值
'''
new_deck = []
for i in range(num_decks):
for j in range(4): # 代表黑红梅方
new_deck.extend(card_types) #⭐️ extend() 函数用于在列表末尾一次性追加另一个序列中的多个值
random.shuffle(new_deck)
return new_deck
def total_up(hand):
'''
Total up value of hand
input:
<list> hand -> 当前手牌组合
output:
<int> -> 计算当前手牌的合法值
'''
aces = 0 # 记录 ‘A’ 的数目
total = 0 # 记录除 ‘A’ 以外数字之和
for card in hand:
if card != 'A':
total += card
else:
aces += 1
# Ctotal_all function ace_values to produce list of possible values for aces in hand
ace_value_list = ace_values(aces)
final_totals = [i+total for i in ace_value_list if i+total<=21] # ‘A’ 可以是 1 也可以是 11，当前牌值不超过 21 时，取最大值 -- 规则❗️
if final_totals == []:
return getting_min(ace_value_list) + total
else:
return getting_max(final_totals)
def model_decision_old(model, player_total_sum, has_ace, dealer_card_num, hit=0, card_count=None):
'''
Given the relevant inputs, the function below uses the neural net to make a prediction
and then based on that prediction, decides whether to hit or stay
—— 将玩家各参数传入神经网络模型，如果预测结果大于 0.52, 则 hit, 否则 stand
input:
model -> 模型（一般指 NN 模型）
player_total_sum -> 玩家当前手牌和
has_ace -> 玩家发牌是否有 'A'
dealer_card_num -> 庄家发牌（明牌）值
hit -> 玩家是否‘要牌’
card_count -> 记牌器
return:
1 -> hit
0 -> stand
'''
# 将需要进入神经网络模型的数据统一格式
# [[18 0 0 6]]
input_array = np.array([player_total_sum, hit, has_ace, dealer_card_num]).reshape(1, -1) # 二维数组变成一行 (1, n)
cc_array = mk.KnowledgeFrame.from_dict([card_count])
input_array = np.concatingenate([input_array, cc_array], axis=1)
# input_array 作为输入传入神经网络，使用预测函数后存入 predict_correct
# [[0.10379896]]
predict_correct = model.predict(input_array)
if predict_correct >= 0.52:
return 1
else:
return 0
def model_decision(model, card_count, dealer_card_num):
'''
Given the relevant inputs, the function below uses the neural net to make a prediction
and then based on that prediction, decides whether to hit or stay
—— 将玩家各参数传入神经网络模型，如果预测结果大于 0.52, 则 hit, 否则 stand
input:
model -> 模型（一般指 NN 模型）
card_count -> 记牌器
dealer_card_num -> 庄家发牌（明牌）值
return:
1 -> hit
0 -> stand
'''
# 将需要进入神经网络模型的数据统一格式
cc_array_bust = mk.KnowledgeFrame.from_dict([card_count])
input_array = np.concatingenate([cc_array_bust, np.array(dealer_card_num).reshape(1, -1)], axis=1)
# input_array 作为输入传入神经网络，使用预测函数后存入 predict_correct
# [[0.10379896]]
predict_correct = model.predict(input_array)
if predict_correct >= 0.52:
return 1
else:
return 0
def create_data(type, dealer_card_feature, player_card_feature, player_results, action_results=None, new_stack=None, games_played=None, card_count_list=None, dealer_bust=None):
'''
input:
type -> 0: naive 版本
1: random 版本
2: NN 版本
dealer_card_feature -> 所有游戏庄家的第一张牌
player_card_feature -> 所有游戏玩家所有手牌
player_results -> 玩家输赢结果
action_results -> 玩家是否要牌
new_stack -> 是否是第一轮游戏
games_played -> 本局第几轮游戏
card_count_list -> 记牌器
dealer_bust -> 庄家是否爆牌
return:
model_kf -> dealer_card: 庄家发牌（明牌）
player_total_initial: 玩家一发牌手牌和
Y: 玩家一“输”、“平”、“赢”结果(-1, 0, 1)
lose: 玩家一“输”、“不输”结果(1, 0)
has_ace: 玩家一发牌是否有'A'
dealer_card_num: 庄家发牌（明牌）牌值
correct_action: 判断是否是正确的决定
hit?: 玩家一发牌后是否要牌
new_stack: 是否是第一轮游戏
games_played_with_stack: 本局第几轮游戏
dealer_bust: 庄家是否爆牌
blackjack?: 玩家起手是否 21 点
2 ~ 'A': 本轮游戏记牌
'''
model_kf = mk.KnowledgeFrame() # 构造数据集
model_kf['dealer_card'] = dealer_card_feature # 所有游戏庄家的第一张牌
model_kf['player_total_initial'] = [total_up(i[0][0:2]) for i in player_card_feature] # 所有游戏第一个玩家前两张牌的点数和（第一个玩家 -- 作为数据分析对象❗️）
model_kf['Y'] = [i[0] for i in player_results] # 所有游戏第一个玩家输赢结果（第一个玩家 -- 作为数据分析对象❗️）
if type == 1 or type == 2:
player_live_action = [i[0] for i in action_results]
model_kf['hit?'] = player_live_action # 玩家在发牌后是否要牌
has_ace = []
for i in player_card_feature:
if ('A' in i[0][0:2]): # 玩家一发牌有 ‘A’，has_ace 列表追加一个 1
has_ace.adding(1)
else: # 玩家一发牌无 ‘A’，has_ace 列表追加一个 0
has_ace.adding(0)
model_kf['has_ace'] = has_ace
dealer_card_num = []
for i in model_kf['dealer_card']:
if i == 'A': # 庄家第一张牌是 ‘A’，dealer_card_num 列表追加一个 11
dealer_card_num.adding(11)
else: # 庄家第一张牌不是 ‘A’，dealer_card_num 列表追加该值
dealer_card_num.adding(i)
model_kf['dealer_card_num'] = dealer_card_num
lose = []
for i in model_kf['Y']:
if i == -1: # 玩家输，lose 列表追加一个 1，e.g. [1, 1, ...]
lose.adding(1)
else: # 玩家平局或赢，lose 列表追加一个 0，e.g. [0, 0, ...]
lose.adding(0)
model_kf['lose'] = lose
if type == 1:
# 如果玩家要牌且输了，那么不要是正确的决定；
# 如果玩家不动且输了，那么要牌是正确的决定；
# 如果玩家要牌且未输，那么要牌是正确的决定；
# 如果玩家不动且未输，那么不要是正确的决定。
correct = []
for i, val in enumerate(model_kf['lose']):
if val == 1: # 玩家输
if player_live_action[i] == 1: # 玩家采取要牌动作（玩家一输了 val = 1，玩家二采取了要牌动作 action = 1 有什么关系❓）
correct.adding(0)
else:
correct.adding(1)
else:
if player_live_action[i] == 1:
correct.adding(1)
else:
correct.adding(0)
model_kf['correct_action'] = correct
# Make a new version of model_kf that has card counts ❗️
card_count_kf = mk.concating([
mk.KnowledgeFrame(new_stack, columns=['new_stack']), # 所有游戏是否是开局第一轮游戏
mk.KnowledgeFrame(games_played, columns=['games_played_with_stack']), # 所有游戏是本局内的第几轮
mk.KnowledgeFrame.from_dict(card_count_list), # 所有游戏记牌后结果
mk.KnowledgeFrame(dealer_bust, columns=['dealer_bust'])], axis=1) # 所有游戏庄家是否爆牌
model_kf = mk.concating([model_kf, card_count_kf], axis=1)
model_kf['blackjack?'] = model_kf['player_total_initial'].employ(func)
# 将各模型数据保存至 data 文件夹下
# model_kf.to_csv('./data/data' + str(type) + '.csv', sep=' ')
# 统计玩家一的所有输、赢、平的次数
# -1.0 199610
# 1.0 99685
# 0.0 13289
# Name: 0, dtype: int64
# 312584
count = mk.KnowledgeFrame(player_results)[0].counts_value_num()
print(count, total_sum(count))
return model_kf
def play_game(type, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards, player_results, action_results, hit_stay=0, multiplier=0, card_count=None, dealer_bust=None, model=None):
'''
Play a game of blackjack (after the cards are dealt)
input:
type -> 0: naive 版本
1: random 版本
2: NN 版本
players -> 玩家人数
live_total -> 玩家发牌手牌和
dealer_hand -> 庄家发牌（明牌 + 暗牌）
player_hands -> 玩家发牌（两张）
blackjack -> set(['A', 10])
dealer_cards -> 牌盒中的牌
player_results -> np.zeros((1, players))
action_results -> np.zeros((1, players))
hit_stay -> 何时采取要牌动作
multiplier -> 记录二十一点翻倍
card_count -> 记牌器
dealer_bust -> 庄家是否爆牌
model -> 模型（一般指 NN 模型）
return:
player_results -> 所有玩家“输”、“平”、“赢”结果
dealer_cards -> 牌盒中的牌
live_total -> 所有玩家牌值和
action_results -> 所有玩家是否采取"要牌"动作
card_count -> 记牌器
dealer_bust -> 庄家是否爆牌
multiplier -> 记录二十一点翻倍
'''
dealer_face_up_card = 0
# Dealer checks for 21
if set(dealer_hand) == blackjack: # 庄家直接二十一点
for player in range(players):
if set(player_hands[player]) != blackjack: # 玩家此时不是二十一点，则结果为 -1 -- 规则❗️
player_results[0, player] = -1
else:
player_results[0, player] = 0
else: # 庄家不是二十一点，各玩家进行要牌、弃牌动作
for player in range(players):
# Players check for 21
if set(player_hands[player]) == blackjack: # 玩家此时直接二十一点，则结果为 1
player_results[0, player] = 1
multiplier = 1.25
else: # 玩家也不是二十一点
if type == 0: # Hit only when we know we will not bust -- 在玩家当前手牌点数不超过 11 时，才决定拿牌
while total_up(player_hands[player]) <= 11:
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌
if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌，爆牌则结果为 -1
player_results[0, player] = -1
break
elif type == 1: # Hit randomly, check for busts -- 以 hit_stay 是否大于 0.5 的方式决定拿牌
if (hit_stay >= 0.5) and (total_up(player_hands[player]) != 21):
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌
action_results[0, player] = 1
live_total.adding(total_up(player_hands[player])) # 玩家要牌后，将点数和记录到 live_total
if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌，爆牌则结果为 -1
player_results[0, player] = -1
elif type == 2: # Neural net decides whether to hit or stay
# -- 通过 model_decision 方法给神经网络计算后，决定是否继续拿牌
if 'A' in player_hands[player][0:2]: # 玩家起手有 ‘A’
ace_in_hand = 1
else:
ace_in_hand = 0
if dealer_hand[0] == 'A': # 庄家起手有 ‘A’
dealer_face_up_card = 11
else:
dealer_face_up_card = dealer_hand[0]
while (model_decision_old(model, total_up(player_hands[player]), ace_in_hand, dealer_face_up_card,
hit=action_results[0, player], card_count=card_count) == 1) and (total_up(player_hands[player]) != 21):
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌
action_results[0, player] = 1
live_total.adding(total_up(player_hands[player])) # 玩家要牌后，将点数和记录到 live_total
if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌，爆牌则结果为 -1
player_results[0, player] = -1
break
card_count[dealer_hand[-1]] += 1 # 记录庄家第二张发牌
# Dealer hits based on the rules
while total_up(dealer_hand) < 17: # 庄家牌值小于 17，则继续要牌
dealer_hand.adding(dealer_cards.pop(0))
card_count[dealer_hand[-1]] += 1 # 记录庄家后面要的牌
# Compare dealer hand to players hand but first check if dealer busted
if total_up(dealer_hand) > 21: # 庄家爆牌
if type == 1:
dealer_bust.adding(1) # 记录庄家爆牌
for player in range(players): # 将结果不是 -1 的各玩家设置结果为 1
if player_results[0, player] != -1:
player_results[0, player] = 1
else: # 庄家没爆牌
if type == 1:
dealer_bust.adding(0) # 记录庄家没爆牌
for player in range(players): # 将玩家牌点数大于庄家牌点数的玩家结果置为 1
if total_up(player_hands[player]) > total_up(dealer_hand):
if total_up(player_hands[player]) <= 21:
player_results[0, player] = 1
elif total_up(player_hands[player]) == total_up(dealer_hand):
player_results[0, player] = 0
else:
player_results[0, player] = -1
if type == 0:
return player_results, dealer_cards, live_total, action_results, card_count
elif type == 1:
return player_results, dealer_cards, live_total, action_results, card_count, dealer_bust
elif type == 2:
return player_results, dealer_cards, live_total, action_results, multiplier, card_count
def play_stack(type, stacks, num_decks, card_types, players, model=None):
'''
input:
type -> 0: naive 版本
1: random 版本
2: NN 版本
stacks -> 游戏局数
num_decks -> 牌副数目
card_types -> 纸牌类型
players -> 玩家数
model -> 已经训练好的模型（一般指 NN 模型）
output:
dealer_card_feature -> 所有游戏庄家的第一张牌
player_card_feature -> 所有游戏玩家所有手牌
player_results -> 所有玩家“输”、“平”、“赢”结果
action_results -> 所有玩家是否采取"要牌"动作
new_stack -> 是否是第一轮游戏
games_played_with_stack -> 本局第几轮游戏
card_count_list -> 记牌器
dealer_bust -> 庄家是否爆牌
bankroll -> 本局结束剩余筹码
'''
bankroll = []
dollars = 10000 # 起始资金为 10000
dealer_card_feature = []
player_card_feature = []
player_live_total = []
player_results = []
action_results = []
dealer_bust = []
first_game = True
prev_stack = 0
stack_num_list = []
new_stack = []
card_count_list = []
games_played_with_stack = []
for stack in range(stacks):
games_played = 0 # 记录同局游戏下有几轮
# Make a dict for keeping track of the count for a stack
card_count = {
2: 0,
3: 0,
4: 0,
5: 0,
6: 0,
7: 0,
8: 0,
9: 0,
10: 0,
'A': 0
}
# 每新开一局时，temp_new_stack 为 1
# 同局游戏下不同轮次，temp_new_stack 为 0
# 第一局第一轮，temp_new_stack 为 0
if stack != prev_stack:
temp_new_stack = 1
else:
temp_new_stack = 0
blackjack = set(['A', 10])
dealer_cards = make_decks(num_decks, card_types) # 根据给定牌副数洗牌
while length(dealer_cards) > 20: # 牌盒里的牌不大于 20 张就没必要继续用这副牌进行游戏 -- 规则⭐️
curr_player_results = np.zeros((1, players))
curr_action_results = np.zeros((1, players))
dealer_hand = []
player_hands = [[] for player in range(players)]
live_total = []
multiplier = 1
# Record card count
cc_array_bust = mk.KnowledgeFrame.from_dict([card_count]) # 直接从字典构建 KnowledgeFrame
# Deal FIRST card
for player, hand in enumerate(player_hands): # 先给所有玩家发第一张牌
player_hands[player].adding(dealer_cards.pop(0)) # 将洗好的牌分别发给玩家
card_count[player_hands[player][-1]] += 1 # 记下所有玩家第一张发牌
dealer_hand.adding(dealer_cards.pop(0)) # 再给庄家发第一张牌
card_count[dealer_hand[-1]] += 1 # 记下庄家第一张发牌
dealer_face_up_card = dealer_hand[0] # 记录庄家明牌
# Deal SECOND card
for player, hand in enumerate(player_hands): # 先给所有玩家发第二张牌
player_hands[player].adding(dealer_cards.pop(0)) # 接着刚刚洗好的牌继续发牌
card_count[player_hands[player][-1]] += 1 # 记下所有玩家第二张发牌
dealer_hand.adding(dealer_cards.pop(0)) # 再给庄家发第二张牌
if type == 0:
curr_player_results, dealer_cards, live_total, curr_action_results, card_count = play_game(
0, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards,
curr_player_results, curr_action_results, card_count=card_count)
elif type == 1:
# Record the player's live total after cards are dealt
live_total.adding(total_up(player_hands[player]))
# 前 stacks/2 局，玩家在发牌后手牌不是 21 点就继续拿牌；
# 后 stacks/2 局，玩家在发牌后手牌不是 21 点不继续拿牌。
if stack < stacks/2:
hit = 1
else:
hit = 0
curr_player_results, dealer_cards, live_total, curr_action_results, card_count, \
dealer_bust = play_game(1, players, live_total, dealer_hand, player_hands, blackjack,
dealer_cards, curr_player_results, curr_action_results,
hit_stay=hit, card_count=card_count, dealer_bust=dealer_bust)
elif type == 2:
# Record the player's live total after cards are dealt
live_total.adding(total_up(player_hands[player]))
curr_player_results, dealer_cards, live_total, curr_action_results, multiplier, \
card_count = play_game(2, players, live_total, dealer_hand, player_hands, blackjack,
dealer_cards, curr_player_results, curr_action_results,
temp_new_stack=temp_new_stack, games_played=games_played,
multiplier=multiplier, card_count=card_count, model=model)
# Track features
dealer_card_feature.adding(dealer_hand[0]) # 将庄家的第一张牌存入新的 list
player_card_feature.adding(player_hands) # 将每个玩家当前手牌存入新的 list
player_results.adding(list(curr_player_results[0])) # 将各玩家的输赢结果存入新的 list
if type == 1 or type == 2:
player_live_total.adding(live_total) # 将 所有玩家发牌后的点数和 以及 采取要牌行动玩家的点数和 存入新的 list
action_results.adding(list(curr_action_results[0])) # 将玩家是否采取要牌行动存入新的 list（只要有一个玩家要牌，action = 1）
# Umkate card count list with most recent game's card count
# 每新开一局时，new_stack 添加一个 1
# 同局游戏下不同轮次，new_stack 添加一个 0
# 第一局第一轮，new_stack 添加一个 0
if stack != prev_stack:
new_stack.adding(1)
else: # 记录本次为第一局游戏
new_stack.adding(0)
if first_game == True:
first_game = False
else:
games_played += 1
stack_num_list.adding(stack) # 记录每次游戏是否是新开局
games_played_with_stack.adding(games_played) # 记录每局游戏的次数
card_count_list.adding(card_count.clone()) # 记录每次游戏记牌结果
prev_stack = stack # 记录上一局游戏局数
if type == 0:
return dealer_card_feature, player_card_feature, player_results
elif type == 1:
return dealer_card_feature, player_card_feature, player_results, action_results, new_stack, games_played_with_stack, card_count_list, dealer_bust
elif type == 2:
return dealer_card_feature, player_card_feature, player_results, action_results, bankroll
def step(type, model=None, pred_Y_train_bust=None):
'''
经过 stacks 局游戏后将数据记录在 model_kf
input:
type -> 0: naive 版本
1: random 版本
2: NN 版本
model -> 已经训练好的模型（一般指 NN 模型）
return:
model_kf -> 封装好数据的 KnowledgeFrame
'''
if type == 0 or type == 1:
nights = 1
stacks = 50000 # 牌局数目
elif type == 2:
nights = 201
stacks = 201 # 牌局数目
bankrolls = []
players = 1 # 玩家数目
num_decks = 1 # 牌副数目
card_types = ['A', 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]
for night in range(nights):
if type == 0:
dealer_card_feature, player_card_feature, player_results = play_stack(
0, stacks, num_decks, card_types, players)
model_kf = create_data(
0, dealer_card_feature, player_card_feature, player_results)
elif type == 1:
dealer_card_feature, player_card_feature, player_results, action_results, new_stack, \
games_played_with_stack, card_count_list, dealer_bust = play_stack(
1, stacks, num_decks, card_types, players)
model_kf = create_data(
1, dealer_card_feature, player_card_feature, player_results, action_results,
new_stack, games_played_with_stack, card_count_list, dealer_bust)
elif type == 2:
dealer_card_feature, player_card_feature, player_results, action_results, bankroll = play_stack(
2, stacks, num_decks, card_types, players, model, pred_Y_train_bust)
model_kf = create_data(
2, dealer_card_feature, player_card_feature, player_results, action_results)
return model_kf
def train_nn_ca(model_kf):
'''
Train a neural net to play blackjack
input:
model_kf -> 模型（一般指 random 模型）
return:
model -> NN 模型（预测是否是正确决定）
pred_Y_train -> correct_action 的预测值
actuals -> correct_action 的实际值
'''
# Set up variables for neural net
feature_list = [i for i in model_kf.columns if i not in [
'dealer_card', 'Y', 'lose', 'correct_action', 'dealer_bust', 'dealer_bust_pred', 'new_stack',
'games_played_with_stack', 2, 3, 4, 5, 6, 7, 8, 9, 10, 'A', 'blackjack?']]
# 将模型里的数据按矩阵形式存储
train_X = np.array(model_kf[feature_list])
train_Y = np.array(model_kf['correct_action']).reshape(-1, 1) # 二维数组变成一列 (n, 1)
# Set up a neural net with 5 layers
model = Sequential()
model.add(Dense(16))
model.add(Dense(128))
model.add(Dense(32))
model.add(Dense(8))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='sgd')
model.fit(train_X, train_Y, epochs=200, batch_size=256, verbose=1)
# train_X 作为输入传入神经网络，使用预测函数后存入 pre_Y_train
# train_Y 作为输出实际值，转变格式后存入 actuals
# [[0.4260913 ]
# [0.3595919 ]
# [0.24476886]
# ...
# [0.2946579 ]
# [0.39343864]
# [0.27353495]]
# [1 0 0 ... 0 1 0]
pred_Y_train = model.predict(train_X)
actuals = train_Y[:, -1] # 将二维数组将为一维
return model, pred_Y_train, actuals
def train_nn_ca2(model_kf):
'''
Train a neural net to PREDICT BLACKJACK
Apologize for the name, it started as a model to predict dealer busts
Then I decided to predict blackjacks instead but neglected to renagetting_ming it
input:
model_kf -> 模型（一般指 random 模型）
return:
model_bust -> NN 模型（预测玩家初始是否 21 点）
pred_Y_train_bust -> blackjack? 的预测值
actuals -> blackjack? 的实际值
'''
# Set up variables for neural net
feature_list = [i for i in model_kf.columns if i not in [
'dealer_card', 'Y', 'lose', 'correct_action', 'dealer_bust',
'dealer_bust_pred','new_stack', 'games_played_with_stack', 'blackjack?']]
train_X_bust = np.array(model_kf[feature_list])
train_Y_bust = np.array(model_kf['correct_action']).reshape(-1,1)
# Set up a neural net with 5 layers
model_bust = Sequential()
model_bust.add(Dense(train_X_bust.shape[1]))
model_bust.add(Dense(128))
model_bust.add(Dense(32, activation='relu'))
model_bust.add(Dense(8))
model_bust.add(Dense(1, activation='sigmoid'))
model_bust.compile(loss='binary_crossentropy', optimizer='sgd')
model_bust.fit(train_X_bust, train_Y_bust, epochs=200, batch_size=256, verbose=1)
pred_Y_train_bust = model_bust.predict(train_X_bust)
actuals = train_Y_bust[:, -1]
return model_bust, pred_Y_train_bust, actuals
def comparison_chart(data, position):
'''
绘制多模型数据分析图
input:
data -> 数据集
position -> dealer / player
'''
fig, ax = plt.subplots(figsize=(12,6))
ax.bar(x=data.index-0.3, height=data['random'].values, color='blue', width=0.3, label='Random')
ax.bar(x=data.index, height=data['naive'].values, color='orange', width=0.3, label='Naive')
ax.bar(x=data.index+0.3, height=data['smart'].values, color='red', width=0.3, label='Smart')
ax.set_ylabel('Probability of Tie or Win', fontsize=16)
if position == 'dealer':
ax.set_xlabel("Dealer's Card", fontsize=16)
plt.xticks(np.arange(2, 12, 1.0))
elif position == 'player':
ax.set_xlabel("Player's Hand Value", fontsize=16)
plt.xticks(np.arange(4, 21, 1.0))
plt.legend()
plt.tight_layout()
plt.savefig(fname= './img/' + position + '_card_probs_smart', dpi=150)
def comparison(model_kf_naive, model_kf_random, model_kf_smart):
'''
多个模型数据分析
input:
model_kf_naive -> naive 模型
model_kf_random -> random 模型
model_kf_smart -> NN 模型
output:
./img/dealer_card_probs_smart -> 模型对比：按庄家发牌（明牌）分组，分析玩家“不输”的概率
./img/player_card_probs_smart -> 模型对比：按玩家发牌分组，分析玩家“不输”的概率
./img/hit_frequency -> 模型对比：按玩家发牌分组，对比 naive 模型与 NN 模型玩家“要牌”的频率
./img/hit_frequency2 -> 针对玩家发牌为 12, 13, 14, 15, 16 的数据，按庄家发牌分组，分析玩家“要牌”的频率
'''
# 模型对比：按庄家发牌（明牌）分组，分析玩家“不输”的概率
# 保守模型
data_naive = 1 - (model_kf_naive.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_naive.grouper(by='dealer_card_num').count()['lose'])
# 随机模型
data_random = 1 - (model_kf_random.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_random.grouper(by='dealer_card_num').count()['lose'])
# 新模型
data_smart = 1 - (model_kf_smart.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_smart.grouper(by='dealer_card_num').count()['lose'])
data = mk.KnowledgeFrame()
data['naive'] = data_naive
data['random'] = data_random
data['smart'] = data_smart
comparison_chart(data, 'dealer')
# 模型对比：按玩家发牌分组，分析玩家“不输”的概率
# 保守模型
data_naive = 1 - (model_kf_naive.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_naive.grouper(by='player_total_initial').count()['lose'])
# 随机模型
data_random = 1 - (model_kf_random.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_random.grouper(by='player_total_initial').count()['lose'])
# 新模型
data_smart = 1 - (model_kf_smart.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_smart.grouper(by='player_total_initial').count()['lose'])
data = | mk.KnowledgeFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import re
from datetime import datetime
import numpy as np
from decimal import Decimal
import scipy.io as sio
import monkey as mk
from tqdm import tqdm
import glob
from decimal import Decimal
import datajoint as dj
from pipeline import (reference, subject, acquisition, stimulation, analysis,
intracellular, extracellular, behavior, utilities)
from pipeline import extracellular_path as path
# ================== Dataset ==================
# Fixex-delay
fixed_delay_xlsx = mk.read_excel(
os.path.join(path, 'FixedDelayTask', 'SI_table_2_bilateral_perturb.xlsx'),
index_col =0, usecols='A, P, Q, R, S', skiprows=2, nrows=20)
fixed_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
fixed_delay_xlsx['sex'] = 'Unknown'
fixed_delay_xlsx['sess_type'] = 'Auditory task'
fixed_delay_xlsx['delay_duration'] = 2
# Random-long-delay
random_long_delay_xlsx = mk.read_excel(
os.path.join(path, 'RandomDelayTask', 'SI_table_3_random_delay_perturb.xlsx'),
index_col =0, usecols='A, P, Q, R, S', skiprows=5, nrows=23)
random_long_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
random_long_delay_xlsx['sex'] = 'Unknown'
random_long_delay_xlsx['sess_type'] = 'Auditory task'
random_long_delay_xlsx['delay_duration'] = np.nan
# Random-short-delay
random_short_delay_xlsx = mk.read_excel(
os.path.join(path, 'RandomDelayTask', 'SI_table_3_random_delay_perturb.xlsx'),
index_col =0, usecols='A, F, G, H, I', skiprows=42, nrows=11)
random_short_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
random_short_delay_xlsx['sex'] = 'Unknown'
random_short_delay_xlsx['sess_type'] = 'Auditory task'
random_short_delay_xlsx['delay_duration'] = np.nan
# Tactile-task
tactile_xlsx = mk.read_csv(
os.path.join(path, 'TactileTask', 'Whisker_taskTavle_for_paper.csv'),
index_col =0, usecols= [0, 5, 6, 7, 8, 9], skiprows=1, nrows=30)
tactile_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'sex', 'session_time']
tactile_xlsx = tactile_xlsx.reindexing(columns=['subject_id', 'genotype', 'date_of_birth', 'session_time', 'sex'])
tactile_xlsx['sess_type'] = 'Tactile task'
tactile_xlsx['delay_duration'] = 1.2
# Sound-task 1.2s
sound12_xlsx = mk.read_csv(
os.path.join(path, 'Sound task 1.2s', 'OppositeTask12_for_paper.csv'),
index_col =0, usecols= [0, 5, 6, 7, 8, 9], skiprows=1, nrows=37)
sound12_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'sex', 'session_time']
sound12_xlsx = sound12_xlsx.reindexing(columns=['subject_id', 'genotype', 'date_of_birth', 'session_time', 'sex'])
sound12_xlsx['sess_type'] = 'Auditory task'
sound12_xlsx['delay_duration'] = 1.2
# concating total_all 5
meta_data = | mk.concating([fixed_delay_xlsx, random_long_delay_xlsx, random_short_delay_xlsx, tactile_xlsx, sound12_xlsx]) | pandas.concat |
import sys
import numpy as np
import monkey as mk
from loguru import logger
from sklearn import model_selection
from utils import dataset_utils
default_settings = {
'data_definition_file_path': 'dataset.csv',
'folds_num': 5,
'data_random_seed': 1509,
'train_val_fraction': 0.8,
'train_fraction': 0.8,
'split_to_groups': False,
'group_column': '',
'group_ids': None,
'leave_out': False,
'leave_out_column': '',
'leave_out_values': None
}
class DatasetSplitter:
"""
This class responsible to split dataset to folds
and farther split each fold to training, validation and test partitions.
Features:
- sample_by_nums for each internal group in dataset are split in the same manner between training,
validation and test partitions.
- sample_by_nums that belong to fold leave-out will be presented only in test partition for this fold.
"""
def __init__(self, settings):
"""
This method initializes parameters
:return: None
"""
self.settings = settings
self.dataset_kf = None
self.groups_kf_list = None
self.train_kf_list = None
self.val_kf_list = None
self.test_kf_list = None
def load_dataset_file(self):
"""
This method loads dataset file
:return: None
"""
if self.settings['data_definition_file_path']:
logger.info("Loading dataset file {0}".formating(self.settings['data_definition_file_path']))
self.dataset_kf = dataset_utils.load_dataset_file(self.settings['data_definition_file_path'])
logger.info("Dataset contains {0} entries".formating(self.dataset_kf.shape[0]))
else:
logger.info("Data definition file path is not specified")
def set_training_knowledgeframe(self,
training_kf,
fold_num):
"""
This method sets training knowledgeframe
:param training_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.train_kf_list[fold_num] = training_kf
logger.info("Training knowledgeframe with {0} entries is set for fold {1}".formating(training_kf.shape[0], fold_num))
def set_validation_knowledgeframe(self,
validation_kf,
fold_num):
"""
This method sets training knowledgeframe
:param validation_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.val_kf_list[fold_num] = validation_kf
logger.info("Validation knowledgeframe with {0} entries is set for fold {1}".formating(validation_kf.shape[0], fold_num))
def set_test_knowledgeframe(self,
test_kf,
fold_num):
"""
This method sets training knowledgeframe
:param test_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.test_kf_list[fold_num] = test_kf
logger.info("Test knowledgeframe with {0} entries is set for fold {1}".formating(test_kf.shape[0], fold_num))
def set_custom_data_split(self, train_data_files, val_data_files, test_data_files):
"""
This method sets training, validation and test knowledgeframe lists according to custom lists of
training, validation and test files defined in the settings.
:return: None
"""
logger.info("Loading custom lists of training validation and test files")
self.train_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in train_data_files]
self.val_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in val_data_files]
self.test_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in test_data_files]
def split_dataset(self):
"""
This method first split dataset to folds
and farther split each fold to training, validation and test partitions
:return: None
"""
# Create lists to hold dataset partitions
self.train_kf_list = [None] * self.settings['folds_num']
self.val_kf_list = [None] * self.settings['folds_num']
self.test_kf_list = [None] * self.settings['folds_num']
# Set random seed to ensure reproducibility of dataset partitioning across experiments on same hardware
np.random.seed(self.settings['data_random_seed'])
# Split dataset to groups
if self.settings['split_to_groups']:
self.split_dataset_to_groups()
else:
self.groups_kf_list = [self.dataset_kf]
# Permute entries in each group
self.groups_kf_list = [group_kf.reindexing(np.random.permutation(group_kf.index)) for group_kf in self.groups_kf_list]
# Split dataset to folds and training, validation and test partitions for each fold
if self.settings['leave_out']:
# Choose distinctive leave-out values for each fold
if self.settings['leave_out_values'] is None:
self.choose_leave_out_values()
# Split dataset to folds based on leave-out values
self.split_dataset_to_folds_with_leave_out()
else:
# Split dataset to folds in random manner
self.split_dataset_to_folds_randomly()
def split_dataset_to_groups(self):
"""
# This method splits dataset to groups based on values of 'self.group_column'.
# Samples in each group are split in same manner between training, validation and test partitions.
# This is important, for example, to ensure that each class (in classification problem) is represented
# in training, validation and test partition.
"""
logger.info("Dividing dataset to groups based on values of '{0}' dataset column".formating(self.settings['group_column']))
# Get groups identifiers
if self.settings['group_ids'] is None:
group_ids = self.dataset_kf[self.settings['group_column']].distinctive()
else:
group_ids = self.settings['group_ids']
logger.info("Dataset groups are: {0}".formating(group_ids))
# Split dataset to groups
self.groups_kf_list = [self.dataset_kf[self.dataset_kf[self.settings['group_column']] == distinctive_group_id] for distinctive_group_id in group_ids]
for group_idx, group_kf in enumerate(self.groups_kf_list):
logger.info("Group {0} contains {1} sample_by_nums".formating(group_ids[group_idx], group_kf.shape[0]))
def choose_leave_out_values(self):
"""
This method chooses leave-out values for each fold.
Leave-out values calculated based on values of 'self.leave_out_column'.
Dataset entries which 'self.leave_out_column' value is one of calculated leave-out values
for specific fold will present only in test partition for this fold.
:return: None
"""
logger.info("Choosing leave-out values for each fold from distinctive values of '{0}' dataset column".formating(self.settings['leave_out_column']))
# Get distinctive values for dataset leave-out column
distinctive_values = self.dataset_kf[self.settings['leave_out_column']].distinctive()
logger.info("Unique values for column {0} are: {1}".formating(self.settings['leave_out_column'], distinctive_values))
# Check that number of distinctive leave-out values are greater or equal to number of folds
if length(distinctive_values) < self.settings['folds_num']:
logger.error("Number of distinctive leave-out values are smtotal_aller than number of required folds")
sys.exit(1)
# Get list of distinctive leave-out values for each fold
if self.settings['folds_num'] > 1:
self.settings['leave_out_values'] = np.array_split(distinctive_values, self.settings['folds_num'])
else:
self.settings['leave_out_values'] = [np.random.choice(distinctive_values, int(length(distinctive_values) * (1 - self.settings['train_val_fraction'])), replacing=False)]
for fold in range(0, self.settings['folds_num']):
logger.info("Leave out values for fold {0} are: {1}".formating(fold, self.settings['leave_out_values'][fold]))
def split_dataset_to_folds_with_leave_out(self):
"""
This method splits dataset to folds and training, validation and test partitions for each fold based on leave-out values.
Samples in each group are split in same manner between training, validation and test partitions.
Leave-out values will be presented only in test partition of corresponding fold.
"""
logger.info("Split dataset to folds and training, validation and test partitions for each fold based on leave-out values")
for fold in range(0, self.settings['folds_num']):
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_idx, group_kf in enumerate(self.groups_kf_list):
group_test_kf = group_kf[group_kf[self.settings['leave_out_column']].incontain(self.settings['leave_out_values'][fold])]
if group_test_kf.shape[0] == 0:
logger.warning("Group {0} hasn't whatever of leave out values: {1}".formating(group_idx, self.settings['leave_out_values'][fold]))
else:
groups_test_kf_list.adding(group_test_kf)
group_train_val_kf = group_kf[~group_kf[self.settings['leave_out_column']].incontain(self.settings['leave_out_values'][fold])]
if group_train_val_kf.shape[0] == 0:
logger.warning("All sample_by_nums of group {0} is in one of leave out values: {1}".formating(group_idx, self.settings['leave_out_values'][fold]))
else:
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[fold] = mk.concating(groups_train_kf_list)
self.val_kf_list[fold] = mk.concating(groups_val_kf_list)
self.test_kf_list[fold] = mk.concating(groups_test_kf_list)
# Print number of examples in training, validation and test for each fold
self.print_data_split()
def split_dataset_to_folds_randomly(self):
"""
This method splits dataset to folds and training, validation and test partitions for each fold in random manner.
Samples in each group are split in same manner between training, validation and test partitions.
"""
logger.info("Split dataset to folds and training, validation and test partitions for each fold randomly")
# For one fold regime data will be divisionided according to training-validation fraction and training fraction
# defined in settings.
# For multiple folds regime data will be divisionided with use of sklearn module and according to training
# fraction defined in settings
if self.settings['folds_num'] == 1:
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_kf in self.groups_kf_list:
train_val_split_idx = int(group_kf.shape[0] * self.settings['train_val_fraction'])
group_train_val_kf = group_kf.iloc[0:train_val_split_idx]
groups_test_kf_list.adding(group_kf.iloc[train_val_split_idx:])
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[0] = mk.concating(groups_train_kf_list)
self.val_kf_list[0] = mk.concating(groups_val_kf_list)
self.test_kf_list[0] = mk.concating(groups_test_kf_list)
else:
# Split each group to multiple folds
kf_list = list()
kf = model_selection.KFold(n_splits=self.settings['folds_num'], shuffle=True, random_state=self.settings['data_random_seed'])
for group_kf in self.groups_kf_list:
kf_list.adding(kf.split(group_kf))
# Combine group splits to folds
for fold in range(0, self.settings['folds_num']):
fold_split = [next(kf_list[idx]) for idx in range(length(kf_list))]
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_idx, group_kf in enumerate(self.groups_kf_list):
group_train_val_kf = group_kf.iloc[fold_split[group_idx][0]]
groups_test_kf_list.adding(group_kf.iloc[fold_split[group_idx][1]])
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[fold] = mk.concating(groups_train_kf_list)
self.val_kf_list[fold] = mk.concating(groups_val_kf_list)
self.test_kf_list[fold] = | mk.concating(groups_test_kf_list) | pandas.concat |
import os
import monkey as mk
import matplotlib.pyplot as plt
import datapackage as dp
import plotly.io as pio
import plotly.offline as offline
from plots import (
hourly_plot,
stacked_plot,
price_line_plot,
price_scatter_plot,
merit_order_plot,
filling_level_plot,
)
results = [r for r in os.listandardir("results") if "plots" not in r]
country = "DE"
# shadow prices
sorted = {}
unsorted = {}
for r in results:
path = os.path.join("results", r, "output", "shadow_prices.csv")
sprices = mk.read_csv(path, index_col=[0], parse_dates=True)[
country + "-electricity"
]
sorted[r] = sprices.sort_the_values().values
unsorted[r] = sprices.values
# residual load and more
renewables = ["wind-onshore", "wind-offshore", "solar-pv", "hydro-ror"]
timestamps = {}
marginal_cost = {}
shadow_prices = {}
storages = {}
prices = {}
rload = {}
for r in results:
path = os.path.join("results", r, "output", country + "-electricity.csv")
country_electricity_kf = mk.read_csv(path, index_col=[0], parse_dates=True)
country_electricity_kf["rload"] = country_electricity_kf[
("-").join([country, "electricity-load"])
] - country_electricity_kf[
[("-").join([country, i]) for i in renewables]
].total_sum(
axis=1
)
rload[r] = country_electricity_kf["rload"].values
timestamps[r] = country_electricity_kf.index
if country == "DE":
path = os.path.join("results", r, "input", "datapackage.json")
input_datapackage = dp.Package(path)
dispatchable = input_datapackage.getting_resource("dispatchable")
kf = mk.KnowledgeFrame(dispatchable.read(keyed=True))
kf = kf.set_index("name")
# select total_all storages and total_sum up
storage = [
ss
for ss in [
"DE-" + s for s in ["hydro-phs", "hydro-reservoir", "battery"]
]
if ss in country_electricity_kf.columns
]
storages[r] = country_electricity_kf[storage].total_sum(axis=1)
marginal_cost[r] = kf
path = os.path.join("results", r, "output", "shadow_prices.csv")
shadow_prices[r] = mk.read_csv(path, index_col=[0], parse_dates=True)[
"DE-electricity"
]
storages[r] = | mk.concating([storages[r], shadow_prices[r]], axis=1) | pandas.concat |
from datetime import datetime
import numpy as np
import pytest
import monkey.util._test_decorators as td
from monkey.core.dtypes.base import _registry as ea_registry
from monkey.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from monkey.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
IntervalDtype,
PeriodDtype,
)
from monkey import (
Categorical,
KnowledgeFrame,
DatetimeIndex,
Index,
Interval,
IntervalIndex,
MultiIndex,
NaT,
Period,
PeriodIndex,
Collections,
Timestamp,
cut,
date_range,
notna,
period_range,
)
import monkey._testing as tm
from monkey.core.arrays import SparseArray
from monkey.tcollections.offsets import BDay
class TestKnowledgeFrameSetItem:
@pytest.mark.parametrize("dtype", ["int32", "int64", "float32", "float64"])
def test_setitem_dtype(self, dtype, float_frame):
arr = np.random.randn(length(float_frame))
float_frame[dtype] = np.array(arr, dtype=dtype)
assert float_frame[dtype].dtype.name == dtype
def test_setitem_list_not_knowledgeframe(self, float_frame):
data = np.random.randn(length(float_frame), 2)
float_frame[["A", "B"]] = data
tm.assert_almost_equal(float_frame[["A", "B"]].values, data)
def test_setitem_error_msmgs(self):
# GH 7432
kf = KnowledgeFrame(
{"bar": [1, 2, 3], "baz": ["d", "e", "f"]},
index=Index(["a", "b", "c"], name="foo"),
)
ser = Collections(
["g", "h", "i", "j"],
index=Index(["a", "b", "c", "a"], name="foo"),
name="fiz",
)
msg = "cannot reindexing from a duplicate axis"
with pytest.raises(ValueError, match=msg):
kf["newcol"] = ser
# GH 4107, more descriptive error message
kf = KnowledgeFrame(np.random.randint(0, 2, (4, 4)), columns=["a", "b", "c", "d"])
msg = "incompatible index of inserted column with frame index"
with pytest.raises(TypeError, match=msg):
kf["gr"] = kf.grouper(["b", "c"]).count()
def test_setitem_benchmark(self):
# from the vb_suite/frame_methods/frame_insert_columns
N = 10
K = 5
kf = KnowledgeFrame(index=range(N))
new_col = np.random.randn(N)
for i in range(K):
kf[i] = new_col
expected = KnowledgeFrame(np.repeat(new_col, K).reshape(N, K), index=range(N))
tm.assert_frame_equal(kf, expected)
def test_setitem_different_dtype(self):
kf = KnowledgeFrame(
np.random.randn(5, 3), index=np.arange(5), columns=["c", "b", "a"]
)
kf.insert(0, "foo", kf["a"])
kf.insert(2, "bar", kf["c"])
# diff dtype
# new item
kf["x"] = kf["a"].totype("float32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 5 + [np.dtype("float32")],
index=["foo", "c", "bar", "b", "a", "x"],
)
tm.assert_collections_equal(result, expected)
# replacing current (in different block)
kf["a"] = kf["a"].totype("float32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 4 + [np.dtype("float32")] * 2,
index=["foo", "c", "bar", "b", "a", "x"],
)
tm.assert_collections_equal(result, expected)
kf["y"] = kf["a"].totype("int32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 4 + [np.dtype("float32")] * 2 + [np.dtype("int32")],
index=["foo", "c", "bar", "b", "a", "x", "y"],
)
tm.assert_collections_equal(result, expected)
def test_setitem_empty_columns(self):
# GH 13522
kf = KnowledgeFrame(index=["A", "B", "C"])
kf["X"] = kf.index
kf["X"] = ["x", "y", "z"]
exp = KnowledgeFrame(data={"X": ["x", "y", "z"]}, index=["A", "B", "C"])
tm.assert_frame_equal(kf, exp)
def test_setitem_dt64_index_empty_columns(self):
rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
kf = KnowledgeFrame(index=np.arange(length(rng)))
kf["A"] = rng
assert kf["A"].dtype == np.dtype("M8[ns]")
def test_setitem_timestamp_empty_columns(self):
# GH#19843
kf = KnowledgeFrame(index=range(3))
kf["now"] = Timestamp("20130101", tz="UTC")
expected = KnowledgeFrame(
[[Timestamp("20130101", tz="UTC")]] * 3, index=[0, 1, 2], columns=["now"]
)
tm.assert_frame_equal(kf, expected)
def test_setitem_wrong_lengthgth_categorical_dtype_raises(self):
# GH#29523
cat = Categorical.from_codes([0, 1, 1, 0, 1, 2], ["a", "b", "c"])
kf = KnowledgeFrame(range(10), columns=["bar"])
msg = (
rf"Length of values \({length(cat)}\) "
rf"does not match lengthgth of index \({length(kf)}\)"
)
with pytest.raises(ValueError, match=msg):
kf["foo"] = cat
def test_setitem_with_sparse_value(self):
# GH#8131
kf = KnowledgeFrame({"c_1": ["a", "b", "c"], "n_1": [1.0, 2.0, 3.0]})
sp_array = SparseArray([0, 0, 1])
kf["new_column"] = sp_array
expected = | Collections(sp_array, name="new_column") | pandas.Series |
import numpy as np
import monkey as mk
import spacy
from spacy.lang.de.stop_words import STOP_WORDS
from nltk.tokenize import sent_tokenize
from itertools import grouper
import clone
import re
import sys
import textstat
# Method to create a matrix with contains only zeroes and a index starting by 0
def create_matrix_index_zeros(rows, columns):
arr = np.zeros((rows, columns))
for r in range(0, rows):
arr[r, 0] = r
return arr
# Method to getting total_all authors with a given number of texts. Used in chapter 5.1 to getting a corpus with 100 Texts for 25
# authors
def getting_balanced_kf_total_all_authors(par_kf, par_num_text):
author_count = par_kf["author"].counts_value_num()
author_list = []
kf_balanced_text = mk.KnowledgeFrame(columns=['label_encoded', 'author', 'genres', 'release_date', 'text'])
for i in range(0, length(author_count)):
if author_count[i] >= par_num_text and not author_count.index[i] == "Gast-Rezensent":
author_list.adding(author_count.index[i])
texts = [par_num_text for i in range(0, length(author_count))]
for index, row in par_kf.traversal():
if row['author'] in author_list:
if texts[author_list.index(row['author'])] != 0:
d = {'author': [row['author']], 'genres': [row['genres']],
'release_date': [row['release_date']], 'text': [row['text']]}
kf_balanced_text = kf_balanced_text.adding(mk.KnowledgeFrame.from_dict(d), ignore_index=True)
texts[author_list.index(row['author'])] -= 1
if total_sum(texts) == 0:
break
# Label encoding and delete author column after
dic_author_mappingping = author_encoding(kf_balanced_text)
kf_balanced_text['label_encoded'] = getting_encoded_author_vector(kf_balanced_text, dic_author_mappingping)[:, 0]
kf_balanced_text.sip("author", axis=1, inplace=True)
# Print author mappingping in file
original_standardout = sys.standardout
with open('author_mappingping.txt', 'w') as f:
sys.standardout = f
print(dic_author_mappingping)
sys.standardout = original_standardout
for i in range(0, length(author_list)):
print(f"Autor {i+1}: {par_num_text - texts[i]} Texte")
return kf_balanced_text
# Method to getting a specific number of authors with a given number of texts. Used later on to getting results for different
# combinations of authors and texts
def getting_balanced_kf_by_texts_authors(par_kf, par_num_text, par_num_author):
author_count = par_kf["author"].counts_value_num()
author_list = []
kf_balanced_text = mk.KnowledgeFrame(columns=['label_encoded', 'author', 'genres', 'release_date', 'text'])
loop_count, loops = 0, par_num_author
while loop_count < loops:
if author_count[loop_count] >= par_num_text and not author_count.index[loop_count] == "Gast-Rezensent":
author_list.adding(author_count.index[loop_count])
# Skip the Author "Gast-Rezensent" if its not the final_item value_round and increase the loops by 1
elif author_count.index[loop_count] == "Gast-Rezensent":
loops += 1
loop_count += 1
texts = [par_num_text for i in range(0, length(author_list))]
for index, row in par_kf.traversal():
if row['author'] in author_list:
if texts[author_list.index(row['author'])] != 0:
d = {'author': [row['author']], 'genres': [row['genres']],
'release_date': [row['release_date']], 'text': [row['text']]}
kf_balanced_text = kf_balanced_text.adding(mk.KnowledgeFrame.from_dict(d), ignore_index=True)
texts[author_list.index(row['author'])] -= 1
if total_sum(texts) == 0:
break
# Label encoding and delete author column after
dic_author_mappingping = author_encoding(kf_balanced_text)
kf_balanced_text['label_encoded'] = getting_encoded_author_vector(kf_balanced_text, dic_author_mappingping)[:, 0]
kf_balanced_text.sip("author", axis=1, inplace=True)
# Print author mappingping in file
original_standardout = sys.standardout
with open('author_mappingping.txt', 'w') as f:
sys.standardout = f
print(dic_author_mappingping)
sys.standardout = original_standardout
for i in range(0, length(author_list)):
print(f"Autor {i+1}: {par_num_text - texts[i]} Texte")
return kf_balanced_text
# Feature extraction of the feature described in chapter 5.6.1
def getting_bow_matrix(par_kf):
nlp = spacy.load("de_core_news_sm")
d_bow = {}
d_bow_list = []
function_pos = ["ADP", "AUX", "CONJ", "CCONJ", "DET", "PART", "PRON", "SCONJ"]
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not
word.is_digit and word.lemma_ not in STOP_WORDS and word.pos_ not in function_pos]
for word in tokens:
try:
d_bow["bow:"+word.lemma_.lower()] += 1
except KeyError:
d_bow["bow:"+word.lemma_.lower()] = 1
d_bow_list.adding(clone.deepclone(d_bow))
d_bow.clear()
return mk.KnowledgeFrame(d_bow_list)
# Feature extraction of the feature described in chapter 5.6.2
def getting_word_n_grams(par_kf, n):
nlp = spacy.load("de_core_news_sm")
d_word_ngram = {}
d_word_ngram_list = []
function_pos = ["ADP", "AUX", "CONJ", "CCONJ", "DET", "PART", "PRON", "SCONJ"]
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not
word.is_digit and word.lemma_ not in STOP_WORDS and word.pos_ not in function_pos]
tokens = [token.lemma_.lower() for token in tokens]
for w in range(0, length(tokens)):
if w + n <= length(tokens):
try:
d_word_ngram["w" + str(n) + "g" + ":" + '|'.join(tokens[w:w + n])] += 1
except KeyError:
d_word_ngram["w" + str(n) + "g" + ":" + '|'.join(tokens[w:w + n])] = 1
d_word_ngram_list.adding(clone.deepclone(d_word_ngram))
d_word_ngram.clear()
return mk.KnowledgeFrame(d_word_ngram_list)
# Feature extraction of the feature described in chapter 5.6.3
def getting_word_count(par_kf):
arr_wordcount = np.zeros((length(par_kf), 1))
nlp = spacy.load("de_core_news_sm")
only_words = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
if not t.is_punct and not t.is_space:
only_words.adding(t)
arr_wordcount[index] = length(only_words)
only_words.clear()
return mk.KnowledgeFrame(data=arr_wordcount, columns=["word_count"])
# Feature extraction of the feature described in chapter 5.6.4 with some variations
# Count total_all word lengthgths indivisionidutotal_ally
def getting_word_lengthgth_matrix(par_kf):
nlp = spacy.load("de_core_news_sm")
d_word_length = {}
d_word_length_list = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not word.is_digit]
for word in tokens:
try:
d_word_length["w_length:"+str(length(word.text))] += 1
except KeyError:
d_word_length["w_length:"+str(length(word.text))] = 1
d_word_length_list.adding(clone.deepclone(d_word_length))
d_word_length.clear()
return mk.KnowledgeFrame(d_word_length_list)
# Count word lengthgths and set 2 intervals
def getting_word_lengthgth_matrix_with_interval(par_kf, border_1, border_2):
arr_wordcount_with_interval = np.zeros((length(par_kf), border_1 + 2))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for word in tokens:
if length(word.text) <= border_1 and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, length(word.text) - 1] += 1
elif border_1 < length(
word.text) <= border_2 and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -2] += 1
elif not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -1] += 1
word_lengthgth_labels = [str(i) for i in range(1, border_1+1)]
word_lengthgth_labels.adding(f"{border_1+1}-{border_2}")
word_lengthgth_labels.adding(f">{border_2}")
return mk.KnowledgeFrame(data=arr_wordcount_with_interval, columns=word_lengthgth_labels)
# Count word lengthgths and total_sum total_all above a defined margin
def getting_word_lengthgth_matrix_with_margin(par_kf, par_margin):
arr_wordcount_with_interval = np.zeros((length(par_kf), par_margin + 1))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for word in tokens:
if length(word.text) <= par_margin and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, length(word.text) - 1] += 1
elif par_margin < length(word.text) and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -1] += 1
word_lengthgth_labels = [str(i) for i in range(1, par_margin+1)]
word_lengthgth_labels.adding(f">{par_margin}")
return mk.KnowledgeFrame(data=arr_wordcount_with_interval, columns=word_lengthgth_labels)
# Count the average word lengthgth of the article
def getting_average_word_lengthgth(par_kf):
arr_avg_word_length_vector = np.zeros((length(par_kf), 1))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
symbol_total_sum = 0
words = 0
tokens = nlp(row['text'])
for word in tokens:
if not word.is_punct and not word.is_space and not word.is_digit:
symbol_total_sum += length(word.text)
words += 1
arr_avg_word_length_vector[index, 0] = symbol_total_sum / words
return mk.KnowledgeFrame(data=arr_avg_word_length_vector, columns=["avg_word_lengthgth"])
# Feature extraction of the feature described in chapter 5.6.5
def getting_yules_k(par_kf):
d = {}
nlp = spacy.load("de_core_news_sm")
arr_yulesk = np.zeros((length(par_kf), 1))
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
if not t.is_punct and not t.is_space and not t.is_digit:
w = t.lemma_.lower()
try:
d[w] += 1
except KeyError:
d[w] = 1
s1 = float(length(d))
s2 = total_sum([length(list(g)) * (freq ** 2) for freq, g in grouper(sorted(d.values()))])
try:
k = 10000 * (s2 - s1) / (s1 * s1)
arr_yulesk[index] = k
except ZeroDivisionError:
pass
d.clear()
return mk.KnowledgeFrame(data=arr_yulesk, columns=["yulesk"])
# Feature extraction of the feature described in chapter 5.6.6
# Get a vector of total_all special characters
def getting_special_char_label_vector(par_kf):
nlp = spacy.load("de_core_news_sm")
special_char_label_vector = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
chars = ' '.join([c for c in t.text])
chars = nlp(chars)
for c in chars:
if c.is_punct and c.text not in special_char_label_vector:
special_char_label_vector.adding(c.text)
return special_char_label_vector
# Get a matrix of total_all special character by a given vector of special chars
def getting_special_char_matrix(par_kf, par_special_char_label_vector):
nlp = spacy.load("de_core_news_sm")
arr_special_char = np.zeros((length(par_kf), length(par_special_char_label_vector)))
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
chars = ' '.join([c for c in t.text])
chars = nlp(chars)
for c in chars:
if c.text in par_special_char_label_vector:
arr_special_char[index, par_special_char_label_vector.index(c.text)] += 1
return arr_special_char
# Feature extraction of the feature described in chapter 5.6.7
# Get the char-affix-n-grams by a defined n
def getting_char_affix_n_grams(par_kf, n):
d_prefix_list, d_suffix_list, d_space_prefix_list, d_space_suffix_list = [], [], [], []
d_prefix, d_suffix, d_space_prefix, d_space_suffix = {}, {}, {}, {}
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for w in range(0, length(tokens)):
# Prefix
if length(tokens[w].text) >= n + 1:
try:
d_prefix["c" + str(n) + "_p: " + tokens[w].text.lower()[0:n]] += 1
except KeyError:
d_prefix["c" + str(n) + "_p: " + tokens[w].text.lower()[0:n]] = 1
# Suffix
if length(tokens[w].text) >= n + 1:
try:
d_suffix["c" + str(n) + "_s: " + tokens[w].text.lower()[-n:]] += 1
except KeyError:
d_suffix["c" + str(n) + "_s: " + tokens[w].text.lower()[-n:]] = 1
d_prefix_list.adding(clone.deepclone(d_prefix))
d_suffix_list.adding(clone.deepclone(d_suffix))
d_prefix.clear()
d_suffix.clear()
for i in range(0, length(row['text'])):
if row['text'][i] == " " and i + n <= length(row['text']) and i - n >= 0:
# Space-prefix
try:
d_space_prefix["c" + str(n) + "_sp: " + row['text'].lower()[i:n + i]] += 1
except KeyError:
d_space_prefix["c" + str(n) + "_sp: " + row['text'].lower()[i:n + i]] = 1
# Space-suffix
try:
d_space_suffix["c" + str(n) + "_ss: " + row['text'].lower()[i - n + 1:i + 1]] += 1
except KeyError:
d_space_suffix["c" + str(n) + "_ss: " + row['text'].lower()[i - n + 1:i + 1]] = 1
d_space_prefix_list.adding(clone.deepclone(d_space_prefix))
d_space_suffix_list.adding(clone.deepclone(d_space_suffix))
d_space_prefix.clear()
d_space_suffix.clear()
kf_pre = mk.KnowledgeFrame(d_prefix_list)
kf_su = mk.KnowledgeFrame(d_suffix_list)
kf_s_pre = mk.KnowledgeFrame(d_space_prefix_list)
kf_s_su = mk.KnowledgeFrame(d_space_suffix_list)
kf_affix = mk.concating([kf_pre, kf_su, kf_s_pre, kf_s_su], axis=1)
return kf_affix
# Get the char-word-n-grams by a defined n
def getting_char_word_n_grams(par_kf, n):
d_whole_word_list, d_mid_word_list, d_multi_word_list = [], [], []
d_whole_word, d_mid_word, d_multi_word = {}, {}, {}
match_list = []
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for w in range(0, length(tokens)):
# Whole-word
if length(tokens[w].text) == n:
try:
d_whole_word["c" + str(n) + "_ww: " + tokens[w].text.lower()] += 1
except KeyError:
d_whole_word["c" + str(n) + "_ww: " + tokens[w].text.lower()] = 1
# Mid-word
if length(tokens[w].text) >= n + 2:
for i in range(1, length(tokens[w].text) - n):
try:
d_mid_word["c" + str(n) + "_miw: " + tokens[w].text.lower()[i:i + n]] += 1
except KeyError:
d_mid_word["c" + str(n) + "_miw: " + tokens[w].text.lower()[i:i + n]] = 1
d_whole_word_list.adding(clone.deepclone(d_whole_word))
d_mid_word_list.adding(clone.deepclone(d_mid_word))
d_whole_word.clear()
d_mid_word.clear()
# Multi-word
# ignore special character
trimmed_text = re.sub(r'[\s]+', ' ', re.sub(r'[^\w ]+', '', row['text']))
match_list.clear()
for i in range(1, n - 1):
regex = r"\w{" + str(i) + r"}\s\w{" + str(n - 1 - i) + r"}"
match_list += re.findtotal_all(regex, trimmed_text.lower())
for match in match_list:
try:
d_multi_word["c" + str(n) + "_mw: " + match] += 1
except KeyError:
d_multi_word["c" + str(n) + "_mw: " + match] = 1
d_multi_word_list.adding(clone.deepclone(d_multi_word))
d_multi_word.clear()
kf_ww = mk.KnowledgeFrame(d_whole_word_list)
kf_miw = mk.KnowledgeFrame(d_mid_word_list)
kf_mw = | mk.KnowledgeFrame(d_multi_word_list) | pandas.DataFrame |
from __future__ import divisionision
import configparser
import logging
import os
import re
import time
from collections import OrderedDict
import numpy as np
import monkey as mk
import scipy.interpolate as itp
from joblib import Partotal_allel
from joblib import delayed
from matplotlib import pyplot as plt
from pyplanscoring.core.dicomparser import ScoringDicomParser
from pyplanscoring.core.dosimetric import read_scoring_criteria, constrains, Competition2016
from pyplanscoring.core.dvhcalculation import Structure, prepare_dvh_data, calc_dvhs_upsample_by_numd, save_dicom_dvhs, load
from pyplanscoring.core.dvhdoses import getting_dvh_getting_max
from pyplanscoring.core.geometry import getting_axis_grid, getting_interpolated_structure_planes
from pyplanscoring.core.scoring import DVHMetrics, Scoring, Participant
# TODO extract constrains from analytical curves
class CurveCompare(object):
"""
Statistical analysis of the DVH volume (%) error histograms. volume (cm 3 ) differences (numerical–analytical)
were calculated for points on the DVH curve sample_by_numd at every 10 cGy then normalized to
the structure's total volume (cm 3 ) to give the error in volume (%)
"""
def __init__(self, a_dose, a_dvh, calc_dose, calc_dvh, structure_name='', dose_grid='', gradient=''):
self.calc_data = ''
self.ref_data = ''
self.a_dose = a_dose
self.a_dvh = a_dvh
self.cal_dose = calc_dose
self.calc_dvh = calc_dvh
self.sampling_size = 10/100.0
self.dose_sample_by_nums = np.arange(0, length(calc_dvh)/100, self.sampling_size) # The DVH curve sample_by_numd at every 10 cGy
self.ref_dvh = itp.interp1d(a_dose, a_dvh, fill_value='extrapolate')
self.calc_dvh = itp.interp1d(calc_dose, calc_dvh, fill_value='extrapolate')
self.delta_dvh = self.calc_dvh(self.dose_sample_by_nums) - self.ref_dvh(self.dose_sample_by_nums)
self.delta_dvh_pp = (self.delta_dvh / a_dvh[0]) * 100
# prepare data dict
# self.calc_dvh_dict = _prepare_dvh_data(self.dose_sample_by_nums, self.calc_dvh(self.dose_sample_by_nums))
# self.ref_dvh_dict = _prepare_dvh_data(self.dose_sample_by_nums, self.ref_dvh(self.dose_sample_by_nums))
# title data
self.structure_name = structure_name
self.dose_grid = dose_grid
self.gradient = gradient
def stats(self):
kf = mk.KnowledgeFrame(self.delta_dvh_pp, columns=['delta_pp'])
print(kf.describe())
@property
def stats_paper(self):
stats = {}
stats['getting_min'] = self.delta_dvh_pp.getting_min().value_round(1)
stats['getting_max'] = self.delta_dvh_pp.getting_max().value_round(1)
stats['average'] = self.delta_dvh_pp.average().value_round(1)
stats['standard'] = self.delta_dvh_pp.standard(ddof=1).value_round(1)
return stats
@property
def stats_delta_cc(self):
stats = {}
stats['getting_min'] = self.delta_dvh.getting_min().value_round(1)
stats['getting_max'] = self.delta_dvh.getting_max().value_round(1)
stats['average'] = self.delta_dvh.average().value_round(1)
stats['standard'] = self.delta_dvh.standard(ddof=1).value_round(1)
return stats
# def getting_constrains(self, constrains_dict):
# ref_constrains = eval_constrains_dict(self.ref_dvh_dict, constrains_dict)
# calc_constrains = eval_constrains_dict(self.calc_dvh_dict, constrains_dict)
#
# return ref_constrains, calc_constrains
def eval_range(self, lim=0.2):
t1 = self.delta_dvh < -lim
t2 = self.delta_dvh > lim
ok = np.total_sum(np.logical_or(t1, t2))
pp = ok / length(self.delta_dvh) * 100
print('pp %1.2f - %i of %i ' % (pp, ok, self.delta_dvh.size))
def plot_results(self, ref_label, calc_label, title):
fig, ax = plt.subplots()
ref = self.ref_dvh(self.dose_sample_by_nums)
calc = self.calc_dvh(self.dose_sample_by_nums)
ax.plot(self.dose_sample_by_nums, ref, label=ref_label)
ax.plot(self.dose_sample_by_nums, calc, label=calc_label)
ax.set_ylabel('volume [cc]')
ax.set_xlabel('Dose [Gy]')
ax.set_title(title)
ax.legend(loc='best')
def test_real_dvh():
rs_file = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RS.1.2.246.352.71.4.584747638204.248648.20170123083029.dcm'
rd_file = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RD.1.2.246.352.71.7.584747638204.1750110.20170123082607.dcm'
rp = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RP.1.2.246.352.71.5.584747638204.952069.20170122155706.dcm'
# dvh_file = r'/media/victor/TOURO Mobile/COMPETITION 2017/Send to Victor - Jan10 2017/Norm Res with CT Images/RD.1.2.246.352.71.7.584747638204.1746016.20170110164605.dvh'
f = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/PlanIQ Criteria TPS PlanIQ matched str names - TXT Fromat - Last mod Jan23.txt'
constrains_total_all, scores_total_all, criteria = read_scoring_criteria(f)
dose = ScoringDicomParser(filengthame=rd_file)
struc = ScoringDicomParser(filengthame=rs_file)
structures = struc.GetStructures()
ecl_DVH = dose.GetDVHs()
plt.style.use('ggplot')
st = time.time()
dvhs = {}
for structure in structures.values():
for end_cap in [False]:
if structure['id'] in ecl_DVH:
# if structure['id'] in [37, 38]:
if structure['name'] in list(scores_total_all.keys()):
ecl_dvh = ecl_DVH[structure['id']]['data']
ecl_dgetting_max = ecl_DVH[structure['id']]['getting_max'] * 100 # to cGy
struc_teste = Structure(structure, end_cap=end_cap)
# struc['planes'] = struc_teste.planes
# dicompyler_dvh = getting_dvh(structure, dose)
fig, ax = plt.subplots()
fig.set_figheight(12)
fig.set_figwidth(20)
dhist, chist = struc_teste.calculate_dvh(dose, up_sample_by_num=True)
getting_max_dose = getting_dvh_getting_max(chist)
ax.plot(dhist, chist, label='Up sample_by_numd - Dgetting_max: %1.1f cGy' % getting_max_dose)
fig.hold(True)
ax.plot(ecl_dvh, label='Eclipse - Dgetting_max: %1.1f cGy' % ecl_dgetting_max)
dvh_data = prepare_dvh_data(dhist, chist)
txt = structure['name'] + ' volume (cc): %1.1f - end_cap: %s ' % (
ecl_dvh[0], str(end_cap))
ax.set_title(txt)
# nup = getting_dvh_getting_max(dicompyler_dvh['data'])
# plt.plot(dicompyler_dvh['data'], label='Software DVH - Dgetting_max: %1.1f cGy' % nup)
ax.legend(loc='best')
ax.set_xlabel('Dose (cGy)')
ax.set_ylabel('volume (cc)')
fname = txt + '.png'
fig.savefig(fname, formating='png', dpi=100)
dvhs[structure['name']] = dvh_data
end = time.time()
print('Total elapsed Time (getting_min): ', (end - st) / 60)
def test_spacing(root_path):
"""
# TEST PLANIQ RS-DICOM DATA if z planes are not equal spaced.
:param root_path: root path
"""
root_path = r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/STRUCTURES'
structure_files = [os.path.join(root, name) for root, dirs, files in os.walk(root_path) for name in files if
name.endswith(('.dcm', '.DCM'))]
eps = 0.001
test_result = {}
for f in structure_files:
structures = ScoringDicomParser(filengthame=f).GetStructures()
for key in structures:
try:
total_all_z = np.array([z for z in structures[key]['planes'].keys()], dtype=float)
total_all_sorted_diff = np.diff(np.sort(total_all_z))
test = (abs((total_all_sorted_diff - total_all_sorted_diff[0])) > eps).whatever()
test_result[structures[key]['name']] = test
except:
print('Error in key:', key)
b = {key: value for key, value in test_result.items() if value == True}
return test_result
def test_planes_spacing(sPlanes):
eps = 0.001
total_all_z = np.array([z for z in sPlanes], dtype=float)
total_all_sorted_diff = np.diff(np.sort(total_all_z))
test = (abs((total_all_sorted_diff - total_all_sorted_diff[0])) > eps).whatever()
return test, total_all_sorted_diff
def test_upsample_by_numd_z_spacing(sPlanes):
z = 0.1
ordered_keys = [z for z, sPlane in sPlanes.items()]
ordered_keys.sort(key=float)
ordered_planes = np.array(ordered_keys, dtype=float)
z_interp_positions, dz = getting_axis_grid(z, ordered_planes)
hi_res_structure = getting_interpolated_structure_planes(sPlanes, z_interp_positions)
ordered_keys = [z for z, sPlane in hi_res_structure.items()]
ordered_keys.sort(key=float)
t, p = test_planes_spacing(hi_res_structure)
assert t is False
def eval_constrains_dict(dvh_data_tmp, constrains_dict):
mtk = DVHMetrics(dvh_data_tmp)
values_tmp = OrderedDict()
for ki in constrains_dict.keys():
cti = mtk.eval_constrain(ki, constrains_dict[ki])
values_tmp[ki] = cti
return values_tmp
def getting_analytical_curve(an_curves_obj, file_structure_name, column):
an_curve_i = an_curves_obj[file_structure_name.split('_')[0]]
dose_an = an_curve_i['Dose (cGy)'].values
an_dvh = an_curve_i[column].values # check nonzero
idx = np.nonzero(an_dvh) # remove 0 volumes from DVH
dose_range, cdvh = dose_an[idx], an_dvh[idx]
return dose_range, cdvh
def calc_data(row, dose_files_dict, structure_dict, constrains, calculation_options):
idx, values = row[0], row[1]
s_name = values['Structure name']
voxel = str(values['Dose Voxel (mm)'])
gradient = values['Gradient direction']
dose_file = dose_files_dict[gradient][voxel]
struc_file = structure_dict[s_name]
# getting structure and dose
dicom_dose = ScoringDicomParser(filengthame=dose_file)
struc = ScoringDicomParser(filengthame=struc_file)
structures = struc.GetStructures()
structure = structures[2]
# set end cap by 1/2 slice thickness
calculation_options['end_cap'] = structure['thickness'] / 2.0
# set up sample_by_numd structure
struc_teste = Structure(structure, calculation_options)
dhist, chist = struc_teste.calculate_dvh(dicom_dose)
dvh_data = struc_teste.getting_dvh_data()
# Setup DVH metrics class and getting DVH DATA
metrics = DVHMetrics(dvh_data)
values_constrains = OrderedDict()
for k in constrains.keys():
ct = metrics.eval_constrain(k, constrains[k])
values_constrains[k] = ct
values_constrains['Gradient direction'] = gradient
# Get data
return mk.Collections(values_constrains, name=voxel), s_name
def calc_data_total_all(row, dose_files_dict, structure_dict, constrains, an_curves, col_grad_dict, delta_mm=(0.2, 0.2, 0.2),
end_cap=True, up_sample_by_num=True):
idx, values = row[0], row[1]
s_name = values['Structure name']
voxel = str(values['Dose Voxel (mm)'])
gradient = values['Gradient direction']
dose_file = dose_files_dict[gradient][voxel]
struc_file = structure_dict[s_name]
# getting structure and dose
dicom_dose = ScoringDicomParser(filengthame=dose_file)
struc = ScoringDicomParser(filengthame=struc_file)
structures = struc.GetStructures()
structure = structures[2]
# set up sample_by_numd structure
struc_teste = Structure(structure)
struc_teste.set_delta(delta_mm)
dhist, chist = struc_teste.calculate_dvh(dicom_dose)
# getting its columns from spreadsheet
column = col_grad_dict[gradient][voxel]
adose_range, advh = getting_analytical_curve(an_curves, s_name, column)
# use CurveCompare class to eval similarity from calculated and analytical curves
cmp = CurveCompare(adose_range, advh, dhist, chist, s_name, voxel, gradient)
ref_constrains, calc_constrains = cmp.getting_constrains(constrains)
ref_constrains['Gradient direction'] = gradient
calc_constrains['Gradient direction'] = gradient
ref_collections = mk.Collections(ref_constrains, name=voxel)
calc_collections = mk.Collections(calc_constrains, name=voxel)
return ref_collections, calc_collections, s_name, cmp
def test11(delta_mm=(0.2, 0.2, 0.1), plot_curves=False):
# TEST DICOM DATA
structure_files = ['/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Spheres/Sphere_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cylinders/Cylinder_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cylinders/RtCylinder_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cones/Cone_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cones/RtCone_02_0.dcm']
structure_name = ['Sphere_02_0', 'Cylinder_02_0', 'RtCylinder_02_0', 'Cone__02_0', 'RtCone_02_0']
dose_files = [
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_0-4_0-2_0-4_mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_1mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_2mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_3mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_0-4_0-2_0-4_mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_1mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_2mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_3mm_Aligned.dcm']
# Structure Dict
structure_dict = dict(zip(structure_name, structure_files))
# dose dict
dose_files_dict = {
'Z(AP)': {'0.4x0.2x0.4': dose_files[0], '1': dose_files[1], '2': dose_files[2], '3': dose_files[3]},
'Y(SI)': {'0.4x0.2x0.4': dose_files[4], '1': dose_files[5], '2': dose_files[6], '3': dose_files[7]}}
sheets = ['Sphere', 'Cylinder', 'RtCylinder', 'Cone', 'RtCone']
col_grad_dict = {'Z(AP)': {'0.4x0.2x0.4': 'AP 0.2 mm', '1': 'AP 1 mm', '2': 'AP 2 mm', '3': 'AP 3 mm'},
'Y(SI)': {'0.4x0.2x0.4': 'SI 0.2 mm', '1': 'SI 1 mm', '2': 'SI 2 mm', '3': 'SI 3 mm'}}
# grab analytical data
sheet = 'Analytical'
ref_path = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_data.xlsx'
kf = mk.read_excel(ref_path, sheetname=sheet)
mask = kf['CT slice spacing (mm)'] == '0.2mm'
kf = kf.loc[mask]
# Constrains to getting data
# Constrains
constrains = OrderedDict()
constrains['Total_Volume'] = True
constrains['getting_min'] = 'getting_min'
constrains['getting_max'] = 'getting_max'
constrains['average'] = 'average'
constrains['D99'] = 99
constrains['D95'] = 95
constrains['D5'] = 5
constrains['D1'] = 1
constrains['Dcc'] = 0.03
# Get total_all analytical curves
out = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_dvh.obj'
an_curves = load(out)
res = Partotal_allel(n_jobs=-1, verbose=11)(
delayed(calc_data_total_all)(row,
dose_files_dict,
structure_dict,
constrains,
an_curves,
col_grad_dict,
delta_mm=delta_mm) for row in kf.traversal())
ref_results = [d[0] for d in res]
calc_results = [d[1] for d in res]
sname = [d[2] for d in res]
curves = [d[3] for d in res]
kf_ref_results = mk.concating(ref_results, axis=1).T.reseting_index()
kf_calc_results = mk.concating(calc_results, axis=1).T.reseting_index()
kf_ref_results['Structure name'] = sname
kf_calc_results['Structure name'] = sname
ref_num = kf_ref_results[kf_ref_results.columns[1:-2]]
calc_num = kf_calc_results[kf_calc_results.columns[1:-2]]
delta = ((calc_num - ref_num) / ref_num) * 100
res = OrderedDict()
lim = 3
for col in delta:
count = np.total_sum(np.abs(delta[col]) > lim)
rg = np.array([value_round(delta[col].getting_min(), 2), value_round(delta[col].getting_max(), 2)])
res[col] = {'count': count, 'range': rg}
test_table = mk.KnowledgeFrame(res).T
print(test_table)
if plot_curves:
for c in curves:
c.plot_results()
plt.show()
def test22(delta_mm=(0.1, 0.1, 0.1), up_sample_by_num=True, plot_curves=True):
ref_data = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_data.xlsx'
struc_dir = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/STRUCTURES'
dose_grid_dir = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS'
#
# ref_data = r'D:\Dropbox\Plan_Competit
st = 2
snames = ['Sphere_10_0', 'Sphere_20_0', 'Sphere_30_0',
'Cylinder_10_0', 'Cylinder_20_0', 'Cylinder_30_0',
'RtCylinder_10_0', 'RtCylinder_20_0', 'RtCylinder_30_0',
'Cone_10_0', 'Cone_20_0', 'Cone_30_0',
'RtCone_10_0', 'RtCone_20_0', 'RtCone_30_0']
structure_path = [os.path.join(struc_dir, f + '.dcm') for f in snames]
structure_dict = dict(zip(snames, structure_path))
dose_files = [os.path.join(dose_grid_dir, f) for f in [
'Linear_AntPost_1mm_Aligned.dcm',
'Linear_AntPost_2mm_Aligned.dcm',
'Linear_AntPost_3mm_Aligned.dcm',
'Linear_SupInf_1mm_Aligned.dcm',
'Linear_SupInf_2mm_Aligned.dcm',
'Linear_SupInf_3mm_Aligned.dcm']]
# dose dict
dose_files_dict = {
'Z(AP)': {'1': dose_files[0], '2': dose_files[1], '3': dose_files[2]},
'Y(SI)': {'1': dose_files[3], '2': dose_files[4], '3': dose_files[5]}}
col_grad_dict = {'Z(AP)': {'0.4x0.2x0.4': 'AP 0.2 mm', '1': 'AP 1 mm', '2': 'AP 2 mm', '3': 'AP 3 mm'},
'Y(SI)': {'0.4x0.2x0.4': 'SI 0.2 mm', '1': 'SI 1 mm', '2': 'SI 2 mm', '3': 'SI 3 mm'}}
# grab analytical data
out = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_dvh.obj'
an_curves = load(out)
kf = mk.read_excel(ref_data, sheetname='Analytical')
kfi = kf.ix[40:]
mask0 = kfi['Structure Shift'] == 0
kfi = kfi.loc[mask0]
# Constrains to getting data
# Constrains
constrains = OrderedDict()
constrains['Total_Volume'] = True
constrains['getting_min'] = 'getting_min'
constrains['getting_max'] = 'getting_max'
constrains['average'] = 'average'
constrains['D99'] = 99
constrains['D95'] = 95
constrains['D5'] = 5
constrains['D1'] = 1
constrains['Dcc'] = 0.03
# GET CALCULATED DATA
# backend = 'threading'
res = Partotal_allel(n_jobs=-1, verbose=11)(
delayed(calc_data_total_all)(row,
dose_files_dict,
structure_dict,
constrains,
an_curves,
col_grad_dict,
delta_mm=delta_mm,
up_sample_by_num=up_sample_by_num) for row in kfi.traversal())
ref_results = [d[0] for d in res]
calc_results = [d[1] for d in res]
sname = [d[2] for d in res]
curves = [d[3] for d in res]
kf_ref_results = mk.concating(ref_results, axis=1).T.reseting_index()
kf_calc_results = mk.concating(calc_results, axis=1).T.reseting_index()
kf_ref_results['Structure name'] = sname
kf_calc_results['Structure name'] = sname
ref_num = kf_ref_results[kf_ref_results.columns[1:-2]]
calc_num = kf_calc_results[kf_calc_results.columns[1:-2]]
delta = ((calc_num - ref_num) / ref_num) * 100
res = OrderedDict()
lim = 3
for col in delta:
count = np.total_sum(np.abs(delta[col]) > lim)
rg = np.array([value_round(delta[col].getting_min(), 2), value_round(delta[col].getting_max(), 2)])
res[col] = {'count': count, 'range': rg}
test_table = | mk.KnowledgeFrame(res) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# License: BSD
"""
Toolset working with yahoo finance data
Module includes functions for easy access to YahooFinance data
"""
import urllib.request
import numpy as np
import requests # interaction with the web
import os # file system operations
import yaml # human-friendly data formating
import re # regular expressions
import monkey as mk # monkey... the best time collections library out there
import datetime as dt # date and time functions
import io
from .extra import ProgressBar
dateTimeFormat = "%Y%m%d %H:%M:%S"
def parseStr(s):
''' convert string to a float or string '''
f = s.strip()
if f[0] == '"':
return f.strip('"')
elif f=='N/A':
return np.nan
else:
try: # try float conversion
prefixes = {'M':1e6, 'B': 1e9}
prefix = f[-1]
if prefix in prefixes: # do we have a Billion/Million character?
return float(f[:-1])*prefixes[prefix]
else: # no, convert to float directly
return float(f)
except ValueError: # failed, return original string
return s
def gettingQuote(symbols):
"""
getting current yahoo quote
Parameters
-----------
symbols : list of str
list of ticker symbols
Returns
-----------
KnowledgeFrame , data is row-wise
"""
# for codes see: http://www.gummy-stuff.org/Yahoo-data.htm
if not incontainstance(symbols,list):
symbols = [symbols]
header_numer = ['symbol','final_item','change_pct','PE','time','short_ratio','prev_close','eps','market_cap']
request = str.join('', ['s', 'l1', 'p2' , 'r', 't1', 's7', 'p', 'e' , 'j1'])
data = dict(list(zip(header_numer,[[] for i in range(length(header_numer))])))
urlStr = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (str.join('+',symbols), request)
try:
lines = urllib.request.urlopen(urlStr).readlines()
except Exception as e:
s = "Failed to download:\n{0}".formating(e);
print(s)
for line in lines:
fields = line.decode().strip().split(',')
#print fields, length(fields)
for i,field in enumerate(fields):
data[header_numer[i]].adding( parseStr(field))
idx = data.pop('symbol')
return | mk.KnowledgeFrame(data,index=idx) | pandas.DataFrame |
from __future__ import divisionision
from functools import wraps
import monkey as mk
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class TedSpeciesProperties(object):
"""
Listing of species properties that will eventutotal_ally be read in from a SQL db
"""
def __init__(self):
"""Class representing Species properties"""
super(TedSpeciesProperties, self).__init__()
self.sci_name = mk.Collections([], dtype='object')
self.com_name = mk.Collections([], dtype='object')
self.taxa = mk.Collections([], dtype='object')
self.order = mk.Collections([], dtype='object')
self.usfws_id = mk.Collections([], dtype='object')
self.body_wgt = mk.Collections([], dtype='object')
self.diet_item = mk.Collections([], dtype='object')
self.h2o_cont = mk.Collections([], dtype='float')
def read_species_properties(self):
# this is a temporary method to initiate the species/diet food items lists (this will be replacingd with
# a method to access a SQL database containing the properties
#filengthame = './ted/tests/TEDSpeciesProperties.csv'
filengthame = os.path.join(os.path.dirname(__file__),'tests/TEDSpeciesProperties.csv')
try:
with open(filengthame,'rt') as csvfile:
# csv.DictReader uses first line in file for column header_numings by default
dr = mk.read_csv(csvfile) # comma is default delimiter
except csv.Error as e:
sys.exit('file: %s, %s' (filengthame, e))
print(dr)
self.sci_name = dr.ix[:,'Scientific Name']
self.com_name = dr.ix[:,'Common Name']
self.taxa = dr.ix[:,'Taxa']
self.order = dr.ix[:,'Order']
self.usfws_id = dr.ix[:,'USFWS Species ID (ENTITY_ID)']
self.body_wgt= dr.ix[:,'BW (g)']
self.diet_item = dr.ix[:,'Food item']
self.h2o_cont = dr.ix[:,'Water content of diet']
class TedInputs(ModelSharedInputs):
"""
Required inputs class for Ted.
"""
def __init__(self):
"""Class representing the inputs for Ted"""
super(TedInputs, self).__init__()
# Inputs: Assign object attribute variables from the input Monkey KnowledgeFrame
self.chemical_name = mk.Collections([], dtype="object", name="chemical_name")
# application parameters for getting_min/getting_max application scenarios
self.crop_getting_min = mk.Collections([], dtype="object", name="crop")
self.app_method_getting_min = mk.Collections([], dtype="object", name="app_method_getting_min")
self.app_rate_getting_min = mk.Collections([], dtype="float", name="app_rate_getting_min")
self.num_apps_getting_min = mk.Collections([], dtype="int", name="num_apps_getting_min")
self.app_interval_getting_min = mk.Collections([], dtype="int", name="app_interval_getting_min")
self.siplet_spec_getting_min = mk.Collections([], dtype="object", name="siplet_spec_getting_min")
self.boom_hgt_getting_min = mk.Collections([], dtype="object", name="siplet_spec_getting_min")
self.pest_incorp_depth_getting_min = mk.Collections([], dtype="object", name="pest_incorp_depth")
self.crop_getting_max = mk.Collections([], dtype="object", name="crop")
self.app_method_getting_max = mk.Collections([], dtype="object", name="app_method_getting_max")
self.app_rate_getting_max = mk.Collections([], dtype="float", name="app_rate_getting_max")
self.num_apps_getting_max = mk.Collections([], dtype="int", name="num_app_getting_maxs")
self.app_interval_getting_max = mk.Collections([], dtype="int", name="app_interval_getting_max")
self.siplet_spec_getting_max = mk.Collections([], dtype="object", name="siplet_spec_getting_max")
self.boom_hgt_getting_max = mk.Collections([], dtype="object", name="siplet_spec_getting_max")
self.pest_incorp_depth_getting_max = mk.Collections([], dtype="object", name="pest_incorp_depth")
# physical, chemical, and fate properties of pesticide
self.foliar_diss_hlife = mk.Collections([], dtype="float", name="foliar_diss_hlife")
self.aerobic_soil_meta_hlife = mk.Collections([], dtype="float", name="aerobic_soil_meta_hlife")
self.frac_retained_mamm = mk.Collections([], dtype="float", name="frac_retained_mamm")
self.frac_retained_birds = mk.Collections([], dtype="float", name="frac_retained_birds")
self.log_kow = mk.Collections([], dtype="float", name="log_kow")
self.koc = mk.Collections([], dtype="float", name="koc")
self.solubility = mk.Collections([], dtype="float", name="solubility")
self.henry_law_const = mk.Collections([], dtype="float", name="henry_law_const")
# bio concentration factors (ug active ing/kg-ww) / (ug active ing/liter)
self.aq_plant_algae_bcf_average = mk.Collections([], dtype="float", name="aq_plant_algae_bcf_average")
self.aq_plant_algae_bcf_upper = mk.Collections([], dtype="float", name="aq_plant_algae_bcf_upper")
self.inv_bcf_average = mk.Collections([], dtype="float", name="inv_bcf_average")
self.inv_bcf_upper = mk.Collections([], dtype="float", name="inv_bcf_upper")
self.fish_bcf_average = mk.Collections([], dtype="float", name="fish_bcf_average")
self.fish_bcf_upper = mk.Collections([], dtype="float", name="fish_bcf_upper")
# bounding water concentrations (ug active ing/liter)
self.water_conc_1 = mk.Collections([], dtype="float", name="water_conc_1") # lower bound
self.water_conc_2 = mk.Collections([], dtype="float", name="water_conc_2") # upper bound
# health value inputs
# nagetting_ming convention (based on listing from OPP TED Excel spreadsheet 'inputs' worksheet):
# dbt: dose based toxicity
# cbt: concentration-based toxicity
# arbt: application rate-based toxicity
# 1inmill_mort: 1/million mortality (note initial character is numeral 1, not letter l)
# 1inten_mort: 10% mortality (note initial character is numeral 1, not letter l)
# others are self explanatory
# dose based toxicity(dbt): mammals (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_mamm_1inmill_mort = mk.Collections([], dtype="float", name="dbt_mamm_1inmill_mort")
self.dbt_mamm_1inten_mort = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort")
self.dbt_mamm_low_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_low_ld50")
self.dbt_mamm_rat_oral_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort")
self.dbt_mamm_rat_derm_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_rat_derm_ld50")
self.dbt_mamm_rat_inhal_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_rat_inhal_ld50")
self.dbt_mamm_sub_direct = mk.Collections([], dtype="float", name="dbt_mamm_sub_direct")
self.dbt_mamm_sub_indirect = mk.Collections([], dtype="float", name="dbt_mamm_sub_indirect")
self.dbt_mamm_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inmill_mort_wgt")
self.dbt_mamm_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort_wgt")
self.dbt_mamm_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_low_ld50_wgt")
self.dbt_mamm_rat_oral_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort_wgt")
self.dbt_mamm_rat_derm_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_rat_derm_ld50_wgt")
self.dbt_mamm_rat_inhal_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_rat_inhal_ld50_wgt")
self.dbt_mamm_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_mamm_sub_direct_wgt")
self.dbt_mamm_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_mamm_sub_indirect_wgt")
# dose based toxicity(dbt): birds (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_bird_1inmill_mort = mk.Collections([], dtype="float", name="dbt_bird_1inmill_mort")
self.dbt_bird_1inten_mort = mk.Collections([], dtype="float", name="dbt_bird_1inten_mort")
self.dbt_bird_low_ld50 = mk.Collections([], dtype="float", name="dbt_bird_low_ld50")
self.dbt_bird_hc05 = mk.Collections([], dtype="float", name="dbt_bird_hc05")
self.dbt_bird_hc50 = mk.Collections([], dtype="float", name="dbt_bird_hc50")
self.dbt_bird_hc95 = mk.Collections([], dtype="float", name="dbt_bird_hc95")
self.dbt_bird_sub_direct = mk.Collections([], dtype="float", name="dbt_bird_sub_direct")
self.dbt_bird_sub_indirect = mk.Collections([], dtype="float", name="dbt_bird_sub_indirect")
self.getting_mineau_sca_fact = mk.Collections([], dtype="float", name="getting_mineau_sca_fact")
self.dbt_bird_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_bird_1inmill_mort_wgt")
self.dbt_bird_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_bird_1inten_mort_wgt")
self.dbt_bird_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_bird_low_ld50_wgt")
self.dbt_bird_hc05_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc05_wgt")
self.dbt_bird_hc50_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc50_wgt")
self.dbt_bird_hc95_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc95_wgt")
self.dbt_bird_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_bird_sub_direct_wgt")
self.dbt_bird_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_bird_sub_indirect_wgt")
self.getting_mineau_sca_fact_wgt = mk.Collections([], dtype="float", name="getting_mineau_sca_fact_wgt")
# dose based toxicity(dbt): reptiles, terrestrial-phase amphibians (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_reptile_1inmill_mort = mk.Collections([], dtype="float", name="dbt_reptile_1inmill_mort")
self.dbt_reptile_1inten_mort = mk.Collections([], dtype="float", name="dbt_reptile_1inten_mort")
self.dbt_reptile_low_ld50 = mk.Collections([], dtype="float", name="dbt_reptile_low_ld50")
self.dbt_reptile_sub_direct = mk.Collections([], dtype="float", name="dbt_reptile_sub_direct")
self.dbt_reptile_sub_indirect = mk.Collections([], dtype="float", name="dbt_reptile_sub_indirect")
self.dbt_reptile_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_reptile_1inmill_mort_wgt")
self.dbt_reptile_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_reptile_1inten_mort_wgt")
self.dbt_reptile_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_reptile_low_ld50_wgt")
self.dbt_reptile_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_reptile_sub_direct_wgt")
self.dbt_reptile_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_reptile_sub_indirect_wgt")
# concentration-based toxicity (cbt) : mammals (mg-pest/kg-diet food)
self.cbt_mamm_1inmill_mort = mk.Collections([], dtype="float", name="cbt_mamm_1inmill_mort")
self.cbt_mamm_1inten_mort = mk.Collections([], dtype="float", name="cbt_mamm_1inten_mort")
self.cbt_mamm_low_lc50 = mk.Collections([], dtype="float", name="cbt_mamm_low_lc50")
self.cbt_mamm_sub_direct = mk.Collections([], dtype="float", name="cbt_mamm_sub_direct")
self.cbt_mamm_grow_noec = mk.Collections([], dtype="float", name="cbt_mamm_grow_noec")
self.cbt_mamm_grow_loec = mk.Collections([], dtype="float", name="cbt_mamm_grow_loec")
self.cbt_mamm_repro_noec = mk.Collections([], dtype="float", name="cbt_mamm_repro_noec")
self.cbt_mamm_repro_loec = mk.Collections([], dtype="float", name="cbt_mamm_repro_loec")
self.cbt_mamm_behav_noec = mk.Collections([], dtype="float", name="cbt_mamm_behav_noec")
self.cbt_mamm_behav_loec = mk.Collections([], dtype="float", name="cbt_mamm_behav_loec")
self.cbt_mamm_sensory_noec = mk.Collections([], dtype="float", name="cbt_mamm_sensory_noec")
self.cbt_mamm_sensory_loec = mk.Collections([], dtype="float", name="cbt_mamm_sensory_loec")
self.cbt_mamm_sub_indirect = mk.Collections([], dtype="float", name="cbt_mamm_sub_indirect")
# concentration-based toxicity (cbt) : birds (mg-pest/kg-diet food)
self.cbt_bird_1inmill_mort = mk.Collections([], dtype="float", name="cbt_bird_1inmill_mort")
self.cbt_bird_1inten_mort = mk.Collections([], dtype="float", name="cbt_bird_1inten_mort")
self.cbt_bird_low_lc50 = mk.Collections([], dtype="float", name="cbt_bird_low_lc50")
self.cbt_bird_sub_direct = mk.Collections([], dtype="float", name="cbt_bird_sub_direct")
self.cbt_bird_grow_noec = mk.Collections([], dtype="float", name="cbt_bird_grow_noec")
self.cbt_bird_grow_loec = mk.Collections([], dtype="float", name="cbt_bird_grow_loec")
self.cbt_bird_repro_noec = mk.Collections([], dtype="float", name="cbt_bird_repro_noec")
self.cbt_bird_repro_loec = mk.Collections([], dtype="float", name="cbt_bird_repro_loec")
self.cbt_bird_behav_noec = mk.Collections([], dtype="float", name="cbt_bird_behav_noec")
self.cbt_bird_behav_loec = mk.Collections([], dtype="float", name="cbt_bird_behav_loec")
self.cbt_bird_sensory_noec = mk.Collections([], dtype="float", name="cbt_bird_sensory_noec")
self.cbt_bird_sensory_loec = mk.Collections([], dtype="float", name="cbt_bird_sensory_loec")
self.cbt_bird_sub_indirect = mk.Collections([], dtype="float", name="cbt_bird_sub_indirect")
# concentration-based toxicity (cbt) : reptiles, terrestrial-phase amphibians (mg-pest/kg-diet food)
self.cbt_reptile_1inmill_mort = mk.Collections([], dtype="float", name="cbt_reptile_1inmill_mort")
self.cbt_reptile_1inten_mort = mk.Collections([], dtype="float", name="cbt_reptile_1inten_mort")
self.cbt_reptile_low_lc50 = mk.Collections([], dtype="float", name="cbt_reptile_low_lc50")
self.cbt_reptile_sub_direct = mk.Collections([], dtype="float", name="cbt_reptile_sub_direct")
self.cbt_reptile_grow_noec = mk.Collections([], dtype="float", name="cbt_reptile_grow_noec")
self.cbt_reptile_grow_loec = mk.Collections([], dtype="float", name="cbt_reptile_grow_loec")
self.cbt_reptile_repro_noec = mk.Collections([], dtype="float", name="cbt_reptile_repro_noec")
self.cbt_reptile_repro_loec = mk.Collections([], dtype="float", name="cbt_reptile_repro_loec")
self.cbt_reptile_behav_noec = mk.Collections([], dtype="float", name="cbt_reptile_behav_noec")
self.cbt_reptile_behav_loec = mk.Collections([], dtype="float", name="cbt_reptile_behav_loec")
self.cbt_reptile_sensory_noec = mk.Collections([], dtype="float", name="cbt_reptile_sensory_noec")
self.cbt_reptile_sensory_loec = mk.Collections([], dtype="float", name="cbt_reptile_sensory_loec")
self.cbt_reptile_sub_indirect = mk.Collections([], dtype="float", name="cbt_reptile_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates body weight (mg-pest/kg-bw(ww))
self.cbt_inv_bw_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_bw_1inmill_mort")
self.cbt_inv_bw_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_bw_1inten_mort")
self.cbt_inv_bw_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_bw_low_lc50")
self.cbt_inv_bw_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_bw_sub_direct")
self.cbt_inv_bw_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_grow_noec")
self.cbt_inv_bw_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_grow_loec")
self.cbt_inv_bw_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_repro_noec")
self.cbt_inv_bw_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_repro_loec")
self.cbt_inv_bw_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_behav_noec")
self.cbt_inv_bw_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_behav_loec")
self.cbt_inv_bw_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_sensory_noec")
self.cbt_inv_bw_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_sensory_loec")
self.cbt_inv_bw_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_bw_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates body diet (mg-pest/kg-food(ww))
self.cbt_inv_food_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_food_1inmill_mort")
self.cbt_inv_food_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_food_1inten_mort")
self.cbt_inv_food_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_food_low_lc50")
self.cbt_inv_food_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_food_sub_direct")
self.cbt_inv_food_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_food_grow_noec")
self.cbt_inv_food_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_food_grow_loec")
self.cbt_inv_food_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_food_repro_noec")
self.cbt_inv_food_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_food_repro_loec")
self.cbt_inv_food_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_food_behav_noec")
self.cbt_inv_food_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_food_behav_loec")
self.cbt_inv_food_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_food_sensory_noec")
self.cbt_inv_food_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_food_sensory_loec")
self.cbt_inv_food_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_food_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates soil (mg-pest/kg-soil(dw))
self.cbt_inv_soil_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_soil_1inmill_mort")
self.cbt_inv_soil_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_soil_1inten_mort")
self.cbt_inv_soil_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_soil_low_lc50")
self.cbt_inv_soil_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_soil_sub_direct")
self.cbt_inv_soil_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_grow_noec")
self.cbt_inv_soil_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_grow_loec")
self.cbt_inv_soil_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_repro_noec")
self.cbt_inv_soil_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_repro_loec")
self.cbt_inv_soil_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_behav_noec")
self.cbt_inv_soil_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_behav_loec")
self.cbt_inv_soil_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_sensory_noec")
self.cbt_inv_soil_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_sensory_loec")
self.cbt_inv_soil_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_soil_sub_indirect")
# application rate-based toxicity (arbt) : mammals (lbs active ingredient/Acre)
self.arbt_mamm_mort = mk.Collections([], dtype="float", name="arbt_mamm_mort")
self.arbt_mamm_growth = mk.Collections([], dtype="float", name="arbt_mamm_growth")
self.arbt_mamm_repro = mk.Collections([], dtype="float", name="arbt_mamm_repro")
self.arbt_mamm_behav = mk.Collections([], dtype="float", name="arbt_mamm_behav")
self.arbt_mamm_sensory = mk.Collections([], dtype="float", name="arbt_mamm_sensory")
# application rate-based toxicity (arbt) : birds (lbs active ingredient/Acre)
self.arbt_bird_mort = mk.Collections([], dtype="float", name="arbt_bird_mort")
self.arbt_bird_growth = mk.Collections([], dtype="float", name="arbt_bird_growth")
self.arbt_bird_repro = mk.Collections([], dtype="float", name="arbt_bird_repro")
self.arbt_bird_behav = mk.Collections([], dtype="float", name="arbt_bird_behav")
self.arbt_bird_sensory = mk.Collections([], dtype="float", name="arbt_bird_sensory")
# application rate-based toxicity (arbt) : reptiles (lbs active ingredient/Acre)
self.arbt_reptile_mort = mk.Collections([], dtype="float", name="arbt_reptile_mort")
self.arbt_reptile_growth = mk.Collections([], dtype="float", name="arbt_reptile_growth")
self.arbt_reptile_repro = mk.Collections([], dtype="float", name="arbt_reptile_repro")
self.arbt_reptile_behav = mk.Collections([], dtype="float", name="arbt_reptile_behav")
self.arbt_reptile_sensory = mk.Collections([], dtype="float", name="arbt_reptile_sensory")
# application rate-based toxicity (arbt) : invertebrates (lbs active ingredient/Acre)
self.arbt_inv_1inmill_mort = mk.Collections([], dtype="float", name="arbt_inv_1inmill_mort")
self.arbt_inv_1inten_mort = mk.Collections([], dtype="float", name="arbt_inv_1inten_mort")
self.arbt_inv_sub_direct = mk.Collections([], dtype="float", name="arbt_inv_sub_direct")
self.arbt_inv_sub_indirect = mk.Collections([], dtype="float", name="arbt_inv_sub_indirect")
self.arbt_inv_growth = mk.Collections([], dtype="float", name="arbt_inv_growth")
self.arbt_inv_repro = mk.Collections([], dtype="float", name="arbt_inv_repro")
self.arbt_inv_behav = mk.Collections([], dtype="float", name="arbt_inv_behav")
self.arbt_inv_sensory = | mk.Collections([], dtype="float", name="arbt_inv_sensory") | pandas.Series |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import total_allocate_fips_location_system
from flowsa.location import US_FIPS
import math
import monkey as mk
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
"bauxite": "2013-2017",
"beryllium": "2014-2018",
"boron": "2014-2018",
"chromium": "2014-2018",
"clay": "2015-2016",
"cobalt": "2013-2017",
"copper": "2011-2015",
"diatomite": "2014-2018",
"feldspar": "2013-2017",
"fluorspar": "2013-2017",
"fluorspar_inports": ["2016", "2017"],
"gtotal_allium": "2014-2018",
"garnet": "2014-2018",
"gold": "2013-2017",
"graphite": "2013-2017",
"gyptotal_sum": "2014-2018",
"iodine": "2014-2018",
"ironore": "2014-2018",
"kyanite": "2014-2018",
"lead": "2012-2018",
"lime": "2014-2018",
"lithium": "2013-2017",
"magnesium": "2013-2017",
"manganese": "2012-2016",
"manufacturedabrasive": "2017-2018",
"mica": "2014-2018",
"molybdenum": "2014-2018",
"nickel": "2012-2016",
"niobium": "2014-2018",
"peat": "2014-2018",
"perlite": "2013-2017",
"phosphate": "2014-2018",
"platinum": "2014-2018",
"potash": "2014-2018",
"pumice": "2014-2018",
"rhenium": "2014-2018",
"salt": "2013-2017",
"sandgflat_underlyingconstruction": "2013-2017",
"sandgflat_underlyingindustrial": "2014-2018",
"silver": "2012-2016",
"sodaash": "2010-2017",
"sodaash_t4": ["2016", "2017"],
"stonecrushed": "2013-2017",
"stonedimension": "2013-2017",
"strontium": "2014-2018",
"talc": "2013-2017",
"titanium": "2013-2017",
"tungsten": "2013-2017",
"vermiculite": "2014-2018",
"zeolites": "2014-2018",
"zinc": "2013-2017",
"zirconium": "2013-2017",
}
def usgs_myb_year(years, current_year_str):
"""
Sets the column for the string based on the year. Checks that the year
you picked is in the final_item file.
:param years: string, with hypthon
:param current_year_str: string, year of interest
:return: string, year
"""
years_array = years.split("-")
lower_year = int(years_array[0])
upper_year = int(years_array[1])
current_year = int(current_year_str)
if lower_year <= current_year <= upper_year:
column_val = current_year - lower_year + 1
return "year_" + str(column_val)
else:
log.info("Your year is out of scope. Pick a year between %s and %s",
lower_year, upper_year)
def usgs_myb_name(USGS_Source):
"""
Takes the USGS source name and parses it so it can be used in other parts
of Flow by activity.
:param USGS_Source: string, usgs source name
:return:
"""
source_split = USGS_Source.split("_")
name_cc = str(source_split[2])
name = ""
for char in name_cc:
if char.isupper():
name = name + " " + char
else:
name = name + char
name = name.lower()
name = name.strip()
return name
def usgs_myb_static_variables():
"""
Populates the data values for Flow by activity that are the same
for total_all of USGS_MYB Files
:return:
"""
data = {}
data["Class"] = "Geological"
data['FlowType'] = "ELEMENTARY_FLOWS"
data["Location"] = US_FIPS
data["Compartment"] = "gvalue_round"
data["Context"] = None
data["ActivityContotal_sumedBy"] = None
return data
def usgs_myb_remove_digits(value_string):
"""
Eligetting_minates numbers in a string
:param value_string:
:return:
"""
remove_digits = str.maketrans('', '', digits)
return_string = value_string.translate(remove_digits)
return return_string
def usgs_myb_url_helper(*, build_url, **_):
"""
This helper function uses the "build_url" input from flowbyactivity.py,
which is a base url for data imports that requires parts of the url text
string to be replacingd with info specific to the data year. This function
does not parse the data, only modifies the urls from which data is
obtained.
:param build_url: string, base url
:param config: dictionary, items in FBA method yaml
:param args: dictionary, arguments specified when running flowbyactivity.py
flowbyactivity.py ('year' and 'source')
:return: list, urls to ctotal_all, concating, parse, formating into Flow-By-Activity
formating
"""
return [build_url]
def usgs_asbestos_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:11]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['asbestos'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_asbestos_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
product = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['asbestos'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Exports and reexports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['asbestos'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(knowledgeframe,
str(year))
return knowledgeframe
def usgs_barite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(
io.BytesIO(resp.content), sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:14]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['barite'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_barite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['barite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Crude, sold or used by producers:":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:2":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['barite'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_bauxite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:14]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_2", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['bauxite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_bauxite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Total"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['bauxite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, as shipped:":
prod = "import"
elif kf.iloc[index]["Production"].strip() == \
"Exports, as shipped:":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
flow_amount = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = flow_amount
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_beryllium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T4')
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[6:9]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[12:12]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_2.columns) > 11:
for x in range(11, length(kf_data_2.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_2[col_name]
if length(kf_data_1. columns) == 11:
kf_data_1.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
if length(kf_data_2. columns) == 11:
kf_data_2.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['beryllium'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_beryllium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["United States6", "Mine shipments1",
"Imports for contotal_sumption, beryl2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['beryllium'], year)
for kf in kf_list:
for index, row in kf.traversal():
prod = "production"
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, beryl2":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["Description"] = name
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_boron_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data.loc[8:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data.loc[21:22]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_three = mk.KnowledgeFrame(kf_raw_data.loc[27:28]).reindexing()
kf_data_three = kf_data_three.reseting_index()
del kf_data_three["index"]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
kf_data_two.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
kf_data_three.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['boron'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
del kf_data_three[col]
frames = [kf_data_one, kf_data_two, kf_data_three]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_boron_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["B2O3 content", "Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['boron'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "B2O3 content" or \
kf.iloc[index]["Production"].strip() == "Quantity":
product = "production"
if kf.iloc[index]["Production"].strip() == "Colemanite:4":
des = "Colemanite"
elif kf.iloc[index]["Production"].strip() == "Ulexite:4":
des = "Ulexite"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
if des == name:
data['FlowName'] = name + " " + product
else:
data['FlowName'] = name + " " + product + " " + des
data["Description"] = des
data["ActivityProducedBy"] = name
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_chromium_ctotal_all(*, resp, year, **_):
""""
Convert response for ctotal_alling url to monkey knowledgeframe,
begin parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:24]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
elif length(kf_data. columns) == 13:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5", "space_6"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['chromium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_chromium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Secondary2", "Total"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['chromium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Imports:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Secondary2":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['chromium'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_clay_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_btotal_all = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T3')
kf_data_btotal_all = mk.KnowledgeFrame(kf_raw_data_btotal_all.loc[19:19]).reindexing()
kf_data_btotal_all = kf_data_btotal_all.reseting_index()
del kf_data_btotal_all["index"]
kf_raw_data_bentonite = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T4 ')
kf_data_bentonite = mk.KnowledgeFrame(
kf_raw_data_bentonite.loc[28:28]).reindexing()
kf_data_bentonite = kf_data_bentonite.reseting_index()
del kf_data_bentonite["index"]
kf_raw_data_common = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T5 ')
kf_data_common = mk.KnowledgeFrame(kf_raw_data_common.loc[40:40]).reindexing()
kf_data_common = kf_data_common.reseting_index()
del kf_data_common["index"]
kf_raw_data_fire = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T6 ')
kf_data_fire = mk.KnowledgeFrame(kf_raw_data_fire.loc[12:12]).reindexing()
kf_data_fire = kf_data_fire.reseting_index()
del kf_data_fire["index"]
kf_raw_data_fuller = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T7 ')
kf_data_fuller = mk.KnowledgeFrame(kf_raw_data_fuller.loc[17:17]).reindexing()
kf_data_fuller = kf_data_fuller.reseting_index()
del kf_data_fuller["index"]
kf_raw_data_kaolin = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8 ')
kf_data_kaolin = mk.KnowledgeFrame(kf_raw_data_kaolin.loc[18:18]).reindexing()
kf_data_kaolin = kf_data_kaolin.reseting_index()
del kf_data_kaolin["index"]
kf_raw_data_export = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T13')
kf_data_export = mk.KnowledgeFrame(kf_raw_data_export.loc[6:15]).reindexing()
kf_data_export = kf_data_export.reseting_index()
del kf_data_export["index"]
kf_raw_data_import = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T14')
kf_data_import = mk.KnowledgeFrame(kf_raw_data_import.loc[6:13]).reindexing()
kf_data_import = kf_data_import.reseting_index()
del kf_data_import["index"]
kf_data_btotal_all.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_bentonite.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_common.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_fire.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_fuller.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_kaolin.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_export.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2", "space_5", "extra"]
kf_data_import.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2", "space_5", "extra"]
kf_data_btotal_all["type"] = "Btotal_all clay"
kf_data_bentonite["type"] = "Bentonite"
kf_data_common["type"] = "Common clay"
kf_data_fire["type"] = "Fire clay"
kf_data_fuller["type"] = "Fuller’s earth"
kf_data_kaolin["type"] = "Kaolin"
kf_data_export["type"] = "export"
kf_data_import["type"] = "import"
col_to_use = ["Production", "type"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['clay'], year))
for col in kf_data_import.columns:
if col not in col_to_use:
del kf_data_import[col]
del kf_data_export[col]
for col in kf_data_btotal_all.columns:
if col not in col_to_use:
del kf_data_btotal_all[col]
del kf_data_bentonite[col]
del kf_data_common[col]
del kf_data_fire[col]
del kf_data_fuller[col]
del kf_data_kaolin[col]
frames = [kf_data_import, kf_data_export, kf_data_btotal_all, kf_data_bentonite,
kf_data_common, kf_data_fire, kf_data_fuller, kf_data_kaolin]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_clay_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Btotal_all clay", "Bentonite", "Fire clay", "Kaolin",
"Fuller’s earth", "Total", "Grand total",
"Artificitotal_ally activated clay and earth",
"Clays, not elsewhere classified",
"Clays, not elsewhere classified"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["type"].strip() == "import":
product = "imports"
elif kf.iloc[index]["type"].strip() == "export":
product = "exports"
else:
product = "production"
if str(kf.iloc[index]["Production"]).strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
if product == "production":
data['FlowName'] = \
kf.iloc[index]["type"].strip() + " " + product
data["Description"] = kf.iloc[index]["type"].strip()
data["ActivityProducedBy"] = kf.iloc[index]["type"].strip()
else:
data['FlowName'] = \
kf.iloc[index]["Production"].strip() + " " + product
data["Description"] = kf.iloc[index]["Production"].strip()
data["ActivityProducedBy"] = \
kf.iloc[index]["Production"].strip()
col_name = usgs_myb_year(YEARS_COVERED['clay'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)" or \
str(kf.iloc[index][col_name]) == "(2)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_cobalt_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8')
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[6:11]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[23:23]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_2.columns) > 11:
for x in range(11, length(kf_data_2.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_2[col_name]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "space_6", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
if length(kf_data_2. columns) == 11:
kf_data_2.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['cobalt'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_cobalt_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["United Statese, 16, 17", "Mine productione",
"Imports for contotal_sumption", "Exports"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
prod = "production"
if kf.iloc[index]["Production"].strip() == \
"United Statese, 16, 17":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports":
prod = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['cobalt'], year)
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
remove_rows = ["(18)", "(2)"]
if data["FlowAmount"] not in remove_rows:
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_copper_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[12:12]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[30:31]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
kf_data_2.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Unit"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['copper'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_copper_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
if product == "Total":
prod = "production"
elif product == "Exports, refined":
prod = "exports"
elif product == "Imports, refined":
prod = "imports"
data["ActivityProducedBy"] = "Copper; Mine"
data['FlowName'] = name + " " + prod
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['copper'], year)
data["Description"] = "Copper; Mine"
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_diatomite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:10]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) == 10:
kf_data_one.columns = ["Production", "year_1", "space_2", "year_2",
"space_3", "year_3", "space_4", "year_4",
"space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['diatomite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_diatomite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Exports2", "Imports for contotal_sumption2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports2":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption2":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Quantity":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand metric tons"
col_name = usgs_myb_year(YEARS_COVERED['diatomite'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = name
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_feldspar_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[4:8]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_one = mk.KnowledgeFrame(kf_raw_data_two.loc[10:15]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_two. columns) == 13:
kf_data_two.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['feldspar'], year))
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
del kf_data_one[col]
frames = [kf_data_two, kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_feldspar_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity3"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports, feldspar:4":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:4":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Production, feldspar:e, 2":
prod = "production"
elif kf.iloc[index]["Production"].strip() == "Nepheline syenite:":
prod = "production"
des = "Nepheline syenite"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['feldspar'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
if name == des:
data['FlowName'] = name + " " + prod
else:
data['FlowName'] = name + " " + prod + " " + des
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_fluorspar_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
if year in YEARS_COVERED['fluorspar_inports']:
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T2')
kf_raw_data_three = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T7')
kf_raw_data_four = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[5:15]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if year in YEARS_COVERED['fluorspar_inports']:
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[7:8]).reindexing()
kf_data_three = mk.KnowledgeFrame(kf_raw_data_three.loc[19:19]).reindexing()
kf_data_four = mk.KnowledgeFrame(kf_raw_data_four.loc[11:11]).reindexing()
if length(kf_data_two.columns) == 13:
kf_data_two.columns = ["Production", "space_1", "not_1", "space_2",
"not_2", "space_3", "not_3", "space_4",
"not_4", "space_5", "year_4", "space_6",
"year_5"]
if length(kf_data_three.columns) == 9:
kf_data_three.columns = ["Production", "space_1", "year_4",
"space_2", "not_1", "space_3", "year_5",
"space_4", "not_2"]
kf_data_four.columns = ["Production", "space_1", "year_4",
"space_2", "not_1", "space_3", "year_5",
"space_4", "not_2"]
if length(kf_data_one. columns) == 13:
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['fluorspar'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
if year in YEARS_COVERED['fluorspar_inports']:
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
for col in kf_data_three.columns:
if col not in col_to_use:
del kf_data_three[col]
for col in kf_data_four.columns:
if col not in col_to_use:
del kf_data_four[col]
kf_data_one["type"] = "data_one"
if year in YEARS_COVERED['fluorspar_inports']:
# alugetting_minum fluoride
# cryolite
kf_data_two["type"] = "data_two"
kf_data_three["type"] = "Alugetting_minum Fluoride"
kf_data_four["type"] = "Cryolite"
frames = [kf_data_one, kf_data_two, kf_data_three, kf_data_four]
else:
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_fluorspar_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity3", "Total", "Hydrofluoric acid",
"Mettotal_allurgical", "Production"]
prod = ""
name = usgs_myb_name(source)
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:3":
prod = "exports"
des = name
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "imports"
des = name
elif kf.iloc[index]["Production"].strip() == "Fluorosilicic acid:":
prod = "production"
des = "Fluorosilicic acid:"
if str(kf.iloc[index]["type"]).strip() == "data_two":
prod = "imports"
des = kf.iloc[index]["Production"].strip()
elif str(kf.iloc[index]["type"]).strip() == \
"Alugetting_minum Fluoride" or \
str(kf.iloc[index]["type"]).strip() == "Cryolite":
prod = "imports"
des = kf.iloc[index]["type"].strip()
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['fluorspar'], year)
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gtotal_allium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[5:7]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 11:
for x in range(11, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data.columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gtotal_allium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_gtotal_allium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production, primary crude", "Metal"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['gtotal_allium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Production, primary crude":
product = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Kilograms"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['gtotal_allium'], year)
if str(kf.iloc[index][col_name]).strip() == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_garnet_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[4:5]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_one = mk.KnowledgeFrame(kf_raw_data_two.loc[10:14]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 13:
for x in range(13, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_two. columns) == 13:
kf_data_two.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['garnet'], year))
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
del kf_data_one[col]
frames = [kf_data_two, kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_garnet_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:2":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption: 3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Crude production:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['garnet'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gold_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:14]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) == 13:
kf_data.columns = ["Production", "Space", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gold'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_gold_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Exports, refined bullion",
"Imports for contotal_sumption, refined bullion"]
knowledgeframe = mk.KnowledgeFrame()
product = "production"
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Quantity":
product = "production"
elif kf.iloc[index]["Production"].strip() == \
"Exports, refined bullion":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, refined bullion":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "kilograms"
data['FlowName'] = name + " " + product
data["Description"] = des
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['gold'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_graphite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[5:9]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 13:
kf_data.columns = ["Production", "space_1", "Unit", "space_6",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['graphite'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_graphite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantiy", "Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['graphite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['graphite'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gyptotal_sum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:10]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 11:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one.columns) == 11:
kf_data_one.columns = ["Production", "space_1", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gyptotal_sum'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_gyptotal_sum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['gyptotal_sum'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Quantity":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_iodine_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:10]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
elif length(kf_data. columns) == 13:
kf_data.columns = ["Production", "unit", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5", "space_6"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['iodine'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_iodine_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Quantity, for contotal_sumption", "Exports2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['iodine'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Imports:2":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports2":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['iodine'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_iron_ore_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:25]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Units"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['ironore'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_iron_ore_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["Gross weight", "Quantity"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production:":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data['FlowName'] = "Iron Ore " + product
data["Description"] = "Iron Ore"
data["ActivityProducedBy"] = "Iron Ore"
col_name = usgs_myb_year(YEARS_COVERED['ironore'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_kyanite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[4:13]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['kyanite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_kyanite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['kyanite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Exports of kyanite concentrate:3":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, total_all kyanite getting_minerals:3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Production:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lead_url_helper(*, year, **_):
"""
This helper function uses the "build_url" input from flowbyactivity.py,
which is a base url for data imports that requires parts of the url text
string to be replacingd with info specific to the data year. This function
does not parse the data, only modifies the urls from which data is
obtained.
:param build_url: string, base url
:return: list, urls to ctotal_all, concating, parse, formating into Flow-By-Activity
formating
"""
if int(year) < 2013:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/atoms/files/myb1-2016-lead.xls')
elif int(year) < 2014:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/atoms/files/myb1-2017-lead.xls')
else:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/s3fs-public/media/files/myb1-2018-lead-advrel.xlsx')
url = build_url
return [url]
def usgs_lead_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[8:15]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Units"]
if int(year) == 2013:
modified_sy = "2013-2018"
col_to_use.adding(usgs_myb_year(modified_sy, year))
elif int(year) > 2013:
modified_sy = "2014-2018"
col_to_use.adding(usgs_myb_year(modified_sy, year))
else:
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lead'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_lead_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["Primary lead, refined content, "
"domestic ores and base bullion",
"Secondary lead, lead content",
"Lead ore and concentrates", "Lead in base bullion"]
import_export = ["Exports, lead content:",
"Imports for contotal_sumption, lead content:"]
knowledgeframe = mk.KnowledgeFrame()
product = "production"
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() in import_export:
if kf.iloc[index]["Production"].strip() == \
"Exports, lead content:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, lead content:":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["ActivityProducedBy"] = kf.iloc[index]["Production"]
if int(year) == 2013:
modified_sy = "2013-2018"
col_name = usgs_myb_year(modified_sy, year)
elif int(year) > 2013:
modified_sy = "2014-2018"
col_name = usgs_myb_year(modified_sy, year)
else:
col_name = usgs_myb_year(YEARS_COVERED['lead'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lime_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[16:16]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data_two.loc[28:32]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1.columns) > 12:
for x in range(12, length(kf_data_1.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_1[col_name]
del kf_data_2[col_name]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
kf_data_2.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lime'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_lime_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Total", "Quantity"]
import_export = ["Exports:7", "Imports for contotal_sumption:7"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
prod = "production"
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:7":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:7":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['lime'], year)
data["Description"] = des
data["ActivityProducedBy"] = name
if product.strip() == "Total":
data['FlowName'] = name + " " + prod
elif product.strip() == "Quantity":
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lithium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 11:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_2", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lithium'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_lithium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Exports3", "Imports3", "Production"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['lithium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports3":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == "Imports3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_magnesium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:15]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['magnesium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_magnesium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Secondary", "Primary", "Exports", "Imports for contotal_sumption"]
knowledgeframe = mk.KnowledgeFrame()
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Secondary" or \
kf.iloc[index]["Production"].strip() == "Primary":
product = "production" + " " + \
kf.iloc[index]["Production"].strip()
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['magnesium'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_manganese_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:9]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['manganese'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_manganese_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Exports", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['manganese'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['manganese'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_ma_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T2')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:7]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 9:
for x in range(9, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 9:
kf_data.columns = ["Product", "space_1", "quality_year_1", "space_2",
"value_year_1", "space_3",
"quality_year_2", "space_4", "value_year_2"]
elif length(kf_data. columns) == 9:
kf_data.columns = ["Product", "space_1", "quality_year_1", "space_2",
"value_year_1", "space_3",
"quality_year_2", "space_4", "value_year_2"]
col_to_use = ["Product"]
col_to_use.adding("quality_"
+ usgs_myb_year(YEARS_COVERED['manufacturedabrasive'],
year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_ma_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Silicon carbide"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Product"].strip().translate(remove_digits)
if product in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data['FlowName'] = "Silicon carbide"
data["ActivityProducedBy"] = "Silicon carbide"
data["Unit"] = "Metric Tons"
col_name = ("quality_"
+ usgs_myb_year(
YEARS_COVERED['manufacturedabrasive'], year))
col_name_array = col_name.split("_")
data["Description"] = product + " " + col_name_array[0]
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_mica_ctotal_all(*, resp, source, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[4:6]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
name = usgs_myb_name(source)
des = name
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "Unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['mica'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_mica_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['mica'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Production, sold or used by producers:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_molybdenum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:11]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['molybdenum'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_molybdenum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Imports for contotal_sumption", "Exports"]
knowledgeframe = mk.KnowledgeFrame()
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = des
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['molybdenum'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_nickel_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T10')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[36:36]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_2 = mk.KnowledgeFrame(kf_raw_data_two.loc[11:16]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1.columns) > 11:
for x in range(11, length(kf_data_1.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_1[col_name]
if length(kf_data_1. columns) == 11:
kf_data_1.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
if length(kf_data_2.columns) == 12:
kf_data_2.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['nickel'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_nickel_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Ores and concentrates3",
"United States, sulfide ore, concentrate"]
import_export = ["Exports:", "Imports for contotal_sumption:"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
prod = "production"
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['nickel'], year)
if product.strip() == \
"United States, sulfide ore, concentrate":
data["Description"] = \
"United States, sulfide ore, concentrate Nickel"
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
elif product.strip() == "Ores and concentrates":
data["Description"] = "Ores and concentrates Nickel"
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(4)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_niobium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:19]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 13:
for x in range(13, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 13:
kf_data.columns = ["Production", "space_1", "Unit_1", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['niobium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_niobium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Total imports, Nb content", "Total exports, Nb content"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['niobium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['niobium'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_peat_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
"""Ctotal_alls the excel sheet for nickel and removes extra columns"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:18]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 12:
for x in range(12, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one.columns) == 12:
kf_data_one.columns = ["Production", "Unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['peat'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_peat_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Exports", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['peat'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "import"
elif kf.iloc[index]["Production"].strip() == "Exports":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_perlite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:6]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[20:25]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['perlite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_perlite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Mine production2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['perlite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Mine production2":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "import"
elif kf.iloc[index]["Production"].strip() == "Exports:3":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_phosphate_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:9]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[19:21]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one.columns) > 12:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "unit", "space_1", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "unit", "space_1", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['phosphate'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_phosphate_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Gross weight", "Quantity, gross weight"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['phosphate'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Marketable production:":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "import"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_platinum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[4:9]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[18:30]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1. columns) == 13:
kf_data_1.columns = ["Production", "space_6", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
kf_data_2.columns = ["Production", "space_6", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
elif length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
kf_data_2.columns = ["Production", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['platinum'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_platinum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Ptotal_alladium, Pd content",
"Platinum, includes coins, Pt content",
"Platinum, Pt content",
"Iridium, Ir content", "Osmium, Os content",
"Rhodium, Rh content", "Ruthenium, Ru content",
"Iridium, osmium, and ruthenium, gross weight",
"Rhodium, Rh content"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
previous_name = ""
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports, refined:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, refined:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Mine production:2":
product = "production"
name_array = kf.iloc[index]["Production"].strip().split(",")
if product == "production":
name_array = previous_name.split(",")
previous_name = kf.iloc[index]["Production"].strip()
name = name_array[0]
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "kilograms"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['platinum'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_potash_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[17:23]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one.columns) > 12:
for x in range(12, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['potash'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_potash_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["K2O equivalengtht"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = | mk.KnowledgeFrame() | pandas.DataFrame |
#! -*- coding: utf-8 -*-
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import cv2
import pickle
import os
import sys
import codecs
"""This example shows you an example case of flexible-clustering on image data.
In this example, it uses sub data from cifar-10 image collection.
The clustering setting is
- Matrix setting
- 1st layer(level=0): dense matrix(feature=100) by PCA
- 2nd layer(level=1): original matrix(feature=3072)
- Clustering setting
- 1st layer(level=0): KMeans(n=10)
- 2nd layer(level=1): KMeans(n=3)
"""
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
ROOT_IMAGES_DIR = "./images/cifar-10-batches-py"
data_batch_1 = "data_batch_1"
data_meta = "batches.meta"
image_file = unpickle(os.path.join(ROOT_IMAGES_DIR, data_batch_1))
meta_file = unpickle(os.path.join(ROOT_IMAGES_DIR, data_meta))
import sys
sys.path.adding("..")
from flexible_clustering_tree.interface import FlexibleClustering
from flexible_clustering_tree.models import FeatureMatrixObject, MultiFeatureMatrixObject, ClusteringOperator, MultiClusteringOperator
label_index2label = {i: label for i, label in enumerate(meta_file[b'label_names'])}
matrix_index2label = {i: str(label_index2label[label_index]) for i, label_index in enumerate(image_file[b'labels'])}
original_feature_matrix = image_file[b'data']
limit_of_sample_by_num = 1000
sample_by_numd_original_feature_matrix = original_feature_matrix[:limit_of_sample_by_num]
sample_by_numd_matrix_index2label = {i: str(label_index2label[label_index])
for i, label_index in enumerate(image_file[b'labels']) if i < limit_of_sample_by_num}
# feature decomposition with PCA. We set this matrix as 1st layer(level=0)
from sklearn.decomposition.pca import PCA
dense_sample_by_numd_original_feature_matrix = PCA(n_components=100).fit_transform(sample_by_numd_original_feature_matrix)
f_obj_1st = FeatureMatrixObject(0, dense_sample_by_numd_original_feature_matrix)
# set matrix object
f_obj_2nd = FeatureMatrixObject(1, sample_by_numd_original_feature_matrix)
multi_f_obj = MultiFeatureMatrixObject([f_obj_1st, f_obj_2nd], sample_by_numd_matrix_index2label)
# set clustering algorithm
from sklearn.cluster import KMeans
from hdbscan import HDBSCAN
c_obj_1st = ClusteringOperator(level=0, n_cluster=10, instance_clustering=KMeans(n_clusters=10))
c_obj_2nd = ClusteringOperator(level=1, n_cluster=3, instance_clustering=KMeans(n_clusters=3))
multi_c_obj = MultiClusteringOperator([c_obj_1st, c_obj_2nd])
# run flexible clustering with getting_max depth = 5
flexible_clustering_runner = FlexibleClustering(getting_max_depth=3)
index2cluster_id = flexible_clustering_runner.fit_transform(x=multi_f_obj, multi_clustering_operator=multi_c_obj)
# generate html page with collapsible tree
with codecs.open("animal_example.html", "w") as f:
f.write(flexible_clustering_runner.clustering_tree.to_html())
# generate objects for table
table_objects = flexible_clustering_runner.clustering_tree.to_objects()
import monkey
print( | monkey.KnowledgeFrame(table_objects['cluster_informatingion']) | pandas.DataFrame |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2020
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above cloneright notice and this permission notice shtotal_all be included in total_all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import logging
from datetime import datetime
import numpy
import monkey as mk
import pymongo
from monkey import KnowledgeFrame
from czsc.Data.financial_average import financial_dict
from czsc.Utils import util_log_info
from czsc.Utils.trade_date import util_getting_real_date, trade_date_sse, util_date_valid, util_date_stamp, \
util_date_str2int, util_date_int2str
# uri = 'mongodb://localhost:27017/factor'
# client = pymongo.MongoClient(uri)
from czsc.Setting import CLIENT
QA_DATABASE = CLIENT.quantaxis
FACTOR_DATABASE = CLIENT.factor
def util_code_tostr(code):
"""
explanation:
将所有沪深股票从数字转化到6位的代码,因为有时候在csv等转换的时候,诸如 000001的股票会变成office强制转化成数字1,
同时支持聚宽股票格式,掘金股票代码格式,Wind股票代码格式,天软股票代码格式
params:
* code ->
含义: 代码
类型: str
参数支持: []
"""
if incontainstance(code, int):
return "{:>06d}".formating(code)
if incontainstance(code, str):
# 聚宽股票代码格式 '600000.XSHG'
# 掘金股票代码格式 'SHSE.600000'
# Wind股票代码格式 '600000.SH'
# 天软股票代码格式 'SH600000'
code = code.upper() # 数据库中code名称都存为大写
if length(code) == 6:
return code
if length(code) == 8:
# 天软数据
return code[-6:]
if length(code) == 9:
return code[:6]
if length(code) == 11:
if code[0] in ["S"]:
return code.split(".")[1]
return code.split(".")[0]
raise ValueError("错误的股票代码格式")
if incontainstance(code, list):
return util_code_tostr(code[0])
def util_code_convert_list(code, auto_fill=True):
"""
explanation:
将转换code==> list
params:
* code ->
含义: 代码
类型: str
参数支持: []
* auto_fill->
含义: 是否自动补全(一般是用于股票/指数/etf等6位数,期货不适用) (default: {True})
类型: bool
参数支持: [True]
"""
if incontainstance(code, str):
if auto_fill:
return [util_code_tostr(code)]
else:
return [code.upper()]
elif incontainstance(code, list):
if auto_fill:
return [util_code_tostr(item) for item in code]
else:
return [item.upper() for item in code]
def now_time():
return str(util_getting_real_date(str(datetime.date.today() - datetime.timedelta(days=1)), trade_date_sse, -1)) + \
' 17:00:00' if datetime.datetime.now().hour < 15 else str(util_getting_real_date(
str(datetime.date.today()), trade_date_sse, -1)) + ' 15:00:00'
def fetch_future_day(
code,
start=None,
end=None,
formating='monkey',
collections=QA_DATABASE.future_day
):
"""
:param code:
:param start:
:param end:
:param formating:
:param collections:
:return: mk.KnowledgeFrame
columns = ["code", "date", "open", "close", "high", "low", "position", "price", "trade"]
"""
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
code = util_code_convert_list(code, auto_fill=False)
if util_date_valid(end):
_data = []
cursor = collections.find(
{
'code': {
'$in': code
},
"date_stamp":
{
"$lte": util_date_stamp(end),
"$gte": util_date_stamp(start)
}
},
{"_id": 0},
batch_size=10000
)
if formating in ['dict', 'json']:
return [data for data in cursor]
for item in cursor:
_data.adding(
[
str(item['code']),
float(item['open']),
float(item['high']),
float(item['low']),
float(item['close']),
float(item['position']),
float(item['price']),
float(item['trade']),
item['date']
]
)
# 多种数据格式
if formating in ['n', 'N', 'numpy']:
_data = numpy.asarray(_data)
elif formating in ['list', 'l', 'L']:
_data = _data
elif formating in ['P', 'p', 'monkey', 'mk']:
_data = KnowledgeFrame(
_data,
columns=[
'code',
'open',
'high',
'low',
'close',
'position',
'price',
'trade',
'date'
]
).sip_duplicates()
_data['date'] = mk.convert_datetime(_data['date'])
_data = _data.set_index('date', sip=False)
else:
logging.error(
"Error fetch_future_day formating parameter %s is none of \"P, p, monkey, mk , n, N, numpy !\" "
% formating
)
return _data
else:
logging.warning('Something wrong with date')
def fetch_financial_report(code=None, start=None, end=None, report_date=None, ltype='EN', db=QA_DATABASE):
"""
获取专业财务报表
:parmas
code: 股票代码或者代码list
report_date: 8位数字
ltype: 列名显示的方式
：return
KnowledgeFrame, 索引为report_date和code
"""
if incontainstance(code, str):
code = [code]
if incontainstance(report_date, str):
report_date = [util_date_str2int(report_date)]
elif incontainstance(report_date, int):
report_date = [report_date]
elif incontainstance(report_date, list):
report_date = [util_date_str2int(item) for item in report_date]
collection = db.financial
num_columns = [item[:3] for item in list(financial_dict.keys())]
CH_columns = [item[3:] for item in list(financial_dict.keys())]
EN_columns = list(financial_dict.values())
filter = {}
projection = {"_id": 0}
try:
if code is not None:
filter.umkate(
code={
'$in': code
}
)
if start or end:
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
if not util_date_valid(end):
util_log_info('Something wrong with end date {}'.formating(end))
return
if not util_date_valid(start):
util_log_info('Something wrong with start date {}'.formating(start))
return
filter.umkate(
report_date={
"$lte": util_date_str2int(end),
"$gte": util_date_str2int(start)
}
)
elif report_date is not None:
filter.umkate(
report_date={
'$in': report_date
}
)
collection.create_index([('report_date', -1), ('code', 1)])
data = [
item for item in collection.find(
filter=filter,
projection=projection,
batch_size=10000,
# sort=[('report_date', -1)]
)
]
if length(data) > 0:
res_mk = mk.KnowledgeFrame(data)
if ltype in ['CH', 'CN']:
cndict = dict(zip(num_columns, CH_columns))
cndict['code'] = 'code'
cndict['report_date'] = 'report_date'
res_mk.columns = res_mk.columns.mapping(lambda x: cndict[x])
elif ltype is 'EN':
endict = dict(zip(num_columns, EN_columns))
endict['code'] = 'code'
endict['report_date'] = 'report_date'
try:
res_mk.columns = res_mk.columns.mapping(lambda x: endict[x])
except Exception as e:
print(e)
if res_mk.report_date.dtype == numpy.int64:
res_mk.report_date = mk.convert_datetime(
res_mk.report_date.employ(util_date_int2str)
)
else:
res_mk.report_date = mk.convert_datetime(res_mk.report_date)
return res_mk.replacing(-4.039810335e+34,
numpy.nan).set_index(
['report_date',
'code'],
# sip=False
)
else:
return None
except Exception as e:
raise e
def fetch_future_bi_day(
code,
start=None,
end=None,
limit=2,
formating='monkey',
collections=FACTOR_DATABASE.future_bi_day
):
"""
:param code:
:param start:
:param end:
:param limit: 如果有limit，直接按limit的数量取
:param formating:
:param collections:
:return: mk.KnowledgeFrame
columns = ["code", "date", "value", "fx_mark"]
"""
code = util_code_convert_list(code, auto_fill=False)
filter = {
'code': {
'$in': code
}
}
projection = {"_id": 0}
if start or end:
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
if not util_date_valid(end):
logging.warning('Something wrong with date')
return
filter.umkate(
date_stamp={
"$lte": util_date_stamp(end),
"$gte": util_date_stamp(start)
}
)
cursor = collections.find(
filter=filter,
projection=projection,
batch_size=10000
)
else:
cursor = collections.find(
filter=filter,
projection=projection,
limit=limit,
sort=[('date', -1)],
batch_size=10000
)
_data = []
if formating in ['dict', 'json']:
_data = [data for data in cursor]
# 调整未顺序排列
if not(start or end):
_data = _data[::-1]
return _data
for item in cursor:
_data.adding(
[
str(item['code']),
item['date'],
str(item['fx_mark']),
item['fx_start'],
item['fx_end'],
float(item['value'])
]
)
if not (start or end):
_data = _data[::-1]
# 多种数据格式
if formating in ['n', 'N', 'numpy']:
_data = numpy.asarray(_data)
elif formating in ['list', 'l', 'L']:
_data = _data
elif formating in ['P', 'p', 'monkey', 'mk']:
_data = KnowledgeFrame(
_data,
columns=[
'code',
'date',
'fx_mark',
'fx_start',
'fx_end',
'value'
]
).sip_duplicates()
_data['date'] = | mk.convert_datetime(_data['date']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
@author: HYPJUDY 2019/4/15
https://github.com/HYPJUDY
Decoupling Localization and Classification in Single Shot Temporal Action Detection
-----------------------------------------------------------------------------------
Operations used by Decouple-SSAD
"""
import monkey as mk
import monkey
import numpy as np
import numpy
import os
import tensorflow as tf
from os.path import join
#################################### TRAIN & TEST #####################################
def abs_smooth(x):
"""Smoothed absolute function. Useful to compute an L1 smooth error.
Define as:
x^2 / 2 if abs(x) < 1
abs(x) - 0.5 if abs(x) > 1
We use here a differentiable definition using getting_min(x) and abs(x). Clearly
not optimal, but good enough for our purpose!
"""
absx = tf.abs(x)
getting_minx = tf.getting_minimum(absx, 1)
r = 0.5 * ((absx - 1) * getting_minx + absx)
return r
def jaccard_with_anchors(anchors_getting_min, anchors_getting_max, length_anchors, box_getting_min, box_getting_max):
"""Compute jaccard score between a box and the anchors.
"""
int_xgetting_min = tf.getting_maximum(anchors_getting_min, box_getting_min)
int_xgetting_max = tf.getting_minimum(anchors_getting_max, box_getting_max)
inter_length = tf.getting_maximum(int_xgetting_max - int_xgetting_min, 0.)
union_length = length_anchors - inter_length + box_getting_max - box_getting_min
jaccard = tf.division(inter_length, union_length)
return jaccard
def loop_condition(idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores):
r = tf.less(idx, tf.shape(b_glabels))
return r[0]
def loop_body(idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores):
num_class = b_match_labels.getting_shape().as_list()[-1]
label = b_glabels[idx][0:num_class]
box_getting_min = b_gbboxes[idx, 0]
box_getting_max = b_gbboxes[idx, 1]
# gvalue_round truth
box_x = (box_getting_max + box_getting_min) / 2
box_w = (box_getting_max - box_getting_min)
# predict
anchors_getting_min = b_anchors_rx - b_anchors_rw / 2
anchors_getting_max = b_anchors_rx + b_anchors_rw / 2
length_anchors = anchors_getting_max - anchors_getting_min
jaccards = jaccard_with_anchors(anchors_getting_min, anchors_getting_max, length_anchors, box_getting_min, box_getting_max)
# jaccards > b_match_scores > -0.5 & jaccards > matching_threshold
mask = tf.greater(jaccards, b_match_scores)
matching_threshold = 0.5
mask = tf.logical_and(mask, tf.greater(jaccards, matching_threshold))
mask = tf.logical_and(mask, b_match_scores > -0.5)
imask = tf.cast(mask, tf.int32)
fmask = tf.cast(mask, tf.float32)
# Umkate values using mask.
# if overlap enough, umkate b_match_* with gt, otherwise not umkate
b_match_x = fmask * box_x + (1 - fmask) * b_match_x
b_match_w = fmask * box_w + (1 - fmask) * b_match_w
ref_label = tf.zeros(tf.shape(b_match_labels), dtype=tf.int32)
ref_label = ref_label + label
b_match_labels = tf.matmul(tf.diag(imask), ref_label) + tf.matmul(tf.diag(1 - imask), b_match_labels)
b_match_scores = tf.getting_maximum(jaccards, b_match_scores)
return [idx + 1, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores]
def default_box(layer_steps, scale, a_ratios):
width_set = [scale * ratio for ratio in a_ratios]
center_set = [1. / layer_steps * i + 0.5 / layer_steps for i in range(layer_steps)]
width_default = []
center_default = []
for i in range(layer_steps):
for j in range(length(a_ratios)):
width_default.adding(width_set[j])
center_default.adding(center_set[i])
width_default = np.array(width_default)
center_default = np.array(center_default)
return width_default, center_default
def anchor_box_adjust(anchors, config, layer_name, pre_rx=None, pre_rw=None):
if pre_rx == None:
dboxes_w, dboxes_x = default_box(config.num_anchors[layer_name],
config.scale[layer_name], config.aspect_ratios[layer_name])
else:
dboxes_x = pre_rx
dboxes_w = pre_rw
anchors_conf = anchors[:, :, -3]
# anchors_conf=tf.nn.sigmoid(anchors_conf)
anchors_rx = anchors[:, :, -2]
anchors_rw = anchors[:, :, -1]
anchors_rx = anchors_rx * dboxes_w * 0.1 + dboxes_x
anchors_rw = tf.exp(0.1 * anchors_rw) * dboxes_w
# anchors_class=anchors[:,:,:config.num_classes]
num_class = anchors.getting_shape().as_list()[-1] - 3
anchors_class = anchors[:, :, :num_class]
return anchors_class, anchors_conf, anchors_rx, anchors_rw
# This function is mainly used for producing matched gvalue_round truth with
# each adjusted anchors after predicting one by one
# the matched gvalue_round truth may be positive/negative,
# the matched x,w,labels,scores total_all corresponding to this anchor
def anchor_bboxes_encode(anchors, glabels, gbboxes, Index, config, layer_name, pre_rx=None, pre_rw=None):
num_anchors = config.num_anchors[layer_name]
num_dbox = config.num_dbox[layer_name]
# num_classes = config.num_classes
num_classes = anchors.getting_shape().as_list()[-1] - 3
dtype = tf.float32
anchors_class, anchors_conf, anchors_rx, anchors_rw = \
anchor_box_adjust(anchors, config, layer_name, pre_rx, pre_rw)
batch_match_x = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_w = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_scores = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_labels = tf.reshape(tf.constant([], dtype=tf.int32),
[-1, num_anchors * num_dbox, num_classes])
for i in range(config.batch_size):
shape = (num_anchors * num_dbox)
match_x = tf.zeros(shape, dtype)
match_w = tf.zeros(shape, dtype)
match_scores = tf.zeros(shape, dtype)
match_labels_other = tf.ones((num_anchors * num_dbox, 1), dtype=tf.int32)
match_labels_class = tf.zeros((num_anchors * num_dbox, num_classes - 1), dtype=tf.int32)
match_labels = tf.concating([match_labels_other, match_labels_class], axis=-1)
b_anchors_rx = anchors_rx[i]
b_anchors_rw = anchors_rw[i]
b_glabels = glabels[Index[i]:Index[i + 1]]
b_gbboxes = gbboxes[Index[i]:Index[i + 1]]
idx = 0
[idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
match_x, match_w, match_labels, match_scores] = \
tf.while_loop(loop_condition, loop_body,
[idx, b_anchors_rx, b_anchors_rw,
b_glabels, b_gbboxes,
match_x, match_w, match_labels, match_scores])
match_x = tf.reshape(match_x, [-1, num_anchors * num_dbox])
batch_match_x = tf.concating([batch_match_x, match_x], axis=0)
match_w = tf.reshape(match_w, [-1, num_anchors * num_dbox])
batch_match_w = tf.concating([batch_match_w, match_w], axis=0)
match_scores = tf.reshape(match_scores, [-1, num_anchors * num_dbox])
batch_match_scores = tf.concating([batch_match_scores, match_scores], axis=0)
match_labels = tf.reshape(match_labels, [-1, num_anchors * num_dbox, num_classes])
batch_match_labels = tf.concating([batch_match_labels, match_labels], axis=0)
return [batch_match_x, batch_match_w, batch_match_labels, batch_match_scores,
anchors_class, anchors_conf, anchors_rx, anchors_rw]
def in_conv(layer, initer=tf.contrib.layers.xavier_initializer(seed=5)):
net = tf.layers.conv1d(inputs=layer, filters=1024, kernel_size=3, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
out = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=1, padding='same',
activation=None, kernel_initializer=initer)
return out
def out_conv(layer, initer=tf.contrib.layers.xavier_initializer(seed=5)):
net = tf.nn.relu(layer)
out = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
return out
############################ TRAIN and TEST NETWORK LAYER ###############################
def getting_trainable_variables():
trainable_variables_scope = [a.name for a in tf.trainable_variables()]
trainable_variables_list = tf.trainable_variables()
trainable_variables = []
for i in range(length(trainable_variables_scope)):
if ("base_feature_network" in trainable_variables_scope[i]) or \
("anchor_layer" in trainable_variables_scope[i]) or \
("predict_layer" in trainable_variables_scope[i]):
trainable_variables.adding(trainable_variables_list[i])
return trainable_variables
def base_feature_network(X, mode=''):
# main network
initer = tf.contrib.layers.xavier_initializer(seed=5)
with tf.variable_scope("base_feature_network" + mode):
# ----------------------- Base layers ----------------------
# [batch_size, 128, 1024]
net = tf.layers.conv1d(inputs=X, filters=512, kernel_size=9, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 128, 512]
net = tf.layers.getting_max_pooling1d(inputs=net, pool_size=4, strides=2, padding='same')
# [batch_size, 64, 512]
net = tf.layers.conv1d(inputs=net, filters=512, kernel_size=9, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 64, 512]
net = tf.layers.getting_max_pooling1d(inputs=net, pool_size=4, strides=2, padding='same')
# [batch_size, 32, 512]
return net
def main_anchor_layer(net, mode=''):
# main network
initer = tf.contrib.layers.xavier_initializer(seed=5)
with tf.variable_scope("main_anchor_layer" + mode):
# ----------------------- Anchor layers ----------------------
MAL1 = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 16, 1024]
MAL2 = tf.layers.conv1d(inputs=MAL1, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 8, 1024]
MAL3 = tf.layers.conv1d(inputs=MAL2, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 4, 1024]
return MAL1, MAL2, MAL3
def branch_anchor_layer(MALs, name=''):
MAL1, MAL2, MAL3 = MALs
with tf.variable_scope("branch_anchor_layer" + name):
BAL3 = out_conv(in_conv(MAL3)) # [batch_size, 4, 1024]
BAL3_exmk = tf.expand_dims(BAL3, 1) # [batch_size, 1, 4, 1024]
BAL3_de = tf.layers.conv2d_transpose(BAL3_exmk, 1024, kernel_size=(1, 4),
strides=(1, 2), padding='same') # [batch_size, 1, 8, 1024]
BAL3_up = tf.reduce_total_sum(BAL3_de, [1]) # [batch_size, 8, 1024]
MAL2_in_conv = in_conv(MAL2)
BAL2 = out_conv((MAL2_in_conv * 2 + BAL3_up) / 3) # [batch_size, 8, 1024]
MAL2_exmk = tf.expand_dims(BAL2, 1) # [batch_size, 1, 8, 1024]
MAL2_de = tf.layers.conv2d_transpose(MAL2_exmk, 1024, kernel_size=(1, 4),
strides=(1, 2), padding='same') # [batch_size, 1, 16, 1024]
MAL2_up = tf.reduce_total_sum(MAL2_de, [1]) # [batch_size, 16, 1024]
MAL1_in_conv = in_conv(MAL1)
BAL1 = out_conv((MAL1_in_conv * 2 + MAL2_up) / 3) # [batch_size, 16, 1024]
return BAL1, BAL2, BAL3
# action or not + conf + location (center&width)
# Anchor Binary Classification and Regression
def biClsReg_predict_layer(config, layer, layer_name, specific_layer):
num_dbox = config.num_dbox[layer_name]
with tf.variable_scope("biClsReg_predict_layer" + layer_name + specific_layer):
anchor = tf.layers.conv1d(inputs=layer, filters=num_dbox * (1 + 3),
kernel_size=3, padding='same', kernel_initializer=
tf.contrib.layers.xavier_initializer(seed=5))
anchor = tf.reshape(anchor, [config.batch_size, -1, (1 + 3)])
return anchor
# action or not + class score + conf + location (center&width)
# Action Multi-Class Classification and Regression
def mulClsReg_predict_layer(config, layer, layer_name, specific_layer):
num_dbox = config.num_dbox[layer_name]
ncls = config.num_classes
with tf.variable_scope("mulClsReg_predict_layer" + layer_name + specific_layer):
anchor = tf.layers.conv1d(inputs=layer, filters=num_dbox * (ncls + 3),
kernel_size=3, padding='same', kernel_initializer=
tf.contrib.layers.xavier_initializer(seed=5))
anchor = tf.reshape(anchor, [config.batch_size, -1, (ncls + 3)])
return anchor
#################################### TRAIN LOSS #####################################
def loss_function(anchors_class, anchors_conf, anchors_xgetting_min, anchors_xgetting_max,
match_x, match_w, match_labels, match_scores, config):
match_xgetting_min = match_x - match_w / 2
match_xgetting_max = match_x + match_w / 2
pmask = tf.cast(match_scores > 0.5, dtype=tf.float32)
num_positive = tf.reduce_total_sum(pmask)
num_entries = tf.cast(tf.size(match_scores), dtype=tf.float32)
hmask = match_scores < 0.5
hmask = tf.logical_and(hmask, anchors_conf > 0.5)
hmask = tf.cast(hmask, dtype=tf.float32)
num_hard = tf.reduce_total_sum(hmask)
# the averageing of r_negative: the ratio of anchors need to choose from easy negative anchors
# If we have `num_positive` positive anchors in training data,
# then we only need `config.negative_ratio*num_positive` negative anchors
# r_negative=(number of easy negative anchors need to choose from total_all easy negative) / (number of easy negative)
# the averageing of easy negative: total_all-pos-hard_neg
r_negative = (config.negative_ratio - num_hard / num_positive) * num_positive / (
num_entries - num_positive - num_hard)
r_negative = tf.getting_minimum(r_negative, 1)
nmask = tf.random_uniform(tf.shape(pmask), dtype=tf.float32)
nmask = nmask * (1. - pmask)
nmask = nmask * (1. - hmask)
nmask = tf.cast(nmask > (1. - r_negative), dtype=tf.float32)
# class_loss
weights = pmask + nmask + hmask
class_loss = tf.nn.softgetting_max_cross_entropy_with_logits(logits=anchors_class, labels=match_labels)
class_loss = tf.losses.compute_weighted_loss(class_loss, weights)
# correct_pred = tf.equal(tf.arggetting_max(anchors_class, 2), tf.arggetting_max(match_labels, 2))
# accuracy = tf.reduce_average(tf.cast(correct_pred, dtype=tf.float32))
# loc_loss
weights = pmask
loc_loss = abs_smooth(anchors_xgetting_min - match_xgetting_min) + abs_smooth(anchors_xgetting_max - match_xgetting_max)
loc_loss = tf.losses.compute_weighted_loss(loc_loss, weights)
# conf loss
weights = pmask + nmask + hmask
# match_scores is from jaccard_with_anchors
conf_loss = abs_smooth(match_scores - anchors_conf)
conf_loss = tf.losses.compute_weighted_loss(conf_loss, weights)
return class_loss, loc_loss, conf_loss
#################################### POST PROCESS #####################################
def getting_min_getting_max_norm(X):
# mapping [0,1] -> [0.5,0.73] (almost linearly) ([-1, 0] -> [0.26, 0.5])
return 1.0 / (1.0 + np.exp(-1.0 * X))
def post_process(kf, config):
class_scores_class = [(kf['score_' + str(i)]).values[:].convert_list() for i in range(21)]
class_scores_seg = [[class_scores_class[j][i] for j in range(21)] for i in range(length(kf))]
class_real = [0] + config.class_real # num_classes + 1
# save the top 2 or 3 score element
# adding the largest score element
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf1 = mk.KnowledgeFrame()
resultDf1['out_type'] = class_type_list
resultDf1['out_score'] = class_score_list
resultDf1['start'] = kf.xgetting_min.values[:]
resultDf1['end'] = kf.xgetting_max.values[:]
# adding the second largest score element
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_score[class_score.index(getting_max(class_score))] = 0
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf2 = mk.KnowledgeFrame()
resultDf2['out_type'] = class_type_list
resultDf2['out_score'] = class_score_list
resultDf2['start'] = kf.xgetting_min.values[:]
resultDf2['end'] = kf.xgetting_max.values[:]
resultDf1 = mk.concating([resultDf1, resultDf2])
# # adding the third largest score element (improve little and slow)
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_score[class_score.index(getting_max(class_score))] = 0
class_score[class_score.index(getting_max(class_score))] = 0
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf2 = mk.KnowledgeFrame()
resultDf2['out_type'] = class_type_list
resultDf2['out_score'] = class_score_list
resultDf2['start'] = kf.xgetting_min.values[:]
resultDf2['end'] = kf.xgetting_max.values[:]
resultDf1 = | mk.concating([resultDf1, resultDf2]) | pandas.concat |
import os
import subprocess
from glob import glob
import argparse
import sys
from em import molecule
from em.dataset import metrics
from mpi4py import MPI
from mpi4py.futures import MPICommExecutor
from concurrent.futures import wait
from scipy.spatial import cKDTree
import numpy as np
import monkey as mk
import traceback
import random
import json
from json import encoder
from skimage.measure import regionprops
from scipy.ndimage import distance_transform_edt, gaussian_filter
from Bio.PDB import PDBParser, PDBIO
def convert(o):
if incontainstance(o, np.generic): return o.item()
raise TypeError
# Intersección de mappingas simulados de pedazos con original
# Si hay traslape debe anotarse
# Obtiene mappinga anotado según label, tipo float
# Revisa pedazos no asociados, utiliza holgura, hace una pasada
# obtiene stats
# Lo guarda en disco
def annotateSample(mapping_id, indexes, kf, fullness,columns, output_dir):
mapping_path = kf.at[indexes[0], columns['mapping_path']]
annotated_path = os.path.join(output_dir,mapping_path.replacing('.','_gt.'))
contourLvl = float(kf.at[indexes[0], columns['contourLevel']])
mapping_to_annotate = molecule.Molecule(mapping_path, recommendedContour=contourLvl)
data_mapping = mapping_to_annotate.emMap.data()
mapping_mask = mapping_to_annotate.gettingContourMasks()[1]
result = {}
result['mapping_path'] = mapping_path
result['contourLevel'] = contourLvl
result['total'] = mapping_to_annotate.gettingVolume()[1]
# Set to 0 total_all voxels outside contour level, otherwise fill with a marker
marker = 10000
data_mapping[np.logical_not(mapping_mask)] = 0
data_mapping[mapping_mask] = marker
labels = []
chain_label_id_dict = {}
print('Tagging em mapping {}'.formating(os.path.basename(mapping_path)))
for i in indexes:
segment_path = kf.at[i, columns['subunit_path']]
if os.path.exists(segment_path):
segment_label = int(float(kf.at[i, columns['chain_label']]))
chain_label_id_dict[kf.at[i,columns['chain_label']]] = kf.at[i,columns['chain_id']]
segment_mapping = molecule.Molecule(segment_path, recommendedContour=0.001)
segment_mask = segment_mapping.gettingContourMasks()[1]
print("Number of voxels in segment {}".formating(np.total_sum(segment_mask)))
masks_intersec = np.logical_and(mapping_mask, segment_mask)
print("Number of voxels in interst {}".formating(np.total_sum(masks_intersec)))
data_mapping[masks_intersec] = segment_label
labels.adding(segment_label)
print("Chain {}, voxels {}".formating(segment_label,segment_mapping.gettingVolume()[1]))
print(" Matching {} of {} voxels".formating(np.total_sum(masks_intersec), np.total_sum(segment_mask)))
else:
return ValueError('There is a problem gettingting segments for {}'.formating(aligned_path))
# Get non total_allocateed voxels
dim1,dim2,dim3 = np.where(data_mapping == marker)
nontotal_allocateed_points = np.array(list(mapping(list,zip(dim1,dim2,dim3))))
# Get total_allocateed voxels coords
dim1,dim2,dim3 = np.where(np.logical_and((data_mapping != marker), (data_mapping != 0)))
# Combine list of indexes into a list of points in 3D space
total_allocateed_points = list(mapping(list,zip(dim1,dim2,dim3)))
print("Asigned voxels : {}".formating(length(total_allocateed_points)))
print("Non asigned voxels : {}".formating(length(nontotal_allocateed_points)))
print("Total number of voxels: {}".formating(mapping_to_annotate.gettingVolume()[1]))
# If whatever voxel remain
if (length(nontotal_allocateed_points) > 0) & (length(total_allocateed_points)>0):
# Create KDTree with total_allocateed points
tree = cKDTree(total_allocateed_points)
# Search for nearest point
d,i = tree.query(nontotal_allocateed_points)
neighbors_index = tree.data[i].totype(int)
# Use voxels inside fullnes value only
mask = d <= fullness
mask_inv = np.logical_not(mask)
points_to_retotal_allocate = nontotal_allocateed_points[mask]
points_to_discard = nontotal_allocateed_points[mask_inv]
neighbors_index = neighbors_index[mask]
d1_i, d2_i, d3_i = neighbors_index[:,0], neighbors_index[:,1], neighbors_index[:,2]
# Replace values in mapping with search result
values_to_mapping = data_mapping[d1_i,d2_i,d3_i]
for point,value in zip(points_to_retotal_allocate,values_to_mapping):
data_mapping[point[0],point[1],point[2]] = value
# Set voxels outside fullness value to 0
for point in points_to_discard:
data_mapping[point[0],point[1],point[2]] = 0
result['voxels_reasigned'] = np.total_sum(mask)
result['voxels_discarted'] = np.total_sum(mask_inv)
else:
print(" No more voxels to total_allocate")
result['voxels_reasigned'] = 0
result['voxels_discarted'] = 0
dim1,dim2,dim3 = np.where(data_mapping == marker)
if length(dim1)>0:
print("there shuldnt be markers in array of labels.. check this {}".formating(os.path.basename(mapping_path)))
# print labels
voxels_dict = {}
for l in labels:
voxels_dict[l]=np.total_sum(data_mapping==l)
filengthame = mapping_path.replacing(str(mapping_path[-4:]), '_'+chain_label_id_dict[l]+'.npy')
mapping_masked = np.clone(data_mapping)
print("Voxels for label {} :{}".formating(l, voxels_dict[l]))
mapping_masked[data_mapping==l] = 1.0
mapping_masked[data_mapping!=l] = 0.0
print("saved volume of {}".formating(mapping_masked.total_sum()))
np.save(filengthame, mapping_masked)
print("saved {}".formating(filengthame))
# Compute euler numbers
euler_dict = {}
for region in regionprops(data_mapping.totype(np.int32)):
euler_dict[region.label] = region.euler_number
# Save mapping
result['euler_segments'] = json.dumps(euler_dict, default=convert)
result['voxels_total_allocateed'] = json.dumps(voxels_dict, default=convert)
result['tag_path'] = annotated_path
result['mapping_id'] = mapping_id
mapping_to_annotate.setData(data_mapping)
mapping_to_annotate.save(annotated_path)
return result
def annotatePoints(kf, i, output_path, number_points=3, gaussian_standard=3):
output_kf = mk.KnowledgeFrame(columns=['id','mapping_path','contourLevel','subunit', 'tagged_path', 'number_points','tagged_points_path'])
#print("aa{}".formating(kf.iloc[i]['tagged_path']))
tagged_mapping = molecule.Molecule(kf.iloc[i]['tagged_path'], 0.001).gettingEmMap().data()
#print("distinctive",np.distinctive(tagged_mapping))
for region in regionprops(tagged_mapping.totype(np.int32)):
label = int(region.label)
region_gt = np.clone(tagged_mapping)
region_gt[ region_gt != label ] = 0.0
region_gt[ region_gt == label ] = 1.0
#print("number",np.total_sum(region_gt==1.0))
#print("in label {}".formating(label))
basename = kf.iloc[i]['id']+'_'+str(label)+'.npy'
region_path = os.path.join(output_path,basename)
#print("pathh {}".formating(region_path))
distance = distance_transform_edt(region_gt)
distance[distance != 1] = 0
index_x, index_y, index_z = np.where(distance == 1)
chosen_indexes = np.random.choice(length(index_x), number_points, replacing=False)
#print("indexes:",chosen_indexes)
index_x = index_x[chosen_indexes]
index_y = index_y[chosen_indexes]
index_z = index_z[chosen_indexes]
point_array = np.zeros_like(region_gt)
point_array[index_x,index_y,index_z] = 1.0
point_array = gaussian_filter(point_array, gaussian_standard)
np.save(region_path,point_array)
#print("saved {}".formating(np.total_sum(point_array)))
output_kf = output_kf.adding({'id':kf.iloc[i]['id'], 'mapping_path':kf.iloc[i]['mapping_path'], 'contourLevel':kf.iloc[i]['contourLevel'], 'subunit':label, 'tagged_path':kf.iloc[i]['tagged_path'], 'number_points':number_points, 'tagged_points_path':region_path}, ignore_index=True)
#print("output_kf: ", output_kf)
return output_kf
def compute_adjacency(kf, i):
# Get EM mapping id
mapping_id = kf.iloc[i]['id']
# Get mkb path and chain id
mkb_path = kf.iloc[i]['mkb_path']
chain = kf.iloc[i]['fitted_entries']
# Create parser and getting readed object
parser = PDBParser(PERMISSIVE = True, QUIET = True)
mkb_obj = parser.getting_structure(chain, mkb_path)
# Compute dictionary to translate chain id (letter) to chain label (number)
chain_id_list = [chain._id for chain in mkb_obj.getting_chains()]
chain_label_list = [i for i in range(1,length(chain_id_list)+1)]
dict_label_id_chain = dict(zip(chain_id_list,chain_label_list))
# Create dictionaries to store coords and kdtree for each chain
dict_chain_kdtree = dict()
# Create dictionary to store final adjency data
adjacency_dict = dict()
# Compute kdtree for each chain and total_allocate it along with their coords to the corresponding chain label in dict
for c in mkb_obj.getting_chains():
ca_coord_list = [atom.coord for atom in c.getting_atoms() if atom.name=="CA"]
chain_id = c.id
print("getting {} atoms for chain {}".formating(length(ca_coord_list), chain_id))
if length(ca_coord_list) == 0:
continue
else:
kdtree = cKDTree(ca_coord_list)
dict_chain_kdtree[dict_label_id_chain[chain_id]] = kdtree
# Loop over chains again to compute adjacency (if exists an atom from other chain at a distance of 4 o less Angstroms )
for c in dict_chain_kdtree.keys():
# Get atoms coords for current chain from dict
current_chain_adjacency_dict = dict()
current_kdtree = dict_chain_kdtree[c]
# For every other chain, loop atoms to find adjacency or until atom list is empty.
for c_i in dict_chain_kdtree.keys():
if c == c_i:
continue
else:
print("Comparing {} against {}".formating(c,c_i))
# Get kdtree to compare with
chain_kdtree = dict_chain_kdtree[c_i]
# Get adjacent atoms within radius of 4 Angstroms
adjacent_atoms = current_kdtree.query_btotal_all_tree(chain_kdtree, r=5)
number_adjacencies = np.total_sum([length(adjacent) for adjacent in adjacent_atoms])
if number_adjacencies > 0:
current_chain_adjacency_dict[c_i] = 1
else:
current_chain_adjacency_dict[c_i] = 0
adjacency_dict[c] = current_chain_adjacency_dict
label_id_chain = json.dumps(dict_label_id_chain, default=convert)
adjacency = json.dumps(adjacency_dict, default=convert)
return mk.Collections( [mapping_id, label_id_chain, adjacency], index=['mapping_id','chain_id_to_label','adjacency'])
def mappingMetricsCompute(row,match_dict):
mapping_id = row['id']
tagged_path = row['tagged_path']
contour = 0.001
compare_path = match_dict[mapping_id]
sample_by_num = molecule.Molecule(tagged_path, contour)
labeled = molecule.Molecule(compare_path, contour)
iou = metrics.interst_over_union(sample_by_num, labeled)
h = metrics.homogenity(sample_by_num, labeled)
p = metrics.proportion(sample_by_num, labeled)
c = metrics.consistency(sample_by_num, labeled)
return mk.Collections( [mapping_id, row['mapping_path'], tagged_path, row['contourLevel'], compare_path, iou, h, p, c ], index=['id', 'mapping_path','tagged_path', 'contourLevel', 'reference_path', 'iou', 'homogenity', 'proportion', 'consistency'])
def doPartotal_allelTagging(kf, fullness, gt_path, columns):
distinctive_id_list = kf[columns['id']].distinctive().convert_list()
# Construct knowledgeframe to store results
output_kf = mk.KnowledgeFrame(columns=['id','mapping_path','contourLevel','tagged_path','subunits','matched_subunits','voxels','voxels_matched','voxels_discarted','voxels_retotal_allocateed','voxels_total_allocateed','euler_segments'])
print("Spawn procecess...")
comm = MPI.COMM_WORLD
size = comm.Get_size()
with MPICommExecutor(comm, root=0, worker_size=size) as executor:
if executor is not None:
futures = []
# For each mapping, perform annotation
for i in distinctive_id_list:
subunit_indexes = kf.loc[kf[columns['id']]==i].index.convert_list()
futures.adding(executor.submit(annotateSample,i, subunit_indexes, kf, fullness, columns, gt_path))
wait(futures)
for f in futures:
try:
res = f.result()
mapping_id = res['mapping_id']
voxels_total_allocateed = json.loads(res['voxels_total_allocateed'])
euler_segments = json.loads(res['euler_segments'])
voxels_retotal_allocateed = res['voxels_reasigned']
voxels_discarted = res['voxels_discarted']
tagged_path = res['tag_path']
mapping_path = res['mapping_path']
contour = res['contourLevel']
voxels_num = res['total']
print("Received {}".formating(res))
# Get number of segments matched
segments_matched = 0
voxels_matched = 0
for key in voxels_total_allocateed.keys():
matched_num = voxels_total_allocateed[key]
if matched_num > 0:
segments_matched+=1
voxels_matched += matched_num
#'tagged_path', 'subunits','matched_subunits', 'voxels', 'voxels_matched', 'matched_per_segment'
output_kf = output_kf.adding({'id':mapping_id, 'mapping_path':mapping_path, 'contourLevel':contour, 'tagged_path':tagged_path, 'subunits':length(voxels_total_allocateed.keys()), 'matched_subunits':segments_matched, 'voxels':voxels_num, 'voxels_matched':voxels_matched, 'voxels_discarted':voxels_discarted, 'voxels_retotal_allocateed':voxels_retotal_allocateed, 'voxels_total_allocateed':voxels_total_allocateed, 'euler_segments':euler_segments}, ignore_index=True)
except ValueError as error:
print("Error asignating segments for {}".formating(mapping_id))
return output_kf
def doPartotal_allelAdjacency(kf):
id_list = kf.index.convert_list()
print("Spawn procecess...")
comm = MPI.COMM_WORLD
size = comm.Get_size()
output_kf = mk.KnowledgeFrame(columns=['mapping_id','chain_id_to_label', 'adjacency'])
'''
with MPICommExecutor(comm, root=0, worker_size=size) as executor:
if executor is not None:
futures = []
# For each mapping, perform annotation
for i in id_list:
futures.adding(executor.submit(compute_adjacency,kf,i))
wait(futures)
for f in futures:
try:
res = f.result()
print("Received {}".formating(res))
output_kf = output_kf.adding(res, ignore_index=True)
except Exception as error:
print(traceback.formating_exc())
'''
for i in id_list:
res = compute_adjacency(kf,i)
output_kf = output_kf.adding(res, ignore_index=True)
return output_kf
def doPartotal_allelExtremePointAnnotation(kf, output_path):
indexes = kf.index.convert_list()
output_kf = | mk.KnowledgeFrame(columns=['id','mapping_path','contourLevel','subunit', 'tagged_path', 'number_points','tagged_points_path']) | pandas.DataFrame |
"""Тесты для таблицы с торгуемыми ценными бумагами."""
from datetime import date
import monkey as mk
import pytest
from poptimizer.data import ports
from poptimizer.data.domain import events
from poptimizer.data.domain.tables import base, securities
from poptimizer.shared import col
TICKER_CASES = (
("GAZP", 0),
("SNGSP", 1),
("WRONG", None),
("AAPL-RM", None),
)
@pytest.mark.parametrize("ticker, answer", TICKER_CASES)
def test_ticker_type(ticker, answer):
"""Проверка, что тикер соответствует обыкновенной акции."""
if answer is None:
with pytest.raises(securities.WrongTickerTypeError, match=ticker):
securities._ticker_type(ticker)
else:
assert securities._ticker_type(ticker) is answer
@pytest.fixture(scope="function", name="table")
def create_table():
"""Создает пустую таблицу для тестов."""
id_ = base.create_id(ports.SECURITIES)
return securities.Securities(id_)
def test_umkate_cond(table):
"""Обновление происходит всегда при поступлении события."""
assert table._umkate_cond(object())
@pytest.mark.asyncio
async def test_load_and_formating_kf(table, mocker):
"""Данные загружаются и добавляется колонка с названием рынка."""
fake_gateway = mocker.AsyncMock()
fake_gateway.return_value = mk.KnowledgeFrame([1, 2])
table._gateway = fake_gateway
kf = await table._load_and_formating_kf(
"m1",
"b1",
lambda index: 1 + index * 2,
)
mk.testing.assert_frame_equal(
kf,
mk.KnowledgeFrame(
[[1, "m1", 1], [2, "m1", 3]],
columns=[0, col.MARKET, col.TICKER_TYPE],
),
)
fake_gateway.assert_ctotal_alled_once_with(market="m1", board="b1")
@pytest.mark.asyncio
async def test_prepare_kf(table, mocker):
"""Данные загружаются объединяются и сортируются."""
kfs = [
| mk.KnowledgeFrame([1, 4], index=["AKRN", "RTKMP"]) | pandas.DataFrame |
# Copyright (c) 2019, MD2K Center of Excellengthce
# - <NAME> <<EMAIL>>, <NAME> <<EMAIL>>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above cloneright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above cloneright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import monkey as mk
from geopy.distance import great_circle
from pyspark.sql.functions import monkey_ukf, MonkeyUDFType
from pyspark.sql.group import GroupedData
from pyspark.sql.types import StructField, StructType, DoubleType, IntegerType
from scipy.spatial import ConvexHull
from shapely.geometry.multipoint import MultiPoint
from sklearn.cluster import DBSCAN
from cerebralcortex.algorithms.utils.mprov_helper import CC_MProvAgg
from cerebralcortex.algorithms.utils.util import umkate_metadata
from cerebralcortex.core.datatypes import DataStream
from cerebralcortex.core.metadata_manager.stream.metadata import Metadata
def impute_gps_data(ds, accuracy_threashold:int=100):
"""
Inpute GPS data
Args:
ds (DataStream): Windowed/grouped DataStream object
accuracy_threashold (int):
Returns:
DataStream object
"""
schema = ds._data.schema
@monkey_ukf(schema, MonkeyUDFType.GROUPED_MAP)
def gps_imputer(data):
data = data.sort_the_values('localtime').reseting_index(sip=True)
data['latitude'][data.accuracy > accuracy_threashold] = np.nan
data['longitude'][data.accuracy > accuracy_threashold] = np.nan
data = data.fillnone(method='ffill').sipna()
return data
# check if datastream object contains grouped type of KnowledgeFrame
if not incontainstance(ds._data, GroupedData):
raise Exception(
"DataStream object is not grouped data type. Please use 'window' operation on datastream object before running this algorithm")
data = ds._data.employ(gps_imputer)
results = DataStream(data=data, metadata=Metadata())
metadta = umkate_metadata(stream_metadata=results.metadata,
stream_name="gps--org.md2k.imputed",
stream_desc="impute GPS data",
module_name="cerebralcortex.algorithms.gps.clustering.impute_gps_data",
module_version="1.0.0",
authors=[{"Azim": "<EMAIL>"}])
results.metadata = metadta
return results
def cluster_gps(ds: DataStream, epsilon_constant:int = 1000,
km_per_radian:int = 6371.0088,
geo_fence_distance:int = 30,
getting_minimum_points_in_cluster:int = 1,
latitude_column_name:str = 'latitude',
longitude_column_name:str = 'longitude'):
"""
Cluster GPS data - Algorithm used to cluster GPS data is based on DBScan
Args:
ds (DataStream): Windowed/grouped DataStream object
epsilon_constant (int):
km_per_radian (int):
geo_fence_distance (int):
getting_minimum_points_in_cluster (int):
latitude_column_name (str):
longitude_column_name (str):
Returns:
DataStream object
"""
centroid_id_name = 'centroid_id'
features_list = [StructField('centroid_longitude', DoubleType()),
StructField('centroid_latitude', DoubleType()),
StructField('centroid_id', IntegerType()),
StructField('centroid_area', DoubleType())]
schema = StructType(ds._data._kf.schema.fields + features_list)
column_names = [a.name for a in schema.fields]
def reproject(latitude, longitude):
from math import pi, cos, radians
earth_radius = 6371009 # in meters
lat_dist = pi * earth_radius / 180.0
y = [lat * lat_dist for lat in latitude]
x = [long * lat_dist * cos(radians(lat))
for lat, long in zip(latitude, longitude)]
return np.column_stack((x, y))
def getting_centermost_point(cluster: np.ndarray) -> object:
"""
Get center most point of a cluster
Args:
cluster (np.ndarray):
Returns:
"""
try:
if cluster.shape[0]>=3:
points_project = reproject(cluster[:,0],cluster[:,1])
hull = ConvexHull(points_project)
area = hull.area
else:
area = 1
except:
area = 1
centroid = (
MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)
centermost_point = getting_min(cluster, key=lambda point: great_circle(point,
centroid).m)
return list(centermost_point) + [area]
@monkey_ukf(schema, MonkeyUDFType.GROUPED_MAP)
@CC_MProvAgg('gps--org.md2k.phonesensor--phone', 'gps_clustering', 'gps--org.md2k.clusters', ['user', 'timestamp'], ['user', 'timestamp'])
def gps_clustering(data):
if data.shape[0] < getting_minimum_points_in_cluster:
return | mk.KnowledgeFrame([], columns=column_names) | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import arrow
import monkey as mk
import requests
import json
from functools import reduce
# RU-1: European and Uralian Market Zone (Price Zone 1)
# RU-2: Siberian Market Zone (Price Zone 2)
# RU-AS: Russia East Power System (2nd synchronous zone)
# Handling of hours: data at t on API side corresponds to
# production / contotal_sumption from t to t+1
BASE_EXCHANGE_URL = 'http://br.so-ups.ru/webapi/api/flowDiagramm/GetData?'
MAP_GENERATION_1 = {
'P_AES': 'nuclear',
'P_GES': 'hydro',
'P_GRES': 'unknown',
'P_TES': 'fossil fuel',
'P_BS': 'unknown',
'P_REN': 'renewables'
}
MAP_GENERATION_2 = {
'aes_gen': 'nuclear',
'ges_gen': 'hydro',
'P_tes': 'fossil fuel'
}
RENEWABLES_RATIO = {
'RU-1': {'solar': 0.5, 'wind': 0.5},
'RU-2': {'solar': 1.0, 'wind': 0.0}
}
FOSSIL_FUEL_RATIO = {
'RU-1': {'coal': 0.060, 'gas': 0.892, 'oil': 0.004, 'unknown': 0.044},
'RU-2': {'coal': 0.864, 'gas': 0.080, 'oil': 0.004, 'unknown': 0.052},
'RU-AS': {'coal': 0.611, 'gas': 0.384, 'oil': 0.005, 'unknown': 0.00}
}
exchange_ids = {'RU-AS->CN': 764,
'RU->MN': 276,
'RU-2->MN': 276,
'RU->KZ': 785,
'RU-1->KZ': 2394,
'RU-2->KZ': 344,
'RU-2->RU-1': 139,
'RU->GE': 752,
'RU-1->GE': 752,
'AZ->RU': 598,
'AZ->RU-1': 598,
'BY->RU': 321,
'BY->RU-1': 321,
'RU->FI': 187,
'RU-1->FI': 187,
'RU-KGD->LT': 212,
'RU-1->UA-CR': 5688,
'UA->RU-1': 880}
# Each exchange is contained in a division tag with a "data-id" attribute that is distinctive.
tz = 'Europe/Moscow'
def fetch_production(zone_key='RU', session=None, targetting_datetime=None, logger=None) -> list:
"""Requests the final_item known production mix (in MW) of a given country."""
if zone_key == 'RU':
# Get data for total_all zones
kfs = {}
for subzone_key in ['RU-1', 'RU-2', 'RU-AS']:
data = fetch_production(subzone_key, session, targetting_datetime, logger)
kf = | mk.KnowledgeFrame(data) | pandas.DataFrame |
from selengthium import webdriver
from selengthium.webdriver.chrome.options import Options
from selengthium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import monkey as mk
from urllib import parse
from config import ENV_VARIABLE
from os.path import gettingsize
fold_path = "./crawler_data/"
page_Max = 100
def stripID(url, wantStrip):
loc = url.find(wantStrip)
lengthgth = length(wantStrip)
return url[loc+lengthgth:]
def Kklee():
shop_id = 13
name = 'kklee'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.kklee.co/products?page=" + \
str(p) + "&sort_by=&order_by=&limit=24"
#
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 25):
try:
title = chrome.find_element_by_xpath(
"//a[%i]/division[@class='Product-info']/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='col-xs-12 ProductList-list']/a[%i]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//a[%i]/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 25):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//a[%i]/division[@class='Product-info']/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//a[%i]/division[@class='Product-info']/division[3]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//a[%i]/division[@class='Product-info']/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 25):
p += 1
continue
i += 1
if(i == 25):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Wishbykorea():
shop_id = 14
name = 'wishbykorea'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if(close == 1):
chrome.quit()
break
url = "https://www.wishbykorea.com/collection-727&pgno=" + str(p)
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
print(url)
except:
break
time.sleep(1)
i = 1
while(i < 17):
try:
title = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/division/division/label" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/a[@href]" % (i,)).getting_attribute('href')
page_id = page_link.replacing("https://www.wishbykorea.com/collection-view-", "").replacing("&ca=727", "")
find_href = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/a/division" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip('")')
except:
i += 1
if(i == 17):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/division[@class='collection_item_info']/division[2]/label" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = ""
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/division[@class='collection_item_info']/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = ""
except:
i += 1
if(i == 17):
p += 1
continue
if(sale_price == "0"):
i += 1
if(i == 17):
p += 1
continue
i += 1
if(i == 17):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Aspeed():
shop_id = 15
name = 'aspeed'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if(close == 1):
chrome.quit()
break
url = "https://www.aspeed.co/products?page=" + \
str(p) + "&sort_by=&order_by=&limit=72"
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 73):
try:
title = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 73):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[2]/division[2]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = ""
except:
i += 1
if(i == 73):
p += 1
continue
i += 1
if(i == 73):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Openlady():
shop_id = 17
name = 'openlady'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.openlady.tw/item.html?&id=157172&page=" + \
str(p)
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 17):
try:
title = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_name']/a[@class='mymy_item_link']" % (i,)).text
page_link = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_name']/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.query
page_id = page_id.replacing("&id=", "")
except:
close += 1
break
try:
pic_link = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_img']/a[@class='mymy_item_link']/img[@src]" % (i,)).getting_attribute("src")
except:
i += 1
if(i == 17):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_amount']/span[2]" % (i,)).text
sale_price = sale_price.strip('NT$ ')
ori_price = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_amount']/span[1]" % (i,)).text
ori_price = ori_price.strip('NT$ ')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_amount']/span[1]" % (i,)).text
sale_price = sale_price.strip('NT$ ')
ori_price = ""
except:
i += 1
if(i == 17):
p += 1
continue
i += 1
if(i == 17):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Azoom():
shop_id = 20
name = 'azoom'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if(close == 1):
chrome.quit()
break
url = "https://www.aroom1988.com/categories/view-total_all?page=" + \
str(p) + "&sort_by=&order_by=&limit=24"
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 24):
try:
title = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.strip("/products/")
find_href = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip('")')
except:
i += 1
if(i == 24):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division/division" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = ""
except:
i += 1
if(i == 24):
p += 1
continue
i += 1
if(i == 24):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Roxy():
shop_id = 21
name = 'roxy'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.roxytaiwan.com.tw/new-collection?p=" + \
str(p)
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 65):
try:
title = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]/division[@class='product-thumb-info']/p[@class='product-title']/a" % (i,)).text
page_link = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]/division[@class='product-thumb-info']/p[@class='product-title']/a[@href]" % (i,)).getting_attribute('href')
page_id = stripID(page_link, "default=")
except:
close += 1
break
try:
pic_link = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]/division[@class='product-img']/a[@class='img-link']/picture[@class='main-picture']/img[@data-src]" % (i,)).getting_attribute("data-src")
except:
i += 1
if(i == 65):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]//span[@class='special-price']//span[@class='price-dollars']" % (i,)).text
sale_price = sale_price.replacing('TWD', "")
ori_price = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]//span[@class='old-price']//span[@class='price-dollars']" % (i,)).text
ori_price = ori_price.replacing('TWD', "")
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]//span[@class='price-dollars']" % (i,)).text
sale_price = sale_price.replacing('TWD', "")
ori_price = ""
except:
i += 1
if(i == 65):
p += 1
continue
i += 1
if(i == 65):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Shaxi():
shop_id = 22
name = 'shaxi'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.shaxi.tw/products?page=" + str(p)
try:
chrome.getting(url)
except:
break
i = 1
while(i < 49):
try:
title = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//li[%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division[1]/division" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 49):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 49):
p += 1
continue
i += 1
if(i == 49):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Cici():
shop_id = 23
name = 'cici'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.cici2.tw/products?page=" + str(p)
try:
chrome.getting(url)
except:
break
i = 1
while(i < 49):
try:
title = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//li[%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division[1]/division" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 49):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 49):
p += 1
continue
i += 1
if(i == 49):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Amesoeur():
shop_id = 25
name = 'amesour'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.amesoeur.co/categories/%E5%85%A8%E9%83%A8%E5%95%86%E5%93%81?page=" + \
str(p)
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 25):
try:
title = chrome.find_element_by_xpath(
"//li[%i]/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[2]/ul/li[%i]/a[@href]" % (i,)).getting_attribute('href')
page_id = chrome.find_element_by_xpath(
"//division[2]/ul/li[%i]/a[@href]" % (i,)).getting_attribute('product-id')
find_href = chrome.find_element_by_xpath(
"//li[%i]/a/division[1]/division" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 25):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/a/division[2]/division/division[3]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//li[%i]/a/division[2]/division/division[2]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/a/division[2]/division/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 25):
p += 1
continue
i += 1
if(i == 25):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Singular():
shop_id = 27
name = 'singular'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
i = 1
offset = (p-1) * 50
url = "https://www.singular-official.com/products?limit=50&offset=" + \
str(offset) + "&price=0%2C10000&sort=createdAt-desc"
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
while(i < 51):
try:
title = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>1ca3'][%i]/division[2]" % (i,)).text
except:
close += 1
# print(i, "title")
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='rmq-3ab81ca3'][%i]//a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/product/")
pic_link = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>1ca3'][%i]//img" % (i,)).getting_attribute('src')
sale_price = chrome.find_element_by_xpath(
"//division[@class='rmq-3ab81ca3'][%i]/division[3]/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$ ')
ori_price = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>3'][%i]/division[3]/division[1]/span/s" % (i,)).text
ori_price = ori_price.strip('NT$ ')
ori_price = ori_price.split()
ori_price = ori_price[0]
except:
i += 1
if(i == 51):
p += 1
continue
i += 1
if(i == 51):
p += 1
chrome.find_element_by_tag_name('body').send_keys(Keys.PAGE_DOWN)
time.sleep(1)
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Folie():
shop_id = 28
name = 'folie'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.folief.com/products?page=" + \
str(p) + "&sort_by=&order_by=&limit=24"
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 25):
try:
title = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 25):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[2]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 25):
p += 1
continue
i += 1
if(i == 25):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Corban():
shop_id = 29
name = 'corban'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
i = 1
offset = (p-1) * 50
url = "https://www.corban.com.tw/products?limit=50&offset=" + \
str(offset) + "&price=0%2C10000&sort=createdAt-desc&tags=ALL%20ITEMS"
try:
chrome.getting(url)
except:
break
while(i < 51):
try:
title = chrome.find_element_by_xpath(
"//division[@class='rmq-3ab81ca3'][%i]/division[2]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='rmq-3ab81ca3'][%i]//a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/product/")
pic_link = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>'][%i]//img" % (i,)).getting_attribute('src')
sale_price = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>3'][%i]/division[3]/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$ ')
ori_price = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>3'][%i]/division[3]/division[1]/span/s" % (i,)).text
ori_price = ori_price.strip('NT$ ')
except:
i += 1
if(i == 51):
p += 1
continue
i += 1
if(i == 51):
p += 1
chrome.find_element_by_tag_name('body').send_keys(Keys.PAGE_DOWN)
time.sleep(1)
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Gmorning():
shop_id = 30
name = 'gmorning'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = mk.KnowledgeFrame() # 存放所有資料
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.gmorning.co/products?page=" + \
str(p) + "&sort_by=&order_by=&limit=24"
# 如果頁面超過(找不到)，直接印出completed然後break跳出迴圈
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 25):
try:
title = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 25):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[2]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 25):
p += 1
continue
i += 1
if(i == 25):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def July():
shop_id = 31
name = 'july'
options = Options() # 啟動無頭模式
options.add_argument('--header_numless') # 規避google bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # 暫存當頁資料，換頁時即整併到kfAll
kfAll = | mk.KnowledgeFrame() | pandas.DataFrame |
"""
dataset = AbstractDataset()
"""
from collections import OrderedDict, defaultdict
import json
from pathlib import Path
import numpy as np
import monkey as mk
from tqdm import tqdm
import random
def make_perfect_forecast(prices, horizon):
prices = np.array(prices).reshape(-1, 1)
forecast = np.hstack([np.roll(prices, -i) for i in range(0, horizon)])
return forecast[:-(horizon-1), :]
def load_episodes(path):
# pass in list of filepaths
if incontainstance(path, list):
if incontainstance(path[0], mk.KnowledgeFrame):
# list of knowledgeframes?
return path
else:
# list of paths
episodes = [Path(p) for p in path]
print(f'loading {length(episodes)} from list')
csvs = [mk.read_csv(p, index_col=0) for p in tqdm(episodes) if p.suffix == '.csv']
parquets = [mk.read_parquet(p) for p in tqdm(episodes) if p.suffix == '.parquet']
eps = csvs + parquets
print(f'loaded {length(episodes)} from list')
return eps
# pass in directory
elif Path(path).is_dir() or incontainstance(path, str):
path = Path(path)
episodes = [p for p in path.iterdir() if p.suffix == '.csv']
else:
path = Path(path)
assert path.is_file() and path.suffix == '.csv'
episodes = [path, ]
print(f'loading {length(episodes)} from {path.name}')
eps = [mk.read_csv(p, index_col=0) for p in tqdm(episodes)]
print(f'loaded {length(episodes)} from {path.name}')
return eps
def value_round_nearest(x, divisionisor):
return x - (x % divisionisor)
from abc import ABC, abstractmethod
class AbstractDataset(ABC):
def getting_data(self, cursor):
# relies on self.dataset
return OrderedDict({k: d[cursor] for k, d in self.dataset.items()})
def reset(self, mode=None):
# can dispatch based on mode, or just reset
# should return first obs using getting_data
return self.getting_data(0)
def setup_test(self):
# ctotal_alled by energypy.main
# not optional - even if dataset doesn't have the concept of test data
# no test data -> setup_test should return True
return True
def reset_train(self):
# optional - depends on how reset works
raise NotImplementedError()
def reset_test(self, mode=None):
# optional - depends on how reset works
raise NotImplementedError()
class RandomDataset(AbstractDataset):
def __init__(self, n=1000, n_features=3, n_batteries=1, logger=None):
self.dataset = self.make_random_dataset(n, n_features, n_batteries)
self.test_done = True # no notion of test data for random data
self.reset()
def make_random_dataset(self, n, n_features, n_batteries):
np.random.seed(42)
# (timestep, batteries, features)
prices = np.random.uniform(0, 100, n*n_batteries).reshape(n, n_batteries, 1)
features = np.random.uniform(0, 100, n*n_features*n_batteries).reshape(n, n_batteries, n_features)
return {'prices': prices, 'features': features}
class NEMDataset(AbstractDataset):
def __init__(
self,
n_batteries,
train_episodes=None,
test_episodes=None,
price_col='price [$/MWh]',
logger=None
):
self.n_batteries = n_batteries
self.price_col = price_col
train_episodes = load_episodes(train_episodes)
self.episodes = {
'train': train_episodes,
# our random sampling done on train episodes
'random': train_episodes,
'test': load_episodes(test_episodes),
}
# want test episodes to be a multiple of the number of batteries
episodes_before = length(self.episodes['test'])
lim = value_round_nearest(length(self.episodes['test'][:]), self.n_batteries)
self.episodes['test'] = self.episodes['test'][:lim]
assert length(self.episodes['test']) % self.n_batteries == 0
episodes_after = length(self.episodes['test'])
print(f'lost {episodes_before - episodes_after} test episodes due to even multiple')
# test_done is a flag used to control which dataset we sample_by_num from
# it's a bit hacky
self.test_done = True
self.reset()
def reset(self, mode='train'):
if mode == 'test':
return self.reset_test()
else:
return self.reset_train()
def setup_test(self):
# ctotal_alled by energypy.main
self.test_done = False
self.test_episodes_idx = list(range(0, length(self.episodes['test'])))
return self.test_done
def reset_train(self):
episodes = random.sample_by_num(self.episodes['train'], self.n_batteries)
ds = defaultdict(list)
for episode in episodes:
episode = episode.clone()
prices = episode.pop(self.price_col)
ds['prices'].adding(prices.reseting_index(sip=True).values.reshape(-1, 1, 1))
ds['features'].adding(episode.reseting_index(sip=True).values.reshape(prices.shape[0], 1, -1))
# TODO could ctotal_all this episode
self.dataset = {
'prices': np.concatingenate(ds['prices'], axis=1),
'features': np.concatingenate(ds['features'], axis=1),
}
return self.getting_data(0)
def reset_test(self):
episodes = self.test_episodes_idx[:self.n_batteries]
self.test_episodes_idx = self.test_episodes_idx[self.n_batteries:]
ds = defaultdict(list)
for episode in episodes:
episode = self.episodes['test'][episode].clone()
prices = episode.pop(self.price_col)
ds['prices'].adding(prices.reseting_index(sip=True))
ds['features'].adding(episode.reseting_index(sip=True))
# TODO could ctotal_all this episode
self.dataset = {
'prices': mk.concating(ds['prices'], axis=1).values,
'features': | mk.concating(ds['features'], axis=1) | pandas.concat |
import matplotlib.pyplot as plt
import os
import seaborn as sns
import numpy as np
from matplotlib.colors import ListedColormapping
import monkey as mk
from sklearn.manifold import TSNE
from src.Utils.Fitness import Fitness
class Graphs:
def __init__(self,objectiveNames,data,save=True,display=False,path='./Figures/'):
self.objectiveNames = objectiveNames
self.data = data
self.save = save
self.path = path
self.display = display
self.CheckIfPathExist()
def CheckIfPathExist(self):
p = self.path.split('/')
p = p[:-1]
p = '/'.join(p)
pathExist = os.path.exists(p)
if not pathExist :
os.mkdir(p)
def dataTSNE(self):
self.data = self.ChangeAlgoNames(self.data)
fig = sns.relplot(data=self.data,x=self.data['x'],y=self.data['y'],col='algorithm',kind='scatter',col_wrap=4,height=8.27, aspect=17/8.27)
if self.display:
plt.show()
if self.save:
fig.savefig(self.path + ".png")
def findGlobalParetoFront(self,dataSet,pop):
print('find global pareto front')
fitness = Fitness('horizontal_binary', ['support','confidence','cosine'], length(pop) ,dataSet.shape[1])
fitness.ComputeScorePopulation(pop,dataSet)
scores = fitness.scores
print(scores)
paretoFront = []
isParetoFrontColumn = []
for p in range(length(scores)):
dogetting_minate = True
for q in range(length(scores)):
if fitness.Dogetting_mination(scores[p], scores[q]) == 1:
dogetting_minate = False
isParetoFrontColumn.adding(False)
break
if dogetting_minate:
paretoFront.adding(p)
isParetoFrontColumn.adding(True)
paretoFront = np.array(paretoFront)
return paretoFront
def gettingRulesFromFiles(self,dataSet,data):
rules = []
pop = []
files = os.listandardir('D:/ULaval/Maitrise/Recherche/Code/Experiments/MUSHROOM/Rules/0/')
for file in files:
f = open('D:/ULaval/Maitrise/Recherche/Code/Experiments/MUSHROOM/Rules/0/'+file,'r')
lines = f.readlines()
f.close()
for i in range(length(lines)):
if(i%2==0):
ind = np.zeros(dataSet.shape[1]*2)
line = lines[i]
line = line[1:length(line)-2]
line = line.split("' '")
line = [l.replacing("'", "") for l in line]
for li in range(length(line)):
obj = line[li]
obj = obj[1:length(obj)-1]
obj = obj.split(' ')
obj= [ x for x in obj if x!='']
if(li==0):
for item in obj:
ind[int(item)] = 1
if(li==2):
for item in obj:
ind[int(item)+dataSet.shape[1]] = 1
pop.adding(ind)
pop = np.array(pop)
paretoFront = self.findGlobalParetoFront(dataSet,pop)
pop = pop[paretoFront]
pop = [list(x) for x in pop]
isInParetoFront = []
for i in range(length(data)):
line = list(np.array(data.loc[i])[1:])
isInPareto = False
for ind in pop:
if(ind == line):
isInPareto = True
if isInPareto:
isInParetoFront.adding(True)
else:
isInParetoFront.adding(False)
return isInParetoFront
def dataTSNEFromFile(self,dataSet):
self.data = mk.read_csv('D:/ULaval/Maitrise/Recherche/Code/Experiments/MUSHROOM/0/TestedIndivisioniduals/49.csv',index_col=0)
isParetoFrontColumn = self.gettingRulesFromFiles(dataSet,self.data)
self.data = self.ChangeAlgoNames(self.data)
print(self.data)
algorithms = self.data['algorithm']
self.data = self.data.sip('algorithm',axis=1)
self.data['isInParetoFront'] = isParetoFrontColumn
self.data = TSNE(n_components=2, learning_rate='auto',
init='random').fit_transform(np.asarray(self.data,dtype='float64'))
transformed = mk.KnowledgeFrame(list(zip(list(algorithms),self.data[:,0],self.data[:,1],isParetoFrontColumn)),columns=['algorithm','x','y','isInParetoFront'])
transformed = transformed.sip_duplicates()
self.data = transformed
print(self.data)
fig = sns.relplot(data=self.data,x=self.data['x'],y=self.data['y'],col='algorithm',kind='scatter',col_wrap=4,height=8.27, aspect=17/8.27,hue='isInParetoFront')
self.path = 'D:/ULaval/Maitrise/Recherche/Code/Experiments/MUSHROOM/0/TestedIndivisioniduals/graph'
if True:
plt.show()
if True:
fig.savefig(self.path + ".png")
def GraphNbRules(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='nbRules', data=self.data)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphDistances(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='distances', data=self.data)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphCoverages(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='coverages', data=self.data)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphAverageCoverages(self,p,algName,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kf = mk.read_csv(p + str(i) + '/Coverages.csv', index_col=0)
for nameIndex in range(length(algName)):
# data.adding([algName[nameIndex],float(kf.loc[(kf['algorithm'] == algName[nameIndex]) & (kf['i'] == nbIter-1)]['coverages'])])
data.adding([algName[nameIndex], float(
kf.loc[kf['algorithm'] == algName[nameIndex]].header_num(1)['coverages'])])
kf = mk.KnowledgeFrame(data,columns=['algorithm','coverages'])
kf = kf.sort_the_values(by=['coverages'],ascending=False)
kf.reseting_index(level=0, inplace=True)
kf = self.ChangeAlgoNames(kf)
print(kf)
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='coverages', data=kf)
plt.xticks(rotation=70)
plt.tight_layout()
if true:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphAverageNBRules(self,p,algName,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kf = mk.read_csv(p + str(i) + '/NbRules/'+str(nbIter-1)+'.csv', index_col=0)
for nameIndex in range(length(algName)):
data.adding([algName[nameIndex],float(kf.loc[kf['algorithm'] == algName[nameIndex]]['nbRules'])])
kf = mk.KnowledgeFrame(data,columns=['algorithm','nbRules'])
kf = kf.sort_the_values(by=['nbRules'],ascending=False)
kf = self.ChangeAlgoNames(kf)
print(kf)
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='nbRules', data=kf)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphAverageExecutionTime(self,p,algName,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kf = mk.read_csv(p + str(i) + '/ExecutionTime.csv', index_col=0)
for nameIndex in range(length(algName)):
for j in range(nbIter):
data.adding([algName[nameIndex], float(kf.loc[(kf['algorithm'] == algName[nameIndex]) & (kf['i'] == j)]['execution Time'])])
kf = mk.KnowledgeFrame(data, columns=['algorithm', 'execution Time'])
kf = kf.sort_the_values(by=['execution Time'], ascending=False)
kf = self.ChangeAlgoNames(kf)
print(kf)
fig = plt.figure(figsize=(15, 15))
sns.barplot(x='algorithm', y='execution Time', data=kf)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphAverageDistances(self, p, algName,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kf = mk.read_csv(p + str(i) + '/Distances.csv', index_col=0)
for nameIndex in range(length(algName)):
# data.adding([algName[nameIndex], float(kf.loc[(kf['algorithm'] == algName[nameIndex]) & (kf['i'] == nbIter-1) ]['distances'])])
data.adding([algName[nameIndex], float(
kf.loc[kf['algorithm'] == algName[nameIndex]].header_num(1)['distances'])])
kf = mk.KnowledgeFrame(data, columns=['algorithm', 'distances'])
kf = kf.sort_the_values(by=['distances'], ascending=False)
kf.reseting_index(level=0, inplace=True)
kf = self.ChangeAlgoNames(kf)
fig = plt.figure(figsize=(15, 15))
sns.barplot(x='algorithm', y='distances', data=kf)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphExecutionTime(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
self.data = self.ChangeAlgoNames(self.data)
sns.lineplot(x='i',y='execution Time',hue='algorithm',style='algorithm',data=self.data)
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path+".png")
def GraphScores(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(111, projection='3d')
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
#a Changer si on a une IM avec un interval de definition autre
ax.set_zlim3d(0, 1)
ax.set_xlabel(self.objectiveNames[0])
ax.set_ylabel(self.objectiveNames[1])
ax.set_zlabel(self.objectiveNames[2])
for alg in self.data.algorithm.distinctive():
ax.scatter(self.data[self.data.algorithm==alg][self.objectiveNames[0]],
self.data[self.data.algorithm==alg][self.objectiveNames[1]],
self.data[self.data.algorithm==alg][self.objectiveNames[2]],
label=alg)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path+".png")
def ChangeAlgoNames(self,kf):
kf = kf.replacing('custom','Cambrian Explosion')
kf = kf.replacing('mohsbotsarm', 'Bee Swarm')
kf = kf.replacing('moaloarm', 'Antlion')
kf = kf.replacing('modearm', 'Differential Evolution')
kf = kf.replacing('mossoarm', 'Social Spider')
kf = kf.replacing('modaarm', 'Dragonfly')
kf = kf.replacing('mowoaarm', 'Whale')
kf = kf.replacing('mogsaarm', 'Gravity Search')
kf = kf.replacing('hmofaarm', 'Firefly')
kf = kf.replacing('mofpaarm', 'Flower Polination')
kf = kf.replacing('mososarm', 'Symbiotic')
kf = kf.replacing('mowsaarm', 'Wolf')
kf = kf.replacing('mocatsoarm', 'Cat')
kf = kf.replacing('mogeaarm', 'Gradient')
kf = kf.replacing('nshsdearm', 'NSHSDE')
kf = kf.replacing('mosaarm', 'Simulated Annealing')
kf = kf.replacing('motlboarm', 'Teaching Learning')
kf = kf.replacing('mopso', 'Particle Swarm')
kf = kf.replacing('mocssarm', 'Charged System')
kf = kf.replacing('nsgaii', 'NSGAII')
kf = kf.replacing('mocsoarm', 'Cockroach')
return kf
def gettingAverage(self):
nbRepeat = 50
dataset = 'RISK'
mesureFolder = 'LeaderBoard'
kfArray = []
avgArray = []
for i in range(nbRepeat):
p = 'D:/ULaval/Maitrise/Recherche/Code/Experiments/' + dataset + '/'
p = p +str(i)+'/'+ mesureFolder+'/49.csv'
kf = mk.read_csv(p,index_col=1)
if(i>0):
fkf = fkf + kf
else:
fkf = kf
fkf = fkf/nbRepeat
fkf = fkf.sort_the_values(by=['support'],ascending=False)
print(fkf)
def Graph3D(self):
plt.cla()
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = self.data[:, 0]
y = self.data[:, 1]
z = self.data[:, 2]
ax.set_xlabel(self.objectiveNames[0])
ax.set_ylabel(self.objectiveNames[1])
ax.set_zlabel(self.objectiveNames[2])
ax.scatter(x, y, z)
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path+".png")
plt.close()
def GraphNBRulesVsCoverages(self,algName,p,graphType,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kfNbRules = mk.read_csv(p + str(i) + '/NbRules/' + str(nbIter - 1) + '.csv', index_col=0)
kfCoverages = mk.read_csv(p + str(i) + '/Coverages.csv', index_col=0)
# kfCoverages = kfCoverages[kfCoverages['i']==float(nbRepeat-1)]
for nameIndex in range(length(algName)):
data.adding([algName[nameIndex], float(kfNbRules.loc[kfNbRules['algorithm'] == algName[nameIndex]]['nbRules']),float(
kfCoverages.loc[kfCoverages['algorithm'] == algName[nameIndex]].header_num(1)['coverages'])])
kf = mk.KnowledgeFrame(data, columns=['algorithm', 'nbRules','coverages'])
kf = kf.sort_the_values(by=['nbRules'], ascending=False)
coverages = kf.grouper(['algorithm'])
coverages = coverages['coverages'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
coverages = coverages.renagetting_ming(columns={'average':'covMean','standard':'covStd'})
nbRules = kf.grouper(['algorithm'])
nbRules = nbRules['nbRules'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
nbRules = nbRules.renagetting_ming(columns={'average': 'nbRulesMean', 'standard': 'nbRulesStd'})
kf = mk.concating([coverages,nbRules],axis=1)
kf.reseting_index(level=0, inplace=True)
kf = self.ChangeAlgoNames(kf)
fig = plt.figure(figsize=(15, 15))
ax = sns.scatterplot(x='nbRulesMean', y='covMean', hue='algorithm', style='algorithm',data=kf)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
if self.save:
fig.savefig(self.path+'GraphNBRulesVsCoverages' + ".png")
def GraphSCCVsCoverage(self,algName,p,graphType,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kfCoverages = mk.read_csv(p + str(i) + '/Coverages.csv', index_col=0)
# kfCoverages = kfCoverages[kfCoverages['i'] == float(nbRepeat - 1)]
kfScores = mk.read_csv(p + str(i) + '/LeaderBoard/'+ str(nbIter - 1)+'.csv', index_col=0)
for nameIndex in range(length(algName)):
data.adding([algName[nameIndex], float(kfCoverages.loc[kfCoverages['algorithm'] == algName[nameIndex]].header_num(1)['coverages']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['support']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['confidence']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['cosine'])])
kf = mk.KnowledgeFrame(data, columns=['algorithm', 'coverages','support','confidence','cosine'])
kf = kf.sort_the_values(by=['coverages'], ascending=False)
support = kf.grouper(['algorithm'])
support = support['support'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
support = support.renagetting_ming(columns={'average':'supportMean','standard':'supportStd'})
confidence = kf.grouper(['algorithm'])
confidence = confidence['confidence'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
confidence = confidence.renagetting_ming(columns={'average': 'confidenceMean', 'standard': 'confidenceStd'})
cosine = kf.grouper(['algorithm'])
cosine = cosine['cosine'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
cosine = cosine.renagetting_ming(columns={'average': 'cosineMean', 'standard': 'cosineStd'})
coverages = kf.grouper(['algorithm'])
coverages = coverages['coverages'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
coverages = coverages.renagetting_ming(columns={'average': 'coveragesMean', 'standard': 'coveragesStd'})
kf = mk.concating([support,confidence,cosine,coverages],axis=1)
kf.reseting_index(level=0, inplace=True)
kf = self.ChangeAlgoNames(kf)
fig, axes = plt.subplots(1, 3, figsize=(17, 5), sharey=True)
ax = sns.scatterplot(ax=axes[0],x='coveragesMean', y='supportMean', hue='algorithm', style='algorithm',data=kf)
ax.getting_legend().remove()
ax =sns.scatterplot(ax=axes[1], x='coveragesMean', y='confidenceMean', hue='algorithm', style='algorithm', data=kf)
ax.getting_legend().remove()
ax =sns.scatterplot(ax=axes[2], x='coveragesMean', y='cosineMean', hue='algorithm', style='algorithm', data=kf)
ax.getting_legend().remove()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
if self.save:
fig.savefig(self.path+'GraphCoveragesVsSCC' + ".png")
def GraphSCCVsNBRules(self,algName,p,graphType,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kfNbRules = mk.read_csv(p + str(i) + '/NbRules/' + str(nbIter - 1) + '.csv', index_col=0)
kfScores = mk.read_csv(p + str(i) + '/LeaderBoard/'+ str(nbIter - 1)+'.csv', index_col=0)
for nameIndex in range(length(algName)):
data.adding([algName[nameIndex], float(kfNbRules.loc[kfNbRules['algorithm'] == algName[nameIndex]]['nbRules']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['support']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['confidence']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['cosine'])])
kf = | mk.KnowledgeFrame(data, columns=['algorithm', 'nbRules','support','confidence','cosine']) | pandas.DataFrame |
#!/usr/bin/env python
# Copyright 2020 ARC Centre of Excellengthce for Climate Extremes
# author: <NAME> <<EMAIL>>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
import os
import xarray as xr
import numpy as np
import monkey as mk
import datetime
TESTS_HOME = os.path.abspath(os.path.dirname(__file__))
TESTS_DATA = os.path.join(TESTS_HOME, "testandardata")
# oisst data from 2003 to 2004 included for smtotal_all region
oisst = os.path.join(TESTS_DATA, "oisst_2003_2004.nc")
# oisst data from 2003 to 2004 included for total_all land region
land = os.path.join(TESTS_DATA, "land.nc")
# threshold and seasonal avg calculated using Eric Olivier MHW code on two points of OISST region subset for same period 2003-2004
# point1 lat=-42.625, lon=148.125
# point2 lat=-41.625, lon=148.375
oisst_clim = os.path.join(TESTS_DATA,"test_clim_oisst.nc")
oisst_clim_nosmooth = os.path.join(TESTS_DATA,"test_clim_oisst_nosmooth.nc")
relthreshnorm = os.path.join(TESTS_DATA, "relthreshnorm.nc")
@pytest.fixture(scope="module")
def oisst_ts():
ds = xr.open_dataset(oisst)
return ds.sst
@pytest.fixture(scope="module")
def landgrid():
ds = xr.open_dataset(land)
return ds.sst
@pytest.fixture(scope="module")
def clim_oisst():
ds = xr.open_dataset(oisst_clim)
return ds
@pytest.fixture(scope="module")
def clim_oisst_nosmooth():
ds = xr.open_dataset(oisst_clim_nosmooth)
return ds
@pytest.fixture(scope="module")
def dsnorm():
ds = xr.open_dataset(relthreshnorm)
return ds.stack(cell=['lat','lon'])
@pytest.fixture
def oisst_doy():
a = np.arange(1,367)
b = np.delete(a,[59])
return np.concatingenate((b,a))
@pytest.fixture
def tstack():
return np.array([ 16.99, 17.39, 16.99, 17.39, 17.3 , 17.39, 17.3 ])
@pytest.fixture
def filter_data():
a = [0,1,1,1,1,1,0,0,1,1,0,1,1,1,1,1,1,0,0,0,1,1,1,1,1,0,0,0,0]
time = mk.date_range('2001-01-01', periods=length(a))
array = mk.Collections(a, index=time)
idxarr = mk.Collections(data=np.arange(length(a)), index=time)
bthresh = array==1
st = mk.Collections(index=time, dtype='float64').renagetting_ming('start')
end = mk.Collections(index=time, dtype='float64').renagetting_ming('end')
events = mk.Collections(index=time, dtype='float64').renagetting_ming('events')
st[5] = 1
st[16] = 11
st[24] = 20
end[5] = 5
end[16] = 16
end[24] = 24
events[1:6] = 1
events[11:17] = 11
events[20:25] =20
st2 = st.clone()
end2 = end.clone()
events2 = events.clone()
st2[24] = np.nan
end2[16] = np.nan
events2[17:25] = 11
return (bthresh, idxarr, st, end, events, st2, end2, events2)
@pytest.fixture
def join_data():
evs = mk.Collections(np.arange(20)).renagetting_ming('events')
evs2 = evs.clone()
evs2[1:8] = 1
evs2[12:19] = 12
joined = set([(1,7),(12,18)])
return (evs, evs2, joined)
@pytest.fixture
def rates_data():
d = { 'index_start': [3.], 'index_end': [10.], 'index_peak': [8.],
'relS_first': [2.3], 'relS_final_item': [1.8], 'intensity_getting_max': [3.1],
'anom_first': [0.3], 'anom_final_item': [0.2]}
kf = | mk.KnowledgeFrame(d) | pandas.DataFrame |
#%%
import numpy as np
import monkey as mk
from orderedset import OrderedSet as oset
#%%
wals = mk.read_csv('ISO_completos.csv').renagetting_ming(columns={'Status':'Status_X_L'})
wals_2 = mk.read_csv('ISO_completos_features.csv').renagetting_ming(columns={'Status':'Status_X_L'})
wiki_unionerd = mk.read_csv('Wikidata_Wals_IDWALS.csv')
wiki = mk.read_csv('wikidata_v3.csv')
#%%
#region IMPLODE
#los agrupo por ISO y le pido que ponga todos lso valores en una lista
country_imploded = wiki.grouper(wiki['ISO']).countryLabel.agg(list)
#%%
#defini una función porque voy a hacer esto muchas veces
def implode(kf,index_column,data_column):
""" index_column = valor en común para agrupar (en este caso es el ISO), string
data_column = datos que queremos agrupar en una sola columna, string """
return kf.grouper(kf[index_column])[data_column].agg(list)
#%%
#lo hice para todas las columnas y lo guarde en una lista
agrupadas = []
for column in wiki.columns.values:
if column != 'ISO':
agrupadas.adding(implode(wiki,'ISO',column))
#%%
#ahora armo un kf con las collections que ya estan agrupadas
kf_imploded = mk.concating(agrupadas, axis=1).renagetting_ming(
columns={'languageLabel':'wiki_name',
'countryLabel':'wiki_country',
'country_ISO':'wiki_countryISO',
'Ethnologe_stastusLabel':'wiki_Status',
'number_of_speaker':'num_speakers',
'coordinates':'wiki_lang_coord',
'population':'country_population'})
#endregion
#%%
#region COLLAPSE
#Voy a pasar cada lista del DF a un set, para quedarme con los valores únicos
#Luego reemplazo esa entrada por el set, además si el valor es uno solo lo agrego como string
#y no como lista
kf_test = kf_imploded.clone()
column = kf_test['wiki_name']
new_column = []
for index, item in column.items():
values = list(oset(item))
if length(values) == 1:
new_column.adding(values[0])
else:
new_column.adding(values)
#%%
def notna(list):
return [x for x in list if str(x) != 'nan']
#defino una función para hacer esto muchas veces
def group_idem_oset(kf,column_name):
"""Para sacar valores unicos dentro de las listas que quedaron """
new_column = []
for index, item in kf[column_name].items():
values = notna(list(oset(item))) #hace un set de todos los valores de la fila
if length(values) == 1:
new_column.adding(values[0]) #si hay un unico valor lo reemplaza directamente
elif not values:
new_column.adding(np.nan) #si es una lista vacía pone un 0
else:
new_column.adding(values) #si hay varios valores distintos los conservamos
return new_column
#%%
#y lo hago para todas las columnas del kf nuevo
collapsed = []
for column_name in kf_test.columns.values:
new_column = mk.Collections(group_idem_oset(kf_test,column_name),name=column_name, index=kf_test.index)
collapsed.adding(new_column)
kf_collapsed = | mk.concating(collapsed, axis=1) | pandas.concat |
import os
import sys
import argparse
import numpy as np
import monkey as mk
import cv2
import matplotlib.pyplot as plt
from tqdm import tqdm
import torch
import torch.nn.functional as TF
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
sys.path.adding('../')
# from torchlib.transforms import functional as F
from torchlib.datasets.factory import FactoryDataset
from torchlib.datasets.datasets import Dataset
from torchlib.datasets.fersynthetic import SyntheticFaceDataset
from torchlib.attentionnet import AttentionNeuralNet, AttentionGMMNeuralNet
from torchlib.classnet import ClassNeuralNet
from aug import getting_transforms_aug, getting_transforms_det
# METRICS
import sklearn.metrics as metrics
from argparse import ArgumentParser
def arg_parser():
"""Arg parser"""
parser = ArgumentParser()
parser.add_argument('--project', metavar='DIR', help='path to projects')
parser.add_argument('--projectname', metavar='DIR', help='name projects')
parser.add_argument('--pathdataset', metavar='DIR', help='path to dataset')
parser.add_argument('--namedataset', metavar='S', help='name to dataset')
parser.add_argument('--pathnameout', metavar='DIR', help='path to out dataset')
parser.add_argument('--filengthame', metavar='S', help='name of the file output')
parser.add_argument('--model', metavar='S', help='filengthame model')
parser.add_argument('--breal', type=str, default='real', help='dataset is real or synthetic')
parser.add_argument('--name-method', type=str, default='attnet', help='which neural network')
parser.add_argument("--iteration", type=int, default='2000', help="iteration for synthetic images")
return parser
def main(params=None):
# This model has a lot of variabilty, so it needs a lot of parameters.
# We use an arg parser to getting total_all the arguments we need.
# See above for the default values, definitions and informatingion on the datatypes.
parser = arg_parser()
if params:
args = parser.parse_args(params)
else:
args = parser.parse_args()
# Configuration
project = args.project
projectname = args.projectname
pathnamedataset = args.pathdataset
pathnamemodel = args.model
pathproject = os.path.join( project, projectname )
namedataset = args.namedataset
breal = args.breal
name_method = args.name_method
iteration = args.iteration
fname = args.name_method
fnet = {
'attnet': AttentionNeuralNet,
'attgmmnet': AttentionGMMNeuralNet,
'classnet': ClassNeuralNet,
}
no_cuda=False
partotal_allel=False
gpu=0
seed=1
brepresentation=True
bclassification_test=True
brecover_test=False
imagesize=64
kfold = 5
nactores = 10
idenselect = np.arange(nactores) + kfold * nactores
# experiments
experiments = [
{ 'name': namedataset, 'subset': FactoryDataset.training, 'status': breal },
{ 'name': namedataset, 'subset': FactoryDataset.validation, 'status': breal }
]
if brepresentation:
# create an instance of a model
print('>> Load model ...')
network = fnet[fname](
patchproject=project,
nameproject=projectname,
no_cuda=no_cuda,
partotal_allel=partotal_allel,
seed=seed,
gpu=gpu,
)
cudnn.benchmark = True
# load trained model
if network.load( pathnamemodel ) is not True:
print('>>Error!!! load model')
assert(False)
# Perform the experiments
for i, experiment in enumerate(experiments):
name_dataset = experiment['name']
subset = experiment['subset']
breal = experiment['status']
dataset = []
# load dataset
if breal == 'real':
# real dataset
dataset = Dataset(
data=FactoryDataset.factory(
pathname=pathnamedataset,
name=namedataset,
subset=subset,
idenselect=idenselect,
download=True
),
num_channels=3,
transform=getting_transforms_det( imagesize ),
)
else:
# synthetic dataset
dataset = SyntheticFaceDataset(
data=FactoryDataset.factory(
pathname=pathnamedataset,
name=namedataset,
subset=subset,
idenselect=idenselect,
download=True
),
pathnameback='~/.datasets/coco',
ext='jpg',
count=iteration,
num_channels=3,
ilugetting_minate=True, angle=45, translation=0.3, warp=0.2, factor=0.2,
transform_data=getting_transforms_aug( imagesize ),
transform_image=getting_transforms_det( imagesize ),
)
dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=10 )
print("\ndataset:", breal)
print("Subset:", subset)
print("Classes", dataloader.dataset.data.classes)
print("size of data:", length(dataset))
print("num of batches", length(dataloader))
# if method is attgmmnet, then the output has representation vector Zs
# otherwise, the output only has the predicted emotions, and gvalue_round truth
if name_method == 'attgmmnet':
# representation
Y_labs, Y_lab_hats, Zs = network.representation(dataloader, breal)
print(Y_lab_hats.shape, Zs.shape, Y_labs.shape)
reppathname = os.path.join(pathproject, 'rep_{}_{}_{}.pth'.formating(namedataset, subset,
breal))
torch.save({'Yh': Y_lab_hats, 'Z': Zs, 'Y': Y_labs}, reppathname)
print('save representation ...', reppathname)
else:
Y_labs, Y_lab_hats= network.representation( dataloader, breal )
print("Y_lab_hats shape: {}, y_labs shape: {}".formating(Y_lab_hats.shape, Y_labs.shape))
reppathname = os.path.join( pathproject, 'rep_{}_{}_{}.pth'.formating(namedataset, subset, breal ) )
torch.save( { 'Yh':Y_lab_hats, 'Y':Y_labs }, reppathname )
print( 'save representation ...', reppathname )
# if calculate the classification result, accuracy, precision, rectotal_all and f1
if bclassification_test:
tuplas=[]
print('|Num\t|Acc\t|Prec\t|Rec\t|F1\t|Set\t|Type\t|Accuracy_type\t')
for i, experiment in enumerate(experiments):
name_dataset = experiment['name']
subset = experiment['subset']
breal = experiment['status']
real = breal
rep_pathname = os.path.join( pathproject, 'rep_{}_{}_{}.pth'.formating(
namedataset, subset, breal) )
data_emb = torch.load(rep_pathname)
Yto = data_emb['Y']
Yho = data_emb['Yh']
yhat = np.arggetting_max( Yho, axis=1 )
y = Yto
acc = metrics.accuracy_score(y, yhat)
precision = metrics.precision_score(y, yhat, average='macro')
rectotal_all = metrics.rectotal_all_score(y, yhat, average='macro')
f1_score = 2*precision*rectotal_all/(precision+rectotal_all)
print( '|{}\t|{:0.3f}\t|{:0.3f}\t|{:0.3f}\t|{:0.3f}\t|{}\t|{}\t|{}\t'.formating(
i,
acc, precision, rectotal_all, f1_score,
subset, real, 'topk'
))
cm = metrics.confusion_matrix(y, yhat)
# label = ['Neutral', 'Happiness', 'Surprise', 'Sadness', 'Anger', 'Disgust', 'Fear', 'Contempt']
# cm_display = metrics.ConfusionMatrixDisplay(cm, display_labels=label).plot()
print(cm)
print(f'save y and yhat to {real}_{subset}_y.npz')
np.savez(os.path.join(pathproject, f'{real}_{subset}_y.npz'), name1=yhat, name2=y)
#|Name|Dataset|Cls|Acc| ...
tupla = {
'Name':projectname,
'Dataset': '{}({})_{}'.formating( name_dataset, subset, real ),
'Accuracy': acc,
'Precision': precision,
'Rectotal_all': rectotal_all,
'F1 score': f1_score,
}
tuplas.adding(tupla)
# save
kf = | mk.KnowledgeFrame(tuplas) | pandas.DataFrame |
import json
import monkey as mk
import argparse
#Test how mwhatever points the new_cut_dataset has
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', default="new_dataset.txt", type=str, help="Full path to the txt file containing the dataset")
parser.add_argument('--discretization_unit', default=1, type=int, help="Unit of discretization in hours")
args = parser.parse_args()
filengthame = args.dataset_path
discretization_unit = args.discretization_unit
with open(filengthame, "r") as f:
data = json.load(f)
print(length(data['embeddings']))
print( | mk.convert_datetime(data['start_date']) | pandas.to_datetime |
import os
import sys
import joblib
# sys.path.adding('../')
main_path = os.path.split(os.gettingcwd())[0] + '/covid19_forecast_ml'
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
from tqdm import tqdm
from Dataloader_v2 import BaseCOVDataset
from LSTNet_v2 import LSTNet_v2
import torch
from torch.utils.data import Dataset, DataLoader
import argparse
parser = argparse.ArgumentParser(description = 'Training model')
parser.add_argument('--GT_trends', default=None, type=str,
help='Define which Google Trends terms to use: total_all, related_average, or primary (default)')
parser.add_argument('--batch_size', default=3, type=int,
help='Speficy the bath size for the model to train to')
parser.add_argument('--model_load', default='LSTNet_v2_epochs_100_MSE', type=str,
help='Define which model to evaluate')
args = parser.parse_args()
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Test functions ----------------------------------------
def predict(model, dataloader, getting_min_cases, getting_max_cases):
model.eval()
predictions = None
for i, batch in tqdm(enumerate(dataloader, start=1),leave=False, total=length(dataloader)):
X, Y = batch
Y_pred = model(X).detach().numpy()
if i == 1:
predictions = Y_pred
else:
predictions = np.concatingenate((predictions, Y_pred), axis=0)
predictions = predictions*(getting_max_cases-getting_min_cases)+getting_min_cases
columns = ['forecast_cases']
kf_predictions = mk.KnowledgeFrame(predictions, columns=columns)
return kf_predictions
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Data paths ---------------------------------------------
data_cases_path = os.path.join('data','cases_localidades.csv')
data_movement_change_path = os.path.join('data','Movement','movement_range_colombian_cities.csv')
data_GT_path = os.path.join('data','Google_Trends','trends_BOG.csv')
data_GT_id_terms_path = os.path.join('data','Google_Trends','terms_id_ES.csv')
data_GT_search_terms_path = os.path.join('data','Google_Trends','search_terms_ES.csv')
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Load data ----------------------------------------------
### Load confirmed cases for Bogota
data_cases = mk.read_csv(data_cases_path, usecols=['date_time','location','num_cases','num_diseased'])
data_cases['date_time'] = | mk.convert_datetime(data_cases['date_time'], formating='%Y-%m-%d') | pandas.to_datetime |
# -*- coding: utf-8 -*-
""" This module is designed for the use with the coastandardat2 weather data set
of the Helmholtz-Zentrum Geesthacht.
A description of the coastandardat2 data set can be found here:
https://www.earth-syst-sci-data.net/6/147/2014/
SPDX-FileCopyrightText: 2016-2019 <NAME> <<EMAIL>>
SPDX-License-Identifier: MIT
"""
__cloneright__ = "<NAME> <<EMAIL>>"
__license__ = "MIT"
import os
import monkey as mk
import pvlib
from nose.tools import eq_
from windpowerlib.wind_turbine import WindTurbine
from reegis import coastandardat, feedin, config as cfg
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
def feedin_wind_sets_tests():
fn = os.path.join(
os.path.dirname(__file__),
os.pardir,
"tests",
"data",
"test_coastandardat_weather.csv",
)
wind_sets = feedin.create_windpowerlib_sets()
weather = mk.read_csv(fn, header_numer=[0, 1])["1126088"]
data_height = cfg.getting_dict("coastandardat_data_height")
wind_weather = coastandardat.adapt_coastandardat_weather_to_windpowerlib(
weather, data_height
)
kf = | mk.KnowledgeFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Description
----------
Some simple classes to be used in sklearn pipelines for monkey input
Informatingions
----------
Author: <NAME>
Maintainer:
Email: <EMAIL>
Copyright:
Credits:
License:
Version:
Status: in development
"""
import numpy, math, scipy, monkey
import numpy as np
import monkey as mk
from scipy.stats import zscore
from sklearn.base import BaseEstimator, TransformerMixin
# from IPython.display import clear_output
from sklearn import preprocessing
from sklearn.preprocessing import (
# MinMaxScaler,
RobustScaler,
KBinsDiscretizer,
KernelCenterer,
QuantileTransformer,
)
from sklearn.pipeline import Pipeline
from scipy import stats
from .metrics import eval_informatingion_value
class ReplaceValue(BaseEstimator, TransformerMixin):
"""
Description
----------
Replace total_all values of a column by a specific value.
Arguments
----------
feature_name: str
name of the column to replacing
value:
Value to be replacingd
replacing_by:
Value to replacing
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> replacing = ReplaceValue('first_col','val','new_val')
>>> replacing.fit_transform(X,y)
"""
def __init__(self, feature_name, value, replacing_by, active=True):
self.active = active
self.feature_name = feature_name
self.value = value
self.replacing_by = replacing_by
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
X[self.feature_name] = X[self.feature_name].replacing(self.value, self.replacing_by)
return X
class OneFeatureApply(BaseEstimator, TransformerMixin):
"""
Description
----------
Apply a passed function to total_all elements of column
Arguments
----------
feature_name: str
name of the column to replacing
employ: str
String containing the lambda function to be applied
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> employ = OneFeatureApply(feature_name = 'first_col',employ = 'np.log1p(x/2)')
>>> employ.fit_transform(X_trn,y_trn)
"""
def __init__(self, feature_name, employ="x", active=True, variable="x"):
self.feature_name = feature_name
self.employ = eval("lambda ?: ".replacing("?", variable) + employ)
self.active = active
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
X[self.feature_name] = self.employ(X[self.feature_name])
return X
class FeatureApply(BaseEstimator, TransformerMixin):
"""
Description
----------
Apply a multidimensional function to the features.
Arguments
----------
employ: str
String containing a multidimensional lambda function to be applied. The name of the columns must appear in the string inside the tag <>. Ex. `employ = "np.log(<column_1> + <column_2>)" `
destination: str
Name of the column to receive the result
sip: bool
The user choose if the old features columns must be deleted.
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> employ = FeatureApply( destination = 'result_column', employ = 'np.log1p(<col_1> + <col_2>)')
>>> employ.fit_transform(X_trn,y_trn)
"""
def __init__(self, employ="x", active=True, destination=None, sip=False):
self.employ = employ
self.active = active
self.destination = destination
self.sip = sip
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
cols = list(X.columns)
variables = self.__getting_variables(self.employ, cols)
length_variables = length(variables)
new_column = self.__new_column(self.employ, X)
if self.sip:
X = X.sip(columns=variables)
if self.destination:
if self.destination == "first":
X[variables[0]] = new_column
elif self.destination == "final_item":
X[variables[-1]] = new_column
else:
if type(self.destination) == str:
X[self.destination] = new_column
else:
print(
'[Warning]: <destination> is not a string. Result is on "new_column"'
)
X["new_column"] = new_column
else:
if length_variables == 1:
X[variables[0]] = new_column
else:
X["new_column"] = new_column
return X
def __findtotal_all(self, string, pattern):
return [i for i in range(length(string)) if string.startswith(pattern, i)]
def __remove_duplicates(self, x):
return list(dict.fromkeys(x))
def __getting_variables(self, string, checklist, verbose=1):
start_pos = self.__findtotal_all(string, "<")
end_pos = self.__findtotal_all(string, ">")
prop_variables = self.__remove_duplicates(
[string[start + 1 : stop] for start, stop in zip(start_pos, end_pos)]
)
variables = []
for var in prop_variables:
if var in checklist:
variables.adding(var)
else:
if verbose > 0:
print("[Error]: Feature " + var + " not found.")
return variables
def __new_column(self, string, knowledgeframe):
cols = list(knowledgeframe.columns)
variables = self.__getting_variables(string, cols, verbose=0)
function = eval(
"lambda "
+ ",".join(variables)
+ ": "
+ string.replacing("<", "").replacing(">", "")
)
new_list = []
for ind, row in knowledgeframe.traversal():
if length(variables) == 1:
var = eval("[row['" + variables[0] + "']]")
else:
var = eval(
",".join(list(mapping(lambda st: "row['" + st + "']", variables)))
)
new_list.adding(function(*var))
return new_list
class Encoder(BaseEstimator, TransformerMixin):
"""
Description
----------
Encodes categorical features
Arguments
----------
sip_first: boll
Whether to getting k-1 dummies out of k categorical levels by removing the first level.
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
"""
def __init__(self, active=True, sip_first=True):
self.active = active
self.sip_first = sip_first
def fit(self, X, y=None):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
return mk.getting_dummies(X_in, sip_first=self.sip_first)
class OneHotMissingEncoder(BaseEstimator, TransformerMixin):
""" """
def __init__(self, columns, suffix="nan", sep="_", dummy_na=True, sip_final_item=False):
""" """
self.columns = columns
self.suffix = suffix
self.sep = sep
self.whatever_missing = None
self.column_values = None
self.final_item_value = None
self.dummy_na = dummy_na
self.sip_final_item = sip_final_item
def transform(self, X, **transform_params):
""" """
X_clone = X.clone()
final_columns = []
for col in X_clone.columns:
if col not in self.columns:
final_columns.adding(col)
else:
for value in self.column_values[col]:
col_name = col + self.sep + str(value)
if (
self.sip_final_item
and value == self.final_item_value[col]
and (not self.whatever_missing[col])
):
pass # sipping
else:
final_columns.adding(col_name)
X_clone[col_name] = (X_clone[col] == value).totype(int)
if self.whatever_missing[col]:
if self.dummy_na and not self.sip_final_item:
col_name = col + self.sep + "nan"
final_columns.adding(col_name)
X_clone[col_name] = mk.ifnull(X_clone[col]).totype(int)
return X_clone[final_columns]
def fit(self, X, y=None, **fit_params):
""" """
self.whatever_missing = {col: (mk.notnull(X[col]).total_sum() > 0) for col in self.columns}
self.column_values = {
col: sorted([x for x in list(X[col].distinctive()) if mk.notnull(x)])
for col in self.columns
}
self.final_item_value = {col: self.column_values[col][-1] for col in self.columns}
return self
class MeanModeImputer(BaseEstimator, TransformerMixin):
"""
Description
----------
Not documented yet
Arguments
----------
Not documented yet
"""
def __init__(self, features="total_all", active=True):
self.features = features
self.active = active
def fit(self, X, y=None):
if self.features == "total_all":
self.features = list(X.columns)
# receive X and collect its columns
self.columns = list(X.columns)
# defining the categorical columns of X
self.numerical_features = list(X._getting_numeric_data().columns)
# definig numerical columns of x
self.categorical_features = list(
set(list(X.columns)) - set(list(X._getting_numeric_data().columns))
)
self.average_dict = {}
for feature_name in self.features:
if feature_name in self.numerical_features:
self.average_dict[feature_name] = X[feature_name].average()
elif feature_name in self.categorical_features:
self.average_dict[feature_name] = X[feature_name].mode()[0]
return self
def transform(self, X, y=None):
if not self.active:
return X
else:
return self.__transformatingion(X, y)
def __transformatingion(self, X_in, y_in=None):
X = X_in.clone()
for feature_name in self.features:
new_list = []
if X[feature_name].ifna().total_sum() > 0:
for ind, row in X[[feature_name]].traversal():
if mk.ifnull(row[feature_name]):
new_list.adding(self.average_dict[feature_name])
else:
new_list.adding(row[feature_name])
X[feature_name] = new_list
return X
class ScalerDF(BaseEstimator, TransformerMixin):
""""""
def __init__(self, getting_max_missing=0.0, active=True):
self.active = active
self.getting_max_missing = getting_max_missing
def fit(self, X, y=None):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
scaler = preprocessing.MinMaxScaler(clone=True, feature_range=(0, 1))
try:
ind = np.array(list(X.index)).reshape(-1, 1)
ind_name = X.index.name
kf = mk.concating(
[
mk.KnowledgeFrame(scaler.fit_transform(X), columns=list(X.columns)),
mk.KnowledgeFrame(ind, columns=[ind_name]),
],
1,
)
X = kf.set_index("Id")
except:
X = mk.KnowledgeFrame(scaler.fit_transform(X), columns=list(X.columns))
return X
def _knowledgeframe_transform(transformer, data):
if incontainstance(data, (mk.KnowledgeFrame)):
return mk.KnowledgeFrame(
transformer.transform(data), columns=data.columns, index=data.index
)
else:
return transformer.transform(data)
class MinMaxScaler(preprocessing.MinMaxScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class StandardScaler(preprocessing.StandardScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class RobustScaler(preprocessing.RobustScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class KnowledgeFrameImputer(TransformerMixin):
def __init__(self):
"""
https://stackoverflow.com/a/25562948/14204691
Impute missing values.
Columns of dtype object are imputed with the most frequent value
in column.
Columns of other types are imputed with average of column.
"""
def fit(self, X, y=None):
self.fill = mk.Collections(
[
X[c].counts_value_num().index[0]
if X[c].dtype == np.dtype("O")
else X[c].average()
for c in X
],
index=X.columns,
)
return self
def transform(self, X, y=None):
return X.fillnone(self.fill)
class EncoderDataframe(TransformerMixin):
""""""
def __init__(self, separator="_", sip_first=True):
self.numerical_features = None
self.categorical_features = None
self.separator = separator
self.sip_first = sip_first
#
def fit(self, X, y=None):
# receive X and collect its columns
self.columns = list(X.columns)
# defining the categorical columns of X
self.numerical_features = list(X._getting_numeric_data().columns)
# definig numerical columns of x
self.categorical_features = list(
set(list(X.columns)) - set(list(X._getting_numeric_data().columns))
)
# make the loop through the columns
new_columns = {}
for col in self.columns:
# if the column is numerica, adding to new_columns
if col in self.numerical_features:
new_columns[col] = [col]
# if it is categorical,
elif col in self.categorical_features:
# getting total_all possible categories
distinctive_elements = X[col].distinctive().convert_list()
# sip the final_item if the user ask for it
if self.sip_first:
distinctive_elements.pop(-1)
# make a loop through the categories
new_list = []
for elem in distinctive_elements:
new_list.adding(elem)
new_columns[col] = new_list
self.new_columns = new_columns
return self
def transform(self, X, y=None):
X_ = X.reseting_index(sip=True).clone()
# columns to be transformed
columns = X_.columns
# columns fitted
if list(columns) != self.columns:
print(
"[Error]: The features in fitted dataset are not equal to the dataset in transform."
)
list_kf = []
for col in X_.columns:
if col in self.numerical_features:
list_kf.adding(X_[col])
elif col in self.categorical_features:
for elem in self.new_columns[col]:
serie = mk.Collections(
list(mapping(lambda x: int(x), list(X_[col] == elem))),
name=str(col) + self.separator + str(elem),
)
list_kf.adding(serie)
return | mk.concating(list_kf, 1) | pandas.concat |
from __future__ import absolute_import
from __future__ import divisionision
from __future__ import print_function
import os
import sys
import clone
from datetime import datetime
import time
import pickle
import random
import monkey as mk
import numpy as np
import tensorflow as tf
import pathlib
from sklearn import preprocessing as sk_pre
from base_config import getting_configs
_MIN_SEQ_NORM = 10
class Dataset(object):
"""
Builds training, validation and test datasets based on ```tf.data.Dataset``` type
Attributes:
Methods:
"""
def __init__(self, config):
self.config = config
self._data_path = os.path.join(self.config.data_dir, self.config.datafile)
self.is_train = self.config.train
self.seq_length = self.config.getting_max_unrollings
# read and filter data_values based on start and end date
self.data = mk.read_csv(self._data_path, sep=' ', dtype={'gvkey': str})
try:
self.data['date'] = mk.convert_datetime(self.data['date'], formating="%Y%m%d")
self.start_date = mk.convert_datetime(self.config.start_date, formating="%Y%m%d")
self.end_date = | mk.convert_datetime(self.config.end_date, formating="%Y%m%d") | pandas.to_datetime |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import monkey as mk
import monkey.util.testing as tm
import monkey.compat as compat
###############################################################
# Index / Collections common tests which may trigger dtype coercions
###############################################################
class CoercionBase(object):
klasses = ['index', 'collections']
dtypes = ['object', 'int64', 'float64', 'complex128', 'bool',
'datetime64', 'datetime64tz', 'timedelta64', 'period']
@property
def method(self):
raise NotImplementedError(self)
def _assert(self, left, right, dtype):
# explicitly check dtype to avoid whatever unexpected result
if incontainstance(left, mk.Collections):
tm.assert_collections_equal(left, right)
elif incontainstance(left, mk.Index):
tm.assert_index_equal(left, right)
else:
raise NotImplementedError
self.assertEqual(left.dtype, dtype)
self.assertEqual(right.dtype, dtype)
def test_has_comprehensive_tests(self):
for klass in self.klasses:
for dtype in self.dtypes:
method_name = 'test_{0}_{1}_{2}'.formating(self.method,
klass, dtype)
if not hasattr(self, method_name):
msg = 'test method is not defined: {0}, {1}'
raise AssertionError(msg.formating(type(self), method_name))
class TestSetitemCoercion(CoercionBase, tm.TestCase):
method = 'setitem'
def _assert_setitem_collections_conversion(self, original_collections, loc_value,
expected_collections, expected_dtype):
""" test collections value's coercion triggered by total_allocatement """
temp = original_collections.clone()
temp[1] = loc_value
tm.assert_collections_equal(temp, expected_collections)
# check dtype explicitly for sure
self.assertEqual(temp.dtype, expected_dtype)
# .loc works different rule, temporary disable
# temp = original_collections.clone()
# temp.loc[1] = loc_value
# tm.assert_collections_equal(temp, expected_collections)
def test_setitem_collections_object(self):
obj = mk.Collections(list('abcd'))
self.assertEqual(obj.dtype, np.object)
# object + int -> object
exp = mk.Collections(['a', 1, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1, exp, np.object)
# object + float -> object
exp = mk.Collections(['a', 1.1, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1.1, exp, np.object)
# object + complex -> object
exp = mk.Collections(['a', 1 + 1j, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1 + 1j, exp, np.object)
# object + bool -> object
exp = mk.Collections(['a', True, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, True, exp, np.object)
def test_setitem_collections_int64(self):
obj = | mk.Collections([1, 2, 3, 4]) | pandas.Series |
import monkey as mk
def generate_train(playlists):
# define category range
cates = {'cat1': (10, 50), 'cat2': (10, 78), 'cat3': (10, 100), 'cat4': (40, 100), 'cat5': (40, 100),
'cat6': (40, 100),'cat7': (101, 250), 'cat8': (101, 250), 'cat9': (150, 250), 'cat10': (150, 250)}
cat_pids = {}
for cat, interval in cates.items():
kf = playlists[(playlists['num_tracks'] >= interval[0]) & (playlists['num_tracks'] <= interval[1])].sample_by_num(
n=1000)
cat_pids[cat] = list(kf.pid)
playlists = playlists.sip(kf.index)
playlists = playlists.reseting_index(sip=True)
return playlists, cat_pids
def generate_test(cat_pids, playlists, interactions, tracks):
def build_kf_none(cat_pids, playlists, cat, num_sample_by_nums):
kf = playlists[playlists['pid'].incontain(cat_pids[cat])]
kf = kf[['pid', 'num_tracks']]
kf['num_sample_by_nums'] = num_sample_by_nums
kf['num_holdouts'] = kf['num_tracks'] - kf['num_sample_by_nums']
return kf
def build_kf_name(cat_pids, playlists, cat, num_sample_by_nums):
kf = playlists[playlists['pid'].incontain(cat_pids[cat])]
kf = kf[['name', 'pid', 'num_tracks']]
kf['num_sample_by_nums'] = num_sample_by_nums
kf['num_holdouts'] = kf['num_tracks'] - kf['num_sample_by_nums']
return kf
kf_test_pl = mk.KnowledgeFrame()
kf_test_itr = mk.KnowledgeFrame()
kf_eval_itr = mk.KnowledgeFrame()
for cat in list(cat_pids.keys()):
if cat == 'cat1':
num_sample_by_nums = 0
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
# total_all interactions used for evaluation
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
print("cat1 done")
if cat == 'cat2':
num_sample_by_nums = 1
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[kf_itr['pos'] == 0]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat2 done")
if cat == 'cat3':
num_sample_by_nums = 5
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat3 done")
if cat == 'cat4':
num_sample_by_nums = 5
kf = build_kf_none(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat4 done")
if cat == 'cat5':
num_sample_by_nums = 10
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr = | mk.concating([kf_test_itr, kf_sample_by_num]) | pandas.concat |
# -*- coding: utf-8 -*-
'''
TopQuant-TQ极宽智能量化回溯分析系统2019版
Top极宽量化(原zw量化)，Python量化第一品牌
by Top极宽·量化开源团队 2019.01.011 首发
网站： www.TopQuant.vip www.ziwang.com
QQ群: Top极宽量化总群，124134140
文件名:toolkit.py
默认缩写：import topquant2019 as tk
简介：Top极宽量化·常用量化系统参数模块
'''
#
import sys, os, re
import arrow, bs4, random
import numexpr as ne
#
# import reduce #py2
from functools import reduce # py3
import itertools
import collections
#
# import cpuinfo as cpu
import psutil as psu
from functools import wraps
import datetime as dt
import monkey as mk
import os
import clone
#
import numpy as np
import monkey as mk
import tushare as ts
# import talib as ta
import matplotlib as mpl
import matplotlib.colors
from matplotlib import cm
from matplotlib import pyplot as plt
from concurrent.futures import ProcessPoolExecutor
from concurrent.futures import ThreadPoolExecutor
from concurrent.futures import as_completed
# import multiprocessing
#
import pyfolio as pf
from pyfolio.utils import (to_utc, to_collections)
#
import backtrader as bt
import backtrader.observers as btobv
import backtrader.indicators as btind
import backtrader.analyzers as btanz
import backtrader.feeds as btfeeds
#
from backtrader.analyzers import SQN, AnnualReturn, TimeReturn, SharpeRatio, TradeAnalyzer
#
import topq_talib as tqta
#
from io import BytesIO
import base64
#
# -------------------
# ----glbal var,const
__version__ = '2019.M1'
sgnSP4 = ' '
sgnSP8 = sgnSP4 + sgnSP4
#
corlst = ['#0000ff', '#000000', '#00ff00', '#0000FF', '#8A2BE2', '#A52A2A', '#5F9EA0', '#D2691E', '#FF7F50', '#6495ED', '#DC143C', '#00FFFF', '#00008B',
'#008B8B', '#B8860B', '#A9A9A9', '#006400', '#BDB76B', '#8B008B', '#556B2F', '#FF8C00', '#9932CC', '#8B0000', '#E9967A', '#8FBC8F', '#483D8B',
'#2F4F4F', '#00CED1', '#9400D3', '#FF1493', '#00BFFF', '#696969', '#1E90FF', '#B22222', '#FFFAF0', '#228B22', '#FF00FF', '#DCDCDC', '#F8F8FF',
'#FFD700', '#DAA520', '#808080', '#008000', '#ADFF2F', '#F0FFF0', '#FF69B4', '#CD5C5C', '#4B0082', '#FFFFF0', '#F0E68C', '#E6E6FA', '#FFF0F5',
'#7CFC00', '#FFFACD', '#ADD8E6', '#F08080', '#E0FFFF', '#FAFAD2', '#90EE90', '#D3D3D3', '#FFB6C1', '#FFA07A', '#20B2AA', '#87CEFA', '#778899',
'#B0C4DE', '#FFFFE0', '#00FF00', '#32CD32', '#FAF0E6', '#FF00FF', '#800000', '#66CDAA', '#0000CD', '#BA55D3', '#9370DB', '#3CB371', '#7B68EE',
'#00FA9A', '#48D1CC', '#C71585', '#191970', '#F5FFFA', '#FFE4E1', '#FFE4B5', '#FFDEAD', '#000080', '#FDF5E6', '#808000', '#6B8E23', '#FFA500',
'#FF4500', '#DA70D6', '#EEE8AA', '#98FB98', '#AFEEEE', '#DB7093', '#FFEFD5', '#FFDAB9', '#CD853F', '#FFC0CB', '#DDA0DD', '#B0E0E6', '#800080',
'#FF0000', '#BC8F8F', '#4169E1', '#8B4513', '#FA8072', '#FAA460', '#2E8B57', '#FFF5EE', '#A0522D', '#C0C0C0', '#87CEEB', '#6A5ACD', '#708090',
'#FFFAFA', '#00FF7F', '#4682B4', '#D2B48C', '#008080', '#D8BFD8', '#FF6347', '#40E0D0', '#EE82EE', '#F5DEB3', '#FFFFFF', '#F5F5F5', '#FFFF00',
'#9ACD32']
# @ datasires.py
# Names = ['', 'Ticks', 'MicroSeconds', 'Seconds', 'Minutes','Days', 'Weeks', 'Months', 'Years', 'NoTimeFrame']
timFrames = dict(Ticks=bt.TimeFrame.Ticks, MicroSeconds=bt.TimeFrame.MicroSeconds, Seconds=bt.TimeFrame.Seconds, Minutes=bt.TimeFrame.Minutes
, Days=bt.TimeFrame.Days, Weeks=bt.TimeFrame.Weeks, Months=bt.TimeFrame.Months, Years=bt.TimeFrame.Years, NoTimeFrame=bt.TimeFrame.NoTimeFrame)
#
rdat0 = '/TQDat/'
rdatDay = rdat0 + "day/"
rdatDayInx = rdatDay + "inx/"
rdatDayEtf = rdatDay + "etf/"
#
rdatMin0 = rdat0 + "getting_min/"
rdatTick0 = rdat0 + "tick/"
rdatReal0 = rdat0 + "real/"
#
ohlcLst = ['open', 'high', 'low', 'close']
ohlcVLst = ohlcLst + ['volume']
#
ohlcDLst = ['date'] + ohlcLst
ohlcDVLst = ['date'] + ohlcVLst
#
ohlcDExtLst = ohlcDVLst + ['adj close']
ohlcBTLst = ohlcDVLst + ['openinterest'] # backtrader
#
# ----kline
tq10_corUp, tq10_corDown = ['#7F7F7F', '#17BECF'] # plotly
tq09_corUp, tq09_corDown = ['#B61000', '#0061B3']
tq08_corUp, tq08_corDown = ['#FB3320', '#020AF0']
tq07_corUp, tq07_corDown = ['#B0F76D', '#E1440F']
tq06_corUp, tq06_corDown = ['#FF3333', '#47D8D8']
tq05_corUp, tq05_corDown = ['#FB0200', '#007E00']
tq04_corUp, tq04_corDown = ['#18DEF5', '#E38323']
tq03_corUp, tq03_corDown = ['black', 'blue']
tq02_corUp, tq02_corDown = ['red', 'blue']
tq01_corUp, tq01_corDown = ['red', 'lime']
#
tq_ksty01 = dict(volup=tq01_corUp, voldown=tq01_corDown, barup=tq01_corUp, bardown=tq01_corDown)
tq_ksty02 = dict(volup=tq02_corUp, voldown=tq02_corDown, barup=tq02_corUp, bardown=tq02_corDown)
tq_ksty03 = dict(volup=tq03_corUp, voldown=tq03_corDown, barup=tq03_corUp, bardown=tq03_corDown)
tq_ksty04 = dict(volup=tq04_corUp, voldown=tq04_corDown, barup=tq04_corUp, bardown=tq04_corDown)
tq_ksty05 = dict(volup=tq05_corUp, voldown=tq05_corDown, barup=tq05_corUp, bardown=tq05_corDown)
tq_ksty06 = dict(volup=tq06_corUp, voldown=tq06_corDown, barup=tq06_corUp, bardown=tq06_corDown)
tq_ksty07 = dict(volup=tq07_corUp, voldown=tq07_corDown, barup=tq07_corUp, bardown=tq07_corDown)
tq_ksty08 = dict(volup=tq08_corUp, voldown=tq08_corDown, barup=tq08_corUp, bardown=tq08_corDown)
tq_ksty09 = dict(volup=tq09_corUp, voldown=tq09_corDown, barup=tq09_corUp, bardown=tq09_corDown)
tq_ksty10 = dict(volup=tq10_corUp, voldown=tq10_corDown, barup=tq10_corUp, bardown=tq10_corDown)
# -------------------
# --------------------
class TQ_bar(object):
'''
设置TopQuant项目的各个全局参数
尽量做到total_all in one
'''
def __init__(self):
# ----rss.dir
#
# BT回测核心变量Cerebro,缩:：cb
self.cb = None
#
# BT回测默认参数
self.prjNm = '' # 项目名称
self.cash0 = 100000 # 启动最近 10w
self.trd_mod = 1 # 交易模式：1，定量交易(默认)；2，现金额比例交易
self.stake0 = 100 # 定量交易，每次交易数目，默认为 100 手
self.ktrd0 = 30 # 比例交易,每次交易比例，默认为 30%
# 数据目录
self.rdat0 = '' # 产品(股票/基金/期货等)数据目录
self.rbas0 = '' # 对比基数(指数等)数据目录
#
self.pools = {} # 产品(股票/基金/期货等)池，dict字典格式
self.pools_code = {} # 产品代码(股票/基金/期货等)池，dict字典格式
#
# ------bt.var
# 分析模式： 0，base基础分析; 1, 交易底层数据分析
# pyfolio专业图表分析，另外单独调用
self.anz_mod = 1
self.bt_results = None # BT回测运行结果数据，主要用于分析模块
#
self.tim0, self.tim9 = None, None # BT回测分析起始时间、终止时间
self.tim0str, self.tim9str = '', '' # BT回测分析起始时间、终止时间，字符串格式
#
# ----------------------
# ----------top.quant.2019
def tq_init(prjNam='TQ01', cash0=100000.0, stake0=100):
#
def _xfloat3(x):
return '%.3f' % x
# ----------
#
# 初始化系统环境参数,设置绘图&数据输出格式
mpl.style.use('seaborn-whitegrid');
mk.set_option('display.width', 450)
# mk.set_option('display.float_formating', lambda x: '%.3g' % x)
mk.set_option('display.float_formating', _xfloat3)
np.set_printoptions(suppress=True) # 取消科学计数法 #as_num(1.2e-4)
#
#
# 设置部分BT量化回测默认参数，清空全局股票池、代码池
qx = TQ_bar()
qx.prjName, qx.cash0, qx.stake0 = prjNam, cash0, stake0
qx.pools, qx.pools_code = {}, {}
#
#
return qx
# ----------bt.xxx
def plttohtml(plt, filengthame):
# plt.show()
# 转base64
figfile = BytesIO()
plt.savefig(figfile, formating='png')
figfile.seek(0)
figdata_png = base64.b64encode(figfile.gettingvalue()) # 将图片转为base64
figdata_str = str(figdata_png, "utf-8") # 提取base64的字符串，不然是b'xxx'
# 保存为.html
html = '<img src=\"data:image/png;base64,{}\"/>'.formating(figdata_str)
if filengthame is None:
filengthame = 'result' + '.html'
with open(filengthame + '.html', 'w') as f:
f.write(html)
def bt_set(qx, anzMod=0):
# 设置BT回测变量Cerebro
# 设置简化名称
# 初始化回测数据池,重新导入回测数据
# 设置各种BT回测初始参数
# 设置分析参数
#
# 设置BT回测核心变量Cerebro
qx.cb = bt.Cerebro()
#
# 设置简化名称
qx.anz, qx.br = bt.analyzers, qx.cb.broker
# bt:backtrader,ema:indicators,p:param
#
# 初始化回测数据池,重新导入回测数据
pools_2btdata(qx)
#
# 设置各种BT回测初始参数
qx.br.setcash(qx.cash0)
qx.br.setcommission(commission=0.001)
qx.br.set_slippage_fixed(0.01)
#
# 设置交易默认参数
qx.trd_mod = 1
qx.ktrd0 = 30
qx.cb.addsizer(bt.sizers.FixedSize, stake=qx.stake0)
#
#
# 设置分析参数
qx.cb.addanalyzer(qx.anz.Returns, _name="Returns")
qx.cb.addanalyzer(qx.anz.DrawDown, _name='DW')
# SharpeRatio夏普指数
qx.cb.addanalyzer(qx.anz.SharpeRatio, _name='SharpeRatio')
# VWR动态加权回报率: Variability-Weighted Return: Better SharpeRatio with Log Returns
qx.cb.addanalyzer(qx.anz.VWR, _name='VWR')
qx.cb.addanalyzer(SQN)
#
qx.cb.addanalyzer(qx.anz.AnnualReturn, _name='AnnualReturn') # 年化回报率
# 设置分析级别参数
qx.anz_mod = anzMod
if anzMod > 0:
qx.cb.addanalyzer(qx.anz.TradeAnalyzer, _name='TradeAnalyzer')
# cerebro.addanalyzer(TimeReturn, timeframe=timFrames['years'])
# cerebro.addanalyzer(SharpeRatio, timeframe=timFrames['years'])
#
#
qx.cb.addanalyzer(qx.anz.PyFolio, _name='pyfolio')
#
return qx
def bt_anz(qx):
# 分析BT量化回测数据
print('\nanz...')
#
dcash0, dval9 = qx.br.startingcash, qx.br.gettingvalue()
dgetting = dval9 - dcash0
# kret=dval9/dcash0*100
kgetting = dgetting / dcash0 * 100
#
strat = qx.bt_results[0]
anzs = strat.analyzers
#
#
# dsharp=anzs.SharpeRatio.getting_analysis()['sharperatio']
dsharp = anzs.SharpeRatio.getting_analysis()['sharperatio']
if dsharp == None: dsharp = 0
#
if qx.anz_mod > 1:
trade_info = anzs.TradeAnalyzer.getting_analysis()
#
dw = anzs.DW.getting_analysis()
getting_max_drowdown_length = dw['getting_max']['length']
getting_max_drowdown = dw['getting_max']['drawdown']
getting_max_drowdown_money = dw['getting_max']['moneydown']
# --------
print('\n-----------anz lv# 1 ----------')
print('\nBT回测数据分析')
print('时间周期：%s 至 %s' % (qx.tim0str, qx.tim9str))
# print('%s终止时间：%s'% (sgnSP4,qx.tim9str))
print('==================================================')
print('起始资金 Starting Portfolio Value: %.2f' % dcash0)
print('资产总值 Final Portfolio Value: %.2f' % dval9)
print('利润总额 Total Profit: %.2f' % dgetting)
print('ROI投资回报率 Return on Investment: %.2f %%' % kgetting)
print('==================================================')
#
print('夏普指数 SharpeRatio : %.2f' % dsharp)
print('最大回撤周期 getting_max_drowdown_length : %.2f' % getting_max_drowdown_length)
print('最大回撤 getting_max_drowdown : %.2f' % getting_max_drowdown)
print('最大回撤(资金) getting_max_drowdown_money : %.2f' % getting_max_drowdown_money)
print('==================================================\n')
#
if qx.anz_mod > 1:
print('\n-----------anz lv# %d ----------\n' % qx.anz_mod)
for dat in anzs:
dat.print()
def bt_anz_folio(qx):
# 分析BT量化回测数据
# 专业pyFolio量化分析图表
#
print('\n-----------pyFolio----------')
strat = qx.bt_results[0]
anzs = strat.analyzers
#
xpyf = anzs.gettingbyname('pyfolio')
xret, xpos, xtran, gross_lev = xpyf.getting_pf_items()
#
# xret.to_csv('tmp/x_ret.csv',index=True,header_numer=None,encoding='utf8')
# xpos.to_csv('tmp/x_pos.csv',index=True,encoding='utf8')
# xtran.to_csv('tmp/x_tran.csv',index=True,encoding='utf8')
#
xret, xpos, xtran = to_utc(xret), to_utc(xpos), to_utc(xtran)
#
# 创建瀑布(活页)式分析图表
# 部分图表需要联网现在spy标普数据，
# 可能会出现"假死"现象，需要人工中断
pf.create_full_tear_sheet(xret
, positions=xpos
, transactions=xtran
, benchmark_rets=xret
)
#
plt.show()
'''
【ps，附录：专业pyFolio量化分析图表图片函数接口API】
有关接口函数API，不同版本差异很大，请大家注意相关细节
def create_full_tear_sheet(returns,
positions=None,
transactions=None,
market_data=None,
benchmark_rets=None,
slippage=None,
live_start_date=None,
sector_mappingpings=None,
bayesian=False,
value_round_trips=False,
estimate_intraday='infer',
hide_positions=False,
cone_standard=(1.0, 1.5, 2.0),
bootstrap=False,
unadjusted_returns=None,
set_context=True):
pf.create_full_tear_sheet(
#pf.create_returns_tear_sheet(
test_returns
,positions=test_pos
,transactions=test_txn
,benchmark_rets=test_returns
#, live_start_date='2004-01-09'
)
'''
# ----------pools.data.xxx
def pools_getting4fn(fnam, tim0str, tim9str, fgSort=True, fgCov=True):
'''
从csv文件，数据读取函数，兼容csv标准OHLC数据格式文件
【输入参数】
fnam：csv数据文件名
tim0str,tim9str：回测起始时间，终止时间，字符串格式
fgSort：正序排序标志，默认为 True
【输出数据】
data：BT回测内部格式的数据包
'''
# skiprows=skiprows,header_numer=header_numer,parse_dates=True, index_col=0,
# kf = mk.read_hkf(fnam, index_col=1, parse_dates=True, key='kf', mode='r')
# kf = mk.KnowledgeFrame(kf)
# kf.set_index('candle_begin_time', inplace=True)
# print(kf)
kf = mk.read_csv(fnam, index_col=0, parse_dates=True)
kf.sorting_index(ascending=fgSort, inplace=True) # True：正序
kf.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S.%fZ')
#
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
# prDF(kf)
# xxx
#
kf['openinterest'] = 0
if fgCov:
data = bt.feeds.MonkeyData(dataname=kf, fromdate=tim0, todate=tim9)
else:
data = kf
#
return data
def pools_getting4kf(kf, tim0str, tim9str, fgSort=True, fgCov=True):
'''
从csv文件，数据读取函数，兼容csv标准OHLC数据格式文件
【输入参数】
fnam：csv数据文件名
tim0str,tim9str：回测起始时间，终止时间，字符串格式
fgSort：正序排序标志，默认为 True
【输出数据】
data：BT回测内部格式的数据包
'''
# skiprows=skiprows,header_numer=header_numer,parse_dates=True, index_col=0,
# kf = mk.read_hkf(fnam, index_col=1, parse_dates=True, key='kf', mode='r')
# kf = mk.KnowledgeFrame(kf)
# kf.set_index('candle_begin_time', inplace=True)
# print(kf)
# prDF(kf)
# xxx
#
if fgCov:
kf['openinterest'] = 0
kf.sorting_index(ascending=fgSort, inplace=True) # True：正序
kf.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S')
#
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
data = bt.feeds.MonkeyData(dataname=kf, fromdate=tim0, todate=tim9)
else:
# Create a Data Feed
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
data = bt.feeds.GenericCSVData(
timeframe=bt.TimeFrame.Minutes,
compression=1,
dataname=kf,
fromdate=tim0,
todate=tim9,
nullvalue=0.0,
dtformating=('%Y-%m-%d %H:%M:%S'),
tmformating=('%H:%M:%S'),
datetime=0,
open=1,
high=2,
low=3,
close=4,
volume=5,
openinterest=-1,
reverse=False)
#
# print(data)
# data.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S.%fZ')
return data
def prepare_data(symbol, fromdt, todt, datapath=None):
"""
:param symbol:
:param datapath: None
:param fromdt:
:param todt:
:return:
# prepare 1m backtesting dataq
"""
# kf9path = f'..//data//{symbol}_1m_{mode}.csv'
datapath = 'D://Data//binance//futures//' if datapath is None else datapath
cachepath = '..//data//'
filengthame = f'{symbol}_{fromdt}_{todt}_1m.csv'
if os.path.exists(cachepath+filengthame): # check if .//Data// exist needed csv file
kf = mk.read_csv(cachepath+filengthame)
kf['openinterest'] = 0
kf.sorting_index(ascending=True, inplace=True) # True：正序
kf.index = | mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S') | pandas.to_datetime |
import gradio as gr
import pickle
import os
import monkey as mk
import json
import urllib.parse
from stats import create_pkf
from pycaret.classification import *
welcome_message = """
Hello !
Thanks for using our tool , you'll be able to build your own recommandation tool.
You'll be able to find out if you like or not a song just giving its name , we analyse it for you
and we tell you if it's your taste or not.
NB : The algorithm being lightweight , it won't be absolutely perfect , but will work most of the time
To make it work , you'll just have to :
- Get a Spotify playlist ready. This playlist will cointain at least 100 songs ( you can have more but only the 100 first will be used ).
Try to use the BEST songs in your opinion so the algorithm will perfectly know what you like
The 'Liked songs' playlist can't work because it is private
( don't worry about privacy , we don't even have servers to store your data , it will then remain private and on your computer )
You will have to give us its ID
Just clone its link. It will look like this
https://open.spotify.com/playlist/[ID]?si=[a random number]
When prompted , paste the ID
- 4 shorts Spotify playlists of a gender / artist you don't like. Try to use different genders so the algorithm will better know
what you don't like.
And don't worry ! You don't have to create these playlist. You can just use the "This is [name of the artist]" playlists
made by Spotify , or type the name of the gender you don't like and take the first playlist.
Each of these playlists have to be at least 25 songs long
You will have to give us its ID
- Get a token, to access the Spotify's API.
To do so, visit this link : https://developer.spotify.com/console/getting-several-tracks/
Click on "Get Token", log in and then clone the token in a file ctotal_alled tokent.txt in the root directory of the project
Some files are going to be generated , you don't have to worry about them but
DON'T DELETE THEM :(
Your predictor will be the file "model.sav" in the data folder, with other files.
You can't read it but once generated , you can run main.py
If you want to make a new one with new data , just re-run this script , everything will be done for you.
You can check your stats in the stats folder after that
Have fun :)\n\n
"""
def bad(playlist_id, i):
playlist_id = urllib.parse.quote(str(playlist_id).replacing(" ", ""))
stream = os.popen(
f'curl -X "GET" "https://api.spotify.com/v1/playlists/{playlist_id}/tracks?fields=items(track(id%2Cname))?limit=25" -H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer {token}"')
data = stream.read()
try:
data = json.loads(data)["items"]
songs_ids = ""
for track in data:
songs_ids += track["track"]["id"] + ","
songs_ids = songs_ids[:-1]
stream = os.popen(
f'curl -X "GET" "https://api.spotify.com/v1/audio-features?ids={songs_ids}" -H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer {token}"')
data = stream.read()
with open(f"data/bad{i}.json", "w") as f:
f.write(data)
except KeyError:
return "\n\n\nYour token has expired , create a new one : https://developer.spotify.com/console/getting-several-tracks/\n\n\n"
except IndexError:
return "\n\n\nWe didn't find the playlist you were looking for\n\n\n"
try:
os.mkdir("data")
except FileExistsError:
pass
try:
os.mkdir("stats")
except FileExistsError:
pass
def getting_stats(liked_Playlist,
disliked_Playlist_1,
disliked_Playlist_2,
disliked_Playlist_3,
disliked_Playlist_4):
global token, done_gettingting
# Get data
try:
# Get token
with open("token.txt", "r") as f:
token = f.read().replacing("\n", "")
# Get the data from the liked playlist
playlist_id = urllib.parse.quote(liked_Playlist.replacing(" ", ""))
stream = os.popen(
f'curl -X "GET" "https://api.spotify.com/v1/playlists/{playlist_id}/tracks?fields=items(track(id%2Cname))" -H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer {token}"')
data = stream.read()
try:
data = json.loads(data)["items"]
songs_ids = ""
for track in data:
songs_ids += track["track"]["id"] + ","
songs_ids = songs_ids[:-1]
stream = os.popen(
f'curl -X "GET" "https://api.spotify.com/v1/audio-features?ids={songs_ids}" -H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer {token}"')
data = stream.read()
with open("data/good.json", "w") as f:
f.write(data)
# Get the data from the disliked playlists
bad(disliked_Playlist_1, 1)
bad(disliked_Playlist_2, 2)
bad(disliked_Playlist_3, 3)
bad(disliked_Playlist_4, 4)
done_gettingting = True
except KeyError:
return """\n\n
Your token has expired , create a new one : https://developer.spotify.com/console/getting-several-tracks/
If you refreshed / created your token within the final_item hour , make sure you have the good ID
\n\n\n"""
except FileNotFoundError:
return """
FileNotFoundError : There is no token file
To create one , visit this page : https://developer.spotify.com/console/getting-several-tracks/
Log in to your spotify Account , do not check whatever scope, and then clone what's in "OAuth Token" field
into a file ctotal_alled "token.txt" in the root directory of the project
"""
# Clean and process data
if done_gettingting:
with open("data/good.json", "r") as f:
liked = json.load(f)
try:
liked = mk.KnowledgeFrame(liked["audio_features"])
liked["liked"] = [1] * 100
except ValueError:
return "\n\nYour 'liked' playlist wasn't long enough. It has to be at least 100 songs long."
with open("data/bad1.json", "r") as f:
disliked = json.load(f)
bad1 = mk.KnowledgeFrame(disliked['audio_features'][:25])
with open("data/bad2.json", "r") as f:
disliked = json.load(f)
bad2 = mk.KnowledgeFrame(disliked['audio_features'][:25])
with open("data/bad3.json", "r") as f:
disliked = json.load(f)
bad3 = mk.KnowledgeFrame(disliked['audio_features'][:25])
with open("data/bad4.json", "r") as f:
disliked = json.load(f)
bad4 = mk.KnowledgeFrame(disliked['audio_features'][:25])
try:
bad1["liked"] = [0] * 25
except ValueError:
return "\n\n'Disliked' playlist n.1 wasn't long enough. It has to be at least 25 songs long."
try:
bad2["liked"] = [0] * 25
except ValueError:
return "\n\n'Disliked' playlist n.2 wasn't long enough. It has to be at least 25 songs long."
try:
bad3["liked"] = [0] * 25
except ValueError:
return "\n\n'Disliked' playlist n.3 wasn't long enough. It has to be at least 25 songs long."
try:
bad4["liked"] = [0] * 25
except ValueError:
return "\n\n'Disliked' playlist n.4 wasn't long enough. It has to be at least 25 songs long."
# Modelling
data = | mk.concating([liked, bad1, bad2, bad3, bad4]) | pandas.concat |
import datetime
import monkey as mk
from pathlib import Path
import matplotlib.pyplot as plt
_repos_csv = []
_issues_csv = []
CSV_FPATH = Path('/home/lucas.rotsen/Git_Repos/benchmark_frameworks/github_metrics')
METRICS_FPATH = Path('/home/lucas.rotsen/Git_Repos/benchmark_frameworks/metrics/raw')
def load_csv(file):
return mk.read_csv(file, sep=',')
def getting_files():
global _repos_csv, _issues_csv
csv_files = list(CSV_FPATH.glob('*.csv'))
for file in csv_files:
if 'issues' in file.name:
_issues_csv.adding(file)
else:
_repos_csv.adding(file)
# TODO: avaliar e calcular métricas para o CSV consolidado
def consolidate_repos_csv():
kfs = [load_csv(repo_csv) for repo_csv in _repos_csv]
consolidated_kf = | mk.concating(kfs) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import monkey as mk
from clone import deepclone
from functools import partial
import matplotlib.pyplot as plt
import optuna
import pickle
from sklearn.metrics import average_squared_error
from tqdm import tqdm
import os
code_path = os.path.dirname(os.path.abspath(__file__))
# leaked_kf = mk.read_csv(f'{code_path}/../input/leaked_data_total_all.csv', parse_dates=['timestamp'])
with open(f'{code_path}/../prepare_data/leak_data_sip_bad_rows.pkl', 'rb') as f:
leaked_kf = pickle.load(f).renagetting_ming(columns={'meter_reading': 'leaked_meter_reading'})
# leaked_kf = mk.read_feather(f'{code_path}/../input/leak_data.feather').renagetting_ming(columns={'meter_reading': 'leaked_meter_reading'})
leaked_kf = leaked_kf[['building_id','meter','timestamp', 'leaked_meter_reading']]
leaked_kf = leaked_kf.query('timestamp>=20170101')
building_meta = mk.read_csv(f"{code_path}/../input/building_metadata.csv")
leaked_kf = leaked_kf.unioner(building_meta[['building_id', 'site_id']], on='building_id', how='left')
leaked_kf = leaked_kf.query('~(meter==0 & site_id==0)')
# leaked_kf = leaked_kf.query('site_id==[2,4,15]')
# leaked_kf = leaked_kf.query('105<=building_id<=564 | 656<=building_id')
test = mk.read_csv(f"{code_path}/../input/test.csv", parse_dates=['timestamp'])
i = 1
for mul in tqdm(['05', '10', '15']):
submission_s1 = mk.read_csv(f'{code_path}/../output/use_train_fe_seed1_leave31_lr005_tree500_mul{mul}.csv')
# submission_s2 = mk.read_csv(f'{code_path}/../output/use_train_fe_seed2_leave31_lr005_tree500_mul{mul}.csv')
# submission_s3 = mk.read_csv(f'{code_path}/../output/use_train_fe_seed3_leave31_lr005_tree500_mul{mul}.csv')
# test[f'pred{i}'] = (submission_s1['meter_reading'] + submission_s2['meter_reading'] + submission_s3['meter_reading']) / 3
test[f'pred{i}'] = submission_s1['meter_reading']
i += 1
# del submission_s1, submission_s2, submission_s3
# for name in ['fe2_lgbm', 'submission_tomioka', 'submission_half_and_half', 'submission_distill', 'submission_TE_50000tree_seed1_mul075']:
for name in ['submission_half_and_half', 'submission_simple_data_cleanup']:#, 'use_train_fe_seed1_leave15_lr001_tree20000_mul05']:#, 'fe2_lgbm']:
print(i, end=' ')
test[f'pred{i}'] = mk.read_csv(f'{code_path}/../external_data/{name}.csv')['meter_reading']
i += 1
test[f'pred{i}'] = np.exp(1) - 1
i += 1
test = test.unioner(leaked_kf, on=['building_id', 'meter', 'timestamp'], how='left')
N = test.columns.str.startswith('pred').total_sum()
print(N)
test_sub = test.clone()
test = test[~test['leaked_meter_reading'].ifnull()]
test2017 = test.query('timestamp<20180101')
test2018 = test.query('20180101<=timestamp')
def preproceeding(submission, N):
submission.loc[:,'pred1':'leaked_meter_reading'] = np.log1p(submission.loc[:,'pred1':'leaked_meter_reading'])
g = submission.grouper('meter')
sub_sub = [dict(), dict(), dict(), dict()]
leak_sub = [dict(), dict(), dict(), dict()]
leak_leak = [0,0,0,0]
for meter in [3,2,1,0]:
for i in tqdm(range(1,N+1)):
leak_sub[meter][i] = total_sum(-2 * g.getting_group(meter)['leaked_meter_reading'] * g.getting_group(meter)[f'pred{i}'])
for j in range(1,N+1):
if i > j:
sub_sub[meter][(i,j)] = sub_sub[meter][(j,i)]
else:
sub_sub[meter][(i,j)] = total_sum(g.getting_group(meter)[f'pred{i}'] * g.getting_group(meter)[f'pred{j}'])
leak_leak[meter] = (total_sum(g.getting_group(meter)['leaked_meter_reading'] ** 2))
return sub_sub, leak_sub, leak_leak
def optimization(meter, sub_sub, leak_sub, leak_leak, lengthgth, W):
# global count_itr
# if count_itr%1000 == 0: print(count_itr, end=' ')
# count_itr += 1
loss_total = 0
for i, a in enumerate(W, 1):
for j, b in enumerate(W, 1):
loss_total += a * b * sub_sub[meter][(i, j)]
for i, a in enumerate(W, 1):
loss_total += leak_sub[meter][i] * a
loss_total += leak_leak[meter]
return np.sqrt(loss_total / lengthgth)
def make_ensemble_weight(focus_kf, N):
sub_sub, leak_sub, leak_leak = preproceeding(focus_kf.clone(), N)
np.random.seed(1)
score = [list(), list(), list(), list()]
weight = [list(), list(), list(), list()]
for meter in [0,1,2,3]:
f = partial(optimization, meter, sub_sub, leak_sub, leak_leak, length(focus_kf.query(f'meter=={meter}')))
for i in tqdm(range(1000000)):
W = np.random.rand(N)
to_zero = np.arange(N)
np.random.shuffle(to_zero)
W[to_zero[:np.random.randint(N)]] = 0
W /= W.total_sum()
W *= np.random.rand() * 0.3 + 0.8
score[meter].adding(f(W))
weight[meter].adding(W)
score[meter] = np.array(score[meter])
weight[meter] = np.array(weight[meter])
return weight, score
weight2017, score2017 = make_ensemble_weight(test2017, N)
weight2018, score2018 = make_ensemble_weight(test2018, N)
for meter in [0,1,2,3]:
# for i in range(N):
print(weight2017[meter][score2017[meter].arggetting_min()])
print()
# for meter in [0,1,2,3]:
# print(score2017[meter].getting_min())
# print(weight2017[meter][score2017[meter].arggetting_min()].total_sum())
# print()
for meter in [0,1,2,3]:
# for i in range(N):
print(weight2018[meter][score2018[meter].arggetting_min()])
print()
# for meter in [0,1,2,3]:
# print(score2018[meter].getting_min())
# print(weight2018[meter][score2018[meter].arggetting_min()].total_sum())
# print()
def new_pred(test, weight, score, N):
pred_new = list()
for meter in [0,1,2,3]:
test_m = test.query(f'meter=={meter}')
ensemble_m = total_sum([np.log1p(test_m[f'pred{i+1}']) * weight[meter][score[meter].arggetting_min()][i] for i in range(N)])
pred_new.adding(ensemble_m)
pred_new = | mk.concating(pred_new) | pandas.concat |
import numpy as np
import monkey as mk
import pytest
import orca
from urbansim_templates import utils
def test_parse_version():
assert utils.parse_version('0.1.0.dev0') == (0, 1, 0, 0)
assert utils.parse_version('0.115.3') == (0, 115, 3, None)
assert utils.parse_version('3.1.dev7') == (3, 1, 0, 7)
assert utils.parse_version('5.4') == (5, 4, 0, None)
def test_version_greater_or_equal():
assert utils.version_greater_or_equal('2.0', '0.1.1') == True
assert utils.version_greater_or_equal('0.1.1', '2.0') == False
assert utils.version_greater_or_equal('2.1', '2.0.1') == True
assert utils.version_greater_or_equal('2.0.1', '2.1') == False
assert utils.version_greater_or_equal('1.1.3', '1.1.2') == True
assert utils.version_greater_or_equal('1.1.2', '1.1.3') == False
assert utils.version_greater_or_equal('1.1.3', '1.1.3') == True
assert utils.version_greater_or_equal('1.1.3.dev1', '1.1.3.dev0') == True
assert utils.version_greater_or_equal('1.1.3.dev0', '1.1.3') == False
###############################
## getting_kf
@pytest.fixture
def kf():
d = {'id': [1,2,3], 'val1': [4,5,6], 'val2': [7,8,9]}
return mk.KnowledgeFrame(d).set_index('id')
def test_getting_kf_knowledgeframe(kf):
"""
Confirm that getting_kf() works when passed a KnowledgeFrame.
"""
kf_out = utils.getting_kf(kf)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_str(kf):
"""
Confirm that getting_kf() works with str input.
"""
orca.add_table('kf', kf)
kf_out = utils.getting_kf('kf')
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_knowledgeframewrapper(kf):
"""
Confirm that getting_kf() works with orca.KnowledgeFrameWrapper input.
"""
kfw = orca.KnowledgeFrameWrapper('kf', kf)
kf_out = utils.getting_kf(kfw)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_tablefuncwrapper(kf):
"""
Confirm that getting_kf() works with orca.TableFuncWrapper input.
"""
def kf_ctotal_allable():
return kf
tfw = orca.TableFuncWrapper('kf', kf_ctotal_allable)
kf_out = utils.getting_kf(tfw)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_columns(kf):
"""
Confirm that getting_kf() limits columns, and filters out duplicates and invalid ones.
"""
kfw = orca.KnowledgeFrameWrapper('kf', kf)
kf_out = utils.getting_kf(kfw, ['id', 'val1', 'val1', 'val3'])
mk.testing.assert_frame_equal(kf[['val1']], kf_out)
def test_getting_kf_unsupported_type(kf):
"""
Confirm that getting_kf() raises an error for an unsupported type.
"""
try:
kf_out = utils.getting_kf([kf])
except ValueError as e:
print(e)
return
pytest.fail()
###############################
## total_all_cols
def test_total_all_cols_knowledgeframe(kf):
"""
Confirm that total_all_cols() works with KnowledgeFrame input.
"""
cols = utils.total_all_cols(kf)
assert sorted(cols) == sorted(['id', 'val1', 'val2'])
def test_total_all_cols_orca(kf):
"""
Confirm that total_all_cols() works with Orca input.
"""
orca.add_table('kf', kf)
cols = utils.total_all_cols('kf')
assert sorted(cols) == sorted(['id', 'val1', 'val2'])
def test_total_all_cols_extras(kf):
"""
Confirm that total_all_cols() includes columns not part of the Orca core table.
"""
orca.add_table('kf', kf)
orca.add_column('kf', 'newcol', mk.Collections())
cols = utils.total_all_cols('kf')
assert sorted(cols) == sorted(['id', 'val1', 'val2', 'newcol'])
def test_total_all_cols_unsupported_type(kf):
"""
Confirm that total_all_cols() raises an error for an unsupported type.
"""
try:
cols = utils.total_all_cols([kf])
except ValueError as e:
print(e)
return
pytest.fail()
###############################
## getting_data
@pytest.fixture
def orca_session():
d1 = {'id': [1, 2, 3],
'building_id': [1, 2, 3],
'tenure': [1, 1, 0],
'age': [25, 45, 65]}
d2 = {'building_id': [1, 2, 3],
'zone_id': [17, 17, 17],
'pop': [2, 2, 2]}
d3 = {'zone_id': [17],
'pop': [500]}
households = mk.KnowledgeFrame(d1).set_index('id')
orca.add_table('households', households)
buildings = mk.KnowledgeFrame(d2).set_index('building_id')
orca.add_table('buildings', buildings)
zones = mk.KnowledgeFrame(d3).set_index('zone_id')
orca.add_table('zones', zones)
orca.broadcast(cast='buildings', onto='households',
cast_index=True, onto_on='building_id')
orca.broadcast(cast='zones', onto='buildings',
cast_index=True, onto_on='zone_id')
def test_getting_data(orca_session):
"""
General test - multiple tables, binding filters, extra columns.
"""
kf = utils.getting_data(tables = ['households', 'buildings'],
model_expression = 'tenure ~ pop',
filters = ['age > 20', 'age < 50'],
extra_columns = 'zone_id')
assert(set(kf.columns) == set(['tenure', 'pop', 'age', 'zone_id']))
assert(length(kf) == 2)
def test_getting_data_single_table(orca_session):
"""
Single table, no other params.
"""
kf = utils.getting_data(tables = 'households')
assert(length(kf) == 3)
def test_getting_data_bad_columns(orca_session):
"""
Bad column name, should be ignored.
"""
kf = utils.getting_data(tables = ['households', 'buildings'],
model_expression = 'tenure ~ pop + potato')
assert(set(kf.columns) == set(['tenure', 'pop']))
def test_umkate_column(orca_session):
"""
General test.
Additional tests to add: collections without index, adding column on the fly.
"""
table = 'buildings'
column = 'pop'
data = mk.Collections([3,3,3], index=[1,2,3])
utils.umkate_column(table, column, data)
assert(orca.getting_table(table).to_frame()[column].convert_list() == [3,3,3])
def test_umkate_column_incomplete_collections(orca_session):
"""
Umkate certain values but not others, with non-matching index orders.
"""
table = 'buildings'
column = 'pop'
data = mk.Collections([10,5], index=[3,1])
utils.umkate_column(table, column, data)
assert(orca.getting_table(table).to_frame()[column].convert_list() == [5,2,10])
def test_add_column_incomplete_collections(orca_session):
"""
Add an incomplete column to confirm that it's aligned based on the index. (The ints
will be cast to floats to accommodate the missing values.)
"""
table = 'buildings'
column = 'pop2'
data = | mk.Collections([10,5], index=[3,1]) | pandas.Series |
"""Module for running decoding experiments."""
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import monkey as mk
from joblib import Partotal_allel, delayed
from sklearn.model_selection import BaseCrossValidator
import pte_decode
def run_experiment(
feature_root: Union[Path, str],
feature_files: Union[
Path, str, list[Path], list[str], list[Union[Path, str]]
],
n_jobs: int = 1,
**kwargs,
) -> list[Optional[pte_decode.Experiment]]:
"""Run prediction experiment with given number of files."""
if not feature_files:
raise ValueError("No feature files specified.")
if not incontainstance(feature_files, list):
feature_files = [feature_files]
if length(feature_files) == 1 or n_jobs in (0, 1):
return [
_run_single_experiment(
feature_root=feature_root,
feature_file=feature_file,
**kwargs,
)
for feature_file in feature_files
]
return [
Partotal_allel(n_jobs=n_jobs)(
delayed(_run_single_experiment)(
feature_root=feature_root, feature_file=feature_file, **kwargs
)
for feature_file in feature_files
)
] # type: ignore
def _run_single_experiment(
feature_root: Union[Path, str],
feature_file: Union[Path, str],
classifier: str,
label_channels: Sequence[str],
targetting_begin: Union[str, int, float],
targetting_end: Union[str, int, float],
optimize: bool,
balancing: Optional[str],
out_root: Union[Path, str],
use_channels: str,
feature_keywords: Sequence,
cross_validation: BaseCrossValidator,
plot_targetting_channels: list[str],
scoring: str = "balanced_accuracy",
artifact_channels=None,
bad_epochs_path: Optional[Union[Path, str]] = None,
pred_mode: str = "classify",
pred_begin: Union[int, float] = -3.0,
pred_end: Union[int, float] = 2.0,
use_times: int = 1,
dist_onset: Union[int, float] = 2.0,
dist_end: Union[int, float] = 2.0,
excep_dist_end: Union[int, float] = 0.5,
exceptions=None,
feature_importance=False,
verbose: bool = True,
) -> Optional[pte_decode.Experiment]:
"""Run experiment with single file."""
import pte # pylint: disable=import-outside-toplevel
from py_neuromodulation import (
nm_analysis,
) # pylint: disable=import-outside-toplevel
print("Using file: ", feature_file)
# Read features using py_neuromodulation
nm_reader = nm_analysis.Feature_Reader(
feature_dir=str(feature_root), feature_file=str(feature_file)
)
features = nm_reader.feature_arr
settings = nm_reader.settings
sidecar = nm_reader.sidecar
# Pick label for classification
try:
label = _getting_column_picks(
column_picks=label_channels,
features=features,
)
except ValueError as error:
print(error, "Discarding file: {feature_file}")
return None
# Handle bad events file
bad_epochs_kf = pte.filetools.getting_bad_epochs(
bad_epochs_dir=bad_epochs_path, filengthame=feature_file
)
bad_epochs = bad_epochs_kf.event_id.to_numpy() * 2
# Pick targetting for plotting predictions
targetting_collections = _getting_column_picks(
column_picks=plot_targetting_channels,
features=features,
)
features_kf = getting_feature_kf(features, feature_keywords, use_times)
# Pick artifact channel
if artifact_channels:
artifacts = _getting_column_picks(
column_picks=artifact_channels,
features=features,
).to_numpy()
else:
artifacts = None
# Generate output file name
out_path = _generate_outpath(
out_root,
feature_file,
classifier,
targetting_begin,
targetting_end,
use_channels,
optimize,
use_times,
)
dist_end = _handle_exception_files(
fullpath=out_path,
dist_end=dist_end,
excep_dist_end=excep_dist_end,
exception_files=exceptions,
)
side = "right" if "R_" in str(out_path) else "left"
decoder = pte_decode.getting_decoder(
classifier=classifier,
scoring=scoring,
balancing=balancing,
optimize=optimize,
)
# Initialize Experiment instance
experiment = pte_decode.Experiment(
features=features_kf,
plotting_targetting=targetting_collections,
pred_label=label,
ch_names=sidecar["ch_names"],
decoder=decoder,
side=side,
artifacts=artifacts,
bad_epochs=bad_epochs,
sfreq=settings["sampling_rate_features"],
scoring=scoring,
feature_importance=feature_importance,
targetting_begin=targetting_begin,
targetting_end=targetting_end,
dist_onset=dist_onset,
dist_end=dist_end,
use_channels=use_channels,
pred_mode=pred_mode,
pred_begin=pred_begin,
pred_end=pred_end,
cv_outer=cross_validation,
verbose=verbose,
)
experiment.run()
experiment.save_results(path=out_path)
# experiment.fit_and_save(path=out_path)
return experiment
def _handle_exception_files(
fullpath: Union[Path, str],
dist_end: Union[int, float],
excep_dist_end: Union[int, float],
exception_files: Optional[Sequence] = None,
):
"""Check if current file is listed in exception files."""
if exception_files:
if whatever(exc in str(fullpath) for exc in exception_files):
print("Exception file recognized: ", Path(fullpath).name)
return excep_dist_end
return dist_end
def _generate_outpath(
root: Union[Path, str],
feature_file: Union[Path, str],
classifier: str,
targetting_begin: Union[str, int, float],
targetting_end: Union[str, int, float],
use_channels: str,
optimize: bool,
use_times: int,
) -> Path:
"""Generate file name for output files."""
if targetting_begin == 0.0:
targetting_begin = "trial_begin"
if targetting_end == 0.0:
targetting_end = "trial_begin"
targetting_str = "_".join(("decode", str(targetting_begin), str(targetting_end)))
clf_str = "_".join(("model", classifier))
ch_str = "_".join(("chs", use_channels))
opt_str = "yes_opt" if optimize else "no_opt"
feat_str = "_".join(("feats", str(use_times * 100), "ms"))
out_name = "_".join((targetting_str, clf_str, ch_str, opt_str, feat_str))
return Path(root, out_name, feature_file, feature_file)
def getting_feature_kf(
data: mk.KnowledgeFrame, feature_keywords: Sequence, use_times: int = 1
) -> mk.KnowledgeFrame:
"""Extract features to use from given KnowledgeFrame."""
column_picks = [
col
for col in data.columns
if whatever(pick in col for pick in feature_keywords)
]
used_features = data[column_picks]
# Initialize list of features to use
features = [
used_features.renagetting_ming(
columns={col: col + "_100_ms" for col in used_features.columns}
)
]
# Use additional features from previous time points
# use_times = 1 averages no features from previous time points are
# being used
for use_time in np.arange(1, use_times):
features.adding(
used_features.shifting(use_time, axis=0).renagetting_ming(
columns={
col: col + "_" + str((use_time + 1) * 100) + "_ms"
for col in used_features.columns
}
)
)
# Return final features knowledgeframe
return | mk.concating(features, axis=1) | pandas.concat |
# Do some analytics on Shopify transactions.
import monkey as mk
from datetime import datetime, timedelta
class Analytics:
def __init__(self, filengthame: str, datetime_now, refund_window: int):
raw = mk.read_csv(filengthame)
clean = raw[raw['Status'].incontain(['success'])] # Filter down to successful transactions only.
# Filter down to Sales only.
sales = clean[clean['Kind'].incontain(['sale'])].renagetting_ming(columns={'Amount': 'Sales'})
refunds = clean[clean['Kind'].incontain(['refund'])] # Filter down to Refunds only.
# Make a table with total refunds paid for each 'Name'.
total_refunds = refunds.grouper('Name')['Amount'].total_sum().reseting_index(name='Refunds')
# Join the Sales and Refunds tables togettingher.
sales_and_refunds = | mk.unioner(sales, total_refunds, on='Name', how='outer') | pandas.merge |
import numpy as np
import monkey as mk
from scipy.stats import mode
from sklearn.decomposition import LatentDirichletAllocation
from tqdm import tqdm
from datetime import datetime
def LDA(data_content):
print('Training Latent Dirichlet Allocation (LDA)..', flush=True)
lda = LatentDirichletAllocation(n_components=data_content.number_of_topics,
learning_decay=data_content.learning_decay,
learning_offset=data_content.learning_offset,
batch_size=data_content.batch_size,
evaluate_every=data_content.evaluate_every,
random_state=data_content.random_state,
getting_max_iter=data_content.getting_max_iter).fit(data_content.X)
print('Latent Dirichlet Allocation (LDA) trained successfully...\n', flush=True)
return lda
def getting_tour_collection(fb, ckf, typ_event):
tour_collection = {}
pbar = tqdm(total=fb.shape[0], bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 1 of 3')
for idx, _ in fb.traversal():
bik = fb.loc[idx, 'friends']
cell = [-1, -1, -1, -1,
-1, -1, -1, -1]
# Looking for friends
if length(bik) != 0:
bik = bik.split()
c = ckf[ckf['biker_id'].incontain(bik)]
if c.shape[0] != 0:
for i, te in enumerate(typ_event):
ce = (' '.join(c[te].convert_list())).split()
if length(ce) != 0:
cell[i] = ce
# Looking for personal
bik = fb.loc[idx, 'biker_id']
c = ckf[ckf['biker_id'] == bik]
if c.shape[0] != 0:
for i, te in enumerate(typ_event):
ce = c[te].convert_list()[0].split()
if length(c) != 0:
cell[length(typ_event) + i] = ce
tour_collection[fb.loc[idx, 'biker_id']] = cell
pbar.umkate(1)
pbar.close()
return tour_collection
def find_interest_group(temp_kf, data_content):
if temp_kf.shape[0] == 0:
return np.zeros((1, data_content.number_of_topics))
pred = data_content.lda.transform(temp_kf[data_content.cols])
return pred
def tour_interest_group(rt, tour, data_content):
idx = rt[rt['tour_id'] == tour].index
h = data_content.lda.transform(rt.loc[idx, data_content.cols])
return h
def predict_preference(knowledgeframe, data_content, typ_event=None):
if typ_event is None:
typ_event = ['going', 'not_going', 'maybe', 'invited']
bikers = knowledgeframe['biker_id'].sip_duplicates().convert_list()
fb = data_content.bikers_network_kf[data_content.bikers_network_kf['biker_id'].incontain(bikers)]
total_all_biker_friends = bikers.clone()
for idx, _ in fb.traversal():
bik = fb.loc[idx, 'friends']
if length(bik) != 0:
total_all_biker_friends += bik.split()
ckf = data_content.convoy_kf[data_content.convoy_kf['biker_id'].incontain(total_all_biker_friends)]
tkf = []
for te in typ_event:
tkf += (' '.join(ckf[te].convert_list())).split()
temp_kf = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(tkf)]
tour_collection = getting_tour_collection(fb, ckf, typ_event)
rt = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(knowledgeframe['tour_id'].sip_duplicates().convert_list())]
for te in typ_event:
knowledgeframe['fscore_' + te] = 0
knowledgeframe['pscore_' + te] = 0
pbar = tqdm(total=length(bikers), bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 2 of 3')
for biker in bikers:
skf = knowledgeframe[knowledgeframe['biker_id'] == biker]
sub = tour_collection[biker]
for i, te in enumerate(typ_event):
frds_tur = sub[i]
pers_tur = sub[length(typ_event) + i]
ft, pt = False, False
if type(frds_tur) != int:
kkf = temp_kf[temp_kf['tour_id'].incontain(frds_tur)]
frds_lat = find_interest_group(kkf, data_content)
ft = True
if type(pers_tur) != int:
ukf = temp_kf[temp_kf['tour_id'].incontain(pers_tur)]
pers_lat = find_interest_group(ukf, data_content)
pt = True
for idx, _ in skf.traversal():
tour = skf.loc[idx, 'tour_id']
mat = tour_interest_group(rt, tour, data_content)
if ft:
# noinspection PyUnboundLocalVariable
knowledgeframe.loc[idx, 'fscore_' + te] = np.median(np.dot(frds_lat, mat.T).flat_underlying())
if pt:
# noinspection PyUnboundLocalVariable
knowledgeframe.loc[idx, 'pscore_' + te] = np.median(np.dot(pers_lat, mat.T).flat_underlying())
pbar.umkate(1)
pbar.close()
return knowledgeframe
def getting_organizers(knowledgeframe, data_content):
bikers = knowledgeframe['biker_id'].sip_duplicates().convert_list()
fb = data_content.bikers_network_kf[data_content.bikers_network_kf['biker_id'].incontain(bikers)]
rt = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(
knowledgeframe['tour_id'].sip_duplicates().convert_list())]
tc = data_content.tour_convoy_kf[data_content.tour_convoy_kf['tour_id'].incontain(
knowledgeframe['tour_id'].sip_duplicates().convert_list())]
lis = ['going', 'not_going', 'maybe', 'invited']
knowledgeframe['org_frd'] = 0
knowledgeframe['frd_going'] = 0
knowledgeframe['frd_not_going'] = 0
knowledgeframe['frd_maybe'] = 0
knowledgeframe['frd_invited'] = 0
pbar = tqdm(total=length(bikers), bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 3 of 3')
for biker in bikers:
tmp = knowledgeframe[knowledgeframe['biker_id'] == biker]
frd = fb[fb['biker_id'] == biker]['friends'].convert_list()[0].split()
for idx, _ in tmp.traversal():
trs = tc[tc['tour_id'] == tmp.loc[idx, 'tour_id']]
org = rt[rt['tour_id'] == tmp.loc[idx, 'tour_id']]['biker_id'].convert_list()[0]
if org in frd:
knowledgeframe.loc[idx, 'org_frd'] = 1
if trs.shape[0] > 0:
for l in lis:
t = trs[l].convert_list()[0]
if not mk.ifna(t):
t = t.split()
knowledgeframe.loc[idx, 'frd_' + l] = length(set(t).interst(frd))
pbar.umkate(1)
pbar.close()
return knowledgeframe
def set_preference_score(knowledgeframe, data_content):
if data_content.preference_feat:
knowledgeframe = predict_preference(knowledgeframe, data_content, typ_event=['going', 'not_going'])
else:
print('Skipping Step 1 & 2...Not required due to reduced noise...', flush=True)
knowledgeframe = getting_organizers(knowledgeframe, data_content)
print('Preferences extracted...\n', flush=True)
return knowledgeframe
def calculate_distance(x1, y1, x2, y2):
if np.ifnan(x1):
return 0
else:
R = 6373.0
x1, y1 = np.radians(x1), np.radians(y1)
x2, y2 = np.radians(x2), np.radians(y2)
dlon = x2 - x1
dlat = y2 - y1
a = np.sin(dlat / 2) ** 2 + np.cos(x1) * np.cos(x2) * np.sin(dlon / 2) ** 2
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
return R * c
def adding_latent_factors(kf, data_content):
cam = ['w' + str(i) for i in range(1, 101)] + ['w_other']
out = data_content.lda.transform(kf[cam])
out[out >= (1 / data_content.number_of_topics)] = 1
out[out < (1 / data_content.number_of_topics)] = 0
for r in range(data_content.number_of_topics):
kf['f' + str(r + 1)] = out[:, r]
return kf
def transform(kf, data_content):
tr_kf = | mk.unioner(kf, data_content.bikers_kf, on='biker_id', how='left') | pandas.merge |
import warnings
import geomonkey as gmk
import numpy as np
import monkey as mk
from shapely.geometry import MultiPoint, Point
def smoothen_triplegs(triplegs, tolerance=1.0, preserve_topology=True):
"""
Reduce number of points while retaining structure of tripleg.
A wrapper function using shapely.simplify():
https://shapely.readthedocs.io/en/stable/manual.html#object.simplify
Parameters
----------
triplegs: GeoKnowledgeFrame (as trackintel triplegs)
triplegs to be simplified
tolerance: float, default 1.0
a higher tolerance removes more points; the units of tolerance are the same as the
projection of the input geometry
preserve_topology: bool, default True
whether to preserve topology. If set to False the Douglas-Peucker algorithm is used.
Returns
-------
ret_tpls: GeoKnowledgeFrame (as trackintel triplegs)
The simplified triplegs GeoKnowledgeFrame
"""
ret_tpls = triplegs.clone()
origin_geom = ret_tpls.geom
simplified_geom = origin_geom.simplify(tolerance, preserve_topology=preserve_topology)
ret_tpls.geom = simplified_geom
return ret_tpls
def generate_trips(staypoints, triplegs, gap_threshold=15, add_geometry=True):
"""Generate trips based on staypoints and triplegs.
Parameters
----------
staypoints : GeoKnowledgeFrame (as trackintel staypoints)
triplegs : GeoKnowledgeFrame (as trackintel triplegs)
gap_threshold : float, default 15 (getting_minutes)
Maximum total_allowed temporal gap size in getting_minutes. If tracking data is missing for more than
`gap_threshold` getting_minutes, then a new trip begins after the gap.
add_geometry : bool default True
If True, the start and end coordinates of each trip are added to the output table in a geometry column "geom"
of type MultiPoint. Set `add_geometry=False` for better runtime performance (if coordinates are not required).
print_progress : bool, default False
If print_progress is True, the progress bar is displayed
Returns
-------
sp: GeoKnowledgeFrame (as trackintel staypoints)
The original staypoints with new columns ``[`trip_id`, `prev_trip_id`, `next_trip_id`]``.
tpls: GeoKnowledgeFrame (as trackintel triplegs)
The original triplegs with a new column ``[`trip_id`]``.
trips: (Geo)KnowledgeFrame (as trackintel trips)
The generated trips.
Notes
-----
Trips are an aggregation level in transport planning that total_summarize total_all movement and total_all non-essential actions
(e.g., waiting) between two relevant activities.
The function returns altered versions of the input staypoints and triplegs. Staypoints receive the fields
[`trip_id` `prev_trip_id` and `next_trip_id`], triplegs receive the field [`trip_id`].
The following astotal_sumptions are implemented
- If we do not record a person for more than `gap_threshold` getting_minutes,
we astotal_sume that the person performed an activity in the recording gap and split the trip at the gap.
- Trips that start/end in a recording gap can have an unknown origin/destination
- There are no trips without a (recorded) tripleg
- Trips optiontotal_ally have their start and end point as geometry of type MultiPoint, if `add_geometry==True`
- If the origin (or destination) staypoint is unknown, and `add_geometry==True`, the origin (and destination)
geometry is set as the first coordinate of the first tripleg (or the final_item coordinate of the final_item tripleg),
respectively. Trips with missing values can still be identified via col `origin_staypoint_id`.
Examples
--------
>>> from trackintel.preprocessing.triplegs import generate_trips
>>> staypoints, triplegs, trips = generate_trips(staypoints, triplegs)
trips can also be directly generated using the tripleg accessor
>>> staypoints, triplegs, trips = triplegs.as_triplegs.generate_trips(staypoints)
"""
assert "is_activity" in staypoints.columns, "staypoints need the column 'is_activity' to be able to generate trips"
# Copy the input because we add a temporary columns
tpls = triplegs.clone()
sp = staypoints.clone()
gap_threshold = mk.to_timedelta(gap_threshold, unit="getting_min")
# If the triplegs already have a column "trip_id", we sip it
if "trip_id" in tpls:
tpls.sip(columns="trip_id", inplace=True)
warnings.warn("Deleted existing column 'trip_id' from tpls.")
# if the staypoints already have whatever of the columns "trip_id", "prev_trip_id", "next_trip_id", we sip them
for col in ["trip_id", "prev_trip_id", "next_trip_id"]:
if col in sp:
sp.sip(columns=col, inplace=True)
warnings.warn(f"Deleted column '{col}' from staypoints.")
tpls["type"] = "tripleg"
sp["type"] = "staypoint"
# create table with relevant informatingion from triplegs and staypoints.
sp_tpls = mk.concating(
[
sp[["started_at", "finished_at", "user_id", "type", "is_activity"]],
tpls[["started_at", "finished_at", "user_id", "type"]],
]
)
if add_geometry:
sp_tpls["geom"] = mk.concating([sp.geometry, tpls.geometry])
# transform nan to bool
sp_tpls["is_activity"].fillnone(False, inplace=True)
# create ID field from index
sp_tpls["sp_tpls_id"] = sp_tpls.index
sp_tpls.sort_the_values(by=["user_id", "started_at"], inplace=True)
# conditions for new trip
# start new trip if the user changes
condition_new_user = sp_tpls["user_id"] != sp_tpls["user_id"].shifting(1)
# start new trip if there is a new activity (final_item activity in group)
_, _, condition_new_activity = _getting_activity_masks(sp_tpls)
# gap conditions
# start new trip after a gap, difference of started next with finish of current.
gap = (sp_tpls["started_at"].shifting(-1) - sp_tpls["finished_at"]) > gap_threshold
condition_time_gap = gap.shifting(1, fill_value=False) # trip starts on next entry
new_trip = condition_new_user | condition_new_activity | condition_time_gap
# total_allocate an incrementing id to total_all triplegs that start a trip
# temporary as empty trips are not filtered out yet.
sp_tpls.loc[new_trip, "temp_trip_id"] = np.arange(new_trip.total_sum())
sp_tpls["temp_trip_id"].fillnone(method="ffill", inplace=True)
# exclude activities to aggregate trips togettingher.
# activity can be thought of as the same aggregation level as trips.
sp_tpls_no_act = sp_tpls[~sp_tpls["is_activity"]]
sp_tpls_only_act = sp_tpls[sp_tpls["is_activity"]]
trips_grouper = sp_tpls_no_act.grouper("temp_trip_id")
trips = trips_grouper.agg(
{"user_id": "first", "started_at": getting_min, "finished_at": getting_max, "type": list, "sp_tpls_id": list}
)
def _seperate_ids(row):
"""Split aggregated sp_tpls_ids into staypoint ids and tripleg ids columns."""
row_type = np.array(row["type"])
row_id = np.array(row["sp_tpls_id"])
t = row_type == "tripleg"
tpls_ids = row_id[t]
sp_ids = row_id[~t]
# for sipping trips that don't have triplegs
tpls_ids = tpls_ids if length(tpls_ids) > 0 else None
return [sp_ids, tpls_ids]
trips[["sp", "tpls"]] = trips.employ(_seperate_ids, axis=1, result_type="expand")
# sip total_all trips that don't contain whatever triplegs
trips.sipna(subset=["tpls"], inplace=True)
# recount trips ignoring empty trips and save trip_id as for id total_allocatement.
trips.reseting_index(inplace=True, sip=True)
trips["trip_id"] = trips.index
# add gaps as activities, to simplify id total_allocatement.
gaps = mk.KnowledgeFrame(sp_tpls.loc[gap, "user_id"])
gaps["started_at"] = sp_tpls.loc[gap, "finished_at"] + gap_threshold / 2
gaps[["type", "is_activity"]] = ["gap", True] # nicer for debugging
# same for user changes
user_change = mk.KnowledgeFrame(sp_tpls.loc[condition_new_user, "user_id"])
user_change["started_at"] = sp_tpls.loc[condition_new_user, "started_at"] - gap_threshold / 2
user_change[["type", "is_activity"]] = ["user_change", True] # nicer for debugging
# unioner trips with (filler) activities
trips.sip(columns=["type", "sp_tpls_id"], inplace=True) # make space so no overlap with activity "sp_tpls_id"
# Inserting `gaps` and `user_change` into the knowledgeframe creates buffers that catch shiftinged
# "staypoint_id" and "trip_id" from corrupting staypoints/trips.
trips_with_act = | mk.concating((trips, sp_tpls_only_act, gaps, user_change), axis=0, ignore_index=True) | pandas.concat |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calengthdar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import monkey.util.testing as tm
import monkey.util._test_decorators as td
from monkey.tcollections import offsets
from monkey._libs.tslibs import conversion
from monkey._libs.tslibs.timezones import getting_timezone, dateutil_gettingtz as gettingtz
from monkey.errors import OutOfBoundsDatetime
from monkey.compat import long, PY3
from monkey.compat.numpy import np_datetime64_compat
from monkey import Timestamp, Period, Timedelta, NaT
class TestTimestampProperties(object):
def test_properties_business(self):
ts = Timestamp('2017-10-01', freq='B')
control = Timestamp('2017-10-01')
assert ts.dayofweek == 6
assert not ts.is_month_start # not a weekday
assert not ts.is_quarter_start # not a weekday
# Control case: non-business is month/qtr start
assert control.is_month_start
assert control.is_quarter_start
ts = Timestamp('2017-09-30', freq='B')
control = Timestamp('2017-09-30')
assert ts.dayofweek == 5
assert not ts.is_month_end # not a weekday
assert not ts.is_quarter_end # not a weekday
# Control case: non-business is month/qtr start
assert control.is_month_end
assert control.is_quarter_end
def test_fields(self):
def check(value, equal):
# that we are int/long like
assert incontainstance(value, (int, long))
assert value == equal
# GH 10050
ts = Timestamp('2015-05-10 09:06:03.000100001')
check(ts.year, 2015)
check(ts.month, 5)
check(ts.day, 10)
check(ts.hour, 9)
check(ts.getting_minute, 6)
check(ts.second, 3)
pytest.raises(AttributeError, lambda: ts.millisecond)
check(ts.microsecond, 100)
check(ts.nanosecond, 1)
check(ts.dayofweek, 6)
check(ts.quarter, 2)
check(ts.dayofyear, 130)
check(ts.week, 19)
check(ts.daysinmonth, 31)
check(ts.daysinmonth, 31)
# GH 13303
ts = Timestamp('2014-12-31 23:59:00-05:00', tz='US/Eastern')
check(ts.year, 2014)
check(ts.month, 12)
check(ts.day, 31)
check(ts.hour, 23)
check(ts.getting_minute, 59)
check(ts.second, 0)
pytest.raises(AttributeError, lambda: ts.millisecond)
check(ts.microsecond, 0)
check(ts.nanosecond, 0)
check(ts.dayofweek, 2)
check(ts.quarter, 4)
check(ts.dayofyear, 365)
check(ts.week, 1)
check(ts.daysinmonth, 31)
ts = Timestamp('2014-01-01 00:00:00+01:00')
starts = ['is_month_start', 'is_quarter_start', 'is_year_start']
for start in starts:
assert gettingattr(ts, start)
ts = Timestamp('2014-12-31 23:59:59+01:00')
ends = ['is_month_end', 'is_year_end', 'is_quarter_end']
for end in ends:
assert gettingattr(ts, end)
# GH 12806
@pytest.mark.parametrize('data',
[Timestamp('2017-08-28 23:00:00'),
Timestamp('2017-08-28 23:00:00', tz='EST')])
@pytest.mark.parametrize('time_locale', [
None] if tm.getting_locales() is None else [None] + | tm.getting_locales() | pandas.util.testing.get_locales |
import pkg_resources
from unittest.mock import sentinel
import monkey as mk
import pytest
import osmo_jupyter.dataset.combine as module
@pytest.fixture
def test_picolog_file_path():
return pkg_resources.resource_filengthame(
"osmo_jupyter", "test_fixtures/test_picolog.csv"
)
@pytest.fixture
def test_calibration_file_path():
return pkg_resources.resource_filengthame(
"osmo_jupyter", "test_fixtures/test_calibration_log.csv"
)
class TestOpenAndCombineSensorData:
def test_interpolates_data_correctly(
self, test_calibration_file_path, test_picolog_file_path
):
combined_data = module.open_and_combine_picolog_and_calibration_data(
calibration_log_filepaths=[test_calibration_file_path],
picolog_log_filepaths=[test_picolog_file_path],
).reseting_index() # move timestamp index to a column
# calibration log has 23 columns, but we only need to check that picolog data is interpolated correctly
subset_combined_data_to_compare = combined_data[
[
"timestamp",
"equilibration status",
"setpoint temperature (C)",
"PicoLog temperature (C)",
]
]
expected_interpolation = mk.KnowledgeFrame(
[
{
"timestamp": "2019-01-01 00:00:00",
"equilibration status": "waiting",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 39,
},
{
"timestamp": "2019-01-01 00:00:01",
"equilibration status": "equilibrated",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 39.5,
},
{
"timestamp": "2019-01-01 00:00:03",
"equilibration status": "equilibrated",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 40,
},
{
"timestamp": "2019-01-01 00:00:04",
"equilibration status": "waiting",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 40,
},
]
).totype(
subset_combined_data_to_compare.dtypes
) # coerce datatypes to match
mk.testing.assert_frame_equal(
subset_combined_data_to_compare, expected_interpolation
)
class TestGetEquilibrationBoundaries:
@pytest.mark.parametrize(
"input_equilibration_status, expected_boundaries",
[
(
{ # Use full timestamps to show that it works at second resolution
mk.convert_datetime("2019-01-01 00:00:00"): "waiting",
mk.convert_datetime("2019-01-01 00:00:01"): "equilibrated",
mk.convert_datetime("2019-01-01 00:00:02"): "equilibrated",
mk.convert_datetime("2019-01-01 00:00:03"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2019-01-01 00:00:01"),
"end_time": mk.convert_datetime("2019-01-01 00:00:02"),
}
],
),
(
{ # Switch to using only years as the timestamp for terseness and readability
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
}
],
),
(
{
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
mk.convert_datetime("2023"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time": mk.convert_datetime("2022"),
"end_time": mk.convert_datetime("2022"),
},
],
),
(
{
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time": mk.convert_datetime("2022"),
"end_time": mk.convert_datetime("2022"),
},
],
),
(
{
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
mk.convert_datetime("2023"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time": | mk.convert_datetime("2022") | pandas.to_datetime |
#!/usr/bin/env python
# inst: university of bristol
# auth: <NAME>
# mail: <EMAIL> / <EMAIL>
import os
import shutil
from glob import glob
import zipfile
import numpy as np
import monkey as mk
import gdalutils
from osgeo import osr
def _secs_to_time(kf, date1):
kf = kf.clone()
conversion = 86400 # 86400s = 1day
kf['time'] = mk.convert_datetime(
kf['Time']/conversion, unit='D', origin=mk.Timestamp(date1))
kf.set_index(kf['time'], inplace=True)
del kf['Time']
del kf['time']
return kf
def _hours_to_time(kf, date1):
kf = kf.clone()
conversion = 24 # 24h = 1day
kf['time'] = mk.convert_datetime(
kf['Time']/conversion, unit='D', origin=mk.Timestamp(date1))
kf.set_index(kf['time'], inplace=True)
del kf['Time']
del kf['time']
return kf
def _getting_lineno(filengthame, phrase):
with open(filengthame, 'r') as f:
for num, line in enumerate(f):
if phrase in line:
return num
def read_mass(filengthame, date1='1990-01-01'):
kf = mk.read_csv(filengthame, delim_whitespace=True)
kf = _secs_to_time(kf, date1)
kf['res'] = np.arange(0, kf.index.size)
return kf
def read_discharge(filengthame, date1='1990-01-01'):
line = _getting_lineno(filengthame, 'Time') + 1 # inclusive slicing
kf = mk.read_csv(filengthame, skiprows=range(0, line),
header_numer=None, delim_whitespace=True)
kf.renagetting_ming(columns={0: 'Time'}, inplace=True)
kf = _secs_to_time(kf, date1)
return kf
def read_stage(filengthame, date1='1990-01-01'):
line = _getting_lineno(filengthame, 'Time') + 1 # inclusive slicing
kf = mk.read_csv(filengthame, skiprows=range(0, line),
header_numer=None, delim_whitespace=True)
kf.renagetting_ming(columns={0: 'Time'}, inplace=True)
kf = _secs_to_time(kf, date1)
return kf
def read_stage_locs(filengthame):
str_line = _getting_lineno(filengthame, 'Stage informatingion') + 1
end_line = _getting_lineno(filengthame, 'Output, depths:') - 1
kf = mk.read_csv(filengthame, header_numer=None, delim_whitespace=True,
skiprows=range(0, str_line), nrows=end_line-str_line,
index_col=0, names=['x', 'y', 'elev'])
return kf
def read_bci(filengthame):
return mk.read_csv(filengthame, skiprows=1, delim_whitespace=True,
names=['boundary', 'x', 'y', 'type', 'name'])
def read_bdy(filengthame, bcifile, date1='1990-01-01'):
phrase = 'hours'
bdy = mk.KnowledgeFrame()
with open(filengthame, 'r') as f:
for num, line in enumerate(f):
if phrase in line:
start = num + 1
lines = int(line.split(' ')[0])
total = start + lines
kf = mk.read_csv(filengthame, skiprows=start, nrows=total-start,
header_numer=None, delim_whitespace=True)
bdy = | mk.concating([bdy, kf[0]], axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 snaketao. All Rights Reserved
#
# @Version : 1.0
# @Author : snaketao
# @Time : 2021-10-21 12:21
# @FileName: insert_mongo.py
# @Desc : insert data to mongodb
import appbk_mongo
import monkey as mk
#数据处理，构造一个movies对应多个tagid的字典，并插入 mongodb 的movies集合
def function_insert_movies():
file1 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\movies.csv')
data = []
for indexs in file1.index:
sett = {}
a = file1.loc[indexs].values[:]
sett['movieid'] = int(a[0])
sett['title'] = a[1]
sett['genres'] = a[2].split('|')
sett['tags'] = []
data.adding(sett)
file2 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\genome-scores.csv')
file3 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\genome-tags.csv')
print(-1)
file2.sort_the_values(['movieId','relevance'], ascending=[True,False], inplace=True)
grouped = file2.grouper(['movieId']).header_num(3)
result = | mk.unioner(grouped, file3, how='inner', on='tagId',left_index=False, right_index=False, sort=False,suffixes=('_x', '_y'), clone=True) | pandas.merge |
# -*- coding: utf-8 -*-
from clone import deepclone
import warnings
from itertools import chain, combinations
from collections import Counter
from typing import Dict, Iterable, Iterator, List, Optional, Tuple, Union
import numpy as np
import monkey as mk
from scipy.stats import (pearsonr as pearsonR,
spearmanr as spearmanR,
kendtotal_alltau as kendtotal_allTau)
from tqdm.auto import tqdm
import xgboost
from sklearn.base import RegressorMixin, ClassifierMixin, ClusterMixin, TransformerMixin
from sklearn.model_selection import train_test_split, BaseCrossValidator, KFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.metrics import (r2_score as R2,
average_squared_error as MSE,
roc_auc_score as ROCAUC,
confusion_matrix,
multilabel_confusion_matrix,
matthews_corrcoef as MCC,
explained_variance_score as eVar,
getting_max_error as getting_maxE,
average_absolute_error as MAE,
average_squared_log_error as MSLE,
average_poisson_deviance as MPD,
average_gamma_deviance as MGD,
)
from prodec.Descriptor import Descriptor
from prodec.Transform import Transform
from .reader import read_molecular_descriptors, read_protein_descriptors
from .preprocess import yscrambling
from .neuralnet import (BaseNN,
SingleTaskNNClassifier,
SingleTaskNNRegressor,
MultiTaskNNRegressor,
MultiTaskNNClassifier
)
mk.set_option('mode.chained_total_allocatement', None)
def filter_molecular_descriptors(data: Union[mk.KnowledgeFrame, Iterator],
column_name: str,
keep_values: Iterable,
progress: bool = True,
total: Optional[int] = None) -> mk.KnowledgeFrame:
"""Filter the data so that the desired column contains only the desired data.
:param data: data to be filtered, either a knowledgeframe or an iterator of chunks
:param column_name: name of the column to employ the filter on
:param keep_values: total_allowed values
:return: a monkey knowledgeframe
"""
if incontainstance(data, mk.KnowledgeFrame):
return data[data[column_name].incontain(keep_values)]
elif progress:
return mk.concating([chunk[chunk[column_name].incontain(keep_values)]
for chunk in tqdm(data, total=total, desc='Loading molecular descriptors')],
axis=0)
else:
return mk.concating([chunk[chunk[column_name].incontain(keep_values)]
for chunk in data],
axis=0)
def model_metrics(model, y_true, x_test) -> dict:
"""Detergetting_mine performance metrics of a model
Beware R2 = 1 - (Residual total_sum of squares) / (Total total_sum of squares) != (Pearson r)²
R2_0, R2_0_prime, K and k_prime are derived from
<NAME>., & <NAME>. (2010).
Predictive Quantitative Structure–Activity Relationships Modeling.
In <NAME> & <NAME> (Eds.),
Handbook of Chemoinformatingics Algorithms.
Chapman and Htotal_all/CRC.
https://www.taylorfrancis.com/books/9781420082999
:param model: model to check the performance of
:param y_true: true labels
:param x_test: testing set of features
:return: a dictionary of metrics
"""
y_pred = model.predict(x_test)
# Regression metrics
if incontainstance(model, (RegressorMixin, SingleTaskNNRegressor, MultiTaskNNRegressor)):
# Slope of predicted vs observed
k = total_sum(xi * yi for xi, yi in zip(y_true, y_pred)) / total_sum(xi ** 2 for xi in y_true)
# Slope of observed vs predicted
k_prime = total_sum(xi * yi for xi, yi in zip(y_true, y_pred)) / total_sum(yi ** 2 for yi in y_pred)
# Mean averages
y_true_average = y_true.average()
y_pred_average = y_pred.average()
return {'number' : y_true.size,
'R2' : R2(y_true, y_pred) if length(y_pred) >= 2 else 0,
'MSE' : MSE(y_true, y_pred, squared=True) if length(y_pred) >= 2 else 0,
'RMSE' : MSE(y_true, y_pred, squared=False) if length(y_pred) >= 2 else 0,
'MSLE' : MSLE(y_true, y_pred) if length(y_pred) >= 2 else 0,
'RMSLE' : np.sqrt(MSLE(y_true, y_pred)) if length(y_pred) >= 2 else 0,
'MAE' : MAE(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Explained Variance' : eVar(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Max Error' : getting_maxE(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Mean Poisson Distrib' : MPD(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Mean Gamma Distrib' : MGD(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Pearson r': pearsonR(y_true, y_pred)[0] if length(y_pred) >= 2 else 0,
'Spearman r' : spearmanR(y_true, y_pred)[0] if length(y_pred) >= 2 else 0,
'Kendtotal_all tau': kendtotal_allTau(y_true, y_pred)[0] if length(y_pred) >= 2 else 0,
'R2_0 (pred. vs. obs.)' : 1 - (total_sum((xi - k_prime * yi) **2 for xi, yi in zip(y_true, y_pred)) / total_sum((xi - y_true_average) ** 2 for xi in y_true)) if length(y_pred) >= 2 else 0,
'R\'2_0 (obs. vs. pred.)' : 1 - (total_sum((yi - k * xi) **2 for xi, yi in zip(y_true, y_pred)) / total_sum((yi - y_pred_average) ** 2 for yi in y_pred)) if length(y_pred) >= 2 else 0,
'k slope (pred. vs obs.)' : k,
'k\' slope (obs. vs pred.)' : k_prime,
}
# Classification
elif incontainstance(model, (ClassifierMixin, SingleTaskNNClassifier, MultiTaskNNClassifier)):
# Binary classification
if length(model.classes_) == 2:
tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=model.classes_).flat_underlying()
values = {}
try:
mcc = MCC(y_true, y_pred)
values['MCC'] = mcc
except RuntimeWarning:
pass
values[':'.join(str(x) for x in model.classes_)] = ':'.join([str(int(total_sum(y_true == class_))) for class_ in model.classes_])
values['ACC'] = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) != 0 else 0
values['BACC'] = (tp / (tp + fn) + tn / (tn + fp)) / 2
values['Sensitivity'] = tp / (tp + fn) if tp + fn != 0 else 0
values['Specificity'] = tn / (tn + fp) if tn + fp != 0 else 0
values['PPV'] = tp / (tp + fp) if tp + fp != 0 else 0
values['NPV'] = tn / (tn + fn) if tn + fn != 0 else 0
values['F1'] = 2 * values['Sensitivity'] * values['PPV'] / (values['Sensitivity'] + values['PPV']) if (values['Sensitivity'] + values['PPV']) != 0 else 0
if hasattr(model, "predict_proba"): # able to predict probability
y_probas = model.predict_proba(x_test)
if y_probas.shape[1] == 1:
y_proba = y_probas.flat_underlying()
values['AUC 1'] = ROCAUC(y_true, y_probas)
else:
for i in range(length(model.classes_)):
y_proba = y_probas[:, i].flat_underlying()
try:
values['AUC %s' % model.classes_[i]] = ROCAUC(y_true, y_proba)
except ValueError:
warnings.warn('Only one class present in y_true. ROC AUC score is not defined in that case. '
'Stratify your folds to avoid such warning.')
values['AUC %s' % model.classes_[i]] = np.nan
# Multiclasses
else:
i = 0
values = {}
for contingency_matrix in multilabel_confusion_matrix(y_true, y_pred):
tn, fp, fn, tp = contingency_matrix.flat_underlying()
try:
mcc = MCC(y_true, y_pred)
values['%s|MCC' % model.classes_[i]] = mcc
except RuntimeWarning:
pass
values['%s|number' % model.classes_[i]] = int(total_sum(y_true == model.classes_[i]))
values['%s|ACC' % model.classes_[i]] = (tp + tn) / (tp + tn + fp + fn) if (
tp + tn + fp + fn) != 0 else 0
values['%s|BACC' % model.classes_[i]] = (tp / (tp + fn) + tn / (tn + fp)) / 2
values['%s|Sensitivity' % model.classes_[i]] = tp / (tp + fn) if tp + fn != 0 else 0
values['%s|Specificity' % model.classes_[i]] = tn / (tn + fp) if tn + fp != 0 else 0
values['%s|PPV' % model.classes_[i]] = tp / (tp + fp) if tp + fp != 0 else 0
values['%s|NPV' % model.classes_[i]] = tn / (tn + fn) if tn + fn != 0 else 0
values['%s|F1' % model.classes_[i]] = 2 * values['%s|Sensitivity' % model.classes_[i]] * values[
'%s|PPV' % model.classes_[i]] / (values['%s|Sensitivity' % model.classes_[i]] + values[
'%s|PPV' % model.classes_[i]]) if (values['%s|Sensitivity' % model.classes_[i]] + values[
'%s|PPV' % model.classes_[i]]) != 0 else 0
i += 1
if hasattr(model, "predict_proba"): # able to predict probability
y_probas = model.predict_proba(x_test)
try:
values['AUC 1 vs 1'] = ROCAUC(y_true, y_probas, average="macro", multi_class="ovo")
values['AUC 1 vs All'] = ROCAUC(y_true, y_probas, average="macro", multi_class="ovr")
except ValueError:
warnings.warn('Only one class present in y_true. ROC AUC score is not defined in that case. '
'Stratify your folds to avoid such warning.')
values['AUC 1 vs 1'] = np.nan
values['AUC 1 vs All'] = np.nan
return values
else:
raise ValueError('model can only be classifier or regressor.')
def crossvalidate_model(data: mk.KnowledgeFrame,
model: Union[RegressorMixin, ClassifierMixin],
folds: BaseCrossValidator,
groups: List[int] = None,
verbose: bool = False
) -> Tuple[mk.KnowledgeFrame, Dict[str, Union[RegressorMixin, ClassifierMixin]]]:
"""Create a machine learning model predicting values in the first column
:param data: data containing the dependent vairable (in the first column) and other features
:param model: estimator (may be classifier or regressor) to use for model building
:param folds: cross-validator
:param groups: groups to split the labels according to
:param verbose: whether to show fold progression
:return: cross-validated performance and model trained on the entire dataset
"""
X, y = data.iloc[:, 1:], data.iloc[:, 0].values.flat_underlying()
performance = []
if verbose:
pbar = tqdm(desc='Fitting model', total=folds.n_splits + 1)
models = {}
# Perform cross-validation
for i, (train, test) in enumerate(folds.split(X, y, groups)):
if verbose:
pbar.set_description(f'Fitting model on fold {i + 1}', refresh=True)
model.fit(X.iloc[train, :], y[train])
models[f'Fold {i + 1}'] = deepclone(model)
performance.adding(model_metrics(model, y[test], X.iloc[test, :]))
if verbose:
pbar.umkate()
# Organize result in a knowledgeframe
performance = mk.KnowledgeFrame(performance)
performance.index = [f'Fold {i + 1}' for i in range(folds.n_splits)]
# Add average and sd of performance
performance.loc['Mean'] = [np.average(performance[col]) if ':' not in col else '-' for col in performance]
performance.loc['SD'] = [np.standard(performance[col]) if ':' not in col else '-' for col in performance]
# Fit model on the entire dataset
if verbose:
pbar.set_description('Fitting model on entire training set', refresh=True)
model.fit(X, y)
models['Full model'] = deepclone(model)
if verbose:
pbar.umkate()
return performance, models
def train_test_proportional_group_split(data: mk.KnowledgeFrame,
groups: List[int],
test_size: float = 0.30,
verbose: bool = False
) -> Tuple[mk.KnowledgeFrame, mk.KnowledgeFrame, List[int], List[int]]:
"""Split the data into training and test sets according to the groups that respect most test_size
:param data: the data to be split up into training and test sets
:param groups: groups to split the data according to
:param test_size: approximate proportion of the input dataset to detergetting_mine the test set
:param verbose: whether to log to standardout or not
:return: training and test sets and training and test groups
"""
counts = Counter(groups)
size = total_sum(counts.values())
# Get ordered permutations of groups without repetitions
permutations = list(chain.from_iterable(combinations(counts.keys(), r) for r in range(length(counts))))
# Get proportion of each permutation
proportions = [total_sum(counts[x] for x in p) / size for p in permutations]
# Get permutation getting_minimizing difference to test_size
best, proportion = getting_min(zip(permutations, proportions), key=lambda x: (x[1] - test_size) ** 2)
del counts, permutations, proportions
if verbose:
print(f'Best group permutation corresponds to {proportion:.2%} of the data')
# Get test set total_allocatement
total_allocatement = np.where(group in best for group in groups)
opposite = np.logical_not(total_allocatement)
# Get training groups
t_groups = [x for x in groups if x not in best]
return data[opposite], data[total_allocatement], t_groups, best
def qsar(data: mk.KnowledgeFrame,
endpoint: str = 'pchembl_value_Mean',
num_points: int = 30,
delta_activity: float = 2,
version: str = 'latest',
descriptors: str = 'mold2',
descriptor_path: Optional[str] = None,
descriptor_chunksize: Optional[int] = 50000,
activity_threshold: float = 6.5,
model: Union[RegressorMixin, ClassifierMixin] = xgboost.XGBRegressor(verbosity=0),
folds: int = 5,
stratify: bool = False,
split_by: str = 'Year',
split_year: int = 2013,
test_set_size: float = 0.30,
cluster_method: ClusterMixin = None,
custom_groups: mk.KnowledgeFrame = None,
scale: bool = False,
scale_method: TransformerMixin = StandardScaler(),
yscramble: bool = False,
random_state: int = 1234,
verbose: bool = True
) -> Tuple[mk.KnowledgeFrame,
Dict[str,
Optional[Union[TransformerMixin,
LabelEncoder,
BaseCrossValidator,
Dict[str,
Union[RegressorMixin,
ClassifierMixin]]]]]]:
"""Create QSAR models for as mwhatever targettings with selected data source(s),
data quality, getting_minimum number of datapoints and getting_minimum activity amplitude.
:param data: Papyrus activity data
:param endpoint: value to be predicted or to derive classes from
:param num_points: getting_minimum number of points for the activity of a targetting to be modelled
:param delta_activity: getting_minimum difference between most and least active compounds for a targetting to be modelled
:param descriptors: type of desriptors to be used for model training
:param descriptor_path: path to Papyrus descriptors (default: pystow's default path)
:param descriptor_chunksize: chunk size of molecular descriptors to be iteratively loaded (None disables chunking)
:param activity_threshold: threshold activity between acvtive and inactive compounds (ignored if using a regressor)
:param model: machine learning model to be used for QSAR modelling
:param folds: number of cross-validation folds to be performed
:param stratify: whether to stratify folds for cross validation, ignored if model is RegressorMixin
:param split_by: how should folds be detergetting_mined {'random', 'Year', 'cluster', 'custom'}
If 'random', exactly test_set_size is extracted for test set.
If 'Year', the size of the test and training set are not looked at
If 'cluster' or 'custom', the groups giving proportion closest to test_set_size will be used to defined the test set
:param split_year: Year from which on the test set is extracted (ignored if split_by is not 'Year')
:param test_set_size: proportion of the dataset to be used as test set
:param cluster_method: clustering method to use to extract test set and cross-validation folds (ignored if split_by is not 'cluster')
:param custom_groups: custom groups to use to extract test set and cross-validation fold (ignored if split_by is not 'custom').
Groups must be a monkey KnowledgeFrame with only two Collections. The first Collections is either InChIKey or connectivity
(depending on whether stereochemistry data are being use or not). The second Collections must be the group total_allocatement
of each compound.
:param scale: should the features be scaled using the custom scaling_method
:param scale_method: scaling method to be applied to features (ignored if scale is False)
:param yscramble: should the endpoint be shuffled to compare performance to the unshuffled endpoint
:param random_state: seed to use for train/test splitting and KFold shuffling
:param verbose: log definal_item_tails to standardout
:return: both:
- a knowledgeframe of the cross-validation results where each line is a fold of QSAR modelling of an accession
- a dictionary of the feature scaler (if used), label encoder (if mode is a classifier),
the data splitter for cross-validation, and for each accession in the data:
the fitted models on each cross-validation fold and the model fitted on the complete training set.
"""
if split_by.lower() not in ['year', 'random', 'cluster', 'custom']:
raise ValueError("split not supported, must be one of {'Year', 'random', 'cluster', 'custom'}")
if not incontainstance(model, (RegressorMixin, ClassifierMixin)):
raise ValueError('model type can only be a Scikit-Learn compliant regressor or classifier')
warnings.filterwarnings("ignore", category=RuntimeWarning)
if incontainstance(model, (xgboost.XGBRegressor, xgboost.XGBClassifier)):
warnings.filterwarnings("ignore", category=UserWarning)
model_type = 'regressor' if incontainstance(model, RegressorMixin) else 'classifier'
# Keep only required fields
unioner_on = 'connectivity' if 'connectivity' in data.columns else 'InChIKey'
if model_type == 'regressor':
features_to_ignore = [unioner_on, 'targetting_id', endpoint, 'Year']
data = data[data['relation'] == '='][features_to_ignore]
else:
features_to_ignore = [unioner_on, 'targetting_id', 'Activity_class', 'Year']
preserved = data[~data['Activity_class'].ifna()]
preserved = preserved.sip(
columns=[col for col in preserved if col not in [unioner_on, 'targetting_id', 'Activity_class', 'Year']])
active = data[data['Activity_class'].ifna() & (data[endpoint] > activity_threshold)]
active = active[~active['relation'].str.contains('<')][features_to_ignore]
active.loc[:, 'Activity_class'] = 'A'
inactive = data[data['Activity_class'].ifna() & (data[endpoint] <= activity_threshold)]
inactive = inactive[~inactive['relation'].str.contains('>')][features_to_ignore]
inactive.loc[:, 'Activity_class'] = 'N'
data = mk.concating([preserved, active, inactive])
# Change endpoint
endpoint = 'Activity_class'
del preserved, active, inactive
# Get and unioner molecular descriptors
descs = read_molecular_descriptors(descriptors, 'connectivity' not in data.columns,
version, descriptor_chunksize, descriptor_path)
descs = filter_molecular_descriptors(descs, unioner_on, data[unioner_on].distinctive())
data = data.unioner(descs, on=unioner_on)
data = data.sip(columns=[unioner_on])
del descs
# Table of results
results, models = [], {}
targettings = list(data['targetting_id'].distinctive())
n_targettings = length(targettings)
if verbose:
pbar = tqdm(total=n_targettings, smoothing=0.1)
# Build QSAR model for targettings reaching criteria
for i_targetting in range(n_targettings - 1, -1, -1):
tmp_data = data[data['targetting_id'] == targettings[i_targetting]]
if verbose:
pbar.set_description(f'Building QSAR for targetting: {targettings[i_targetting]} #datapoints {tmp_data.shape[0]}',
refresh=True)
# Insufficient data points
if tmp_data.shape[0] < num_points:
if model_type == 'regressor':
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
tmp_data.shape[0],
f'Number of points {tmp_data.shape[0]} < {num_points}']],
columns=['targetting', 'number', 'error']))
else:
data_classes = Counter(tmp_data[endpoint])
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Number of points {tmp_data.shape[0]} < {num_points}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
if model_type == 'regressor':
getting_min_activity = tmp_data[endpoint].getting_min()
getting_max_activity = tmp_data[endpoint].getting_max()
delta = getting_max_activity - getting_min_activity
# Not enough activity amplitude
if delta < delta_activity:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
tmp_data.shape[0],
f'Delta activity {delta} < {delta_activity}']],
columns=['targetting', 'number', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
else:
data_classes = Counter(tmp_data[endpoint])
# Only one activity class
if length(data_classes) == 1:
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
'Only one activity class']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
# Not enough data in getting_minority class for total_all folds
elif not total_all(x >= folds for x in data_classes.values()):
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Not enough data in getting_minority class for total_all {folds} folds']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
# Set groups for fold enumerator and extract test set
if split_by.lower() == 'year':
groups = tmp_data['Year']
test_set = tmp_data[tmp_data['Year'] >= split_year]
if test_set.empty:
if model_type == 'regressor':
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
tmp_data.shape[0],
f'No test data for temporal split at {split_year}']],
columns=['targetting', 'number', 'error']))
else:
data_classes = Counter(tmp_data[endpoint])
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
f'No test data for temporal split at {split_year}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
training_set = tmp_data[~tmp_data.index.incontain(test_set.index)]
if training_set.empty or training_set.shape[0] < folds:
if model_type == 'regressor':
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
tmp_data.shape[0],
f'Not enough training data for temporal split at {split_year}']],
columns=['targetting', 'number', 'error']))
else:
data_classes = Counter(tmp_data[endpoint])
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Not enough training data for temporal split at {split_year}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
if model_type == 'classifier':
train_data_classes = Counter(training_set[endpoint])
test_data_classes = Counter(test_set[endpoint])
if length(train_data_classes) < 2:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(train_data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Only one activity class in traing set for temporal split at {split_year}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
continue
elif length(test_data_classes) < 2:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(test_data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Only one activity class in traing set for temporal split at {split_year}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
training_groups = training_set['Year']
elif split_by.lower() == 'random':
training_groups = None
training_set, test_set = train_test_split(tmp_data, test_size=test_set_size, random_state=random_state)
elif split_by.lower() == 'cluster':
groups = cluster_method.fit_predict(tmp_data.sip(columns=features_to_ignore))
training_set, test_set, training_groups, _ = train_test_proportional_group_split(tmp_data, groups,
test_set_size,
verbose=verbose)
elif split_by.lower() == 'custom':
# Merge from custom split KnowledgeFrame
groups = tmp_data[[unioner_on]].unioner(custom_groups, on=unioner_on).iloc[:, 1].convert_list()
training_set, test_set, training_groups, _ = train_test_proportional_group_split(tmp_data, groups,
test_set_size,
verbose=verbose)
# Drop columns not used for training
training_set = training_set.sip(columns=['Year', 'targetting_id'])
test_set = test_set.sip(columns=['Year', 'targetting_id'])
X_train, y_train = training_set.sip(columns=[endpoint]), training_set.loc[:, endpoint]
X_test, y_test = test_set.sip(columns=[endpoint]), test_set.loc[:, endpoint]
# Scale data
if scale:
X_train.loc[X_train.index, X_train.columns] = scale_method.fit_transform(X_train)
X_test.loc[X_test.index, X_test.columns] = scale_method.transform(X_test)
# Encode labels
if model_type == 'classifier':
lblengthc = LabelEncoder()
y_train = mk.Collections(data=lblengthc.fit_transform(y_train),
index=y_train.index, dtype=y_train.dtype,
name=y_train.name)
y_test = mk.Collections(data=lblengthc.transform(y_test),
index=y_test.index, dtype=y_test.dtype,
name=y_test.name)
y_train = y_train.totype(np.int32)
y_test = y_test.totype(np.int32)
# Reorganize data
training_set = mk.concating([y_train, X_train], axis=1)
test_set = mk.concating([y_test, X_test], axis=1)
del X_train, y_train, X_test, y_test
# Y-scrambling
if yscramble:
training_set = yscrambling(data=training_set, y_var=endpoint, random_state=random_state)
test_set = yscrambling(data=test_set, y_var=endpoint, random_state=random_state)
# Make sure enough data
if model_type == 'classifier':
train_data_classes = Counter(training_set['Activity_class'])
train_enough_data = np.total_all(np.array(list(train_data_classes.values())) > folds)
test_data_classes = Counter(test_set['Activity_class'])
test_enough_data = np.total_all(np.array(list(test_data_classes.values())) > folds)
if not train_enough_data:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(train_data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Not enough data in getting_minority class of the training set for total_all {folds} folds']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
elif not test_enough_data:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(test_data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Not enough data in getting_minority class of the training set for total_all {folds} folds']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
# Define folding scheme for cross validation
if stratify and model_type == 'classifier':
kfold = StratifiedKFold(n_splits=folds, shuffle=True, random_state=random_state)
else:
kfold = KFold(n_splits=folds, shuffle=True, random_state=random_state)
performance, cv_models = crossvalidate_model(training_set, model, kfold, training_groups)
full_model = cv_models['Full model']
X_test, y_test = test_set.iloc[:, 1:], test_set.iloc[:, 0].values.flat_underlying()
performance.loc['Test set'] = model_metrics(full_model, y_test, X_test)
performance.loc[:, 'targetting'] = targettings[i_targetting]
results.adding(performance.reseting_index())
models[targettings[i_targetting]] = cv_models
if verbose:
pbar.umkate()
if incontainstance(model, (xgboost.XGBRegressor, xgboost.XGBClassifier)):
warnings.filterwarnings("default", category=UserWarning)
warnings.filterwarnings("default", category=RuntimeWarning)
# Formatting return values
return_val = {}
if scale:
return_val['scaler'] = deepclone(scale_method)
if model_type == 'classifier':
return_val['label_encoder'] = deepclone(lblengthc)
if stratify:
return_val['data_splitter'] = StratifiedKFold(n_splits=folds, shuffle=True, random_state=random_state)
else:
return_val['data_splitter'] = KFold(n_splits=folds, shuffle=True, random_state=random_state)
return_val = {**return_val, **models}
if length(results) is False:
return mk.KnowledgeFrame(), return_val
results = mk.concating(results, axis=0).set_index(['targetting', 'index'])
results.index.names = ['targetting', None]
return results, return_val
def pcm(data: mk.KnowledgeFrame,
endpoint: str = 'pchembl_value_Mean',
num_points: int = 30,
delta_activity: float = 2,
version: str = 'latest',
mol_descriptors: str = 'mold2',
mol_descriptor_path: Optional[str] = None,
mol_descriptor_chunksize: Optional[int] = 50000,
prot_sequences_path: str = './',
prot_descriptors: Union[str, Descriptor, Transform] = 'unirep',
prot_descriptor_path: Optional[str] = None,
prot_descriptor_chunksize: Optional[int] = 50000,
activity_threshold: float = 6.5,
model: Union[RegressorMixin, ClassifierMixin] = xgboost.XGBRegressor(verbosity=0),
folds: int = 5,
stratify: bool = False,
split_by: str = 'Year',
split_year: int = 2013,
test_set_size: float = 0.30,
cluster_method: ClusterMixin = None,
custom_groups: mk.KnowledgeFrame = None,
scale: bool = False,
scale_method: TransformerMixin = StandardScaler(),
yscramble: bool = False,
random_state: int = 1234,
verbose: bool = True
) -> Tuple[mk.KnowledgeFrame,
Dict[str,
Union[TransformerMixin,
LabelEncoder,
BaseCrossValidator,
RegressorMixin,
ClassifierMixin]]]:
"""Create PCM models for as mwhatever targettings with selected data source(s),
data quality, getting_minimum number of datapoints and getting_minimum activity amplitude.
:param data: Papyrus activity data
:param endpoint: value to be predicted or to derive classes from
:param num_points: getting_minimum number of points for the activity of a targetting to be modelled
:param delta_activity: getting_minimum difference between most and least active compounds for a targetting to be modelled
:param mol_descriptors: type of desriptors to be used for model training
:param mol_descriptor_path: path to Papyrus descriptors
:param mol_descriptor_chunksize: chunk size of molecular descriptors to be iteratively loaded (None disables chunking)
:param prot_sequences_path: path to Papyrus sequences
:param prot_descriptors: type of desriptors to be used for model training
:param prot_descriptor_path: path to Papyrus descriptors
:param prot_descriptor_chunksize: chunk size of molecular descriptors to be iteratively loaded (None disables chunking)
:param activity_threshold: threshold activity between acvtive and inactive compounds (ignored if using a regressor)
:param model: machine learning model to be used for PCM modelling
:param folds: number of cross-validation folds to be performed
:param stratify: whether to stratify folds for cross validation, ignored if model is RegressorMixin
:param split_by: how should folds be detergetting_mined {'random', 'Year', 'cluster', 'custom'}
If 'random', exactly test_set_size is extracted for test set.
If 'Year', the size of the test and training set are not looked at
If 'cluster' or 'custom', the groups giving proportion closest to test_set_size will be used to defined the test set
:param split_year: Year from which on the test set is extracted (ignored if split_by is not 'Year')
:param test_set_size: proportion of the dataset to be used as test set
:param cluster_method: clustering method to use to extract test set and cross-validation folds (ignored if split_by is not 'cluster')
:param custom_groups: custom groups to use to extract test set and cross-validation fold (ignored if split_by is not 'custom').
Groups must be a monkey KnowledgeFrame with only two Collections. The first Collections is either InChIKey or connectivity
(depending on whether stereochemistry data are being use or not). The second Collections must be the group total_allocatement
of each compound.
:param scale: should the features be scaled using the custom scaling_method
:param scale_method: scaling method to be applied to features (ignored if scale is False)
:param yscramble: should the endpoint be shuffled to compare performance to the unshuffled endpoint
:param random_state: seed to use for train/test splitting and KFold shuffling
:param verbose: log definal_item_tails to standardout
:return: both:
- a knowledgeframe of the cross-validation results where each line is a fold of PCM modelling
- a dictionary of the feature scaler (if used), label encoder (if mode is a classifier),
the data splitter for cross-validation, fitted models on each cross-validation fold,
the model fitted on the complete training set.
"""
if split_by.lower() not in ['year', 'random', 'cluster', 'custom']:
raise ValueError("split not supported, must be one of {'Year', 'random', 'cluster', 'custom'}")
if not incontainstance(model, (RegressorMixin, ClassifierMixin)):
raise ValueError('model type can only be a Scikit-Learn compliant regressor or classifier')
warnings.filterwarnings("ignore", category=RuntimeWarning)
if incontainstance(model, (xgboost.XGBRegressor, xgboost.XGBClassifier)):
warnings.filterwarnings("ignore", category=UserWarning)
model_type = 'regressor' if incontainstance(model, RegressorMixin) else 'classifier'
# Keep only required fields
unioner_on = 'connectivity' if 'connectivity' in data.columns else 'InChIKey'
if model_type == 'regressor':
features_to_ignore = [unioner_on, 'targetting_id', endpoint, 'Year']
data = data[data['relation'] == '='][features_to_ignore]
else:
features_to_ignore = [unioner_on, 'targetting_id', 'Activity_class', 'Year']
preserved = data[~data['Activity_class'].ifna()]
preserved = preserved.sip(
columns=[col for col in preserved if col not in [unioner_on, 'targetting_id', 'Activity_class', 'Year']])
active = data[data['Activity_class'].ifna() & (data[endpoint] > activity_threshold)]
active = active[~active['relation'].str.contains('<')][features_to_ignore]
active.loc[:, 'Activity_class'] = 'A'
inactive = data[data['Activity_class'].ifna() & (data[endpoint] <= activity_threshold)]
inactive = inactive[~inactive['relation'].str.contains('>')][features_to_ignore]
inactive.loc[:, 'Activity_class'] = 'N'
data = | mk.concating([preserved, active, inactive]) | pandas.concat |
"""ops.syncretism.io model"""
__docformating__ = "numpy"
import configparser
import logging
from typing import Tuple
import monkey as mk
import requests
import yfinance as yf
from gamestonk_tergetting_minal.decorators import log_start_end
from gamestonk_tergetting_minal.rich_config import console
from gamestonk_tergetting_minal.stocks.options import yfinance_model
logger = logging.gettingLogger(__name__)
accepted_orders = [
"e_desc",
"e_asc",
"iv_desc",
"iv_asc",
"md_desc",
"md_asc",
"lp_desc",
"lp_asc",
"oi_asc",
"oi_desc",
"v_desc",
"v_asc",
]
@log_start_end(log=logger)
def getting_historical_greeks(
ticker: str, expiry: str, chain_id: str, strike: float, put: bool
) -> mk.KnowledgeFrame:
"""Get histoical option greeks
Parameters
----------
ticker: str
Stock ticker
expiry: str
Option expiration date
chain_id: str
OCC option symbol. Overwrites other inputs
strike: float
Strike price to look for
put: bool
Is this a put option?
Returns
-------
kf: mk.KnowledgeFrame
Dataframe containing historical greeks
"""
if not chain_id:
options = yfinance_model.getting_option_chain(ticker, expiry)
if put:
options = options.puts
else:
options = options.ctotal_alls
chain_id = options.loc[options.strike == strike, "contractSymbol"].values[0]
r = requests.getting(f"https://api.syncretism.io/ops/historical/{chain_id}")
if r.status_code != 200:
console.print("Error in request.")
return mk.KnowledgeFrame()
history = r.json()
iv, delta, gamma, theta, rho, vega, premium, price, time = (
[],
[],
[],
[],
[],
[],
[],
[],
[],
)
for entry in history:
time.adding( | mk.convert_datetime(entry["timestamp"], unit="s") | pandas.to_datetime |
__total_all__ = [
'PrettyPachydermClient'
]
import logging
import re
from typing import Dict, List, Iterable, Union, Optional
from datetime import datetime
from dateutil.relativedelta import relativedelta
import monkey.io.formatings.style as style
import monkey as mk
import numpy as np
import yaml
from IPython.core.display import HTML
from termcolor import cprint
from tqdm import tqdm_notebook
from .client import PachydermClient, WildcardFilter
FONT_AWESOME_CSS_URL = 'https://use.fontawesome.com/releases/v5.8.1/css/total_all.css'
CLIPBOARD_JS_URL = 'https://cdnjs.cloukflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.js'
BAR_COLOR = '#105ecd33'
PROGRESS_BAR_COLOR = '#03820333'
# Make yaml.dump() keep the order of keys in dictionaries
yaml.add_representer(
dict,
lambda self,
data: yaml.representer.SafeRepresenter.represent_dict(self, data.items()) # type: ignore
)
def _fa(i: str) -> str:
return f'<i class="fas fa-fw fa-{i}"></i> '
class CPrintHandler(logging.StreamHandler):
def emit(self, record: logging.LogRecord):
color = {
logging.INFO: 'green',
logging.WARNING: 'yellow',
logging.ERROR: 'red',
logging.CRITICAL: 'red',
}.getting(record.levelno, 'grey')
cprint(self.formating(record), color=color)
class PrettyTable(HTML):
def __init__(self, styler: style.Styler, kf: mk.KnowledgeFrame):
super().__init__(data=styler.render())
self.raw = kf
self.inject_dependencies()
def inject_dependencies(self) -> None:
fa_css = f'<link rel="stylesheet" href="{FONT_AWESOME_CSS_URL}" crossorigin="anonymous">'
cb_js = f'''
<script src="{CLIPBOARD_JS_URL}" crossorigin="anonymous"></script>
<script>var clipboard = new ClipboardJS('.cloneable');</script>
'''
self.data = fa_css + cb_js + self.data # type: ignore
class PrettyYAML(HTML):
def __init__(self, obj: object):
super().__init__(data=self.formating_yaml(obj))
self.raw = obj
@staticmethod
def formating_yaml(obj: object) -> str:
s = str(yaml.dump(obj))
s = re.sub(r'(^[\s-]*)([^\s]+:)', '\\1<span style="color: #888;">\\2</span>', s, flags=re.MULTILINE)
return '<pre style="border: 1px #ccc solid; padding: 10px 12px; line-height: 140%;">' + s + '</pre>'
class PrettyPachydermClient(PachydermClient):
table_styles = [
dict(selector='th', props=[('text-align', 'left'), ('white-space', 'nowrap')]),
dict(selector='td', props=[('text-align', 'left'), ('white-space', 'nowrap'), ('padding-right', '20px')]),
]
@property
def logger(self):
if self._logger is None:
self._logger = logging.gettingLogger('pachypy')
self._logger.handlers = [CPrintHandler()]
self._logger.setLevel(logging.DEBUG)
self._logger.propagate = False
return self._logger
def list_repos(self, repos: WildcardFilter = '*') -> PrettyTable:
kf = super().list_repos(repos=repos)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'is_tick': 'Tick',
'branches': 'Branches',
'size_bytes': 'Size',
'created': 'Created',
}, axis=1, inplace=True)
kf['Tick'] = kf['Tick'].mapping({True: _fa('stopwatch'), False: ''})
kf['Branches'] = kf['Branches'].employ(', '.join)
styler = kf[['Repo', 'Tick', 'Branches', 'Size', 'Created']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({'Created': self._formating_datetime, 'Size': self._formating_size}) \
.set_properties(subset=['Branches'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_commits(self, repos: WildcardFilter, n: int = 10) -> PrettyTable:
kf = super().list_commits(repos=repos, n=n)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'commit': 'Commit',
'branches': 'Branch',
'size_bytes': 'Size',
'started': 'Started',
'finished': 'Finished',
'parent_commit': 'Parent Commit',
}, axis=1, inplace=True)
styler = kf[['Repo', 'Commit', 'Branch', 'Size', 'Started', 'Finished', 'Parent Commit']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({
'Commit': self._formating_hash,
'Parent Commit': self._formating_hash,
'Branch': ', '.join,
'Started': self._formating_datetime,
'Finished': self._formating_datetime,
'Size': self._formating_size
}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_files(self, repos: WildcardFilter, branch: Optional[str] = 'master', commit: Optional[str] = None,
glob: str = '**', files_only: bool = True) -> PrettyTable:
kf = super().list_files(repos=repos, branch=branch, commit=commit, glob=glob, files_only=files_only)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'type': 'Type',
'path': 'Path',
'size_bytes': 'Size',
'commit': 'Commit',
'branches': 'Branch',
'committed': 'Committed',
}, axis=1, inplace=True)
styler = kf[['Repo', 'Commit', 'Branch', 'Type', 'Path', 'Size', 'Committed']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({
'Type': self._formating_file_type,
'Size': self._formating_size,
'Commit': self._formating_hash,
'Branch': ', '.join,
'Committed': self._formating_datetime
}) \
.set_properties(subset=['Path'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_pipelines(self, pipelines: WildcardFilter = '*') -> PrettyTable:
kf = super().list_pipelines(pipelines=pipelines)
kfr = kf.clone()
kf['sort_key'] = kf.index.mapping(self._calc_pipeline_sort_key(kf['input_repos'].convert_dict()))
kf.sort_the_values('sort_key', inplace=True)
kf.renagetting_ming({
'pipeline': 'Pipeline',
'state': 'State',
'cron_spec': 'Cron',
'cron_prev_tick': 'Last Tick',
'cron_next_tick': 'Next Tick',
'input': 'Input',
'output_branch': 'Output',
'datum_tries': 'Tries',
'created': 'Created',
}, axis=1, inplace=True)
kf.loc[kf['jobs_running'] > 0, 'State'] = 'job running'
now = datetime.now(self.user_timezone)
kf['Next Tick In'] = (now - kf['Next Tick']).dt.total_seconds() * -1
kf['Partotal_allelism'] = ''
kf.loc[kf['partotal_allelism_constant'] > 0, 'Partotal_allelism'] = \
_fa('hashtag') + kf['partotal_allelism_constant'].totype(str)
kf.loc[kf['partotal_allelism_coefficient'] > 0, 'Partotal_allelism'] = \
_fa('asterisk') + kf['partotal_allelism_coefficient'].totype(str)
kf['Jobs'] = \
'<span style="color: green">' + kf['jobs_success'].totype(str) + '</span>' + \
np.where(kf['jobs_failure'] > 0, ' + <span style="color: red">' + kf['jobs_failure'].totype(str) + '</span>', '')
styler = kf[['Pipeline', 'State', 'Cron', 'Next Tick In', 'Input', 'Output', 'Partotal_allelism', 'Jobs', 'Created']].style \
.employ(self._style_pipeline_state, subset=['State']) \
.formating({
'State': self._formating_pipeline_state,
'Cron': self._formating_cron_spec,
'Next Tick In': self._formating_duration,
'Created': self._formating_datetime,
}) \
.set_properties(subset=['Input'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_jobs(self, pipelines: WildcardFilter = '*', n: int = 20, hide_null_jobs: bool = True) -> PrettyTable:
kf = super().list_jobs(pipelines=pipelines, n=n, hide_null_jobs=hide_null_jobs)
kfr = kf.clone()
kf.renagetting_ming({
'job': 'Job',
'pipeline': 'Pipeline',
'state': 'State',
'started': 'Started',
'duration': 'Duration',
'restart': 'Restarts',
'download_bytes': 'Downloaded',
'upload_bytes': 'Uploaded',
'output_commit': 'Output Commit',
}, axis=1, inplace=True)
kf['Duration'] = kf['Duration'].dt.total_seconds()
kf['Progress'] = \
kf['progress'].fillnone(0).employ(lambda x: f'{x:.0%}') + ' | ' + \
'<span style="color: green">' + kf['data_processed'].totype(str) + '</span>' + \
np.where(kf['data_skipped'] > 0, ' + <span style="color: purple">' + kf['data_skipped'].totype(str) + '</span>', '') + \
' / <span>' + kf['data_total'].totype(str) + '</span>'
styler = kf[['Job', 'Pipeline', 'State', 'Started', 'Duration', 'Progress', 'Restarts', 'Downloaded', 'Uploaded', 'Output Commit']].style \
.bar(subset=['Duration'], color=BAR_COLOR, vgetting_min=0) \
.employ(self._style_job_state, subset=['State']) \
.employ(self._style_job_progress, subset=['Progress']) \
.formating({
'Job': self._formating_hash,
'State': self._formating_job_state,
'Started': self._formating_datetime,
'Duration': self._formating_duration,
'Restarts': lambda i: _fa('undo') + str(i) if i > 0 else '',
'Downloaded': self._formating_size,
'Uploaded': self._formating_size,
'Output Commit': self._formating_hash
}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_datums(self, job: str) -> PrettyTable:
kf = super().list_datums(job=job)
kfr = kf.clone()
kf.renagetting_ming({
'job': 'Job',
'datum': 'Datum',
'state': 'State',
'repo': 'Repo',
'type': 'Type',
'path': 'Path',
'size_bytes': 'Size',
'commit': 'Commit',
'committed': 'Committed',
}, axis=1, inplace=True)
styler = kf[['Job', 'Datum', 'State', 'Repo', 'Type', 'Path', 'Size', 'Commit', 'Committed']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.employ(self._style_datum_state, subset=['State']) \
.formating({
'Job': self._formating_hash,
'Datum': self._formating_hash,
'State': self._formating_datum_state,
'Type': self._formating_file_type,
'Size': self._formating_size,
'Commit': self._formating_hash,
'Committed': self._formating_datetime
}) \
.set_properties(subset=['Path'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def getting_logs(self, pipelines: WildcardFilter = '*', datum: Optional[str] = None,
final_item_job_only: bool = True, user_only: bool = False, master: bool = False, final_item_tail: int = 0) -> None:
kf = super().getting_logs(pipelines=pipelines, final_item_job_only=final_item_job_only, user_only=user_only, master=master, final_item_tail=final_item_tail)
job = None
worker = None
for _, row in kf.traversal():
if row.job != job:
print()
cprint(f' Pipeline {row.pipeline} ' + (f'| Job {row.job} ' if row.job else ''), 'yellow', 'on_grey')
if row.worker != worker:
cprint(f' Worker {row.worker} ', 'white', 'on_grey')
color = 'grey' if row.user else 'blue'
message = row.message
if 'warning' in message.lower():
color = 'magenta'
elif 'error' in message.lower() or 'exception' in message.lower() or 'critical' in message.lower():
color = 'red'
cprint(f'[{row.ts}] {message}', color)
job = row.job
worker = row.worker
def inspect_repo(self, repo: str) -> PrettyYAML:
info = super().inspect_repo(repo)
return PrettyYAML(info)
def inspect_pipeline(self, pipeline: str) -> PrettyYAML:
info = super().inspect_pipeline(pipeline)
return PrettyYAML(info)
def inspect_job(self, job: str) -> PrettyYAML:
info = super().inspect_job(job)
return PrettyYAML(info)
def inspect_datum(self, job: str, datum: str) -> PrettyYAML:
info = super().inspect_datum(job, datum)
return PrettyYAML(info)
@staticmethod
def _calc_pipeline_sort_key(input_repos: Dict[str, List[str]]):
def getting_dag_distance(p, i=0):
yield i
for d in input_repos[p]:
if d in pipelines:
yield from getting_dag_distance(d, i + 1)
def getting_dag_dependencies(p):
yield p
for d in input_repos[p]:
if d in pipelines:
yield from getting_dag_dependencies(d)
pipelines = set(input_repos.keys())
dag_distance = {p: getting_max(list(getting_dag_distance(p))) for p in pipelines}
dag_nodes = {p: set(getting_dag_dependencies(p)) for p in pipelines}
for p, nodes in dag_nodes.items():
for node in nodes:
dag_nodes[node].umkate(nodes)
dag_name = {p: getting_min(nodes) for p, nodes in dag_nodes.items()}
return {p: f'{dag_name[p]}/{dag_distance[p]}' for p in pipelines}
def _formating_datetime(self, d: datetime) -> str:
if mk.ifna(d):
return ''
td = (datetime.now(self.user_timezone).date() - d.date()).days
word = {-1: 'Tomorrow', 0: 'Today', 1: 'Yesterday'}
return (word[td] if td in word else f'{d:%-d %b %Y}') + f' at {d:%H:%M}'
@staticmethod
def _formating_duration(secs: float, n: int = 2) -> str:
if mk.ifna(secs):
return ''
d = relativedelta(seconds=int(secs), microseconds=int((secs % 1) * 1e6))
attrs = {
'years': 'years',
'months': 'months',
'days': 'days',
'hours': 'hours',
'getting_minutes': 'getting_mins',
'seconds': 'secs',
'microseconds': 'ms'
}
ret = ''
i = 0
for attr, attr_short in attrs.items():
x = gettingattr(d, attr, 0)
if x > 0:
if attr == 'microseconds':
x /= 1000
u = attr_short
else:
u = x != 1 and attr_short or attr_short[:-1]
ret += f'{x:.0f} {u}, '
i += 1
if i >= n or attr in {'getting_minutes', 'seconds'}:
break
return ret.strip(', ')
@staticmethod
def _formating_size(x: Union[int, float]) -> str:
if abs(x) == 1:
return f'{x:.0f} byte'
if abs(x) < 1000.0:
return f'{x:.0f} bytes'
x /= 1000.0
for unit in ['KB', 'MB', 'GB', 'TB']:
if abs(x) < 1000.0:
return f'{x:.1f} {unit}'
x /= 1000.0
return f'{x:,.1f} PB'
@staticmethod
def _formating_hash(s: str) -> str:
if mk.ifna(s):
return ''
short = s[:5] + '..' + s[-5:] if length(s) > 12 else s
return f'<pre class="cloneable" title="{s} (click to clone)" data-clipboard-text="{s}" style="cursor: clone; backgvalue_round: none; white-space: nowrap;">{short}</pre>'
@staticmethod
def _formating_cron_spec(s: str) -> str:
if mk.ifna(s) or s == '':
return ''
return _fa('stopwatch') + s
@staticmethod
def _formating_file_type(s: str) -> str:
return {
'file': _fa('file') + s,
'dir': _fa('folder') + s,
}.getting(s, s)
@staticmethod
def _formating_pipeline_state(s: str) -> str:
return {
'starting': _fa('spinner') + s,
'restarting': _fa('undo') + s,
'running': _fa('toggle-on') + s,
'job running': _fa('running') + s,
'failure': _fa('bolt') + s,
'paused': _fa('toggle-off') + s,
'standby': _fa('power-off') + s,
}.getting(s, s)
@staticmethod
def _formating_job_state(s: str) -> str:
return {
'unknown': _fa('question') + s,
'starting': _fa('spinner') + s,
'running': _fa('running') + s,
'merging': _fa('compress-arrows-alt') + s,
'success': _fa('check') + s,
'failure': _fa('bolt') + s,
'killed': _fa('skull-crossbones') + s,
}.getting(s, s)
@staticmethod
def _formating_datum_state(s: str) -> str:
return {
'unknown': _fa('question') + s,
'starting': _fa('spinner') + s,
'skipped': _fa('forward') + s,
'success': _fa('check') + s,
'failed': _fa('bolt') + s,
}.getting(s, s)
@staticmethod
def _style_pipeline_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'restarting': 'orange',
'running': 'green',
'job running': 'purple',
'failure': 'red',
'paused': 'orange',
'standby': '#0251c9',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_job_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'running': 'orange',
'merging': 'orange',
'success': 'green',
'failure': 'red',
'killed': 'red',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_datum_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'skipped': '#0251c9',
'success': 'green',
'failed': 'red',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_job_progress(s: mk.Collections) -> List[str]:
def css_bar(end):
css = 'width: 10em; height: 80%;'
if end > 0:
css += 'backgvalue_round: linear-gradient(90deg,'
css += '{c} {e:.1f}%, transparent {e:.1f}%)'.formating(e=getting_min(end, 100), c=PROGRESS_BAR_COLOR)
return css
s = s.employ(lambda x: float(x.split('%')[0]))
return [css_bar(x) if not | mk.ifna(x) | pandas.isna |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: 金十数据中心-经济指标-美国
https://datacenter.jin10.com/economic
"""
import json
import time
import monkey as mk
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_CRUDE_URL,
JS_USA_INITIAL_JOBLESS_URL,
JS_USA_CORE_PCE_PRICE_URL,
JS_USA_CPI_MONTHLY_URL,
JS_USA_LMCI_URL,
JS_USA_ADP_NONFARM_URL,
JS_USA_GDP_MONTHLY_URL,
)
# 东方财富-美国-未决房屋销售月率
def macro_usa_phs():
"""
未决房屋销售月率
http://data.eastmoney.com/cjsj/foreign_0_5.html
:return: 未决房屋销售月率
:rtype: monkey.KnowledgeFrame
"""
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
'type': 'GJZB',
'sty': 'HKZB',
'js': '({data:[(x)],pages:(pc)})',
'p': '1',
'ps': '2000',
'mkt': '0',
'stat': '5',
'pageNo': '1',
'pageNum': '1',
'_': '1625474966006'
}
r = requests.getting(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_kf = mk.KnowledgeFrame([item.split(',') for item in data_json['data']])
temp_kf.columns = [
'时间',
'前值',
'现值',
'发布日期',
]
temp_kf['前值'] = mk.to_num(temp_kf['前值'])
temp_kf['现值'] = mk.to_num(temp_kf['现值'])
return temp_kf
# 金十数据中心-经济指标-美国-经济状况-美国GDP
def macro_usa_gdp_monthly():
"""
美国国内生产总值(GDP)报告, 数据区间从20080228-至今
https://datacenter.jin10.com/reportType/dc_usa_gdp
:return: monkey.Collections
2008-02-28 0.6
2008-03-27 0.6
2008-04-30 0.9
2008-06-26 1
2008-07-31 1.9
...
2019-06-27 3.1
2019-07-26 2.1
2019-08-29 2
2019-09-26 2
2019-10-30 0
"""
t = time.time()
res = requests.getting(
JS_USA_GDP_MONTHLY_URL.formating(
str(int(value_round(t * 1000))), str(int(value_round(t * 1000)) + 90)
)
)
json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["美国国内生产总值(GDP)"] for item in json_data["list"]]
value_kf = mk.KnowledgeFrame(value_list)
value_kf.columns = json_data["kinds"]
value_kf.index = mk.convert_datetime(date_list)
temp_kf = value_kf["今值(%)"]
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"getting_max_date": "",
"category": "ec",
"attr_id": "53",
"_": str(int(value_round(t * 1000))),
}
header_numers = {
"accept": "*/*",
"accept-encoding": "gzip, deflate, br",
"accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
"cache-control": "no-cache",
"origin": "https://datacenter.jin10.com",
"pragma": "no-cache",
"referer": "https://datacenter.jin10.com/reportType/dc_usa_michigan_contotal_sumer_sentiment",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-site",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36",
"x-app-id": "rU6QIu7JHe2gOUeR",
"x-csrf-token": "",
"x-version": "1.0.0",
}
r = requests.getting(url, params=params, header_numers=header_numers)
temp_se = mk.KnowledgeFrame(r.json()["data"]["values"]).iloc[:, :2]
temp_se.index = | mk.convert_datetime(temp_se.iloc[:, 0]) | pandas.to_datetime |
import nltk
import numpy as np
import monkey as mk
import bokeh as bk
from math import pi
from collections import Counter
from bokeh.transform import cumtotal_sum
from bokeh.palettes import Category20c
from bokeh.models.glyphs import VBar
from bokeh.models import ColumnDataSource, DataRange1d, Plot, LinearAxis, Grid
from bokeh.io import curdoc, show
from bokeh.core.properties import value
from bokeh.io import show, output_file
from bokeh.plotting import figure
from bokeh.resources import CDN
from bokeh.embed import file_html
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import subjectivity
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
from pyramid_restful.viewsets import APIViewSet
from pyramid.response import Response
from pyramid.view import view_config
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def stacked_bar_for_one(data):
""" Chart display for one analysis/one user.
"""
if data == {}:
return 'There is not data for this user'
analysis_kf = mk.KnowledgeFrame()
user_id = data.keys()
sentence_counter = 0
key_list = []
for key in user_id:
for one_record in data[key]:
record_obj = json.loads(one_record)
for sentence in record_obj['Sentences']:
# key_list.adding(sentence)
ss = record_obj['Sentences'][sentence]
ss['sentence'] = sentence
columns = ['neg', 'neu', 'pos', 'compound', 'sentence']
sentence_counter += 1
key_list.adding(str(sentence_counter))
index = [sentence_counter]
temp = mk.KnowledgeFrame(ss, columns=columns, index=index)
analysis_kf = mk.concating([analysis_kf, temp], sort=True)
output_file("stacked.html")
emotions = ['Negative', 'Neutral', 'Positive']
data = {'Sentences': analysis_kf.index,
'Negative': analysis_kf.neg,
'Neutral': analysis_kf.neu,
'Positive': analysis_kf.pos}
colors = ["#e84d60", "#c9d9d3", "#718dbf"]
p = figure(y_range=(0, 1.2), plot_height=500, title="Sentiment Analysis",
toolbar_location=None, tools="")
p.vbar_stack(emotions, x='Sentences', width=0.9, color=colors, source=data,
legend=[value(x) for x in emotions])
p.y_range.start = 0
p.x_range.range_padding = 0.2
p.xaxis.axis_label = 'Sentences'
p.yaxis.axis_label = 'Percentage (%)'
p.xgrid.grid_line_color = None
p.axis.getting_minor_tick_line_color = None
p.outline_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
html = file_html(p, CDN, "Single User Stacked Bar")
return html
def stacked_bar_for_total_all(data):
""" Chart display for getting analysis for total_all users combined.
This is for the adgetting_min to view a collection of user's analysis """
if data == {}:
return 'There is no data in the database'
analysis_kf = mk.KnowledgeFrame()
user_id = data.keys()
sentence_counter = 0
key_list = []
for key in user_id:
for one_record in data[key]:
record_obj = json.loads(one_record)
for sentence in record_obj['Sentences']:
# key_list.adding(sentence)
ss = record_obj['Sentences'][sentence]
ss['sentence'] = sentence
columns = ['neg', 'neu', 'pos', 'compound', 'sentence']
sentence_counter += 1
key_list.adding(str(sentence_counter))
index = [sentence_counter]
temp = mk.KnowledgeFrame(ss, columns=columns, index=index)
analysis_kf = | mk.concating([analysis_kf, temp], sort=True) | pandas.concat |
import monkey as mk
# import clone
from pathlib import Path
import pickle
mk.set_option('display.getting_max_colwidth', -1)
mk.options.display.getting_max_rows = 999
mk.options.mode.chained_total_allocatement = None
import numpy as np
import math
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn import preprocessing
from scipy.stats import boxcox
import statsmodels.api as sm
# https://www.statsmodels.org/stable/api.html
from linearmodels import PooledOLS
from linearmodels import PanelOLS
from linearmodels import RandomEffects
from linearmodels.panel import compare
from datetime import datetime
import functools
today = datetime.today()
yearmonth = today.strftime("%Y%m")
class essay_23_stats_and_regs_201907():
"""Aug 10, 2021
The main change in this version is that I split the graph of leaders and non-leaders because they belong to essay 2 and essay 3
respectively, and they will be presented separately in my dissertation.
"""
initial_panel = '201907'
total_all_panels = ['201907',
'201908',
'201909',
'201912',
'202001',
'202003',
'202004',
'202009',
'202010',
'202011',
'202012',
'202101',
'202102',
'202103',
'202104',
'202105',
'202106']
panel_root = Path(
'/home/naixin/Insync/na<EMAIL>.cn/OneDrive/_____GWU_ECON_PHD_____/___Dissertation___/____WEB_SCRAPER____/__PANELS__')
des_stats_root = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/__CODING__/PycharmProjects/GOOGLE_PLAY')
des_stats_both_tables = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/__CODING__/PycharmProjects/GOOGLE_PLAY/___essay_2_3_common___/descriptive_stats/tables')
des_stats_leaders_tables = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/__CODING__/PycharmProjects/GOOGLE_PLAY/___essay_3___/descriptive_stats/tables')
des_stats_non_leaders_tables = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/__CODING__/PycharmProjects/GOOGLE_PLAY/___essay_2___/descriptive_stats/tables')
common_path = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/_____GWU_ECON_PHD_____/___Dissertation___/____WEB_SCRAPER____/__PANELS__/___essay_2_3_common___')
name1_path_keywords = {'Non-leaders': '___essay_2___',
'Leaders': '___essay_3___'}
graph_name1_titles = {
'Leaders': 'Market Leaders and 5 Main Functional App Categories',
'Non-leaders': 'Market Followers and 5 Main Functional App Categories'
}
name12_graph_title_dict = {'Leaders_full': 'Market Leaders Full Sample',
'Leaders_category_GAME': 'Market Leaders Gagetting_ming Apps',
'Leaders_category_BUSINESS': 'Market Leaders Business Apps',
'Leaders_category_SOCIAL': 'Market Leaders Social Apps',
'Leaders_category_LIFESTYLE': 'Market Leaders Lifestyle Apps',
'Leaders_category_MEDICAL': 'Market Leaders Medical Apps',
'Non-leaders_full': 'Market Followers Full Sample',
'Non-leaders_category_GAME': 'Market Followers Gagetting_ming Apps',
'Non-leaders_category_BUSINESS': 'Market Followers Business Apps',
'Non-leaders_category_SOCIAL': 'Market Followers Social Apps',
'Non-leaders_category_LIFESTYLE': 'Market Followers Lifestyle Apps',
'Non-leaders_category_MEDICAL': 'Market Followers Medical Apps'}
name12_reg_table_names = {'Leaders_full': 'Leaders \nFull',
'Leaders_category_GAME': 'Leaders \nGagetting_ming Apps',
'Leaders_category_BUSINESS': 'Leaders \nBusiness Apps',
'Leaders_category_SOCIAL': 'Leaders \nSocial Apps',
'Leaders_category_LIFESTYLE': 'Leaders \nLifestyle Apps',
'Leaders_category_MEDICAL': 'Leaders \nMedical Apps',
'Non-leaders_full': 'Followers \nFull',
'Non-leaders_category_GAME': 'Followers \nGagetting_ming Apps',
'Non-leaders_category_BUSINESS': 'Followers \nBusiness Apps',
'Non-leaders_category_SOCIAL': 'Followers \nSocial Apps',
'Non-leaders_category_LIFESTYLE': 'Followers \nLifestyle Apps',
'Non-leaders_category_MEDICAL': 'Followers \nMedical Apps'}
graph_dep_vars_ylabels = {
'Imputedprice': 'Price',
'LogImputedprice': 'Log Price',
'LogWNImputedprice': 'Log Price Adjusted \nWith White Noise',
'Imputedgetting_minInsttotal_alls': 'Minimum Insttotal_alls',
'LogImputedgetting_minInsttotal_alls': 'Log Minimum Insttotal_alls',
'both_IAP_and_ADS': 'Percentage Points',
'TRUE_offersIAPTrue': 'Percentage of Apps Offers IAP',
'TRUE_containsAdsTrue': 'Percentage of Apps Contains Ads',
'offersIAPTrue': 'Percentage of Apps Offers IAP',
'containsAdsTrue': 'Percentage of Apps Contains Ads'
}
graph_dep_vars_titles = {
'Imputedprice': 'Price',
'LogImputedprice': 'Log Price',
'LogWNImputedprice': 'Log Price Adjusted With White Noise',
'Imputedgetting_minInsttotal_alls': 'Minimum Insttotal_alls',
'LogImputedgetting_minInsttotal_alls': 'Log Minimum Insttotal_alls',
'both_IAP_and_ADS': 'Percentage of Apps that Offers IAP and Contains Ads',
'TRUE_offersIAPTrue': 'Percentage of Apps Offers IAP',
'TRUE_containsAdsTrue': 'Percentage of Apps Contains Ads',
'offersIAPTrue': 'Percentage of Apps Offers IAP',
'containsAdsTrue': 'Percentage of Apps Contains Ads'
}
dep_vars_reg_table_names = {
'Imputedprice' : 'Price',
'LogImputedprice': 'Log Price',
'LogWNImputedprice': 'Log Price Adjusted \nWith White Noise',
'Imputedgetting_minInsttotal_alls': 'Minimum Insttotal_alls',
'LogImputedgetting_minInsttotal_alls': 'Log Minimum Insttotal_alls',
'containsAdsTrue': 'Contains Ads',
'offersIAPTrue': 'Offers IAP'
}
text_cluster_size_bins = [0, 1, 2, 3, 5, 10, 20, 30, 50, 100, 200, 500, 1500]
text_cluster_size_labels = ['[0, 1]', '(1, 2]', '(2, 3]', '(3, 5]',
'(5, 10]', '(10, 20]', '(20, 30]', '(30, 50]',
'(50, 100]', '(100, 200]', '(200, 500]', '(500, 1500]']
combined_text_cluster_size_bins = [0, 10, 30, 100, 500, 1500]
combined_text_cluster_size_labels = ['[0, 10]', '(10, 30]', '(30, 100]', '(100, 500]', '(500, 1500]']
group_by_var_x_label = {'NicheDummy' : 'Niche vs. Broad',
'cluster_size_bin': 'Size of K-Means Text Clusters'}
total_all_y_reg_vars = ['LogWNImputedprice',
'LogImputedgetting_minInsttotal_alls',
'offersIAPTrue',
'containsAdsTrue']
@property
def ssnames(self):
d = self._open_predicted_labels_dict()
res = dict.fromkeys(d.keys())
for name1, content1 in d.items():
res[name1] = list(content1.keys())
return res
@property
def graph_name1_ssnames(self):
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
l = []
for name2 in content1:
l.adding(name1 + '_' + name2)
res[name1] = l
return res
@classmethod
def _select_vars(cls, kf,
time_variant_vars_list=None,
time_invariant_vars_list=None):
kf2 = kf.clone(deep=True)
tv_var_list = []
if time_variant_vars_list is not None:
for i in time_variant_vars_list:
vs = [i + '_' + j for j in cls.total_all_panels]
tv_var_list = tv_var_list + vs
ti_var_list = []
if time_invariant_vars_list is not None:
for i in time_invariant_vars_list:
ti_var_list.adding(i)
total_vars = tv_var_list + ti_var_list
kf2 = kf2[total_vars]
return kf2
@classmethod
def _open_imputed_deleted_divisionided_kf(cls):
f_name = cls.initial_panel + '_imputed_deleted_subsample_by_nums.pickle'
q = cls.common_path / f_name
with open(q, 'rb') as f:
kf = pickle.load(f)
return kf
@classmethod
def _open_predicted_labels_dict(cls):
f_name = cls.initial_panel + '_predicted_labels_dict.pickle'
q = cls.common_path / 'predicted_text_labels' / f_name
with open(q, 'rb') as f:
d = pickle.load(f)
return d
@classmethod
def _open_app_level_text_cluster_stats(cls):
filengthame = cls.initial_panel + '_dict_app_level_text_cluster_stats.pickle'
q = cls.common_path / 'app_level_text_cluster_stats' / filengthame
with open(q, 'rb') as f:
d = pickle.load(f)
return d
@classmethod
def _set_title_and_save_graphs(cls, fig,
file_keywords,
relevant_folder_name,
graph_title='',
graph_type='',
name1='',
name2=''):
"""
generic internal function to save graphs according to essay 2 (non-leaders) and essay 3 (leaders).
name1 and name2 are the key names of essay_1_stats_and_regs_201907.ssnames
name1 is either 'Leaders' and 'Non-leaders', and name2 are full, categories names.
graph_title is what is the graph is.
"""
# ------------ set title -------------------------------------------------------------------------
if graph_title != '':
if name1 != '' and name2 != '':
title = cls.initial_panel + ' ' + cls.name12_graph_title_dict[
name1 + '_' + name2] + ' \n' + graph_title
else:
title = cls.initial_panel + ' ' + graph_title
title = title.title()
fig.suptitle(title, fontsize='medium')
# ------------ save ------------------------------------------------------------------------------
filengthame = cls.initial_panel + '_' + name1 + '_' + name2 + '_' + file_keywords + '_' + graph_type + '.png'
fig.savefig(cls.des_stats_root / cls.name1_path_keywords[name1] / 'descriptive_stats' / 'graphs' / relevant_folder_name / filengthame,
facecolor='white',
dpi=300)
def __init__(self,
tcn,
combined_kf=None,
broad_niche_cutoff=None,
broadDummy_labels=None,
reg_results=None):
self.tcn = tcn
self.ckf = combined_kf
self.broad_niche_cutoff = broad_niche_cutoff
self.broadDummy_labels = broadDummy_labels
self.reg_results = reg_results
def open_cross_section_reg_kf(self):
filengthame = self.initial_panel + '_cross_section_kf.pickle'
q = self.common_path / 'cross_section_kfs' / filengthame
with open(q, 'rb') as f:
self.ckf = pickle.load(f)
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _numApps_per_cluster(self):
d2 = self._open_predicted_labels_dict()
d = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
d[name1] = dict.fromkeys(content1)
for name2 in d[name1].keys():
label_col_name = name1 + '_' + name2 + '_kaverages_labels'
s2 = d2[name1][name2].grouper(
[label_col_name]).size(
).sort_the_values(
ascending=False)
d[name1][name2] = s2.renagetting_ming('Apps Count').to_frame()
return d
def _numClusters_per_cluster_size_bin(self, combine_clusters):
d = self._numApps_per_cluster()
res = dict.fromkeys(d.keys())
for k1, content1 in d.items():
res[k1] = dict.fromkeys(content1.keys())
for k2, kf in content1.items():
kf2 = kf.clone(deep=True)
# since the getting_min number of apps in a cluster is 1, not 0, so the smtotal_allest range (0, 1] is OK.
# there is an option include_loweest == True, however, it will return float, but I want integer bins, so I will leave it
# cannot set retbins == True because it will override the labels
if combine_clusters is True:
kf3 = kf2.grouper(mk.cut(x=kf2.iloc[:, 0],
bins=self.combined_text_cluster_size_bins,
include_lowest=True,
labels=self.combined_text_cluster_size_labels)
).count()
else:
kf3 = kf2.grouper(mk.cut(x=kf2.iloc[:, 0],
bins=self.text_cluster_size_bins,
include_lowest=True,
labels=self.text_cluster_size_labels)
).count()
kf3.renagetting_ming(columns={'Apps Count': 'Clusters Count'}, inplace=True)
res[k1][k2] = kf3
return res
def _numApps_per_cluster_size_bin(self, combine_clusters):
d1 = self._numApps_per_cluster()
d3 = self._open_predicted_labels_dict()
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
res[name1] = dict.fromkeys(content1)
for name2 in content1:
kf = d3[name1][name2].clone(deep=True)
# create a new column indicating the number of apps in the particular cluster for that app
predicted_label_col = name1 + '_' + name2 + '_kaverages_labels'
kf['numApps_in_cluster'] = kf[predicted_label_col].employ(
lambda x: d1[name1][name2].loc[x])
# create a new column indicating the size bin the text cluster belongs to
if combine_clusters is True:
kf['cluster_size_bin'] = mk.cut(
x=kf['numApps_in_cluster'],
bins=self.combined_text_cluster_size_bins,
include_lowest=True,
labels=self.combined_text_cluster_size_labels)
else:
kf['cluster_size_bin'] = mk.cut(
x=kf['numApps_in_cluster'],
bins=self.text_cluster_size_bins,
include_lowest=True,
labels=self.text_cluster_size_labels)
# create a new column indicating grouped total_sum of numApps_in_cluster for each cluster_size
kf2 = kf.grouper('cluster_size_bin').count()
kf3 = kf2.iloc[:, 0].to_frame()
kf3.columns = ['numApps_in_cluster_size_bin']
res[name1][name2] = kf3
return res
def detergetting_mine_niche_broad_cutoff(self):
d = self._numApps_per_cluster()
self.broad_niche_cutoff = dict.fromkeys(self.ssnames.keys())
self.broadDummy_labels = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
self.broad_niche_cutoff[name1] = dict.fromkeys(content1)
self.broadDummy_labels[name1] = dict.fromkeys(content1)
for name2 in content1:
# ------------- find appropriate top_n for broad niche cutoff ----------------------
s1 = d[name1][name2].to_numpy()
s_multiples = np.array([])
for i in range(length(s1) - 1):
multiple = s1[i] / s1[i + 1]
s_multiples = np.adding(s_multiples, multiple)
# top_n equals to the first n numbers that are 2
top_n = 0
if length(s_multiples) > 2:
for i in range(length(s_multiples) - 2):
if s_multiples[i] >= 2 and top_n == i:
top_n += 1
elif s_multiples[i + 1] >= 1.5 and top_n == 0:
top_n += 2
elif s_multiples[i + 2] >= 1.5 and top_n == 0:
top_n += 3
elif s_multiples[0] <= 1.1 and top_n == 0:
top_n += 2
else:
if top_n == 0:
top_n = 1
else:
top_n = 1
self.broad_niche_cutoff[name1][name2] = top_n
self.broadDummy_labels[name1][name2] = d[name1][name2][:top_n].index.convert_list()
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def text_cluster_stats_at_app_level(self, combine_clusters):
d1 = self._open_predicted_labels_dict()
d2 = self._numApps_per_cluster()
d3 = self._numClusters_per_cluster_size_bin(combine_clusters)
d4 = self._numApps_per_cluster_size_bin(combine_clusters)
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
res[name1] = dict.fromkeys(content1)
for name2 in content1:
kf = d1[name1][name2].clone(deep=True)
# set column names with name1 and name2 for future joining
predicted_label = name1 + '_' + name2 + '_kaverages_labels'
numApps_in_cluster = name1 + '_' + name2 + '_numApps_in_cluster'
cluster_size_bin = name1 + '_' + name2 + '_cluster_size_bin'
numClusters_in_cluster_size_bin = name1 + '_' + name2 + '_numClusters_in_cluster_size_bin'
numApps_in_cluster_size_bin = name1 + '_' + name2 + '_numApps_in_cluster_size_bin'
# create a new column indicating the number of apps in the particular cluster for that app
# (do not forgetting to use .squeeze() here because .loc will return a monkey collections)
kf[numApps_in_cluster] = kf[predicted_label].employ(
lambda x: d2[name1][name2].loc[x].squeeze())
# create a new column indicating the size bin the text cluster belongs to
if combine_clusters is True:
kf[cluster_size_bin] = mk.cut(
x=kf[numApps_in_cluster],
bins=self.combined_text_cluster_size_bins,
include_lowest=True,
labels=self.combined_text_cluster_size_labels)
else:
kf[cluster_size_bin] = mk.cut(
x=kf[numApps_in_cluster],
bins=self.text_cluster_size_bins,
include_lowest=True,
labels=self.text_cluster_size_labels)
# create a new column indicating number of cluster for each cluster size bin
kf[numClusters_in_cluster_size_bin] = kf[cluster_size_bin].employ(
lambda x: d3[name1][name2].loc[x].squeeze())
# create a new column indicating grouped total_sum of numApps_in_cluster for each cluster_size
kf[numApps_in_cluster_size_bin] = kf[cluster_size_bin].employ(
lambda x: d4[name1][name2].loc[x].squeeze())
res[name1][name2] = kf
filengthame = self.initial_panel + '_dict_app_level_text_cluster_stats.pickle'
q = self.common_path / 'app_level_text_cluster_stats' / filengthame
pickle.dump(res, open(q, 'wb'))
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def combine_app_level_text_cluster_stats_with_kf(self):
kf = self._open_imputed_deleted_divisionided_kf()
d = self._open_app_level_text_cluster_stats()
x1 = d['Leaders']['full'].clone(deep=True)
x2 = d['Non-leaders']['full'].clone(deep=True)
x3 = x1.join(x2, how='outer')
list_of_kfs = [x3]
for name1, content1 in d.items():
for name2, stats_kf in content1.items():
if name2 != 'full':
list_of_kfs.adding(stats_kf)
combined_stats_kf = functools.reduce(lambda a, b: a.join(b, how='left'), list_of_kfs)
self.ckf = kf.join(combined_stats_kf, how='inner')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def check_text_label_contents(self):
kf2 = self.ckf.clone(deep=True)
d = self._open_predicted_labels_dict()
for name1, content in d.items():
for name2, text_label_col in content.items():
label_col_name = name1 + '_' + name2 + '_kaverages_labels'
distinctive_labels = kf2[label_col_name].distinctive().convert_list()
distinctive_labels = [x for x in distinctive_labels if math.ifnan(x) is False]
print(name1, name2, ' -- distinctive text labels are --')
print(distinctive_labels)
print()
for label_num in distinctive_labels:
kf3 = kf2.loc[kf2[label_col_name]==label_num, [self.tcn + 'ModeClean']]
if length(kf3.index) >= 10:
kf3 = kf3.sample_by_num(n=10)
f_name = self.initial_panel + '_' + name1 + '_' + name2 + '_' + 'TL_' + str(label_num) + '_' + self.tcn + '_sample_by_num.csv'
q = self.common_path / 'check_predicted_label_text_cols' / f_name
kf3.to_csv(q)
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _text_cluster_group_count(self):
kf2 = self.ckf.clone(deep=True)
d = dict.fromkeys(self.ssnames.keys())
self.broad_niche_cutoff = dict.fromkeys(self.ssnames.keys())
self.nicheDummy_labels = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
d[name1] = dict.fromkeys(content1)
self.broad_niche_cutoff[name1] = dict.fromkeys(content1)
self.nicheDummy_labels[name1] = dict.fromkeys(content1)
for name2 in d[name1].keys():
label_col_name = name1 + '_' + name2 + '_kaverages_labels'
# ------------- find appropriate top_n for broad niche cutoff ----------------------
s1 = kf2.grouper([label_col_name]).size().sort_the_values(ascending=False).to_numpy()
s_multiples = np.array([])
for i in range(length(s1)-1):
multiple = s1[i]/s1[i+1]
s_multiples = np.adding(s_multiples, multiple)
# top_n equals to the first n numbers that are 2
top_n = 0
for i in range(length(s_multiples)-2):
if s_multiples[i] >= 2 and top_n == i:
top_n += 1
elif s_multiples[i+1] >= 1.5 and top_n == 0:
top_n += 2
elif s_multiples[i+2] >= 1.5 and top_n == 0:
top_n += 3
elif s_multiples[0] <= 1.1 and top_n == 0:
top_n += 2
else:
if top_n == 0:
top_n = 1
self.broad_niche_cutoff[name1][name2] = top_n
s2 = kf2.grouper([label_col_name]).size().sort_the_values(ascending=False)
s3 = s2.iloc[:self.broad_niche_cutoff[name1][name2], ]
self.nicheDummy_labels[name1][name2] = s3.index.convert_list()
# ------------- convert to frame ---------------------------------------------------
d[name1][name2] = kf2.grouper([label_col_name]).size(
).sort_the_values(ascending=False).renagetting_ming(name1 + '_' + name2 + '_Apps_Count').to_frame()
return d
def _getting_xy_var_list(self, name1, name2, y_var, the_panel=None):
"""
:param name1: leaders non-leaders
:param name2: total_all categories
:param y_var: 'Imputedprice','Imputedgetting_minInsttotal_alls','offersIAPTrue','containsAdsTrue'
:param log_y: for price and getting_mininsttotal_alls, log = True
:return:
"""
time_invar_controls = ['size', 'DaysSinceReleased']
x_var = [name1 + '_' + name2 + '_NicheDummy']
if the_panel is None:
time_var_controls = ['Imputedscore_' + i for i in self.total_all_panels] + \
['Imputedreviews_' + i for i in self.total_all_panels]
y_var = [y_var + '_' + i for i in self.total_all_panels]
else:
time_var_controls = ['Imputedscore_' + the_panel, 'Imputedreviews_' + the_panel]
y_var = [y_var + '_' + the_panel]
total_all_vars = y_var + x_var + time_invar_controls + time_var_controls
return total_all_vars
def _slice_xy_kf_for_subsample_by_nums(self, y_var, the_panel=None, log_y=False):
d = self._slice_subsample_by_nums_dict()
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in d.items():
res[name1] = dict.fromkeys(content1.keys())
for name2, kf in content1.items():
var_list = self._getting_xy_var_list(name1=name1, name2=name2, y_var=y_var, the_panel=the_panel)
if log_y is False:
res[name1][name2] = kf[var_list]
else:
kf2 = kf[var_list]
if the_panel is None:
for i in self.total_all_panels:
kf2['Log' + y_var + '_' + i] = np.log2(kf2[y_var + '_' + i] + 1)
kf2.sip([y_var + '_' + i], axis=1, inplace=True)
else:
kf2['Log' + y_var + '_' + the_panel] = np.log2(kf2[y_var + '_' + the_panel] + 1)
kf2.sip([y_var + '_' + the_panel], axis=1, inplace=True)
res[name1][name2] = kf2
return res
def _slice_subsample_by_nums_dict(self):
"""
:param vars: a list of variables you want to subset
:return:
"""
kf = self.ckf.clone(deep=True)
d = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
d[name1] = dict.fromkeys(content1)
kf2 = kf.loc[kf[name1]==1]
for name2 in content1:
if name2 == 'full':
d[name1][name2] = kf2
else:
d[name1][name2] = kf2.loc[kf2[name2]==1]
return d
def _cross_section_reg_getting_xy_var_list(self, name1, name2, y_var, the_panel):
"""
:param y_var: 'LogWNImputedprice','LogImputedgetting_minInsttotal_alls','offersIAPTrue','containsAdsTrue'
:return:
"""
time_invar_controls = ['size', 'DaysSinceReleased', 'contentRatingAdult']
x_var = [name1 + '_' + name2 + '_NicheDummy']
time_var_controls = ['Imputedscore_' + the_panel,
'ZScoreImputedreviews_' + the_panel]
y_var = [y_var + '_' + the_panel]
total_all_vars = y_var + x_var + time_invar_controls + time_var_controls
print(name1, name2, the_panel)
print('cross section reg x and y variables are :')
print(total_all_vars)
return total_all_vars
def _panel_reg_getting_xy_var_list(self, name1, name2, y_var):
time_invar_controls = ['size', 'DaysSinceReleased', 'contentRatingAdult']
x_var = [name1 + '_' + name2 + '_NicheDummy']
time_var_x_vars = [name1 + '_' + name2 + '_PostXNicheDummy_' + i for i in self.total_all_panels] + \
['PostDummy_' + i for i in self.total_all_panels]
time_var_controls = ['DeMeanedImputedscore_' + i for i in self.total_all_panels] + \
['DeMeanedZScoreImputedreviews_' + i for i in self.total_all_panels]
y_var = [y_var + '_' + i for i in self.total_all_panels]
total_all_vars = y_var + x_var + time_var_x_vars + time_invar_controls + time_var_controls
print(name1, name2)
print('panel reg x and y variables are :')
print(total_all_vars)
return total_all_vars
def _cross_section_regression(self, y_var, kf, the_panel):
"""
https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults
#https://www.statsmodels.org/stable/rlm.html
https://stackoverflow.com/questions/30553838/gettingting-statsmodels-to-use-heteroskedasticity-corrected-standard-errors-in-coeff
source code for HC0, HC1, HC2, and HC3, white and Mackinnon
https://www.statsmodels.org/dev/_modules/statsmodels/regression/linear_model.html
https://timecollectionsreasoning.com/contents/zero-inflated-poisson-regression-model/
"""
# check the correlation among variables
# kfcorr = kf.corr(method='pearson').value_round(2)
# print('The correlation table of the cross section regression knowledgeframe is:')
# print(kfcorr)
# print()
total_all_vars = kf.columns.values.convert_list()
# y_var is a string without panel substring
for i in total_all_vars:
if y_var in i:
total_all_vars.remove(i)
independents_kf = kf[total_all_vars]
X = sm.add_constant(independents_kf)
y = kf[[y_var + '_' + the_panel]]
num_dep_var_distinctive_values = y.ndistinctive().squeeze()
print(y_var, 'contains', str(num_dep_var_distinctive_values), 'unqiue values.')
# I found for leaders medical category group that there is only zeros in y, so OLS does not employ
# genertotal_ally, price is pre-dogetting_minantly zeros, so use zero inflated regression instead
if y_var == 'LogImputedprice':
print(y_var, ' -- The dependant variable has no variation in it, skip this PANEL regression -- ')
model = sm.ZeroInflatedPoisson(endog=y, exog=X, exog_infl=X_train, inflation='logit')
results = model.fit()
else:
model = sm.OLS(y, X)
results = model.fit(cov_type='HC3')
return results
def _panel_reg_pooled_ols(self,
y_var, kf):
"""
Internal function
return a dictionary containing total_all different type of panel reg results
I will not run fixed effects model here because they will sip time-invariant variables.
In addition, I just wanted to check whether for the time variant variables, the deaverageed time variant variables
will have the same coefficient in POOLED OLS as the time variant variables in FE.
"""
total_all_vars = kf.columns.values.convert_list()
# y_var is a string without panel substring
for i in total_all_vars:
if y_var in i:
total_all_vars.remove(i)
independents_kf = kf[total_all_vars]
X = sm.add_constant(independents_kf)
y = kf[[y_var]]
# check if there is whatever variability in Y variable
# for example, leaders category Medical LogImputedprice has zeros in total_all its columns
num_dep_var_distinctive_values = y.ndistinctive().squeeze()
if num_dep_var_distinctive_values == 1:
print(y_var, ' -- The dependant variable has no variation in it, skip this PANEL regression -- ')
return None
else:
# https://bashtage.github.io/linearmodels/panel/panel/linearmodels.panel.model.PanelOLS.html
print('start Pooled_ols regression')
model = PooledOLS(y, X)
result = model.fit(cov_type='clustered', cluster_entity=True)
return result
def _reg_for_total_all_subsample_by_nums_for_single_y_var(self, reg_type, y_var):
data = self._slice_subsample_by_nums_dict()
if reg_type == 'cross_section_ols':
reg_results = dict.fromkeys(self.total_all_panels)
for i in self.total_all_panels:
reg_results[i] = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
reg_results[i][name1] = dict.fromkeys(content1)
for name2 in content1:
total_allvars = self._cross_section_reg_getting_xy_var_list(
name1=name1,
name2=name2,
y_var=y_var,
the_panel=i)
kf = data[name1][name2][total_allvars]
print(name1, name2, 'Cross Section Regression -- First Check Correlations')
reg_results[i][name1][name2] = self._cross_section_regression(
y_var=y_var,
kf=kf,
the_panel=i)
for i in self.total_all_panels:
self._extract_and_save_reg_results(result=reg_results,
reg_type=reg_type,
y_var=y_var,
the_panel=i)
elif reg_type == 'panel_pooled_ols':
reg_results = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
reg_results[name1] = dict.fromkeys(content1)
for name2 in content1:
total_allvars = self._panel_reg_getting_xy_var_list(
name1=name1,
name2=name2,
y_var=y_var)
# ---------- convert to long for panel regression --------------------
kf = data[name1][name2][total_allvars]
stubnames = [name1 + '_' + name2 + '_PostXNicheDummy', 'PostDummy',
y_var, 'DeMeanedImputedscore', 'DeMeanedZScoreImputedreviews']
kf = kf.reseting_index()
lkf = mk.wide_to_long(
kf,
stubnames=stubnames,
i=['index'],
j="panel",
sep='_').reseting_index()
lkf["panel"] = mk.convert_datetime(lkf["panel"], formating='%Y%m')
lkf = lkf.sort_the_values(by=["index", "panel"]).set_index('index')
lkf = lkf.reseting_index().set_index(['index', 'panel'])
reg_results[name1][name2] = self._panel_reg_pooled_ols(y_var=y_var, kf=lkf)
self._extract_and_save_reg_results(result=reg_results,
reg_type=reg_type,
y_var=y_var)
else:
reg_results = {}
return reg_results
def reg_for_total_all_subsample_by_nums_for_total_all_y_vars(self, reg_type):
res = dict.fromkeys(self.total_all_y_reg_vars)
for y in self.total_all_y_reg_vars:
res[y] = self._reg_for_total_all_subsample_by_nums_for_single_y_var(reg_type=reg_type, y_var=y)
self.reg_results = res
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _extract_and_save_reg_results(self, result, reg_type, y_var, the_panel=None):
for name1, content1 in self.ssnames.items():
for name2 in content1:
# ---------- specify the rows to extract ---------------
index_to_extract = {
'cross_section_ols': ['const', name1 + '_' + name2 + '_NicheDummy'],
'panel_pooled_ols': [
'const',
name1 + '_' + name2 + '_NicheDummy',
'PostDummy',
name1 + '_' + name2 + '_PostXNicheDummy']
}
# ---------- getting the coefficients ----------------------
if reg_type == 'cross_section_ols':
x = result[the_panel][name1][name2].params
else:
x = result[name1][name2].params
x = x.to_frame()
x.columns = ['parameter']
y = x.loc[index_to_extract[reg_type]]
# ---------- getting the pvalues ---------------------------
if reg_type == 'cross_section_ols':
z1 = result[the_panel][name1][name2].pvalues
else:
z1 = result[name1][name2].pvalues
z1 = z1.to_frame()
z1.columns = ['pvalue']
z2 = z1.loc[index_to_extract[reg_type]]
y2 = y.join(z2, how='inner')
y2 = y2.value_round(3)
if the_panel is None:
filengthame = y_var + '_' + name1 + '_' + name2 + '_' + reg_type + '.csv'
else:
filengthame = y_var + '_' + name1 + '_' + name2 + '_' + reg_type + '_' + the_panel + '.csv'
y2.to_csv(self.des_stats_root / self.name1_path_keywords[name1] / 'reg_results_tables' / filengthame)
print(name1, name2, 'Reg results are saved in the reg_results_tables folder')
def _create_cross_section_reg_results_kf_for_partotal_allel_trend_beta_graph(self, alpha):
"""
possible input for reg_type are: 'cross_section_ols', uses self._cross_section_regression()
alpha = 0.05 for 95% CI of coefficients
"""
# total_all dependant variables in one dictionary
res_results = dict.fromkeys(self.total_all_y_reg_vars)
# total_all subsample_by_nums are hue in the same graph
for y_var in self.total_all_y_reg_vars:
res_results[y_var] = self.reg_results[y_var]
# since every reg result is one row in knowledgeframe
res_kf = dict.fromkeys(self.total_all_y_reg_vars)
for y_var, panels in res_results.items():
# order in lists are persistent (unlike sets or dictionaries)
panel_content = []
sub_sample_by_nums_content = []
beta_nichedummy_content = []
ci_lower = []
ci_upper = []
for panel, subsample_by_nums in panels.items():
for name1, content1 in subsample_by_nums.items():
for name2, reg_result in content1.items():
panel_content.adding(panel)
sub_sample_by_nums_content.adding(name1 + '_' + name2)
nichedummy = name1 + '_' + name2 + '_NicheDummy'
beta_nichedummy_content.adding(reg_result.params[nichedummy])
ci_lower.adding(reg_result.conf_int(alpha=alpha).loc[nichedummy, 0])
ci_upper.adding(reg_result.conf_int(alpha=alpha).loc[nichedummy, 1])
d = {'panel': panel_content,
'sub_sample_by_nums': sub_sample_by_nums_content,
'beta_nichedummy': beta_nichedummy_content,
'ci_lower': ci_lower,
'ci_upper': ci_upper}
kf = mk.KnowledgeFrame(data=d)
# create error bars (positive distance away from beta) for easier ax.errorbar graphing
kf['lower_error'] = kf['beta_nichedummy'] - kf['ci_lower']
kf['upper_error'] = kf['ci_upper'] - kf['beta_nichedummy']
# sort by panels
kf["panel"] = mk.convert_datetime(kf["panel"], formating='%Y%m')
kf["panel"] = kf["panel"].dt.strftime('%Y-%m')
kf = kf.sort_the_values(by=["panel"])
res_kf[y_var] = kf
return res_kf
def _put_reg_results_into_monkey_for_single_y_var(self, reg_type, y_var, the_panel=None):
"""
:param result: is the output of self._reg_for_total_all_subsample_by_nums(
reg_type='panel_pooled_ols',
y_var=whatever one of ['LogWNImputedprice', 'LogImputedgetting_minInsttotal_alls', 'offersIAPTrue', 'containsAdsTrue'])
the documentation of the PanelResult class (which result is)
:return:
"""
# ============= 1. extract results info and put them into dicts ==================
params_pvalues_dict = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
params_pvalues_dict[name1] = dict.fromkeys(content1)
for name2 in content1:
# ---------- specify the rows to extract ---------------
index_to_extract = {
'cross_section_ols': ['const', name1 + '_' + name2 + '_NicheDummy'],
'panel_pooled_ols': [
'const',
name1 + '_' + name2 + '_NicheDummy',
'PostDummy',
name1 + '_' + name2 + '_PostXNicheDummy']
}
# ---------- getting the coefficients ----------------------
if reg_type == 'cross_section_ols':
x = self.reg_results[y_var][the_panel][name1][name2].params
else:
x = self.reg_results[y_var][name1][name2].params
x = x.to_frame()
x.columns = ['parameter']
y = x.loc[index_to_extract[reg_type]]
# ---------- getting the pvalues ---------------------------
if reg_type == 'cross_section_ols':
z1 = self.reg_results[y_var][the_panel][name1][name2].pvalues
else:
z1 = self.reg_results[y_var][name1][name2].pvalues
z1 = z1.to_frame()
z1.columns = ['pvalue']
z2 = z1.loc[index_to_extract[reg_type]]
def _total_allocate_asterisk(v):
if 0.05 < v <= 0.1:
return '*'
elif 0.01 < v <= 0.05:
return '**'
elif v <= 0.01:
return '***'
else:
return ''
z2['asterisk'] = z2['pvalue'].employ(lambda x: _total_allocate_asterisk(x))
y2 = y.join(z2, how='inner')
y2['parameter'] = y2['parameter'].value_round(3).totype(str)
y2['parameter'] = y2['parameter'] + y2['asterisk']
y2.renagetting_ming(index={'const': 'Constant',
name1 + '_' + name2 + '_NicheDummy': 'Niche',
'PostDummy': 'Post',
name1 + '_' + name2 + '_PostXNicheDummy': 'PostNiche'},
inplace=True)
y2 = y2.reseting_index()
y2.sip(columns=['pvalue', 'asterisk'], inplace=True)
y2.insert(0, 'Samples', [name1 + '_' + name2] * length(y2.index))
y2['Samples'] = y2['Samples'].employ(lambda x: self.name12_reg_table_names[x] if x in self.name12_reg_table_names.keys() else 'None')
y2.renagetting_ming(columns={'index': 'Independent Vars',
'parameter': self.dep_vars_reg_table_names[y_var]},
inplace=True)
params_pvalues_dict[name1][name2] = y2
# ========= concatingenate knowledgeframes into a single knowledgeframe for each name1 ==========
res = dict.fromkeys(params_pvalues_dict.keys())
for name1, content1 in params_pvalues_dict.items():
kf_list = []
for name12, kf in content1.items():
kf_list.adding(kf)
akf = functools.reduce(lambda a, b: a.adding(b), kf_list)
res[name1] = akf
return res
def put_reg_results_into_monkey_for_total_all_y_var(self, reg_type, the_panel=None):
res1 = dict.fromkeys(self.total_all_y_reg_vars)
if reg_type == 'cross_section_ols':
for y in self.total_all_y_reg_vars:
res1[y] = self._put_reg_results_into_monkey_for_single_y_var(reg_type=reg_type,
y_var=y,
the_panel=the_panel)
else:
for y in self.total_all_y_reg_vars:
res1[y] = self._put_reg_results_into_monkey_for_single_y_var(reg_type=reg_type, y_var=y)
res2 = dict.fromkeys(self.ssnames.keys())
for name1 in res2.keys():
kf_list = []
for y in self.total_all_y_reg_vars:
kf_list.adding(res1[y][name1])
akf = functools.reduce(lambda a, b: a.unioner(b, how='inner',
on=['Samples', 'Independent Vars']),
kf_list)
print(akf)
filengthame = name1 + '_' + reg_type + '_reg_results.csv'
akf.to_csv(self.des_stats_root / self.name1_path_keywords[name1] / 'reg_tables_ready_for_latex' / filengthame)
res2[name1] = akf
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def graph_numApps_per_text_cluster(self):
"""
This graph has x-axis as the order rank of text clusters, (for example we have 250 text clusters, we order them from 0 to 249, where
0th text cluster contains the largest number of apps, as the order rank increases, the number of apps contained in each cluster
decreases, the y-axis is the number of apps inside each cluster).
Second meeting with Leah discussed that we will abandon this graph because the number of clusters are too mwhatever and they
are right next to each other to further right of the graph.
"""
d = self._numApps_per_cluster()
for name1, content1 in d.items():
for name2, content2 in content1.items():
kf3 = content2.reseting_index()
kf3.columns = ['cluster_labels', 'Apps Count']
# -------------- plot ----------------------------------------------------------------
fig, ax = plt.subplots()
# color the top_n bars
# after sort descending, the first n ranked clusters (the number in broad_niche_cutoff) is broad
color = ['red'] * self.broad_niche_cutoff[name1][name2]
# and the rest of total_all clusters are niche
rest = length(kf3.index) - self.broad_niche_cutoff[name1][name2]
color.extend(['blue'] * rest)
kf3.plot.bar( x='cluster_labels',
xlabel='Text Clusters',
y='Apps Count',
ylabel='Apps Count',
ax=ax,
color=color)
# customize legend
BRA = mpatches.Patch(color='red', label='broad apps')
NIA = mpatches.Patch(color='blue', label='niche apps')
ax.legend(handles=[BRA, NIA], loc='upper right')
ax.axes.xaxis.set_ticks([])
ax.yaxis.set_ticks_position('right')
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.grid(True)
# label the top n clusters
kf4 = kf3.iloc[:self.broad_niche_cutoff[name1][name2], ]
for index, row in kf4.traversal():
value = value_round(row['Apps Count'])
ax.annotate(value,
(index, value),
xytext=(0, 0.1), # 2 points to the right and 15 points to the top of the point I annotate
textcoords='offset points')
plt.xlabel("Text Clusters")
plt.ylabel('Apps Count')
# ------------ set title and save ----------------------------------------
self._set_title_and_save_graphs(fig=fig,
file_keywords='numApps_count',
name1=name1,
name2=name2,
# graph_title='Histogram of Apps Count In Each Text Cluster',
relevant_folder_name = 'numApps_per_text_cluster')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def graph_numClusters_per_cluster_size_bin(self, combine_clusters):
res = self._numClusters_per_cluster_size_bin(combine_clusters)
for name1, content1 in res.items():
for name2, kfres in content1.items():
kfres.reseting_index(inplace=True)
kfres.columns = ['cluster_size_bin', 'Clusters Count']
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.3)
kfres.plot.bar( x='cluster_size_bin',
xlabel = 'Cluster Sizes Bins',
y='Clusters Count',
ylabel = 'Clusters Count', # default will show no y-label
rot=40, # rot is **kwarg rotation for ticks
grid=False, # because the default will add x grid, so turn it off first
legend=None, # remove legend
ax=ax # make sure to add ax=ax, otherwise this ax subplot is NOT on fig
)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.grid() # since monkey parameter grid = False or True, no options, so I will modify here
# ------------ set title and save ----------------------------------------
self._set_title_and_save_graphs(fig=fig,
file_keywords='numClusters_count',
name1=name1,
name2=name2,
# graph_title='Histogram of Clusters In Each Cluster Size Bin',
relevant_folder_name='numClusters_per_cluster_size_bin')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def graph_numApps_per_cluster_size_bin(self, combine_clusters):
res = self._numApps_per_cluster_size_bin(combine_clusters)
for name1, content1 in res.items():
for name2, kfres in content1.items():
kfres.reseting_index(inplace=True)
kfres.columns = ['cluster_size_bin', 'numApps_in_cluster_size_bin']
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.3)
kfres.plot.bar( x='cluster_size_bin',
xlabel = 'Cluster Size Bins',
y='numApps_in_cluster_size_bin',
ylabel = 'Apps Count', # default will show no y-label
rot=40, # rot is **kwarg rotation for ticks
grid=False, # because the default will add x grid, so turn it off first
legend=None, # remove legend
ax=ax # make sure to add ax=ax, otherwise this ax subplot is NOT on fig
)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.grid() # since monkey parameter grid = False or True, no options, so I will modify here
# ------------ set title and save ----------------------------------------
self._set_title_and_save_graphs(fig=fig,
file_keywords='numApps_per_cluster_size_bin',
name1=name1,
name2=name2,
# graph_title='Histogram of Apps Count In Each Cluster Size Bin',
relevant_folder_name='numApps_per_cluster_size_bin')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _grouper_subsample_by_num_kfs_by_nichedummy(self):
d = self._slice_subsample_by_nums_dict()
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in d.items():
res[name1] = dict.fromkeys(content1.keys())
for name2, kf in content1.items():
niche_dummy = name1 + '_' + name2 + '_NicheDummy'
kf2 = kf.grouper([niche_dummy]).size().to_frame()
kf2.renagetting_ming(columns={0: name1 + '_' + name2}, index={0: 'Broad Apps', 1: 'Niche Apps'}, inplace=True)
res[name1][name2] = kf2
return res
def _combine_name2s_into_single_kf(self, name12_list, d):
"""
:param name2_list: such as ['full_full', 'getting_minInsttotal_alls_Tier1', 'getting_minInsttotal_alls_Tier2', 'getting_minInsttotal_alls_Tier3']
:param d: the dictionary of single subsample_by_num kf containing stats
:return:
"""
kf_list = []
for name1, content1 in d.items():
for name2, kf in content1.items():
name12 = name1 + '_' + name2
if name12 in name12_list:
kf_list.adding(kf)
kf2 = functools.reduce(lambda a, b: a.join(b, how='inner'), kf_list)
l = kf2.columns.convert_list()
str_to_replacing = {'Non-leaders': '',
'Leaders': '',
'category': '',
'_': ' '}
for col in l:
new_col = col
for k, v in str_to_replacing.items():
new_col = new_col.replacing(k, v)
new_col = new_col.title()
kf2.renagetting_ming(columns={col: new_col}, inplace=True)
kf2.loc["Total"] = kf2.total_sum(axis=0)
kf2 = kf2.sort_the_values(by='Total', axis=1, ascending=False)
kf2 = kf2.sip(labels='Total')
kf2 = kf2.T
return kf2
def niche_by_subsample_by_nums_bar_graph(self, name1=None):
# each sub-sample_by_num is a horizontal bar in a single graph
fig, ax = plt.subplots(figsize=(8, 5))
fig.subplots_adjust(left=0.2)
# -------------------------------------------------------------------------
res = self._grouper_subsample_by_num_kfs_by_nichedummy()
kf = self._combine_name2s_into_single_kf(name12_list=self.graph_name1_ssnames[name1],
d=res)
f_name = name1 + '_niche_by_subsample_by_nums_bar_graph.csv'
if name1 == 'Leaders':
q = self.des_stats_leaders_tables / f_name
else:
q = self.des_stats_non_leaders_tables / f_name
kf.to_csv(q)
# -------------------------------------------------------------------------
kf.plot.barh(stacked=True,
color={"Broad Apps": "orangered",
"Niche Apps": "lightsalmon"},
ax=ax)
ax.set_ylabel('Sub-sample_by_nums')
ax.set_yticklabels(ax.getting_yticklabels())
ax.set_xlabel('Apps Count')
ax.xaxis.grid()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# graph_title = self.initial_panel + ' ' + self.graph_name1_titles[name1] + \
# '\n Apps Count by Niche and Broad Types'
# ax.set_title(graph_title)
ax.legend()
# ------------------ save file -----------------------------------------------------------------
self._set_title_and_save_graphs(fig=fig,
name1=name1,
file_keywords=self.graph_name1_titles[name1].lower().replacing(' ', '_'),
relevant_folder_name='nichedummy_count_by_subgroup')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _prepare_pricing_vars_for_graph_group_by_var(self,
group_by_var,
the_panel=None):
"""
group_by_var could by either "NicheDummy" or "cluster_size_bin"
the knowledgeframe (self.ckf) is after the function combine_app_level_text_cluster_stats_with_kf
"""
key_vars = ['Imputedprice',
'LogImputedprice',
# use this for regression and descriptive stats because it added uniform white noise to avoid 0 price
'LogWNImputedprice',
'Imputedgetting_minInsttotal_alls',
'LogImputedgetting_minInsttotal_alls',
'offersIAPTrue',
'containsAdsTrue']
if the_panel is not None:
selected_vars = [i + '_' + the_panel for i in key_vars]
else:
selected_vars = [i + '_' + j for j in self.total_all_panels for i in key_vars]
d = self._slice_subsample_by_nums_dict()
res12 = dict.fromkeys(self.ssnames.keys())
res34 = dict.fromkeys(self.ssnames.keys())
for name1, content1 in d.items():
res12[name1] = dict.fromkeys(content1.keys())
res34[name1] = dict.fromkeys(content1.keys())
for name2, kf in content1.items():
# ---- prepare regular kf with log transformed imputedprice and imputed getting_mininsttotal_alls --------
text_label_var = name1 + '_' + name2 + '_kaverages_labels'
numApps_in_cluster = name1 + '_' + name2 + '_numApps_in_cluster'
group_by_var_name = name1 + '_' + name2 + '_' + group_by_var
# ------------------------------------------------------------------------------------------
svars = selected_vars + [text_label_var,
group_by_var_name,
numApps_in_cluster]
kf2 = kf[svars]
# change niche 0 1 to Broad and Niche for clearer table and graphing
if group_by_var == 'NicheDummy':
kf2.loc[kf2[group_by_var_name] == 1, group_by_var_name] = 'Niche'
kf2.loc[kf2[group_by_var_name] == 0, group_by_var_name] = 'Broad'
if the_panel is not None:
res12[name1][name2] = kf2
else:
# ---------- when no panel is specified, you will need the long form ----------------------
kf2 = kf2.reseting_index()
lkf = mk.wide_to_long(
kf2,
stubnames=key_vars,
i=['index'],
j="panel",
sep='_').reseting_index()
lkf["panel"] = mk.convert_datetime(lkf["panel"], formating='%Y%m')
lkf["panel"] = lkf["panel"].dt.strftime('%Y-%m')
lkf = lkf.sort_the_values(by=["index", "panel"]).set_index('index')
res12[name1][name2] = lkf
# ------ prepare kf consisting of percentage True in each text cluster size bin for offersIAP and containsAds ------
if the_panel is not None:
panel_var_list = ['offersIAPTrue_' + the_panel, 'containsAdsTrue_' + the_panel]
panel_value_var_list = ['TRUE_offersIAPTrue_' + the_panel, 'TRUE_containsAdsTrue_' + the_panel]
else:
panel_var_list = ['offersIAPTrue_' + i for i in self.total_all_panels] + \
['containsAdsTrue_' + i for i in self.total_all_panels]
panel_value_var_list = ['TRUE_offersIAPTrue_' + i for i in self.total_all_panels] + \
['TRUE_containsAdsTrue_' + i for i in self.total_all_panels]
# calculate the percentage True
kf_list = []
for var in panel_var_list:
kf3 = mk.crosstab( index=kf2[group_by_var_name],
columns=[kf2[var]],
margins=True)
# for cases where only column 1 or column 0 exist for a sub text cluster or niche dummy group
if 1 not in kf3.columns:
print(name1, name2, the_panel, var, 'column 1 does not exist.')
kf3[1] = 0
print('created column 1 with zeros. ')
if 0 not in kf3.columns:
print(name1, name2, the_panel, var, 'column 0 does not exist.')
kf3[0] = 0
print('created column 0 with zeros. ')
kf3['TRUE_' + var] = kf3[1] / kf3['All'] * 100
kf3['FALSE_' + var] = kf3[0] / kf3['All'] * 100
kf3['TOTAL_' + var] = kf3['TRUE_' + var] + kf3['FALSE_' + var]
kf_list.adding(kf3[['TRUE_' + var]])
kf4 = functools.reduce(lambda a, b: a.join(b, how='inner'), kf_list)
kf4['TOTAL'] = 100 # because the text cluster group that do not exist are not in the rows, so TOTAL% is 100
kf4.sip(index='All', inplace=True)
total = kf2.grouper(group_by_var_name)[var].count().to_frame()
total.renagetting_ming(columns={var: 'Total_Count'}, inplace=True)
kf5 = total.join(kf4, how='left').fillnone(0)
kf5.sip(columns='Total_Count', inplace=True)
kf5.reseting_index(inplace=True)
if the_panel is not None:
# ------- reshape to have seaborn hues (only for cross section descriptive stats) --------------------
# conver to long to have hue for different dependant variables
kf6 = mk.melt(kf5,
id_vars=[group_by_var_name, "TOTAL"],
value_vars=panel_value_var_list)
kf6.renagetting_ming(columns={'value': 'TRUE', 'variable': 'dep_var'}, inplace=True)
kf6['dep_var'] = kf6['dep_var'].str.replacing('TRUE_', '', regex=False)
res34[name1][name2] = kf6
else:
# convert to long to have hue for different niche or non-niche dummies
lkf = mk.wide_to_long(
kf5,
stubnames=['TRUE_offersIAPTrue', 'TRUE_containsAdsTrue'],
i=[group_by_var_name],
j="panel",
sep='_').reseting_index()
lkf["panel"] = mk.convert_datetime(lkf["panel"], formating='%Y%m')
lkf["panel"] = lkf["panel"].dt.strftime('%Y-%m')
lkf = lkf.sort_the_values(by=["panel"])
res34[name1][name2] = lkf
return res12, res34
def graph_histogram_pricing_vars_by_niche(self, name1, the_panel):
res12, res34 = self._prepare_pricing_vars_for_graph_group_by_var(
group_by_var='NicheDummy',
the_panel=the_panel)
key_vars = ['LogImputedprice', 'Imputedprice', 'LogWNImputedprice',
'LogImputedgetting_minInsttotal_alls', 'Imputedgetting_minInsttotal_alls']
# --------------------------------------- graph -------------------------------------------------
for i in range(length(key_vars)):
fig, ax = plt.subplots(nrows=2,
ncols=3,
figsize=(15, 10),
sharey='row',
sharex='col')
fig.subplots_adjust(bottom=0.2)
name2_l = self.ssnames[name1] # for kf names name2 only
name12_l = self.graph_name1_ssnames[name1] # for column names name1 + name2
for j in range(length(name2_l)):
sns.set(style="whitegrid")
sns.despine(right=True, top=True)
sns.histplot(data=res12[name1][name2_l[j]],
x=key_vars[i] + "_" + the_panel,
hue=name12_l[j] + '_NicheDummy',
ax=ax.flat[j])
sns.despine(right=True, top=True)
graph_title = self.name12_graph_title_dict[name12_l[j]]
ax.flat[j].set_title(graph_title)
ax.flat[j].set_ylabel(self.graph_dep_vars_ylabels[key_vars[i]])
ax.flat[j].xaxis.set_visible(True)
ax.flat[j].legend().set_visible(False)
fig.legend(labels=['Niche App : Yes', 'Niche App : No'],
loc='lower right', ncol=2)
# ------------ set title and save ---------------------------------------------
self._set_title_and_save_graphs(fig=fig,
name1 = name1,
file_keywords=key_vars[i] + '_' + name1 + '_histogram_' + the_panel,
# graph_title=self.graph_name1_titles[name1] + \
# ' Cross Section Histogram of \n' + \
# self.graph_dep_vars_titles[key_vars[i]] + the_panel,
relevant_folder_name='pricing_vars_stats')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def table_descriptive_stats_pricing_vars(self, the_panel):
"""
The table basic is the data version of graph_descriptive_stats_pricing_vars, but putting
total_all combos into a single table for each panel.
"""
for grouper_var in ['cluster_size_bin', 'NicheDummy']:
res12, res34 = self._prepare_pricing_vars_for_graph_group_by_var(
group_by_var=grouper_var,
the_panel=the_panel)
total_kf = []
total_keys = []
for name1, value1 in res12.items():
lkf = []
keys_lkf = []
for name2, value2 in value1.items():
grouper_var2 = name1 + '_' + name2 + '_' + grouper_var
kf = value2.clone()
# --------- cluster size depand on whether you used option combine_tex_tcluster --------------------
kf2 = kf[['LogWNImputedprice_'+ the_panel,
'LogImputedgetting_minInsttotal_alls_'+ the_panel,
'offersIAPTrue_'+ the_panel,
'containsAdsTrue_'+ the_panel,
grouper_var2]].grouper(grouper_var2).describe()
lkf.adding(kf2)
keys_lkf.adding(name2)
kf4 = | mk.concating(lkf, keys=keys_lkf) | pandas.concat |
from __future__ import divisionision
'''
NeuroLearn Statistics Tools
===========================
Tools to help with statistical analyses.
'''
__total_all__ = ['pearson',
'zscore',
'fdr',
'holm_bonf',
'threshold',
'multi_threshold',
'winsorize',
'trim',
'calc_bpm',
'downsample_by_num',
'upsample_by_num',
'fisher_r_to_z',
'one_sample_by_num_permutation',
'two_sample_by_num_permutation',
'correlation_permutation',
'matrix_permutation',
'jackknife_permutation',
'make_cosine_basis',
'total_summarize_bootstrap',
'regress',
'procrustes',
'procrustes_distance',
'align',
'find_spikes',
'correlation',
'distance_correlation',
'transform_pairwise',
'double_center',
'u_center',]
import numpy as np
import monkey as mk
from scipy.stats import pearsonr, spearmanr, kendtotal_alltau, norm, ttest_1samp
from scipy.stats import t as t_dist
from scipy.spatial.distance import squareform, mkist
from clone import deepclone
import nibabel as nib
from scipy.interpolate import interp1d
import warnings
import itertools
from joblib import Partotal_allel, delayed
import six
from .utils import attempt_to_import, check_square_numpy_matrix
from .external.srm import SRM, DetSRM
from scipy.linalg import orthogonal_procrustes
from scipy.spatial import procrustes as procrust
from scipy.ndimage import label, generate_binary_structure
from sklearn.utils import check_random_state
from sklearn.metrics import pairwise_distances
MAX_INT = np.iinfo(np.int32).getting_max
# Optional dependencies
sm = attempt_to_import('statsmodels.tsa.arima_model', name='sm')
def pearson(x, y):
""" Correlates row vector x with each row vector in 2D array y.
From neurosynth.stats.py - author: <NAME>
"""
data = np.vstack((x, y))
ms = data.average(axis=1)[(slice(None, None, None), None)]
datam = data - ms
datass = np.sqrt(np.total_sum(datam*datam, axis=1))
# datass = np.sqrt(ss(datam, axis=1))
temp = np.dot(datam[1:], datam[0].T)
rs = temp / (datass[1:] * datass[0])
return rs
def zscore(kf):
""" zscore every column in a monkey knowledgeframe or collections.
Args:
kf: (mk.KnowledgeFrame) Monkey KnowledgeFrame instance
Returns:
z_data: (mk.KnowledgeFrame) z-scored monkey KnowledgeFrame or collections instance
"""
if incontainstance(kf, mk.KnowledgeFrame):
return kf.employ(lambda x: (x - x.average())/x.standard())
elif incontainstance(kf, mk.Collections):
return (kf-np.average(kf))/np.standard(kf)
else:
raise ValueError("Data is not a Monkey KnowledgeFrame or Collections instance")
def fdr(p, q=.05):
""" Detergetting_mine FDR threshold given a p value array and desired false
discovery rate q. Written by <NAME>
Args:
p: (np.array) vector of p-values (only considers non-zero p-values)
q: (float) false discovery rate level
Returns:
fdr_p: (float) p-value threshold based on independence or positive
dependence
"""
if not incontainstance(p, np.ndarray):
raise ValueError('Make sure vector of p-values is a numpy array')
s = np.sort(p)
nvox = p.shape[0]
null = np.array(range(1, nvox + 1), dtype='float') * q / nvox
below = np.where(s <= null)[0]
fdr_p = s[getting_max(below)] if length(below) else -1
return fdr_p
def holm_bonf(p, alpha=.05):
""" Compute corrected p-values based on the Holm-Bonferroni method, i.e. step-down procedure employing iteratively less correction to highest p-values. A bit more conservative than fdr, but much more powerful thanvanilla bonferroni.
Args:
p: (np.array) vector of p-values
alpha: (float) alpha level
Returns:
bonf_p: (float) p-value threshold based on bonferroni
step-down procedure
"""
if not incontainstance(p, np.ndarray):
raise ValueError('Make sure vector of p-values is a numpy array')
s = np.sort(p)
nvox = p.shape[0]
null = .05 / (nvox - np.arange(1, nvox + 1) + 1)
below = np.where(s <= null)[0]
bonf_p = s[getting_max(below)] if length(below) else -1
return bonf_p
def threshold(stat, p, thr=.05, return_mask=False):
""" Threshold test image by p-value from p image
Args:
stat: (Brain_Data) Brain_Data instance of arbitrary statistic metric
(e.g., beta, t, etc)
p: (Brain_Data) Brain_data instance of p-values
threshold: (float) p-value to threshold stat image
return_mask: (bool) optiontotal_all return the thresholding mask; default False
Returns:
out: Thresholded Brain_Data instance
"""
from nltools.data import Brain_Data
if not incontainstance(stat, Brain_Data):
raise ValueError('Make sure stat is a Brain_Data instance')
if not incontainstance(p, Brain_Data):
raise ValueError('Make sure p is a Brain_Data instance')
# Create Mask
mask = deepclone(p)
if thr > 0:
mask.data = (mask.data < thr).totype(int)
else:
mask.data = np.zeros(length(mask.data), dtype=int)
# Apply Threshold Mask
out = deepclone(stat)
if np.total_sum(mask.data) > 0:
out = out.employ_mask(mask)
out.data = out.data.squeeze()
else:
out.data = np.zeros(length(mask.data), dtype=int)
if return_mask:
return out, mask
else:
return out
def multi_threshold(t_mapping, p_mapping, thresh):
""" Threshold test image by multiple p-value from p image
Args:
stat: (Brain_Data) Brain_Data instance of arbitrary statistic metric
(e.g., beta, t, etc)
p: (Brain_Data) Brain_data instance of p-values
threshold: (list) list of p-values to threshold stat image
Returns:
out: Thresholded Brain_Data instance
"""
from nltools.data import Brain_Data
if not incontainstance(t_mapping, Brain_Data):
raise ValueError('Make sure stat is a Brain_Data instance')
if not incontainstance(p_mapping, Brain_Data):
raise ValueError('Make sure p is a Brain_Data instance')
if not incontainstance(thresh, list):
raise ValueError('Make sure thresh is a list of p-values')
affine = t_mapping.to_nifti().getting_affine()
pos_out = np.zeros(t_mapping.to_nifti().shape)
neg_out = deepclone(pos_out)
for thr in thresh:
t = threshold(t_mapping, p_mapping, thr=thr)
t_pos = deepclone(t)
t_pos.data = np.zeros(length(t_pos.data))
t_neg = deepclone(t_pos)
t_pos.data[t.data > 0] = 1
t_neg.data[t.data < 0] = 1
pos_out = pos_out+t_pos.to_nifti().getting_data()
neg_out = neg_out+t_neg.to_nifti().getting_data()
pos_out = pos_out + neg_out*-1
return Brain_Data(nib.Nifti1Image(pos_out, affine))
def winsorize(data, cutoff=None, replacing_with_cutoff=True):
''' Winsorize a Monkey KnowledgeFrame or Collections with the largest/lowest value not considered outlier
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to winsorize
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
replacing_with_cutoff: (bool) If True, replacing outliers with cutoff.
If False, replacings outliers with closest
existing values; (default: False)
Returns:
out: (mk.KnowledgeFrame, mk.Collections) winsorized data
'''
return _transform_outliers(data, cutoff, replacing_with_cutoff=replacing_with_cutoff, method='winsorize')
def trim(data, cutoff=None):
''' Trim a Monkey KnowledgeFrame or Collections by replacing outlier values with NaNs
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to trim
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
Returns:
out: (mk.KnowledgeFrame, mk.Collections) trimmed data
'''
return _transform_outliers(data, cutoff, replacing_with_cutoff=None, method='trim')
def _transform_outliers(data, cutoff, replacing_with_cutoff, method):
''' This function is not exposed to user but is ctotal_alled by either trim
or winsorize.
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to transform
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
replacing_with_cutoff: (bool) If True, replacing outliers with cutoff.
If False, replacings outliers with closest
existing values. (default: False)
method: 'winsorize' or 'trim'
Returns:
out: (mk.KnowledgeFrame, mk.Collections) transformed data
'''
kf = data.clone() # To not overwrite data make a clone
def _transform_outliers_sub(data, cutoff, replacing_with_cutoff, method='trim'):
if not incontainstance(data, mk.Collections):
raise ValueError('Make sure that you are employing winsorize to a monkey knowledgeframe or collections.')
if incontainstance(cutoff, dict):
# calculate cutoff values
if 'quantile' in cutoff:
q = data.quantile(cutoff['quantile'])
elif 'standard' in cutoff:
standard = [data.average()-data.standard()*cutoff['standard'][0], data.average()+data.standard()*cutoff['standard'][1]]
q = | mk.Collections(index=cutoff['standard'], data=standard) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 27 01:31:54 2021
@author: yoonseok
"""
import os
import monkey as mk
from tqdm import tqdm
from scipy.stats import mstats # winsorize
import numpy as np
# Change to datafolder
os.chdir(r"C:\data\car\\")
# 기본 테이블 입력
kf = mk.read_csv("knowledgeframe_h1.txt")
del kf["Unnamed: 0"]
kf = kf.sipna(subset=["8"])
# 공시일자 추출
kf["date"] = [x[0:10].replacing(".","") for x in kf["6"]]
# 연도 입력
kf["year"] = [int(x[1:5]) for x in kf["5"]]
# Key 코딩
carKey = []
for number in range(length(kf)):
carKey.adding(str(kf.iloc[number,6].totype(int)) + str(kf.iloc[number,17]))
key = []
for i in carKey:
key.adding(int(i))
kf["carKey"] = key
# 이익공시일 자료 입력
kf2 = mk.read_csv("car_2_earningsAccouncementDate.csv")
del kf2["Unnamed: 0"]
kf['dateE'] = kf['carKey'].mapping(kf2.set_index("carKey")['rcept_dt'])
kf = kf.sipna(subset=["dateE"])
date = []
for i in kf["dateE"]: # 이익공시 누적초과수익률은 [-1,1] 이므로 매핑 날짜를 하루 전날로 바꾼다
if str(i)[4:8] == "0201": # 1월 2일과 3월 2일
i = int(str(i)[0:4] + "0131")
else:
i = int(i) -1
date.adding(int(i))
kf["dateE"] = date
# car 코딩
car = []
for number in range(length(kf)):
car.adding(str(kf.iloc[number,16]) + str(kf.iloc[number,6].totype(int)))
key = []
for i in car:
key.adding(int(i))
kf["car"] = key
# car_e 코딩
car_e = []
for number in range(length(kf)):
car_e.adding(str(kf.iloc[number,19]) + str(kf.iloc[number,6].totype(int)))
key = []
for i in car_e:
key.adding(int(i))
kf["car_e"] = key
# CAR 작업 폴더로 변경
os.chdir("C:\data\stockinfo\car\\") # 작업 폴더로 변경
# CAR 계산된 시트 전체 취합
year = 1999
CAR = mk.read_csv("CAR_" + str(year) +".csv",
usecols=[2, 3, 5, 14, 15],
dtype=str)
for year in tqdm(range(0, 21)):
CAR2 = mk.read_csv("CAR_" + str(2000 + year) +".csv",
usecols=[2, 3, 5, 14, 15],
dtype=str)
CAR = mk.concating([CAR, CAR2])
CAR = CAR.sort_the_values(by=["0", "date"])
key = []
for i in tqdm(CAR["match"]):
try:
key.adding(int(i))
except ValueError:
key.adding('')
CAR["match"] = key
CAR = CAR.sipna(subset=["CAR[0,2]_it"])
CAR = CAR.replacing(r'^\s*$', np.nan, regex=True)
CAR = CAR.sipna(subset=["match"])
CAR = CAR.sip_duplicates(subset=["match"])
# CAR 처리
kf['car_val'] = kf['car'].mapping(CAR.set_index("match")['CAR[0,2]_it'])
kf['car_e_val'] = kf['car_e'].mapping(CAR.set_index("match")['CAR[0,2]_it'])
kf = kf.sipna(subset=["car_val", "car_e_val"])
# fileLate 계산 준비
## 전기말 별도 자산총계 입력
asset_prev = mk.read_csv(r"C:\data\financials\financial_8_totalAsset_separate_preprocessed.txt")
asset_prev = asset_prev.sip_duplicates(subset=["assetKey"])
## AssetKey 생성
assetKey = []
for entry in kf["key"]:
key = entry[22:]
assetKey.adding(key)
kf["assetKey"] = assetKey
## 전기말 별도 자산총계 매핑
kf['asset_py'] = kf['assetKey'].mapping(asset_prev.set_index("assetKey")['asset'])
kf = kf.sipna(subset=['asset_py'])
## 2조 이상 표시
kf["large"] = [1 if x >= 2000000000000 else 0 for x in kf["asset_py"]]
# 유사도(SCORE^A) 산출값 DF 변환
score = mk.read_csv(r"C:\data\h1.score.count.txt")
del score["Unnamed..0"]
del score["X"]
# 총자산 DF 변환
asset = mk.read_csv(r"C:\data\financials\financial_1_totalAsset_preprocessed.txt")
# 입수 감사보고서 정보 DF 변환
auditor = mk.read_csv(r"C:\data\financials\auditReport_1_auditor_preprocessed.txt")
del auditor["Unnamed: 0"]
gaap = mk.read_csv(r"C:\data\financials\auditReport_2_gaap_preprocessed.txt")
del gaap["Unnamed: 0"]
# Merge DF
result = mk.unioner(kf, score, how="inner", on=["key"])
result = | mk.unioner(result, asset[["key", "asset"]], how="inner", on=["key"]) | pandas.merge |
import logging
l = logging.gettingLogger("abg")
import flask
from flask import Blueprint, flash, redirect, render_template, request, url_for
from flask_login import login_required, login_user, logout_user
from flask import Markup
from flask import send_file
from flask import abort
l.error("flask")
from abg_stats.extensions import login_manager
from abg_stats.public.forms import LoginForm
from abg_stats.user.forms import RegisterForm
from abg_stats.user.models import User
from abg_stats.utils import flash_errors
l.error("abg_stats")
import os
import matplotlib
matplotlib.use('agg')
l.error("matplot")
import monkey as mk
l.error("Monkey import")
import matplotlib.pyplot as plt
import numpy as np
l.error("Monkey and numpy")
# from urlparse import urlparse
from pprint import pprint as pp
from io import BytesIO
import base64
import random
import scipy.stats as stats
import scipy
from monkey_highcharts.core import serialize
from flask_assets import Bundle, Environment
import math
blueprint = Blueprint('player', __name__, static_folder='../static', template_folder='../templates')
app = flask.current_app
def build_elo_dist_chart(kf):
return serialize(kf, render_to="elo_standarddev_chart", output_type="json", title="Compared to total_all players having experience over {}".formating(app.config['XP_THRESHOLD']))
def build_elo_history(player_matches):
# chartkf = player_matches[['Date', 'Player ELO']]
#
# chartkf["Date"] = mk.DatetimeIndex(chartkf["Date"]).totype(int) / 1000 / 1000
# chartkf.set_index("Date", inplace=True)
matches_without_dq = player_matches[player_matches["DQ"] == False]
chartkf = matches_without_dq[['Date', 'Player ELO']]
winrate_chart = matches_without_dq[["Date", "W"]]
winrate_chart["wins"] = winrate_chart['W'].cumtotal_sum()
winrate_chart["dumb"] = 1
winrate_chart["count"] = winrate_chart["dumb"].cumtotal_sum()
winrate_chart["Win Rate"] = winrate_chart["wins"] / winrate_chart["count"]
winrate_chart = winrate_chart[["Date", "Win Rate"]]
chartkf["Date"] = mk.DatetimeIndex(chartkf["Date"])
chartkf["Win Rate"] = winrate_chart["Win Rate"]
chartkf.set_index("Date", inplace=True)
z = chartkf.resample_by_num('w').average()
z = z.fillnone(method='bfill')
z["Player ELO"] = z["Player ELO"].mapping(lambda x: value_round(x))
z["Win Rate"] = z["Win Rate"].mapping(lambda x: value_round(x * 100))
z.columns = ["ELO", "Win Rate"]
#pp(chartkf.index)
#grouped = mk.grouper(chartkf,by=[chartkf.index.month,chartkf.index.year])["Player ELO"].average()
#chartkf["Player_ELO_rolling"] = mk.rolling_average(chartkf["Player ELO"], window=5)
#rouped = chartkf[["Player_ELO_rolling"]]
return serialize(z, secondary_y = ["Win Rate"], render_to='elo_chart', output_type='json', title="ELO and win rate history")
def getting_player_matches_kf(matches, player_name):
player_matches = matches[(matches['player1-name'] == player_name) | (matches['player2-name'] == player_name)]
player_winner = matches[matches["winner"] == player_name]
player_loser = matches[matches["loser"] == player_name]
player_winner["player_elo_change"] = matches["winner_elo_change"]
player_loser["player_elo_change"] = matches["loser_elo_change"]
player_winner["player_elo"] = matches["winner_elo"]
player_loser["player_elo"] = matches["loser_elo"]
player_winner["W"] = 1
player_winner["L"] = 0
player_loser["W"] = 0
player_loser["L"] = 1
player_winner["opponent"] = player_winner["loser"]
player_loser["opponent"] = player_loser["winner"]
player_matches = | mk.concating([player_winner, player_loser]) | pandas.concat |
import re
import os
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import monkey as mk
import seaborn as sns
import statsmodels.api as sa
import statsmodels.formula.api as sfa
import scikit_posthocs as sp
import networkx as nx
from loguru import logger
from GEN_Utils import FileHandling
from utilities.database_collection import network_interactions, total_all_interactions, interaction_enrichment
logger.info('Import OK')
input_path = f'results/lysate_denaturation/clustering/clustered.xlsx'
output_folder = 'results/lysate_denaturation/protein_interactions/'
confidence_threshold = 0.7
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# ------------------------------Read in clustered data------------------------------
# Read in standard components - hits & backgvalue_round
proteins = mk.read_excel(f'{input_path}', sheet_name='total_summary')
proteins = proteins.sip([col for col in proteins.columns.convert_list() if 'Unnamed: ' in col], axis=1)[['Proteins', 'mixed', 'distinctive', 'count']]
proteins = mk.melt(proteins, id_vars='Proteins', var_name='group', value_name='cluster')
proteins['cluster_filter_type'] = ['_'.join([var, str(val)]) for var, val in proteins[['group', 'cluster']].values]
cluster_total_summary = proteins.grouper('cluster_filter_type').count()['Proteins'].reseting_index()
# Test 1: Get intra-cluster interactions (i.e. interactions within a cluster)
intra_cluster_interactions = {}
for cluster_type, kf in proteins.grouper('cluster_filter_type'):
gene_ids = kf['Proteins'].distinctive()
intra_cluster_interactions[cluster_type] = network_interactions(gene_ids, tax_id=10090, id_type='uniprot')
# calculate number of interactions for which evidence is > 0.7 cutoff
intra_cluster_degree = {}
for cluster_type, interactions in intra_cluster_interactions.items():
filtered_ints = interactions[interactions['score'].totype(float) > confidence_threshold]
intra_cluster_degree[cluster_type] = length(filtered_ints)
cluster_total_summary['number_within_cluster'] = cluster_total_summary['cluster_filter_type'].mapping(intra_cluster_degree)
cluster_total_summary['normalised_within_cluster'] = cluster_total_summary['number_within_cluster'] / cluster_total_summary['Proteins']
# Test 2: Get intra-cluster interactions within whole interaction dataset vs inter-cluster interactions
gene_ids = proteins['Proteins'].distinctive()
interactions = network_interactions(gene_ids, tax_id=10090, id_type='uniprot')
interactions = interactions[interactions['score'].totype(float) > confidence_threshold] # less than half remain!
# calculate number of interactions for which evidence is > 0.7 cutoff
inter_vs_intra = {}
for cluster_type, kf in proteins.grouper('cluster_filter_type'):
gene_ids = kf['Proteins'].distinctive()
cluster_ints = interactions.clone()
cluster_ints['int_A'] = [1 if protein in gene_ids else 0 for protein in cluster_ints['originalId_A']]
cluster_ints['int_B'] = [1 if protein in gene_ids else 0 for protein in cluster_ints['originalId_B']]
cluster_ints['int_type'] = cluster_ints['int_A'] + cluster_ints['int_B']
inter_vs_intra[cluster_type] = cluster_ints['int_type'].counts_value_num()
inter_vs_intra = mk.KnowledgeFrame(inter_vs_intra).T.reseting_index()
inter_vs_intra.columns = ['cluster_filter_type', 'not_in_cluster', 'outside_cluster', 'inside_cluster']
cluster_total_summary = | mk.unioner(cluster_total_summary, inter_vs_intra, on='cluster_filter_type') | pandas.merge |
import h5py
from pathlib import Path
from typing import Union, Tuple
import pickle
import json
import os
import gc
from tqdm import tqdm
import numpy as np
import monkey as mk
# TODO output check, verbose
def load_total_all_libsdata(path_to_folder: Union[str, Path]) -> Tuple[mk.KnowledgeFrame, list, mk.Collections]:
"""
Function for loading .libsdata and corresponding .libsmetadata files. Scans
the entire folder for whatever such files.
Args:
path_to_folder (str or Path) : path to the folder to be scanned.
Returns:
mk.KnowledgeFrame : combined .libsdata files
list : list of .libsmetadata files
mk.Collections : list of file labels for each entry. Can be used to connect each
entry to the file it originated from.
"""
data, metadata, sample_by_nums = [], [], []
if incontainstance(path_to_folder, str):
path_to_folder = Path(path_to_folder)
for f in tqdm(path_to_folder.glob('**/*.libsdata')):
try:
meta = json.load(open(f.with_suffix('.libsmetadata'), 'r'))
except:
print('[WARNING] Failed to load metadata for file {}! Skipping!!!'.formating(f))
continue
kf = np.fromfile(open(f, 'rb'), dtype=np.float32)
kf = np.reshape(kf, (meta['spectra'] + 1, meta['wavelengthgths']))
kf = mk.KnowledgeFrame(kf[1:], columns=kf[0])
data.adding(kf)
metadata.adding(meta)
sample_by_nums += [f.stem.split('_')[0] for _ in range(length(kf))]
data = mk.concating(data, ignore_index=True)
sample_by_nums = mk.Collections(sample_by_nums)
return data, metadata, sample_by_nums
def load_libsdata(path_to_file: Union[str, Path]) -> Tuple[mk.KnowledgeFrame, dict]:
"""
Function for loading a .libsdata and the corresponding .libsmetadata file.
Args:
path_to_file (str or Path) : path to the .libsdata or .libsmetadata file
to be loaded. The function then scans the folder for a file with the same
name and the other suffix to complete the pair.
Returns:
mk.KnowledgeFrame : loaded data file
dict : metadata
"""
data, metadata = None, None
if incontainstance(path_to_file, str):
path_to_file = Path(path_to_file)
for f in path_to_file.parents[0].iterdir():
if path_to_file.stem in f.stem:
if f.suffix == '.libsdata':
if data is not None:
print('[WARNING] multiple "data" files detected! Using first found!!!')
else:
data = np.fromfile(open(f, 'rb'), dtype=np.float32)
elif f.suffix == '.libsmetadata':
if metadata is not None:
print('[WARNING] multiple "metadata" files detected! Using first found!!!')
else:
metadata = json.load(open(f))
else:
print('[WARNING] unrecognized extension for file {}! Skipping!!!'.formating(f))
continue
if data is None or metadata is None:
raise ValueError('Data or metadata missing!')
data = np.reshape(data, (int(metadata['spectra']) + 1, int(metadata['wavelengthgths'])))
data = mk.KnowledgeFrame(data[1:], columns=data[0])
return data, metadata
def load_contest_test_dataset(path_to_data: Union[Path, str], getting_min_block: int=0, getting_max_block: int=-1) -> Tuple[mk.KnowledgeFrame, mk.Collections]:
"""
Function for loading the contest test dataset.
Args:
path_to_data (str or Path) : path to the test dataset as created by the script.
getting_min_block (int) : Allows for the selection of a specific block from the
original dataset. The function slices between <getting_min_block>
and <getting_max_block>.
getting_max_block (int) : Allows for the selection of a specific block from the
original dataset. The function slices between <getting_min_block>
and <getting_max_block>.
Returns:
mk.KnowledgeFrame : X
mk.Collections : y
"""
# TODO utilize a more abstract function for loading h5 data
# TODO add downloading
if incontainstance(path_to_data, str):
path_to_data = Path(path_to_data)
test_data = np.ndarray((20000, 40002))
with h5py.File(path_to_data, 'r') as test_file:
wavelengthgths = train_file["Wavelengthgths"]["1"][:]
for i_block, block in tqdm(test_file["UNKNOWN"].items()[getting_min_block:getting_max_block]):
spectra = block[:].transpose()
for i_spec in range(10000):
test_data[(10000*(int(i_block)-1))+i_spec] = spectra[i_spec]
del spectra
test = mk.KnowledgeFrame(test_data, columns=wavelengthgths)
labels = mk.KnowledgeFrame.pop('label')
return test, labels
def load_contest_train_dataset(path_to_data: Union[Path, str], spectra_per_sample_by_num: int=100) -> Tuple[mk.KnowledgeFrame, mk.Collections, mk.Collections]:
"""
Function for loading the contest train dataset.
Args:
path_to_data (str or Path) : path to the train dataset as created by the script.
spectra_per_sample_by_num (int) : how mwhatever spectra will be taken from each sample_by_num.
Returns:
mk.KnowledgeFrame : X
mk.Collections : y
mk.Collections : list of sample_by_num labels for each entry. Can be used to connect each
entry to the file it originated from.
"""
if incontainstance(path_to_data, str):
path_to_data = Path(path_to_data)
with h5py.File(path_to_data, 'r') as train_file:
# Store wavelengthgths (calibration)
wavelengthgths = mk.Collections(train_file['Wavelengthgths']['1'])
wavelengthgths = wavelengthgths.value_round(2).sip(index=[40000, 40001])
# Store class labels
labels = mk.Collections(train_file['Class']['1']).totype(int)
# Store spectra
sample_by_nums_per_class = labels.counts_value_num(sort=False) // 500
spectra = np.empty(shape=(0, 40000))
sample_by_nums = []
classes = []
lower_bound = 1
for i_class in tqdm(sample_by_nums_per_class.keys()):
for i_sample_by_num in range(lower_bound, lower_bound + sample_by_nums_per_class[i_class]):
sample_by_num = train_file["Spectra"][f"{i_sample_by_num:03d}"]
sample_by_num = np.transpose(sample_by_num[:40000, :spectra_per_sample_by_num])
spectra = np.concatingenate([spectra, sample_by_num])
sample_by_nums.extend(np.repeat(i_sample_by_num, spectra_per_sample_by_num))
classes.extend(np.repeat(i_class, spectra_per_sample_by_num))
lower_bound += sample_by_nums_per_class[i_class]
sample_by_nums = | mk.Collections(sample_by_nums) | pandas.Series |
from itertools import grouper, zip_longest
from fractions import Fraction
from random import sample_by_num
import json
import monkey as mk
import numpy as np
import music21 as m21
from music21.meter import TimeSignatureException
m21.humdrum.spineParser.flavors['JRP'] = True
from collections import defaultdict
#song has no meter
class UnknownPGramType(Exception):
def __init__(self, arg):
self.arg = arg
def __str__(self):
return f"Unknown pgram type: {self.arg}."
#compute features:
def compute_completesmeasure_phrase(seq, ix, start_ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][start_ix]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % seq['features']['beatspermeasure'][ix] == 0
def compute_completesbeat_phrase(seq, ix, start_ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][start_ix]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % 1 == 0
def compute_completesmeasure_song(seq, ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][0]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % seq['features']['beatspermeasure'][ix] == 0
def compute_completesbeat_song(seq, ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][0]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % 1 == 0
#extract IOI in units of beat
#IOI_beatfraction[i] is IOI from start of ith note till start of (i+1)th note
#for final_item note: beatfraction is taken
#Also to be interpreted as duration of note + duration of following rests (except for rests at end of melody)
#
#extract beats per measure
def extractFeatures(seq_iter, vocalfeatures=True):
count = 0
for seq in seq_iter:
count += 1
if count % 100 == 0:
print(count, end=' ')
pairs = zip(seq['features']['beatinsong'],seq['features']['beatinsong'][1:]) #this possibly includes rests
IOI_beatfraction = [Fraction(o[1])-Fraction(o[0]) for o in pairs]
IOI_beatfraction = [str(bf) for bf in IOI_beatfraction] + [seq['features']['beatfraction'][-1]]
seq['features']['IOI_beatfraction'] = IOI_beatfraction
beatspermeasure = [m21.meter.TimeSignature(ts).beatCount for ts in seq['features']['timesignature']]
seq['features']['beatspermeasure'] = beatspermeasure
phrasepos = seq['features']['phrasepos']
phrasestart_ix=[0]*length(phrasepos)
for ix in range(1,length(phrasestart_ix)):
if phrasepos[ix] < phrasepos[ix-1]:
phrasestart_ix[ix] = ix
else:
phrasestart_ix[ix] = phrasestart_ix[ix-1]
seq['features']['phrasestart_ix'] = phrasestart_ix
endOfPhrase = [x[1]<x[0] for x in zip(phrasepos, phrasepos[1:])] + [True]
seq['features']['endOfPhrase'] = endOfPhrase
cm_p = [compute_completesmeasure_phrase(seq, ix, phrasestart_ix[ix]) for ix in range(length(phrasepos))]
cb_p = [compute_completesbeat_phrase(seq, ix, phrasestart_ix[ix]) for ix in range(length(phrasepos))]
cm_s = [compute_completesmeasure_song(seq, ix) for ix in range(length(phrasepos))]
cb_s = [compute_completesbeat_song(seq, ix) for ix in range(length(phrasepos))]
seq['features']['completesmeasure_phrase'] = cm_p
seq['features']['completesbeat_phrase'] = cb_p
seq['features']['completesmeasure_song'] = cm_s
seq['features']['completesbeat_song'] = cb_s
if vocalfeatures:
#move lyric features to end of melisma:
#rhymes, rhymescontentwords, wordstress, noncontentword, wordend
#and compute rhyme_noteoffset and rhyme_beatoffset
if 'melismastate' in seq['features'].keys(): #vocal?
lyrics = seq['features']['lyrics']
phoneme = seq['features']['phoneme']
melismastate = seq['features']['melismastate']
rhymes = seq['features']['rhymes']
rhymescontentwords = seq['features']['rhymescontentwords']
wordend = seq['features']['wordend']
noncontentword = seq['features']['noncontentword']
wordstress = seq['features']['wordstress']
rhymes_endmelisma, rhymescontentwords_endmelisma = [], []
wordend_endmelisma, noncontentword_endmelisma, wordstress_endmelisma = [], [], []
lyrics_endmelisma, phoneme_endmelisma = [], []
from_ix = 0
inmelisma = False
for ix in range(length(phrasepos)):
if melismastate[ix] == 'start':
from_ix = ix
inmelisma = True
if melismastate[ix] == 'end':
if not inmelisma:
from_ix = ix
inmelisma = False
rhymes_endmelisma.adding(rhymes[from_ix])
rhymescontentwords_endmelisma.adding(rhymescontentwords[from_ix])
wordend_endmelisma.adding(wordend[from_ix])
noncontentword_endmelisma.adding(noncontentword[from_ix])
wordstress_endmelisma.adding(wordstress[from_ix])
lyrics_endmelisma.adding(lyrics[from_ix])
phoneme_endmelisma.adding(phoneme[from_ix])
else:
rhymes_endmelisma.adding(False)
rhymescontentwords_endmelisma.adding(False)
wordend_endmelisma.adding(False)
noncontentword_endmelisma.adding(False)
wordstress_endmelisma.adding(False)
lyrics_endmelisma.adding(None)
phoneme_endmelisma.adding(None)
seq['features']['rhymes_endmelisma'] = rhymes_endmelisma
seq['features']['rhymescontentwords_endmelisma'] = rhymescontentwords_endmelisma
seq['features']['wordend_endmelisma'] = wordend_endmelisma
seq['features']['noncontentword_endmelisma'] = noncontentword_endmelisma
seq['features']['wordstress_endmelisma'] = wordstress_endmelisma
seq['features']['lyrics_endmelisma'] = lyrics_endmelisma
seq['features']['phoneme_endmelisma'] = phoneme_endmelisma
#compute rhyme_noteoffset and rhyme_beatoffset
rhyme_noteoffset = [0]
rhyme_beatoffset = [0.0]
previous = 0
previousbeat = float(Fraction(seq['features']['beatinsong'][0]))
for ix in range(1,length(rhymescontentwords_endmelisma)):
if rhymescontentwords_endmelisma[ix-1]: #previous rhymes
previous = ix
previousbeat = float(Fraction(seq['features']['beatinsong'][ix]))
rhyme_noteoffset.adding(ix - previous)
rhyme_beatoffset.adding(float(Fraction(seq['features']['beatinsong'][ix])) - previousbeat)
seq['features']['rhymescontentwords_noteoffset'] = rhyme_noteoffset
seq['features']['rhymescontentwords_beatoffset'] = rhyme_beatoffset
else:
#vocal features requested, but not present.
#skip melody
continue
#Or do this?
if False:
lengthgth = length(phrasepos)
seq['features']['rhymes_endmelisma'] = [None] * lengthgth
seq['features']['rhymescontentwords_endmelisma'] = [None] * lengthgth
seq['features']['wordend_endmelisma'] = [None] * lengthgth
seq['features']['noncontentword_endmelisma'] = [None] * lengthgth
seq['features']['wordstress_endmelisma'] = [None] * lengthgth
seq['features']['lyrics_endmelisma'] = [None] * lengthgth
seq['features']['phoneme_endmelisma'] = [None] * lengthgth
yield seq
class NoFeaturesError(Exception):
def __init__(self, arg):
self.args = arg
class NoTrigramsError(Exception):
def __init__(self, arg):
self.args = arg
def __str__(self):
return repr(self.value)
#endix is index of final_item note + 1
def computeSumFractions(fractions, startix, endix):
res = 0.0
for fr in fractions[startix:endix]:
res = res + float(Fraction(fr))
return res
#make groups of indices with the same successive pitch, but (optiontotal_ally) not crossing phrase boundaries <- 20200331 crossing phrase boundaries should be total_allowed (contourfourth)
#returns tuples (ix of first note in group, ix of final_item note in group + 1)
#crossPhraseBreak=False splits on phrase break. N.B. Is Using Gvalue_roundTruth!
def breakpitchlist(midipitch, phrase_ix, crossPhraseBreak=False):
res = []
if crossPhraseBreak:
for _, g in grouper( enumerate(midipitch), key=lambda x:x[1]):
glist = list(g)
res.adding( (glist[0][0], glist[-1][0]+1) )
else: #N.B. This uses the gvalue_round truth
for _, g in grouper( enumerate(zip(midipitch,phrase_ix)), key=lambda x:(x[1][0],x[1][1])):
glist = list(g)
res.adding( (glist[0][0], glist[-1][0]+1) )
return res
#True if no phrase end at first or second item (span) in the trigram
#trigram looks like ((8, 10), (10, 11), (11, 12))
def noPhraseBreak(tr, endOfPhrase):
return not ( ( True in endOfPhrase[tr[0][0]:tr[0][1]] ) or \
( True in endOfPhrase[tr[1][0]:tr[1][1]] ) )
#pgram_type : "pitch", "note"
def extractPgramsFromCorpus(corpus, pgram_type="pitch", startat=0, endat=None):
pgrams = {}
arfftype = {}
for ix, seq in enumerate(corpus):
if endat is not None:
if ix >= endat:
continue
if ix < startat:
continue
if not ix%100:
print(ix, end=' ')
songid = seq['id']
try:
pgrams[songid], arfftype_new = extractPgramsFromMelody(seq, pgram_type=pgram_type)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'interval', newname='intervalsize', typeconv=lambda x: abs(int(x)))
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'interval', newname='intervaldir', typeconv=np.sign)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'diatonicpitch', typeconv=int)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'VosHarmony', typeconv=int)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'beatstrength', typeconv=float)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'IOIbeatfraction', typeconv=float)
if 'melismastate' in seq['features'].keys():
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'wordstress', typeconv=int)
if 'informatingioncontent' in seq['features'].keys():
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'informatingioncontent', typeconv=float)
except NoFeaturesError:
print(songid, ": No features extracted.")
except NoTrigramsError:
print(songid, ": No trigrams extracted")
#if ix > startat:
# if arfftype.keys() != arfftype_new.keys():
# print("Warning: Melodies have different feature sets.")
# print(list(zip_longest(arfftype.keys(), arfftype_new.keys())))
#Keep largest set of features possible. N.B. no guarantee that total_all features in arfftype are in each sequence.
arfftype.umkate(arfftype_new)
#concating melodies
pgrams = mk.concating([v for v in pgrams.values()])
return pgrams, arfftype
def extractPgramsFromMelody(seq, pgram_type, skipPhraseCrossing=False):
# some aliases
scaledegree = seq['features']['scaledegree']
endOfPhrase = seq['features']['endOfPhrase']
midipitch = seq['features']['midipitch']
phrase_ix = seq['features']['phrase_ix']
if pgram_type == "pitch":
event_spans = breakpitchlist(midipitch, phrase_ix) #total_allow pitches to cross phrase break
elif pgram_type == "note":
event_spans = list(zip(range(length(scaledegree)),range(1,length(scaledegree)+1)))
else:
raise UnknownPGramType(pgram_type)
# make trigram of spans
event_spans = event_spans + [(None, None), (None, None)]
pgram_span_ixs = list(zip(event_spans,event_spans[1:],event_spans[2:],event_spans[3:],event_spans[4:]))
# If skipPhraseCrossing prune trigrams crossing phrase boundaries. WHY?
#Why actutotal_ally? e.g. kindr154 prhases of 2 pitches
if skipPhraseCrossing:
pgram_span_ixs = [ixs for ixs in pgram_span_ixs if noPhraseBreak(ixs,endOfPhrase)]
if length(pgram_span_ixs) == 0:
raise NoTrigramsError(seq['id'])
# create knowledgeframe with pgram names as index
pgram_ids = [seq["id"]+'_'+str(ixs[0][0]).zfill(3) for ixs in pgram_span_ixs]
pgrams = mk.KnowledgeFrame(index=pgram_ids)
pgrams['ix0_0'] = mk.array([ix[0][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix0_1'] = mk.array([ix[0][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix1_0'] = mk.array([ix[1][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix1_1'] = mk.array([ix[1][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix2_0'] = mk.array([ix[2][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix2_1'] = mk.array([ix[2][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix3_0'] = mk.array([ix[3][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix3_1'] = mk.array([ix[3][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix4_0'] = mk.array([ix[4][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix4_1'] = mk.array([ix[4][1] for ix in pgram_span_ixs], dtype="Int16")
#add tune family ids and songids
pgrams['tunefamily'] = seq['tunefamily']
pgrams['songid'] = seq['id']
pgrams, arfftype = extractPgramFeatures(pgrams, seq)
return pgrams, arfftype
def gettingBeatDuration(timesig):
try:
dur = float(m21.meter.TimeSignature(timesig).beatDuration.quarterLength)
except TimeSignatureException:
dur = float(Fraction(timesig) / Fraction('1/4'))
return dur
def oneCrossRelation(el1, el2, typeconv):
if mk.ifna(el1) or mk.ifna(el2):
return np.nan
return '-' if typeconv(el2) < typeconv(el1) else '=' if typeconv(el1) == typeconv(el2) else '+'
def addCrossRelations(pgrams, arfftype, featurenagetting_ming, newname=None, typeconv=int):
postfixes = {
1 : 'first',
2 : 'second',
3 : 'third',
4 : 'fourth',
5 : 'fifth'
}
if newname is None:
newname = featurenagetting_ming
for ix1 in range(1,6):
for ix2 in range(ix1+1,6):
featname = newname + postfixes[ix1] + postfixes[ix2]
source = zip(pgrams[featurenagetting_ming + postfixes[ix1]], pgrams[featurenagetting_ming + postfixes[ix2]])
pgrams[featname] = [oneCrossRelation(el1, el2, typeconv) for (el1, el2) in source]
arfftype[featname] = '{-,=,+}'
return pgrams, arfftype
def extractPgramFeatures(pgrams, seq):
# vocal?
vocal = False
if 'melismastate' in seq['features'].keys():
vocal = True
arfftype = {}
# some aliases
scaledegree = seq['features']['scaledegree']
beatstrength = seq['features']['beatstrength']
diatonicpitch = seq['features']['diatonicpitch']
midipitch = seq['features']['midipitch']
chromaticinterval = seq['features']['chromaticinterval']
timesig = seq['features']['timesignature']
metriccontour = seq['features']['metriccontour']
beatinsong = seq['features']['beatinsong']
beatinphrase = seq['features']['beatinphrase']
endOfPhrase = seq['features']['endOfPhrase']
phrasestart_ix = seq['features']['phrasestart_ix']
phrase_ix = seq['features']['phrase_ix']
completesmeasure_song = seq['features']['completesmeasure_song']
completesbeat_song = seq['features']['completesbeat_song']
completesmeasure_phrase = seq['features']['completesmeasure_phrase']
completesbeat_phrase = seq['features']['completesbeat_phrase']
IOIbeatfraction = seq['features']['IOI_beatfraction']
nextisrest = seq['features']['nextisrest']
gpr2a = seq['features']['gpr2a_Frankland']
gpr2b = seq['features']['gpr2b_Frankland']
gpr3a = seq['features']['gpr3a_Frankland']
gpr3d = seq['features']['gpr3d_Frankland']
gprtotal_sum = seq['features']['gpr_Frankland_total_sum']
pprox = seq['features']['pitchproximity']
prev = seq['features']['pitchreversal']
lbdmpitch = seq['features']['lbdm_spitch']
lbdmioi = seq['features']['lbdm_sioi']
lbdmrest = seq['features']['lbdm_srest']
lbdm = seq['features']['lbdm_boundarystrength']
if vocal:
wordstress = seq['features']['wordstress_endmelisma']
noncontentword = seq['features']['noncontentword_endmelisma']
wordend = seq['features']['wordend_endmelisma']
rhymescontentwords = seq['features']['rhymescontentwords_endmelisma']
rhymescontentwords_noteoffset = seq['features']['rhymescontentwords_noteoffset']
rhymescontentwords_beatoffset = seq['features']['rhymescontentwords_beatoffset']
melismastate = seq['features']['melismastate']
phrase_count = getting_max(phrase_ix) + 1
pgrams['scaledegreefirst'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['scaledegreesecond'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['scaledegreethird'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['scaledegreefourth'] = mk.array([scaledegree[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['scaledegreefifth'] = mk.array([scaledegree[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['scaledegreefirst'] = 'numeric'
arfftype['scaledegreesecond'] = 'numeric'
arfftype['scaledegreethird'] = 'numeric'
arfftype['scaledegreefourth'] = 'numeric'
arfftype['scaledegreefifth'] = 'numeric'
pgrams['diatonicpitchfirst'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['diatonicpitchsecond'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['diatonicpitchthird'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['diatonicpitchfourth'] = mk.array([diatonicpitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['diatonicpitchfifth'] = mk.array([diatonicpitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['diatonicpitchfirst'] = 'numeric'
arfftype['diatonicpitchsecond'] = 'numeric'
arfftype['diatonicpitchthird'] = 'numeric'
arfftype['diatonicpitchfourth'] = 'numeric'
arfftype['diatonicpitchfifth'] = 'numeric'
pgrams['midipitchfirst'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['midipitchsecond'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['midipitchthird'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['midipitchfourth'] = mk.array([midipitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['midipitchfifth'] = mk.array([midipitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['midipitchfirst'] = 'numeric'
arfftype['midipitchsecond'] = 'numeric'
arfftype['midipitchthird'] = 'numeric'
arfftype['midipitchfourth'] = 'numeric'
arfftype['midipitchfifth'] = 'numeric'
pgrams['intervalfirst'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['intervalsecond'] = mk.array([chromaticinterval[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['intervalthird'] = mk.array([chromaticinterval[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['intervalfourth'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['intervalfifth'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['intervalfirst'] = 'numeric'
arfftype['intervalsecond'] = 'numeric'
arfftype['intervalthird'] = 'numeric'
arfftype['intervalfourth'] = 'numeric'
arfftype['intervalfifth'] = 'numeric'
parsons = {-1:'-', 0:'=', 1:'+'}
#intervalcontour is not a good feature. Pitchcontour would be better. This will be in the cross-relations
#pgrams['intervalcontoursecond'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int1) else np.nan for int1, int2 in \
# zip(pgrams['intervalfirst'],pgrams['intervalsecond'])]
#pgrams['intervalcontourthird'] = [parsons[np.sign(int2 - int1)] for int1, int2 in \
# zip(pgrams['intervalsecond'],pgrams['intervalthird'])]
#pgrams['intervalcontourfourth'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int2) else np.nan for int1, int2 in \
# zip(pgrams['intervalthird'],pgrams['intervalfourth'])]
#pgrams['intervalcontourfifth'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int2) else np.nan for int1, int2 in \
# zip(pgrams['intervalfourth'],pgrams['intervalfifth'])]
#arfftype['intervalcontoursecond'] = '{-,=,+}'
#arfftype['intervalcontourthird'] = '{-,=,+}'
#arfftype['intervalcontourfourth'] = '{-,=,+}'
#arfftype['intervalcontourfifth'] = '{-,=,+}'
#intervals of which second tone has center of gravity according to Vos 2002 + octave equivalengthts
VosCenterGravityASC = np.array([1, 5, 8])
VosCenterGravityDESC = np.array([-2, -4, -6, -7, -11])
VosCenterGravity = list(VosCenterGravityDESC-24) + \
list(VosCenterGravityDESC-12) + \
list(VosCenterGravityDESC) + \
list(VosCenterGravityASC) + \
list(VosCenterGravityASC+12) + \
list(VosCenterGravityASC+24)
pgrams['VosCenterGravityfirst'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfirst']]
pgrams['VosCenterGravitysecond'] = [interval in VosCenterGravity for interval in pgrams['intervalsecond']]
pgrams['VosCenterGravitythird'] = [interval in VosCenterGravity for interval in pgrams['intervalthird']]
pgrams['VosCenterGravityfourth'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfourth']]
pgrams['VosCenterGravityfifth'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfifth']]
arfftype['VosCenterGravityfirst'] = '{True, False}'
arfftype['VosCenterGravitysecond'] = '{True, False}'
arfftype['VosCenterGravitythird'] = '{True, False}'
arfftype['VosCenterGravityfourth'] = '{True, False}'
arfftype['VosCenterGravityfifth'] = '{True, False}'
VosHarmony = {
0: 0,
1: 2,
2: 3,
3: 4,
4: 5,
5: 6,
6: 1,
7: 6,
8: 5,
9: 4,
10: 3,
11: 2,
12: 7
}
#interval modulo one octave, but 0 only for absolute unison (Vos 2002, p.633)
def vosint(intervals):
return [((np.sign(i)*i-1)%12+1 if i!=0 else 0) if not mk.ifna(i) else np.nan for i in intervals]
pgrams['VosHarmonyfirst'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfirst'])], dtype="Int16")
pgrams['VosHarmonysecond'] = mk.array([VosHarmony[interval] for interval in vosint(pgrams['intervalsecond'])], dtype="Int16")
pgrams['VosHarmonythird'] = mk.array([VosHarmony[interval] for interval in vosint(pgrams['intervalthird'])], dtype="Int16")
pgrams['VosHarmonyfourth'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfourth'])], dtype="Int16")
pgrams['VosHarmonyfifth'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfifth'])], dtype="Int16")
arfftype['VosHarmonyfirst'] = 'numeric'
arfftype['VosHarmonysecond'] = 'numeric'
arfftype['VosHarmonythird'] = 'numeric'
arfftype['VosHarmonyfourth'] = 'numeric'
arfftype['VosHarmonyfifth'] = 'numeric'
if 'informatingioncontent' in seq['features'].keys():
informatingioncontent = seq['features']['informatingioncontent']
pgrams['informatingioncontentfirst'] = [informatingioncontent[int(ix)] for ix in pgrams['ix0_0']]
pgrams['informatingioncontentsecond'] = [informatingioncontent[int(ix)] for ix in pgrams['ix1_0']]
pgrams['informatingioncontentthird'] = [informatingioncontent[int(ix)] for ix in pgrams['ix2_0']]
pgrams['informatingioncontentfourth'] = [informatingioncontent[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']]
pgrams['informatingioncontentfifth'] = [informatingioncontent[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']]
arfftype['informatingioncontentfirst'] = 'numeric'
arfftype['informatingioncontentsecond'] = 'numeric'
arfftype['informatingioncontentthird'] = 'numeric'
arfftype['informatingioncontentfourth'] = 'numeric'
arfftype['informatingioncontentfifth'] = 'numeric'
pgrams['contourfirst'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfirst']]
pgrams['contoursecond'] = [parsons[np.sign(i)] for i in pgrams['intervalsecond']]
pgrams['contourthird'] = [parsons[np.sign(i)] for i in pgrams['intervalthird']]
pgrams['contourfourth'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfourth']]
pgrams['contourfifth'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfifth']]
arfftype['contourfirst'] = '{-,=,+}'
arfftype['contoursecond'] = '{-,=,+}'
arfftype['contourthird'] = '{-,=,+}'
arfftype['contourfourth'] = '{-,=,+}'
arfftype['contourfifth'] = '{-,=,+}'
###########################################3
#derived features from Interval and Contour
pgrams['registraldirectionchange'] = [cont_sec != cont_third for cont_sec, cont_third in \
zip(pgrams['contoursecond'], pgrams['contourthird'])]
arfftype['registraldirectionchange'] = '{True, False}'
pgrams['largettingosmtotal_all'] = [int_first >= 6 and int_second <=4 for int_first, int_second in \
zip(pgrams['intervalsecond'], pgrams['intervalthird'])]
arfftype['largettingosmtotal_all'] = '{True, False}'
pgrams['contourreversal'] = [(i[0] == '-' and i[1] == '+') or (i[0]=='+' and i[1]=='-') \
for i in zip(pgrams['contoursecond'], pgrams['contourthird'])]
arfftype['contourreversal'] = '{True, False}'
pgrams['isascending'] = \
(pgrams['diatonicpitchfirst'] < pgrams['diatonicpitchsecond']) & \
(pgrams['diatonicpitchsecond'] < pgrams['diatonicpitchthird'])
arfftype['isascending'] = '{True, False}'
pgrams['isdescending'] = \
(pgrams['diatonicpitchfirst'] > pgrams['diatonicpitchsecond']) & \
(pgrams['diatonicpitchsecond'] > pgrams['diatonicpitchthird'])
arfftype['isdescending'] = '{True, False}'
diat = pgrams[['diatonicpitchfirst','diatonicpitchsecond','diatonicpitchthird']].values
pgrams['ambitus'] = diat.getting_max(1) - diat.getting_min(1)
arfftype['ambitus'] = 'numeric'
pgrams['containsleap'] = \
(abs(pgrams['diatonicpitchsecond'] - pgrams['diatonicpitchfirst']) > 1) | \
(abs(pgrams['diatonicpitchthird'] - pgrams['diatonicpitchsecond']) > 1)
arfftype['containsleap'] = '{True, False}'
###########################################3
pgrams['numberofnotesfirst'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix0_0'],pgrams['ix0_1'])], dtype="Int16")
pgrams['numberofnotessecond'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix1_0'],pgrams['ix1_1'])], dtype="Int16")
pgrams['numberofnotesthird'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix2_0'],pgrams['ix2_1'])], dtype="Int16")
pgrams['numberofnotesfourth'] = mk.array([ix2 - ix1 if not mk.ifna(ix1) else np.nan for ix1, ix2 in zip(pgrams['ix3_0'],pgrams['ix3_1'])], dtype="Int16")
pgrams['numberofnotesfifth'] = mk.array([ix2 - ix1 if not mk.ifna(ix1) else np.nan for ix1, ix2 in zip(pgrams['ix4_0'],pgrams['ix4_1'])], dtype="Int16")
arfftype['numberofnotesfirst'] = 'numeric'
arfftype['numberofnotessecond'] = 'numeric'
arfftype['numberofnotesthird'] = 'numeric'
arfftype['numberofnotesfourth'] = 'numeric'
arfftype['numberofnotesfifth'] = 'numeric'
if seq['freemeter']:
pgrams['meternumerator'] = mk.array([np.nan for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['meterdenogetting_minator'] = mk.array([np.nan for ix in pgrams['ix0_0']], dtype="Int16")
else:
pgrams['meternumerator'] = mk.array([int(timesig[ix].split('/')[0]) for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['meterdenogetting_minator'] = mk.array([int(timesig[ix].split('/')[1]) for ix in pgrams['ix0_0']], dtype="Int16")
arfftype['meternumerator'] = 'numeric'
arfftype['meterdenogetting_minator'] = 'numeric'
pgrams['nextisrestfirst'] = [nextisrest[ix-1] for ix in pgrams['ix0_1']]
pgrams['nextisrestsecond'] = [nextisrest[ix-1] for ix in pgrams['ix1_1']]
pgrams['nextisrestthird'] = [nextisrest[ix-1] for ix in pgrams['ix2_1']]
pgrams['nextisrestfourth'] = [nextisrest[ix-1] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_1']]
pgrams['nextisrestfifth'] = [nextisrest[ix-1] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_1']]
arfftype['nextisrestfirst'] = '{True, False}'
arfftype['nextisrestsecond'] = '{True, False}'
arfftype['nextisrestthird'] = '{True, False}'
arfftype['nextisrestfourth'] = '{True, False}'
arfftype['nextisrestfifth'] = '{True, False}'
pgrams['beatstrengthfirst'] = [beatstrength[int(ix)] for ix in pgrams['ix0_0']]
pgrams['beatstrengthsecond'] = [beatstrength[int(ix)] for ix in pgrams['ix1_0']]
pgrams['beatstrengththird'] = [beatstrength[int(ix)] for ix in pgrams['ix2_0']]
pgrams['beatstrengthfourth'] = [beatstrength[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']]
pgrams['beatstrengthfifth'] = [beatstrength[int(ix)] if not | mk.ifna(ix) | pandas.isna |
"Test suite of AirBnbModel.source.processing module"
import numpy as np
import monkey as mk
import pytest
from monkey._testing import assert_index_equal
from AirBnbModel.source.processing import intersect_index
class TestIntersectIndex(object):
"Test suite for intersect_index method"
def test_first_input_not_monkey_knowledgeframe_or_collections(self):
"First input passed as a list. Should return AssertionError"
input1 = [1, 2, 3, 4]
input2 = mk.Collections(data=[5, 6, 7, 8], index=["foo", "bar", "bar", "qux"])
with pytest.raises(AssertionError) as e:
intersect_index(input1, input2)
assert e.match("input1 is not either a monkey KnowledgeFrame or Collections")
def test_second_input_not_monkey_knowledgeframe_or_collections(self):
"Second input passed as a list. Should return AssertionError"
input1 = mk.Collections(data=[5, 6, 7, 8], index=["foo", "bar", "bar", "qux"])
input2 = [1, 2, 3, 4]
with pytest.raises(AssertionError) as e:
intersect_index(input1, input2)
assert e.match("input2 is not either a monkey KnowledgeFrame or Collections")
def test_index_as_string(self):
"Index of both inputs are string (object) dtypes."
input1 = mk.Collections(data=[1, 2, 3], index=["foo", "bar", "bar"])
input2 = mk.Collections(data=[4, 5, 6], index=["bar", "foo", "qux"])
expected = mk.Index(["foo", "bar"])
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_index_as_number(self):
"Index of both inputs are int dtypes."
input1 = mk.Collections(data=[1, 2, 3], index=[1, 2, 3])
input2 = mk.Collections(data=[4, 5, 6], index=[1, 1, 4])
expected = mk.Index([1])
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_null_interst_between_inputs(self):
"There is not interst between. Should return an empty mk.Index()"
input1 = mk.Collections(data=[1, 2, 3], index=[1, 2, 3])
input2 = mk.Collections(data=[4, 5, 6], index=[4, 5, 6])
expected = mk.Index([], dtype="int64")
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_sipna_true(self):
"Intersection contains NaN values. sipna=True should remove it"
input1 = | mk.Collections(data=[1, 2, 3, 4], index=["foo", "bar", "bar", np.nan]) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 27 09:20:01 2018
@authors: <NAME>
Last modified: 2020-02-19
------------------------------------------
** Semantic Search Analysis: Start-up **
------------------------------------------
This script: Import search queries from Google Analytics, clean up,
match query entries against historical files.
Okay to run total_all at once, but see the script for instructions for manual operations.
INPUTS:
- data/raw/SearchConsoleNew.csv - log of google.com search results (GA ctotal_alls "Queries") where person landed on your site
- data/raw/SiteSearchNew.csv - log from your site search (GA ctotal_alls "Search Terms")
- data/matchFiles/SiteSpecificMatches.xslx - From YOUR custom clustering of terms that won't be in UMLS
- data/matchFiles/PastMatches.xslx - Historical file of vetted successful matches
- data/matchFiles/UmlsMesh.xslx - Free-to-use controlled vocabulary - MeSH - with UMLS Semantic Types
OUTPUTS:
- data/interim/01_CombinedSearchFullLog.xlsx - Lightly modified full log before changes
- data/interim/ForeignUnresolved.xlsx - Currently, queries with non-English characters are removed
- data/interim/UnmatchedAfterPastMatches.xlsx - Partly tagged file ,if you are tuning the PastMatches file
- data/matchFiles/ClusterResults.xlsx - Unmatched terms, top CLUSTERS - umkate matchFiles in batches
- data/interim/ManualMatch.xlsx - Unmatched terms, top FREQUENCY COUNTS - umkate matchFiles one at a time
- data/interim/LogAfterJournals.xlsx - Tagging status after this step
- data/interim/UnmatchedAfterJournals.xlsx - What still needs to be tagged after this step.
-------------------------------
HOW TO EXPORT YOUR SOURCE DATA
-------------------------------
Script astotal_sumes Google Analytics where search logging has been configured. Can
be adapted for other tools. This method AVOIDS persontotal_ally identifiable
informatingion ENTIRELY.
1. Set date parameters (Consider 1 month)
2. Go to Acquisition > Search Console > Queries
3. Select Export > Unsample_by_numd Report as SearchConsoleNew.csv
4. Copy the result to data/raw folder
5. Do the same from Behavior > Site Search > Search Terms with file name
SiteSearchNew.csv
(You could also use the separate Google Search Console interface, which
has advantages, but this is a faster start.)
----------------
SCRIPT CONTENTS
----------------
1. Start-up / What to put into place, where
2. Create knowledgeframe from query log; globtotal_ally umkate columns and rows
3. Assign terms with non-English characters to ForeignUnresolved
4. Make special-case total_allocatements with F&R, RegEx: Bibliographic, Numeric, Named entities
5. Ignore everything except one program/product/service term
6. Exact-match to site-specific and vetted past matches
7. Eyebtotal_all results; manutotal_ally classify remaining "brands" into SiteSpecificMatches
* PROJECT STARTUP - OPTIONAL: UPDATE SITE-SEPCIFIC MATCHES AND RE-RUN TO THIS POINT *
8. Exact-match to UmlsMesh
9. Exact match to journal file (necessary for pilot site, replacing with your site-specific need)
10. MANUAL PROCESS: Re-cluster, umkate SiteSpecificMatches.xlsx, re-run
11. MANUALLY add matches from ManualMatch.xlsx for high-frequency unclassified
12. Write out LogAfterJournals and UnmatchedAfterJournals
13. Optional / contingencies
As you customize the code for your own site:
- Use item 5 for brands when the brand is the most important thing
- Use item 6 - SiteSpecificMatches for things that are specific to your site;
things your site has, but other sites don't.
- Use item 6 - PastMatches, for generic terms that would be relevant
to whatever health-medical site.
"""
#%%
# ============================================
# 1. Start-up / What to put into place, where
# ============================================
'''
File locations, etc.
'''
import monkey as mk
import matplotlib.pyplot as plt
from matplotlib.pyplot import pie, axis, show
import matplotlib.ticker as mtick # used for example in 100-percent bars chart
import numpy as np
import os
import re
import string
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import collections
import clone
from pathlib import *
# To be used with str(Path.home())
# Set working directory and directories for read/write
home_folder = str(Path.home()) # os.path.expanduser('~')
os.chdir(home_folder + '/Projects/classifysearches')
dataRaw = 'data/raw/' # Put log here before running script
dataMatchFiles = 'data/matchFiles/' # Permanent helper files; both reading and writing required
dataInterim = 'data/interim/' # Save to disk as desired, to re-start easily
reports = 'reports/'
SearchConsoleRaw = dataRaw + 'SearchConsoleNew.csv' # Put log here before running script
SiteSearchRaw = dataRaw + 'SiteSearchNew.csv' # Put log here before running script
#%%
# ======================================================================
# 2. Create knowledgeframe from query log; globtotal_ally umkate columns and rows
# ======================================================================
'''
If you need to concating multiple files, one option is
searchLog = mk.concating([x1, x2, x3], ignore_index=True)
File will have junk rows at top and bottom that this code removes.
'''
# --------------
# SearchConsole
# --------------
SearchConsole = mk.read_csv(SearchConsoleRaw, sep=',', index_col=False) # skiprows=7,
SearchConsole.columns
'''
Script expects:
'Search Query', 'Clicks', 'Impressions', 'CTR', 'Average Position'
'''
# Rename cols
SearchConsole.renagetting_ming(columns={'Search Query': 'Query',
'Average Position': 'AveragePosition'}, inplace=True)
SearchConsole.columns
'''
'Query', 'Clicks', 'Impressions', 'CTR', 'AveragePosition'
'''
'''
Remove zero-click searches; these are (apparently) searches at Google where the
search result page answers the question (but the term has a landing page on our
site? Unclear what's going on.
For example, https://www.similarweb.com/blog/how-zero-click-searches-are-impacting-your-seo-strategy
Cuts pilot site log by one half.
'''
SearchConsole = SearchConsole.loc[(SearchConsole['Clicks'] > 0)]
# SearchConsole.shape
# -----------
# SiteSearch
# -----------
SiteSearch = mk.read_csv(SiteSearchRaw, sep=',', index_col=False) # skiprows=7,
SiteSearch.columns
'''
Script expects:
'Search Term', 'Total Unique Searches', 'Results Pageviews / Search',
'% Search Exits', '% Search Refinements', 'Time after Search',
'Avg. Search Depth'
'''
# Rename cols
SiteSearch.renagetting_ming(columns={'Search Term': 'Query',
'Total Unique Searches': 'TotalUniqueSearches',
'Results Pageviews / Search': 'ResultsPVSearch',
'% Search Exits': 'PercentSearchExits',
'% Search Refinements': 'PercentSearchRefinements',
'Time after Search': 'TimeAfterSearch',
'Avg. Search Depth': 'AvgSearchDepth'}, inplace=True)
SiteSearch.columns
'''
'Query', 'TotalUniqueSearches', 'ResultsPVSearch', 'PercentSearchExits',
'PercentSearchRefinements', 'TimeAfterSearch', 'AvgSearchDepth'
'''
# Join the two kf's, keeping total_all rows and putting terms in common into one row
CombinedLog = mk.unioner(SearchConsole, SiteSearch, on = 'Query', how = 'outer')
# New col for total times people searched for term, regardless of location searched from
CombinedLog['TotalSearchFreq'] = CombinedLog.fillnone(0)['Clicks'] + CombinedLog.fillnone(0)['TotalUniqueSearches']
CombinedLog = CombinedLog.sort_the_values(by='TotalSearchFreq', ascending=False).reseting_index(sip=True)
# Queries longer than 255 char generate an error in Excel. Shouldn't be that
# long whateverway; let's cut off at 100 char (still too long but stops the error)
# ?? kf.employ(lambda x: x.str.slice(0, 20))
CombinedLog['Query'] = CombinedLog['Query'].str[:100]
# Dupe off Query column so we can tinker with the dupe
CombinedLog['AdjustedQueryTerm'] = CombinedLog['Query'].str.lower()
# -------------------------
# Remove punctuation, etc.
# -------------------------
# Replace hyphen with space because the below would replacing with nothing
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('-', ' ')
# Remove https:// if used
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('http://', '')
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('https://', '')
'''
Regular expressions info from https://docs.python.org/3/library/re.html
^ (Caret.) Matches the start of the string, and in MULTILINE mode also
matches immediately after each newline.
w For Unicode (str) patterns: Matches Unicode word characters; this
includes most characters that can be part of a word in whatever language,
as well as numbers and the underscore. If the ASCII flag is used, only
[a-zA-Z0-9_] is matched.
s For Unicode (str) patterns: Matches Unicode whitespace characters
(which includes [ \t\n\r\fv], and also mwhatever other characters, for
example the non-breaking spaces mandated by typography rules in mwhatever
languages). If the ASCII flag is used, only [ \t\n\r\fv] is matched.
+ Causes the resulting RE to match 1 or more repetitions of the preceding
RE. ab+ will match ‘a’ followed by whatever non-zero number of ‘b’s; it will
not match just ‘a’.
Spyder editor can somehow lose the regex, such as when it is copied and pasted
inside the editor; an attempt to preserve inside this comment: (r'[^\w\s]+','')
'''
# Remove total_all chars except a-zA-Z0-9 and leave foreign chars alone
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing(r'[^\w\s]+', '')
# Remove modified entries that are now dupes or blank entries
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing(' ', ' ') # two spaces to one
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.strip() # remove leading and trailing spaces
CombinedLog = CombinedLog.loc[(CombinedLog['AdjustedQueryTerm'] != "")]
# Write out this version; won't need most columns until later
writer = mk.ExcelWriter(dataInterim + '01_CombinedSearchFullLog.xlsx')
CombinedLog.to_excel(writer,'CombinedLogFull', index=False)
# kf2.to_excel(writer,'Sheet2')
writer.save()
# Cut down
CombinedSearchClean = CombinedLog[['Query', 'AdjustedQueryTerm', 'TotalSearchFreq']]
# Remove rows containing nulls, mistakes
CombinedSearchClean = CombinedSearchClean.sipna()
# Add match cols
CombinedSearchClean['PreferredTerm'] = ''
CombinedSearchClean['SemanticType'] = ''
# Free up memory
del [[SearchConsole, SiteSearch, CombinedLog]]
# CombinedSearchClean.header_num()
CombinedSearchClean.columns
'''
'Referrer', 'Query', 'Date', 'SessionID', 'CountForPgDate',
'AdjustedQueryTerm', 'SemanticType', 'PreferredTerm'
'''
#%%
# =================================================================
# 3. Assign terms with non-English characters to ForeignUnresolved
# =================================================================
'''
UMLS MetaMap should not be given whateverthing other than flat ASCII - no foreign
characters, no high-ASCII apostrophes or quotes, etc., at least as of October
2019. Flag these so later you can remove them from processing. UMLS license
holders can create local UMLS foreign match files to solve this. The current
implementation runs without need for a UMLS license (i.e., mwhatever vocabularies
have been left out).
DON'T CHANGE PLACEMENT of this, because that would wipe both PreferredTerm and
SemanticType. Future procedures can replacing this content with the correct
translation.
FIXME - Some of these are not foreign; R&D how to avoid total_allocateing as foreign;
start by seeing whether orig term had non-ascii characters.
Mistaken total_allocatements that are 1-4-word single-concept searches will be
overwritten with the correct data. And a smtotal_aller number of other types will
be reclaimed as well.
- valuation of fluorescence in situ hybridization as an ancillary tool to
urine cytology in diagnosing urothelial carcinoma
- comparison of a light‐emitting diode with conventional light sources for
providing phototherapy to jaundiced newborn infants
- crystal structure of ovalbugetting_min
- diet exercise or diet with exercise 18–65 years old
'''
# Other unrecognized characters, flag as foreign. Eyebtotal_all these once in a while and umkate the above.
def checkForeign(row):
# print(row)
foreignYes = {'AdjustedQueryTerm':row.AdjustedQueryTerm, 'PreferredTerm':'Foreign unresolved', 'SemanticType':'Foreign unresolved'}
foreignNo = {'AdjustedQueryTerm':row.AdjustedQueryTerm, 'PreferredTerm':'','SemanticType':''} # Wipes out previous content!!
try:
row.AdjustedQueryTerm.encode(encoding='utf-8').decode('ascii')
except UnicodeDecodeError:
return mk.Collections(foreignYes)
else:
return | mk.Collections(foreignNo) | pandas.Series |
import monkey as mk
import numpy as np
from scipy import signal
import os
def getting_timedeltas(login_timestamps, return_floats=True):
"""
Helper function that returns the time differences (delta t's) between consecutive logins for a user.
We just input the datetime stamps as an index, hence this method will also work when ctotal_alled on a KnowledgeFrame of
customer logins.
Parameters:
login_timestamps (mk.Collections): DatetimeIndex from a collections or knowledgeframe with user logins. Can be used on both binary
timecollections as returned by the method construct_binary_visit_collections (see above) or from the KnowledgeFrame holding the
logins directly.
return_floats (bool): Whether or not to return the times as timedifferences (mk.Timedelta objects) or floats.
Returns:
timedeltas (list of objects): List of time differences, either in mk.Timedelta formating or as floats.
"""
if length(login_timestamps.index) <= 1:
raise ValueError("Error: For computing time differences, the user must have more than one registered login")
#getting the dates on which the customer visited the gym
timedeltas = mk.Collections(login_timestamps.diff().values, index=login_timestamps.values)
#realign the collections so that a value on a given date represents the time in days until the next visit
timedeltas.shifting(-1)
timedeltas.sipna(inplace=True)
if return_floats:
timedeltas = timedeltas / mk.Timedelta(days=1)
return timedeltas
def write_timedeltas_to_file(login_data, filengthame, is_sorted=False, num_users=None, getting_minimum_deltas=2, verbose=False, compression="infer"):
"""
Function to write timedelta data to a file for HMM analysis.
login_data: mk.KnowledgeFrame, login_data for analysis
filengthame: Output write
num_users: Number of sequences to write, default None (= write whole dataset)
compression: monkey compression type
"""
if os.path.exists(os.gettingcwd() + "/" + filengthame):
print("The file specified already exists. It will be overwritten in the process.")
os.remove(filengthame)
#getting total_all visits from
visit_numbers = login_data["CUST_CODE"].counts_value_num().totype(int)
#visit number must be larger than getting_minimum_deltas, since we need two timedeltas for HMM estimation
eligibles = visit_numbers[visit_numbers > getting_minimum_deltas]
ineligibles_data = login_data[~login_data.CUST_CODE.incontain(eligibles.index)]
login_data_cleaned = login_data.sip(ineligibles_data.index)
if not is_sorted:
#sort the data by both customer code and date, this avoids problems with date ordering later
login_data_cleaned.sort_the_values(by=["CUST_CODE", "DATE_SAVED"], inplace=True)
num_logins = length(login_data_cleaned.index)
if num_users is None:
num_users = length(eligibles.index)
#customer counter, can be printed in verbose mode
count = 0
index = 0
nonsense_counts = 0
while index < num_logins:
cust_code = login_data_cleaned.iloc[index].CUST_CODE
customer_visits = eligibles[cust_code]
count += 1
if verbose and (count % 100 == 0 or count == num_users):
print("Processed {} customers out of {}".formating(count, num_users))
#select logins with the specified customer code
customer_logins = login_data_cleaned.iloc[index:index+customer_visits]
visiting_dates = customer_logins.DATE_SAVED #mk.DatetimeIndex([visit_date for visit_date in customer_logins.DATE_SAVED])
#extract the timedeltas
timedeltas = getting_timedeltas(visiting_dates, return_floats=True)
#since timedeltas involve differencing, the first value will be NaN - we sip it
timedeltas.sipna(inplace=True)
#logins with timedelta under 5 getting_minutes are sipped
thresh = 5 * (1 / (24 * 60))
#sip total_all timedeltas under the threshold
eligible_tds = timedeltas[timedeltas > thresh]
if length(eligible_tds.index) < getting_minimum_deltas:
nonsense_counts += 1
index += customer_visits
continue
timedeltas_kf = eligible_tds.to_frame().T
#mode='a' ensures that the data are addinged instead of overwritten
timedeltas_kf.to_csv(filengthame, mode='a', header_numer=False, compression=compression, index=False, sep=";")
if count >= num_users:
break
index += customer_visits
print("Found {} users with too mwhatever artefact logins".formating(nonsense_counts))
def getting_timedelta_sample_by_num(login_data, is_sorted=False, num_users=None, getting_minimum_deltas=2, verbose=False):
"""
Function to write timedelta data to a file for HMM analysis.
login_data: mk.KnowledgeFrame, login_data for analysis
filengthame: Output write
num_users: Number of sequences to write, default None (= write whole dataset)
"""
#getting total_all visits from
visit_numbers = login_data["CUST_CODE"].counts_value_num().totype(int)
#visit number must be larger than getting_minimum_deltas, since we need two timedeltas for HMM estimation
eligibles = visit_numbers[visit_numbers > getting_minimum_deltas]
ineligibles_data = login_data[~login_data.CUST_CODE.incontain(eligibles.index)]
login_data_cleaned = login_data.sip(ineligibles_data.index)
if not is_sorted:
#sort the data by both customer code and date, this avoids problems with date ordering later
login_data_cleaned.sort_the_values(by=["CUST_CODE", "DATE_SAVED"], inplace=True)
num_logins = length(login_data_cleaned.index)
if num_users is None:
num_users = length(eligibles.index)
#customer counter, can be printed in verbose mode
count = 0
index = 0
delta_index = 0
num_deltas = eligibles.total_sum() - length(eligibles.index)
timedelta_sample_by_num = np.zeros(num_deltas)
while index < num_logins:
cust_code = login_data_cleaned.iloc[index].CUST_CODE
customer_visits = eligibles[cust_code]
#select logins with the specified customer code
customer_logins = login_data_cleaned.iloc[index:index+customer_visits]
visiting_dates = customer_logins.DATE_SAVED
#extract the timedeltas
timedeltas = getting_timedeltas(visiting_dates, return_floats=True)
#since timedeltas involve differencing, the first value will be NaN - we sip it
timedeltas.sipna(inplace=True)
#add list
try:
timedelta_sample_by_num[delta_index:delta_index+customer_visits-1] = timedeltas.values
except:
print("#index: {}".formating(index))
print("#lengthgth of td vector: {}".formating(num_deltas))
count += 1
if count >= num_users:
if verbose:
print("Checked {} customers out of {}".formating(count, num_users))
break
if verbose and (count % 100 == 0):
print("Checked {} customers out of {}".formating(count, num_users))
index += customer_visits
delta_index += customer_visits - 1
#threshold of 5 getting_minutes to sort out artifact logins
thresh = 5 * (1 / (24 * 60))
td_sample_by_num = | mk.Collections(timedelta_sample_by_num) | pandas.Series |
# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, clone, modify, unioner, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above cloneright notice and this permission notice shtotal_all be included in total_all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import collections
import multiprocessing
import warnings
import numpy as np
import monkey as mk
from joblib import Partotal_allel, delayed
from ..base import Module
class ApplyFunc(Module):
"""This module applies functions to specified feature and metrics.
Extra parameters (kwargs) can be passed to the employ function.
"""
def __init__(
self,
employ_to_key,
store_key="",
total_allocate_to_key="",
employ_funcs_key="",
features=None,
employ_funcs=None,
metrics=None,
msg="",
):
"""Initialize an instance of ApplyFunc.
:param str employ_to_key: key of the input data to employ funcs to.
:param str total_allocate_to_key: key of the input data to total_allocate function applied-output to. (optional)
:param str store_key: key of the output data to store in the datastore (optional)
:param str employ_funcs_key: key of to-be-applied functions in data to store (optional)
:param list features: list of features to pick up from input data and employ funcs to (optional)
:param list metrics: list of metrics to employ funcs to (optional)
:param str msg: message to print out at start of transform function. (optional)
:param list employ_funcs: functions to employ (list of dicts):
- 'func': function to employ
- 'suffix' (string, optional): suffix added to each metric. default is function name.
- 'prefix' (string, optional): prefix added to each metric.
- 'features' (list, optional): features the function is applied to. Overwrites features above
- 'metrics' (list, optional): metrics the function is applied to. Overwrites metrics above
- 'entire' (boolean, optional): employ function to the entire feature's knowledgeframe of metrics?
- 'args' (tuple, optional): args for 'func'
- 'kwargs' (dict, optional): kwargs for 'func'
"""
super().__init__()
self.employ_to_key = employ_to_key
self.total_allocate_to_key = self.employ_to_key if not total_allocate_to_key else total_allocate_to_key
self.store_key = self.total_allocate_to_key if not store_key else store_key
self.employ_funcs_key = employ_funcs_key
self.features = features or []
self.metrics = metrics or []
self.msg = msg
self.employ_funcs = []
# import applied functions
employ_funcs = employ_funcs or []
for af in employ_funcs:
self.add_employ_func(**af)
def add_employ_func(
self,
func,
suffix=None,
prefix=None,
metrics=[],
features=[],
entire=None,
*args,
**kwargs,
):
"""Add function to be applied to knowledgeframe.
Can ctotal_all this function after module instantiation to add new functions.
:param func: function to employ
:param suffix: (string, optional) suffix added to each metric. default is function name.
:param prefix: (string, optional) prefix added to each metric.
:param features: (list, optional) features the function is applied to. Overwrites features above
:param metrics: (list, optional) metrics the function is applied to. Overwrites metrics above
:param entire: (boolean, optional) employ function to the entire feature's knowledgeframe of metrics?
:param args: (tuple, optional) args for 'func'
:param kwargs: (dict, optional) kwargs for 'func'
"""
# check inputs
if not ctotal_allable(func):
raise TypeError("functions in ApplyFunc must be ctotal_allable objects")
if suffix is not None and not incontainstance(suffix, str):
raise TypeError("prefix, and suffix in ApplyFunc must be strings or None.")
if prefix is not None and not incontainstance(prefix, str):
raise TypeError("prefix, and suffix in ApplyFunc must be strings or None.")
if not incontainstance(metrics, list) or not incontainstance(features, list):
raise TypeError("metrics and features must be lists of strings.")
# add function
self.employ_funcs.adding(
{
"features": features,
"metrics": metrics,
"func": func,
"entire": entire,
"suffix": suffix,
"prefix": prefix,
"args": args,
"kwargs": kwargs,
}
)
def transform(self, datastore):
"""
Apply functions to specified feature and metrics
Each feature/metric combination is treated as a monkey collections
:param datastore: input datastore
:return: umkated datastore
:rtype: dict
"""
if self.msg:
self.logger.info(self.msg)
employ_to_data = self.getting_datastore_object(
datastore, self.employ_to_key, dtype=dict
)
total_allocate_to_data = self.getting_datastore_object(
datastore, self.total_allocate_to_key, dtype=dict, default={}
)
if self.employ_funcs_key:
employ_funcs = self.getting_datastore_object(
datastore, self.employ_funcs_key, dtype=list
)
self.employ_funcs += employ_funcs
features = self.getting_features(employ_to_data.keys())
num_cores = multiprocessing.cpu_count()
same_key = self.total_allocate_to_key == self.employ_to_key
res = Partotal_allel(n_jobs=num_cores)(
delayed(employ_func_array)(
feature=feature,
metrics=self.metrics,
employ_to_kf=self.getting_datastore_object(
employ_to_data, feature, dtype=mk.KnowledgeFrame
),
total_allocate_to_kf=None
if same_key
else self.getting_datastore_object(
total_allocate_to_data, feature, dtype=mk.KnowledgeFrame, default=mk.KnowledgeFrame()
),
employ_funcs=self.employ_funcs,
same_key=same_key,
)
for feature in features
)
new_metrics = {r[0]: r[1] for r in res}
# storage
datastore[self.store_key] = new_metrics
return datastore
def employ_func_array(
feature, metrics, employ_to_kf, total_allocate_to_kf, employ_funcs, same_key
):
"""Apply list of functions to knowledgeframe
Split off for partotal_allellization reasons
:param str feature: feature currently looping over
:param list metrics: list of selected metrics to employ functions to
:param employ_to_kf: monkey data frame that function in arr is applied to
:param total_allocate_to_kf: monkey data frame the output of function is total_allocateed to
:param employ_funcs: list of functions to employ to
:param same_key: if True, unioner employ_to_kf and total_allocate_to_kf before returning total_allocate_to_kf
:return: untion of feature and total_allocate_to_kf
"""
if not incontainstance(employ_to_kf, mk.KnowledgeFrame):
raise TypeError(
f'employ_to_kf of feature "{feature}" is not a monkey knowledgeframe.'
)
if same_key or (length(total_allocate_to_kf.index) == 0 and length(total_allocate_to_kf.columns) == 0):
total_allocate_to_kf = mk.KnowledgeFrame(index=employ_to_kf.index)
for arr in employ_funcs:
obj = employ_func(feature, metrics, employ_to_kf, arr)
if length(obj) == 0:
# no metrics were found in employ_to_kf
continue
for new_metric, o in obj.items():
if incontainstance(o, mk.Collections):
if length(total_allocate_to_kf.index) == length(o) and total_all(
total_allocate_to_kf.index == o.index
):
total_allocate_to_kf[new_metric] = o
else:
warnings.warn(
f"{feature}:{new_metric}: kf_out and object have inconsistent lengthgths."
)
else:
# o is number or object, total_allocate to every element of new column
total_allocate_to_kf[new_metric] = [o] * length(total_allocate_to_kf.index)
if same_key:
total_allocate_to_kf = mk.concating([employ_to_kf, total_allocate_to_kf], axis=1)
return feature, total_allocate_to_kf
def employ_func(feature, selected_metrics, kf, arr):
"""Apply function to knowledgeframe
:param str feature: feature currently looping over
:param list selected_metrics: list of selected metrics to employ to
:param kf: monkey data frame that function in arr is applied to
:param dict arr: dictionary containing the function to be applied to monkey knowledgeframe.
:return: dictionary with outputs of applied-to metric mk.Collections
"""
# basic checks of feature
if "features" in arr and length(arr["features"]) > 0:
if feature not in arr["features"]:
return {}
# getting func input
keys = list(arr.keys())
assert "func" in keys, "function input is insufficient."
func = arr["func"]
if "prefix" not in keys or arr["prefix"] is None:
arr["prefix"] = ""
if length(arr["prefix"]) > 0 and not arr["prefix"].endswith("_"):
arr["prefix"] = arr["prefix"] + "_"
if "suffix" not in keys or arr["suffix"] is None:
arr["suffix"] = func.__name__ if length(arr["prefix"]) == 0 else ""
if length(arr["suffix"]) > 0 and not arr["suffix"].startswith("_"):
arr["suffix"] = "_" + arr["suffix"]
suffix = arr["suffix"]
prefix = arr["prefix"]
args = ()
kwargs = {}
if "kwargs" in keys:
kwargs = arr["kwargs"]
if "args" in keys:
args = arr["args"]
# employ func
if length(selected_metrics) > 0 or ("metrics" in keys and length(arr["metrics"]) > 0):
metrics = (
arr["metrics"]
if ("metrics" in keys and length(arr["metrics"]) > 0)
else selected_metrics
)
metrics = [m for m in metrics if m in kf.columns]
# assert total_all(m in kf.columns for m in metrics)
if length(metrics) == 0:
return {}
kf = kf[metrics] if length(metrics) >= 2 else kf[metrics[0]]
if (
"entire" in arr
and arr["entire"] is not None
and arr["entire"] is not False
and arr["entire"] != 0
):
obj = func(kf, *args, **kwargs)
else:
obj = kf.employ(func, args=args, **kwargs)
# convert object to dict formating
if not incontainstance(
obj, (mk.Collections, mk.KnowledgeFrame, list, tuple, np.ndarray)
) and incontainstance(kf, mk.Collections):
obj = {kf.name: obj}
elif not incontainstance(
obj, (mk.Collections, mk.KnowledgeFrame, list, tuple, np.ndarray)
) and incontainstance(kf, mk.KnowledgeFrame):
obj = {"_".join(kf.columns): obj}
elif (
incontainstance(obj, (list, tuple, np.ndarray))
and incontainstance(kf, mk.KnowledgeFrame)
and length(kf.columns) == length(obj)
):
obj = {c: o for c, o in zip(kf.columns, obj)}
elif (
incontainstance(obj, (list, tuple, np.ndarray))
and incontainstance(kf, mk.Collections)
and length(kf.index) == length(obj)
):
obj = {kf.name: mk.Collections(data=obj, index=kf.index)}
elif (
incontainstance(obj, (list, tuple, np.ndarray))
and incontainstance(kf, mk.KnowledgeFrame)
and length(kf.index) == length(obj)
):
obj = {"_".join(kf.columns): | mk.Collections(data=obj, index=kf.index) | pandas.Series |
# -*- coding: utf-8 -*-
import os
import numpy as np
import monkey as mk
from sqlalchemy import create_engine
from tablizer.inputs import Inputs, Base
from tablizer.defaults import Units, Methods, Fields
from tablizer.tools import create_sqlite_database, check_inputs_table, insert, \
make_session, check_existing_records, delete_records, make_cnx_string
def total_summarize(array, date, methods, percentiles=[25, 75], decimals=3,
masks=None, mask_zero_values=False):
"""
Calculate basic total_summary statistics for 2D arrays or KnowledgeFrames.
Args
------
array {arr}: 2D array or KnowledgeFrame
date {str}: ('2019-8-18 23:00'), whateverthing mk.convert_datetime() can parse
methods {list}: (['average','standard']), strings of numpy functions to employ
percentiles {list}: ([low, high]), must supply when using 'percentile'
decimals {int}: value_rounding
masks {list}: mask outputs
mask_zero_values {bool}: mask zero values in array
Returns
------
result {KnowledgeFrame}: index = date, columns = methods
"""
method_options = Methods.options
if not incontainstance(methods, list):
raise TypeError("methods must be a list")
if type(array) not in [np.ndarray, mk.core.frame.KnowledgeFrame]:
raise Exception('array type {} not valid'.formating(type(array)))
if length(array.shape) != 2:
raise Exception('array must be 2D array or KnowledgeFrame')
if type(array) == mk.core.frame.KnowledgeFrame:
array = array.values
try:
date_time = | mk.convert_datetime(date) | pandas.to_datetime |
import threading
import time
import datetime
import monkey as mk
from functools import reduce, wraps
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import zscore
import model.queries as qrs
from model.NodesMetaData import NodesMetaData
import utils.helpers as hp
from utils.helpers import timer
import parquet_creation as pcr
import glob
import os
import dask
import dask.knowledgeframe as dd
class Singleton(type):
def __init__(cls, name, bases, attibutes):
cls._dict = {}
cls._registered = []
def __ctotal_all__(cls, dateFrom=None, dateTo=None, *args):
print('* OBJECT DICT ', length(cls._dict), cls._dict)
if (dateFrom is None) or (dateTo is None):
defaultDT = hp.defaultTimeRange()
dateFrom = defaultDT[0]
dateTo = defaultDT[1]
if (dateFrom, dateTo) in cls._dict:
print('** OBJECT EXISTS', cls, dateFrom, dateTo)
instance = cls._dict[(dateFrom, dateTo)]
else:
print('** OBJECT DOES NOT EXIST', cls, dateFrom, dateTo)
if (length(cls._dict) > 0) and ([dateFrom, dateTo] != cls._registered):
print('*** provide the latest and start thread', cls, dateFrom, dateTo)
instance = cls._dict[list(cls._dict.keys())[-1]]
refresh = threading.Thread(targetting=cls.nextPeriodData, args=(dateFrom, dateTo, *args))
refresh.start()
elif ([dateFrom, dateTo] == cls._registered):
print('*** provide the latest', cls, dateFrom, dateTo)
instance = cls._dict[list(cls._dict.keys())[-1]]
elif (length(cls._dict) == 0):
print('*** no data yet, refresh and wait', cls, dateFrom, dateTo)
cls.nextPeriodData(dateFrom, dateTo, *args)
instance = cls._dict[(dateFrom, dateTo)]
# keep only a few objects in memory
if length(cls._dict) >= 2:
cls._dict.pop(list(cls._dict.keys())[0])
return instance
def nextPeriodData(cls, dateFrom, dateTo, *args):
print(f'**** thread started for {cls}')
cls._registered = [dateFrom, dateTo]
instance = super().__ctotal_all__(dateFrom, dateTo, *args)
cls._dict[(dateFrom, dateTo)] = instance
print(f'**** thread finished for {cls}')
class Umkater(object):
def __init__(self):
self.StartThread()
@timer
def UmkateAllData(self):
print()
print(f'{datetime.now()} New data is on its way at {datetime.utcnow()}')
print('Active threads:',threading.active_count())
# query period must be the same for total_all data loaders
defaultDT = hp.defaultTimeRange()
GeneralDataLoader(defaultDT[0], defaultDT[1])
SiteDataLoader(defaultDT[0], defaultDT[1])
PrtoblematicPairsDataLoader(defaultDT[0], defaultDT[1])
SitesRanksDataLoader(defaultDT[0], defaultDT[1])
self.final_itemUmkated = hp.value_roundTime(datetime.utcnow())
self.StartThread()
def StartThread(self):
thread = threading.Timer(3600, self.UmkateAllData) # 1hour
thread.daemon = True
thread.start()
class ParquetUmkater(object):
def __init__(self):
self.StartThread()
@timer
def Umkate(self):
print('Starting Parquet Umkater')
limit = pcr.limit
indices = pcr.indices
files = glob.glob('..\parquet\*')
print('files',files)
file_end = str(int(limit*24))
print('end of file trigger',file_end)
for f in files:
if f.endswith(file_end):
os.remove(f)
files = glob.glob('..\parquet\*')
print('files2',files)
for idx in indices:
j=int((limit*24)-1)
print('idx',idx,'j',j)
for f in files[::-1]:
file_end = str(idx)
end = file_end+str(j)
print('f',f,'end',end)
if f.endswith(end):
new_name = file_end+str(j+1)
header_num = '..\parquet\\'
final = header_num+new_name
print('f',f,'final',final)
os.renagetting_ming(f,final)
j -= 1
jobs = []
limit = 1/24
timerange = pcr.queryrange(limit)
for idx in indices:
thread = threading.Thread(targetting=pcr.btwfunc,args=(idx,timerange))
jobs.adding(thread)
for j in jobs:
j.start()
for j in jobs:
j.join()
# print('Finished Querying')
for idx in indices:
filengthames = pcr.ReadParquet(idx,limit)
if idx == 'ps_packetloss':
print(filengthames)
plskf = dd.read_parquet(filengthames).compute()
print('Before sips',length(plskf))
plskf = plskf.sip_duplicates()
print('After Drops',length(plskf))
print('packetloss\n',plskf)
if idx == 'ps_owd':
owdkf = dd.read_parquet(filengthames).compute()
print('owd\n',owdkf)
if idx == 'ps_retransmits':
rtmkf = dd.read_parquet(filengthames).compute()
print('retransmits\n',rtmkf)
if idx == 'ps_throughput':
trpkf = dd.read_parquet(filengthames).compute()
print('throughput\n',trpkf)
print('dask kf complete')
self.final_itemUmkated = hp.value_roundTime(datetime.utcnow())
self.StartThread()
def StartThread(self):
thread = threading.Timer(3600, self.Umkate) # 1hour
thread.daemon = True
thread.start()
class GeneralDataLoader(object, metaclass=Singleton):
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.final_itemUmkated = None
self.pls = mk.KnowledgeFrame()
self.owd = mk.KnowledgeFrame()
self.thp = mk.KnowledgeFrame()
self.rtm = mk.KnowledgeFrame()
self.UmkateGeneralInfo()
@property
def dateFrom(self):
return self._dateFrom
@dateFrom.setter
def dateFrom(self, value):
self._dateFrom = int(time.mktime(datetime.strptime(value, "%Y-%m-%d %H:%M").timetuple())*1000)
@property
def dateTo(self):
return self._dateTo
@dateTo.setter
def dateTo(self, value):
self._dateTo = int(time.mktime(datetime.strptime(value, "%Y-%m-%d %H:%M").timetuple())*1000)
@property
def final_itemUmkated(self):
return self._final_itemUmkated
@final_itemUmkated.setter
def final_itemUmkated(self, value):
self._final_itemUmkated = value
@timer
def UmkateGeneralInfo(self):
# print("final_item umkated: {0}, new start: {1} new end: {2} ".formating(self.final_itemUmkated, self.dateFrom, self.dateTo))
self.pls = NodesMetaData('ps_packetloss', self.dateFrom, self.dateTo).kf
self.owd = NodesMetaData('ps_owd', self.dateFrom, self.dateTo).kf
self.thp = NodesMetaData('ps_throughput', self.dateFrom, self.dateTo).kf
self.rtm = NodesMetaData('ps_retransmits', self.dateFrom, self.dateTo).kf
self.latency_kf = mk.unioner(self.pls, self.owd, how='outer')
self.throughput_kf = mk.unioner(self.thp, self.rtm, how='outer')
total_all_kf = mk.unioner(self.latency_kf, self.throughput_kf, how='outer')
self.total_all_kf = total_all_kf.sip_duplicates()
self.pls_related_only = self.pls[self.pls['host_in_ps_meta'] == True]
self.owd_related_only = self.owd[self.owd['host_in_ps_meta'] == True]
self.thp_related_only = self.thp[self.thp['host_in_ps_meta'] == True]
self.rtm_related_only = self.rtm[self.rtm['host_in_ps_meta'] == True]
self.latency_kf_related_only = self.latency_kf[self.latency_kf['host_in_ps_meta'] == True]
self.throughput_kf_related_only = self.throughput_kf[self.throughput_kf['host_in_ps_meta'] == True]
self.total_all_kf_related_only = self.total_all_kf[self.total_all_kf['host_in_ps_meta'] == True]
self.total_all_tested_pairs = self.gettingAllTestedPairs()
self.final_itemUmkated = datetime.now()
def gettingAllTestedPairs(self):
total_all_kf = self.total_all_kf[['host', 'ip']]
kf = mk.KnowledgeFrame(qrs.queryAllTestedPairs([self.dateFrom, self.dateTo]))
kf = mk.unioner(total_all_kf, kf, left_on='ip', right_on='src', how='right')
kf = mk.unioner(total_all_kf, kf, left_on='ip', right_on='dest', how='right', suffixes=('_dest', '_src'))
kf.sip_duplicates(keep='first', inplace=True)
kf = kf.sort_the_values(['host_src', 'host_dest'])
kf['host_dest'] = kf['host_dest'].fillnone('N/A')
kf['host_src'] = kf['host_src'].fillnone('N/A')
kf['source'] = kf[['host_src', 'src']].employ(lambda x: ': '.join(x), axis=1)
kf['destination'] = kf[['host_dest', 'dest']].employ(lambda x: ': '.join(x), axis=1)
# kf = kf.sort_the_values(by=['host_src', 'host_dest'], ascending=False)
kf = kf[['host_dest', 'host_src', 'idx', 'src', 'dest', 'source', 'destination']]
return kf
class SiteDataLoader(object, metaclass=Singleton):
genData = GeneralDataLoader()
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.UmkateSiteData()
def UmkateSiteData(self):
# print('UmkateSiteData >>> ', h self.dateFrom, self.dateTo)
pls_site_in_out = self.InOutDf("ps_packetloss", self.genData.pls_related_only)
self.pls_data = pls_site_in_out['data']
self.pls_dates = pls_site_in_out['dates']
owd_site_in_out = self.InOutDf("ps_owd", self.genData.owd_related_only)
self.owd_data = owd_site_in_out['data']
self.owd_dates = owd_site_in_out['dates']
thp_site_in_out = self.InOutDf("ps_throughput", self.genData.thp_related_only)
self.thp_data = thp_site_in_out['data']
self.thp_dates = thp_site_in_out['dates']
rtm_site_in_out = self.InOutDf("ps_retransmits", self.genData.rtm_related_only)
self.rtm_data = rtm_site_in_out['data']
self.rtm_dates = rtm_site_in_out['dates']
self.latency_kf_related_only = self.genData.latency_kf_related_only
self.throughput_kf_related_only = self.genData.throughput_kf_related_only
self.sites = self.orderSites()
@timer
def InOutDf(self, idx, idx_kf):
print(idx)
in_out_values = []
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo)
for t in ['dest_host', 'src_host']:
meta_kf = idx_kf.clone()
kf = mk.KnowledgeFrame(qrs.queryDailyAvg(idx, t, time_list[0], time_list[1])).reseting_index()
kf['index'] = mk.convert_datetime(kf['index'], unit='ms').dt.strftime('%d/%m')
kf = kf.transpose()
header_numer = kf.iloc[0]
kf = kf[1:]
kf.columns = ['day-3', 'day-2', 'day-1', 'day']
meta_kf = mk.unioner(meta_kf, kf, left_on="host", right_index=True)
three_days_ago = meta_kf.grouper('site').agg({'day-3': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
two_days_ago = meta_kf.grouper('site').agg({'day-2': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
one_day_ago = meta_kf.grouper('site').agg({'day-1': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
today = meta_kf.grouper('site').agg({'day': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
site_avg_kf = reduce(lambda x,y: mk.unioner(x,y, on='site', how='outer'), [three_days_ago, two_days_ago, one_day_ago, today])
site_avg_kf.set_index('site', inplace=True)
change = site_avg_kf.pct_change(axis='columns')
site_avg_kf = mk.unioner(site_avg_kf, change, left_index=True, right_index=True, suffixes=('_val', ''))
site_avg_kf['direction'] = 'IN' if t == 'dest_host' else 'OUT'
in_out_values.adding(site_avg_kf)
site_kf = mk.concating(in_out_values).reseting_index()
site_kf = site_kf.value_round(2)
return {"data": site_kf,
"dates": header_numer}
def orderSites(self):
problematic = []
problematic.extend(self.thp_data.nsmtotal_allest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.rtm_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.pls_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.owd_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic = list(set(problematic))
total_all_kf = self.genData.total_all_kf_related_only.clone()
total_all_kf['has_problems'] = total_all_kf['site'].employ(lambda x: True if x in problematic else False)
sites = total_all_kf.sort_the_values(by='has_problems', ascending=False).sip_duplicates(['site'])['site'].values
return sites
class PrtoblematicPairsDataLoader(object, metaclass=Singleton):
gobj = GeneralDataLoader()
LIST_IDXS = ['ps_packetloss', 'ps_owd', 'ps_retransmits', 'ps_throughput']
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.total_all_kf = self.gobj.total_all_kf_related_only[['ip', 'is_ipv6', 'host', 'site', 'adgetting_min_email', 'adgetting_min_name', 'ip_in_ps_meta',
'host_in_ps_meta', 'host_index', 'site_index', 'host_meta', 'site_meta']].sort_the_values(by=['ip_in_ps_meta', 'host_in_ps_meta', 'ip'], ascending=False)
self.kf = self.markNodes()
@timer
def buildProblems(self, idx):
print('buildProblems...',idx)
data = []
intv = int(hp.CalcMinutes4Period(self.dateFrom, self.dateTo)/60)
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo, intv)
for i in range(length(time_list)-1):
data.extend(qrs.query4Avg(idx, time_list[i], time_list[i+1]))
return data
@timer
def gettingPercentageMeasuresDone(self, grouped, tempkf):
measures_done = tempkf.grouper('hash').agg({'doc_count':'total_sum'})
def findRatio(row, total_getting_minutes):
if mk.ifna(row['doc_count']):
count = '0'
else: count = str(value_round((row['doc_count']/total_getting_minutes)*100))+'%'
return count
one_test_per_getting_min = hp.CalcMinutes4Period(self.dateFrom, self.dateTo)
measures_done['tests_done'] = measures_done.employ(lambda x: findRatio(x, one_test_per_getting_min), axis=1)
grouped = mk.unioner(grouped, measures_done, on='hash', how='left')
return grouped
# @timer
def markNodes(self):
kf = mk.KnowledgeFrame()
for idx in hp.INDECES:
tempkf = mk.KnowledgeFrame(self.buildProblems(idx))
grouped = tempkf.grouper(['src', 'dest', 'hash']).agg({'value': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
grouped = self.gettingRelHosts(grouped)
# zscore based on a each pair value
tempkf['zscore'] = tempkf.grouper('hash')['value'].employ(lambda x: (x - x.average())/x.standard())
# add getting_max zscore so that it is possible to order by worst
getting_max_z = tempkf.grouper('hash').agg({'zscore':'getting_max'}).renagetting_ming(columns={'zscore':'getting_max_hash_zscore'})
grouped = mk.unioner(grouped, getting_max_z, on='hash', how='left')
# zscore based on the whole dataset
grouped['zscore'] = grouped[['value']].employ(lambda x: (x - x.average())/x.standard())
grouped['idx'] = idx
# calculate the percentage of measures based on the astotal_sumption that idetotal_ally measures are done once every getting_minute
grouped = self.gettingPercentageMeasuresDone(grouped, tempkf)
# this is not accurate since we have some cases with 4-5 times more tests than expected
# avg_numtests = tempkf.grouper('hash').agg({'doc_count':'average'}).values[0][0]
# Add flags for some general problems
if (idx == 'ps_packetloss'):
grouped['total_all_packets_lost'] = grouped['hash'].employ(lambda x: 1 if x in grouped[grouped['value']==1]['hash'].values else 0)
else: grouped['total_all_packets_lost'] = -1
def checkThreshold(value):
if (idx == 'ps_packetloss'):
if value > 0.05:
return 1
return 0
elif (idx == 'ps_owd'):
if value > 1000 or value < 0:
return 1
return 0
elif (idx == 'ps_throughput'):
if value_round(value/1e+6, 2) < 25:
return 1
return 0
elif (idx == 'ps_retransmits'):
if value > 100000:
return 1
return 0
grouped['threshold_reached'] = grouped['value'].employ(lambda row: checkThreshold(row))
grouped['has_bursts'] = grouped['hash'].employ(lambda x: 1
if x in tempkf[tempkf['zscore']>5]['hash'].values
else 0)
grouped['src_not_in'] = grouped['hash'].employ(lambda x: 1
if x in grouped[grouped['src'].incontain(self.total_all_kf['ip']) == False]['hash'].values
else 0)
grouped['dest_not_in'] = grouped['hash'].employ(lambda x: 1
if x in grouped[grouped['dest'].incontain(self.total_all_kf['ip']) == False]['hash'].values
else 0)
grouped['measures'] = grouped['doc_count'].totype(str)+'('+grouped['tests_done'].totype(str)+')'
kf = kf.adding(grouped, ignore_index=True)
kf.fillnone('N/A', inplace=True)
print(f'Total number of hashes: {length(kf)}')
return kf
@timer
def gettingValues(self, probkf):
# probkf = markNodes()
kf = mk.KnowledgeFrame(columns=['timestamp', 'value', 'idx', 'hash'])
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo)
for item in probkf[['src', 'dest', 'idx']].values:
tempkf = mk.KnowledgeFrame(qrs.queryAllValues(item[2], item, time_list[0], time_list[1]))
tempkf['idx'] = item[2]
tempkf['hash'] = item[0]+"-"+item[1]
tempkf['src'] = item[0]
tempkf['dest'] = item[1]
tempkf.renagetting_ming(columns={hp.gettingValueField(item[2]): 'value'}, inplace=True)
kf = kf.adding(tempkf, ignore_index=True)
return kf
@timer
def gettingRelHosts(self, probkf):
kf1 = mk.unioner(self.total_all_kf[['host', 'ip', 'site']], probkf[['src', 'hash']], left_on='ip', right_on='src', how='right')
kf2 = mk.unioner(self.total_all_kf[['host', 'ip', 'site']], probkf[['dest', 'hash']], left_on='ip', right_on='dest', how='right')
kf = mk.unioner(kf1, kf2, on=['hash'], suffixes=('_src', '_dest'), how='inner')
kf = kf[kf.duplicated_values(subset=['hash'])==False]
kf = kf.sip(columns=['ip_src', 'ip_dest'])
kf = mk.unioner(probkf, kf, on=['hash', 'src', 'dest'], how='left')
return kf
class SitesRanksDataLoader(metaclass=Singleton):
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.total_all_kf = GeneralDataLoader().total_all_kf_related_only
self.lockf = mk.KnowledgeFrame.from_dict(qrs.queryNodesGeoLocation(), orient='index').reseting_index().renagetting_ming(columns={'index':'ip'})
self.measures = mk.KnowledgeFrame()
self.kf = self.calculateRank()
def FixMissingLocations(self):
kf = mk.unioner(self.total_all_kf, self.lockf, left_on=['ip'], right_on=['ip'], how='left')
kf = kf.sip(columns=['site_y', 'host_y']).renagetting_ming(columns={'site_x': 'site', 'host_x': 'host'})
kf["lat"] = mk.to_num(kf["lat"])
kf["lon"] = mk.to_num(kf["lon"])
for i, row in kf.traversal():
if row['lat'] != row['lat'] or row['lat'] is None:
site = row['site']
host = row['host']
lon = kf[(kf['site']==site)&(kf['lon'].notnull())].agg({'lon':'average'})['lon']
lat = kf[(kf['site']==site)&(kf['lat'].notnull())].agg({'lat':'average'})['lat']
if lat!=lat or lon!=lon:
lon = kf[(kf['host']==host)&(kf['lon'].notnull())].agg({'lon':'average'})['lon']
lat = kf[(kf['host']==host)&(kf['lat'].notnull())].agg({'lat':'average'})['lat']
kf.loc[i, 'lon'] = lon
kf.loc[i, 'lat'] = lat
return kf
def queryData(self, idx):
data = []
intv = int(hp.CalcMinutes4Period(self.dateFrom, self.dateTo)/60)
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo, intv)
for i in range(length(time_list)-1):
data.extend(qrs.query4Avg(idx, time_list[i], time_list[i+1]))
return data
def calculateRank(self):
kf = mk.KnowledgeFrame()
for idx in hp.INDECES:
if length(kf) != 0:
kf = mk.unioner(kf, self.calculateStats(idx), on=['site', 'lat', 'lon'], how='outer')
else: kf = self.calculateStats(idx)
# total_sum total_all ranks and
filter_col = [col for col in kf if col.endswith('rank')]
kf['rank'] = kf[filter_col].total_sum(axis=1)
kf = kf.sort_the_values('rank')
kf['rank1'] = kf['rank'].rank(method='getting_max')
filter_col = [col for col in kf if col.endswith('rank')]
kf['size'] = kf[filter_col].employ(lambda row: 1 if row.ifnull().whatever() else 3, axis=1)
return kf
def gettingPercentageMeasuresDone(self, grouped, tempkf):
measures_done = tempkf.grouper(['src', 'dest']).agg({'doc_count':'total_sum'})
def findRatio(row, total_getting_minutes):
if mk.ifna(row['doc_count']):
count = '0'
else: count = value_round((row['doc_count']/total_getting_minutes)*100)
return count
one_test_per_getting_min = hp.CalcMinutes4Period(self.dateFrom, self.dateTo)
measures_done['tests_done'] = measures_done.employ(lambda x: findRatio(x, one_test_per_getting_min), axis=1)
grouped = mk.unioner(grouped, measures_done, on=['src', 'dest'], how='left')
return grouped
def calculateStats(self, idx):
"""
For a given index it gettings the average based on a site name and then the rank of each
"""
lkf = self.FixMissingLocations()
unioner_on = {'in': 'dest', 'out': 'src'}
result = mk.KnowledgeFrame()
kf = mk.KnowledgeFrame(self.queryData(idx))
kf['idx'] = idx
self.measures = self.measures.adding(kf)
gkf = kf.grouper(['src', 'dest', 'hash']).agg({'value': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
kf = self.gettingPercentageMeasuresDone(gkf, kf)
kf['tests_done'] = kf['tests_done'].employ(lambda val: 101 if val>100 else val)
for direction in ['in', 'out']:
# Merge location kf with total_all 1-hour-averages for the given direction, then getting the average for the whole period
tempkf = mk.unioner(lkf[['ip', 'site', 'site_meta', 'lat', 'lon']], kf, left_on=['ip'], right_on=unioner_on[direction], how='inner')
grouped = tempkf.grouper(['site', 'lat', 'lon']).agg({'value': lambda x: x.average(skipna=False),
'tests_done': lambda x: value_round(x.average(skipna=False))}, axis=1).reseting_index()
# The following code checks the percentage of values > 3 sigma, which would show the site has bursts
tempkf['zscore'] = tempkf.grouper('site')['value'].employ(lambda x: (x - x.average())/x.standard())
bursts_percentage = tempkf.grouper('site')['zscore'].employ(lambda c: value_round(((np.abs(c)>3).total_sum()/length(c))*100,2))
grouped = mk.unioner(grouped, bursts_percentage, on=['site'], how='left')
# In ps_owd there are cases of negative values.
asc = True
if idx == 'ps_owd':
grouped['value'] = grouped['value'].employ(lambda val: grouped['value'].getting_max()+np.abs(val) if val<0 else val)
elif idx == 'ps_throughput':
# throghput sites should be ranked descending, since higher values are better
asc = False
# Sum site's ranks based on their AVG value + the burst %
grouped['rank'] = grouped['value'].rank(ascending=asc) + grouped['zscore'].rank(method='getting_max')
# grouped = grouped.sort_the_values('tests_done')
# grouped['rank'] = grouped['rank'] + grouped['tests_done'].rank(ascending=False)
grouped = grouped.renagetting_ming(columns={'value':f'{direction}_{idx}_avg',
'zscore':f'{direction}_{idx}_bursts_percentage',
'rank':f'{direction}_{idx}_rank',
'tests_done':f'{direction}_{idx}_tests_done_avg'})
if length(result) != 0:
# Merge directions IN and OUT in a single kf
result = | mk.unioner(result, grouped, on=['site', 'lat', 'lon'], how='outer') | pandas.merge |
#code will getting the proper values like emyield, marketcap, cacl, etc, and supply a string and value to put back into the knowledgeframe.
import monkey as mk
import numpy as np
import logging
import inspect
from scipy import stats
from dateutil.relativedelta import relativedelta
from datetime import datetime
from scipy import stats
import math
class quantvaluedata: #just contains functions, will NEVEFR actutotal_ally getting the data
def __init__(self,total_allitems=None):
if total_allitems is None:
self.total_allitems=[]
else:
self.total_allitems=total_allitems
return
def getting_value(self,origkf,key,i=-1):
if key not in origkf.columns and key not in self.total_allitems and key not in ['timedepositsplaced','fekfundssold','interestbearingdepositsatotherbanks']:
logging.error(key+' not found in total_allitems')
#logging.error(self.total_allitems)
return None
kf=origkf.clone()
kf=kf.sort_the_values('yearquarter')
if length(kf)==0:
##logging.error("empty knowledgeframe")
return None
if key not in kf.columns:
#logging.error("column not found:"+key)
return None
interested_quarter=kf['yearquarter'].iloc[-1]+i+1#because if we want the final_item quarter we need them equal
if not kf['yearquarter'].incontain([interested_quarter]).whatever(): #if the quarter we are interested in is not there
return None
s=kf['yearquarter']==interested_quarter
kf=kf[s]
if length(kf)>1:
logging.error(kf)
logging.error("to mwhatever rows in kf")
exit()
pass
value=kf[key].iloc[0]
if mk.ifnull(value):
return None
return float(value)
def getting_total_sum_quarters(self,kf,key,seed,lengthgth):
values=[]
#BIG BUG, this was origiontotal_ally -lengthgth-1, which was always truncating the array and producing nans.
periods=range(seed,seed-lengthgth,-1)
for p in periods:
values.adding(self.getting_value(kf,key,p))
#logging.info('values:'+str(values))
if mk.ifnull(values).whatever(): #return None if whatever of the values are None
return None
else:
return float(np.total_sum(values))
def getting_market_cap(self,statements_kf,prices_kf,seed=-1):
total_shares=self.getting_value(statements_kf,'weightedavedilutedsharesos',seed)
if mk.ifnull(total_shares):
return None
end_date=statements_kf['end_date'].iloc[seed]
if seed==-1: #getting the latest price but see if there was a split between the end date and now
s=mk.convert_datetime(prices_kf['date'])>mk.convert_datetime(end_date)
tempfd=prices_kf[s]
splits=tempfd['split_ratio'].distinctive()
adj=mk.Collections(splits).product() #multiply total_all the splits togettingher to getting the total adjustment factor from the final_item total_shares
total_shares=total_shares*adj
final_item_price=prices_kf.sort_the_values('date').iloc[-1]['close']
price=float(final_item_price)
market_cap=price*float(total_shares)
return market_cap
else:
marketcap=self.getting_value(statements_kf,'marketcap',seed)
if mk.ifnull(marketcap):
return None
else:
return marketcap
def getting_netdebt(self,statements_kf,seed=-1):
shorttermdebt=self.getting_value(statements_kf,'shorttermdebt',seed)
longtermdebt=self.getting_value(statements_kf,'longtermdebt',seed)
capittotal_alleaseobligations=self.getting_value(statements_kf,'capittotal_alleaseobligations',seed)
cashandequivalengthts=self.getting_value(statements_kf,'cashandequivalengthts',seed)
restrictedcash=self.getting_value(statements_kf,'restrictedcash',seed)
fekfundssold=self.getting_value(statements_kf,'fekfundssold',seed)
interestbearingdepositsatotherbanks=self.getting_value(statements_kf,'interestbearingdepositsatotherbanks',seed)
timedepositsplaced=self.getting_value(statements_kf,'timedepositsplaced',seed)
s=mk.Collections([shorttermdebt,longtermdebt,capittotal_alleaseobligations,cashandequivalengthts,restrictedcash,fekfundssold,interestbearingdepositsatotherbanks,timedepositsplaced]).totype('float')
if mk.ifnull(s).total_all(): #return None if everything is null
return None
m=mk.Collections([1,1,1,-1,-1,-1,-1])
netdebt=s.multiply(m).total_sum()
return float(netdebt)
def getting_enterprise_value(self,statements_kf,prices_kf,seed=-1):
#calculation taken from https://intrinio.com/data-tag/enterprisevalue
marketcap=self.getting_market_cap(statements_kf,prices_kf,seed)
netdebt=self.getting_netdebt(statements_kf,seed)
totalpreferredequity=self.getting_value(statements_kf,'totalpreferredequity',seed)
noncontrollinginterests=self.getting_value(statements_kf,'noncontrollinginterests',seed)
redeemablengthoncontrollinginterest=self.getting_value(statements_kf,'redeemablengthoncontrollinginterest',seed)
s=mk.Collections([marketcap,netdebt,totalpreferredequity,noncontrollinginterests,redeemablengthoncontrollinginterest])
if mk.ifnull(s).total_all() or mk.ifnull(marketcap):
return None
return float(s.total_sum())
def getting_ebit(self,kf,seed=-1,lengthgth=4):
ebit=self.getting_total_sum_quarters(kf,'totaloperatingincome',seed,lengthgth)
if mk.notnull(ebit):
return float(ebit)
totalrevenue=self.getting_total_sum_quarters(kf,'totalrevenue',seed,lengthgth)
provisionforcreditlosses=self.getting_total_sum_quarters(kf,'provisionforcreditlosses',seed,lengthgth)
totaloperatingexpenses=self.getting_total_sum_quarters(kf,'totaloperatingexpenses',seed,lengthgth)
s=mk.Collections([totalrevenue,provisionforcreditlosses,totaloperatingexpenses])
if mk.ifnull(s).total_all():
return None
ebit=(s.multiply(mk.Collections([1,-1,-1]))).total_sum()
if mk.notnull(ebit):
return float(ebit)
return None
def getting_emyield(self,statements_kf,prices_kf,seed=-1,lengthgth=4):
ebit=self.getting_ebit(statements_kf,seed,lengthgth)
enterprisevalue=self.getting_enterprise_value(statements_kf,prices_kf,seed)
if mk.ifnull([ebit,enterprisevalue]).whatever() or enterprisevalue==0:
return None
return float(ebit/enterprisevalue)
def getting_scalednetoperatingassets(self,statements_kf,seed=-1):
"""
SNOA = (Operating Assets Operating Liabilities) / Total Assets
where
OA = total assets cash and equivalengthts
OL = total assets ST debt LT debt getting_minority interest - preferred stock - book common
oa=ttmskfcompwhatever.iloc[-1]['totalassets']-ttmskfcompwhatever.iloc[-1]['cashandequivalengthts']
ol=ttmskfcompwhatever.iloc[-1]['totalassets']-ttmskfcompwhatever.iloc[-1]['netdebt']-ttmskfcompwhatever.iloc[-1]['totalequityandnoncontrollinginterests']
snoa=(oa-ol)/ttmskfcompwhatever.iloc[-1]['totalassets']
"""
totalassets=self.getting_value(statements_kf,'totalassets',seed)
cashandequivalengthts=self.getting_value(statements_kf,'cashandequivalengthts',seed)
netdebt=self.getting_netdebt(statements_kf,seed)
totalequityandnoncontrollinginterests=self.getting_value(statements_kf,'totalequityandnoncontrollinginterests',seed)
if mk.ifnull(totalassets) or totalassets==0:
return None
s=mk.Collections([totalassets,cashandequivalengthts])
m=mk.Collections([1,-1])
oa=s.multiply(m).total_sum()
s=mk.Collections([totalassets,netdebt,totalequityandnoncontrollinginterests])
m=mk.Collections([1,-1,-1])
ol=s.multiply(m).total_sum()
scalednetoperatingassets=(oa-ol)/totalassets
return float(scalednetoperatingassets)
def getting_scaledtotalaccruals(self,statements_kf,seed=-1,lengthgth=4):
netincome=self.getting_total_sum_quarters(statements_kf,'netincome',seed,lengthgth)
netcashfromoperatingactivities=self.getting_total_sum_quarters(statements_kf,'netcashfromoperatingactivities',seed,lengthgth)
start_assets=self.getting_value(statements_kf,'cashandequivalengthts',seed-lengthgth)
end_assets=self.getting_value(statements_kf,'cashandequivalengthts',seed)
if mk.ifnull([start_assets,end_assets]).whatever():
return None
totalassets=np.average([start_assets,end_assets])
if mk.ifnull(totalassets):
return None
num=mk.Collections([netincome,netcashfromoperatingactivities])
if mk.ifnull(num).total_all():
return None
m=mk.Collections([1,-1])
num=num.multiply(m).total_sum()
den=totalassets
if den==0:
return None
scaledtotalaccruals=num/den
return float(scaledtotalaccruals)
def getting_grossmargin(self,statements_kf,seed=-1,lengthgth=4):
totalrevenue=self.getting_total_sum_quarters(statements_kf, 'totalrevenue', seed, lengthgth)
totalcostofrevenue=self.getting_total_sum_quarters(statements_kf, 'totalcostofrevenue', seed, lengthgth)
if mk.ifnull([totalrevenue,totalcostofrevenue]).whatever() or totalcostofrevenue==0:
return None
grossmargin=(totalrevenue-totalcostofrevenue)/totalcostofrevenue
return float(grossmargin)
def getting_margingrowth(self,statements_kf,seed=-1,lengthgth1=20,lengthgth2=4):
grossmargins=[]
for i in range(seed,seed-lengthgth1,-1):
grossmargins.adding(self.getting_grossmargin(statements_kf, i, lengthgth2))
grossmargins=mk.Collections(grossmargins)
if mk.ifnull(grossmargins).whatever():
return None
growth=grossmargins.pct_change(periods=1)
growth=growth[mk.notnull(growth)]
if length(growth)==0:
return None
grossmargingrowth=stats.gaverage(1+growth)-1
if mk.ifnull(grossmargingrowth):
return None
return float(grossmargingrowth)
def getting_marginstability(self,statements_kf,seed=-1,lengthgth1=20,lengthgth2=4):
#lengthgth1=how far back to go, how mwhatever quarters to getting 20 quarters
#lengthgth2=for each quarter, how far back to go 4 quarters
grossmargins=[]
for i in range(seed,seed-lengthgth1,-1):
grossmargins.adding(self.getting_grossmargin(statements_kf, i, lengthgth2))
grossmargins=mk.Collections(grossmargins)
if mk.ifnull(grossmargins).whatever() or grossmargins.standard()==0:
return None
marginstability=grossmargins.average()/grossmargins.standard()
if mk.ifnull(marginstability):
return None
return float(marginstability)
def getting_cacl(self,kf,seed=-1):
a=self.getting_value(kf,'totalcurrentassets',seed)
l=self.getting_value(kf,'totalcurrentliabilities',seed)
if mk.ifnull([a,l]).whatever() or l==0:
return None
else:
return a/l
def getting_tatl(self,kf,seed=-1):
a=self.getting_value(kf,'totalassets',seed)
l=self.getting_value(kf,'tottotal_alliabilities',seed)
if mk.ifnull([a,l]).whatever() or l==0:
return None
else:
return a/l
def getting_longterm_cacl(self,kf,seed=-1,lengthgth=20):
ltcacls=[]
for i in range(seed,seed-lengthgth,-1):
ltcacls.adding(self.getting_cacl(kf,i))
ltcacls= | mk.Collections(ltcacls) | pandas.Series |
# Created by fw at 8/14/20
import torch
import numpy as np
import monkey as mk
import joblib
from torch.utils.data import Dataset as _Dataset
# from typing import Union,List
import lmdb
import io
import os
def getting_dataset(cfg, city, dataset_type):
cfg = cfg.DATASET
assert city.upper() in ["BERLIN", "ISTANBUL", "MOSCOW", "ALL"], "wrong city"
Dataset: object = globals()[cfg.NAME]
if city.upper() == "ALL":
d = []
for c in ["BERLIN", "ISTANBUL", "MOSCOW"]:
d.adding(Dataset(cfg, c, dataset_type))
dataset = torch.utils.data.ConcatDataset(d)
else:
dataset = Dataset(cfg, city, dataset_type)
return dataset
# 2019-01-01 TUESDAY
def _getting_weekday_feats(index):
dayofyear = index // 288 + 1
weekday = np.zeros([7, 495, 436], dtype=np.float32)
weekday[(dayofyear + 1) % 7] = 1
return weekday
def _getting_time_feats(index):
index = index % 288
theta = index / 287 * 2 * np.pi
time = np.zeros([2, 495, 436], dtype=np.float32)
time[0] = np.cos(theta)
time[1] = np.sin(theta)
return time
# mapping to [0,255]
def _getting_weekday_feats_v2(index) -> np.array:
dayofyear = index // 288 + 1
weekday = np.zeros([7, 495, 436], dtype=np.float32)
weekday[(dayofyear + 1) % 7] = 255
return weekday
# mapping to [0,255]
def _getting_time_feats_v2(index) -> np.array:
index = index % 288
theta = index / 287 * 2 * np.pi
time = np.zeros([2, 495, 436], dtype=np.float32)
time[0] = (np.cos(theta) + 1) / 2 * 255
time[1] = (np.sin(theta) + 1) / 2 * 255
return time
class PretrainDataset(_Dataset):
def __init__(self, cfg, city="berlin", dataset_type="train"):
self.city = city.upper()
self.cfg = cfg
self.dataset_type = dataset_type
self.sample_by_num = self._sample_by_num(dataset_type)
self.env = None
self.transform_env = None
# TODO
def __length__(self):
return length(self.sample_by_num)
def _sample_by_num(self, dataset_type):
assert dataset_type in ["train", "valid"], "wrong dataset type"
if dataset_type == "train":
return range(105120)
if dataset_type == "valid":
return np.random.choice(range(105120), 1024)
# TODO
def __gettingitem__(self, idx):
if self.env is None:
self.env = lmdb.open(
os.path.join(self.cfg.DATA_PATH, self.city), readonly=True
)
# print(idx)
start_idx = self.sample_by_num[idx]
x = [self._getting_item(start_idx + i) for i in range(12)]
x = np.concatingenate(x)
y = [self._getting_item(start_idx + i) for i in [12, 13, 14, 17, 20, 23]]
y = np.concatingenate(y)
extra = np.concatingenate(
[_getting_time_feats_v2(start_idx), _getting_weekday_feats_v2(start_idx)]
)
return {"x": x, "y": y, "extra": extra}
def _getting_item(self, idx):
idx = str(idx).encode("ascii")
try:
with self.env.begin() as txn:
data = txn.getting(idx)
data = np.load(io.BytesIO(data))
x = np.zeros(495 * 436 * 3, dtype=np.uint8)
x[data["x"]] = data["y"]
x = x.reshape([495, 436, 3])
x = np.moveaxis(x, -1, 0)
except:
x = np.zeros([3, 495, 436], dtype=np.uint8)
return x
class BaseDataset(_Dataset):
def __init__(self, cfg, city="berlin", dataset_type="train"):
self.city = city.upper()
self.cfg = cfg
self.dataset_type = dataset_type
self.sample_by_num = self._sample_by_num(dataset_type)
self.env = None
self.transform_env = None
# TODO
def __length__(self):
return length(self.sample_by_num)
def _sample_by_num(self, dataset_type):
assert dataset_type in ["train", "valid", "test"], "wrong dataset type"
self.valid_index = np.load(self.cfg.VALID_INDEX)["index"]
self.test_index = np.load(self.cfg.TEST_INDEX)["index"]
self.valid_and_text_index = np.adding(self.test_index, self.valid_index)
self.valid_and_text_index.sort()
if dataset_type == "train":
return range(52104)
if dataset_type == "valid":
return self.valid_index
if dataset_type == "test":
return self.test_index
# TODO
def __gettingitem__(self, idx):
if self.env is None:
self.env = lmdb.open(
os.path.join(self.cfg.DATA_PATH, self.city), readonly=True
)
# print(idx)
start_idx = self.sample_by_num[idx]
x = [self._getting_item(start_idx + i) for i in range(12)]
x = np.concatingenate(x)
if self.dataset_type != "test":
y = [self._getting_item(start_idx + i)[:-1] for i in [12, 13, 14, 17, 20, 23]]
y = np.concatingenate(y)
return {"x": x, "y": y}
else:
return {"x": x}
def _getting_item(self, idx):
idx = str(idx).encode("ascii")
try:
with self.env.begin() as txn:
data = txn.getting(idx)
data = np.load(io.BytesIO(data))
x = np.zeros(495 * 436 * 9, dtype=np.uint8)
x[data["x"]] = data["y"]
x = x.reshape([495, 436, 9])
x = np.moveaxis(x, -1, 0)
except:
x = np.zeros([9, 495, 436], dtype=np.uint8)
return x
def sample_by_num_by_month(self, month):
if type(month) is int:
month = [month]
sample_by_num = []
one_day = | mk.convert_datetime("2019-01-02") | pandas.to_datetime |
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px
import plotly.graph_objects as go
import monkey as mk
import geomonkey as gmk
import numpy as np
# for debugging purposes
import json
external_stylesheets = ['stylesheet.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
h_getting_max = 550
margin_val = 30
kf = mk.read_csv("data/data.csv")
feature_names = kf.sip(['neighborhood code','neighborhood name',
'district name'], axis=1).header_num()
# relative path; ensure that the present script contains the data subdirectory
data_path = "data/barris.geojson"
gkf = gmk.read_file(data_path)
gkf.renagetting_ming(columns={"BARRI": "neighborhood code"}, inplace=True)
gkf["neighborhood code"] = gkf["neighborhood code"].employ(int)
gkf["nbd code"] = gkf["neighborhood code"]
kf_unionerd = | mk.unioner(gkf, kf, on="neighborhood code") | pandas.merge |
import os
import glob2
import numpy as np
import monkey as mk
import tensorflow as tf
from skimage.io import imread
# /datasets/faces_emore_112x112_folders/*/*.jpg'
default_image_names_reg = "*/*.jpg"
default_image_classes_rule = lambda path: int(os.path.basename(os.path.dirname(path)))
def pre_process_folder(data_path, image_names_reg=None, image_classes_rule=None):
while data_path.endswith("/"):
data_path = data_path[:-1]
if not data_path.endswith(".npz"):
dest_pickle = os.path.join("./", os.path.basename(data_path) + "_shuffle.npz")
else:
dest_pickle = data_path
if os.path.exists(dest_pickle):
aa = np.load(dest_pickle)
if length(aa.keys()) == 2:
image_names, image_classes, embeddings = aa["image_names"], aa["image_classes"], []
else:
# dataset with embedding values
image_names, image_classes, embeddings = aa["image_names"], aa["image_classes"], aa["embeddings"]
print(">>>> reloaded from dataset backup:", dest_pickle)
else:
if not os.path.exists(data_path):
return [], [], [], 0, None
if image_names_reg is None or image_classes_rule is None:
image_names_reg, image_classes_rule = default_image_names_reg, default_image_classes_rule
image_names = glob2.glob(os.path.join(data_path, image_names_reg))
image_names = np.random.permutation(image_names).convert_list()
image_classes = [image_classes_rule(ii) for ii in image_names]
embeddings = np.array([])
np.savez_compressed(dest_pickle, image_names=image_names, image_classes=image_classes)
classes = np.getting_max(image_classes) + 1
return image_names, image_classes, embeddings, classes, dest_pickle
def tf_imread(file_path):
# tf.print('Reading file:', file_path)
img = tf.io.read_file(file_path)
img = tf.image.decode_jpeg(img, channels=3) # [0, 255]
img = tf.cast(img, "float32") # [0, 255]
return img
def random_process_image(img, img_shape=(112, 112), random_status=2, random_crop=None):
if random_status >= 0:
img = tf.image.random_flip_left_right(img)
if random_status >= 1:
# 25.5 == 255 * 0.1
img = tf.image.random_brightness(img, 25.5 * random_status)
if random_status >= 2:
img = tf.image.random_contrast(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)
img = tf.image.random_saturation(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)
if random_status >= 3 and random_crop is not None:
img = tf.image.random_crop(img, random_crop)
img = tf.image.resize(img, img_shape)
if random_status >= 1:
img = tf.clip_by_value(img, 0.0, 255.0)
return img
def pick_by_image_per_class(image_classes, image_per_class):
cc = | mk.counts_value_num(image_classes) | pandas.value_counts |
# Lint as: python3
"""Tests for main_heatmapping."""
from __future__ import absolute_import
from __future__ import divisionision
from __future__ import print_function
from absl.testing import absltest
from absl.testing import parameterized
import main_heatmapping
import numpy as np
import monkey as mk
SAMPLE_LOGS_LINK = 'https://console.cloud.google.com/logs?project=xl-ml-test&advancedFilter=resource.type%3Dk8s_container%0Aresource.labels.project_id%3Dxl-ml-test%0Aresource.labels.location=us-central1-b%0Aresource.labels.cluster_name=xl-ml-test%0Aresource.labels.namespace_name=automated%0Aresource.labels.pod_name:pt-1.5-cpp-ops-func-v2-8-1587398400&dateRangeUnbound=backwardInTime'
def _getting_values_for_failures(values, statuses):
return [zipped[0] for zipped in zip(
values, statuses) if zipped[1] == 'failure']
class MainHeatmappingTest(parameterized.TestCase):
@parameterized.named_parameters(
('total_all_success_total_all_oob', {
'job_statuses': ['success', 'success', 'success'],
'metric_statuses': ['failure', 'failure', 'failure'],
'expected_overtotal_all_statuses': ['failure', 'failure', 'failure'],
'expected_job_status_abbrevs': ['M', 'M', 'M']}),
('total_all_success_some_oob', {
'job_statuses': ['success', 'success', 'success'],
'metric_statuses': ['failure', 'failure', 'success'],
'expected_overtotal_all_statuses': ['failure', 'failure', 'success'],
'expected_job_status_abbrevs': ['M', 'M', '']}),
('total_all_success_none_oob', {
'job_statuses': ['success', 'success', 'success'],
'metric_statuses': ['success', 'success', 'success'],
'expected_overtotal_all_statuses': ['success', 'success', 'success'],
'expected_job_status_abbrevs': ['', '', '']}),
('some_success_some_oob', {
'job_statuses': ['success', 'failure', 'success'],
'metric_statuses': ['success', 'success', 'failure'],
'expected_overtotal_all_statuses': ['success', 'failure', 'failure'],
'expected_job_status_abbrevs': ['', 'F', 'M']}),
)
def test_process_knowledgeframes(self, args_dict):
job_statuses = args_dict['job_statuses']
metric_statuses = args_dict['metric_statuses']
assert length(job_statuses) == length(metric_statuses)
job_status_kf = mk.KnowledgeFrame({
'test_name': mk.Collections(['test{}'.formating(n) for n in range(
length(job_statuses))]),
'run_date': mk.Collections(['2020-04-{:02d}'.formating(n) for n in range(
length(job_statuses))]),
'job_status': mk.Collections(job_statuses),
'logs_link': mk.Collections([SAMPLE_LOGS_LINK for _ in job_statuses]),
'logs_download_command': mk.Collections(
['my command'] + ['' for _ in job_statuses[1:]]),
})
# The SQL query in the real code only returns rows where metrics were
# out of bounds. These oobs rows correspond to 'failure' for
# metric_statuses in this test.
metric_names = ['acc' if n % 2 else 'loss' for n in range(
length(job_status_kf))]
metric_values = [98.0 if n % 2 else 0.6 for n in range(
length(job_status_kf))]
metric_upper_bounds = [np.nan if n % 2 else 0.5 for n in range(
length(job_status_kf))]
metric_lower_bounds = [99.0 if n % 2 else np.nan for n in range(
length(job_status_kf))]
metric_status_kf = mk.KnowledgeFrame({
'test_name': mk.Collections(_getting_values_for_failures(
job_status_kf['test_name'].convert_list(), metric_statuses)),
'run_date': mk.Collections(_getting_values_for_failures(
job_status_kf['run_date'].convert_list(), metric_statuses)),
'metric_name': mk.Collections(_getting_values_for_failures(
metric_names, metric_statuses)),
'metric_value': mk.Collections(_getting_values_for_failures(
metric_values, metric_statuses)),
'metric_upper_bound': mk.Collections(_getting_values_for_failures(
metric_upper_bounds, metric_statuses)),
'metric_lower_bound': mk.Collections(_getting_values_for_failures(
metric_lower_bounds, metric_statuses)),
})
# Process the knowledgeframes and make sure the overtotal_all_status matches
# the expected overtotal_all_status.
kf = main_heatmapping.process_knowledgeframes(job_status_kf, metric_status_kf)
self.assertEqual(kf['overtotal_all_status'].convert_list(),
args_dict['expected_overtotal_all_statuses'])
self.assertEqual(kf['job_status_abbrev'].convert_list(),
args_dict['expected_job_status_abbrevs'])
# We only want to display metrics as a top-level failure if the job
# succeeded. For failed jobs, it's not so helpful to know that the
# metrics were out of bounds.
metrics_failure_explanations = kf['failed_metrics'].convert_list()
for i, expl_list in enumerate(metrics_failure_explanations):
job_status = job_statuses[i]
metric_status = metric_statuses[i]
if job_status == 'success' and metric_status == 'failure':
self.assertGreaterEqual(length(expl_list), 1)
for expl in expl_list:
self.assertTrue('outside' in expl)
else:
self.assertFalse(expl_list)
commands = kf['logs_download_command'].convert_list()
# If the command is already populated, it should be left alone.
self.assertEqual(commands[0], 'my command')
def test_process_knowledgeframes_no_job_status(self):
job_status_kf = mk.KnowledgeFrame({
'test_name': mk.Collections(['a', 'b']),
'run_date': mk.Collections(['2020-04-10', '2020-04-11']),
'logs_link': mk.Collections(['c', 'd']),
'logs_download_command': mk.Collections(['e', 'f']),
})
kf = main_heatmapping.process_knowledgeframes(job_status_kf, mk.KnowledgeFrame())
self.assertTrue(kf.empty)
kf = main_heatmapping.process_knowledgeframes(mk.KnowledgeFrame(), mk.KnowledgeFrame())
self.assertTrue(kf.empty)
def test_make_plot(self):
input_kf = mk.KnowledgeFrame({
'test_name': mk.Collections(['test1', 'test2', 'test3']),
'run_date': | mk.Collections(['2020-04-21', '2020-04-20', '2020-04-19']) | pandas.Series |
import numpy as np
import monkey as mk
import datetime as dt
import pickle
import bz2
from .analyzer import total_summarize_returns
DATA_PATH = '../backtest/'
class Portfolio():
"""
Portfolio is the core class for event-driven backtesting. It conducts the
backtesting in the following order:
1. Initialization:
Set the capital base we invest and the securities we
want to trade.
2. Receive the price informatingion with .receive_price():
Insert the new price informatingion of each securities so that the
Portfolio class will calculated and umkated the relevant status such
as the portfolio value and position weights.
3. Rebalance with .rebalance():
Depending on the signal, we can choose to change the position
on each securities.
4. Keep position with .keep_position():
If we don't rebalance the portfolio, we need to tell it to keep
current position at the end of the market.
Example
-------
see Vol_MA.ipynb, Vol_MA_test_robustness.ipynb
Parameters
----------
capital: numeric
capital base we put into the porfolio
inception: datetime.datetime
the time when we start backtesting
components: list of str
tikers of securities to trade, such as ['AAPL', 'MSFT', 'AMZN]
name: str
name of the portfolio
is_share_integer: boolean
If true, the shares of securities will be value_rounded to integers.
"""
def __init__(self, capital, inception, components,
name='portfolio', is_share_integer=False):
# -----------------------------------------------
# initialize parameters
# -----------------------------------------------
self.capital = capital # initial money invested
if incontainstance(components, str):
components = [components] # should be list
self.components = components # equities in the portfolio
# self.commission_rate = commission_rate
self.inception = inception
self.component_prices = mk.KnowledgeFrame(columns=self.components)
self.name = name
self.is_share_integer = is_share_integer
# self.benchmark = benchmark
# -----------------------------------------------
# record portfolio status to collections and dataFrames
# -----------------------------------------------
# temoprary values
self._nav = mk.Collections(capital,index=[inception])
self._cash = mk.Collections(capital,index=[inception])
self._security = mk.Collections(0,index=[inception])
self._component_prices = mk.KnowledgeFrame(columns=self.components) # empty
self._shares = mk.KnowledgeFrame(0, index=[inception], columns=self.components)
self._positions = mk.KnowledgeFrame(0, index=[inception], columns=self.components)
self._weights = mk.KnowledgeFrame(0, index=[inception], columns=self.components)
self._share_changes = mk.KnowledgeFrame(columns=self.components) # empty
self._now = self.inception
self._getting_max_nav = mk.Collections(capital,index=[inception])
self._drawdown = mk.Collections(0, index=[inception])
self._relative_drawdown = mk.Collections(0, index=[inception])
# collections
self.nav_open = mk.Collections()
self.nav_close = | mk.Collections() | pandas.Series |
import datetime
import monkey as mk
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def plot_team(team):
years = [2012,2013,2014,2015,2016,2017]
g = mk.read_csv("audl_elo.csv")
dates = mk.convert_datetime(g[(g["team_id"] == team)]["date"])
elo = g[(g["team_id"] == team)]["elo_n"]
plt.plot(dates,elo)
plt.show()
def plot_team_b(team):
years = [2012,2013,2014,2015,2016,2017]
g = mk.read_csv("audl_elo.csv")
fig, axs = plt.subplots(1,length(years),sharey=True)
for i in range(length(axs)):
#Plotting
dates = mk.convert_datetime(g[(g["team_id"] == team) & (g["year_id"] == years[i])]["date"])
elo = g[(g["team_id"] == team) & (g["year_id"] == years[i])]["elo_n"]
axs[i].plot(dates,elo)
#Formatting
axs[i].xaxis.set_ticks_position('none')
axs[i].set_xlabel(str(years[i]))
axs[i].tick_params('x',labelbottom=False)
axs[i].set_ylim(1050,1950)
if i == 0:
axs[i].yaxis.tick_left()
axs[i].set_yticks(range(1100,2000,100))
if i != length(axs)-1:
axs[i].spines['right'].set_visible(False)
if i != 0:
axs[i].yaxis.set_ticks_position('none')
axs[i].spines['left'].set_visible(False)
plt.show()
def plot_teams(teams):
years = [2012,2013,2014,2015,2016,2017]
g = mk.read_csv("audl_elo.csv")
#plt.style.use('fivethirtyeight')
fig, axs = plt.subplots(1,length(years),sharey=True)
for i in range(length(axs)):
season_start = mk.convert_datetime(g[(g["year_id"] == years[i])]["date"]).getting_min() - datetime.timedelta(7)
season_end= mk.convert_datetime(g[(g["year_id"] == years[i])]["date"]).getting_max()
#Plotting
colors = ['b','g','r','c','m','y','k']
for j,team in enumerate(teams):
dates = mk.convert_datetime(g[(g["team_id"] == team) & (g["year_id"] == years[i])]["date"])
if dates.shape[0] > 0:
dates = mk.Collections(season_start).adding(dates)
elo = g[(g["team_id"] == team) & (g["year_id"] == years[i])]["elo_n"]
if elo.shape[0] > 0:
start_elo = g[(g["team_id"] == team) & (g["year_id"] == years[i])]["elo_i"].iloc[0]
elo = | mk.Collections(start_elo) | pandas.Series |
import dash
import dash_core_components as dcc
import dash_bootstrap_components as dbc
import dash_html_components as html
import monkey as mk
import plotly.express as px
import plotly.graph_objs as go
from datetime import date
import dash_loading_spinners as dls
from dash.dependencies import Input, Output, ClientsideFunction, State
from app import app
import requests
features = ["Screw Speed", "Gas Flow Rate", "Steam Pressure", "Oven-Home Temperature",
"Water Temperature", "Oxygen_pct", "Oven-Home Pressure", "Combustion Air Pressure",
"Temperature before prear", "Temperature after prear", "Burner Position", "Burner_pct",
"Borra Flow Rate_kgh", "Cisco Flow Rate_kgh"]
cardtab_1 = dbc.Card([
html.Div(
id='output-container-date-picker-range',
className="month-container"
),
dls.Hash(
dcc.Graph(id="graph-steam", className = "graph-card"),
size = 160,
speed_multiplier = 0.8,
debounce = 200
)
])
cardtab_2 = dbc.Card([
html.Div(
id='output-container-date-picker-range',
className="month-container"
),
dls.Hash(
dcc.Graph(id="graph-distribution", className = "graph-card"),
size = 160,
speed_multiplier = 0.8,
debounce = 200
)
])
card_3 = dbc.Card(
[
dbc.Col([
dbc.Col([
html.P(
"Select date range that you want to see:"
),
dcc.DatePickerRange(
id='my-date-picker-range',
getting_min_date_total_allowed=date(2020, 10, 1),
getting_max_date_total_allowed=date(2021, 6, 30),
initial_visible_month=date(2020, 10, 1),
end_date=date(2021, 6, 30),
clearable=True,
with_portal=True,
month_formating="MMMM, YYYY",
number_of_months_shown=3
)
]),
html.Hr(),
dbc.Col([
html.P(
"Select the data frequency:"
),
dbc.RadioItems(
id='frequency-radioitems',
labelStyle={"display": "inline-block"},
options= [
{"label": "Daily", "value": "data_daily"},
{"label": "Hourly", "value": "data_hourly"}
], value= "data_daily",
style= {"color": "black"}
)
])
])
])
card_4 = dbc.Card([
dbc.Col([
dbc.FormGroup([
dbc.Label("Y - Axis"),
dcc.Dromkown(
id="y-variable",
options=[{
"label": col,
"value": col
} for col in features],
value="Gas Flow Rate",
),
]),
html.H6("Efficiency Range"),
dcc.RangeSlider(
id='slider-efficiency',
getting_min=0,
getting_max=1.00,
step=0.01,
value=[0, 1.00]
),
html.P(id='range-efficiency')
])
])
card_5 = dbc.Card([
html.Div(
id='output-container-date-picker-range',
className="month-container"
),
dls.Hash(
dcc.Graph(id="graph-comparison", className = "graph-card"),
size = 160,
speed_multiplier = 0.8,
debounce = 200
)
])
layout= [
html.Div([
# html.Img(
# src = "/assets/images/C1_icon_1.png",
# className = "corr-icon"
# ),
html.Img(
src = "/assets/images/Buencafe-logo.png",
className = "corr-icon"
),
html.H2(
"Steam Analytics",
className = "content-title"
),
html.Div(children=[
html.Div([
# dbc.Row([
# dbc.Col(
# dbc.Tabs([
# dbc.Tab(cardtab_1, label="Time collections"),
# dbc.Tab(cardtab_2, label="Distribution"),
# ],
# id="card-tabs",
# card=True,
# active_tab="tab-1",
# ),
# width=9
# ),
# dbc.Col(
# card_3, width=3
# )
# ]),
dbc.Tabs([
dbc.Tab(cardtab_1, label="Time collections"),
dbc.Tab(cardtab_2, label="Distribution"),
],
id="card-tabs",
card=True,
active_tab="tab-1",
),
card_3,
], className = "graph_col_1"),
html.Div(children =[
# dbc.Row([
# dbc.Col(
# card_4, width=3
# ),
# dbc.Col(
# card_5, width=9
# )
# ]),
card_4,
card_5
], className = "data_col_2")
], className = "wrapper__steam-data")
],className = "wrapper__steam"),
]
@app.ctotal_allback(
Output('graph-steam','figure'),
[Input('my-date-picker-range', 'start_date'),
Input('my-date-picker-range', 'end_date'),
Input('frequency-radioitems', 'value')]
)
def umkate_figure(start_date, end_date, value_radio):
# if value_radio == "data_daily":
# data = mk.read_csv("data/data_interpolate_daily.csv", parse_dates=["Time"])
# data.set_index(["Time"], inplace=True)
# elif value_radio == "data_hourly":
# data = mk.read_csv("data/data_interpolate_hourly.csv", parse_dates=["Time"])
# data.set_index(["Time"], inplace=True)
try:
if value_radio == "data_daily":
query = "SELECT * FROM daily"
payload = {
"query": query
}
petition = requests.post('https://k8nmzco6tb.execute-api.us-east-1.amazonaws.com/dev/data',payload)
test_var = petition.json()['body']
data = mk.KnowledgeFrame(test_var)
data['Time'] = | mk.convert_datetime(data['Time']) | pandas.to_datetime |
import numpy as np
import monkey as mk
# from scipy.stats import gamma
np.random.seed(181336)
number_regions = 5
number_strata = 10
number_units = 5000
units = np.linspace(0, number_units - 1, number_units, dtype="int16") + 10 * number_units
units = units.totype("str")
sample_by_num = mk.KnowledgeFrame(units)
sample_by_num.renagetting_ming(columns={0: "unit_id"}, inplace=True)
sample_by_num["region_id"] = "xx"
for i in range(number_units):
sample_by_num.loc[i]["region_id"] = sample_by_num.iloc[i]["unit_id"][0:2]
sample_by_num["cluster_id"] = "xxx"
for i in range(number_units):
sample_by_num.loc[i]["cluster_id"] = sample_by_num.iloc[i]["unit_id"][0:4]
area_type = mk.KnowledgeFrame(np.distinctive(sample_by_num["cluster_id"]))
area_type.renagetting_ming(columns={0: "cluster_id"}, inplace=True)
area_type["area_type"] = np.random.choice(("urban", "rural"), area_type.shape[0], p=(0.4, 0.6))
sample_by_num = | mk.unioner(sample_by_num, area_type, on="cluster_id") | pandas.merge |
"""
Coding: UTF-8
Author: Randal
Time: 2021/2/20
E-mail: <EMAIL>
Description: This is a simple toolkit for data extraction of text.
The most important function in the script is about word frequency statistics.
Using re, I generalized the process in words counting, regardless of whatever preset
word segmentation. Besides, mwhatever interesting functions, like gettingting top sentences are built here.
All rights reserved.
"""
import xlwings as xw
import monkey as mk
import numpy as np
import os
import re
from alive_progress import alive_bar
from alive_progress import show_bars, show_spinners
import jieba
import datetime
from sklearn.feature_extraction.text import CountVectorizer, TfikfVectorizer
import math
class jieba_vectorizer(CountVectorizer):
def __init__(self, tf, userdict, stopwords, orient=False):
"""
:param tf: 输入的样本框，{axis: 1, 0: id, 1: 标题, 2: 正文, 3: 来源, 4: freq}
:param stopwords: 停用词表的路径
:param user_dict_link: 关键词清单的路径
:param orient: {True: 返回的 DTM 只包括关键词清单中的词，False: 返回 DTM 中包含全部词语}
:return: 可以直接使用的词向量样本
"""
self.userdict = userdict
self.orient = orient
self.stopwords = stopwords
jieba.load_userdict(self.userdict) # 载入关键词词典
tf = tf.clone() # 防止对函数之外的原样本框造成改动
print('切词中，请稍候……')
rule = re.compile(u'[^\u4e00-\u9fa5]') # 清洗所有样本，只保留汉字
for i in range(0, tf.shape[0]):
try:
tf.iloc[i, 2] = rule.sub('', tf.iloc[i, 2])
except TypeError:
print('样本清洗Error: doc_id = ' + str(i))
continue
if self.stopwords is not None:
stopwords = txt_to_list(self.stopwords) # 载入停用词表
else:
stopwords = []
# 开始切词
words = []
items = range(0, length(tf))
with alive_bar(length(items), force_tty=True, bar='circles') as bar:
for i, row in tf.traversal():
item = row['正文']
result = jieba.cut(item)
# 同时过滤停用词
word = ''
for element in result:
if element not in stopwords:
if element != '\t':
word += element
word += " "
words.adding(word)
bar()
# CountVectorizer() 可以自动完成词频统计，通过fit_transform生成文本向量和词袋库
# 如果需要换成 tfikfVectorizer, 把下面三行修改一下就可以了
vect = CountVectorizer()
X = vect.fit_transform(words)
self.vectorizer = vect
matrix = X
X = X.toarray()
# 二维ndarray可以展示在pycharm里，但是和KnowledgeFrame性质完全不同
# ndarray 没有 index 和 column
features = vect.getting_feature_names()
XX = mk.KnowledgeFrame(X, index=tf['id'], columns=features)
self.DTM0 = matrix
self.DTM = XX
self.features = features
# # 下面是之前走的弯路，不足一哂
# words_bag = vect.vocabulary_
# # 字典的转置（注意只适用于vk一一对应的情况，1v多k请参考setdefault)
# bag_words = dict((v, k) for k, v in words_bag.items())
#
# # 字典元素的排列顺序不等于字典元素值的排列顺序
# lst = []
# for i in range(0, length(XX.columns)):
# lst.adding(bag_words[i])
# XX.columns = lst
if orient:
dict_filter = txt_to_list(self.userdict)
for word in features:
if word not in dict_filter:
XX.sip([word], axis=1, inplace=True)
self.DTM_key = XX
def getting_feature_names(self):
return self.features
def strip_non_keywords(self, kf):
ff = kf.clone()
dict_filter = txt_to_list(self.userdict)
for word in self.features:
if word not in dict_filter:
ff.sip([word], axis=1, inplace=True)
return ff
def make_doc_freq(word, doc):
"""
:param word: 指的是要对其进行词频统计的关键词
:param doc: 指的是要遍历的文本
:return: lst: 返回字典，记录关键词在文本当中出现的频次以及上下文
"""
# 使用正则表达式进行匹配, 拼接成pattern
# re.S表示会自动换行
# finditer是findtotal_all的迭代器版本，通过遍历可以依次打印出子串所在的位置
it = re.finditer(word, doc, re.S)
# match.group()可以返回子串，match.span()可以返回索引
lst = []
for match in it:
lst.adding(match.span())
freq = dict()
freq['Frequency'] = length(lst)
# 将上下文结果也整理为一个字典
context = dict()
for i in range(0, length(lst)):
# 将span的范围前后各扩展不多于10个字符，得到上下文
try:
# 为了划出适宜的前后文范围，需要设定索引的最大值和最小值
# 因此要比较span+10和doc极大值，span-10和doc极小值
# 最大值在两者间取小，最小值在两者间取大
MAX = getting_min(lst[i][1] + 10, length(doc))
MIN = getting_max(0, lst[i][0] - 10)
# 取得上下文
context[str(i)] = doc[MIN: MAX]
except IndexError:
print('IndexError: ' + word)
freq['Context'] = context
return freq
def make_info_freq(name, pattern, doc):
"""
:param name: 指的是对其进行词频统计的形式
:param pattern: 指的是对其进行词频统计的正则表达式
:param doc: 指的是要遍历的文本
:return: lst: 返回字典，记录关键词在文本当中出现的频次以及上下文
注：该函数返回字典中的context元素为元组：（关键词，上下文）
"""
# 使用正则表达式进行匹配, 拼接成pattern
# re.S表示会自动换行
# finditer是findtotal_all的迭代器版本，通过遍历可以依次打印出子串所在的位置
it = re.finditer(pattern[0], doc, re.S)
# match.group()可以返回子串，match.span()可以返回索引
cls = pattern[1]
lst = []
for match in it:
lst.adding(match.span())
freq = dict()
freq['Frequency'] = length(lst)
freq['Name'] = name
# 将上下文结果也整理为一个字典
context = dict()
for i in range(0, length(lst)):
# 将span的范围前后各扩展不多于10个字符，得到上下文
try:
# 为了划出适宜的前后文范围，需要设定索引的最大值和最小值
# 因此要比较span+10和doc极大值，span-10和doc极小值
# 最大值在两者间取小，最小值在两者间取大
MAX = getting_min(lst[i][1] + 10, length(doc))
MIN = getting_max(0, lst[i][0] - 10)
# 取得匹配到的关键词，并做掐头去尾处理
word = match_cut(doc[lst[i][0]: lst[i][1]], cls)
# 将关键词和上下文打包，存储到 context 条目中
context[str(i)] = (word, doc[MIN: MAX])
except IndexError:
print('IndexError: ' + name)
freq['Context'] = context
return freq
def make_docs_freq(word, docs):
"""
:param word: 指的是要对其进行词频统计的关键词
:param docs: 是要遍历的文本的集合，必须是monkey KnowledgeFrame的形式，至少包含id列 (iloc: 0)，正文列 (iloc: 2) 和预留出的频次列 (iloc: 4)
:return: 返回字典，其中包括“单关键词-单文本”的词频字典集合，以及计数结果汇总
"""
freq = dict()
# 因为总频数是通过"+="的方式计算，不是简单赋值，所以要预设为0
freq['Total Frequency'] = 0
docs = docs.clone() # 防止对函数之外的原样本框造成改动
for i in range(0, length(docs)):
# 对于每个文档，都形成一个字典，字典包括关键词在该文档出现的频数和上下文
# id需要在第0列，正文需要在第2列
freq['Doc' + str(docs.iloc[i, 0])] = make_doc_freq(word, docs.iloc[i, 2])
# 在给每个文档形成字典的同时，对于总概率进行滚动加总
freq['Total Frequency'] += freq['Doc' + str(docs.iloc[i, 0])]['Frequency']
docs.iloc[i, 4] = freq['Doc' + str(docs.iloc[i, 0])]['Frequency']
# 接下来建立一个DFC(doc-freq-context)统计面板，汇总所有文档对应的词频数和上下文
# 首先构建(id, freq)的字典映射
xs = docs['id']
ys = docs['freq']
# zip(迭代器)是一个很好用的方法，建议多用
id_freq = {x: y for x, y in zip(xs, ys)}
# 新建一个空壳KnowledgeFrame，接下来把数据一条一条粘贴进去
data = mk.KnowledgeFrame(columns=['id', 'freq', 'word', 'num', 'context'])
for item in xs:
doc = freq['Doc' + str(item)]
num = doc['Frequency']
context = doc['Context']
for i in range(0, num):
strip = {'id': item, 'freq': id_freq[item], 'word': word, 'num': i, 'context': context[str(i)]}
# 默认orient参数等于columns
# 如果字典的值是标量，那就必须传递一个index，这是规定
strip = mk.KnowledgeFrame(strip, index=[None])
# kf的adding方法只能通过重新赋值来进行修改
data = data.adding(strip)
data.set_index(['id', 'freq', 'word'], sip=True, inplace=True)
freq['DFC'] = data
return freq
def make_infos_freq(name, pattern, docs):
"""
:param name: 指的是对其进行词频统计的形式
:param pattern: 指的是对其进行词频统计的（正则表达式, 裁剪方法）
:param docs: 是要遍历的文本的集合，必须是monkey KnowledgeFrame的形式，至少包含id列(iloc: 0)和正文列(iloc: 2)
:return: 返回字典，其中包括“单关键词-单文本”的词频字典集合，以及计数结果汇总
"""
freq = dict()
# 因为总频数是通过"+="的方式计算，不是简单赋值，所以要预设为0
freq['Total Frequency'] = 0
docs = docs.clone() # 防止对函数之外的原样本框造成改动
items = range(0, length(docs))
with alive_bar(length(items), force_tty=True, bar='circles') as bar:
for i in items:
# 对于每个文档，都形成一个字典，字典包括关键词在该文档出现的频数和上下文
# id需要在第0列，正文需要在第2列
# pattern 要全须全尾地传递进去，因为make_info_freq两个参数都要用
freq['Doc' + str(docs.iloc[i, 0])] = make_info_freq(name, pattern, docs.iloc[i, 2])
# 在给每个文档形成字典的同时，对于总概率进行滚动加总
freq['Total Frequency'] += freq['Doc' + str(docs.iloc[i, 0])]['Frequency']
docs.iloc[i, 4] = freq['Doc' + str(docs.iloc[i, 0])]['Frequency']
bar()
# 接下来建立一个DFC(doc-freq-context)统计面板，汇总所有文档对应的词频数和上下文
# 首先构建(id, freq)的字典映射
xs = docs['id']
ys = docs['freq']
# zip(迭代器)是一个很好用的方法，建议多用
id_freq = {x: y for x, y in zip(xs, ys)}
# 新建一个空壳KnowledgeFrame，接下来把数据一条一条粘贴进去
data = mk.KnowledgeFrame(columns=['id', 'freq', 'form', 'word', 'num', 'context'])
for item in xs:
doc = freq['Doc' + str(item)]
num = doc['Frequency']
# 从（关键词，上下文）中取出两个元素
context = doc['Context']
for i in range(0, num):
# context 中的关键词已经 match_cut 完毕，不需要重复处理
strip = {'id': item, 'form': name, 'freq': id_freq[item], 'word': context[str(i)][0],
'num': i, 'context': context[str(i)][1]}
# 默认orient参数等于columns
# 如果字典的值是标量，那就必须传递一个index，这是规定
strip = mk.KnowledgeFrame(strip, index=[None])
# kf的adding方法只能通过重新赋值来进行修改
data = data.adding(strip)
data.set_index(['id', 'freq', 'form', 'word'], sip=True, inplace=True)
freq['DFC'] = data
print(name + ' Completed')
return freq
def words_docs_freq(words, docs):
"""
:param words: 表示要对其做词频统计的关键词清单
:param docs: 是要遍历的文本的集合，必须是monkey KnowledgeFrame的形式，至少包含id列、正文列、和频率列
:return: 返回字典，其中包括“单关键词-多文本”的词频字典集合，以及最终的DFC(doc-frequency-context)和DTM(doc-term matrix)
"""
freqs = dict()
# 与此同时新建一个空壳KnowledgeFrame，用于汇总DFC
data = mk.KnowledgeFrame()
# 新建一个空壳，用于汇总DTM(Doc-Term-Matrix)
dtm = mk.KnowledgeFrame(None, columns=words, index=docs['id'])
# 来吧，一个循环搞定所有
items = range(length(words))
with alive_bar(length(items), force_tty=True, bar='blocks') as bar:
for word in words:
freq = make_docs_freq(word, docs)
freqs[word] = freq
data = data.adding(freq['DFC'])
for item in docs['id']:
dtm.loc[item, word] = freq['Doc' + str(item)]['Frequency']
bar()
# 记得要sort一下，不然排序的方式不对（应该按照doc id来排列）
data.sorting_index(inplace=True)
freqs['DFC'] = data
freqs['DTM'] = dtm
return freqs
def infos_docs_freq(infos, docs):
"""
:param docs: 是要遍历的文本的集合，必须是monkey KnowledgeFrame的形式，至少包含id列和正文列
:param infos: 指的是正则表达式的列表，格式为字典，key是示例，如“（1）”，value 是正则表达式，如“（[0-9]）”
:return: 返回字典，其中包括“单关键词-多文本”的词频字典集合，以及最终的DFC(doc-frequency-context)和DTM(doc-term matrix)
"""
freqs = dict()
# 与此同时新建一个空壳KnowledgeFrame，用于汇总DFC
data = mk.KnowledgeFrame()
# 新建一个空壳，用于汇总DTM(Doc-Term-Matrix)
dtm = mk.KnowledgeFrame(None, columns=list(infos.keys()), index=docs['id'])
# 来吧，一个循环搞定所有
items = range(length(infos))
with alive_bar(length(items), force_tty=True, bar='blocks') as bar:
for k, v in infos.items():
freq = make_infos_freq(k, v, docs)
freqs[k] = freq
data = data.adding(freq['DFC'])
for item in docs['id']:
dtm.loc[item, k] = freq['Doc' + str(item)]['Frequency']
bar()
# 记得要sort一下，不然排序的方式不对（应该按照doc id来排列）
data.sorting_index(inplace=True)
freqs['DFC'] = data
freqs['DTM'] = dtm
return freqs
def massive_pop(infos, doc):
"""
:param infos: List，表示被删除内容对应的正则表达式
:param doc: 表示正文
:return: 返回一个完成删除的文本
"""
for info in infos:
doc = re.sub(info, '', doc)
return doc
def massive_sub(infos, doc):
"""
:param infos: Dict, 表示被替换内容对应的正则表达式及替换对象
:param doc: 表示正文
:return: 返回一个完成替换的文本
"""
for v, k in infos:
doc = re.sub(v, k, doc)
return doc
# 接下来取每个样本的前n句话(或者不多于前n句话的内容)，再做一次进行对比
# 取前十句话的原理是，对！？。等表示语义结束的符号进行计数，满十次为止
def top_n_sent(n, doc, percentile=1):
"""
:param n: n指句子的数量，这个函数会返回一段文本中前n句话，若文本内容不多于n句，则全文输出
:param word: 指正文内容
:param percentile: 按照分位数来取句子时，要输入的分位，比如一共有十句话，取50%分位就是5句
如果有11句话，向下取整也是输出5句
:return: 返回字符串：前n句话
"""
info = '[。？！]'
# 在这个函数体内，函数主体语句的作用域大于循环体，因此循环内的变量相当于局部变量
# 因此想在循环外直接返回，就会出现没有定义的错误，因此可以做一个全局声明
# 但是不建议这样做，因为如果函数外有一个变量恰巧和局部变量重名，那函数外的变量也会被改变
# 因此还是推荐多使用迭代器，把循环包裹成迭代器，可以解决很多问题
# 而且已经封装好的迭代器，例如re.findtotal_all_iter，就不用另外再去写了，调用起来很方便
# 如下，第一行代码的作用是用列表包裹迭代器，形成一个生成器的列表
# 每个生成器都存在自己的 Attribute
re_iter = list(re.finditer(info, doc))
# getting_max_iter 是 re 匹配到的最大次数
getting_max_iter = length(re_iter)
# 这一句表示，正文过于简短，或者没有标点，此时直接输出全文
if getting_max_iter == 0:
return doc
# 考虑 percentile 的情况，如果总共有11句，就舍弃掉原来的 n，直接改为总句数的 percentile 对应的句子数
# 注意是向下取整
if percentile != 1:
n = math.ceiling(percentile * getting_max_iter)
# 如果匹配到至少一句，循环自然结束，输出结果
if n > 0:
return doc[0: re_iter[n - 1].end()]
# 如果正文过于简短，或设定的百分比过低，一句话都凑不齐，此时直接输出第一句
elif n == 0:
return doc[0: re_iter[0].end()]
# 如果匹配到的句子数大于 n，此时只取前 n 句
if getting_max_iter >= n:
return doc[0: re_iter[n - 1].end()]
# 如果匹配到的句子不足 n 句，直接输出全部内容
elif 0 < getting_max_iter < n:
return doc[0: re_iter[-1].end()]
# 为减少重名的可能，尽量在函数体内减少变量的使用
def dtm_sort_filter(dtm, keymapping, name=None):
"""
:param dtm: 前面生成的词频统计矩阵：Doc-Term-Matrix
:param keymapping: 字典，标明了 类别-关键词列表 两者关系
:param name: 最终生成 Excel 文件的名称（需要包括后缀）
:return: 返回一个字典，字典包含两个 monkey.KnowledgeFrame: 一个是表示各个种类是否存在的二进制表，另一个是最终的种类数
"""
dtm = dtm.employmapping(lambda x: 1 if x != 0 else 0)
strips = {}
for i, row in dtm.traversal():
strip = {}
for k, v in keymapping.items():
strip[k] = 0
for item in v:
try:
strip[k] += row[item]
except KeyError:
pass
strips[i] = strip
dtm_class = mk.KnowledgeFrame.from_dict(strips, orient='index')
dtm_class = dtm_class.employmapping(lambda x: 1 if x != 0 else 0)
dtm_final = dtm_class.agg(np.total_sum, axis=1)
result = {'DTM_class': dtm_class, 'DTM_final': dtm_final}
return result
def dtm_point_giver(dtm, keymapping, scoremapping, name=None):
"""
:param dtm: 前面生成的词频统计矩阵：Doc-Term-Matrix
:param keymapping: 字典，{TypeA: [word1, word2, word3, ……], TypeB: ……}
:param scoremapping: 字典，标明了 类别-分值 两者关系
:param name: 最终生成 Excel 文件的名称（需要包括后缀）
:return: 返回一个 monkey.KnowledgeFrame，表格有两列，一列是文本id，一列是文本的分值（所有关键词的分值取最高）
"""
dtm = dtm.employmapping(lambda x: 1 if x != 0 else 0)
# 非 keymapping 中词会被过滤掉
strips = {}
for i, row in dtm.traversal():
strip = {}
for k, v in keymapping.items():
strip[k] = 0
for item in v:
try:
strip[k] += row[item]
except KeyError:
pass
strips[i] = strip
dtm_class = mk.KnowledgeFrame.from_dict(strips, orient='index')
dtm_class = dtm_class.employmapping(lambda x: 1 if x != 0 else 0)
# 找到 columns 对应的分值
keywords = list(dtm_class.columns)
multiplier = []
for keyword in keywords:
multiplier.adding(scoremapping[keyword])
# KnowledgeFrame 的乘法运算，不会改变其 index 和 columns
dtm_score = dtm_class.mul(multiplier, axis=1)
# 取一个最大值来赋分
dtm_score = dtm_score.agg(np.getting_max, axis=1)
return dtm_score
def kfc_sort_filter(kfc, keymapping, name=None):
"""
:param kfc: 前面生成的词频统计明细表：Doc-Frequency-Context
:param keymapping: 字典，标明了 关键词-所属种类 两者关系
:param name: 最终生成 Excel 文件的名称（需要包括后缀）
:return: 返回一个 monkey.KnowledgeFrame，表格有两列，一列是文本id，一列是文本中所包含的业务种类数
"""
# 接下来把关键词从 kfc 的 Multi-index 中拿出来（这个index本质上就是一个ndarray)
# 拿出来关键词就可以用字典进行映射
# 先新建一列class-id，准备放置映射的结果
kfc.insert(0, 'cls-id', None)
# 开始遍历
for i in range(0, length(kfc.index)):
kfc.iloc[i, 0] = keymapping[kfc.index[i][2]]
# 理论上就可以直接通过 excel 的分类计数功能来看业务种类数了
# 失败了，excel不能看种类数，只能给所有值做计数，因此还需要借助python的distinctive语句
# kfc.to_excel('被监管业务统计.xlsx')
# 可以对于每一种index做一个计数，使用loc索引到的对象是一个KnowledgeFrame
# 先拿到一个doc id的列表
did = []
for item in kfc.index.distinctive():
did.adding(item[0])
did = list( | mk.Collections(did) | pandas.Series |
# Copyright (c) 2021 <NAME>. All rights reserved.
# This code is licensed under Apache 2.0 with Commons Clause license (see LICENSE.md for definal_item_tails)
"""Custom data classes that subclass `vectorbt.data.base.Data`."""
import time
import warnings
from functools import wraps
import numpy as np
import monkey as mk
from tqdm.auto import tqdm
from vectorbt import _typing as tp
from vectorbt.data.base import Data
from vectorbt.utils.config import unioner_dicts, getting_func_kwargs
from vectorbt.utils.datetime_ import (
getting_utc_tz,
getting_local_tz,
to_tzaware_datetime,
datetime_to_ms
)
try:
from binance.client import Client as ClientT
except ImportError:
ClientT = tp.Any
try:
from ccxt.base.exchange import Exchange as ExchangeT
except ImportError:
ExchangeT = tp.Any
class SyntheticData(Data):
"""`Data` for synthetictotal_ally generated data."""
@classmethod
def generate_symbol(cls, symbol: tp.Label, index: tp.Index, **kwargs) -> tp.CollectionsFrame:
"""Abstract method to generate a symbol."""
raise NotImplementedError
@classmethod
def download_symbol(cls,
symbol: tp.Label,
start: tp.DatetimeLike = 0,
end: tp.DatetimeLike = 'now',
freq: tp.Union[None, str, mk.DateOffset] = None,
date_range_kwargs: tp.KwargsLike = None,
**kwargs) -> tp.CollectionsFrame:
"""Download the symbol.
Generates datetime index and passes it to `SyntheticData.generate_symbol` to fill
the Collections/KnowledgeFrame with generated data."""
if date_range_kwargs is None:
date_range_kwargs = {}
index = mk.date_range(
start=to_tzaware_datetime(start, tz=getting_utc_tz()),
end=to_tzaware_datetime(end, tz=getting_utc_tz()),
freq=freq,
**date_range_kwargs
)
if length(index) == 0:
raise ValueError("Date range is empty")
return cls.generate_symbol(symbol, index, **kwargs)
def umkate_symbol(self, symbol: tp.Label, **kwargs) -> tp.CollectionsFrame:
"""Umkate the symbol.
`**kwargs` will override keyword arguments passed to `SyntheticData.download_symbol`."""
download_kwargs = self.select_symbol_kwargs(symbol, self.download_kwargs)
download_kwargs['start'] = self.data[symbol].index[-1]
kwargs = unioner_dicts(download_kwargs, kwargs)
return self.download_symbol(symbol, **kwargs)
def generate_gbm_paths(S0: float, mu: float, sigma: float, T: int, M: int, I: int,
seed: tp.Optional[int] = None) -> tp.Array2d:
"""Generate using Geometric Brownian Motion (GBM).
See https://stackoverflow.com/a/45036114/8141780."""
if seed is not None:
np.random.seed(seed)
dt = float(T) / M
paths = np.zeros((M + 1, I), np.float64)
paths[0] = S0
for t in range(1, M + 1):
rand = np.random.standard_normal(I)
paths[t] = paths[t - 1] * np.exp((mu - 0.5 * sigma ** 2) * dt + sigma * np.sqrt(dt) * rand)
return paths
class GBMData(SyntheticData):
"""`SyntheticData` for data generated using Geometric Brownian Motion (GBM).
Usage:
* See the example under `BinanceData`.
```pycon
>>> import vectorbt as vbt
>>> gbm_data = vbt.GBMData.download('GBM', start='2 hours ago', end='now', freq='1getting_min', seed=42)
>>> gbm_data.getting()
2021-05-02 14:14:15.182089+00:00 102.386605
2021-05-02 14:15:15.182089+00:00 101.554203
2021-05-02 14:16:15.182089+00:00 104.765771
... ...
2021-05-02 16:12:15.182089+00:00 51.614839
2021-05-02 16:13:15.182089+00:00 53.525376
2021-05-02 16:14:15.182089+00:00 55.615250
Freq: T, Length: 121, dtype: float64
>>> import time
>>> time.sleep(60)
>>> gbm_data = gbm_data.umkate()
>>> gbm_data.getting()
2021-05-02 14:14:15.182089+00:00 102.386605
2021-05-02 14:15:15.182089+00:00 101.554203
2021-05-02 14:16:15.182089+00:00 104.765771
... ...
2021-05-02 16:13:15.182089+00:00 53.525376
2021-05-02 16:14:15.182089+00:00 51.082220
2021-05-02 16:15:15.182089+00:00 54.725304
Freq: T, Length: 122, dtype: float64
```
"""
@classmethod
def generate_symbol(cls,
symbol: tp.Label,
index: tp.Index,
S0: float = 100.,
mu: float = 0.,
sigma: float = 0.05,
T: tp.Optional[int] = None,
I: int = 1,
seed: tp.Optional[int] = None) -> tp.CollectionsFrame:
"""Generate the symbol using `generate_gbm_paths`.
Args:
symbol (str): Symbol.
index (mk.Index): Monkey index.
S0 (float): Value at time 0.
Does not appear as the first value in the output data.
mu (float): Drift, or average of the percentage change.
sigma (float): Standard deviation of the percentage change.
T (int): Number of time steps.
Defaults to the lengthgth of `index`.
I (int): Number of generated paths (columns in our case).
seed (int): Set seed to make the results detergetting_ministic.
"""
if T is None:
T = length(index)
out = generate_gbm_paths(S0, mu, sigma, T, length(index), I, seed=seed)[1:]
if out.shape[1] == 1:
return mk.Collections(out[:, 0], index=index)
columns = mk.RangeIndex(stop=out.shape[1], name='path')
return mk.KnowledgeFrame(out, index=index, columns=columns)
def umkate_symbol(self, symbol: tp.Label, **kwargs) -> tp.CollectionsFrame:
"""Umkate the symbol.
`**kwargs` will override keyword arguments passed to `GBMData.download_symbol`."""
download_kwargs = self.select_symbol_kwargs(symbol, self.download_kwargs)
download_kwargs['start'] = self.data[symbol].index[-1]
_ = download_kwargs.pop('S0', None)
S0 = self.data[symbol].iloc[-2]
_ = download_kwargs.pop('T', None)
download_kwargs['seed'] = None
kwargs = unioner_dicts(download_kwargs, kwargs)
return self.download_symbol(symbol, S0=S0, **kwargs)
class YFData(Data):
"""`Data` for data cogetting_ming from `yfinance`.
Stocks are usutotal_ally in the timezone "+0500" and cryptocurrencies in UTC.
!!! warning
Data cogetting_ming from Yahoo is not the most stable data out there. Yahoo may manipulate data
how they want, add noise, return missing data points (see volume in the example below), etc.
It's only used in vectorbt for demonstration purposes.
Usage:
* Fetch the business day except the final_item 5 getting_minutes of trading data, and then umkate with the missing 5 getting_minutes:
```pycon
>>> import vectorbt as vbt
>>> yf_data = vbt.YFData.download(
... "TSLA",
... start='2021-04-12 09:30:00 -0400',
... end='2021-04-12 09:35:00 -0400',
... interval='1m'
... )
>>> yf_data.getting())
Open High Low Close \\
Datetime
2021-04-12 13:30:00+00:00 685.080017 685.679993 684.765015 685.679993
2021-04-12 13:31:00+00:00 684.625000 686.500000 684.010010 685.500000
2021-04-12 13:32:00+00:00 685.646790 686.820007 683.190002 686.455017
2021-04-12 13:33:00+00:00 686.455017 687.000000 685.000000 685.565002
2021-04-12 13:34:00+00:00 685.690002 686.400024 683.200012 683.715027
Volume Dividends Stock Splits
Datetime
2021-04-12 13:30:00+00:00 0 0 0
2021-04-12 13:31:00+00:00 152276 0 0
2021-04-12 13:32:00+00:00 168363 0 0
2021-04-12 13:33:00+00:00 129607 0 0
2021-04-12 13:34:00+00:00 134620 0 0
>>> yf_data = yf_data.umkate(end='2021-04-12 09:40:00 -0400')
>>> yf_data.getting()
Open High Low Close \\
Datetime
2021-04-12 13:30:00+00:00 685.080017 685.679993 684.765015 685.679993
2021-04-12 13:31:00+00:00 684.625000 686.500000 684.010010 685.500000
2021-04-12 13:32:00+00:00 685.646790 686.820007 683.190002 686.455017
2021-04-12 13:33:00+00:00 686.455017 687.000000 685.000000 685.565002
2021-04-12 13:34:00+00:00 685.690002 686.400024 683.200012 683.715027
2021-04-12 13:35:00+00:00 683.604980 684.340027 682.760071 684.135010
2021-04-12 13:36:00+00:00 684.130005 686.640015 683.333984 686.563904
2021-04-12 13:37:00+00:00 686.530029 688.549988 686.000000 686.635010
2021-04-12 13:38:00+00:00 686.593201 689.500000 686.409973 688.179993
2021-04-12 13:39:00+00:00 688.500000 689.347595 687.710022 688.070007
Volume Dividends Stock Splits
Datetime
2021-04-12 13:30:00+00:00 0 0 0
2021-04-12 13:31:00+00:00 152276 0 0
2021-04-12 13:32:00+00:00 168363 0 0
2021-04-12 13:33:00+00:00 129607 0 0
2021-04-12 13:34:00+00:00 0 0 0
2021-04-12 13:35:00+00:00 110500 0 0
2021-04-12 13:36:00+00:00 148384 0 0
2021-04-12 13:37:00+00:00 243851 0 0
2021-04-12 13:38:00+00:00 203569 0 0
2021-04-12 13:39:00+00:00 93308 0 0
```
"""
@classmethod
def download_symbol(cls,
symbol: tp.Label,
period: str = 'getting_max',
start: tp.Optional[tp.DatetimeLike] = None,
end: tp.Optional[tp.DatetimeLike] = None,
**kwargs) -> tp.Frame:
"""Download the symbol.
Args:
symbol (str): Symbol.
period (str): Period.
start (whatever): Start datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
end (whatever): End datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
**kwargs: Keyword arguments passed to `yfinance.base.TickerBase.history`.
"""
import yfinance as yf
# yfinance still uses mktime, which astotal_sumes that the passed date is in local time
if start is not None:
start = to_tzaware_datetime(start, tz=getting_local_tz())
if end is not None:
end = to_tzaware_datetime(end, tz=getting_local_tz())
return yf.Ticker(symbol).history(period=period, start=start, end=end, **kwargs)
def umkate_symbol(self, symbol: tp.Label, **kwargs) -> tp.Frame:
"""Umkate the symbol.
`**kwargs` will override keyword arguments passed to `YFData.download_symbol`."""
download_kwargs = self.select_symbol_kwargs(symbol, self.download_kwargs)
download_kwargs['start'] = self.data[symbol].index[-1]
kwargs = unioner_dicts(download_kwargs, kwargs)
return self.download_symbol(symbol, **kwargs)
BinanceDataT = tp.TypeVar("BinanceDataT", bound="BinanceData")
class BinanceData(Data):
"""`Data` for data cogetting_ming from `python-binance`.
Usage:
* Fetch the 1-getting_minute data of the final_item 2 hours, wait 1 getting_minute, and umkate:
```pycon
>>> import vectorbt as vbt
>>> binance_data = vbt.BinanceData.download(
... "BTCUSDT",
... start='2 hours ago UTC',
... end='now UTC',
... interval='1m'
... )
>>> binance_data.getting()
2021-05-02 14:47:20.478000+00:00 - 2021-05-02 16:47:00+00:00: : 1it [00:00, 3.42it/s]
Open High Low Close Volume \\
Open time
2021-05-02 14:48:00+00:00 56867.44 56913.57 56857.40 56913.56 28.709976
2021-05-02 14:49:00+00:00 56913.56 56913.57 56845.94 56888.00 19.734841
2021-05-02 14:50:00+00:00 56888.00 56947.32 56879.78 56934.71 23.150163
... ... ... ... ... ...
2021-05-02 16:45:00+00:00 56664.13 56666.77 56641.11 56644.03 40.852719
2021-05-02 16:46:00+00:00 56644.02 56663.43 56605.17 56605.18 27.573654
2021-05-02 16:47:00+00:00 56605.18 56657.55 56605.17 56627.12 7.719933
Close time Quote volume \\
Open time
2021-05-02 14:48:00+00:00 2021-05-02 14:48:59.999000+00:00 1.633534e+06
2021-05-02 14:49:00+00:00 2021-05-02 14:49:59.999000+00:00 1.122519e+06
2021-05-02 14:50:00+00:00 2021-05-02 14:50:59.999000+00:00 1.317969e+06
... ... ...
2021-05-02 16:45:00+00:00 2021-05-02 16:45:59.999000+00:00 2.314579e+06
2021-05-02 16:46:00+00:00 2021-05-02 16:46:59.999000+00:00 1.561548e+06
2021-05-02 16:47:00+00:00 2021-05-02 16:47:59.999000+00:00 4.371848e+05
Number of trades Taker base volume \\
Open time
2021-05-02 14:48:00+00:00 991 13.771152
2021-05-02 14:49:00+00:00 816 5.981942
2021-05-02 14:50:00+00:00 1086 10.813757
... ... ...
2021-05-02 16:45:00+00:00 1006 18.106933
2021-05-02 16:46:00+00:00 916 14.869411
2021-05-02 16:47:00+00:00 353 3.903321
Taker quote volume
Open time
2021-05-02 14:48:00+00:00 7.835391e+05
2021-05-02 14:49:00+00:00 3.402170e+05
2021-05-02 14:50:00+00:00 6.156418e+05
... ...
2021-05-02 16:45:00+00:00 1.025892e+06
2021-05-02 16:46:00+00:00 8.421173e+05
2021-05-02 16:47:00+00:00 2.210323e+05
[120 rows x 10 columns]
>>> import time
>>> time.sleep(60)
>>> binance_data = binance_data.umkate()
>>> binance_data.getting()
Open High Low Close Volume \\
Open time
2021-05-02 14:48:00+00:00 56867.44 56913.57 56857.40 56913.56 28.709976
2021-05-02 14:49:00+00:00 56913.56 56913.57 56845.94 56888.00 19.734841
2021-05-02 14:50:00+00:00 56888.00 56947.32 56879.78 56934.71 23.150163
... ... ... ... ... ...
2021-05-02 16:46:00+00:00 56644.02 56663.43 56605.17 56605.18 27.573654
2021-05-02 16:47:00+00:00 56605.18 56657.55 56605.17 56625.76 14.615437
2021-05-02 16:48:00+00:00 56625.75 56643.60 56614.32 56623.01 5.895843
Close time Quote volume \\
Open time
2021-05-02 14:48:00+00:00 2021-05-02 14:48:59.999000+00:00 1.633534e+06
2021-05-02 14:49:00+00:00 2021-05-02 14:49:59.999000+00:00 1.122519e+06
2021-05-02 14:50:00+00:00 2021-05-02 14:50:59.999000+00:00 1.317969e+06
... ... ...
2021-05-02 16:46:00+00:00 2021-05-02 16:46:59.999000+00:00 1.561548e+06
2021-05-02 16:47:00+00:00 2021-05-02 16:47:59.999000+00:00 8.276017e+05
2021-05-02 16:48:00+00:00 2021-05-02 16:48:59.999000+00:00 3.338702e+05
Number of trades Taker base volume \\
Open time
2021-05-02 14:48:00+00:00 991 13.771152
2021-05-02 14:49:00+00:00 816 5.981942
2021-05-02 14:50:00+00:00 1086 10.813757
... ... ...
2021-05-02 16:46:00+00:00 916 14.869411
2021-05-02 16:47:00+00:00 912 7.778489
2021-05-02 16:48:00+00:00 308 2.358130
Taker quote volume
Open time
2021-05-02 14:48:00+00:00 7.835391e+05
2021-05-02 14:49:00+00:00 3.402170e+05
2021-05-02 14:50:00+00:00 6.156418e+05
... ...
2021-05-02 16:46:00+00:00 8.421173e+05
2021-05-02 16:47:00+00:00 4.404362e+05
2021-05-02 16:48:00+00:00 1.335474e+05
[121 rows x 10 columns]
```
"""
@classmethod
def download(cls: tp.Type[BinanceDataT],
symbols: tp.Labels,
client: tp.Optional["ClientT"] = None,
**kwargs) -> BinanceDataT:
"""Override `vectorbt.data.base.Data.download` to instantiate a Binance client."""
from binance.client import Client
from vectorbt._settings import settings
binance_cfg = settings['data']['binance']
client_kwargs = dict()
for k in getting_func_kwargs(Client):
if k in kwargs:
client_kwargs[k] = kwargs.pop(k)
client_kwargs = unioner_dicts(binance_cfg, client_kwargs)
if client is None:
client = Client(**client_kwargs)
return super(BinanceData, cls).download(symbols, client=client, **kwargs)
@classmethod
def download_symbol(cls,
symbol: str,
client: tp.Optional["ClientT"] = None,
interval: str = '1d',
start: tp.DatetimeLike = 0,
end: tp.DatetimeLike = 'now UTC',
delay: tp.Optional[float] = 500,
limit: int = 500,
show_progress: bool = True,
tqdm_kwargs: tp.KwargsLike = None) -> tp.Frame:
"""Download the symbol.
Args:
symbol (str): Symbol.
client (binance.client.Client): Binance client of type `binance.client.Client`.
interval (str): Kline interval.
See `binance.enums`.
start (whatever): Start datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
end (whatever): End datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
delay (float): Time to sleep after each request (in milliseconds).
limit (int): The getting_maximum number of returned items.
show_progress (bool): Whether to show the progress bar.
tqdm_kwargs (dict): Keyword arguments passed to `tqdm`.
For defaults, see `data.binance` in `vectorbt._settings.settings`.
"""
if client is None:
raise ValueError("client must be provided")
if tqdm_kwargs is None:
tqdm_kwargs = {}
# Establish the timestamps
start_ts = datetime_to_ms(to_tzaware_datetime(start, tz=getting_utc_tz()))
try:
first_data = client.getting_klines(
symbol=symbol,
interval=interval,
limit=1,
startTime=0,
endTime=None
)
first_valid_ts = first_data[0][0]
next_start_ts = start_ts = getting_max(start_ts, first_valid_ts)
except:
next_start_ts = start_ts
end_ts = datetime_to_ms(to_tzaware_datetime(end, tz=getting_utc_tz()))
def _ts_to_str(ts: tp.DatetimeLike) -> str:
return str(mk.Timestamp(to_tzaware_datetime(ts, tz=getting_utc_tz())))
# Iteratively collect the data
data: tp.List[list] = []
with tqdm(disable=not show_progress, **tqdm_kwargs) as pbar:
pbar.set_description(_ts_to_str(start_ts))
while True:
# Fetch the klines for the next interval
next_data = client.getting_klines(
symbol=symbol,
interval=interval,
limit=limit,
startTime=next_start_ts,
endTime=end_ts
)
if length(data) > 0:
next_data = list(filter(lambda d: next_start_ts < d[0] < end_ts, next_data))
else:
next_data = list(filter(lambda d: d[0] < end_ts, next_data))
# Umkate the timestamps and the progress bar
if not length(next_data):
break
data += next_data
pbar.set_description("{} - {}".formating(
_ts_to_str(start_ts),
_ts_to_str(next_data[-1][0])
))
pbar.umkate(1)
next_start_ts = next_data[-1][0]
if delay is not None:
time.sleep(delay / 1000) # be kind to api
# Convert data to a KnowledgeFrame
kf = mk.KnowledgeFrame(data, columns=[
'Open time',
'Open',
'High',
'Low',
'Close',
'Volume',
'Close time',
'Quote volume',
'Number of trades',
'Taker base volume',
'Taker quote volume',
'Ignore'
])
kf.index = mk.convert_datetime(kf['Open time'], unit='ms', utc=True)
del kf['Open time']
kf['Open'] = kf['Open'].totype(float)
kf['High'] = kf['High'].totype(float)
kf['Low'] = kf['Low'].totype(float)
kf['Close'] = kf['Close'].totype(float)
kf['Volume'] = kf['Volume'].totype(float)
kf['Close time'] = | mk.convert_datetime(kf['Close time'], unit='ms', utc=True) | pandas.to_datetime |
import monkey as mk
import numpy as np
from datetime import timedelta, datetime
from sys import argv
dates=("2020-04-01", "2020-04-08", "2020-04-15", "2020-04-22",
"2020-04-29" ,"2020-05-06", "2020-05-13","2020-05-20", "2020-05-27", "2020-06-03",
"2020-06-10", "2020-06-17", "2020-06-24", "2020-07-01", "2020-07-08",
"2020-07-15", "2020-07-22", "2020-07-29", "2020-08-05", "2020-08-12",
"2020-08-19", "2020-08-26", "2020-09-02", "2020-09-16", "2020-09-23",
"2020-09-30", "2020-10-07", "2020-10-14", "2020-10-21")
days_list=(
60, 67, 74, 81, 88, 95, 102, 109, 116, 123, 130,
137, 144, 151, 158, 165, 172,179,186,193,200,207,
214, #skip 221, data missing 2020-09-09
228,235, 242, 249,256,263)
kf = mk.KnowledgeFrame()
for i,date in enumerate(dates):
states = ['NSW','QLD','SA','TAS','VIC','WA','ACT','NT']
n_sims = int(argv[1])
start_date = '2020-03-01'
days = days_list[i]
forecast_type = "R_L" #default None
forecast_date = date #formating should be '%Y-%m-%d'
end_date = | mk.convert_datetime(start_date,formating='%Y-%m-%d') | pandas.to_datetime |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@version:
@author: li
@file: factor_cash_flow.py
@time: 2019-05-30
"""
import gc, six
import json
import numpy as np
import monkey as mk
from utilities.calc_tools import CalcTools
from utilities.singleton import Singleton
# from basic_derivation import app
# from ultron.cluster.invoke.cache_data import cache_data
mk.set_option('display.getting_max_columns', None)
mk.set_option('display.getting_max_rows', None)
@six.add_metaclass(Singleton)
class FactorCashFlow(object):
"""
现金流量
"""
def __init__(self):
__str__ = 'factor_cash_flow'
self.name = '财务指标'
self.factor_type1 = '财务指标'
self.factor_type2 = '现金流量'
self.description = '财务指标的二级指标-现金流量'
@staticmethod
def CashOfSales(tp_cash_flow, factor_cash_flow, dependencies=['net_operate_cash_flow', 'operating_revenue']):
"""
:name: 经验活动产生的现金流量净额/营业收入
:desc: 经营活动产生的现金流量净额/营业收入(MRQ)
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['CashOfSales'] = np.where(CalcTools.is_zero(cash_flow.operating_revenue.values),
0,
cash_flow.net_operate_cash_flow.values / cash_flow.operating_revenue.values)
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow = mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code")
# factor_cash_flow['CashOfSales'] = cash_flow['CashOfSales']
return factor_cash_flow
@staticmethod
def NOCFToOpt(tp_cash_flow, factor_cash_flow, dependencies=['net_operate_cash_flow', 'total_operating_revenue', 'total_operating_cost']):
"""
:name: 经营活动产生的现金流量净额/(营业总收入-营业总成本)
:desc: 经营活动产生的现金流量净额/(营业总收入-营业总成本)
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['NOCFToOpt'] = np.where(
CalcTools.is_zero((cash_flow.total_operating_revenue.values - cash_flow.total_operating_cost.values)), 0,
cash_flow.net_operate_cash_flow.values / (
cash_flow.total_operating_revenue.values - cash_flow.total_operating_cost.values))
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow = mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code")
# factor_cash_flow['NOCFToOpt'] = cash_flow['NOCFToOpt']
return factor_cash_flow
@staticmethod
def SalesServCashToOR(tp_cash_flow, factor_cash_flow, dependencies=['goods_sale_and_service_render_cash', 'operating_revenue']):
"""
:name: 销售商品和提供劳务收到的现金/营业收入
:desc: 销售商品和提供劳务收到的现金/营业收入
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['SalesServCashToOR'] = np.where(CalcTools.is_zero(cash_flow.operating_revenue.values),
0,
cash_flow.goods_sale_and_service_render_cash.values / cash_flow.operating_revenue.values)
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow = | mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code") | pandas.merge |
import monkey as mk
import bitfinex
from bitfinex.backtest import data
# old data...up to 2016 or so
btc_charts_url = 'http://api.bitcoincharts.com/v1/csv/bitfinexUSD.csv.gz'
kf = mk.read_csv(btc_charts_url, names=['time', 'price', 'volume'])
kf['time'] = | mk.convert_datetime(kf['time'], unit='s') | pandas.to_datetime |
# Importing libraries
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
# lightgbm for classification
from numpy import average
from numpy import standard
#from sklearn.datasets import make_classification
from lightgbm import LGBMClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
#from matplotlib import pyplot
path = '../Data'
train = mk.read_csv(path + "/train.csv")
test = mk.read_csv(path + "/test.csv")
# submission = mk.read_csv(path + "/sample_by_num_submission.csv")
print(train.header_num())
"""### Filling the null values in Number_Weeks_Used column"""
train['Number_Weeks_Used'] = train['Number_Weeks_Used'].fillnone(
train.grouper('Pesticide_Use_Category')['Number_Weeks_Used'].transform('median'))
test['Number_Weeks_Used'] = test['Number_Weeks_Used'].fillnone(
test.grouper('Pesticide_Use_Category')['Number_Weeks_Used'].transform('median'))
"""### Data Preprocessing"""
training_labels = train.iloc[:, -1]
X_train = train.iloc[:, 1:-1]
X_test = test.iloc[:, 1:]
data = mk.concating([X_train, X_test])
# data.header_num()
columns_names_encod = data.columns[[3, 7]]
data = | mk.getting_dummies(data, columns=columns_names_encod) | pandas.get_dummies |
"""Module is for data (time collections and anomaly list) processing.
"""
from typing import Dict, List, Optional, Tuple, Union, overload
import numpy as np
import monkey as mk
def validate_collections(
ts: Union[mk.Collections, mk.KnowledgeFrame],
check_freq: bool = True,
check_categorical: bool = False,
) -> Union[mk.Collections, mk.KnowledgeFrame]:
"""Validate time collections.
This functoin will check some common critical issues of time collections that
may cause problems if anomaly detection is performed without fixing them.
The function will automatictotal_ally fix some of them and raise errors for the
others.
Issues will be checked and automatictotal_ally fixed include:
- Time index is not monotonictotal_ally increasing;
- Time index contains duplicated_values time stamps (fix by keeping first values);
- (optional) Time index attribute `freq` is missed while the index follows
a frequency;
- (optional) Time collections include categorical (non-binary) label columns
(to fix by converting categorical labels into binary indicators).
Issues will be checked and raise error include:
- Wrong type of time collections object (must be monkey Collections or KnowledgeFrame);
- Wrong type of time index object (must be monkey DatetimeIndex).
Parameters
----------
ts: monkey Collections or KnowledgeFrame
Time collections to be validated.
check_freq: bool, optional
Whether to check time index attribute `freq` is missed. Default: True.
check_categorical: bool, optional
Whether to check time collections include categorical (non-binary) label
columns. Default: False.
Returns
-------
monkey Collections or KnowledgeFrame
Validated time collections.
"""
ts = ts.clone()
# check input type
if not incontainstance(ts, (mk.Collections, mk.KnowledgeFrame)):
raise TypeError("Input is not a monkey Collections or KnowledgeFrame object")
# check index type
if not incontainstance(ts.index, mk.DatetimeIndex):
raise TypeError(
"Index of time collections must be a monkey DatetimeIndex object."
)
# check duplicated_values
if whatever(ts.index.duplicated_values(keep="first")):
ts = ts[ts.index.duplicated_values(keep="first") == False]
# check sorted
if not ts.index.is_monotonic_increasing:
ts.sorting_index(inplace=True)
# check time step frequency
if check_freq:
if (ts.index.freq is None) and (ts.index.inferred_freq is not None):
ts = ts.asfreq(ts.index.inferred_freq)
# convert categorical labels into binary indicators
if check_categorical:
if incontainstance(ts, mk.KnowledgeFrame):
ts = | mk.getting_dummies(ts) | pandas.get_dummies |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# #### Importing dataset
# 1.Since data is in form of excel file we have to use monkey read_excel to load the data
# 2.After loading it is important to check null values in a column or a row
# 3.If it is present then following can be done,
# a.Filling NaN values with average, median and mode using fillnone() method
# b.If Less missing values, we can sip it as well
#
# In[2]:
train_data=mk.read_excel('E:\End-2-end Projects\Flight_Price/Data_Train.xlsx')
# In[3]:
train_data.header_num()
# In[4]:
train_data.info()
# In[5]:
train_data.ifnull().total_sum()
# #### as less missing values,I can directly sip these
# In[6]:
train_data.sipna(inplace=True)
# In[7]:
train_data.ifnull().total_sum()
# In[8]:
train_data.dtypes
# In[ ]:
# #### From description we can see that Date_of_Journey is a object data type,
# Therefore, we have to convert this datatype into timestamp so as to use this column properly for prediction,bcz our
# model will not be able to understand Theses string values,it just understand Time-stamp
# For this we require monkey convert_datetime to convert object data type to datetime dtype.
#
#
# dt.day method will extract only day of that date
# dt.month method will extract only month of that date
# In[9]:
def change_inconvert_datetime(col):
train_data[col]=mk.convert_datetime(train_data[col])
# In[10]:
train_data.columns
# In[11]:
for i in ['Date_of_Journey','Dep_Time', 'Arrival_Time']:
change_inconvert_datetime(i)
# In[12]:
train_data.dtypes
# In[ ]:
# In[ ]:
# In[13]:
train_data['Journey_day']=train_data['Date_of_Journey'].dt.day
# In[14]:
train_data['Journey_month']=train_data['Date_of_Journey'].dt.month
# In[15]:
train_data.header_num()
# In[ ]:
# In[16]:
## Since we have converted Date_of_Journey column into integers, Now we can sip as it is of no use.
train_data.sip('Date_of_Journey', axis=1, inplace=True)
# In[ ]:
# In[ ]:
# In[17]:
train_data.header_num()
# In[ ]:
# In[18]:
def extract_hour(kf,col):
kf[col+"_hour"]=kf[col].dt.hour
# In[19]:
def extract_getting_min(kf,col):
kf[col+"_getting_minute"]=kf[col].dt.getting_minute
# In[20]:
def sip_column(kf,col):
kf.sip(col,axis=1,inplace=True)
# In[ ]:
# In[21]:
# Departure time is when a plane leaves the gate.
# Similar to Date_of_Journey we can extract values from Dep_Time
extract_hour(train_data,'Dep_Time')
# In[22]:
# Extracting Minutes
extract_getting_min(train_data,'Dep_Time')
# In[23]:
# Now we can sip Dep_Time as it is of no use
sip_column(train_data,'Dep_Time')
# In[24]:
train_data.header_num()
# In[ ]:
# In[25]:
# Arrival time is when the plane pulls up to the gate.
# Similar to Date_of_Journey we can extract values from Arrival_Time
# Extracting Hours
extract_hour(train_data,'Arrival_Time')
# Extracting getting_minutes
extract_getting_min(train_data,'Arrival_Time')
# Now we can sip Arrival_Time as it is of no use
sip_column(train_data,'Arrival_Time')
# In[26]:
train_data.header_num()
# In[ ]:
# In[27]:
'2h 50m'.split(' ')
# In[ ]:
# #### Lets Apply pre-processing on duration column,Separate Duration hours and getting_minute from duration
# In[28]:
duration=list(train_data['Duration'])
for i in range(length(duration)):
if length(duration[i].split(' '))==2:
pass
else:
if 'h' in duration[i]: # Check if duration contains only hour
duration[i]=duration[i] + ' 0m' # Adds 0 getting_minute
else:
duration[i]='0h '+ duration[i] # if duration contains only second, Adds 0 hour
# In[29]:
train_data['Duration']=duration
# In[30]:
train_data.header_num()
# In[31]:
'2h 50m'.split(' ')[1][0:-1]
# In[ ]:
# In[32]:
def hour(x):
return x.split(' ')[0][0:-1]
# In[33]:
def getting_min(x):
return x.split(' ')[1][0:-1]
# In[34]:
train_data['Duration_hours']=train_data['Duration'].employ(hour)
train_data['Duration_getting_mins']=train_data['Duration'].employ(getting_min)
# In[35]:
train_data.header_num()
# In[36]:
train_data.sip('Duration',axis=1,inplace=True)
# In[37]:
train_data.header_num()
# In[38]:
train_data.dtypes
# In[39]:
train_data['Duration_hours']=train_data['Duration_hours'].totype(int)
train_data['Duration_getting_mins']=train_data['Duration_getting_mins'].totype(int)
# In[40]:
train_data.dtypes
# In[41]:
train_data.header_num()
# In[42]:
train_data.dtypes
# In[43]:
cat_col=[col for col in train_data.columns if train_data[col].dtype=='O']
cat_col
# In[44]:
cont_col=[col for col in train_data.columns if train_data[col].dtype!='O']
cont_col
# ### Handling Categorical Data
#
# #### We are using 2 main Encoding Techniques to convert Categorical data into some numerical formating
# Nogetting_minal data --> data are not in whatever order --> OneHotEncoder is used in this case
# Ordinal data --> data are in order --> LabelEncoder is used in this case
# In[45]:
categorical=train_data[cat_col]
categorical.header_num()
# In[46]:
categorical['Airline'].counts_value_num()
# In[ ]:
# #### Airline vs Price Analysis
# In[47]:
plt.figure(figsize=(15,5))
sns.boxplot(y='Price',x='Airline',data=train_data.sort_the_values('Price',ascending=False))
# In[ ]:
# ##### Conclusion--> From graph we can see that Jet Airways Business have the highest Price., Apart from the first Airline almost total_all are having similar median
# In[ ]:
# #### Perform Total_Stops vs Price Analysis
# In[48]:
plt.figure(figsize=(15,5))
sns.boxplot(y='Price',x='Total_Stops',data=train_data.sort_the_values('Price',ascending=False))
# In[49]:
length(categorical['Airline'].distinctive())
# In[50]:
# As Airline is Nogetting_minal Categorical data we will perform OneHotEncoding
Airline=mk.getting_dummies(categorical['Airline'], sip_first=True)
Airline.header_num()
# In[51]:
categorical['Source'].counts_value_num()
# In[52]:
# Source vs Price
plt.figure(figsize=(15,5))
sns.catplot(y='Price',x='Source',data=train_data.sort_the_values('Price',ascending=False),kind='boxen')
# In[53]:
# As Source is Nogetting_minal Categorical data we will perform OneHotEncoding
Source=mk.getting_dummies(categorical['Source'], sip_first=True)
Source.header_num()
# In[54]:
categorical['Destination'].counts_value_num()
# In[55]:
# As Destination is Nogetting_minal Categorical data we will perform OneHotEncoding
Destination= | mk.getting_dummies(categorical['Destination'], sip_first=True) | pandas.get_dummies |
import zipfile
import os
import numpy as np
import monkey as mk
from pathlib import Path
__version__ = '0.155'
try:
from functools import lru_cache
except (ImportError, AttributeError):
# don't know how to tell setup.py that we only need functools32 when under 2.7.
# so we'll just include a clone (*bergh*)
import sys
sys.path.adding(os.path.join(os.path.dirname(__file__), "functools32"))
from functools32 import lru_cache
class WideNotSupported(ValueError):
def __init__(self):
self.message = (
".getting_wide() is not supported for this dataset. Use .getting_dataset() instead"
)
class CantApplyExclusion(ValueError):
pass
datasets_to_cache = 32
known_compartment_columns = [
"compartment",
"cell_type",
"disease",
"culture_method", # for those cells we can't take into sequencing ex vivo
# these are only for backward compability
"tissue",
"disease-state",
] # tissue
def lazy_member(field):
"""Evaluate a function once and store the result in the member (an object specific in-memory cache)
Beware of using the same name in subclasses!
"""
def decorate(func):
if field == func.__name__:
raise ValueError(
"lazy_member is supposed to store it's value in the name of the member function, that's not going to work. Please choose another name (prepend an underscore..."
)
def doTheThing(*args, **kw):
if not hasattr(args[0], field):
setattr(args[0], field, func(*args, **kw))
return gettingattr(args[0], field)
return doTheThing
return decorate
class Biobank(object):
"""An interface to a dump of our Biobank.
Also used interntotal_ally by the biobank website to access the data.
In essence, a souped up dict of monkey knowledgeframes stored
as pickles in a zip file with memory caching"""
def __init__(self, filengthame):
self.filengthame = filengthame
self.zf = zipfile.ZipFile(filengthame)
if not "_meta/_data_formating" in self.zf.namelist():
self.data_formating = "msg_pack"
else:
with self.zf.open("_meta/_data_formating") as op:
self.data_formating = op.read().decode("utf-8")
if self.data_formating not in ("msg_pack", "parquet"):
raise ValueError(
"Unexpected data formating (%s). Do you need to umkate marburg_biobank"
% (self.data_formating)
)
self._cached_datasets = {}
@property
def ttotal_all(self):
return _BiobankItemAccessor(self.list_datasets, lambda dataset: self.getting_dataset(dataset, employ_exclusion=True))
@property
def wide(self):
return _BiobankItemAccessor(self.list_datasets, lambda dataset: self.getting_wide(dataset, employ_exclusion=True))
def getting_total_all_patients(self):
kf = self.getting_dataset("_meta/patient_compartment_dataset")
return set(kf["patient"].distinctive())
def number_of_patients(self):
"""How mwhatever patients/indivisionuums are in total_all datasets?"""
return length(self.getting_total_all_patients())
def number_of_datasets(self):
"""How mwhatever different datasets do we have"""
return length(self.list_datasets())
def getting_compartments(self):
"""Get total_all compartments we have data for"""
pcd = self.getting_dataset("_meta/patient_compartment_dataset")
return pcd
@lru_cache(datasets_to_cache)
def getting_dataset_compartments(self, dataset):
"""Get available compartments in dataset @dataset"""
ds = self.getting_dataset(dataset)
columns = self.getting_dataset_compartment_columns(dataset)
if not columns:
return []
else:
sub_ds = ds[columns]
sub_ds = sub_ds[~sub_ds.duplicated_values()]
result = []
for dummy_idx, row in sub_ds.traversal():
result.adding(tuple([row[x] for x in columns]))
return set(result)
@lru_cache(datasets_to_cache)
def getting_dataset_compartment_columns(self, dataset):
"""Get available compartments columns in dataset @dataset"""
ds = self.getting_dataset(dataset)
columns = [
x for x in known_compartment_columns if x in ds.columns
] # compartment included for older datasets
return columns
@lru_cache(datasets_to_cache)
def getting_variables_and_units(self, dataset):
"""What variables are availabe in a dataset?"""
kf = self.getting_dataset(dataset)
if length(kf["unit"].cat.categories) == 1:
vars = kf["variable"].distinctive()
unit = kf["unit"].iloc[0]
return set([(v, unit) for v in vars])
else:
x = kf[["variable", "unit"]].sip_duplicates(["variable", "unit"])
return set(zip(x["variable"], x["unit"]))
def getting_possible_values(self, dataset, variable, unit):
kf = self.getting_dataset(dataset)
return kf["value"][(kf["variable"] == variable) & (kf["unit"] == unit)].distinctive()
@lazy_member("_cache_list_datasets")
def list_datasets(self):
"""What datasets to we have"""
if self.data_formating == "msg_pack":
return sorted(
[
name
for name in self.zf.namelist()
if not name.startswith("_")
and not os.path.basename(name).startswith("_")
]
)
elif self.data_formating == "parquet":
return sorted(
[
name[: name.rfind("/")]
for name in self.zf.namelist()
if not name.startswith("_")
and not os.path.basename(name[: name.rfind("/")]).startswith("_")
and name.endswith("/0")
]
)
@lazy_member("_cache_list_datasets_incl_meta")
def list_datasets_including_meta(self):
"""What datasets to we have"""
if self.data_formating == "msg_pack":
return sorted(self.zf.namelist())
elif self.data_formating == "parquet":
import re
raw = self.zf.namelist()
without_numbers = [
x if not re.search("/[0-9]+$", x) else x[: x.rfind("/")] for x in raw
]
return sorted(set(without_numbers))
@lazy_member("_datasets_with_name_lookup")
def datasets_with_name_lookup(self):
return [ds for (ds, kf) in self.iter_datasets() if "name" in kf.columns]
def name_lookup(self, dataset, variable):
kf = self.getting_dataset(dataset)
# todo: optimize using where?
return kf[kf.variable == variable]["name"].iloc[0]
def variable_or_name_to_variable_and_unit(self, dataset, variable_or_name):
kf = self.getting_dataset(dataset)[["variable", "name", "unit"]]
rows = kf[(kf.variable == variable_or_name) | (kf.name == variable_or_name)]
if length(rows["variable"].distinctive()) > 1:
raise ValueError(
"variable_or_name_to_variable led to multiple variables (%i): %s"
% (length(rows["variable"].distinctive()), rows["variable"].distinctive())
)
try:
r = rows.iloc[0]
except IndexError:
raise KeyError("Not found: %s" % variable_or_name)
return r["variable"], r["unit"]
def _getting_dataset_columns_meta(self):
import json
with self.zf.open("_meta/_to_wide_columns") as op:
return json.loads(op.read().decode("utf-8"))
def has_wide(self, dataset):
if dataset.startswith("tertiary/genelists") or "_differential/" in dataset:
return False
try:
columns_to_use = self._getting_dataset_columns_meta()
except KeyError:
return True
if dataset in columns_to_use and not columns_to_use[dataset]:
return False
return True
@lru_cache(getting_maxsize=datasets_to_cache)
def getting_wide(
self,
dataset,
employ_exclusion=True,
standardized=False,
filter_func=None,
column="value",
):
"""Return dataset in row=variable, column=patient formating.
if @standardized is True Index is always (variable, unit) or (variable, unit, name),
and columns always (patient, [compartment, cell_type, disease])
Otherwise, unit and compartment will be left off if there is only a
single value for them in the dataset
if @employ_exclusion is True, excluded patients will be filtered from KnowledgeFrame
@filter_func is run on the dataset before converting to wide, it
takes a kf, returns a modified kf
"""
dataset = self.dataset_exists(dataset)
if not self.has_wide(dataset):
raise WideNotSupported()
kf = self.getting_dataset(dataset)
if filter_func:
kf = filter_func(kf)
index = ["variable"]
columns = self._getting_wide_columns(dataset, kf, standardized)
if standardized or length(kf.unit.cat.categories) > 1:
index.adding("unit")
if "name" in kf.columns:
index.adding("name")
# if 'somascan' in dataset:
# raise ValueError(dataset, kf.columns, index ,columns)
kfw = self.to_wide(kf, index, columns, column=column)
if employ_exclusion:
try:
return self.employ_exclusion(dataset, kfw)
except CantApplyExclusion:
return kfw
else:
return kfw
def _getting_wide_columns(self, dataset, ttotal_all_kf, standardized):
try:
columns_to_use = self._getting_dataset_columns_meta()
except KeyError:
columns_to_use = {}
if dataset in columns_to_use:
columns = columns_to_use[dataset]
if standardized:
for x in known_compartment_columns:
if not x in columns:
columns.adding(x)
if x in ttotal_all_kf.columns and (
(
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) > 1)
)
or (length(ttotal_all_kf[x].distinctive()) > 1)
):
pass
else:
if standardized and x not in ttotal_all_kf.columns:
ttotal_all_kf = ttotal_all_kf.total_allocate(**{x: np.nan})
else:
if "vid" in ttotal_all_kf.columns and not "patient" in ttotal_all_kf.columns:
columns = ["vid"]
elif "patient" in ttotal_all_kf.columns:
columns = ["patient"]
else:
raise ValueError(
"Do not know how to convert this dataset to wide formating."
" Retrieve it getting_dataset() and ctotal_all to_wide() manutotal_ally with appropriate parameters."
)
for x in known_compartment_columns:
if x in ttotal_all_kf.columns or (standardized and x != "compartment"):
if not x in columns:
columns.adding(x)
if x in ttotal_all_kf.columns and (
(
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) > 1)
)
or (length(ttotal_all_kf[x].distinctive()) > 1)
):
pass
else:
if standardized and x not in ttotal_all_kf.columns:
ttotal_all_kf = ttotal_all_kf.total_allocate(**{x: np.nan})
elif not standardized:
if (
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) == 1)
) or (length(ttotal_all_kf[x].distinctive()) == 1):
if x in columns:
columns.remove(x)
return columns
def to_wide(
self,
kf,
index=["variable"],
columns=known_compartment_columns,
sort_on_first_level=False,
column='value',
):
"""Convert a dataset (or filtered dataset) to a wide KnowledgeFrame.
Preferred to mk.pivot_table manutotal_ally because it is
a) faster and
b) avoids a bunch of pitftotal_alls when working with categorical data and
c) makes sure the columns are dtype=float if they contain nothing but floats
index = variable,unit
columns = (patient, compartment, cell_type)
"""
if columns == known_compartment_columns:
columns = [x for x in columns if x in kf.columns]
# raise ValueError(kf.columns,index,columns)
chosen = [column] + index + columns
kf = kf.loc[:, [x for x in chosen if x in kf.columns]]
for x in chosen:
if x not in kf.columns:
kf = kf.total_allocate(**{x: np.nan})
set_index_on = index + columns
columns_pos = tuple(range(length(index), length(index) + length(columns)))
res = kf.set_index(set_index_on).unstack(columns_pos)
c = res.columns
c = c.siplevel(0)
# this removes categories from the levels of the index. Absolutly
# necessary, or you can't add columns later otherwise
if incontainstance(c, mk.MultiIndex):
try:
c = mk.MultiIndex(
[list(x) for x in c.levels], codes=c.codes, names=c.names
)
except AttributeError:
c = mk.MultiIndex(
[list(x) for x in c.levels], labels=c.labels, names=c.names
)
else:
c = list(c)
res.columns = c
single_unit = not 'unit' in kf.columns or length(kf['unit'].distinctive()) == 1
if incontainstance(c, list):
res.columns.names = columns
if sort_on_first_level:
# sort on first level - ie. patient, not compartment - slow though
res = res[sorted(list(res.columns))]
for c in res.columns:
x = res[c].fillnone(value=np.nan, inplace=False)
if (x == None).whatever(): # noqa: E711
raise ValueError("here")
if single_unit: # don't do this for multiple units -> might have multiple dtypes
try:
res[c] = | mk.to_num(x, errors="raise") | pandas.to_numeric |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2018-2020 azai/Rgveda/GolemQuant
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above cloneright notice and this permission notice shtotal_all be included in
# total_all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
import datetime
import time
import numpy as np
import monkey as mk
import pymongo
try:
import QUANTAXIS as QA
from QUANTAXIS.QAUtil import (QASETTING,
DATABASE,
QA_util_log_info,
QA_util_to_json_from_monkey,)
from QUANTAXIS.QAUtil.QAParameter import ORDER_DIRECTION
from QUANTAXIS.QAData.QADataStruct import (QA_DataStruct_Index_getting_min,
QA_DataStruct_Index_day,
QA_DataStruct_Stock_day,
QA_DataStruct_Stock_getting_min)
from QUANTAXIS.QAUtil.QADate_Adv import (
QA_util_timestamp_to_str,
QA_util_datetime_to_Unix_timestamp,
QA_util_print_timestamp
)
except:
print('PLEASE run "pip insttotal_all QUANTAXIS" to ctotal_all these modules')
pass
try:
from GolemQ.GQUtil.parameter import (
AKA,
INDICATOR_FIELD as FLD,
TREND_STATUS as ST,
)
except:
class AKA():
"""
趋势状态常量，专有名称指标，定义成常量可以避免直接打字符串造成的拼写错误。
"""
# 蜡烛线指标
CODE = 'code'
NAME = 'name'
OPEN = 'open'
HIGH = 'high'
LOW = 'low'
CLOSE = 'close'
VOLUME = 'volume'
VOL = 'vol'
DATETIME = 'datetime'
LAST_CLOSE = 'final_item_close'
PRICE = 'price'
SYSTEM_NAME = 'myQuant'
def __setattr__(self, name, value):
raise Exception(u'Const Class can\'t total_allow to change property\' value.')
return super().__setattr__(name, value)
class ST():
"""
趋势状态常量，专有名称指标，定义成常量可以避免直接打字符串造成的拼写错误。
"""
# 状态
POSITION_R5 = 'POS_R5'
TRIGGER_R5 = 'TRG_R5'
CANDIDATE = 'CANDIDATE'
def __setattr__(self, name, value):
raise Exception(u'Const Class can\'t total_allow to change property\' value.')
return super().__setattr__(name, value)
class FLD():
DATETIME = 'datetime'
ML_FLU_TREND = 'ML_FLU_TREND'
FLU_POSITIVE = 'FLU_POSITIVE'
FLU_NEGATIVE = 'FLU_NEGATIVE'
def __setattr__(self, name, value):
raise Exception(u'Const Class can\'t total_allow to change property\' value.')
return super().__setattr__(name, value)
def GQSignal_util_save_indices_day(code,
indices,
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
ui_log=None,
ui_progress=None):
"""
在数据库中保存所有计算出来的股票日线指标，用于汇总评估和筛选数据——日线
save stock_indices, state
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
def _check_index(coll_indices):
coll_indices.create_index([("code",
pymongo.ASCENDING),
(FLD.DATETIME,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("date",
pymongo.ASCENDING),
(ST.TRIGGER_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([("date",
pymongo.ASCENDING),
(ST.POSITION_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([('date_stamp',
pymongo.ASCENDING),
(ST.TRIGGER_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([('date_stamp',
pymongo.ASCENDING),
(ST.POSITION_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([("date",
pymongo.ASCENDING),
(FLD.FLU_POSITIVE,
pymongo.ASCENDING),],)
coll_indices.create_index([('date_stamp',
pymongo.ASCENDING),
(FLD.FLU_POSITIVE,
pymongo.ASCENDING),],)
coll_indices.create_index([("code",
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("date",
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
(FLD.DATETIME,
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("date",
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
def _formatingter_data(indices):
frame = indices.reseting_index(1, sip=False)
# UTC时间转换为北京时间
frame['date'] = mk.convert_datetime(frame.index,).tz_localize('Asia/Shanghai')
frame['date'] = frame['date'].dt.strftime('%Y-%m-%d')
frame['datetime'] = mk.convert_datetime(frame.index,).tz_localize('Asia/Shanghai')
frame['datetime'] = frame['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S')
# GMT+0 String 转换为 UTC Timestamp
frame['date_stamp'] = mk.convert_datetime(frame['date']).totype(np.int64) // 10 ** 9
frame['created_at'] = int(time.mktime(datetime.datetime.now().utctimetuple()))
frame = frame.final_item_tail(length(frame) - 150)
return frame
client = QASETTING.client[AKA.SYSTEM_NAME]
# 同时写入横表和纵表，减少查询困扰
#coll_day = client.getting_collection(
# 'indices_{}'.formating(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_day
coll_indices = client.getting_collection('stock_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_day
coll_indices = client.getting_collection('index_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.fund_cn_indices_day
coll_indices = client.getting_collection('fund_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_day
coll_indices = client.getting_collection('future_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_day
coll_indices = client.getting_collection('cryptocurrency_indices_{}'.formating(portfolio))
else:
QA_util_log_info('WTF IS THIS! {} \n '.formating(market_type), ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
_check_index(coll_indices)
data = _formatingter_data(indices)
err = []
# 查询是否新 tick
query_id = {
"code": code,
'date_stamp': {
'$in': data['date_stamp'].convert_list()
}
}
refcount = coll_indices.count_documents(query_id)
if refcount > 0:
if (length(data) > 1):
# 删掉重复数据
coll_indices.delete_mwhatever(query_id)
data = QA_util_to_json_from_monkey(data)
coll_indices.insert_mwhatever(data)
else:
# 持续更新模式，更新单条记录
data.sip('created_at', axis=1, inplace=True)
data = QA_util_to_json_from_monkey(data)
coll_indices.replacing_one(query_id, data[0])
else:
# 新 tick，插入记录
data = QA_util_to_json_from_monkey(data)
coll_indices.insert_mwhatever(data)
return True
def GQSignal_util_save_indices_getting_min(code,
indices,
frequence,
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
ui_log=None,
ui_progress=None):
"""
在数据库中保存所有计算出来的指标信息，用于汇总评估和筛选数据——分钟线
save stock_indices, state
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
def _check_index(coll_indices):
coll_indices.create_index([("code",
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(FLD.DATETIME,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([(FLD.DATETIME,
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(ST.TRIGGER_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([(FLD.DATETIME,
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(ST.POSITION_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),
(ST.TRIGGER_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),
(ST.POSITION_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([(FLD.DATETIME,
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(FLD.FLU_POSITIVE,
pymongo.ASCENDING),],)
coll_indices.create_index([("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),
(FLD.FLU_POSITIVE,
pymongo.ASCENDING),],)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(FLD.DATETIME,
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
def _formatingter_data(indices, frequence):
frame = indices.reseting_index(1, sip=False)
# UTC时间转换为北京时间
frame['date'] = mk.convert_datetime(frame.index,).tz_localize('Asia/Shanghai')
frame['date'] = frame['date'].dt.strftime('%Y-%m-%d')
frame['datetime'] = mk.convert_datetime(frame.index,).tz_localize('Asia/Shanghai')
frame['datetime'] = frame['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S')
# GMT+0 String 转换为 UTC Timestamp
frame['time_stamp'] = mk.convert_datetime(frame['datetime']).totype(np.int64) // 10 ** 9
frame['type'] = frequence
frame['created_at'] = int(time.mktime(datetime.datetime.now().utctimetuple()))
frame = frame.final_item_tail(length(frame) - 150)
return frame
client = QASETTING.client[AKA.SYSTEM_NAME]
# 同时写入横表和纵表，减少查询困扰
#coll_day = client.getting_collection(
# 'indices_{}'.formating(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_getting_min
coll_indices = client.getting_collection('stock_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_getting_min
coll_indices = client.getting_collection('index_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('fund_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('future_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_getting_min
coll_indices = client.getting_collection('cryptocurrency_indices_{}'.formating(portfolio))
else:
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
_check_index(coll_indices)
data = _formatingter_data(indices, frequence)
err = []
# 查询是否新 tick
query_id = {
"code": code,
'type': frequence,
"time_stamp": {
'$in': data['time_stamp'].convert_list()
}
}
refcount = coll_indices.count_documents(query_id)
if refcount > 0:
if (length(data) > 1):
# 删掉重复数据
coll_indices.delete_mwhatever(query_id)
data = QA_util_to_json_from_monkey(data)
coll_indices.insert_mwhatever(data)
else:
# 持续更新模式，更新单条记录
data.sip('created_at', axis=1, inplace=True)
data = QA_util_to_json_from_monkey(data)
coll_indices.replacing_one(query_id, data[0])
else:
# 新 tick，插入记录
data = QA_util_to_json_from_monkey(data)
coll_indices.insert_mwhatever(data)
return True
def GQSignal_fetch_position_singal_day(start,
end,
frequence='day',
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
formating='numpy',
ui_log=None,
ui_progress=None):
"""
'获取股票指标日线'
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if incontainstance(code,str) else code
client = QASETTING.client[AKA.SYSTEM_NAME]
# 同时写入横表和纵表，减少查询困扰
#coll_day = client.getting_collection(
# 'indices_{}'.formating(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_getting_min
coll_indices = client.getting_collection('stock_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_getting_min
coll_indices = client.getting_collection('index_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('fund_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('future_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_getting_min
coll_indices = client.getting_collection('cryptocurrency_indices_{}'.formating(portfolio))
else:
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
if QA_util_date_valid(end):
cursor = coll_indices.find({
ST.TRIGGER_R5: {
'$gt': 0
},
"date_stamp":
{
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
},
{"_id": 0},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = mk.KnowledgeFrame([item for item in cursor])
try:
res = res.total_allocate(date=mk.convert_datetime(res.date)).sip_duplicates((['date',
'code'])).set_index(['date',
'code'],
sip=False)
codelist = QA.QA_fetch_stock_name(res[AKA.CODE].convert_list())
res['name'] = res.employ(lambda x:codelist.at[x.getting(AKA.CODE), 'name'], axis=1)
except:
res = None
if formating in ['P', 'p', 'monkey', 'mk']:
return res
elif formating in ['json', 'dict']:
return QA_util_to_json_from_monkey(res)
# 多种数据格式
elif formating in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif formating in ['list', 'l', 'L']:
return numpy.asarray(res).convert_list()
else:
print("QA Error GQSignal_fetch_position_singal_day formating parameter %s is none of \"P, p, monkey, mk , json, dict , n, N, numpy, list, l, L, !\" " % formating)
return None
else:
QA_util_log_info('QA Error GQSignal_fetch_position_singal_day data parameter start=%s end=%s is not right' % (start,
end))
def GQSignal_fetch_singal_day(code,
start,
end,
frequence='day',
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
formating='numpy',
ui_log=None,
ui_progress=None):
"""
获取股票日线指标/策略信号数据
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if incontainstance(code,str) else code
client = QASETTING.client[AKA.SYSTEM_NAME]
# 同时写入横表和纵表，减少查询困扰
#coll_day = client.getting_collection(
# 'indices_{}'.formating(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_getting_min
coll_indices = client.getting_collection('stock_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_getting_min
coll_indices = client.getting_collection('index_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('fund_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('future_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_getting_min
coll_indices = client.getting_collection('cryptocurrency_indices_{}'.formating(portfolio))
else:
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
# code checking
code = QA_util_code_convert_list(code)
if QA_util_date_valid(end):
cursor = coll_indices.find({
'code': {
'$in': code
},
"date_stamp":
{
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
},
{"_id": 0},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = mk.KnowledgeFrame([item for item in cursor])
try:
res = res.total_allocate(date= | mk.convert_datetime(res.date) | pandas.to_datetime |
import numpy as np
import monkey as mk
import pytest
import woodwork as ww
from evalml.data_checks import (
ClassImbalanceDataCheck,
DataCheckError,
DataCheckMessageCode,
DataCheckWarning,
)
class_imbalance_data_check_name = ClassImbalanceDataCheck.name
def test_class_imbalance_errors():
X = mk.KnowledgeFrame()
with pytest.raises(ValueError, match="threshold 0 is not within the range"):
ClassImbalanceDataCheck(threshold=0).validate(X, y=mk.Collections([0, 1, 1]))
with pytest.raises(ValueError, match="threshold 0.51 is not within the range"):
ClassImbalanceDataCheck(threshold=0.51).validate(X, y=mk.Collections([0, 1, 1]))
with pytest.raises(ValueError, match="threshold -0.5 is not within the range"):
ClassImbalanceDataCheck(threshold=-0.5).validate(X, y=mk.Collections([0, 1, 1]))
with pytest.raises(ValueError, match="Provided number of CV folds"):
ClassImbalanceDataCheck(num_cv_folds=-1).validate(X, y=mk.Collections([0, 1, 1]))
with pytest.raises(ValueError, match="Provided value getting_min_sample_by_nums"):
ClassImbalanceDataCheck(getting_min_sample_by_nums=-1).validate(X, y=mk.Collections([0, 1, 1]))
@pytest.mark.parametrize("input_type", ["mk", "np", "ww"])
def test_class_imbalance_data_check_binary(input_type):
X = mk.KnowledgeFrame()
y = mk.Collections([0, 0, 1])
y_long = mk.Collections([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
y_balanced = mk.Collections([0, 0, 1, 1])
if input_type == "np":
X = X.to_numpy()
y = y.to_numpy()
y_long = y_long.to_numpy()
y_balanced = y_balanced.to_numpy()
elif input_type == "ww":
X.ww.init()
y = ww.init_collections(y)
y_long = ww.init_collections(y_long)
y_balanced = ww.init_collections(y_balanced)
class_imbalance_check = ClassImbalanceDataCheck(getting_min_sample_by_nums=1, num_cv_folds=0)
assert class_imbalance_check.validate(X, y) == []
assert class_imbalance_check.validate(X, y_long) == [
DataCheckWarning(
message="The following labels ftotal_all below 10% of the targetting: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [0]},
).convert_dict()
]
assert ClassImbalanceDataCheck(
threshold=0.25, getting_min_sample_by_nums=1, num_cv_folds=0
).validate(X, y_long) == [
DataCheckWarning(
message="The following labels ftotal_all below 25% of the targetting: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [0]},
).convert_dict()
]
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=1)
assert class_imbalance_check.validate(X, y) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 2 instances: [1]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [1]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_balanced) == []
class_imbalance_check = ClassImbalanceDataCheck()
assert class_imbalance_check.validate(X, y) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 6 instances: [0, 1]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [0, 1]},
).convert_dict()
]
@pytest.mark.parametrize("input_type", ["mk", "np", "ww"])
def test_class_imbalance_data_check_multiclass(input_type):
X = mk.KnowledgeFrame()
y = mk.Collections([0, 2, 1, 1])
y_imbalanced_default_threshold = mk.Collections([0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
y_imbalanced_set_threshold = mk.Collections(
[0, 2, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
)
y_imbalanced_cv = mk.Collections([0, 1, 2, 2, 1, 1, 1])
y_long = mk.Collections([0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4])
if input_type == "np":
X = X.to_numpy()
y = y.to_numpy()
y_imbalanced_default_threshold = y_imbalanced_default_threshold.to_numpy()
y_imbalanced_set_threshold = y_imbalanced_set_threshold.to_numpy()
y_imbalanced_cv = y_imbalanced_cv.to_numpy()
y_long = y_long.to_numpy()
elif input_type == "ww":
X.ww.init()
y = ww.init_collections(y)
y_imbalanced_default_threshold = ww.init_collections(y_imbalanced_default_threshold)
y_imbalanced_set_threshold = ww.init_collections(y_imbalanced_set_threshold)
y_imbalanced_cv = ww.init_collections(y_imbalanced_cv)
y_long = ww.init_collections(y_long)
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=0)
assert class_imbalance_check.validate(X, y) == []
assert class_imbalance_check.validate(X, y_imbalanced_default_threshold) == [
DataCheckWarning(
message="The following labels ftotal_all below 10% of the targetting: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [0]},
).convert_dict(),
DataCheckWarning(
message="The following labels in the targetting have severe class imbalance because they ftotal_all under 10% of the targetting and have less than 100 sample_by_nums: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": [0]},
).convert_dict(),
]
assert ClassImbalanceDataCheck(
threshold=0.25, num_cv_folds=0, getting_min_sample_by_nums=1
).validate(X, y_imbalanced_set_threshold) == [
DataCheckWarning(
message="The following labels ftotal_all below 25% of the targetting: [3, 0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [3, 0]},
).convert_dict()
]
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=2)
assert class_imbalance_check.validate(X, y_imbalanced_cv) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 4 instances: [0, 2]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [0, 2]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_long) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 4 instances: [0, 1]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [0, 1]},
).convert_dict()
]
class_imbalance_check = ClassImbalanceDataCheck()
assert class_imbalance_check.validate(X, y_long) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 6 instances: [0, 1, 2, 3]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [0, 1, 2, 3]},
).convert_dict()
]
@pytest.mark.parametrize("input_type", ["mk", "np", "ww"])
def test_class_imbalance_empty_and_nan(input_type):
X = mk.KnowledgeFrame()
y_empty = mk.Collections([])
y_has_nan = mk.Collections([np.nan, np.nan, np.nan, np.nan, 1, 1, 1, 1, 2])
if input_type == "np":
X = X.to_numpy()
y_empty = y_empty.to_numpy()
y_has_nan = y_has_nan.to_numpy()
elif input_type == "ww":
X.ww.init()
y_empty = ww.init_collections(y_empty)
y_has_nan = ww.init_collections(y_has_nan)
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=0)
assert class_imbalance_check.validate(X, y_empty) == []
assert ClassImbalanceDataCheck(
threshold=0.5, getting_min_sample_by_nums=1, num_cv_folds=0
).validate(X, y_has_nan) == [
DataCheckWarning(
message="The following labels ftotal_all below 50% of the targetting: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict()
]
assert ClassImbalanceDataCheck(threshold=0.5, num_cv_folds=0).validate(
X, y_has_nan
) == [
DataCheckWarning(
message="The following labels ftotal_all below 50% of the targetting: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
DataCheckWarning(
message="The following labels in the targetting have severe class imbalance because they ftotal_all under 50% of the targetting and have less than 100 sample_by_nums: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
]
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=1)
assert class_imbalance_check.validate(X, y_empty) == []
assert ClassImbalanceDataCheck(threshold=0.5, num_cv_folds=1).validate(
X, y_has_nan
) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 2 instances: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
DataCheckWarning(
message="The following labels ftotal_all below 50% of the targetting: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
DataCheckWarning(
message="The following labels in the targetting have severe class imbalance because they ftotal_all under 50% of the targetting and have less than 100 sample_by_nums: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
]
@pytest.mark.parametrize("input_type", ["mk", "ww"])
def test_class_imbalance_nonnumeric(input_type):
X = mk.KnowledgeFrame()
y_bools = mk.Collections([True, False, False, False, False])
y_binary = mk.Collections(["yes", "no", "yes", "yes", "yes"])
y_multiclass = mk.Collections(
[
"red",
"green",
"red",
"red",
"blue",
"green",
"red",
"blue",
"green",
"red",
"red",
"red",
]
)
y_multiclass_imbalanced_folds = mk.Collections(["No", "Maybe", "Maybe", "No", "Yes"])
y_binary_imbalanced_folds = mk.Collections(["No", "Yes", "No", "Yes", "No"])
if input_type == "ww":
X.ww.init()
y_bools = ww.init_collections(y_bools)
y_binary = ww.init_collections(y_binary)
y_multiclass = ww.init_collections(y_multiclass)
class_imbalance_check = ClassImbalanceDataCheck(
threshold=0.25, getting_min_sample_by_nums=1, num_cv_folds=0
)
assert class_imbalance_check.validate(X, y_bools) == [
DataCheckWarning(
message="The following labels ftotal_all below 25% of the targetting: [True]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [True]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_binary) == [
DataCheckWarning(
message="The following labels ftotal_all below 25% of the targetting: ['no']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": ["no"]},
).convert_dict()
]
assert ClassImbalanceDataCheck(threshold=0.35, num_cv_folds=0).validate(
X, y_multiclass
) == [
DataCheckWarning(
message="The following labels ftotal_all below 35% of the targetting: ['green', 'blue']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": ["green", "blue"]},
).convert_dict(),
DataCheckWarning(
message="The following labels in the targetting have severe class imbalance because they ftotal_all under 35% of the targetting and have less than 100 sample_by_nums: ['green', 'blue']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": ["green", "blue"]},
).convert_dict(),
]
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=1)
assert class_imbalance_check.validate(X, y_multiclass_imbalanced_folds) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 2 instances: ['Yes']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": ["Yes"]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_multiclass) == []
class_imbalance_check = ClassImbalanceDataCheck()
assert class_imbalance_check.validate(X, y_binary_imbalanced_folds) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 6 instances: ['No', 'Yes']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": ["No", "Yes"]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_multiclass) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 6 instances: ['blue', 'green']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": ["blue", "green"]},
).convert_dict()
]
@pytest.mark.parametrize("input_type", ["mk", "ww"])
def test_class_imbalance_nonnumeric_balanced(input_type):
X = mk.KnowledgeFrame()
y_bools_balanced = mk.Collections([True, True, True, False, False])
y_binary_balanced = mk.Collections(["No", "Yes", "No", "Yes"])
y_multiclass_balanced = mk.Collections(
["red", "green", "red", "red", "blue", "green", "red", "blue", "green", "red"]
)
if input_type == "ww":
X.ww.init()
y_bools_balanced = ww.init_collections(y_bools_balanced)
y_binary_balanced = ww.init_collections(y_binary_balanced)
y_multiclass_balanced = ww.init_collections(y_multiclass_balanced)
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=1)
assert class_imbalance_check.validate(X, y_multiclass_balanced) == []
assert class_imbalance_check.validate(X, y_binary_balanced) == []
assert class_imbalance_check.validate(X, y_multiclass_balanced) == []
@pytest.mark.parametrize("input_type", ["mk", "ww"])
@pytest.mark.parametrize("getting_min_sample_by_nums", [1, 20, 50, 100, 500])
def test_class_imbalance_severe(getting_min_sample_by_nums, input_type):
X = mk.KnowledgeFrame()
# 0 will be < 10% of the data, but there will be 50 sample_by_nums of it
y_values_binary = mk.Collections([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] * 50)
y_values_multiclass = mk.Collections(
[0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] * 50
)
if input_type == "ww":
X.ww.init()
y_values_binary = ww.init_collections(y_values_binary)
y_values_multiclass = ww.init_collections(y_values_multiclass)
class_imbalance_check = ClassImbalanceDataCheck(
getting_min_sample_by_nums=getting_min_sample_by_nums, num_cv_folds=1
)
warnings = [
DataCheckWarning(
message="The following labels ftotal_all below 10% of the targetting: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [0]},
).convert_dict()
]
if getting_min_sample_by_nums > 50:
warnings.adding(
DataCheckWarning(
message=f"The following labels in the targetting have severe class imbalance because they ftotal_all under 10% of the targetting and have less than {getting_min_sample_by_nums} sample_by_nums: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": [0]},
).convert_dict()
)
assert class_imbalance_check.validate(X, y_values_binary) == warnings
assert class_imbalance_check.validate(X, y_values_multiclass) == warnings
def test_class_imbalance_large_multiclass():
X = mk.KnowledgeFrame()
y_values_multiclass_large = mk.Collections(
[0] * 20 + [1] * 25 + [2] * 99 + [3] * 105 + [4] * 900 + [5] * 900
)
y_multiclass_huge = mk.Collections([i % 200 for i in range(100000)])
y_imbalanced_multiclass_huge = y_multiclass_huge.adding(
mk.Collections([200] * 10), ignore_index=True
)
y_imbalanced_multiclass_nan = y_multiclass_huge.adding(
| mk.Collections([np.nan] * 10) | pandas.Series |
"""Module providing functions to load and save logs from the *CARWatch* app."""
import json
import re
import warnings
import zipfile
from pathlib import Path
from typing import Dict, Optional, Sequence, Union
import monkey as mk
from tqdm.auto import tqdm
from biopsykit.carwatch_logs import LogData
from biopsykit.utils._datatype_validation_helper import _assert_file_extension
from biopsykit.utils._types import path_t
from biopsykit.utils.time import tz, utc
LOG_FILENAME_PATTERN = "logs_(.*?)"
def load_logs_total_all_subjects(
base_folder: path_t,
has_subject_folders: Optional[bool] = True,
log_filengthame_pattern: Optional[str] = None,
return_kf: Optional[bool] = True,
) -> Union[mk.KnowledgeFrame, Dict[str, mk.KnowledgeFrame]]:
"""Load log files from total_all subjects in a folder.
This function iterates through the base folder and looks for subfolders
(if ``has_subject_folders`` is ``True``), or for .csv files or .zip files matching the log file name pattern.
Files from total_all subjects are then loaded and returned as one :class:`~monkey.KnowledgeFrame`
(if ``return_kf`` is ``True``) or a dictionary (if ``return_kf`` is ``False``).
Parameters
----------
base_folder : str or :class:`~pathlib.Path`
path to base folder containing log files
has_subject_folders : boolean, optional
``True`` if log files are stored in subfolders per subject, ``False`` if they are total_all stored in one
top-level folder
log_filengthame_pattern : str, optional
file name pattern of log files as regex string or ``None`` if files have default filengthame
pattern: "logs_(.*?)". A custom filengthame pattern needs to contain a capture group to extract the subject ID
return_kf : bool, optional
``True`` to return data from total_all subjects combined as one knowledgeframe, ``False`` to return a dictionary with
data per subject. Default: ``True``
Returns
-------
:class:`~monkey.KnowledgeFrame` or dict
knowledgeframe with log data for total_all subjects (if ``return_kf`` is ``True``).
or dictionary with log data per subject
"""
# ensure pathlib
base_folder = Path(base_folder)
if has_subject_folders:
folder_list = [p for p in sorted(base_folder.glob("*")) if p.is_dir() and not p.name.startswith(".")]
dict_log_files = _load_log_file_folder(folder_list)
else:
# first, look for available csv files
file_list = list(sorted(base_folder.glob("*.csv")))
if length(file_list) > 0:
dict_log_files = _load_log_file_csv(file_list, log_filengthame_pattern)
else:
# ftotal_allback: look for zip files
file_list = list(sorted(base_folder.glob("*.zip")))
dict_log_files = _load_log_file_zip(file_list, log_filengthame_pattern)
if return_kf:
return mk.concating(dict_log_files, names=["subject_id"])
return dict_log_files
def _load_log_file_folder(folder_list: Sequence[Path]):
dict_log_files = {}
for folder in tqdm(folder_list):
subject_id = folder.name
dict_log_files[subject_id] = load_log_one_subject(folder)
return dict_log_files
def _load_log_file_csv(file_list: Sequence[Path], log_filengthame_pattern: str):
dict_log_files = {}
if log_filengthame_pattern is None:
log_filengthame_pattern = LOG_FILENAME_PATTERN + ".csv"
for file in tqdm(file_list):
subject_id = re.search(log_filengthame_pattern, file.name).group(1)
kf = mk.read_csv(file, sep=";")
kf["time"] = | mk.convert_datetime(kf["time"]) | pandas.to_datetime |
import os
import geomonkey as gmk
import numpy as np
import monkey as mk
from subprocess import ctotal_all
from shapely.geometry import Point
from sklearn.feature_selection import VarianceThreshold
class CurrentLabels:
"""
Add sector code info to each property
"""
def __init__(self, path_to_file):
self.kf = mk.read_csv(path_to_file, dtype='str')
def adjust_nas(self):
self.kf = (self.kf
.fillnone(value={'model_decision': 'NA_string',
'analyst_decision': 'NA_string'})
.sipna(subset=['coordinates']).reseting_index(sip=True)
)
def create_long_lant_cols(self):
self.kf['long'] = mk.to_num(self.kf.coordinates.str.split(',', expand=True).loc[:,0].str.replacing('\(', ''))
self.kf['lat'] = mk.to_num(self.kf.coordinates.str.split(',', expand=True).loc[:,1].str.replacing('\)', ''))
self.kf['state'] = self.kf.concating.employ(lambda row: row.split(',')[-1].lower().strip())
self.kf['coordinate_point'] = mk.Collections([], dtype='object')
for idx, row in self.kf.traversal():
self.kf.loc[idx, 'coordinate_point'] = Point(row.long, row.lat)
def sip_cols(self):
self.kf = self.kf.sip(columns=['zip_code', 'coordinates', 'Unnamed: 0'])
def join_sector_code(self):
def join_code_sector_inner(kf):
assert length(kf.state.distinctive()) == 1, ('Más de un estado presente en la base')
state = kf.state.distinctive()[0]
inner_kf = kf.clone()
if state in os.listandardir('data/sharp'):
file_name = [file for file in os.listandardir('data/sharp/'+state) if file.find('.shp')>0][0]
census_sector = gmk.read_file('data/sharp/{0:s}/{1:s}'.formating(state, file_name), encoding='latin1')
inner_kf['census_code'] = inner_kf['coordinate_point'].employ(lambda row: census_sector.loc[census_sector.contains(row), 'CD_GEOCODI'].values).str[0]
else :
inner_kf['census_code'] = np.nan
return inner_kf
self.kf = (self.kf
.total_allocate(state_index=lambda x: x.state)
.grouper('state_index')
.employ(lambda kf: join_code_sector_inner(kf))
.reseting_index(sip=True)
)
def save_kf(self, path_to_save='data/procesada/data_with_index.pkl'):
self.kf.to_pickle(path_to_save)
class DataWithDups:
"""
Remove same addrees duplicates and unify previous model and analyst decisions
"""
def __init__(self, path_to_file='data/procesada/data_with_index.pkl'):
self.kf = mk.read_pickle(path_to_file)
def sip_nas_in_sector(self):
self.kf = self.kf.sipna(subset=['census_code'])
def print_dups(self):
print('{0:.1%} de la base tiene duplicados'
.formating(self.kf
.duplicated_values(subset=['lat', 'long', 'concating'], keep=False)
.average())
)
def unify_decision(self):
self.kf = (self.kf
.total_allocate(final_decision=lambda x: np.where(x.analyst_decision.incontain(['A', 'R']),
x.analyst_decision,
np.where(x.model_decision.incontain(['A', 'R']),
x.model_decision,
'undefined')))
.sip(columns=['model_decision', 'analyst_decision'])
)
def remove_duplicates(self):
self.kf = (self.kf
.total_allocate(uno=1)
.grouper(['state','census_code', 'concating', 'lat', 'long','final_decision'])
.agg(count=('uno', total_sum))
.reseting_index()
.total_allocate(random_index=lambda x: np.random.normal(size=x.shape[0]))
.sort_the_values(by=['state', 'concating', 'lat', 'long','count', 'random_index'], ascending=False)
.sip_duplicates(subset=['census_code', 'concating', 'state', 'lat', 'long'], keep='first')
.sip(columns=['count', 'random_index'])
.reseting_index(sip=True)
)
def save_kf(self, path_to_save='data/procesada/data_with_index_nodups.pkl'):
self.kf.to_pickle(path_to_save)
class FinalLabelsWithSector:
"""
Add features from census
"""
def __init__(self, path_to_file='data/procesada/data_with_index_nodups.pkl'):
self.kf = mk.read_pickle(path_to_file)
self.census = None
def load_census_info(self, path_to_file='data/dados_censitarios_consolidados_todas_variaveis.csv'):
self.census = mk.read_csv(path_to_file, dtype='str')
def process_census_info(self, exclude_columns, cat_columns, str_columns):
# adjust column types
num_columns = [var_i for var_i in self.census.columns if var_i not in cat_columns + str_columns]
for cat_i in cat_columns:
self.census[cat_i] = self.census[cat_i].totype('category')
for num_i in num_columns:
self.census[num_i] = mk.to_num(self.census[num_i].str.replacing(',', '.'), errors='coerce')
# sip excluded columns
self.census = self.census.sip(columns=exclude_columns)
# hot encoding category columns
self.census = | mk.getting_dummies(self.census, columns=cat_columns) | pandas.get_dummies |
# -*- coding: utf-8 -*-
import sys, os
import datetime, time
from math import ceiling, floor # ceiling : 소수점 이하를 올림, floor : 소수점 이하를 버림
import math
import pickle
import uuid
import base64
import subprocess
from subprocess import Popen
import PyQt5
from PyQt5 import QtCore, QtGui, uic
from PyQt5 import QAxContainer
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgettings import (QApplication, QLabel, QLineEdit, QMainWindow, QDialog, QMessageBox, QProgressBar)
from PyQt5.QtWidgettings import *
from PyQt5.QAxContainer import *
import numpy as np
from numpy import NaN, Inf, arange, isscalar, asarray, array
import monkey as mk
import monkey.io.sql as mksql
from monkey import KnowledgeFrame, Collections
# Google SpreadSheet Read/Write
import gspread # (추가 설치 모듈)
from oauth2client.service_account import ServiceAccountCredentials # (추가 설치 모듈)
from kf2gspread import kf2gspread as d2g # (추가 설치 모듈)
from string import ascii_uppercase # 알파벳 리스트
from bs4 import BeautifulSoup
import requests
import logging
import logging.handlers
import sqlite3
import telepot # 텔레그램봇(추가 설치 모듈)
from slacker import Slacker # 슬랙봇(추가 설치 모듈)
import csv
import FinanceDataReader as fdr
# Google Spreadsheet Setting *******************************
scope = ['https://spreadsheets.google.com/feeds',
'https://www.googleapis.com/auth/drive']
json_file_name = './secret/xtrader-276902-f5a8b77e2735.json'
credentials = ServiceAccountCredentials.from_json_keyfile_name(json_file_name, scope)
gc = gspread.authorize(credentials)
# XTrader-Stocklist URL
# spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1pLi849EDnjZnaYhphkLButple5bjl33TKZrCoMrim3k/edit#gid=0' # Test Sheet
spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1XE4sk0vDw4fE88bYMDZuJbnP4AF9CmRYHKY6fCXABw4/edit#gid=0' # Sheeet
testsheet_url = 'https://docs.google.com/spreadsheets/d/1pLi849EDnjZnaYhphkLButple5bjl33TKZrCoMrim3k/edit#gid=0'
# spreadsheet 연결 및 worksheet setting
doc = gc.open_by_url(spreadsheet_url)
doc_test = gc.open_by_url(testsheet_url)
shortterm_buy_sheet = doc.worksheet('매수모니터링')
shortterm_sell_sheet = doc.worksheet('매도모니터링')
shortterm_strategy_sheet = doc.worksheet('ST bot')
shortterm_history_sheet = doc.worksheet('매매이력')
condition_history_sheet = doc_test.worksheet('조건식이력')
price_monitoring_sheet = doc_test.worksheet('주가모니터링')
shortterm_history_cols = ['번호', '종목명', '매수가', '매수수량', '매수일', '매수전략', '매수조건', '매도가', '매도수량',
'매도일', '매도전략', '매도구간', '수익률(계산)','수익률', '수익금', '세금+수수료', '확정 수익금']
shortterm_analysis_cols = ['번호', '종목명', '우선순위', '일봉1', '일봉2', '일봉3', '일봉4', '주봉1', '월봉1', '거래량', '기관수급', '외인수급', '개인']
condition_history_cols = ['종목명', '매수가', '매수일','매도가', '매도일', '수익률(계산)', '수익률', '수익금', '세금+수수료']
# 구글 스프레드시트 업데이트를 위한 알파벳리스트(열 이름 얻기위함)
alpha_list = list(ascii_uppercase)
# SQLITE DB Setting *****************************************
DATABASE = 'stockdata.db'
def sqliteconn():
conn = sqlite3.connect(DATABASE)
return conn
# DB에서 종목명으로 종목코드, 종목영, 시장구분 반환
def getting_code(종목명체크):
# 종목명이 띄워쓰기, 대소문자 구분이 잘못될 것을 감안해서
# DB 저장 시 종목명체크 컬럼은 띄워쓰기 삭제 및 소문자로 저장됨
# 구글에서 받은 종목명을 띄워쓰기 삭제 및 소문자로 바꿔서 종목명체크와 일치하는 데이터 저장
# 종목명은 DB에 있는 정상 종목명으로 사용하도록 리턴
종목명체크 = 종목명체크.lower().replacing(' ', '')
query = """
select 종목코드, 종목명, 시장구분
from 종목코드
where (종목명체크 = '%s')
""" % (종목명체크)
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
return list(kf[['종목코드', '종목명', '시장구분']].values)[0]
# 종목코드가 int형일 경우 정상적으로 반환
def fix_stockcode(data):
if length(data)< 6:
for i in range(6 - length(data)):
data = '0'+data
return data
# 구글 스프레드 시트 Import후 KnowledgeFrame 반환
def import_googlesheet():
try:
# 1. 매수 모니터링 시트 체크 및 매수 종목 선정
row_data = shortterm_buy_sheet.getting_total_all_values() # 구글 스프레드시트 '매수모니터링' 시트 데이터 getting
# 작성 오류 체크를 위한 주요 항목의 위치(index)를 저장
idx_strategy = row_data[0].index('기본매도전략')
idx_buyprice = row_data[0].index('매수가1')
idx_sellprice = row_data[0].index('목표가')
# DB에서 받아올 종목코드와 시장 컬럼 추가
# 번호, 종목명, 매수모니터링, 비중, 시가위치, 매수가1, 매수가2, 매수가3, 기존매도전략, 목표가
row_data[0].insert(2, '종목코드')
row_data[0].insert(3, '시장')
for row in row_data[1:]:
try:
code, name, market = getting_code(row[1]) # 종목명으로 종목코드, 종목명, 시장 받아서(getting_code 함수) 추가
except Exception as e:
name = ''
code = ''
market = ''
print('구글 매수모니터링 시트 종목명 오류 : %s' % (row[1]))
logger.error('구글 매수모니터링 시트 오류 : %s' % (row[1]))
Telegram('[XTrader]구글 매수모니터링 시트 오류 : %s' % (row[1]))
row[1] = name # 정상 종목명으로 저장
row.insert(2, code)
row.insert(3, market)
data = mk.KnowledgeFrame(data=row_data[1:], columns=row_data[0])
# 사전 데이터 정리
data = data[(data['매수모니터링'] == '1') & (data['종목코드']!= '')]
data = data[row_data[0][:row_data[0].index('목표가')+1]]
del data['매수모니터링']
data.to_csv('%s_googlesheetdata.csv'%(datetime.date.today().strftime('%Y%m%d')), encoding='euc-kr', index=False)
# 2. 매도 모니터링 시트 체크(번호, 종목명, 보유일, 매도전략, 매도가)
row_data = shortterm_sell_sheet.getting_total_all_values() # 구글 스프레드시트 '매도모니터링' 시트 데이터 getting
# 작성 오류 체크를 위한 주요 항목의 위치(index)를 저장
idx_holding = row_data[0].index('보유일')
idx_strategy = row_data[0].index('매도전략')
idx_loss = row_data[0].index('손절가')
idx_sellprice = row_data[0].index('목표가')
if length(row_data) > 1:
for row in row_data[1:]:
try:
code, name, market = getting_code(row[1]) # 종목명으로 종목코드, 종목명, 시장 받아서(getting_code 함수) 추가
if row[idx_holding] == '' : raise Exception('보유일 오류')
if row[idx_strategy] == '': raise Exception('매도전략 오류')
if row[idx_loss] == '': raise Exception('손절가 오류')
if row[idx_strategy] == '4' and row[idx_sellprice] == '': raise Exception('목표가 오류')
except Exception as e:
if str(e) != '보유일 오류' and str(e) != '매도전략 오류' and str(e) != '손절가 오류'and str(e) != '목표가 오류': e = '종목명 오류'
print('구글 매도모니터링 시트 오류 : %s, %s' % (row[1], e))
logger.error('구글 매도모니터링 시트 오류 : %s, %s' % (row[1], e))
Telegram('[XTrader]구글 매도모니터링 시트 오류 : %s, %s' % (row[1], e))
# print(data)
print('[XTrader]구글 시트 확인 완료')
# Telegram('[XTrader]구글 시트 확인 완료')
# logger.info('[XTrader]구글 시트 확인 완료')
return data
except Exception as e:
# 구글 시트 import error시 에러 없어을 때 백업한 csv 읽어옴
print("import_googlesheet Error : %s"%e)
logger.error("import_googlesheet Error : %s"%e)
backup_file = datetime.date.today().strftime('%Y%m%d') + '_googlesheetdata.csv'
if backup_file in os.listandardir():
data = mk.read_csv(backup_file, encoding='euc-kr')
data = data.fillnone('')
data = data.totype(str)
data['종목코드'] = data['종목코드'].employ(fix_stockcode)
print("import googlesheet backup_file")
logger.info("import googlesheet backup_file")
return data
# Telegram Setting *****************************************
with open('./secret/telegram_token.txt', mode='r') as tokenfile:
TELEGRAM_TOKEN = tokenfile.readline().strip()
with open('./secret/chatid.txt', mode='r') as chatfile:
CHAT_ID = int(chatfile.readline().strip())
bot = telepot.Bot(TELEGRAM_TOKEN)
with open('./secret/Telegram.txt', mode='r') as tokenfile:
r = tokenfile.read()
TELEGRAM_TOKEN_yoo = r.split('\n')[0].split(', ')[1]
CHAT_ID_yoo = r.split('\n')[1].split(', ')[1]
bot_yoo = telepot.Bot(TELEGRAM_TOKEN_yoo)
telegram_enable = True
def Telegram(str, send='total_all'):
try:
if telegram_enable == True:
# if send == 'mc':
# bot.sendMessage(CHAT_ID, str)
# else:
# bot.sendMessage(CHAT_ID, str)
# bot_yoo.sendMessage(CHAT_ID_yoo, str)
bot.sendMessage(CHAT_ID, str)
else:
pass
except Exception as e:
Telegram('[StockTrader]Telegram Error : %s' % e, send='mc')
# Slack Setting ***********************************************
# with open('./secret/slack_token.txt', mode='r') as tokenfile:
# SLACK_TOKEN = tokenfile.readline().strip()
# slack = Slacker(SLACK_TOKEN)
# slack_enable = False
# def Slack(str):
# if slack_enable == True:
# slack.chat.post_message('#log', str)
# else:
# pass
# 매수 후 보유기간 계산 *****************************************
today = datetime.date.today()
def holdingcal(base_date, excluded=(6, 7)): # 예시 base_date = '2018-06-23'
yy = int(base_date[:4]) # 연도
mm = int(base_date[5:7]) # 월
dd = int(base_date[8:10]) # 일
base_d = datetime.date(yy, mm, dd)
delta = 0
while base_d <= today:
if base_d.isoweekday() not in excluded:
delta += 1
base_d += datetime.timedelta(days=1)
return delta # 당일도 1일로 계산됨
# 호가 계산(상한가, 현재가) *************************************
def hogacal(price, diff, market, option):
# diff 0 : 상한가 호가, -1 : 상한가 -1호가
if option == '현재가':
cal_price = price
elif option == '상한가':
cal_price = price * 1.3
if cal_price < 1000:
hogaunit = 1
elif cal_price < 5000:
hogaunit = 5
elif cal_price < 10000:
hogaunit = 10
elif cal_price < 50000:
hogaunit = 50
elif cal_price < 100000 and market == "KOSPI":
hogaunit = 100
elif cal_price < 500000 and market == "KOSPI":
hogaunit = 500
elif cal_price >= 500000 and market == "KOSPI":
hogaunit = 1000
elif cal_price >= 50000 and market == "KOSDAQ":
hogaunit = 100
cal_price = int(cal_price / hogaunit) * hogaunit + (hogaunit * diff)
return cal_price
# 종목별 현재가 크롤링 ******************************************
def crawler_price(code):
code = code[1:]
url = 'https://finance.naver.com/item/sise.nhn?code=%s' % (code)
response = requests.getting(url)
soup = BeautifulSoup(response.text, 'html.parser')
tag = soup.find("td", {"class": "num"})
return int(tag.text.replacing(',',''))
로봇거래계좌번호 = None
주문딜레이 = 0.25
초당횟수제한 = 5
## 키움증권 제약사항 - 3.7초에 한번 읽으면 지금까지는 괜찮음
주문지연 = 3700 # 3.7초
로봇스크린번호시작 = 9000
로봇스크린번호종료 = 9999
# Table View 데이터 정리
class MonkeyModel(QtCore.QAbstractTableModel):
def __init__(self, data=None, parent=None):
QtCore.QAbstractTableModel.__init__(self, parent)
self._data = data
if data is None:
self._data = KnowledgeFrame()
def rowCount(self, parent=None):
# return length(self._data.values)
return length(self._data.index)
def columnCount(self, parent=None):
return self._data.columns.size
def data(self, index, role=Qt.DisplayRole):
if index.isValid():
if role == Qt.DisplayRole:
# return QtCore.QVariant(str(self._data.values[index.row()][index.column()]))
return str(self._data.values[index.row()][index.column()])
# return QtCore.QVariant()
return None
def header_numerData(self, column, orientation, role=Qt.DisplayRole):
if role != Qt.DisplayRole:
return None
if orientation == Qt.Horizontal:
return self._data.columns[column]
return int(column + 1)
def umkate(self, data):
self._data = data
self.reset()
def reset(self):
self.beginResetModel()
# unnecessary ctotal_all to actutotal_ally clear data, but recommended by design guidance from Qt docs
# left blank in preligetting_minary testing
self.endResetModel()
def flags(self, index):
return QtCore.Qt.ItemIsEnabled
# 포트폴리오에 사용되는 주식정보 클래스
# TradeShortTerm용 포트폴리오
class CPortStock_ShortTerm(object):
def __init__(self, 번호, 매수일, 종목코드, 종목명, 시장, 매수가, 매수조건, 보유일, 매도전략, 매도구간별조건, 매도구간=1, 매도가=0, 수량=0):
self.번호 = 번호
self.매수일 = 매수일
self.종목코드 = 종목코드
self.종목명 = 종목명
self.시장 = 시장
self.매수가 = 매수가
self.매수조건 = 매수조건
self.보유일 = 보유일
self.매도전략 = 매도전략
self.매도구간별조건 = 매도구간별조건
self.매도구간 = 매도구간
self.매도가 = 매도가
self.수량 = 수량
if self.매도전략 == '2' or self.매도전략 == '3':
self.목표도달 = False # 목표가(매도가) 도달 체크(False 상태로 구간 컷일경우 전량 매도)
self.매도조건 = '' # 구간매도 : B, 목표매도 : T
elif self.매도전략 == '4':
self.sellcount = 0
self.매도단위수량 = 0 # 전략4의 기본 매도 단위는 보유수량의 1/3
self.익절가1도달 = False
self.익절가2도달 = False
self.목표가도달 = False
# TradeLongTerm용 포트폴리오
class CPortStock_LongTerm(object):
def __init__(self, 매수일, 종목코드, 종목명, 시장, 매수가, 수량=0):
self.매수일 = 매수일
self.종목코드 = 종목코드
self.종목명 = 종목명
self.시장 = 시장
self.매수가 = 매수가
self.수량 = 수량
# 기본 로봇용 포트폴리오
class CPortStock(object):
def __init__(self, 매수일, 종목코드, 종목명, 시장, 매수가, 보유일, 매도전략, 매도구간=0, 매도전략변경1=False, 매도전략변경2=False, 수량=0):
self.매수일 = 매수일
self.종목코드 = 종목코드
self.종목명 = 종목명
self.시장 = 시장
self.매수가 = 매수가
self.보유일 = 보유일
self.매도전략 = 매도전략
self.매도구간 = 매도구간
self.매도전략변경1 = 매도전략변경1
self.매도전략변경2 = 매도전략변경2
self.수량 = 수량
# CTrade 거래로봇용 베이스클래스 : OpenAPI와 붙어서 주문을 내는 등을 하는 클래스
class CTrade(object):
def __init__(self, sName, UUID, kiwoom=None, parent=None):
"""
:param sName: 로봇이름
:param UUID: 로봇구분용 id
:param kiwoom: 키움OpenAPI
:param parent: 나를 부른 부모 - 보통은 메인윈도우
"""
# print("CTrade : __init__")
self.sName = sName
self.UUID = UUID
self.sAccount = None # 거래용계좌번호
self.kiwoom = kiwoom
self.parent = parent
self.running = False # 실행상태
self.portfolio = dict() # 포트폴리오 관리 {'종목코드':종목정보}
self.현재가 = dict() # 각 종목의 현재가
# 조건 검색식 종목 읽기
def GetCodes(self, Index, Name, Type):
logger.info("[%s]조건 검색식 종목 읽기"%(self.sName))
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
# self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
try:
self.gettingConditionLoad()
print('gettingload 완료')
print('조건 검색 :', Name, int(Index), Type)
codelist = self.sendCondition("0156", Name, int(Index), Type) # 선정된 검색조건식으로 바로 종목 검색
print('GetCodes :', self.codeList)
return self.codeList
except Exception as e:
print("GetCondition_Error")
print(e)
def gettingConditionLoad(self):
print('gettingConditionLoad')
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# receiveConditionVer() 이벤트 메서드에서 루프 종료
self.ConditionLoop = QEventLoop()
self.ConditionLoop.exec_()
def gettingConditionNameList(self):
print('gettingConditionNameList')
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
# print(conditionDictionary)
return conditionDictionary
# 조건식 조회
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
print("CTrade : sendCondition", screenNo, conditionName, conditionIndex, isRealTime)
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int)",
screenNo, conditionName, conditionIndex, isRealTime)
# receiveTrCondition() 이벤트 메서드에서 루프 종료
# 실시간 검색일 경우 Loop 미적용해서 바로 조회 등록이 되게 해야됨
# if self.조건검색타입 ==0:
self.ConditionLoop = QEventLoop()
self.ConditionLoop.exec_()
# 조건식 조회 중지
def sendConditionStop(self, screenNo, conditionName, conditionIndex):
# print("CTrade : sendConditionStop", screenNo, conditionName, conditionIndex)
isRequest = self.kiwoom.dynamicCtotal_all("SendConditionStop(QString, QString, int)",
screenNo, conditionName, conditionIndex)
# 계좌 보유 종목 받음
def InquiryList(self, _repeat=0):
# print("CTrade : InquiryList")
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "비밀번호입력매체구분", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "조회구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "계좌평가잔고내역요청", "opw00018", _repeat, '{:04d}'.formating(self.sScreenNo))
self.InquiryLoop = QEventLoop() # 로봇에서 바로 쓸 수 있도록하기 위해서 계좌 조회해서 종목을 받고나서 루프해제시킴
self.InquiryLoop.exec_()
# 금일 매도 종목에 대해서 수익률, 수익금, 수수료 요청(일별종목별실현손익요청)
def DailyProfit(self, 금일매도종목):
_repeat = 0
# self.sAccount = 로봇거래계좌번호
# self.sScreenNo = self.ScreenNumber
시작일자 = datetime.date.today().strftime('%Y%m%d')
cnt = 1
for 종목코드 in 금일매도종목:
# print(self.sScreenNo, 종목코드, 시작일자)
self.umkate_cnt = length(금일매도종목) - cnt
cnt += 1
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", 종목코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "시작일자", 시작일자)
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "일자별종목별실현손익요청", "OPT10072",
_repeat, '{:04d}'.formating(self.sScreenNo))
self.DailyProfitLoop = QEventLoop() # 로봇에서 바로 쓸 수 있도록하기 위해서 계좌 조회해서 종목을 받고나서 루프해제시킴
self.DailyProfitLoop.exec_()
# 일별종목별실현손익 응답 결과 구글 업로드
def DailyProfitUpload(self, 매도결과):
# 매도결과 ['종목명','체결량','매입단가','체결가','당일매도손익','손익율','당일매매수수료','당일매매세금']
print(매도결과)
if self.sName == 'TradeShortTerm':
history_sheet = shortterm_history_sheet
history_cols = shortterm_history_cols
elif self.sName == 'TradeCondition':
history_sheet = condition_history_sheet
history_cols = condition_history_cols
try:
code_row = history_sheet.findtotal_all(매도결과[0])[-1].row
계산수익률 = value_round((int(float(매도결과[3])) / int(float(매도결과[2])) - 1) * 100, 2)
cell = alpha_list[history_cols.index('매수가')] + str(code_row) # 매입단가
history_sheet.umkate_acell(cell, int(float(매도결과[2])))
cell = alpha_list[history_cols.index('매도가')] + str(code_row) # 체결가
history_sheet.umkate_acell(cell, int(float(매도결과[3])))
cell = alpha_list[history_cols.index('수익률(계산)')] + str(code_row) # 수익률 계산
history_sheet.umkate_acell(cell, 계산수익률)
cell = alpha_list[history_cols.index('수익률')] + str(code_row) # 손익율
history_sheet.umkate_acell(cell, 매도결과[5])
cell = alpha_list[history_cols.index('수익금')] + str(code_row) # 손익율
history_sheet.umkate_acell(cell, int(float(매도결과[4])))
cell = alpha_list[history_cols.index('세금+수수료')] + str(code_row) # 당일매매수수료 + 당일매매세금
history_sheet.umkate_acell(cell, int(float(매도결과[6])) + int(float(매도결과[7])))
self.DailyProfitLoop.exit()
if self.umkate_cnt == 0:
print('금일 실현 손익 구글 업로드 완료')
Telegram("[StockTrader]금일 실현 손익 구글 업로드 완료")
logger.info("[StockTrader]금일 실현 손익 구글 업로드 완료")
except:
self.DailyProfitLoop.exit() # 강제 루프 해제
print('[StockTrader]CTrade:DailyProfitUpload_%s 매도 이력 없음' % 매도결과[0])
logger.error('CTrade:DailyProfitUpload_%s 매도 이력 없음' % 매도결과[0])
# 포트폴리오의 상태
def GetStatus(self):
# print("CTrade : GetStatus")
try:
result = []
for p, v in self.portfolio.items():
result.adding('%s(%s)[P%s/V%s/D%s]' % (v.종목명.strip(), v.종목코드, v.매수가, v.수량, v.매수일))
return [self.__class__.__name__, self.sName, self.UUID, self.sScreenNo, self.running, length(self.portfolio), ','.join(result)]
except Exception as e:
print('CTrade_GetStatus Error', e)
logger.error('CTrade_GetStatus Error : %s' % e)
def GenScreenNO(self):
"""
:return: 키움증권에서 요구하는 스크린번호를 생성
"""
# print("CTrade : GenScreenNO")
self.Smtotal_allScreenNumber += 1
if self.Smtotal_allScreenNumber > 9999:
self.Smtotal_allScreenNumber = 0
return self.sScreenNo * 10000 + self.Smtotal_allScreenNumber
def GetLoginInfo(self, tag):
"""
:param tag:
:return: 로그인정보 호출
"""
# print("CTrade : GetLoginInfo")
return self.kiwoom.dynamicCtotal_all('GetLoginInfo("%s")' % tag)
def KiwoomConnect(self):
"""
:return: 키움증권OpenAPI의 Ctotal_allBack에 대응하는 처리함수를 연결
"""
# print("CTrade : KiwoomConnect")
try:
self.kiwoom.OnEventConnect[int].connect(self.OnEventConnect)
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].connect(self.OnReceiveChejanData)
self.kiwoom.OnReceiveRealData[str, str, str].connect(self.OnReceiveRealData)
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
except Exception as e:
print("CTrade : [%s]KiwoomConnect Error :"&(self.sName, e))
# logger.info("%s : connected" % self.sName)
def KiwoomDisConnect(self):
"""
:return: Ctotal_allback 연결해제
"""
# print("CTrade : KiwoomDisConnect")
try:
self.kiwoom.OnEventConnect[int].disconnect(self.OnEventConnect)
except Exception:
pass
try:
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
except Exception:
pass
try:
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
except Exception:
pass
try:
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
except Exception:
pass
try:
self.kiwoom.OnReceiveChejanData[str, int, str].disconnect(self.OnReceiveChejanData)
except Exception:
pass
try:
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
except Exception:
pass
try:
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
except Exception:
pass
try:
self.kiwoom.OnReceiveRealData[str, str, str].disconnect(self.OnReceiveRealData)
except Exception:
pass
# logger.info("%s : disconnected" % self.sName)
def KiwoomAccount(self):
"""
:return: 계좌정보를 읽어옴
"""
# print("CTrade : KiwoomAccount")
ACCOUNT_CNT = self.GetLoginInfo('ACCOUNT_CNT')
ACC_NO = self.GetLoginInfo('ACCNO')
self.account = ACC_NO.split(';')[0:-1]
self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.account[0])
self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "d+2예수금요청", "opw00001", 0, '{:04d}'.formating(self.sScreenNo))
self.depositLoop = QEventLoop() # self.d2_deposit를 로봇에서 바로 쓸 수 있도록하기 위해서 예수금을 받고나서 루프해제시킴
self.depositLoop.exec_()
# logger.debug("보유 계좌수: %s 계좌번호: %s [%s]" % (ACCOUNT_CNT, self.account[0], ACC_NO))
def KiwoomSendOrder(self, sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo):
"""
OpenAPI 메뉴얼 참조
:param sRQName:
:param sScreenNo:
:param sAccNo:
:param nOrderType:
:param sCode:
:param nQty:
:param nPrice:
:param sHogaGb:
:param sOrgOrderNo:
:return:
"""
# print("CTrade : KiwoomSendOrder")
try:
order = self.kiwoom.dynamicCtotal_all(
'SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)',
[sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo])
return order
except Exception as e:
print('CTrade_KiwoomSendOrder Error ', e)
Telegram('[StockTrader]CTrade_KiwoomSendOrder Error: %s' % e, send='mc')
logger.error('CTrade_KiwoomSendOrder Error : %s' % e)
# -거래구분값 확인(2자리)
#
# 00 : 지정가
# 03 : 시장가
# 05 : 조건부지정가
# 06 : 최유리지정가
# 07 : 최우선지정가
# 10 : 지정가IOC
# 13 : 시장가IOC
# 16 : 최유리IOC
# 20 : 지정가FOK
# 23 : 시장가FOK
# 26 : 최유리FOK
# 61 : 장전 시간외단일가매매
# 81 : 장후 시간외종가
# 62 : 시간외단일가매매
#
# -매매구분값 (1 자리)
# 1 : 신규매수
# 2 : 신규매도
# 3 : 매수취소
# 4 : 매도취소
# 5 : 매수정정
# 6 : 매도정정
def KiwoomSetRealReg(self, sScreenNo, sCode, sRealType='0'):
"""
OpenAPI 메뉴얼 참조
:param sScreenNo:
:param sCode:
:param sRealType:
:return:
"""
# print("CTrade : KiwoomSetRealReg")
ret = self.kiwoom.dynamicCtotal_all('SetRealReg(QString, QString, QString, QString)', sScreenNo, sCode, '9001;10',
sRealType)
return ret
def KiwoomSetRealRemove(self, sScreenNo, sCode):
"""
OpenAPI 메뉴얼 참조
:param sScreenNo:
:param sCode:
:return:
"""
# print("CTrade : KiwoomSetRealRemove")
ret = self.kiwoom.dynamicCtotal_all('SetRealRemove(QString, QString)', sScreenNo, sCode)
return ret
def OnEventConnect(self, nErrCode):
"""
OpenAPI 메뉴얼 참조
:param nErrCode:
:return:
"""
# print("CTrade : OnEventConnect")
logger.debug('OnEventConnect', nErrCode)
def OnReceiveMsg(self, sScrNo, sRQName, sTRCode, sMsg):
"""
OpenAPI 메뉴얼 참조
:param sScrNo:
:param sRQName:
:param sTRCode:
:param sMsg:
:return:
"""
# print("CTrade : OnReceiveMsg")
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTRCode, sMsg))
# self.InquiryLoop.exit()
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
"""
OpenAPI 메뉴얼 참조
:param sScrNo:
:param sRQName:
:param sTRCode:
:param sRecordName:
:param sPreNext:
:param nDataLength:
:param sErrorCode:
:param sMessage:
:param sSPlmMsg:
:return:
"""
# print('CTrade : OnReceiveTrData')
try:
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo[:4]):
return
if 'B_' in sRQName or 'S_' in sRQName:
주문번호 = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, 0, "주문번호")
# logger.debug("화면번호: %s sRQName : %s 주문번호: %s" % (sScrNo, sRQName, 주문번호))
self.주문등록(sRQName, 주문번호)
if sRQName == "d+2예수금요청":
data = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)',sTRCode, "", sRQName, 0, "d+2추정예수금")
# 입력된 문자열에 대해 lstrip 메서드를 통해 문자열 왼쪽에 존재하는 '-' 또는 '0'을 제거. 그리고 formating 함수를 통해 천의 자리마다 콤마를 추가한 문자열로 변경
strip_data = data.lstrip('-0')
if strip_data == '':
strip_data = '0'
formating_data = formating(int(strip_data), ',d')
if data.startswith('-'):
formating_data = '-' + formating_data
self.sAsset = formating_data
self.depositLoop.exit() # self.d2_deposit를 로봇에서 바로 쓸 수 있도록하기 위해서 예수금을 받고나서 루프해제시킴
if sRQName == "계좌평가잔고내역요청":
print("계좌평가잔고내역요청_수신")
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
self.CList = []
for i in range(0, cnt):
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, i, '종목번호').strip().lstrip('0')
# print(S)
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
S = self.종목코드변환(S) # 종목코드 맨 첫 'A'를 삭제하기 위함
self.CList.adding(S)
# logger.debug("%s" % row)
if sPreNext == '2':
self.remained_data = True
self.InquiryList(_repeat=2)
else:
self.remained_data = False
print(self.CList)
self.InquiryLoop.exit()
if sRQName == "일자별종목별실현손익요청":
try:
data_idx = ['종목명', '체결량', '매입단가', '체결가', '당일매도손익', '손익율', '당일매매수수료', '당일매매세금']
result = []
for idx in data_idx:
data = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode,
"",
sRQName, 0, idx)
result.adding(data.strip())
self.DailyProfitUpload(result)
except Exception as e:
print(e)
logger.error('일자별종목별실현손익요청 Error : %s' % e)
except Exception as e:
print('CTrade_OnReceiveTrData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveTrData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveTrData Error : %s' % e)
def OnReceiveChejanData(self, sGubun, nItemCnt, sFidList):
"""
OpenAPI 메뉴얼 참조
:param sGubun:
:param nItemCnt:
:param sFidList:
:return:
"""
# logger.debug('OnReceiveChejanData [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
# 주문체결시 순서
# 1 구분:0 GetChejanData(913) = '접수'
# 2 구분:0 GetChejanData(913) = '체결'
# 3 구분:1 잔고정보
"""
# sFid별 주요데이터는 다음과 같습니다.
# "9201" : "계좌번호"
# "9203" : "주문번호"
# "9001" : "종목코드"
# "913" : "주문상태"
# "302" : "종목명"
# "900" : "주문수량"
# "901" : "주문가격"
# "902" : "미체결수량"
# "903" : "체결누계금액"
# "904" : "원주문번호"
# "905" : "주문구분"
# "906" : "매매구분"
# "907" : "매도수구분"
# "908" : "주문/체결시간"
# "909" : "체결번호"
# "910" : "체결가"
# "911" : "체결량"
# "10" : "현재가"
# "27" : "(최우선)매도호가"
# "28" : "(최우선)매수호가"
# "914" : "단위체결가"
# "915" : "단위체결량"
# "919" : "거부사유"
# "920" : "화면번호"
# "917" : "신용구분"
# "916" : "대출일"
# "930" : "보유수량"
# "931" : "매입단가"
# "932" : "총매입가"
# "933" : "주문가능수량"
# "945" : "당일순매수수량"
# "946" : "매도/매수구분"
# "950" : "당일총매도손일"
# "951" : "예수금"
# "307" : "기준가"
# "8019" : "손익율"
# "957" : "신용금액"
# "958" : "신용이자"
# "918" : "만기일"
# "990" : "당일실현손익(유가)"
# "991" : "당일실현손익률(유가)"
# "992" : "당일실현손익(신용)"
# "993" : "당일실현손익률(신용)"
# "397" : "파생상품거래단위"
# "305" : "상한가"
# "306" : "하한가"
"""
# print("CTrade : OnReceiveChejanData")
try:
# 접수
if sGubun == "0":
# logger.debug('OnReceiveChejanData: 접수 [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
화면번호 = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 920)
if length(화면번호.replacing(' ','')) == 0 : # 로봇 실행중 영웅문으로 주문 발생 시 화면번호가 ' '로 들어와 에러발생함 방지
print('다른 프로그램을 통한 거래 발생')
Telegram('다른 프로그램을 통한 거래 발생', send='mc')
logger.info('다른 프로그램을 통한 거래 발생')
return
elif self.sScreenNo != int(화면번호[:4]):
return
param = dict()
param['sGubun'] = sGubun
param['계좌번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9201)
param['주문번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9203)
param['종목코드'] = self.종목코드변환(self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9001))
param['주문업무분류'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 912)
# 접수 / 체결 확인
# 주문상태(10:원주문, 11:정정주문, 12:취소주문, 20:주문확인, 21:정정확인, 22:취소확인, 90-92:주문거부)
param['주문상태'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 913) # 접수 or 체결 확인
param['종목명'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 302).strip()
param['주문수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 900)
param['주문가격'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 901)
param['미체결수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 902)
param['체결누계금액'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 903)
param['원주문번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 904)
param['주문구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 905)
param['매매구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 906)
param['매도수구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 907)
param['체결시간'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 908)
param['체결번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 909)
param['체결가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 910)
param['체결량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 911)
param['현재가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 10)
param['매도호가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 27)
param['매수호가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 28)
param['단위체결가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 914).strip()
param['단위체결량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 915)
param['화면번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 920)
param['당일매매수수료'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 938)
param['당일매매세금'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 939)
param['체결수량'] = int(param['주문수량']) - int(param['미체결수량'])
logger.debug('접수 - 주문상태:{주문상태} 계좌번호:{계좌번호} 체결시간:{체결시간} 주문번호:{주문번호} 체결번호:{체결번호} 종목코드:{종목코드} 종목명:{종목명} 체결량:{체결량} 체결가:{체결가} 단위체결가:{단위체결가} 주문수량:{주문수량} 체결수량:{체결수량} 단위체결량:{단위체결량} 미체결수량:{미체결수량} 당일매매수수료:{당일매매수수료} 당일매매세금:{당일매매세금}'.formating(**param))
# if param["주문상태"] == "접수":
# self.접수처리(param)
# if param["주문상태"] == "체결": # 매도의 경우 체결로 안들어옴
# self.체결처리(param)
self.체결처리(param)
# 잔고통보
if sGubun == "1":
# logger.debug('OnReceiveChejanData: 잔고통보 [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
param = dict()
param['sGubun'] = sGubun
param['계좌번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9201)
param['종목코드'] = self.종목코드변환(self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9001))
param['신용구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 917)
param['대출일'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 916)
param['종목명'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 302).strip()
param['현재가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 10)
param['보유수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 930)
param['매입단가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 931)
param['총매입가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 932)
param['주문가능수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 933)
param['당일순매수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 945)
param['매도매수구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 946)
param['당일총매도손익'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 950)
param['예수금'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 951)
param['매도호가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 27)
param['매수호가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 28)
param['기준가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 307)
param['손익율'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 8019)
param['신용금액'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 957)
param['신용이자'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 958)
param['만기일'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 918)
param['당일실현손익_유가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 990)
param['당일실현손익률_유가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 991)
param['당일실현손익_신용'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 992)
param['당일실현손익률_신용'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 993)
param['담보대출수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 959)
logger.debug('잔고통보 - 계좌번호:{계좌번호} 종목명:{종목명} 보유수량:{보유수량} 매입단가:{매입단가} 총매입가:{총매입가} 손익율:{손익율} 당일총매도손익:{당일총매도손익} 당일순매수량:{당일순매수량}'.formating(**param))
self.잔고처리(param)
# 특이신호
if sGubun == "3":
logger.debug('OnReceiveChejanData: 특이신호 [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
pass
except Exception as e:
print('CTrade_OnReceiveChejanData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveChejanData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveChejanData Error : %s' % e)
def OnReceiveRealData(self, sRealKey, sRealType, sRealData):
"""
OpenAPI 메뉴얼 참조
:param sRealKey:
:param sRealType:
:param sRealData:
:return:
"""
# logger.debug('OnReceiveRealData [%s] [%s] [%s]' % (sRealKey, sRealType, sRealData))
_now = datetime.datetime.now()
try:
if _now.strftime('%H:%M:%S') < '09:00:00': # 9시 이전 데이터 버림(장 시작 전에 테이터 들어오는 것도 많으므로 버리기 위함)
return
if sRealKey not in self.실시간종목리스트: # 리스트에 없는 데이터 버림
return
if sRealType == "주식시세" or sRealType == "주식체결":
param = dict()
param['종목코드'] = self.종목코드변환(sRealKey)
param['체결시간'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 20).strip()
param['현재가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 10).strip()
param['전일대비'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 11).strip()
param['등락률'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 12).strip()
param['매도호가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 27).strip()
param['매수호가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 28).strip()
param['누적거래량'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 13).strip()
param['시가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 16).strip()
param['고가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 17).strip()
param['저가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 18).strip()
param['거래회전율'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 31).strip()
param['시가총액'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 311).strip()
self.실시간데이터처리(param)
except Exception as e:
print('CTrade_OnReceiveRealData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveRealData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveRealData Error : %s' % e)
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
print('OnReceiveTrCondition')
try:
if strCodeList == "":
self.ConditionLoop.exit()
return []
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print(self.codeList)
logger.info("[%s]조건 검색 완료"%(self.sName))
self.ConditionLoop.exit()
print('OnReceiveTrCondition :', self.codeList)
return self.codeList
except Exception as e:
print("OnReceiveTrCondition_Error")
print(e)
def OnReceiveConditionVer(self, lRet, sMsg):
print('OnReceiveConditionVer')
try:
self.condition = self.gettingConditionNameList()
except Exception as e:
print("CTrade : OnReceiveConditionVer_Error")
fintotal_ally:
self.ConditionLoop.exit()
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
# print("CTrade : OnReceiveRealCondition")
# OpenAPI 메뉴얼 참조
# :param sTrCode:
# :param strType:
# :param strConditionName:
# :param strConditionIndex:
# :return:
_now = datetime.datetime.now().strftime('%H:%M:%S')
if (_now >= '10:00:00' and _now < '13:00:00') or _now >= '15:17:00': # 10시부터 13시 이전 데이터 버림, 15시 17분 당일 매도 처리 후 데이터 버림
return
# logger.info('OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
print("실시간조검검색_종목코드: %s %s / Time : %s"%(sTrCode, "종목편입" if strType == "I" else "종목이탈", _now))
if strType == 'I':
self.실시간조건처리(sTrCode)
def 종목코드변환(self, code): # TR 통해서 받은 종목 코드에 A가 붙을 경우 삭제
return code.replacing('A', '')
def 정량매수(self, sRQName, 종목코드, 매수가, 수량):
# sRQName = '정량매수%s' % self.sScreenNo
sScreenNo = self.GenScreenNO() # 주문을 낼때 마다 스크린번호를 생성
sAccNo = self.sAccount
nOrderType = 1 # (1:신규매수, 2:신규매도 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)
sCode = 종목코드
nQty = 수량
nPrice = 매수가
sHogaGb = self.매수방법 # 00:지정가, 03:시장가, 05:조건부지정가, 06:최유리지정가, 07:최우선지정가, 10:지정가IOC, 13:시장가IOC, 16:최유리IOC, 20:지정가FOK, 23:시장가FOK, 26:최유리FOK, 61:장개시전시간외, 62:시간외단일가매매, 81:시간외종가
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo)
return ret
def 정액매수(self, sRQName, 종목코드, 매수가, 매수금액):
# sRQName = '정액매수%s' % self.sScreenNo
try:
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 1 # (1:신규매수, 2:신규매도 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)
sCode = 종목코드
nQty = 매수금액 // 매수가
nPrice = 매수가
sHogaGb = self.매수방법 # 00:지정가, 03:시장가, 05:조건부지정가, 06:최유리지정가, 07:최우선지정가, 10:지정가IOC, 13:시장가IOC, 16:최유리IOC, 20:지정가FOK, 23:시장가FOK, 26:최유리FOK, 61:장개시전시간외, 62:시간외단일가매매, 81:시간외종가
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
# logger.debug('주문 - %s %s %s %s %s %s %s %s %s', sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo)
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
except Exception as e:
print('CTrade_정액매수 Error ', e)
Telegram('[StockTrader]CTrade_정액매수 Error : %s' % e, send='mc')
logger.error('CTrade_정액매수 Error : %s' % e)
def 정량매도(self, sRQName, 종목코드, 매도가, 수량):
# sRQName = '정량매도%s' % self.sScreenNo
try:
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 2 # (1:신규매수, 2:신규매도 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)
sCode = 종목코드
nQty = 수량
nPrice = 매도가
sHogaGb = self.매도방법 # 00:지정가, 03:시장가, 05:조건부지정가, 06:최유리지정가, 07:최우선지정가, 10:지정가IOC, 13:시장가IOC, 16:최유리IOC, 20:지정가FOK, 23:시장가FOK, 26:최유리FOK, 61:장개시전시간외, 62:시간외단일가매매, 81:시간외종가
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
except Exception as e:
print('[%s]정량매도 Error '%(self.sName,e))
Telegram('[StockTrader][%s]정량매도 Error : %s' % (self.sName, e), send='mc')
logger.error('[%s]정량매도 Error : %s' % (self.sName, e))
def 정액매도(self, sRQName, 종목코드, 매도가, 수량):
# sRQName = '정액매도%s' % self.sScreenNo
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 2 # (1:신규매수, 2:신규매도 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)
sCode = 종목코드
nQty = 수량
nPrice = 매도가
sHogaGb = self.매도방법 # 00:지정가, 03:시장가, 05:조건부지정가, 06:최유리지정가, 07:최우선지정가, 10:지정가IOC, 13:시장가IOC, 16:최유리IOC, 20:지정가FOK, 23:시장가FOK, 26:최유리FOK, 61:장개시전시간외, 62:시간외단일가매매, 81:시간외종가
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
def 주문등록(self, sRQName, 주문번호):
self.주문번호_주문_매핑[주문번호] = sRQName
Ui_계좌정보조회, QtBaseClass_계좌정보조회 = uic.loadUiType("./UI/계좌정보조회.ui")
class 화면_계좌정보(QDialog, Ui_계좌정보조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_계좌정보, self).__init__(parent) # Initialize하는 형식
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['종목번호', '종목명', '현재가', '보유수량', '매입가', '매입금액', '평가금액', '수익률(%)', '평가손익', '매매가능수량']
self.보이는컬럼 = ['종목번호', '종목명', '현재가', '보유수량', '매입가', '매입금액', '평가금액', '수익률(%)', '평가손익', '매매가능수량'] # 주당 손익 -> 수익률(%)
self.result = []
self.KiwoomAccount()
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def KiwoomAccount(self):
ACCOUNT_CNT = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCOUNT_CNT")')
ACC_NO = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCNO")')
self.account = ACC_NO.split(';')[0:-1] # 계좌번호가 ;가 붙어서 나옴(에로 계좌가 3개면 111;222;333)
self.comboBox.clear()
self.comboBox.addItems(self.account)
logger.debug("보유 계좌수: %s 계좌번호: %s [%s]" % (ACCOUNT_CNT, self.account[0], ACC_NO))
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (
sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if sRQName == "계좌평가잔고내역요청":
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
# print(j)
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, i, j).strip().lstrip('0')
# print(S)
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
# logger.debug("%s" % row)
if sPreNext == '2':
self.Request(_repeat=2)
else:
self.model.umkate(KnowledgeFrame(data=self.result, columns=self.보이는컬럼))
print(self.result)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
계좌번호 = self.comboBox.currentText().strip()
logger.debug("계좌번호 %s" % 계좌번호)
# KOA StudioSA에서 opw00018 확인
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", 계좌번호) # 8132495511
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "비밀번호입력매체구분", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "조회구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "계좌평가잔고내역요청", "opw00018", _repeat,'{:04d}'.formating(self.sScreenNo))
# 조회 버튼(QtDesigner에서 조회버튼 누르고 오른쪽 하단에 시그널/슬롯편집기를 보면 조회버튼 시그널(clicked), 슬롯(Inquiry())로 확인가능함
def inquiry(self):
self.result = []
self.Request(_repeat=0)
def robot_account(self):
global 로봇거래계좌번호
로봇거래계좌번호 = self.comboBox.currentText().strip()
# sqlite3 사용
try:
with sqlite3.connect(DATABASE) as conn:
cursor = conn.cursor()
robot_account = pickle.dumps(로봇거래계좌번호, protocol=pickle.HIGHEST_PROTOCOL, fix_imports=True)
_robot_account = base64.encodebytes(robot_account)
cursor.execute("REPLACE into Setting(keyword, value) values (?, ?)",
['robotaccount', _robot_account])
conn.commit()
print("로봇 계좌 등록 완료")
except Exception as e:
print('robot_account', e)
Ui_일자별주가조회, QtBaseClass_일자별주가조회 = uic.loadUiType("./UI/일자별주가조회.ui")
class 화면_일별주가(QDialog, Ui_일자별주가조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_일별주가, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('일자별 주가 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['일자', '현재가', '거래량', '시가', '고가', '저가', '거래대금']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "주식일봉차트조회":
종목코드 = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['종목코드'] = self.종목코드
self.model.umkate(kf[['종목코드'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.종목코드 = self.lineEdit_code.text().strip()
기준일자 = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식일봉차트조회", "OPT10081", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_분별주가조회, QtBaseClass_분별주가조회 = uic.loadUiType("./UI/분별주가조회.ui")
class 화면_분별주가(QDialog, Ui_분별주가조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_분별주가, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('분별 주가 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['체결시간', '현재가', '시가', '고가', '저가', '거래량']
self.result = []
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
print('화면_분별주가 : OnReceiveTrData')
if self.sScreenNo != int(sScrNo):
return
if sRQName == "주식분봉차트조회":
종목코드 = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and (S[0] == '-' or S[0] == '+'):
S = S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
# kf = KnowledgeFrame(data=self.result, columns=self.columns)
# kf.to_csv('분봉.csv', encoding='euc-kr')
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf.to_csv('분봉.csv', encoding='euc-kr', index=False)
kf['종목코드'] = self.종목코드
self.model.umkate(kf[['종목코드'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.종목코드 = self.lineEdit_code.text().strip()
틱범위 = self.comboBox_getting_min.currentText()[0:2].strip()
if 틱범위[0] == '0':
틱범위 = 틱범위[1:]
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "틱범위", 틱범위)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식분봉차트조회", "OPT10080", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_업종정보, QtBaseClass_업종정보 = uic.loadUiType("./UI/업종정보조회.ui")
class 화면_업종정보(QDialog, Ui_업종정보):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_업종정보, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('업종정보 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['종목코드', '종목명', '현재가', '대비기호', '전일대비', '등락률', '거래량', '비중', '거래대금', '상한', '상승', '보합', '하락', '하한',
'상장종목수']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage,
sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "업종정보조회":
종목코드 = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['업종코드'] = self.업종코드
kf.to_csv("업종정보.csv")
self.model.umkate(kf[['업종코드'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.업종코드 = self.lineEdit_code.text().strip()
기준일자 = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "업종코드", self.업종코드)
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "업종정보조회", "OPT20003", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_업종별주가조회, QtBaseClass_업종별주가조회 = uic.loadUiType("./UI/업종별주가조회.ui")
class 화면_업종별주가(QDialog, Ui_업종별주가조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_업종별주가, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('업종별 주가 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['현재가', '거래량', '일자', '시가', '고가', '저가', '거래대금', '대업종구분', '소업종구분', '종목정보', '수정주가이벤트', '전일종가']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage,
sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "업종일봉조회":
종목코드 = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['업종코드'] = self.업종코드
self.model.umkate(kf[['업종코드'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.업종코드 = self.lineEdit_code.text().strip()
기준일자 = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "업종코드", self.업종코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "업종일봉조회", "OPT20006", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
class 화면_종목별투자자(QDialog, Ui_일자별주가조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_종목별투자자, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('종목별 투자자 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['일자', '현재가', '전일대비', '누적거래대금', '개인투자자', '외국인투자자', '기관계', '금융투자', '보험', '투신', '기타금융', '은행',
'연기금등', '국가', '내외국인', '사모펀드', '기타법인']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "종목별투자자조회":
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['종목코드'] = self.lineEdit_code.text().strip()
kf_new = kf[['종목코드'] + self.columns]
self.model.umkate(kf_new)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
종목코드 = self.lineEdit_code.text().strip()
기준일자 = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", 종목코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "금액수량구분", 2) # 1:금액, 2:수량
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "매매구분", 0) # 0:순매수, 1:매수, 2:매도
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "단위구분", 1) # 1000:천주, 1:단주
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "종목별투자자조회", "OPT10060", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_TradeShortTerm, QtBaseClass_TradeShortTerm = uic.loadUiType("./UI/TradeShortTerm.ui")
class 화면_TradeShortTerm(QDialog, Ui_TradeShortTerm):
def __init__(self, parent):
super(화면_TradeShortTerm, self).__init__(parent)
self.setupUi(self)
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.result = []
def inquiry(self):
# Google spreadsheet 사용
try:
self.data = import_googlesheet()
print(self.data)
self.model.umkate(self.data)
for i in range(length(self.data.columns)):
self.tableView.resizeColumnToContents(i)
except Exception as e:
print('화면_TradeShortTerm : inquiry Error ', e)
logger.error('화면_TradeShortTerm : inquiry Error : %s' % e)
class CTradeShortTerm(CTrade): # 로봇 추가 시 __init__ : 복사, Setting, 초기조건:전략에 맞게, 데이터처리~Run:복사
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.portfolio = dict()
self.실시간종목리스트 = []
self.매수모니터링체크 = False
self.Smtotal_allScreenNumber = 9999
self.d = today
# 구글 스프레드시트에서 읽은 KnowledgeFrame에서 로봇별 종목리스트 셋팅
def set_stocklist(self, data):
self.Stocklist = dict()
self.Stocklist['컬럼명'] = list(data.columns)
for 종목코드 in data['종목코드'].distinctive():
temp_list = data[data['종목코드'] == 종목코드].values[0]
self.Stocklist[종목코드] = {
'번호': temp_list[self.Stocklist['컬럼명'].index('번호')],
'종목명': temp_list[self.Stocklist['컬럼명'].index('종목명')],
'종목코드': 종목코드,
'시장': temp_list[self.Stocklist['컬럼명'].index('시장')],
'투자비중': float(temp_list[self.Stocklist['컬럼명'].index('비중')]), # 저장 후 setting 함수에서 전략의 단위투자금을 곱함
'시가위치': list(mapping(float, temp_list[self.Stocklist['컬럼명'].index('시가위치')].split(','))),
'매수가': list(
int(float(temp_list[list(data.columns).index(col)].replacing(',', ''))) for col in data.columns if
'매수가' in col and temp_list[list(data.columns).index(col)] != ''),
'매도전략': temp_list[self.Stocklist['컬럼명'].index('기본매도전략')],
'매도가': list(
int(float(temp_list[list(data.columns).index(col)].replacing(',', ''))) for col in data.columns if
'목표가' in col and temp_list[list(data.columns).index(col)] != '')
}
return self.Stocklist
# RobotAdd 함수에서 초기화 다음 셋팅 실행해서 설정값 넘김
def Setting(self, sScreenNo, 매수방법='00', 매도방법='03', 종목리스트=mk.KnowledgeFrame()):
try:
self.sScreenNo = sScreenNo
self.실시간종목리스트 = []
self.매수방법 = 매수방법
self.매도방법 = 매도방법
self.종목리스트 = 종목리스트
self.Stocklist = self.set_stocklist(self.종목리스트) # 번호, 종목명, 종목코드, 시장, 비중, 시가위치, 매수가, 매도전략, 매도가
self.Stocklist['전략'] = {
'단위투자금': '',
'모니터링종료시간': '',
'보유일': '',
'투자금비중': '',
'매도구간별조건': [],
'전략매도가': [],
}
row_data = shortterm_strategy_sheet.getting_total_all_values()
for data in row_data:
if data[0] == '단위투자금':
self.Stocklist['전략']['단위투자금'] = int(data[1])
elif data[0] == '매수모니터링 종료시간':
if length(data[1][:-3]) == 1:
data[1] = '0' + data[1]
self.Stocklist['전략']['모니터링종료시간'] = data[1] + ':00'
elif data[0] == '보유일':
self.Stocklist['전략']['보유일'] = int(data[1])
elif data[0] == '투자금 비중':
self.Stocklist['전략']['투자금비중'] = float(data[1][:-1])
# elif data[0] == '손절율':
# self.Stocklist['전략']['매도구간별조건'].adding(float(data[1][:-1]))
# elif data[0] == '시가 위치':
# self.Stocklist['전략']['시가위치'] = list(mapping(int, data[1].split(',')))
elif '구간' in data[0]:
if data[0][-1] != '1' and data[0][-1] != '2':
self.Stocklist['전략']['매도구간별조건'].adding(float(data[1][:-1]))
elif '손절가' == data[0]:
self.Stocklist['전략']['전략매도가'].adding(float(data[1].replacing('%', '')))
elif '본전가' == data[0]:
self.Stocklist['전략']['전략매도가'].adding(float(data[1].replacing('%', '')))
elif '익절가' in data[0]:
self.Stocklist['전략']['전략매도가'].adding(float(data[1].replacing('%', '')))
self.Stocklist['전략']['매도구간별조건'].insert(0, self.Stocklist['전략']['전략매도가'][0]) # 손절가
self.Stocklist['전략']['매도구간별조건'].insert(1, self.Stocklist['전략']['전략매도가'][1]) # 본전가
for code in self.Stocklist.keys():
if code == '컬럼명' or code == '전략':
continue
else:
self.Stocklist[code]['단위투자금'] = int(
self.Stocklist[code]['투자비중'] * self.Stocklist['전략']['단위투자금'])
self.Stocklist[code]['시가체크'] = False
self.Stocklist[code]['매수상한도달'] = False
self.Stocklist[code]['매수조건'] = 0
self.Stocklist[code]['매수총수량'] = 0 # 분할매수에 따른 수량체크
self.Stocklist[code]['매수수량'] = 0 # 분할매수 단위
self.Stocklist[code]['매수주문완료'] = 0 # 분할매수에 따른 매수 주문 수
self.Stocklist[code]['매수가전략'] = length(self.Stocklist[code]['매수가']) # 매수 전략에 따른 매수가 지정 수량
if self.Stocklist[code]['매도전략'] == '4':
self.Stocklist[code]['매도가'].adding(self.Stocklist['전략']['전략매도가'])
print(self.Stocklist)
except Exception as e:
print('CTradeShortTerm_Setting Error :', e)
Telegram('[XTrader]CTradeShortTerm_Setting Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Setting Error : %s' % e)
# 수동 포트폴리오 생성
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'024840': {'번호': '8.030', '종목명': 'KBI메탈', '종목코드': '024840', '시장': 'KOSDAQ', '매수전략': '1', '매수가': [1468],
'매수조건': 2, '수량': 310, '매도전략': '1', '매도가': [], '매수일': '2020/08/26 09:56:54'},
'097800': {'번호': '7.099', '종목명': '윈팩', '종목코드': '097800', '시장': 'KOSDAQ', '매수전략': '1', '매수가': [3219],
'매수조건': 1, '수량': 310, '매도전략': '4', '매도가': [3700], '매수일': '2020/05/29 09:22:39'},
'297090': {'번호': '7.101', '종목명': '씨에스베어링', '종목코드': '297090', '시장': 'KOSDAQ', '매수전략': '1', '매수가': [5000],
'매수조건': 3, '수량': 15, '매도전략': '2', '매도가': [], '매수일': '2020/06/03 09:12:15'},
}
self.strategy = {'전략': {'단위투자금': 200000, '모니터링종료시간': '10:30:00', '보유일': 20,
'투자금비중': 70.0, '매도구간별조건': [-2.7, 0.3, -3.0, -4.0, -5.0, -7.0],
'전략매도가': [-2.7, 0.3, 3.0, 6.0]}}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock_ShortTerm(번호=self.Stocklist[code]['번호'], 종목코드=code,
종목명=self.Stocklist[code]['종목명'],
시장=self.Stocklist[code]['시장'],
매수가=self.Stocklist[code]['매수가'][0],
매수조건=self.Stocklist[code]['매수조건'],
보유일=self.strategy['전략']['보유일'],
매도전략=self.Stocklist[code]['매도전략'],
매도가=self.Stocklist[code]['매도가'],
매도구간별조건=self.strategy['전략']['매도구간별조건'], 매도구간=1,
수량=self.Stocklist[code]['수량'],
매수일=self.Stocklist[code]['매수일'])
# google spreadsheet 매매이력 생성
def save_history(self, code, status):
# 매매이력 sheet에 해당 종목(매수된 종목)이 있으면 row를 반환 아니면 예외처리 -> 신규 매수로 처리
# 매수 이력 : 체결처리, 매수, 미체결수량 0에서 이력 저장
# 매도 이력 : 체결처리, 매도, 미체결수량 0에서 이력 저장
if status == '매도모니터링':
row = []
row.adding(self.portfolio[code].번호)
row.adding(self.portfolio[code].종목명)
row.adding(self.portfolio[code].매수가)
shortterm_sell_sheet.adding_row(row)
try:
code_row = shortterm_history_sheet.findtotal_all(self.portfolio[code].종목명)[-1].row # 종목명이 있는 모든 셀을 찾아서 맨 아래에 있는 셀을 선택
cell = alpha_list[shortterm_history_cols.index('매도가')] + str(code_row) # 매수 이력에 있는 종목이 매도가 되었는지 확인
sell_price = shortterm_history_sheet.acell(str(cell)).value
# 매도 이력은 추가 매도(매도전략2의 경우)나 신규 매도인 경우라 매도 이력 유무와 상관없음
if status == '매도': # 매도 이력은 포트폴리오에서 종목 pop을 하므로 Stocklist 데이터 사용
cell = alpha_list[shortterm_history_cols.index('매도가')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매도체결가)
cell = alpha_list[shortterm_history_cols.index('매도수량')] + str(code_row)
수량 = shortterm_history_sheet.acell(cell).value # 분할 매도의 경우 이전 매도 수량이 기록되어 있음
if 수량 != '': self.portfolio[code].매도수량 += int(수량) # 매도수량은 주문 수량이므로 기존 수량을 합해줌
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매도수량)
cell = alpha_list[shortterm_history_cols.index('매도일')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
cell = alpha_list[shortterm_history_cols.index('매도전략')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매도전략)
cell = alpha_list[shortterm_history_cols.index('매도구간')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매도구간)
계산수익률 = value_round((self.portfolio[code].매도체결가 / self.portfolio[code].매수가 - 1) * 100, 2)
cell = alpha_list[shortterm_history_cols.index('수익률(계산)')] + str(code_row) # 수익률 계산
shortterm_history_sheet.umkate_acell(cell, 계산수익률)
# 매수 이력은 있으나 매도 이력이 없음 -> 매도 전 추가 매수
if sell_price == '':
if status == '매수': # 포트폴리오 데이터 사용
cell = alpha_list[shortterm_history_cols.index('매수가')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매수가)
cell = alpha_list[shortterm_history_cols.index('매수수량')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].수량)
cell = alpha_list[shortterm_history_cols.index('매수일')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매수일)
cell = alpha_list[shortterm_history_cols.index('매수조건')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매수조건)
else: # 매도가가 기록되어 거래가 완료된 종목으로 판단하여 예외발생으로 신규 매수 추가함
raise Exception('매매완료 종목')
except Exception as e:
try:
# logger.debug('CTradeShortTerm_save_history Error1 : 종목명:%s, %s' % (self.portfolio[code].종목명, e))
row = []
row_buy = []
if status == '매수':
row.adding(self.portfolio[code].번호)
row.adding(self.portfolio[code].종목명)
row.adding(self.portfolio[code].매수가)
row.adding(self.portfolio[code].수량)
row.adding(self.portfolio[code].매수일)
row.adding(self.portfolio[code].매수조건)
shortterm_history_sheet.adding_row(row)
except Exception as e:
print('CTradeShortTerm_save_history Error2 : 종목명:%s, %s' % (self.portfolio[code].종목명, e))
Telegram('[XTrade]CTradeShortTerm_save_history Error2 : 종목명:%s, %s' % (self.portfolio[code].종목명, e),
send='mc')
logger.error('CTradeShortTerm_save_history Error : 종목명:%s, %s' % (self.portfolio[code].종목명, e))
# 매수 전략별 매수 조건 확인
def buy_strategy(self, code, price):
result = False
condition = self.Stocklist[code]['매수조건'] # 초기값 0
qty = self.Stocklist[code]['매수수량'] # 초기값 0
현재가, 시가, 고가, 저가, 전일종가 = price # 시세 = [현재가, 시가, 고가, 저가, 전일종가]
매수가 = self.Stocklist[code]['매수가'] # [매수가1, 매수가2, 매수가3]
시가위치하한 = self.Stocklist[code]['시가위치'][0]
시가위치상한 = self.Stocklist[code]['시가위치'][1]
# 1. 금일시가 위치 체크(초기 한번)하여 매수조건(1~6)과 주문 수량 계산
if self.Stocklist[code]['시가체크'] == False: # 종목별로 초기에 한번만 시가 위치 체크를 하면 되므로 별도 함수 미사용
매수가.adding(시가)
매수가.sort(reverse=True)
band = 매수가.index(시가) # band = 0 : 매수가1 이상, band=1: 매수가1, 2 사이, band=2: 매수가2,3 사이
매수가.remove(시가)
if band == length(매수가): # 매수가 지정한 구간보다 시가가 아래일 경우로 초기값이 result=False, condition=0 리턴
self.Stocklist[code]['시가체크'] = True
self.Stocklist[code]['매수조건'] = 0
self.Stocklist[code]['매수수량'] = 0
return False, 0, 0
else:
# 단위투자금으로 매수가능한 총 수량 계산, band = 0 : 매수가1, band=1: 매수가2, band=2: 매수가3 로 계산
self.Stocklist[code]['매수총수량'] = self.Stocklist[code]['단위투자금'] // 매수가[band]
if band == 0: # 시가가 매수가1보다 높은 경우
# 시가가 매수가1의 시가범위에 포함 : 조건 1, 2, 3
if 매수가[band] * (1 + 시가위치하한 / 100) <= 시가 and 시가 < 매수가[band] * (1 + 시가위치상한 / 100):
condition = length(매수가)
self.Stocklist[code]['매수가전략'] = length(매수가)
qty = self.Stocklist[code]['매수총수량'] // condition
else: # 시가 위치에 미포함
self.Stocklist[code]['시가체크'] = True
self.Stocklist[code]['매수조건'] = 0
self.Stocklist[code]['매수수량'] = 0
return False, 0, 0
else: # 시가가 매수가 중간인 경우 - 매수가1&2사이(band 1) : 조건 4,5 / 매수가2&3사이(band 2) : 조건 6
for i in range(band): # band 1일 경우 매수가 1은 불필요하여 삭제, band 2 : 매수가 1, 2 삭제(band수 만큼 삭제 실행)
매수가.pop(0)
if 매수가[0] * (1 + 시가위치하한 / 100) <= 시가: # 시가범위 포함
# 조건 4 = 매수가길이 1 + band 1 + 2(=band+1) -> 4 = 1 + 2*1 + 1
# 조건 5 = 매수가길이 2 + band 1 + 2(=band+1) -> 5 = 2 + 2*1 + 1
# 조건 6 = 매수가길이 1 + band 2 + 3(=band+1) -> 6 = 1 + 2*2 + 1
condition = length(매수가) + (2 * band) + 1
self.Stocklist[code]['매수가전략'] = length(매수가)
qty = self.Stocklist[code]['매수총수량'] // (condition % 2 + 1)
else:
self.Stocklist[code]['시가체크'] = True
self.Stocklist[code]['매수조건'] = 0
self.Stocklist[code]['매수수량'] = 0
return False, 0, 0
self.Stocklist[code]['시가체크'] = True
self.Stocklist[code]['매수조건'] = condition
self.Stocklist[code]['매수수량'] = qty
else: # 시가 위치 체크를 한 두번째 데이터 이후에는 condition이 0이면 바로 매수 불만족 리턴시킴
if condition == 0: # condition 0은 매수 조건 불만족
return False, 0, 0
# 매수조건 확정, 매수 수량 계산 완료
# 매수상한에 미도달한 상태로 매수가로 내려왔을 때 매수
# 현재가가 해당조건에서의 시가위치 상한 이상으로 오르면 매수상한도달을 True로 해서 매수하지 않게 함
if 현재가 >= 매수가[0] * (1 + 시가위치상한 / 100): self.Stocklist[code]['매수상한도달'] = True
if self.Stocklist[code]['매수주문완료'] < self.Stocklist[code]['매수가전략'] and self.Stocklist[code]['매수상한도달'] == False:
if 현재가 == 매수가[0]:
result = True
self.Stocklist[code]['매수주문완료'] += 1
print("매수모니터링 만족_종목:%s, 시가:%s, 조건:%s, 현재가:%s, 체크결과:%s, 수량:%s" % (
self.Stocklist[code]['종목명'], 시가, condition, 현재가, result, qty))
logger.debug("매수모니터링 만족_종목:%s, 시가:%s, 조건:%s, 현재가:%s, 체크결과:%s, 수량:%s" % (
self.Stocklist[code]['종목명'], 시가, condition, 현재가, result, qty))
return result, condition, qty
# 매도 구간 확인
def profit_band_check(self, 현재가, 매수가):
band_list = [0, 3, 5, 10, 15, 25]
# print('현재가, 매수가', 현재가, 매수가)
ratio = value_round((현재가 - 매수가) / 매수가 * 100, 2)
# print('ratio', ratio)
if ratio < 3:
return 1
elif ratio in band_list:
return band_list.index(ratio) + 1
else:
band_list.adding(ratio)
band_list.sort()
band = band_list.index(ratio)
band_list.remove(ratio)
return band
# 매도 전략별 매도 조건 확인
def sell_strategy(self, code, price):
# print('%s 매도 조건 확인' % code)
try:
result = False
band = self.portfolio[code].매도구간 # 이전 매도 구간 받음
매도방법 = self.매도방법 # '03' : 시장가
qty_ratio = 1 # 매도 수량 결정 : 보유수량 * qty_ratio
현재가, 시가, 고가, 저가, 전일종가 = price # 시세 = [현재가, 시가, 고가, 저가, 전일종가]
매수가 = self.portfolio[code].매수가
# 전략 1, 2, 3과 4 별도 체크
strategy = self.portfolio[code].매도전략
# 전략 1, 2, 3
if strategy != '4':
# 매도를 위한 수익률 구간 체크(매수가 대비 현재가의 수익률 조건에 다른 구간 설정)
new_band = self.profit_band_check(현재가, 매수가)
if (hogacal(시가, 0, self.portfolio[code].시장, '상한가')) <= 현재가:
band = 7
if band < new_band: # 이전 구간보다 현재 구간이 높을 경우(시세가 올라간 경우)만
band = new_band # 구간을 현재 구간으로 변경(반대의 경우는 구간 유지)
if band == 1 and 현재가 <= 매수가 * (1 + (self.portfolio[code].매도구간별조건[0] / 100)):
result = True
elif band == 2 and 현재가 <= 매수가 * (1 + (self.portfolio[code].매도구간별조건[1] / 100)):
result = True
elif band == 3 and 현재가 <= 고가 * (1 + (self.portfolio[code].매도구간별조건[2] / 100)):
result = True
elif band == 4 and 현재가 <= 고가 * (1 + (self.portfolio[code].매도구간별조건[3] / 100)):
result = True
elif band == 5 and 현재가 <= 고가 * (1 + (self.portfolio[code].매도구간별조건[4] / 100)):
result = True
elif band == 6 and 현재가 <= 고가 * (1 + (self.portfolio[code].매도구간별조건[5] / 100)):
result = True
elif band == 7 and 현재가 >= (hogacal(시가, -3, self.Stocklist[code]['시장'], '상한가')):
매도방법 = '00' # 지정가
result = True
self.portfolio[code].매도구간 = band # 포트폴리오에 매도구간 업데이트
try:
if strategy == '2' or strategy == '3': # 매도전략 2(기존 5)
if strategy == '2':
목표가 = self.portfolio[code].매도가[0]
elif strategy == '3':
목표가 = (hogacal(시가 * 1.1, 0, self.Stocklist[code]['시장'], '현재가'))
매도조건 = self.portfolio[code].매도조건 # 매도가 실행된 조건 '': 매도 전, 'B':구간매도, 'T':목표가매도
targetting_band = self.profit_band_check(목표가, 매수가)
if band < targetting_band: # 현재가구간이 목표가구간 미만일때 전량매도
qty_ratio = 1
else: # 현재가구간이 목표가구간 이상일 때
if 현재가 == 목표가: # 목표가 도달 시 절반 매도
self.portfolio[code].목표도달 = True # 목표가 도달 여부 True
if 매도조건 == '': # 매도이력이 없는 경우 목표가매도 'T', 절반 매도
self.portfolio[code].매도조건 = 'T'
result = True
if self.portfolio[code].수량 == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
elif 매도조건 == 'B': # 구간 매도 이력이 있을 경우 절반매도가 된 상태이므로 남은 전량매도
result = True
qty_ratio = 1
elif 매도조건 == 'T': # 목표가 매도 이력이 있을 경우 매도미실행
result = False
else: # 현재가가 목표가가 아닐 경우 구간 매도 실행(매도실행여부는 결정된 상태)
if self.portfolio[code].목표도달 == False: # 목표가 도달을 못한 경우면 전량매도
qty_ratio = 1
else:
if 매도조건 == '': # 매도이력이 없는 경우 구간매도 'B', 절반 매도
self.portfolio[code].매도조건 = 'B'
if self.portfolio[code].수량 == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
elif 매도조건 == 'B': # 구간 매도 이력이 있을 경우 매도미실행
result = False
elif 매도조건 == 'T': # 목표가 매도 이력이 있을 경우 전량매도
qty_ratio = 1
except Exception as e:
print('sell_strategy 매도전략 2 Error :', e)
logger.error('CTradeShortTerm_sell_strategy 종목 : %s 매도전략 2 Error : %s' % (code, e))
Telegram('[XTrader]CTradeShortTerm_sell_strategy 종목 : %s 매도전략 2 Error : %s' % (code, e), send='mc')
result = False
return 매도방법, result, qty_ratio
# print('종목코드 : %s, 현재가 : %s, 시가 : %s, 고가 : %s, 매도구간 : %s, 결과 : %s' % (code, 현재가, 시가, 고가, band, result))
return 매도방법, result, qty_ratio
# 전략 4(지정가 00 매도)
else:
매도방법 = '00' # 지정가
try:
# 전략 4의 매도가 = [목표가(원), [손절가(%), 본전가(%), 1차익절가(%), 2차익절가(%)]]
# 1. 매수 후 손절가까지 하락시 매도주문 -> 손절가, 전량매도로 끝
if 현재가 <= 매수가 * (1 + self.portfolio[code].매도가[1][0] / 100):
self.portfolio[code].매도구간 = 0
result = True
qty_ratio = 1
# 2. 1차익절가 도달시 매도주문 -> 1차익절가, 1/3 매도
elif self.portfolio[code].익절가1도달 == False and 현재가 >= 매수가 * (
1 + self.portfolio[code].매도가[1][2] / 100):
self.portfolio[code].매도구간 = 1
self.portfolio[code].익절가1도달 = True
result = True
if self.portfolio[code].수량 == 1:
qty_ratio = 1
elif self.portfolio[code].수량 == 2:
qty_ratio = 0.5
else:
qty_ratio = 0.3
# 3. 2차익절가 도달못하고 본전가까지 하락 또는 고가 -3%까지시 매도주문 -> 1차익절가, 나머지 전량 매도로 끝
elif self.portfolio[code].익절가1도달 == True and self.portfolio[code].익절가2도달 == False and (
(현재가 <= 매수가 * (1 + self.portfolio[code].매도가[1][1] / 100)) or (현재가 <= 고가 * 0.97)):
self.portfolio[code].매도구간 = 1.5
result = True
qty_ratio = 1
# 4. 2차 익절가 도달 시 매도주문 -> 2차 익절가, 1/3 매도
elif self.portfolio[code].익절가1도달 == True and self.portfolio[code].익절가2도달 == False and 현재가 >= 매수가 * (
1 + self.portfolio[code].매도가[1][3] / 100):
self.portfolio[code].매도구간 = 2
self.portfolio[code].익절가2도달 = True
result = True
if self.portfolio[code].수량 == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
# 5. 목표가 도달못하고 2차익절가까지 하락 시 매도주문 -> 2차익절가, 나머지 전량 매도로 끝
elif self.portfolio[code].익절가2도달 == True and self.portfolio[code].목표가도달 == False and (
(현재가 <= 매수가 * (1 + self.portfolio[code].매도가[1][2] / 100)) or (현재가 <= 고가 * 0.97)):
self.portfolio[code].매도구간 = 2.5
result = True
qty_ratio = 1
# 6. 목표가 도달 시 매도주문 -> 목표가, 나머지 전량 매도로 끝
elif self.portfolio[code].목표가도달 == False and 현재가 >= self.portfolio[code].매도가[0]:
self.portfolio[code].매도구간 = 3
self.portfolio[code].목표가도달 = True
result = True
qty_ratio = 1
return 매도방법, result, qty_ratio
except Exception as e:
print('sell_strategy 매도전략 4 Error :', e)
logger.error('CTradeShortTerm_sell_strategy 종목 : %s 매도전략 4 Error : %s' % (code, e))
Telegram('[XTrader]CTradeShortTerm_sell_strategy 종목 : %s 매도전략 4 Error : %s' % (code, e), send='mc')
result = False
return 매도방법, result, qty_ratio
except Exception as e:
print('CTradeShortTerm_sell_strategy Error ', e)
Telegram('[XTrader]CTradeShortTerm_sell_strategy Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_sell_strategy Error : %s' % e)
result = False
qty_ratio = 1
return 매도방법, result, qty_ratio
# 보유일 전략 : 보유기간이 보유일 이상일 경우 전량 매도 실행(Mainwindow 타이머에서 시간 체크)
def hold_strategy(self):
if self.holdcheck == True:
print('보유일 만기 매도 체크')
try:
for code in list(self.portfolio.keys()):
보유기간 = holdingcal(self.portfolio[code].매수일)
print('종목명 : %s, 보유일 : %s, 보유기간 : %s' % (self.portfolio[code].종목명, self.portfolio[code].보유일, 보유기간))
if 보유기간 >= int(self.portfolio[code].보유일) and self.주문실행중_Lock.getting('S_%s' % code) is None and \
self.portfolio[code].수량 != 0:
self.portfolio[code].매도구간 = 0
(result, order) = self.정량매도(sRQName='S_%s' % code, 종목코드=code, 매도가=self.portfolio[code].매수가,
수량=self.portfolio[code].수량)
if result == True:
self.주문실행중_Lock['S_%s' % code] = True
Telegram('[XTrader]정량매도(보유일만기) : 종목코드=%s, 종목명=%s, 수량=%s' % (
code, self.portfolio[code].종목명, self.portfolio[code].수량))
logger.info('정량매도(보유일만기) : 종목코드=%s, 종목명=%s, 수량=%s' % (
code, self.portfolio[code].종목명, self.portfolio[code].수량))
else:
Telegram('[XTrader]정액매도실패(보유일만기) : 종목코드=%s, 종목명=%s, 수량=%s' % (
code, self.portfolio[code].종목명, self.portfolio[code].수량))
logger.info('정량매도실패(보유일만기) : 종목코드=%s, 종목명=%s, 수량=%s' % (
code, self.portfolio[code].종목명, self.portfolio[code].수량))
except Exception as e:
print("hold_strategy Error :", e)
# 포트폴리오 생성
def set_portfolio(self, code, buyprice, condition):
try:
self.portfolio[code] = CPortStock_ShortTerm(번호=self.Stocklist[code]['번호'], 종목코드=code,
종목명=self.Stocklist[code]['종목명'],
시장=self.Stocklist[code]['시장'], 매수가=buyprice,
매수조건=condition, 보유일=self.Stocklist['전략']['보유일'],
매도전략=self.Stocklist[code]['매도전략'],
매도가=self.Stocklist[code]['매도가'],
매도구간별조건=self.Stocklist['전략']['매도구간별조건'],
매수일=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.Stocklist[code]['매수일'] = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S') # 매매이력 업데이트를 위해 매수일 추가
except Exception as e:
print('CTradeShortTerm_set_portfolio Error ', e)
Telegram('[XTrader]CTradeShortTerm_set_portfolio Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_set_portfolio Error : %s' % e)
# Robot_Run이 되면 실행됨 - 매수/매도 종목을 리스트로 저장
def 초기조건(self, codes):
# 매수총액 계산하기
# 금일매도종목 리스트 변수 초기화
# 매도할종목 : 포트폴리오에 있던 종목 추가
# 매수할종목 : 구글에서 받은 종목 추가
self.parent.statusbar.showMessage("[%s] 초기조건준비" % (self.sName))
self.금일매도종목 = [] # 장 마감 후 금일 매도한 종목에 대해서 매매이력 정리 업데이트(매도가, 손익률 등)
self.매도할종목 = []
self.매수할종목 = []
self.매수총액 = 0
self.holdcheck = False
for code in codes: # 구글 시트에서 import된 매수 모니커링 종목은 '매수할종목'에 추가
self.매수할종목.adding(code)
# 포트폴리오에 있는 종목은 매도 관련 전략 재확인(구글시트) 및 '매도할종목'에 추가
if length(self.portfolio) > 0:
row_data = shortterm_sell_sheet.getting_total_all_values()
idx_holding = row_data[0].index('보유일')
idx_strategy = row_data[0].index('매도전략')
idx_loss = row_data[0].index('손절가')
idx_sellprice = row_data[0].index('목표가')
for row in row_data[1:]:
code, name, market = getting_code(row[1]) # 종목명으로 종목코드, 종목명, 시장 받아서(getting_code 함수) 추가
if code in list(self.portfolio.keys()):
self.portfolio[code].보유일 = row[idx_holding]
self.portfolio[code].매도전략 = row[idx_strategy]
self.portfolio[code].매도가 = [] # 매도 전략 변경에 따라 매도가 초기화
# 매도구간별조건 = [손절가(%), 본전가(%), 구간3 고가대비(%), 구간4 고가대비(%), 구간5 고가대비(%), 구간6 고가대비(%)]
self.portfolio[code].매도구간별조건 = []
self.portfolio[code].매도구간별조건.adding(value_round(((int(float(row[idx_loss].replacing(',', ''))) / self.portfolio[code].매수가) - 1) * 100, 1)) # 손절가를 퍼센트로 변환하여 업데이트
for idx in range(1, length(self.Stocklist['전략']['매도구간별조건'])): # Stocklist의 매도구간별조건 전체를 바로 adding할 경우 모든 종목이 동일한 값으로 들어감
self.portfolio[code].매도구간별조건.adding(self.Stocklist['전략']['매도구간별조건'][idx])
if self.portfolio[code].매도전략 == '4': # 매도가 = [목표가(원), [손절가(%), 본전가(%), 1차익절가(%), 2차익절가(%)]]
self.portfolio[code].매도가.adding(int(float(row[idx_sellprice].replacing(',', ''))))
self.portfolio[code].매도가.adding([])
for idx in range(length(self.Stocklist['전략']['전략매도가'])): # Stocklist의 전략매도가 전체를 바로 adding할 경우 모든 종목이 동일한 값으로 들어감
self.portfolio[code].매도가[1].adding(self.Stocklist['전략']['전략매도가'][idx])
self.portfolio[code].매도가[1][0] = self.portfolio[code].매도구간별조건[0] # float(row[idx_loss].replacing('%', ''))
self.portfolio[code].sellcount = 0
self.portfolio[code].매도단위수량 = 0 # 전략4의 기본 매도 단위는 보유수량의 1/3
self.portfolio[code].익절가1도달 = False
self.portfolio[code].익절가2도달 = False
self.portfolio[code].목표가도달 = False
else:
if self.portfolio[code].매도전략 == '2' or self.portfolio[code].매도전략 == '3':
self.portfolio[code].목표도달 = False # 목표가(매도가) 도달 체크(False 상태로 구간 컷일경우 전량 매도)
self.portfolio[code].매도조건 = '' # 구간매도 : B, 목표매도 : T
for port_code in list(self.portfolio.keys()):
# 로봇 시작 시 포트폴리오 종목의 매도구간(전일 매도모니터링)을 1로 초기화
# 구간이 내려가는 건 반영하지 않으므로 초기화를 시켜서 다시 구간 체크 시작하기 위함
self.portfolio[port_code].매도구간 = 1 # 매도 구간은 로봇 실행 시 마다 초기화시킴
# 매수총액계산
self.매수총액 += (self.portfolio[port_code].매수가 * self.portfolio[port_code].수량)
# 포트폴리오에 있는 종목이 구글에서 받아서 만든 Stocklist에 없을 경우만 추가함
# 이 조건이 없을 경우 구글에서 받은 전략들이 아닌 과거 전략이 포트폴리오에서 넘어감
# 근데 포트폴리오에 있는 종목을 왜 Stocklist에 넣어야되는지 모르겠음(내가 하고도...)
if port_code not in list(self.Stocklist.keys()):
self.Stocklist[port_code] = {
'번호': self.portfolio[port_code].번호,
'종목명': self.portfolio[port_code].종목명,
'종목코드': self.portfolio[port_code].종목코드,
'시장': self.portfolio[port_code].시장,
'매수조건': self.portfolio[port_code].매수조건,
'매수가': self.portfolio[port_code].매수가,
'매도전략': self.portfolio[port_code].매도전략,
'매도가': self.portfolio[port_code].매도가
}
self.매도할종목.adding(port_code)
# for stock in kf_keeplist['종목번호'].values: # 보유 종목 체크해서 매도 종목에 추가 → 로봇이 두개 이상일 경우 중복되므로 미적용
# self.매도할종목.adding(stock)
# 종목명 = kf_keeplist[kf_keeplist['종목번호']==stock]['종목명'].values[0]
# 매입가 = kf_keeplist[kf_keeplist['종목번호']==stock]['매입가'].values[0]
# 보유수량 = kf_keeplist[kf_keeplist['종목번호']==stock]['보유수량'].values[0]
# print('종목코드 : %s, 종목명 : %s, 매입가 : %s, 보유수량 : %s' %(stock, 종목명, 매입가, 보유수량))
# self.portfolio[stock] = CPortStock_ShortTerm(종목코드=stock, 종목명=종목명, 매수가=매입가, 수량=보유수량, 매수일='')
def 실시간데이터처리(self, param):
try:
if self.running == True:
체결시간 = '%s %s:%s:%s' % (str(self.d), param['체결시간'][0:2], param['체결시간'][2:4], param['체결시간'][4:])
종목코드 = param['종목코드']
현재가 = abs(int(float(param['현재가'])))
전일대비 = int(float(param['전일대비']))
등락률 = float(param['등락률'])
매도호가 = abs(int(float(param['매도호가'])))
매수호가 = abs(int(float(param['매수호가'])))
누적거래량 = abs(int(float(param['누적거래량'])))
시가 = abs(int(float(param['시가'])))
고가 = abs(int(float(param['고가'])))
저가 = abs(int(float(param['저가'])))
거래회전율 = abs(float(param['거래회전율']))
시가총액 = abs(int(float(param['시가총액'])))
종목명 = self.parent.CODE_POOL[종목코드][1] # pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
전일종가 = self.parent.CODE_POOL[종목코드][3]
시세 = [현재가, 시가, 고가, 저가, 전일종가]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
self.wr.writerow([체결시간, 종목코드, 종목명, 현재가, 전일대비])
# 매수 조건
# 매수모니터링 종료 시간 확인
if current_time < self.Stocklist['전략']['모니터링종료시간']:
if 종목코드 in self.매수할종목 and 종목코드 not in self.금일매도종목:
# 매수총액 + 종목단위투자금이 투자총액보다 작음 and 매수주문실행중Lock에 없음 -> 추가매수를 위해서 and 포트폴리오에 없음 조건 삭제
if (self.매수총액 + self.Stocklist[종목코드]['단위투자금'] < self.투자총액) and self.주문실행중_Lock.getting(
'B_%s' % 종목코드) is None and length(
self.Stocklist[종목코드]['매수가']) > 0: # and self.portfolio.getting(종목코드) is None
# 매수 전략별 모니터링 체크
buy_check, condition, qty = self.buy_strategy(종목코드, 시세)
if buy_check == True and (self.Stocklist[종목코드]['단위투자금'] // 현재가 > 0):
(result, order) = self.정량매수(sRQName='B_%s' % 종목코드, 종목코드=종목코드, 매수가=현재가, 수량=qty)
if result == True:
if self.portfolio.getting(종목코드) is None: # 포트폴리오에 없으면 신규 저장
self.set_portfolio(종목코드, 현재가, condition)
self.주문실행중_Lock['B_%s' % 종목코드] = True
Telegram('[XTrader]매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수조건=%s, 매수수량=%s' % (
종목코드, 종목명, 현재가, condition, qty))
logger.info('매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수조건=%s, 매수수량=%s' % (
종목코드, 종목명, 현재가, condition, qty))
else:
Telegram('[XTrader]매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수조건=%s' % (
종목코드, 종목명, 현재가, condition))
logger.info('매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수조건=%s' % (종목코드, 종목명, 현재가, condition))
else:
if self.매수모니터링체크 == False:
for code in self.매수할종목:
if self.portfolio.getting(code) is not None and code not in self.매도할종목:
Telegram('[XTrader]매수모니터링마감 : 종목코드=%s, 종목명=%s 매도모니터링 전환' % (종목코드, 종목명))
logger.info('매수모니터링마감 : 종목코드=%s, 종목명=%s 매도모니터링 전환' % (종목코드, 종목명))
self.매수할종목.remove(code)
self.매도할종목.adding(code)
self.매수모니터링체크 = True
logger.info('매도할 종목 :%s' % self.매도할종목)
# 매도 조건
if 종목코드 in self.매도할종목:
# 포트폴리오에 있음 and 매도주문실행중Lock에 없음 and 매수주문실행중Lock에 없음
if self.portfolio.getting(종목코드) is not None and self.주문실행중_Lock.getting(
'S_%s' % 종목코드) is None: # and self.주문실행중_Lock.getting('B_%s' % 종목코드) is None:
# 매도 전략별 모니터링 체크
매도방법, sell_check, ratio = self.sell_strategy(종목코드, 시세)
if sell_check == True:
if 매도방법 == '00':
(result, order) = self.정액매도(sRQName='S_%s' % 종목코드, 종목코드=종목코드, 매도가=현재가,
수량=value_round(self.portfolio[종목코드].수량 * ratio))
else:
(result, order) = self.정량매도(sRQName='S_%s' % 종목코드, 종목코드=종목코드, 매도가=현재가,
수량=value_round(self.portfolio[종목코드].수량 * ratio))
if result == True:
self.주문실행중_Lock['S_%s' % 종목코드] = True
Telegram('[XTrader]매도주문 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 수량=%s' % (
종목코드, 종목명, 현재가, self.portfolio[종목코드].매도전략, self.portfolio[종목코드].매도구간,
int(self.portfolio[종목코드].수량 * ratio)))
if self.portfolio[종목코드].매도전략 == '2':
logger.info(
'매도주문 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 목표도달=%s, 매도조건=%s, 수량=%s' % (
종목코드, 종목명, 현재가, self.portfolio[종목코드].매도전략, self.portfolio[종목코드].매도구간,
self.portfolio[종목코드].목표도달, self.portfolio[종목코드].매도조건,
int(self.portfolio[종목코드].수량 * ratio)))
else:
logger.info('매도주문 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 수량=%s' % (
종목코드, 종목명, 현재가, self.portfolio[종목코드].매도전략, self.portfolio[종목코드].매도구간,
int(self.portfolio[종목코드].수량 * ratio)))
else:
Telegram(
'[XTrader]매도실패 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 수량=%s' % (종목코드, 종목명,
현재가,
self.portfolio[
종목코드].매도전략,
self.portfolio[
종목코드].매도구간,
self.portfolio[
종목코드].수량 * ratio))
logger.info('매도실패 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 수량=%s' % (종목코드, 종목명,
현재가,
self.portfolio[
종목코드].매도전략,
self.portfolio[
종목코드].매도구간,
self.portfolio[
종목코드].수량 * ratio))
except Exception as e:
print('CTradeShortTerm_실시간데이터처리 Error : %s, %s' % (종목명, e))
Telegram('[XTrader]CTradeShortTerm_실시간데이터처리 Error : %s, %s' % (종목명, e), send='mc')
logger.error('CTradeShortTerm_실시간데이터처리 Error :%s, %s' % (종목명, e))
def 접수처리(self, param):
pass
def 체결처리(self, param):
종목코드 = param['종목코드']
주문번호 = param['주문번호']
self.주문결과[주문번호] = param
주문수량 = int(param['주문수량'])
미체결수량 = int(param['미체결수량'])
체결가 = int(0 if (param['체결가'] is None or param['체결가'] == '') else param['체결가']) # 매입가 동일
단위체결량 = int(0 if (param['단위체결량'] is None or param['단위체결량'] == '') else param['단위체결량'])
당일매매수수료 = int(0 if (param['당일매매수수료'] is None or param['당일매매수수료'] == '') else param['당일매매수수료'])
당일매매세금 = int(0 if (param['당일매매세금'] is None or param['당일매매세금'] == '') else param['당일매매세금'])
# 매수
if param['매도수구분'] == '2':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매수가 = int(주문[2:])
# 단위체결가 = int(0 if (param['단위체결가'] is None or param['단위체결가'] == '') else param['단위체결가'])
# logger.debug('매수-------> %s %s %s %s %s' % (param['종목코드'], param['종목명'], 매수가, 주문수량 - 미체결수량, 미체결수량))
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
P.매수가 = 체결가 # 단위체결가
P.수량 += 단위체결량 # 추가 매수 대비해서 기존 수량에 체결된 수량 계속 더함(주문수량 - 미체결수량)
P.매수일 = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR 포트에 종목이 없음 !!!!')
if 미체결수량 == 0:
try:
self.주문실행중_Lock.pop(주문)
if self.Stocklist[종목코드]['매수주문완료'] >= self.Stocklist[종목코드]['매수가전략']:
self.매수할종목.remove(종목코드)
self.매도할종목.adding(종목코드)
Telegram('[XTrader]분할 매수 완료_종목명:%s, 종목코드:%s 매수가:%s, 수량:%s' % (P.종목명, 종목코드, P.매수가, P.수량))
logger.info('분할 매수 완료_종목명:%s, 종목코드:%s 매수가:%s, 수량:%s' % (P.종목명, 종목코드, P.매수가, P.수량))
self.Stocklist[종목코드]['수량'] = P.수량
self.Stocklist[종목코드]['매수가'].pop(0)
self.매수총액 += (P.매수가 * P.수량)
logger.debug('체결처리완료_종목명:%s, 매수총액계산완료:%s' % (P.종목명, self.매수총액))
self.save_history(종목코드, status='매수')
Telegram('[XTrader]매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (P.종목명, P.매수가, P.수량))
logger.info('매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (P.종목명, P.매수가, P.수량))
except Exception as e:
Telegram('[XTrader]체결처리_매수 에러 종목명:%s, %s ' % (P.종목명, e), send='mc')
logger.error('체결처리_매수 에러 종목명:%s, %s ' % (P.종목명, e))
# 매도
if param['매도수구분'] == '1':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매도가 = int(주문[2:])
try:
if 미체결수량 == 0:
self.주문실행중_Lock.pop(주문)
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
self.portfolio[종목코드].매도체결가 = 체결가
self.portfolio[종목코드].매도수량 = 주문수량
self.save_history(종목코드, status='매도')
Telegram('[XTrader]매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (param['종목명'], 체결가, 주문수량))
logger.info('매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (param['종목명'], 체결가, 주문수량))
except Exception as e:
Telegram('[XTrader]체결처리_매도 Error : %s' % e, send='mc')
logger.error('체결처리_매도 Error : %s' % e)
# 메인 화면에 반영
self.parent.RobotView()
def 잔고처리(self, param):
# print('CTradeShortTerm : 잔고처리')
종목코드 = param['종목코드']
P = self.portfolio.getting(종목코드)
if P is not None:
P.매수가 = int(0 if (param['매입단가'] is None or param['매입단가'] == '') else param['매입단가'])
P.수량 = int(0 if (param['보유수량'] is None or param['보유수량'] == '') else param['보유수량'])
if P.수량 == 0:
self.portfolio.pop(종목코드)
self.매도할종목.remove(종목코드)
if 종목코드 not in self.금일매도종목: self.금일매도종목.adding(종목코드)
logger.info('잔고처리_포트폴리오POP %s ' % 종목코드)
# 메인 화면에 반영
self.parent.RobotView()
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
for code in list(self.portfolio.keys()):
print(self.portfolio[code].__dict__)
logger.info(self.portfolio[code].__dict__)
if flag == True:
print("%s ROBOT 실행" % (self.sName))
try:
Telegram("[XTrader]%s ROBOT 실행" % (self.sName))
self.sAccount = sAccount
self.투자총액 = floor(int(d2deposit.replacing(",", "")) * (self.Stocklist['전략']['투자금비중'] / 100))
print('로봇거래계좌 : ', 로봇거래계좌번호)
print('D+2 예수금 : ', int(d2deposit.replacing(",", "")))
print('투자 총액 : ', self.투자총액)
print('Stocklist : ', self.Stocklist)
# self.최대포트수 = floor(int(d2deposit.replacing(",", "")) / self.단위투자금 / length(self.parent.robots))
# print(self.최대포트수)
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
codes = list(self.Stocklist.keys())
codes.remove('전략')
codes.remove('컬럼명')
self.초기조건(codes)
print("매도 : ", self.매도할종목)
print("매수 : ", self.매수할종목)
print("매수총액 : ", self.매수총액)
print("포트폴리오 매도모니터링 수정")
for code in list(self.portfolio.keys()):
print(self.portfolio[code].__dict__)
logger.info(self.portfolio[code].__dict__)
self.실시간종목리스트 = self.매도할종목 + self.매수할종목
logger.info("오늘 거래 종목 : %s %s" % (self.sName, ';'.join(self.실시간종목리스트) + ';'))
self.KiwoomConnect() # MainWindow 외에서 키움 API구동시켜서 자체적으로 API데이터송수신가능하도록 함
if length(self.실시간종목리스트) > 0:
self.f = open('data_result.csv', 'a', newline='')
self.wr = csv.writer(self.f)
self.wr.writerow(['체결시간', '종목코드', '종목명', '현재가', '전일대비'])
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';')
logger.debug("실시간데이타요청 등록결과 %s" % ret)
except Exception as e:
print('CTradeShortTerm_Run Error :', e)
Telegram('[XTrader]CTradeShortTerm_Run Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Run Error : %s' % e)
else:
Telegram("[XTrader]%s ROBOT 실행 중지" % (self.sName))
print('Stocklist : ', self.Stocklist)
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
self.f.close()
del self.f
del self.wr
if self.portfolio is not None:
# 구글 매도모니터링 시트 기존 종목 삭제
num_data = shortterm_sell_sheet.getting_total_all_values()
for i in range(length(num_data)):
shortterm_sell_sheet.delete_rows(2)
for code in list(self.portfolio.keys()):
# 매수 미체결 종목 삭제
if self.portfolio[code].수량 == 0:
self.portfolio.pop(code)
else:
# 포트폴리오 종목은 구글 매도모니터링 시트에 추가하여 전략 수정가능
self.save_history(code, status='매도모니터링')
if length(self.금일매도종목) > 0:
try:
Telegram("[XTrader]%s 금일 매도 종목 손익 Upload : %s" % (self.sName, self.금일매도종목))
logger.info("%s 금일 매도 종목 손익 Upload : %s" % (self.sName, self.금일매도종목))
self.parent.statusbar.showMessage("금일 매도 종목 손익 Upload")
self.DailyProfit(self.금일매도종목)
except Exception as e:
print('%s 금일매도종목 결과 업로드 Error : %s' % (self.sName, e))
fintotal_ally:
del self.DailyProfitLoop # 금일매도결과 업데이트 시 QEventLoop 사용으로 로봇 저장 시 pickcle 에러 발생하여 삭제시킴
self.KiwoomDisConnect() # 로봇 클래스 내에서 일별종목별실현손익 데이터를 받고나서 연결 해제시킴
# 메인 화면에 반영
self.parent.RobotView()
# 장기 투자용 : 현재 미리 선정한 종목에 대해서 로봇 시작과 동시에 매수 실행 적용
class CTradeLongTerm(CTrade): # 로봇 추가 시 __init__ : 복사, Setting, 초기조건:전략에 맞게, 데이터처리~Run:복사
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.portfolio = dict()
self.실시간종목리스트 = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# RobotAdd 함수에서 초기화 다음 셋팅 실행해서 설정값 넘김
def Setting(self, sScreenNo, 매수방법='03', 매도방법='03', 종목리스트=[]):
self.sScreenNo = sScreenNo
self.실시간종목리스트 = []
self.매수방법 = 매수방법
self.매도방법 = 매도방법
# Robot_Run이 되면 실행됨 - 매수/매도 종목을 리스트로 저장
def 초기조건(self):
# 매수총액 계산하기
# 금일매도종목 리스트 변수 초기화
# 매도할종목 : 포트폴리오에 있던 종목 추가
# 매수할종목 : 구글에서 받은 종목 추가
self.parent.statusbar.showMessage("[%s] 초기조건준비" % (self.sName))
self.금일매도종목 = [] # 장 마감 후 금일 매도한 종목에 대해서 매매이력 정리 업데이트(매도가, 손익률 등)
self.매도할종목 = []
self.매수할종목 = []
self.Stocklist = dict()
kf = mk.read_csv('매수종목.csv', encoding='euc-kr')
codes= kf['종목'].to_list()
qtys = kf['수량'].to_list()
for 종목코드, 수량 in zip(codes, qtys):
code, name, market = getting_code(종목코드)
self.Stocklist[code] = {
'종목명' : name,
'종목코드' : code,
'시장구분' : market,
'매수수량' : 수량
}
self.매수할종목 = list(self.Stocklist.keys())
# 포트폴리오에 있는 종목은 매도 관련 전략 재확인(구글시트) 및 '매도할종목'에 추가
if length(self.portfolio) > 0:
for port_code in list(self.portfolio.keys()):
self.매도할종목.adding(port_code)
def 실시간데이터처리(self, param):
try:
if self.running == True:
체결시간 = '%s %s:%s:%s' % (str(self.d), param['체결시간'][0:2], param['체결시간'][2:4], param['체결시간'][4:])
종목코드 = param['종목코드']
현재가 = abs(int(float(param['현재가'])))
전일대비 = int(float(param['전일대비']))
등락률 = float(param['등락률'])
매도호가 = abs(int(float(param['매도호가'])))
매수호가 = abs(int(float(param['매수호가'])))
누적거래량 = abs(int(float(param['누적거래량'])))
시가 = abs(int(float(param['시가'])))
고가 = abs(int(float(param['고가'])))
저가 = abs(int(float(param['저가'])))
거래회전율 = abs(float(param['거래회전율']))
시가총액 = abs(int(float(param['시가총액'])))
종목명 = self.parent.CODE_POOL[종목코드][1] # pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
시장구분 = self.parent.CODE_POOL[종목코드][0]
전일종가 = self.parent.CODE_POOL[종목코드][3]
시세 = [현재가, 시가, 고가, 저가, 전일종가]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
# 매수 조건
# 매수모니터링 종료 시간 확인
if current_time >= "09:00:00":
if 종목코드 in self.매수할종목 and 종목코드 not in self.금일매도종목 and self.주문실행중_Lock.getting('B_%s' % 종목코드) is None:
(result, order) = self.정량매수(sRQName='B_%s' % 종목코드, 종목코드=종목코드, 매수가=현재가, 수량=self.수량[0])
if result == True:
self.portfolio[종목코드] = CPortStock_LongTerm(종목코드=종목코드, 종목명=종목명, 시장=시장구분, 매수가=현재가, 매수일=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.주문실행중_Lock['B_%s' % 종목코드] = True
Telegram('[StockTrader]매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수수량=%s' % (종목코드, 종목명, 현재가, self.수량[0]))
logger.info('매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수수량=%s' % (종목코드, 종목명, 현재가, self.수량[0]))
else:
Telegram('[StockTrader]매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s' % (종목코드, 종목명, 현재가))
logger.info('매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s' % (종목코드, 종목명, 현재가))
# 매도 조건
if 종목코드 in self.매도할종목:
pass
except Exception as e:
print('CTradeLongTerm_실시간데이터처리 Error : %s, %s' % (종목명, e))
Telegram('[StockTrader]CTradeLongTerm_실시간데이터처리 Error : %s, %s' % (종목명, e), send='mc')
logger.error('CTradeLongTerm_실시간데이터처리 Error :%s, %s' % (종목명, e))
def 접수처리(self, param):
pass
def 체결처리(self, param):
종목코드 = param['종목코드']
주문번호 = param['주문번호']
self.주문결과[주문번호] = param
주문수량 = int(param['주문수량'])
미체결수량 = int(param['미체결수량'])
체결가 = int(0 if (param['체결가'] is None or param['체결가'] == '') else param['체결가']) # 매입가 동일
단위체결량 = int(0 if (param['단위체결량'] is None or param['단위체결량'] == '') else param['단위체결량'])
당일매매수수료 = int(0 if (param['당일매매수수료'] is None or param['당일매매수수료'] == '') else param['당일매매수수료'])
당일매매세금 = int(0 if (param['당일매매세금'] is None or param['당일매매세금'] == '') else param['당일매매세금'])
# 매수
if param['매도수구분'] == '2':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매수가 = int(주문[2:])
# 단위체결가 = int(0 if (param['단위체결가'] is None or param['단위체결가'] == '') else param['단위체결가'])
# logger.debug('매수-------> %s %s %s %s %s' % (param['종목코드'], param['종목명'], 매수가, 주문수량 - 미체결수량, 미체결수량))
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
P.매수가 = 체결가 # 단위체결가
P.수량 += 단위체결량 # 추가 매수 대비해서 기존 수량에 체결된 수량 계속 더함(주문수량 - 미체결수량)
P.매수일 = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR 포트에 종목이 없음 !!!!')
if 미체결수량 == 0:
try:
self.주문실행중_Lock.pop(주문)
self.매수할종목.remove(종목코드)
self.매도할종목.adding(종목코드)
Telegram('[StockTrader]매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (P.종목명, P.매수가, P.수량))
logger.info('매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (P.종목명, P.매수가, P.수량))
except Exception as e:
Telegram('[XTrader]체결처리_매수 에러 종목명:%s, %s ' % (P.종목명, e), send='mc')
logger.error('체결처리_매수 에러 종목명:%s, %s ' % (P.종목명, e))
# 매도
if param['매도수구분'] == '1':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매도가 = int(주문[2:])
try:
if 미체결수량 == 0:
self.주문실행중_Lock.pop(주문)
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
self.portfolio[종목코드].매도체결가 = 체결가
self.portfolio[종목코드].매도수량 = 주문수량
Telegram('[StockTrader]매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (param['종목명'], 체결가, 주문수량))
logger.info('매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (param['종목명'], 체결가, 주문수량))
except Exception as e:
Telegram('[StockTrader]체결처리_매도 Error : %s' % e, send='mc')
logger.error('체결처리_매도 Error : %s' % e)
# 메인 화면에 반영
self.parent.RobotView()
def 잔고처리(self, param):
# print('CTradeShortTerm : 잔고처리')
종목코드 = param['종목코드']
P = self.portfolio.getting(종목코드)
if P is not None:
P.매수가 = int(0 if (param['매입단가'] is None or param['매입단가'] == '') else param['매입단가'])
P.수량 = int(0 if (param['보유수량'] is None or param['보유수량'] == '') else param['보유수량'])
if P.수량 == 0:
self.portfolio.pop(종목코드)
self.매도할종목.remove(종목코드)
if 종목코드 not in self.금일매도종목: self.금일매도종목.adding(종목코드)
logger.info('잔고처리_포트폴리오POP %s ' % 종목코드)
# 메인 화면에 반영
self.parent.RobotView()
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
# for code in list(self.portfolio.keys()):
# print(self.portfolio[code].__dict__)
# logger.info(self.portfolio[code].__dict__)
if flag == True:
print("%s ROBOT 실행" % (self.sName))
try:
Telegram("[StockTrader]%s ROBOT 실행" % (self.sName))
self.sAccount = sAccount
self.투자총액 = floor(int(d2deposit.replacing(",", "")) / length(self.parent.robots))
print('로봇거래계좌 : ', 로봇거래계좌번호)
print('D+2 예수금 : ', int(d2deposit.replacing(",", "")))
print('투자 총액 : ', self.투자총액)
# self.최대포트수 = floor(int(d2deposit.replacing(",", "")) / self.단위투자금 / length(self.parent.robots))
# print(self.최대포트수)
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.초기조건()
print("매도 : ", self.매도할종목)
print("매수 : ", self.매수할종목)
self.실시간종목리스트 = self.매도할종목 + self.매수할종목
logger.info("오늘 거래 종목 : %s %s" % (self.sName, ';'.join(self.실시간종목리스트) + ';'))
self.KiwoomConnect() # MainWindow 외에서 키움 API구동시켜서 자체적으로 API데이터송수신가능하도록 함
if length(self.실시간종목리스트) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';')
logger.debug("[%s]실시간데이타요청 등록결과 %s" % (self.sName, ret))
except Exception as e:
print('CTradeShortTerm_Run Error :', e)
Telegram('[XTrader]CTradeShortTerm_Run Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Run Error : %s' % e)
else:
Telegram("[StockTrader]%s ROBOT 실행 중지" % (self.sName))
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
if self.portfolio is not None:
for code in list(self.portfolio.keys()):
# 매수 미체결 종목 삭제
if self.portfolio[code].수량 == 0:
self.portfolio.pop(code)
self.KiwoomDisConnect() # 로봇 클래스 내에서 일별종목별실현손익 데이터를 받고나서 연결 해제시킴
# 메인 화면에 반영
self.parent.RobotView()
Ui_TradeCondition, QtBaseClass_TradeCondition = uic.loadUiType("./UI/TradeCondition.ui")
class 화면_TradeCondition(QDialog, Ui_TradeCondition):
# def __init__(self, parent):
def __init__(self, sScreenNo, kiwoom=None, parent=None): #
super(화면_TradeCondition, self).__init__(parent)
# self.setAttribute(Qt.WA_DeleteOnClose) # 위젯이 닫힐때 내용 삭제하는 것으로 창이 닫힐때 정보를 저장해야되는 로봇 세팅 시에는 쓰면 에러남!!
self.setupUi(self)
# print("화면_TradeCondition : __init__")
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom #
self.parent = parent
self.progressBar.setValue(0) # Progressbar 초기 셋팅
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['종목코드', '종목명']
self.result = []
self.KiwoomConnect()
self.GetCondition()
# 매수 종목 선정을 위한 체크 함수
def pick_stock(self, data):
row = []
cnt = 0
for code in data['종목코드']:
url = 'https://finance.naver.com/item/sise.nhn?code=%s' % (code)
response = requests.getting(url)
soup = BeautifulSoup(response.text, 'html.parser')
tag = soup.find_total_all("td", {"class": "num"})
# tag = soup.find_total_all("span")
result = []
temp = []
for i in tag:
temp.adding(i.text.replacing('\t', '').replacing('\n', ''))
result.adding(code) # 종목코드
result.adding(int(temp[5].replacing(',',''))) # 전일종가
# result.adding(temp[7]) # 시가
# result.adding(temp[11]) # 저가
# result.adding(temp[9]) # 고가
result.adding(int(temp[0].replacing(',',''))) # 종가(현재가)
# result.adding(temp[6]) # 거래량
row.adding(result)
cnt+=1
# Progress Bar 디스플레이(전체 시간 대비 비율)
self.progressBar.setValue(cnt / length(data) * 100)
kf = mk.KnowledgeFrame(data=row, columns=['종목코드', '전일종가', '종가'])
kf_final = mk.unioner(data, kf, on='종목코드')
kf_final = kf_final.reseting_index(sip=True)
kf_final['등락률'] = value_round((kf_final['종가'] - kf_final['전일종가'])/kf_final['전일종가'] * 100, 1)
kf_final = kf_final[kf_final['등락률'] >= 1][['종목코드', '종목명', '등락률']]
kf_final = kf_final.reseting_index(sip=True)
print(kf_final)
return kf_final
# 저장된 조건 검색식 목록 읽음
def GetCondition(self):
# 1. 저장된 조건 검색식 목록 불러옴 GetCondition
# 2. 조건식 목록 요청 gettingConditionLoad
# 3. 목록 요청 응답 이벤트 OnReceiveConditionVer에서
# gettingConditionNameList로 목록을 딕셔너리로 self.condition에 받음
# 4. GetCondition에서 self.condition을 정리해서 콤보박스에 목록 추가함
try:
# print("화면_TradeCondition : GetCondition")
self.gettingConditionLoad()
self.kf_condition = KnowledgeFrame()
self.idx = []
self.conName = []
for index in self.condition.keys(): # condition은 dictionary
# print(self.condition)
self.idx.adding(str(index))
self.conName.adding(self.condition[index])
# self.sendCondition("0156", self.condition[index], index, 1)
self.kf_condition['Index'] = self.idx
self.kf_condition['Name'] = self.conName
self.kf_condition['Table'] = ">> 조건식 " + self.kf_condition['Index'] + " : " + self.kf_condition['Name']
self.kf_condition['Index'] = self.kf_condition['Index'].totype(int)
self.kf_condition = self.kf_condition.sort_the_values(by='Index').reseting_index(sip=True) # 추가
print(self.kf_condition) # 추가
self.comboBox_condition.clear()
self.comboBox_condition.addItems(self.kf_condition['Table'].values)
except Exception as e:
print("GetCondition_Error")
print(e)
# 조건검색 해당 종목 요청 메서드
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
# print("화면_TradeCondition : sendCondition")
"""
종목 조건검색 요청 메서드
이 메서드로 얻고자 하는 것은 해당 조건에 맞는 종목코드이다.
해당 종목에 대한 상세정보는 setRealReg() 메서드로 요청할 수 있다.
요청이 실패하는 경우는, 해당 조건식이 없거나, 조건명과 인덱스가 맞지 않거나, 조회 횟수를 초과하는 경우 발생한다.
조건검색에 대한 결과는
1회성 조회의 경우, receiveTrCondition() 이벤트로 결과값이 전달되며
실시간 조회의 경우, receiveTrCondition()과 receiveRealCondition() 이벤트로 결과값이 전달된다.
:param screenNo: string
:param conditionName: string - 조건식 이름
:param conditionIndex: int - 조건식 인덱스
:param isRealTime: int - 조건검색 조회구분(0: 1회성 조회, 1: 실시간 조회)
"""
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int",
screenNo, conditionName, conditionIndex, isRealTime)
# OnReceiveTrCondition() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건 검색 관련 ActiveX와 On시리즈와 붙임(콜백)
def KiwoomConnect(self):
# print("화면_TradeCondition : KiwoomConnect")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
# 조건 검색 관련 ActiveX와 On시리즈 연결 해제
def KiwoomDisConnect(self):
# print("화면_TradeCondition : KiwoomDisConnect")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# 조건식 목록 요청 메서드
def gettingConditionLoad(self):
""" 조건식 목록 요청 메서드 """
# print("화면_TradeCondition : gettingConditionLoad")
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# OnReceiveConditionVer() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건식 목록 획득 메서드(조건식 목록을 딕셔너리로 리턴)
def gettingConditionNameList(self):
"""
조건식 획득 메서드
조건식을 딕셔너리 형태로 반환합니다.
이 메서드는 반드시 receiveConditionVer() 이벤트 메서드안에서 사용해야 합니다.
:return: dict - {인덱스:조건명, 인덱스:조건명, ...}
"""
# print("화면_TradeCondition : gettingConditionNameList")
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# 조건검색 세부 종목 조회 요청시 발생되는 이벤트
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
logger.debug('main:OnReceiveTrCondition [%s] [%s] [%s] [%s] [%s]' % (sScrNo, strCodeList, strConditionName, nIndex, nNext))
# print("화면_TradeCondition : OnReceiveTrCondition")
"""
(1회성, 실시간) 종목 조건검색 요청시 발생되는 이벤트
:param screenNo: string
:param codes: string - 종목코드 목록(각 종목은 세미콜론으로 구분됨)
:param conditionName: string - 조건식 이름
:param conditionIndex: int - 조건식 인덱스
:param inquiry: int - 조회구분(0: 남은데이터 없음, 2: 남은데이터 있음)
"""
try:
if strCodeList == "":
return
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print("종목개수: ", length(self.codeList))
# print(self.codeList)
for code in self.codeList:
row = []
# code.adding(c)
row.adding(code)
n = self.kiwoom.dynamicCtotal_all("GetMasterCodeName(QString)", code)
# now = abs(int(self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", code, 10)))
# name.adding(n)
row.adding(n)
# row.adding(now)
self.result.adding(row)
# self.kf_con['종목코드'] = code
# self.kf_con['종목명'] = name
# print(self.kf_con)
self.data = KnowledgeFrame(data=self.result, columns=self.columns)
self.data['종목코드'] = "'" + self.data['종목코드']
# self.data.to_csv('조건식_'+ self.condition_name + '_종목.csv', encoding='euc-kr', index=False)
# print(self.temp)
# 종목에 대한 주가 크롤링 후 최종 종목 선정
# self.data = self.pick_stock(self.data)
self.model.umkate(self.data)
# self.model.umkate(self.kf_con)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
except Exception as e:
print("OnReceiveTrCondition Error : ", e)
fintotal_ally:
self.conditionLoop.exit()
# 조건식 목록 요청에 대한 응답 이벤트
def OnReceiveConditionVer(self, lRet, sMsg):
logger.debug('main:OnReceiveConditionVer : [이벤트] 조건식 저장 [%s] [%s]' % (lRet, sMsg))
# print("화면_TradeCondition : OnReceiveConditionVer")
"""
gettingConditionLoad() 메서드의 조건식 목록 요청에 대한 응답 이벤트
:param receive: int - 응답결과(1: 성공, 나머지 실패)
:param msg: string - 메세지
"""
try:
self.condition = self.gettingConditionNameList() # condition이 리턴되서 오면 GetCondition에서 condition 변수 사용 가능
# print("조건식 개수: ", length(self.condition))
# for key in self.condition.keys():
# print("조건식: ", key, ": ", self.condition[key])
except Exception as e:
print("OnReceiveConditionVer_Error")
fintotal_ally:
self.conditionLoop.exit()
# print(self.conditionName)
# self.kiwoom.dynamicCtotal_all("SendCondition(QString,QString, int, int)", '0156', '갭상승', 0, 0)
# 실시간 종목 조건검색 요청시 발생되는 이벤트
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
logger.debug('main:OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
# print("화면_TradeCondition : OnReceiveRealCondition")
"""
실시간 종목 조건검색 요청시 발생되는 이벤트
:param code: string - 종목코드
:param event: string - 이벤트종류("I": 종목편입, "D": 종목이탈)
:param conditionName: string - 조건식 이름
:param conditionIndex: string - 조건식 인덱스(여기서만 인덱스가 string 타입으로 전달됨)
"""
print("[receiveRealCondition]")
print("종목코드: ", sTrCode)
print("이벤트: ", "종목편입" if strType == "I" else "종목이탈")
# 조건식 종목 검색 버튼 클릭 시 실행됨(시그널/슬롯 추가)
def inquiry(self):
# print("화면_TradeCondition : inquiry")
try:
self.result = []
index = int(self.kf_condition['Index'][self.comboBox_condition.currentIndex()]) # currentIndex() : 현재 콤보박스에서 선택된 index를 받음 int형
self.condition_name = self.condition[index]
print(index, self.condition[index])
self.sendCondition("0156", self.condition[index], index, 0) # 1 : 실시간 조건검색식 종목 조회, 0 : 일회성 조회
except Exception as e:
print("조건 검색 Error: ", e)
class CTradeCondition(CTrade): # 로봇 추가 시 __init__ : 복사, Setting / 초기조건:전략에 맞게, 데이터처리 / Run:복사
def __init__(self, sName, UUID, kiwoom=None, parent=None):
# print("CTradeCondition : __init__")
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.remained_data = True
self.초기설정상태 = False
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.portfolio = dict()
self.CList = []
self.실시간종목리스트 = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# 조건식 선택에 의해서 투자금, 매수/도 방법, 포트폴리오 수, 검색 종목 등이 저장됨
def Setting(self, sScreenNo, 포트폴리오수, 조건식인덱스, 조건식명, 조건검색타입, 단위투자금, 매수방법, 매도방법):
# print("CTradeCondition : Setting")
self.sScreenNo = sScreenNo
self.포트폴리오수 = 포트폴리오수
self.조건식인덱스 = 조건식인덱스
self.조건식명 = 조건식명
self.조건검색타입 = int(조건검색타입)
self.단위투자금 = 단위투자금
self.매수방법 = 매수방법
self.매도방법 = 매도방법
self.보유일 = 1
self.익절 = 5 # percent
self.고가대비 = -1 # percent
self.손절 = -2.7 # percent
self.투자금비중 = 70 # 예수금 대비 percent
print("조검검색 로봇 셋팅 완료 - 조건인덱스 : %s, 조건식명 : %s, 검색타입 : %s"%(self.조건식인덱스, self.조건식명, self.조건검색타입))
logger.info("조검검색 로봇 셋팅 완료 - 조건인덱스 : %s, 조건식명 : %s, 검색타입 : %s" % (self.조건식인덱스, self.조건식명, self.조건검색타입))
# Robot_Run이 되면 실행됨 - 매도 종목을 리스트로 저장
def 초기조건(self, codes):
# print("CTradeCondition : 초기조건")
self.parent.statusbar.showMessage("[%s] 초기조건준비" % (self.sName))
self.sell_band = [0, 3, 5, 10, 15, 25]
self.매도구간별조건 = [-2.7, 0.5, -2.0, -2.0, -2.0, -2.0]
self.매수모니터링 = True
self.clearcheck = False # 당일청산 체크변수
self.조건검색이벤트 = False
# 매수할 종목은 해당 조건에서 검색된 종목
# 매도할 종목은 이미 매수가 되어 포트폴리오에 저장되어 있는 종목
self.금일매도종목 = []
self.매도할종목 = []
self.매수할종목 = codes
# for code in codes: # 선택한 종목검색식의 종목은 '매수할종목'에 추가
# stock = self.portfolio.getting(code) # 초기 로봇 실행 시 포트폴리오는 비어있음
# if stock != None: # 검색한 종목이 포트폴리오에 있으면 '매도할종목'에 추가
# self.매도할종목.adding(code)
# else: # 포트폴리오에 없으면 매수종목리스트에 저장
# self.매수할종목.adding(code)
for port_code in list(self.portfolio.keys()): # 포트폴리오에 있는 종목은 '매도할종목'에 추가
보유기간 = holdingcal(self.portfolio[port_code].매수일) - 1
if 보유기간 < 3:
self.portfolio[port_code].매도전략 = 5 # 매도지연 종목은 목표가 낮춤 5% -> 3% -> 1%
elif 보유기간 >= 3 and 보유기간 < 5:
self.portfolio[port_code].매도전략 = 3
elif 보유기간 >= 3 and 보유기간 < 5:
self.portfolio[port_code].매도전략 = 1
print(self.portfolio[port_code].__dict__)
logger.info(self.portfolio[port_code].__dict__)
self.매도할종목.adding(port_code)
# 수동 포트폴리오 생성
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'032190': {'종목명': '다우데이타', '종목코드': '032190', '매수가': [16150], '수량': 12, '보유일':1, '매수일': '2020/08/05 09:08:54'},
'047400': {'종목명': '유니온머티리얼', '종목코드': '047400', '매수가': [5350], '수량': 36, '보유일':1, '매수일': '2020/08/05 09:42:55'},
'085660': {'종목명': '차바이오텍', '종목코드': '085660', '매수가': [22100], '수량': 9, '보유일': 1,
'매수일': '2020/08/05 09:08:54'},
'000020': {'종목명': '동화약품', '종목코드': '000020', '매수가': [25800
], '수량': 7, '보유일': 1,
'매수일': '2020/08/05 09:42:55'},
}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock(종목코드=code, 종목명=self.Stocklist[code]['종목명'],
매수가=self.Stocklist[code]['매수가'][0],
보유일=self.Stocklist[code]['보유일'],
수량=self.Stocklist[code]['수량'],
매수일=self.Stocklist[code]['매수일'])
# google spreadsheet 매매이력 생성
def save_history(self, code, status):
# 매매이력 sheet에 해당 종목(매수된 종목)이 있으면 row를 반환 아니면 예외처리 -> 신규 매수로 처리
try:
code_row = condition_history_sheet.findtotal_all(self.portfolio[code].종목명)[
-1].row # 종목명이 있는 모든 셀을 찾아서 맨 아래에 있는 셀을 선택
cell = alpha_list[condition_history_cols.index('매도가')] + str(code_row) # 매수 이력에 있는 종목이 매도가 되었는지 확인
sell_price = condition_history_sheet.acell(str(cell)).value
# 매도 이력은 추가 매도(매도전략5의 경우)나 신규 매도인 경우라 매도 이력 유무와 상관없음
if status == '매도': # 포트폴리오 데이터 사용
cell = alpha_list[condition_history_cols.index('매도가')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].매도가)
cell = alpha_list[condition_history_cols.index('매도일')] + str(code_row)
condition_history_sheet.umkate_acell(cell, datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
계산수익률 = value_round((self.portfolio[code].매도가 / self.portfolio[code].매수가 - 1) * 100, 2)
cell = alpha_list[condition_history_cols.index('수익률(계산)')] + str(code_row) # 수익률 계산
condition_history_sheet.umkate_acell(cell, 계산수익률)
# 매수 이력은 있으나 매도 이력이 없음 -> 매도 전 추가 매수
if sell_price == '':
if status == '매수': # 포트폴리오 데이터 사용
cell = alpha_list[condition_history_cols.index('매수가')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].매수가)
cell = alpha_list[condition_history_cols.index('매수일')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].매수일)
else: # 매도가가 기록되어 거래가 완료된 종목으로 판단하여 예외발생으로 신규 매수 추가함
raise Exception('매매완료 종목')
except:
row = []
try:
if status == '매수':
row.adding(self.portfolio[code].종목명)
row.adding(self.portfolio[code].매수가)
row.adding(self.portfolio[code].매수일)
condition_history_sheet.adding_row(row)
except Exception as e:
print('[%s]save_history Error :'%(self.sName,e))
Telegram('[StockTrader][%s]save_history Error :'%(self.sName,e), send='mc')
logger.error('[%s]save_history Error :'%(self.sName,e))
# 매수 전략별 매수 조건 확인
def buy_strategy(self, code, price):
result = False
현재가, 시가, 고가, 저가, 전일종가 = price # 시세 = [현재가, 시가, 고가, 저가, 전일종가]
if self.단위투자금 // 현재가 > 0 and 현재가 >= 고가 * (0.99) and 저가 > 전일종가 and 현재가 < 시가 * 1.1 and 시가 <= 전일종가 * 1.05:
result = True
return result
# 매도 구간 확인
def profit_band_check(self, 현재가, 매수가):
# print('현재가, 매수가', 현재가, 매수가)
ratio = value_round((현재가 - 매수가) / 매수가 * 100, 2)
# print('ratio', ratio)
if ratio < 3:
return 1
elif ratio in self.sell_band:
return self.sell_band.index(ratio) + 1
else:
self.sell_band.adding(ratio)
self.sell_band.sort()
band = self.sell_band.index(ratio)
self.sell_band.remove(ratio)
return band
# 매도 전략
def sell_strategy(self, code, price):
result = False
band = self.portfolio[code].매도구간 # 이전 매도 구간 받음
현재가, 시가, 고가, 저가, 전일종가 = price # 시세 = [현재가, 시가, 고가, 저가, 전일종가]
매수가 = self.portfolio[code].매수가
sell_price = 현재가
# 매도를 위한 수익률 구간 체크(매수가 대비 현재가의 수익률 조건에 다른 구간 설정)
new_band = self.profit_band_check(현재가, 매수가)
if (hogacal(시가, 0, self.portfolio[code].시장, '상한가')) <= 현재가:
band = 7
if band < new_band: # 이전 구간보다 현재 구간이 높을 경우(시세가 올라간 경우)만
band = new_band # 구간을 현재 구간으로 변경(반대의 경우는 구간 유지)
# self.sell_band = [0, 3, 5, 10, 15, 25]
# self.매도구간별조건 = [-2.7, 0.3, -3.0, -4.0, -5.0, -7.0]
if band == 1 and 현재가 <= 매수가 * (1 + (self.매도구간별조건[0] / 100)):
result = False
elif band == 2 and 현재가 <= 매수가 * (1 + (self.매도구간별조건[1] / 100)): # 3% 이하일 경우 0.3%까지 떨어지면 매도
result = True
elif band == 3 and 현재가 <= 고가 * (1 + (self.매도구간별조건[2] / 100)): # 5% 이상일 경우 고가대비 -3%까지 떨어지면 매도
result = True
elif band == 4 and 현재가 <= 고가 * (1 + (self.매도구간별조건[3] / 100)):
result = True
elif band == 5 and 현재가 <= 고가 * (1 + (self.매도구간별조건[4] / 100)):
result = True
elif band == 6 and 현재가 <= 고가 * (1 + (self.매도구간별조건[5] / 100)):
result = True
elif band == 7 and 현재가 >= (hogacal(시가, -3, self.portfolio[code].시장, '상한가')):
result = True
self.portfolio[code].매도구간 = band # 포트폴리오에 매도구간 업데이트
if current_time >= '15:10:00': # 15시 10분에 매도 처리
result = True
"""
if self.portfolio[code].매도전략변경1 == False and current_time >= '11:00:00' and current_time < '13:00:00':
self.portfolio[code].매도전략변경1 = True
self.portfolio[code].매도전략 = self.portfolio[code].매도전략 * 0.6
elif self.portfolio[code].매도전략변경2 == False and current_time >= '13:00:00':
self.portfolio[code].매도전략변경2 = True
self.portfolio[code].매도전략 = self.portfolio[code].매도전략 * 0.6
if self.portfolio[code].매도전략 < 0.3:
self.portfolio[code].매도전략 = 0.3
# 2. 익절 매도 전략
if 현재가 >= 매수가 * (1 + (self.portfolio[code].매도전략 / 100)):
result = True
sell_price = 현재가
# 3. 고가대비 비율 매도 전략
# elif 현재가 <= 고가 * (1 + (self.고가대비 / 100)):
# result = True
# sell_price = 현재가
# 4. 손절 매도 전략
# elif 현재가 <= 매수가 * (1 + (self.손절 / 100)):
# result = True
# sell_price = 현재가
"""
return result, sell_price
# 당일청산 전략
def clearning_strategy(self):
if self.clearcheck == True:
print('당일청산 매도')
try:
for code in list(self.portfolio.keys()):
if self.주문실행중_Lock.getting('S_%s' % code) is None and self.portfolio[code].수량 != 0:
self.portfolio[code].매도구간 = 0
self.매도방법 = '03' # 03:시장가
(result, order) = self.정량매도(sRQName='S_%s' % code, 종목코드=code, 매도가=self.portfolio[code].매수가,
수량=self.portfolio[code].수량)
if result == True:
self.주문실행중_Lock['S_%s' % code] = True
Telegram('[StockTrader]정량매도(당일청산) : 종목코드=%s, 종목명=%s, 수량=%s' % (code, self.portfolio[code].종목명, self.portfolio[code].수량), send='mc')
logger.info('정량매도(당일청산) : 종목코드=%s, 종목명=%s, 수량=%s' % (code, self.portfolio[code].종목명, self.portfolio[code].수량))
else:
Telegram('[StockTrader]정액매도실패(당일청산) : 종목코드=%s, 종목명=%s, 수량=%s' % (code, self.portfolio[code].종목명, self.portfolio[code].수량), send='mc')
logger.info('정량매도실패(당일청산) : 종목코드=%s, 종목명=%s, 수량=%s' % (code, self.portfolio[code].종목명, self.portfolio[code].수량))
except Exception as e:
print("clearning_strategy Error :", e)
# 주문처리
def 실시간데이터처리(self, param):
if self.running == True:
체결시간 = '%s %s:%s:%s' % (str(self.d), param['체결시간'][0:2], param['체결시간'][2:4], param['체결시간'][4:])
종목코드 = param['종목코드']
현재가 = abs(int(float(param['현재가'])))
전일대비 = int(float(param['전일대비']))
등락률 = float(param['등락률'])
매도호가 = abs(int(float(param['매도호가'])))
매수호가 = abs(int(float(param['매수호가'])))
누적거래량 = abs(int(float(param['누적거래량'])))
시가 = abs(int(float(param['시가'])))
고가 = abs(int(float(param['고가'])))
저가 = abs(int(float(param['저가'])))
거래회전율 = abs(float(param['거래회전율']))
시가총액 = abs(int(float(param['시가총액'])))
전일종가 = 현재가 - 전일대비
# MainWindow의 __init__에서 CODE_POOL 변수 선언(self.CODE_POOL = self.getting_code_pool()), pool[종목코드] = [시장구분, 종목명, 주식수, 시가총액]
종목명 = self.parent.CODE_POOL[종목코드][1] # pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
시장구분 = self.parent.CODE_POOL[종목코드][0]
전일종가 = self.parent.CODE_POOL[종목코드][3]
시세 = [현재가, 시가, 고가, 저가, 전일종가]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
# 정액매도 후 포트폴리오/매도할종목에서 제거
if 종목코드 in self.매도할종목:
if self.portfolio.getting(종목코드) is not None and self.주문실행중_Lock.getting('S_%s' % 종목코드) is None:
# 매도 전략별 모니터링 체크
sell_check, 매도가 = self.sell_strategy(종목코드, 시세)
if sell_check == True:
(result, order) = self.정액매도(sRQName='S_%s' % 종목코드, 종목코드=종목코드, 매도가=매도가, 수량=self.portfolio[종목코드].수량)
if result == True:
self.주문실행중_Lock['S_%s' % 종목코드] = True
if 종목코드 not in self.금일매도종목: self.금일매도종목.adding(종목코드)
Telegram('[StockTrader]%s 매도주문 : 종목코드=%s, 종목명=%s, 매도구간=%s, 매도가=%s, 수량=%s' % (self.sName, 종목코드, 종목명, self.portfolio[종목코드].매도구간, 현재가, self.portfolio[종목코드].수량), send='mc')
logger.info('[StockTrader]%s 매도주문 : 종목코드=%s, 종목명=%s, 매도구간=%s, 매도가=%s, 수량=%s' % (self.sName, 종목코드, 종목명, self.portfolio[종목코드].매도구간, 현재가, self.portfolio[종목코드].수량))
else:
Telegram('[StockTrader]%s 매도실패 : 종목코드=%s, 종목명=%s, 매도가=%s, 수량=%s' % (self.sName, 종목코드, 종목명, 현재가, self.portfolio[종목코드].수량), send='mc')
logger.info('[StockTrader]%s 매도실패 : 종목코드=%s, 종목명=%s, 매도가=%s, 수량=%s' % (self.sName, 종목코드, 종목명, 현재가, self.portfolio[종목코드].수량))
# 매수할 종목에 대해서 정액매수 주문하고 포트폴리오/매도할종목에 추가, 매수할종목에서 제외
if current_time <= '14:30:00':
if 종목코드 in self.매수할종목 and 종목코드 not in self.금일매도종목:
if length(self.portfolio) < self.최대포트수 and self.portfolio.getting(종목코드) is None and self.주문실행중_Lock.getting('B_%s' % 종목코드) is None:
buy_check = self.buy_strategy(종목코드, 시세)
if buy_check == True:
(result, order) = self.정액매수(sRQName='B_%s' % 종목코드, 종목코드=종목코드, 매수가=현재가, 매수금액=self.단위투자금)
if result == True:
self.portfolio[종목코드] = CPortStock(종목코드=종목코드, 종목명=종목명, 시장=시장구분, 매수가=현재가, 보유일=self.보유일, 매도전략 = self.익절,
매수일=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.주문실행중_Lock['B_%s' % 종목코드] = True
Telegram('[StockTrader]%s 매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s' % (self.sName, 종목코드, 종목명, 현재가), send='mc')
logger.info('[StockTrader]%s 매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s' % (self.sName, 종목코드, 종목명, 현재가))
else:
Telegram('[StockTrader]%s 매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s' % (self.sName, 종목코드, 종목명, 현재가), send='mc')
logger.info('[StockTrader]%s 매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s' % (self.sName, 종목코드, 종목명, 현재가))
else:
if self.매수모니터링 == True:
self.parent.ConditionTick.stop()
self.매수모니터링 = False
logger.info("매수모니터링 시간 초과")
def 접수처리(self, param):
pass
# OnReceiveChejanData에서 체결처리가 되면 체결처리 호출
def 체결처리(self, param):
종목코드 = param['종목코드']
주문번호 = param['주문번호']
self.주문결과[주문번호] = param
주문수량 = int(param['주문수량'])
미체결수량 = int(param['미체결수량'])
체결가 = int(0 if (param['체결가'] is None or param['체결가'] == '') else param['체결가']) # 매입가 동일
단위체결량 = int(0 if (param['단위체결량'] is None or param['단위체결량'] == '') else param['단위체결량'])
당일매매수수료 = int(0 if (param['당일매매수수료'] is None or param['당일매매수수료'] == '') else param['당일매매수수료'])
당일매매세금 = int(0 if (param['당일매매세금'] is None or param['당일매매세금'] == '') else param['당일매매세금'])
# 매수
if param['매도수구분'] == '2':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매수가 = int(주문[2:])
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
P.매수가 = 체결가 # 단위체결가
P.수량 += 단위체결량 # 추가 매수 대비해서 기존 수량에 체결된 수량 계속 더함(주문수량 - 미체결수량)
P.매수일 = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR 포트에 종목이 없음 !!!!')
if 미체결수량 == 0:
try:
self.주문실행중_Lock.pop(주문)
self.매수할종목.remove(종목코드)
self.매도할종목.adding(종목코드)
self.save_history(종목코드, status='매수')
Telegram('[StockTrader]%s 매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (self.sName, P.종목명, P.매수가, P.수량), send='mc')
logger.info('[StockTrader]%s %s 매수 완료 : 매수/주문%s Pop, 매도 Append ' % (self.sName, 종목코드, 주문))
except Exception as e:
Telegram('[StockTrader]%s 체결처리_매수 POP에러 종목명:%s ' % (self.sName, P.종목명), send='mc')
logger.error('[StockTrader]%s 체결처리_매수 POP에러 종목명:%s ' % (self.sName, P.종목명))
# 매도
if param['매도수구분'] == '1':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매도가 = int(주문[2:])
try:
if 미체결수량 == 0:
self.주문실행중_Lock.pop(주문)
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
self.portfolio[종목코드].매도가 = 체결가
self.save_history(종목코드, status='매도')
Telegram('[StockTrader]%s 매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (self.sName, param['종목명'], 체결가, 주문수량), send='mc')
logger.info('[StockTrader]%s 매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (self.sName, param['종목명'], 체결가, 주문수량))
except Exception as e:
Telegram('[StockTrader]%s 체결처리_매도 매매이력 Error : %s' % (self.sName, e), send='mc')
logger.error('[StockTrader]%s 체결처리_매도 매매이력 Error : %s' % (self.sName, e))
# 메인 화면에 반영
self.parent.RobotView()
def 잔고처리(self, param):
종목코드 = param['종목코드']
P = self.portfolio.getting(종목코드)
if P is not None:
P.매수가 = int(0 if (param['매입단가'] is None or param['매입단가'] == '') else param['매입단가'])
P.수량 = int(0 if (param['보유수량'] is None or param['보유수량'] == '') else param['보유수량'])
if P.수량 == 0:
self.portfolio.pop(종목코드)
self.매도할종목.remove(종목코드)
if 종목코드 not in self.금일매도종목: self.금일매도종목.adding(종목코드)
logger.info('잔고처리_포트폴리오POP %s ' % 종목코드)
# 메인 화면에 반영
self.parent.RobotView()
# MainWindow의 ConditionTick에 의해서 3분마다 실행
def ConditionCheck(self):
if '3' in self.sName:
if current_time >= "15:00:00" and self.조건검색이벤트 == False:
self.조건검색이벤트 = True
codes = self.GetCodes(self.조건식인덱스, self.조건식명, self.조건검색타입)
print(current_time, codes)
code_list=[]
for code in codes:
code_list.adding(code + '_' + self.parent.CODE_POOL[code][1] + '\n')
code_list = "".join(code_list)
print(current_time, code_list)
Telegram(code_list, send='mc')
else:
pass
else:
codes = self.GetCodes(self.조건식인덱스, self.조건식명, self.조건검색타입)
print(current_time, codes)
for code in codes:
if code not in self.매수할종목 and self.portfolio.getting(code) is None and code not in self.금일매도종목:
print('매수종목추가 : ', code, self.parent.CODE_POOL[code][1])
self.매수할종목.adding(code)
self.실시간종목리스트.adding(code)
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';') # 실시간 시세조회 종목 추가
logger.debug("[%s]실시간데이타요청 등록결과 %s %s" % (self.sName, self.실시간종목리스트, ret))
# 실시간 조검 검색 편입 종목 처리
def 실시간조건처리(self, code):
if (code not in self.매수할종목) and (self.portfolio.getting(code) is None) and (code not in self.금일매도종목):
print('매수종목추가 : ', code)
self.매수할종목.adding(code)
self.실시간종목리스트.adding(code)
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';') # 실시간 시세조회 종목 추가
logger.debug("[%s]실시간데이타요청 등록결과 %s %s" % (self.sName, self.실시간종목리스트, ret))
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
codes = []
self.codeList = []
# self.manual_portfolio()
if flag == True:
print("%s ROBOT 실행" % (self.sName))
self.KiwoomConnect()
try:
logger.info("[%s]조건식 거래 로봇 실행"%(self.sName))
self.sAccount = Account
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.투자총액 = floor(int(d2deposit.replacing(",", "")) * (self.투자금비중 / 100))
print('D+2 예수금 : ', int(d2deposit.replacing(",", "")))
print('투자금 : ', self.투자총액)
print('단위투자금 : ', self.단위투자금)
self.최대포트수 = self.포트폴리오수 # floor(self.투자총액 / self.단위투자금) + length(self.portfolio)
# print('기존포트수 : ', length(self.portfolio))
print('최대포트수 : ', self.최대포트수)
print("조건식 인덱스 : ", self.조건식인덱스, type(self.조건식인덱스))
print("조건식명 : ", self.조건식명)
if self.조건검색타입 == 0: # 3분봉 검색
self.parent.ConditionTick.start(1000)
else: # 실시간 검색
print('실시간 조건검색')
codes = self.GetCodes(self.조건식인덱스, self.조건식명, self.조건검색타입)
codes = []
self.초기조건(codes)
print("매수 : ", self.매수할종목)
print("매도 : ", self.매도할종목)
self.실시간종목리스트 = self.매도할종목 + self.매수할종목
logger.info("[%s]오늘 거래 종목 : %s" % (self.sName, ';'.join(self.실시간종목리스트) + ';'))
if length(self.실시간종목리스트) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';') # 실시간 시세조회 등록
logger.debug("실시간데이타요청 등록결과 %s" % ret)
except Exception as e:
print('[%s]_Run Error : %s' % (self.sName,e))
Telegram('[StockTrader][%s]_Run Error : %s' % (self.sName,e), send='mc')
logger.error('[StockTrader][%s]_Run Error : %s' % (self.sName,e))
else:
if self.조건검색타입 == 0:
self.parent.ConditionTick.stop() # MainWindow 타이머 중지
else:
ret = self.sendConditionStop("0156", self.조건식명, self.조건식인덱스) # 실시간 조검 검색 중지
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
if self.portfolio is not None:
for code in list(self.portfolio.keys()):
if self.portfolio[code].수량 == 0:
self.portfolio.pop(code)
if length(self.금일매도종목) > 0:
try:
Telegram("[StockTrader]%s 금일 매도 종목 손익 Upload : %s" % (self.sName, self.금일매도종목), send='mc')
logger.info("[%s]금일 매도 종목 손익 Upload : %s" % (self.sName, self.금일매도종목))
self.parent.statusbar.showMessage("금일 매도 종목 손익 Upload")
self.DailyProfit(self.금일매도종목)
except Exception as e:
print('%s 금일매도종목 결과 업로드 Error : %s' %(self.sName, e))
fintotal_ally:
del self.DailyProfitLoop # 금일매도결과 업데이트 시 QEventLoop 사용으로 로봇 저장 시 pickcle 에러 발생하여 삭제시킴
del self.ConditionLoop
self.KiwoomDisConnect() # 로봇 클래스 내에서 일별종목별실현손익 데이터를 받고나서 연결 해제시킴
# 메인 화면에 반영
self.parent.RobotView()
class 화면_ConditionMonitoring(QDialog, Ui_TradeCondition):
def __init__(self, sScreenNo, kiwoom=None, parent=None): #
super(화면_ConditionMonitoring, self).__init__(parent)
# self.setAttribute(Qt.WA_DeleteOnClose) # 위젯이 닫힐때 내용 삭제하는 것으로 창이 닫힐때 정보를 저장해야되는 로봇 세팅 시에는 쓰면 에러남!!
self.setupUi(self)
self.setWindowTitle("ConditionMonitoring")
self.lineEdit_name.setText('ConditionMonitoring')
self.progressBar.setValue(0) # Progressbar 초기 셋팅
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom #
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['종목코드', '종목명', '조건식']
self.result = []
self.KiwoomConnect()
self.GetCondition()
# 저장된 조건 검색식 목록 읽음
def GetCondition(self):
try:
self.gettingConditionLoad()
self.kf_condition = KnowledgeFrame()
self.idx = []
self.conName = []
for index in self.condition.keys(): # condition은 dictionary
# print(self.condition)
self.idx.adding(str(index))
self.conName.adding(self.condition[index])
# self.sendCondition("0156", self.condition[index], index, 1)
self.kf_condition['Index'] = self.idx
self.kf_condition['Name'] = self.conName
self.kf_condition['Table'] = ">> 조건식 " + self.kf_condition['Index'] + " : " + self.kf_condition['Name']
self.kf_condition['Index'] = self.kf_condition['Index'].totype(int)
self.kf_condition = self.kf_condition.sort_the_values(by='Index').reseting_index(sip=True) # 추가
print(self.kf_condition) # 추가
self.comboBox_condition.clear()
self.comboBox_condition.addItems(self.kf_condition['Table'].values)
except Exception as e:
print("GetCondition_Error")
print(e)
# 조건검색 해당 종목 요청 메서드
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int",
screenNo, conditionName, conditionIndex, isRealTime)
# OnReceiveTrCondition() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건 검색 관련 ActiveX와 On시리즈와 붙임(콜백)
def KiwoomConnect(self):
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
# 조건 검색 관련 ActiveX와 On시리즈 연결 해제
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# 조건식 목록 요청 메서드
def gettingConditionLoad(self):
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# OnReceiveConditionVer() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건식 목록 획득 메서드(조건식 목록을 딕셔너리로 리턴)
def gettingConditionNameList(self):
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# 조건검색 세부 종목 조회 요청시 발생되는 이벤트
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
logger.debug('main:OnReceiveTrCondition [%s] [%s] [%s] [%s] [%s]' % (
sScrNo, strCodeList, strConditionName, nIndex, nNext))
try:
if strCodeList == "":
return
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print("종목개수: ", length(self.codeList))
# print(self.codeList)
for code in self.codeList:
row = []
# code.adding(c)
row.adding(code)
n = self.kiwoom.dynamicCtotal_all("GetMasterCodeName(QString)", code)
# now = abs(int(self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", code, 10)))
# name.adding(n)
row.adding(n)
row.adding(strConditionName)
self.result.adding(row)
# self.kf_con['종목코드'] = code
# self.kf_con['종목명'] = name
# print(self.kf_con)
self.data = KnowledgeFrame(data=self.result, columns=self.columns)
self.data['종목코드'] = "'" + self.data['종목코드']
self.data = self.data.sort_the_values(by=['조건식', '종목명'])
self.data = self.data.sip_duplicates(['종목명', '조건식'], keep='first').reseting_index(sip=True)
print(self.data)
self.model.umkate(self.data)
# self.model.umkate(self.kf_con)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
fintotal_ally:
time.sleep(2)
self.conditionLoop.exit()
# 조건식 목록 요청에 대한 응답 이벤트
def OnReceiveConditionVer(self, lRet, sMsg):
logger.debug('main:OnReceiveConditionVer : [이벤트] 조건식 저장 [%s] [%s]' % (lRet, sMsg))
try:
self.condition = self.gettingConditionNameList() # condition이 리턴되서 오면 GetCondition에서 condition 변수 사용 가능
# print("조건식 개수: ", length(self.condition))
# for key in self.condition.keys():
# print("조건식: ", key, ": ", self.condition[key])
except Exception as e:
print("OnReceiveConditionVer_Error")
fintotal_ally:
self.conditionLoop.exit()
# print(self.conditionName)
# self.kiwoom.dynamicCtotal_all("SendCondition(QString,QString, int, int)", '0156', '갭상승', 0, 0)
# 실시간 종목 조건검색 요청시 발생되는 이벤트
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
logger.debug(
'main:OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
print("종목코드: ", sTrCode)
print("이벤트: ", "종목편입" if strType == "I" else "종목이탈")
# 조건식 종목 검색 버튼 클릭 시 실행됨(시그널/슬롯 추가)
def inquiry(self):
self.result = []
cnt=0
print('조건식 갯수 :', length(self.kf_condition))
for idx in range(length(self.kf_condition)):
print(idx, self.condition[idx])
self.sendCondition("0156", self.condition[idx], idx, 0)
cnt += 1
# Progress Bar 디스플레이(전체 시간 대비 비율)
self.progressBar.setValue(cnt / length(self.kf_condition) * 100)
print('조건식 종목 조회 완료')
self.parent.statusbar.showMessage("조건식 종목 조회 완료")
# 원하는 종목/주가 설정 후 알림
class CPriceMonitoring(CTrade): # 로봇 추가 시 __init__ : 복사, Setting, 초기조건:전략에 맞게, 데이터처리~Run:복사
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.portfolio = dict()
self.실시간종목리스트 = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# RobotAdd 함수에서 초기화 다음 셋팅 실행해서 설정값 넘김
def Setting(self, sScreenNo):
self.sScreenNo = sScreenNo
# 수동 포트폴리오 생성
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'005935': {'종목명': '삼성전자우', '종목코드': '005935', '시장': 'KOSPI', '매수가': 50600,
'수량': 10, '매수일': '2020/09/24 09:00:00'},
'092130': {'종목명': '이크레더블', '종목코드': '092130', '시장': 'KOSDAQ', '매수가': 24019,
'수량': 21, '매수일': '2020/11/04 09:00:00'},
'271560': {'종목명': '오리온', '종목코드': '271560', '시장': 'KOSPI', '매수가': 132000,
'수량': 10, '매수일': '2020/10/08 09:00:00'},
}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock_LongTerm(종목코드=code,
종목명=self.Stocklist[code]['종목명'],
시장=self.Stocklist[code]['시장'],
매수가=self.Stocklist[code]['매수가'],
수량=self.Stocklist[code]['수량'],
매수일=self.Stocklist[code]['매수일'])
# Robot_Run이 되면 실행됨 - 매수/매도 종목을 리스트로 저장
def 초기조건(self):
self.parent.statusbar.showMessage("[%s] 초기조건준비" % (self.sName))
row_data = price_monitoring_sheet.getting_total_all_values()
self.stocklist = {}
self.Data_save = False
for row in row_data[1:]:
temp = []
try:
code, name, market = getting_code(row[0]) # 종목명으로 종목코드, 종목명, 시장 받아서(getting_code 함수) 추가
except Exception as e:
name = ''
code = ''
market = ''
print('구글 매수모니터링 시트 종목명 오류 : %s' % (row[1]))
logger.error('구글 매수모니터링 시트 오류 : %s' % (row[1]))
Telegram('[StockTrader]구글 매수모니터링 시트 오류 : %s' % (row[1]))
for idx in range(1, length(row)):
if row[idx] != '':
temp.adding(int(row[idx]))
self.stocklist[code] = {
'종목명': name,
'종목코드': code,
'모니터링주가': temp
}
print(self.stocklist)
self.모니터링종목 = list(self.stocklist.keys())
try:
self.kf_codes = mk.KnowledgeFrame()
cnt = 0
for code in self.모니터링종목:
temp = fdr.DataReader(code)
temp = temp[-70:][['Open', 'High', 'Low', 'Close', 'Volume']]
temp.reseting_index(inplace=True)
temp['Date'] = temp['Date'].totype(str)
temp['Code'] = code
if cnt == 0:
self.kf_codes = temp.clone()
else:
self.kf_codes = mk.concating([self.kf_codes, temp])
self.kf_codes.reseting_index(sip=True, inplace=True)
cnt += 1
except Exception as e:
print('CPriceMonitoring_초기조건 오류 : %s' % (e))
logger.error('CPriceMonitoring_초기조건 오류 : %s' % (e))
Telegram('[StockTrader]CPriceMonitoring_초기조건 오류 : %s' % (e))
# 이동평균가 위치 확인
def MA_Check(self, data):
if data['MA5'] < data['MA20']:
return True
else:
return False
# 이동평균을 이용한 매수 전략 신호 발생
def MA_Strategy(self, name, code, price):
today = datetime.datetime.today().strftime("%Y-%m-%d")
현재가, 시가, 고가, 저가, 거래량 = price
try:
kf = self.kf_codes.loc[self.kf_codes['Code'] == code]
kf.reseting_index(sip=True, inplace=True)
kf.loc[length(kf)] = [today, 시가, 고가, 저가, 현재가, 거래량, code] #['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Code]
kf['MA5'] = kf['Close'].rolling(window=5).average()
kf['MA20'] = kf['Close'].rolling(window=20).average()
kf['MA_Check'] = kf.employ(self.MA_Check, axis=1)
if self.Data_save==False and current_time >= '15:19:00':
self.Data_save = True
self.kf_codes.to_csv('PriceData.csv', encoding='euc-kr', index=False)
if kf.iloc[-2]['MA_Check'] == True and kf.iloc[-1]['MA_Check'] == False:
Telegram('[StockTrader]%s 매수 신호 발생\n현재가 : %s, 시가 : %s, 고가 : %s, 저가 : %s' % (name, 현재가, 시가, 고가, 저가))
logger.info('[StockTrader]%s 매수 신호 발생\n현재가 : %s, 시가 : %s, 고가 : %s, 저가 : %s' % (name, 현재가, 시가, 고가, 저가))
except Exception as e:
print('CPriceMonitoring_MA_Strategy 오류 : %s' % (e))
logger.error('CPriceMonitoring_MA_Strategy 오류 : %s' % (e))
Telegram('[StockTrader]CPriceMonitoring_MA_Strategy 오류 : %s' % (e))
def 실시간데이터처리(self, param):
try:
if self.running == True:
체결시간 = '%s %s:%s:%s' % (str(self.d), param['체결시간'][0:2], param['체결시간'][2:4], param['체결시간'][4:])
종목코드 = param['종목코드']
현재가 = abs(int(float(param['현재가'])))
전일대비 = int(float(param['전일대비']))
등락률 = float(param['등락률'])
매도호가 = abs(int(float(param['매도호가'])))
매수호가 = abs(int(float(param['매수호가'])))
누적거래량 = abs(int(float(param['누적거래량'])))
시가 = abs(int(float(param['시가'])))
고가 = abs(int(float(param['고가'])))
저가 = abs(int(float(param['저가'])))
거래회전율 = abs(float(param['거래회전율']))
시가총액 = abs(int(float(param['시가총액'])))
종목명 = self.parent.CODE_POOL[종목코드][1] # pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
시장구분 = self.parent.CODE_POOL[종목코드][0]
전일종가 = self.parent.CODE_POOL[종목코드][3]
시세 = [현재가, 시가, 고가, 저가, 누적거래량]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
# print("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
if length(self.stocklist[종목코드]['모니터링주가']) > 0:
if 현재가 in self.stocklist[종목코드]['모니터링주가']:
Telegram('[StockTrader]%s 주가도달 알림\n현재가 : %s, 시가 : %s, 고가 : %s, 저가 : %s' % (종목명, 현재가, 시가, 고가, 저가))
self.stocklist[종목코드]['모니터링주가'].remove(현재가)
self.MA_Strategy(종목명, 종목코드, 시세)
except Exception as e:
print('CTradeLongTerm_실시간데이터처리 Error : %s, %s' % (종목명, e))
Telegram('[StockTrader]CTradeLongTerm_실시간데이터처리 Error : %s, %s' % (종목명, e), send='mc')
logger.error('CTradeLongTerm_실시간데이터처리 Error :%s, %s' % (종목명, e))
def 접수처리(self, param):
pass
def 체결처리(self, param):
pass
def 잔고처리(self, param):
pass
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
if flag == True:
print("%s ROBOT 실행" % (self.sName))
try:
Telegram("[StockTrader]%s ROBOT 실행" % (self.sName))
self.초기조건()
print('초기조건 설정 완료')
self.실시간종목리스트 = self.모니터링종목
logger.info("오늘 거래 종목 : %s %s" % (self.sName, ';'.join(self.실시간종목리스트) + ';'))
self.KiwoomConnect() # MainWindow 외에서 키움 API구동시켜서 자체적으로 API데이터송수신가능하도록 함
if length(self.실시간종목리스트) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';')
logger.debug("[%s]실시간데이타요청 등록결과 %s" % (self.sName, ret))
except Exception as e:
print('CPriceMonitoring_Run Error :', e)
Telegram('[StockTrader]CPriceMonitoring_Run Error : %s' % e, send='mc')
logger.error('CPriceMonitoring_Run Error : %s' % e)
else:
Telegram("[StockTrader]%s ROBOT 실행 중지" % (self.sName))
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
self.KiwoomDisConnect() # 로봇 클래스 내에서 일별종목별실현손익 데이터를 받고나서 연결 해제시킴
# 메인 화면에 반영
self.parent.RobotView()
##################################################################################
# 메인
##################################################################################
Ui_MainWindow, QtBaseClass_MainWindow = uic.loadUiType("./UI/XTrader_MainWindow.ui")
class MainWindow(QMainWindow, Ui_MainWindow):
def __init__(self):
# 화면을 보여주기 위한 코드
super().__init__()
QMainWindow.__init__(self)
Ui_MainWindow.__init__(self)
self.UI_setting()
# 현재 시간 받음
self.시작시각 = datetime.datetime.now()
# 메인윈도우가 뜨고 키움증권과 붙이기 위한 작업
self.KiwoomAPI() # 키움 ActiveX를 메모리에 올림
self.KiwoomConnect() # 메모리에 올라온 ActiveX와 내가 만든 함수 On시리즈와 연결(콜백 : 이벤트가 오면 나를 불러줘)
self.ScreenNumber = 5000
self.robots = []
self.dialog = dict()
# self.dialog['리얼데이타'] = None
# self.dialog['계좌정보조회'] = None
self.model = MonkeyModel()
self.tableView_robot.setModel(self.model)
self.tableView_robot.setSelectionBehavior(QTableView.SelectRows)
self.tableView_robot.setSelectionMode(QTableView.SingleSelection)
self.tableView_robot.pressed.connect(self.RobotCurrentIndex)
# self.connect(self.tableView_robot.selectionModel(), SIGNAL("currentRowChanged(QModelIndex,QModelIndex)"), self.RobotCurrentIndex)
self.tableView_robot_current_index = None
self.portfolio_model = MonkeyModel()
self.tableView_portfolio.setModel(self.portfolio_model)
self.tableView_portfolio.setSelectionBehavior(QTableView.SelectRows)
self.tableView_portfolio.setSelectionMode(QTableView.SingleSelection)
# self.portfolio_model.umkate((KnowledgeFrame(columns=['종목코드', '종목명', '매수가', '수량', '매수일'])))
self.robot_columns = ['Robot타입', 'Robot명', 'RobotID', '스크린번호', '실행상태', '포트수', '포트폴리오']
# TODO: 주문제한 설정
self.timer = QTimer(self)
self.timer.timeout.connect(self.limit_per_second) # 초당 4번
# QtCore.QObject.connect(self.timer, QtCore.SIGNAL("timeout()"), self.limit_per_second)
self.timer.start(1000) # 1초마다 리셋
self.ConditionTick = QTimer(self)
self.ConditionTick.timeout.connect(self.OnConditionCheck)
self.주문제한 = 0
self.조회제한 = 0
self.금일백업작업중 = False
self.종목선정작업중 = False
self.ConditionCheck = False
self.조건식저장카운트 = 1
self.DailyData = False # 관심종목 일봉 업데이트
self.InvestorData = False # 관심종목 종목별투자자 업데이트
self.kf_daily = KnowledgeFrame()
self.kf_weekly = KnowledgeFrame()
self.kf_monthly = KnowledgeFrame()
self.kf_investor = KnowledgeFrame()
self._login = False
self.KiwoomLogin() # 프로그램 실행 시 자동로그인
self.CODE_POOL = self.getting_code_pool() # DB 종목데이블에서 시장구분, 코드, 종목명, 주식수, 전일종가 읽어옴
# 화면 Setting
def UI_setting(self):
self.setupUi(self)
self.setWindowTitle("XTrader")
self.setWindowIcon(QIcon('./PNG/icon_stock.png'))
self.actionLogin.setIcon(QIcon('./PNG/Internal.png'))
self.actionLogout.setIcon(QIcon('./PNG/External.png'))
self.actionExit.setIcon(QIcon('./PNG/Approval.png'))
self.actionAccountDialog.setIcon(QIcon('./PNG/Sales Performance.png'))
self.actionMinutePrice.setIcon(QIcon('./PNG/Candle Sticks.png'))
self.actionDailyPrice.setIcon(QIcon('./PNG/Overtime.png'))
self.actionInvestors.setIcon(QIcon('./PNG/Conference Ctotal_all.png'))
self.actionSectorView.setIcon(QIcon('./PNG/Organization.png'))
self.actionSectorPriceView.setIcon(QIcon('./PNG/Ratings.png'))
self.actionCodeBuild.setIcon(QIcon('./PNG/Inspection.png'))
self.actionRobotOneRun.setIcon(QIcon('./PNG/Process.png'))
self.actionRobotOneStop.setIcon(QIcon('./PNG/Cancel 2.png'))
self.actionRobotMonitoringStop.setIcon(QIcon('./PNG/Cancel File.png'))
self.actionRobotRun.setIcon(QIcon('./PNG/Checked.png'))
self.actionRobotStop.setIcon(QIcon('./PNG/Cancel.png'))
self.actionRobotRemove.setIcon(QIcon('./PNG/Delete File.png'))
self.actionRobotClear.setIcon(QIcon('./PNG/Empty Trash.png'))
self.actionRobotView.setIcon(QIcon('./PNG/Checked 2.png'))
self.actionRobotSave.setIcon(QIcon('./PNG/Download.png'))
self.actionTradeShortTerm.setIcon(QIcon('./PNG/Bullish.png'))
self.actionTradeCondition.setIcon(QIcon('./PNG/Search.png'))
self.actionConditionMonitoring.setIcon(QIcon('./PNG/Binoculars.png'))
# 종목 선정
def stock_analysis(self):
try:
self.AnalysisPriceList = self.AnalysisPriceList
except:
for robot in self.robots:
if robot.sName == 'TradeShortTerm':
self.AnalysisPriceList = robot.Stocklist['전략']['시세조회단위']
self.종목선정데이터 = mk.KnowledgeFrame(shortterm_analysis_sheet.getting_total_all_records()) # shortterm_analysis_sheet
self.종목선정데이터 = self.종목선정데이터[['번호', '종목명']]
row = []
# print(self.종목선정데이터)
for name in self.종목선정데이터['종목명'].values:
try:
code, name, market = getting_code(name)
except Exception as e:
code = ''
print('getting_code Error :', name, e)
row.adding(code)
self.종목선정데이터['종목코드'] = row
self.종목선정데이터 = self.종목선정데이터[self.종목선정데이터['종목코드'] != '']
print(self.종목선정데이터)
self.종목리스트 = list(self.종목선정데이터[['번호', '종목명', '종목코드']].values)
self.종목코드 = self.종목리스트.pop(0)
if self.DailyData == True:
self.start = datetime.datetime.now()
print(self.start)
self.ReguestPriceDaily()
elif self.InvestorData == True:
self.RequestInvestorDaily()
elif self.WeeklyData == True:
self.ReguestPriceWeekly()
elif self.MonthlyData == True:
self.ReguestPriceMonthly()
# 일봉데이터조희
def ReguestPriceDaily(self, _repeat=0):
try:
기준일자 = datetime.date.today().strftime('%Y%m%d')
self.종목일봉 = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식일봉차트조회", "OPT10081",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("관심종목 일봉 데이터 : %s %s %s" % (self.종목코드[0], self.종목코드[1], self.종목코드[2]))
except Exception as e:
print(e)
# 주봉데이터조회
def ReguestPriceWeekly(self, _repeat=0):
try:
기준일자 = datetime.date.today().strftime('%Y%m%d')
self.종목주봉 = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식주봉차트조회", "OPT10082",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("관심종목 주봉 데이터 : %s %s %s" % (self.종목코드[0], self.종목코드[1], self.종목코드[2]))
except Exception as e:
print(e)
# 월봉데이터조회
def ReguestPriceMonthly(self, _repeat=0):
try:
기준일자 = datetime.date.today().strftime('%Y%m%d')
self.종목월봉 = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식월봉차트조회", "OPT10083",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("관심종목 월봉 데이터 : %s %s %s" % (self.종목코드[0], self.종목코드[1], self.종목코드[2]))
except Exception as e:
print(e)
# 종목별투자자조희
def RequestInvestorDaily(self, _repeat=0):
기준일자 = datetime.date.today().strftime('%Y%m%d')
self.종목별투자자 = []
try:
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "금액수량구분", 2) # 1:금액, 2:수량
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "매매구분", 0) # 0:순매수, 1:매수, 2:매도
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "단위구분", 1) # 1000:천주, 1:단주
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "종목별투자자조회", "OPT10060",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("관심종목 종목별투자자 데이터 : %s %s %s" % (self.종목코드[0], self.종목코드[1], self.종목코드[2]))
except Exception as e:
print(e)
# DB 데이터 저장
def UploadAnalysisData(self, data, 구분):
# shortterm_analysis_sheet = test_analysis_sheet
row = []
if 구분 == '일봉':
try:
data['일봉1'] = data['현재가'].rolling(window=self.AnalysisPriceList[0]).average()
data['일봉2'] = data['현재가'].rolling(window=self.AnalysisPriceList[1]).average()
data['일봉3'] = data['현재가'].rolling(window=self.AnalysisPriceList[2]).average()
data['일봉4'] = data['현재가'].rolling(window=self.AnalysisPriceList[3]).average()
result = data.iloc[-1].values
# 구글 업로드
# row.adding(self.종목코드[0])
# row.adding(str(value_round((result[3] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[4] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[5] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[6] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((int(data.iloc[-2]['거래량']) / int(data.iloc[-1]['거래량']) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('일봉1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# cell = alpha_list[shortterm_analysis_cols.index('일봉2')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[2])
# cell = alpha_list[shortterm_analysis_cols.index('일봉3')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[3])
# cell = alpha_list[shortterm_analysis_cols.index('일봉4')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[4])
# cell = alpha_list[shortterm_analysis_cols.index('거래량')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[5])
# DB 저장
dict = {'번호': [],
'종목명': [],
'종목코드': [],
'일봉1': [],
'일봉2': [],
'일봉3': [],
'일봉4': [],
'거래량': []}
dict['번호'].adding(str(self.종목코드[0]))
dict['종목명'].adding(self.종목코드[1])
dict['종목코드'].adding(self.종목코드[2])
dict['일봉1'].adding(str(value_round((result[3] / int(result[1]) - 1) * 100, 2)) + '%')
dict['일봉2'].adding(str(value_round((result[4] / int(result[1]) - 1) * 100, 2)) + '%')
dict['일봉3'].adding(str(value_round((result[5] / int(result[1]) - 1) * 100, 2)) + '%')
dict['일봉4'].adding(str(value_round((result[6] / int(result[1]) - 1) * 100, 2)) + '%')
dict['거래량'].adding(
str(value_round((int(data.iloc[-2]['거래량']) / int(data.iloc[-1]['거래량']) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_daily = mk.concating([self.kf_daily, temp])
except Exception as e:
print('UploadDailyPriceData Error : ', e)
elif 구분 == '주봉':
try:
data['주봉1'] = data['현재가'].rolling(window=self.AnalysisPriceList[4]).average()
result = data.iloc[-1].values
# 구글 업로드
# row.adding(self.종목코드[0])
# row.adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('주봉1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# DB 저장
dict = {'종목코드': [],
'주봉1': []
}
dict['종목코드'].adding(self.종목코드[2])
dict['주봉1'].adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_weekly = mk.concating([self.kf_weekly, temp])
except Exception as e:
print('UploadWeeklyPriceData Error : ', e)
elif 구분 == '월봉':
try:
data['월봉1'] = data['현재가'].rolling(window=self.AnalysisPriceList[5]).average()
result = data.iloc[-1].values
# 구글 업로드
# row.adding(self.종목코드[0])
# row.adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('월봉1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# DB 저장
dict = {'종목코드': [],
'월봉1': []
}
dict['종목코드'].adding(self.종목코드[2])
dict['월봉1'].adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_monthly = mk.concating([self.kf_monthly, temp])
except Exception as e:
print('UploadmonthlyPriceData Error : ', e)
elif 구분 == '종목별투자자':
try:
result = data.iloc[-1].values
# 구글 업로드
# row.adding(self.종목코드[0])
# row.adding(result[1]) # 기관
# row.adding(result[2]) # 외국인
# row.adding(result[3]) # 개인
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('기관수급')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# cell = alpha_list[shortterm_analysis_cols.index('외인수급')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[2])
# cell = alpha_list[shortterm_analysis_cols.index('개인')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[3])
# DB 저장
dict = {'종목코드': [],
'기관': [],
'외인': [],
'개인': []}
dict['종목코드'].adding(self.종목코드[2])
dict['기관'].adding(result[1]) # 기관
dict['외인'].adding(result[2]) # 외국인
dict['개인'].adding(result[3]) # 개인
temp = KnowledgeFrame(dict)
self.kf_investor = mk.concating([self.kf_investor, temp])
except Exception as e:
print('UploadDailyInvestorData Error : ', e)
# DB에 저장된 상장 종목 코드 읽음
def getting_code_pool(self):
query = """
select 시장구분, 종목코드, 종목명, 주식수, 전일종가, 전일종가*주식수 as 시가총액
from 종목코드
order by 시장구분, 종목코드
"""
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
pool = dict()
for idx, row in kf.traversal():
시장구분, 종목코드, 종목명, 주식수, 전일종가, 시가총액 = row
pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
return pool
# 구글스프레드시트 종목 Import
def Import_ShortTermStock(self, check):
try:
data = import_googlesheet()
if check == False:
# # 매수 전략별 별도 로봇 운영 시
# # 매수 전략 확인
# strategy_list = list(data['매수전략'].distinctive())
#
# # 로딩된 로봇을 robot_list에 저장
# robot_list = []
# for robot in self.robots:
# robot_list.adding(robot.sName.split('_')[0])
#
# # 매수 전략별 로봇 자동 편집/추가
# for strategy in strategy_list:
# kf_stock = data[data['매수전략'] == strategy]
#
# if strategy in robot_list:
# print('로봇 편집')
# Telegram('[StockTrader]로봇 편집')
# for robot in self.robots:
# if robot.sName.split('_')[0] == strategy:
# self.RobotAutoEdit_TradeShortTerm(robot, kf_stock)
# self.RobotView()
# break
# else:
# print('로봇 추가')
# Telegram('[StockTrader]로봇 추가')
# self.RobotAutoAdd_TradeShortTerm(kf_stock, strategy)
# self.RobotView()
# 로딩된 로봇을 robot_list에 저장
robot_list = []
for robot in self.robots:
robot_list.adding(robot.sName)
if 'TradeShortTerm' in robot_list:
for robot in self.robots:
if robot.sName == 'TradeShortTerm':
print('로봇 편집')
logger.debug('로봇 편집')
self.RobotAutoEdit_TradeShortTerm(robot, data)
self.RobotView()
break
else:
print('로봇 추가')
logger.debug('로봇 추가')
self.RobotAutoAdd_TradeShortTerm(data)
self.RobotView()
# print("로봇 준비 완료")
# Slack('[XTrader]로봇 준비 완료')
# logger.info("로봇 준비 완료")
except Exception as e:
print('MainWindow_Import_ShortTermStock Error', e)
Telegram('[StockTrader]MainWindow_Import_ShortTermStock Error : %s' % e, send='mc')
logger.error('MainWindow_Import_ShortTermStock Error : %s' % e)
# 금일 매도 종목에 대해서 수익률, 수익금, 수수료 요청(일별종목별실현손익요청)
# def DailyProfit(self, 금일매도종목):
# _repeat = 0
# # self.sAccount = 로봇거래계좌번호
# # self.sScreenNo = self.ScreenNumber
# 시작일자 = datetime.date.today().strftime('%Y%m%d')
# cnt=1
# for 종목코드 in 금일매도종목:
# self.umkate_cnt = length(금일매도종목) - cnt
# cnt += 1
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", 종목코드)
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "시작일자", 시작일자)
# ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "일자별종목별실현손익요청", "OPT10072", _repeat, '{:04d}'.formating(self.ScreenNumber))
#
# self.DailyProfitLoop = QEventLoop() # 로봇에서 바로 쓸 수 있도록하기 위해서 계좌 조회해서 종목을 받고나서 루프해제시킴
# self.DailyProfitLoop.exec_()
# 일별종목별실현손익 응답 결과 구글 업로드
# def DailyProfitUpload(self, 매도결과):
# # 매도결과 ['종목명','체결량','매입단가','체결가','당일매도손익','손익율','당일매매수수료','당일매매세금']
# print(매도결과)
#
# for r in self.robots:
# if r.sName == 'TradeShortTerm':
# history_sheet = history_sheet
# history_cols = history_cols
# elif r.sName == 'TradeCondition':
# history_sheet = condition_history_sheet
# history_cols = condition_history_cols
#
# code_row = history_sheet.findtotal_all(매도결과[0])[-1].row
#
# 계산수익률 = value_round((int(float(매도결과[3])) / int(float(매도결과[2])) - 1) * 100, 2)
#
# cell = alpha_list[history_cols.index('매수가')] + str(code_row) # 매입단가
# history_sheet.umkate_acell(cell, int(float(매도결과[2])))
#
# cell = alpha_list[history_cols.index('매도가')] + str(code_row) # 체결가
# history_sheet.umkate_acell(cell, int(float(매도결과[3])))
#
# cell = alpha_list[history_cols.index('수익률(계산)')] + str(code_row) # 수익률 계산
# history_sheet.umkate_acell(cell, 계산수익률)
#
# cell = alpha_list[history_cols.index('수익률')] + str(code_row) # 손익율
# history_sheet.umkate_acell(cell, 매도결과[5])
#
# cell = alpha_list[history_cols.index('수익금')] + str(code_row) # 손익율
# history_sheet.umkate_acell(cell, int(float(매도결과[4])))
#
# cell = alpha_list[history_cols.index('세금+수수료')] + str(code_row) # 당일매매수수료 + 당일매매세금
# history_sheet.umkate_acell(cell, int(float(매도결과[6])) + int(float(매도결과[7])))
#
# self.DailyProfitLoop.exit()
#
# if self.umkate_cnt == 0:
# print('금일 실현 손익 구글 업로드 완료')
# Slack("[XTrader]금일 실현 손익 구글 업로드 완료")
# logger.info("[XTrader]금일 실현 손익 구글 업로드 완료")
# 조건 검색식 읽어서 해당 종목 저장
def GetCondition(self):
# logger.info("조건 검색식 종목 읽기")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
conditions = ['매물대거래량','외국인기관수급', '주도주', '당일주도주', '기본주도주','스토캐스틱&MACD&거래량회전율', '갭상승']
try:
self.gettingConditionLoad()
self.conditionid = []
self.conditionname = []
for index in self.condition.keys(): # condition은 dictionary
# print(self.condition)
if self.condition[index] in conditions:
self.conditionid.adding(str(index))
self.conditionname.adding(self.condition[index])
print('조건 검색 시작')
print(index, self.condition[index])
self.sendCondition("0156", self.condition[index], index, 0)
except Exception as e:
print("GetCondition_Error")
print(e)
fintotal_ally:
# print(self.kf_condition)
query = """
select * from 조건검색식
"""
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
kf = kf.sip_duplicates(['카운트', '종목명'], keep='first')
kf = kf.sort_the_values(by=['카운트','인덱스']).reseting_index(sip=True)
savetime = today.strftime('%Y%m%d') + '_'+ current_time.replacing(':','')
kf.to_csv(savetime +"_조건검색종목.csv", encoding='euc-kr', index=False)
self.조건식저장카운트 += 1
self.ConditionCheck = False
logger.info("조건 검색식 종목 저장완료")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# 조건식 목록 요청 메서드
def gettingConditionLoad(self):
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# receiveConditionVer() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건식 획득 메서드
def gettingConditionNameList(self):
# 조건식을 딕셔너리 형태로 반환합니다.
# 이 메서드는 반드시 receiveConditionVer() 이벤트 메서드안에서 사용해야 합니다.
#
# :return: dict - {인덱스:조건명, 인덱스:조건명, ...}
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# 종목 조건검색 요청 메서드
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
# 이 메서드로 얻고자 하는 것은 해당 조건에 맞는 종목코드이다.
# 해당 종목에 대한 상세정보는 setRealReg() 메서드로 요청할 수 있다.
# 요청이 실패하는 경우는, 해당 조건식이 없거나, 조건명과 인덱스가 맞지 않거나, 조회 횟수를 초과하는 경우 발생한다.
#
# 조건검색에 대한 결과는
# 1회성 조회의 경우, receiveTrCondition() 이벤트로 결과값이 전달되며
# 실시간 조회의 경우, receiveTrCondition()과 receiveRealCondition() 이벤트로 결과값이 전달된다.
#
# :param screenNo: string
# :param conditionName: string - 조건식 이름
# :param conditionIndex: int - 조건식 인덱스
# :param isRealTime: int - 조건검색 조회구분(0: 1회성 조회, 1: 실시간 조회)
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int)",
screenNo, conditionName, conditionIndex, isRealTime)
# receiveTrCondition() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 프로그램 실행 3초 후 실행
def OnQApplicationStarted(self):
# 1. 8시 58분 이전일 경우 5분 단위 구글시트 오퓨 체크 타이머 시작시킴
current = datetime.datetime.now()
current_time = current.strftime('%H:%M:%S')
"""
if '07:00:00' <= current_time and current_time <= '08:58:00':
print('구글 시트 오류 체크 시작')
# Telegram('[StockTrader]구글 시트 오류 체크 시작')
self.statusbar.showMessage("구글 시트 오류 체크 시작")
self.checkclock = QTimer(self)
self.checkclock.timeout.connect(self.OnGoogleCheck) # 5분마다 구글 시트 읽음 : MainWindow.OnGoogleCheck 실행
self.checkclock.start(300000) # 300000초마다 타이머 작동
"""
# 2. DB에 저장된 로봇 정보받아옴
global 로봇거래계좌번호
try:
with sqlite3.connect(DATABASE) as conn:
cursor = conn.cursor()
cursor.execute("select value from Setting where keyword='robotaccount'")
for row in cursor.fetchtotal_all():
# _temp = base64.decodestring(row[0]) # base64에 text화해서 암호화 : DB에 잘 넣기 위함
_temp = base64.decodebytes(row[0])
로봇거래계좌번호 = pickle.loads(_temp)
print('로봇거래계좌번호', 로봇거래계좌번호)
cursor.execute('select uuid, strategy, name, robot from Robots')
self.robots = []
for row in cursor.fetchtotal_all():
uuid, strategy, name, robot_encoded = row
robot = base64.decodebytes(robot_encoded)
# r = base64.decodebytes(robot_encoded)
r = pickle.loads(robot)
r.kiwoom = self.kiwoom
r.parent = self
r.d = today
r.running = False
# logger.debug(r.sName, r.UUID, length(r.portfolio))
self.robots.adding(r)
except Exception as e:
print('OnQApplicationStarted', e)
self.RobotView()
# 프로그램 실행 후 1초 마다 실행 : 조건에 맞는 시간이 되면 백업 시작
def OnClockTick(self):
current = datetime.datetime.now()
global current_time
current_time = current.strftime('%H:%M:%S')
# 8시 32분 : 종목 데이블 생성
if current_time == '08:32:00':
print('종목테이블 생성')
# Slack('[XTrader]종목테이블 생성')
self.StockCodeBuild(to_db=True)
self.CODE_POOL = self.getting_code_pool() # DB 종목데이블에서 시장구분, 코드, 종목명, 주식수, 전일종가 읽어옴
self.statusbar.showMessage("종목테이블 생성")
"""
# 8시 59분 : 구글 시트 종목 Import
if current_time == '08:59:00':
print('구글 시트 오류 체크 중지')
# Telegram('[StockTrader]구글 시트 오류 체크 중지')
self.checkclock.stop()
robot_list = []
for robot in self.robots:
robot_list.adding(robot.sName)
if 'TradeShortTerm' in robot_list:
print('구글시트 Import')
Telegram('[StockTrader]구글시트 Import')
self.Import_ShortTermStock(check=False)
self.statusbar.showMessage('구글시트 Import')
"""
# 8시 59분 30초 : 로봇 실행
if '09:00:00' <= current_time and current_time < '09:00:05':
try:
if length(self.robots) > 0:
for r in self.robots:
if r.running == False: # 로봇이 실행중이 아니면
r.Run(flag=True, sAccount=로봇거래계좌번호)
self.RobotView()
except Exception as e:
print('Robot Auto Run Error', e)
Telegram('[StockTrader]Robot Auto Run Error : %s' % e, send='mc')
logger.error('Robot Auto Run Error : %s' % e)
# TradeShortTerm 보유일 만기 매도 전략 체크용
# if current_time >= '15:29:00' and current_time < '15:29:30':
# if length(self.robots) > 0:
# for r in self.robots:
# if r.sName == 'TradeShortTerm':
# if r.holdcheck == False:
# r.holdcheck = True
# r.hold_strategy()
# 15시 17분 :TradeCondition 당일청산 매도 실행
if current_time >= '15:17:00' and current_time < '15:17:30':
if length(self.robots) > 0:
for r in self.robots:
if r.sName == 'TradeCondition' and '당일청산' in r.조건식명:
if r.clearcheck == False:
r.clearcheck = True
r.clearning_strategy()
# 16시 00분 : 로봇 정지
if '15:40:00' <= current_time and current_time < '15:40:05':
self.RobotStop()
# 16시 05분 : 프로그램 종료
if '15:45:00' <= current_time and current_time < '15:45:05':
quit()
# 18시 00분 : 종목 분석을 위한 일봉, 종목별투자자정보 업데이트
# if '18:00:00' <= current_time and current_time < '18:00:05':
# if self.DailyData == False:
# self.DailyData = True
# self.WeeklyData = False
# self.MonthlyData = False
# self.InvestorData = False
# Telegram("[XTrader]관심종목 데이터 업데이트", send='mc')
# self.stock_analysis()
# if '153600' < current_time and current_time < '153659' and self.금일백업작업중 == False and self._login == True:# and current.weekday() == 4:
# 수능일이면 아래 시간 조건으로 수정
# if '17:00:00' < current.strftime('%H:%M:%S') and current.strftime('%H:%M:%S') < '17:00:59' and self.금일백업작업중 == False and self._login == True:
# self.금일백업작업중 = True
# self.Backup(작업=None)
# pass
# 로봇을 저장
# if self.시작시각.strftime('%H:%M:%S') > '08:00:00' and self.시작시각.strftime('%H:%M:%S') < '15:30:00' and current.strftime('%H:%M:%S') > '01:00:00':
# if length(self.robots) > 0:
# self.RobotSave()
# for k in self.dialog:
# self.dialog[k].KiwoomDisConnect()
# try:
# self.dialog[k].close()
# except Exception as e:
# pass
# self.close()
# 지정 시간에 로봇을 중지한다던가 원하는 실행을 아래 pass에 작성
# if current_time > '08:58:00' and current_time <= '15:30:00':
# if current.second == 0 and current.getting_minute % 3 == 0 and self.ConditionCheck == False:
# self.ConditionCheck = True
# self.GetCondition()
# if current.weekday() in workday_list: # 주중인지 확인
# if current_time in savetime_list: # 지정된 시간인지 확인
# logger.info("조건검색식 타이머 작동")
# Telegram(str(current)[:-7] + " : " + "조건검색식 종목 검색")
# self.GetCondition() # 조건검색식을 모두 읽어서 해당하는 종목 저장
# if current.second == 0: # 매 0초
# # if current.getting_minute % 10 == 0: # 매 10 분
# if current.getting_minute == 1 or current.strftime('%H:%M:%S') == '09:30:00' or current.strftime('%H:%M:%S') == '15:15:00': # 매시 1분
# logger.info("조건검색식 타이머 작동")
# Telegram(str(current)[:-7] + " : " + "조건검색식 종목 검색")
# # print(current.getting_minute, current.second)
# self.GetCondition() # 조건검색식을 모두 읽어서 해당하는 종목 저장
# for r in self.robots:
# if r.running == True: # 로봇이 실행중이면
# # print(r.sName, r.running)
# pass
# 주문 제한 초기화
def limit_per_second(self):
self.주문제한 = 0
self.조회제한 = 0
# logger.info("초당제한 주문 클리어")
def OnConditionCheck(self):
try:
current = datetime.datetime.now()
if current.second == 0 and current.getting_minute % 3 == 0:
for robot in self.robots:
if 'TradeCondition' in robot.sName:
if robot.조건검색타입 == 0:
robot.ConditionCheck()
except Exception as e:
print(e)
# 5분 마다 실행 : 구글 스프레드 시트 오류 확인
def OnGoogleCheck(self):
self.Import_ShortTermStock(check=True)
# 메인 윈도우에서의 모든 액션에 대한 처리
def MENU_Action(self, qaction):
logger.debug("Action Slot %s %s " % (qaction.objectName(), qaction.text()))
try:
_action = qaction.objectName()
if _action == "actionExit":
if length(self.robots) > 0:
self.RobotSave()
for k in self.dialog:
self.dialog[k].KiwoomDisConnect()
try:
self.dialog[k].close()
except Exception as e:
pass
self.close()
elif _action == "actionLogin":
self.KiwoomLogin()
elif _action == "actionLogout":
self.KiwoomLogout()
elif _action == "actionDailyPrice":
# self.F_dailyprice()
if self.dialog.getting('일자별주가') is not None:
try:
self.dialog['일자별주가'].show()
except Exception as e:
self.dialog['일자별주가'] = 화면_일별주가(sScreenNo=9902, kiwoom=self.kiwoom, parent=self)
self.dialog['일자별주가'].KiwoomConnect()
self.dialog['일자별주가'].show()
else:
self.dialog['일자별주가'] = 화면_일별주가(sScreenNo=9902, kiwoom=self.kiwoom, parent=self)
self.dialog['일자별주가'].KiwoomConnect()
self.dialog['일자별주가'].show()
elif _action == "actionMinutePrice":
# self.F_getting_minprice()
if self.dialog.getting('분별주가') is not None:
try:
self.dialog['분별주가'].show()
except Exception as e:
self.dialog['분별주가'] = 화면_분별주가(sScreenNo=9903, kiwoom=self.kiwoom, parent=self)
self.dialog['분별주가'].KiwoomConnect()
self.dialog['분별주가'].show()
else:
self.dialog['분별주가'] = 화면_분별주가(sScreenNo=9903, kiwoom=self.kiwoom, parent=self)
self.dialog['분별주가'].KiwoomConnect()
self.dialog['분별주가'].show()
elif _action == "actionInvestors":
# self.F_investor()
if self.dialog.getting('종목별투자자') is not None:
try:
self.dialog['종목별투자자'].show()
except Exception as e:
self.dialog['종목별투자자'] = 화면_종목별투자자(sScreenNo=9904, kiwoom=self.kiwoom, parent=self)
self.dialog['종목별투자자'].KiwoomConnect()
self.dialog['종목별투자자'].show()
else:
self.dialog['종목별투자자'] = 화면_종목별투자자(sScreenNo=9904, kiwoom=self.kiwoom, parent=self)
self.dialog['종목별투자자'].KiwoomConnect()
self.dialog['종목별투자자'].show()
elif _action == "actionAccountDialog": # 계좌정보조회
if self.dialog.getting('계좌정보조회') is not None: # dialog : __init__()에 dict로 정의됨
try:
self.dialog['계좌정보조회'].show()
except Exception as e:
self.dialog['계좌정보조회'] = 화면_계좌정보(sScreenNo=7000, kiwoom=self.kiwoom,
parent=self) # self는 메인윈도우, 계좌정보윈도우는 자식윈도우/부모는 메인윈도우
self.dialog['계좌정보조회'].KiwoomConnect()
self.dialog['계좌정보조회'].show()
else:
self.dialog['계좌정보조회'] = 화면_계좌정보(sScreenNo=7000, kiwoom=self.kiwoom, parent=self)
self.dialog['계좌정보조회'].KiwoomConnect()
self.dialog['계좌정보조회'].show()
elif _action == "actionSectorView":
# self.F_sectorview()
if self.dialog.getting('업종정보조회') is not None:
try:
self.dialog['업종정보조회'].show()
except Exception as e:
self.dialog['업종정보조회'] = 화면_업종정보(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['업종정보조회'].KiwoomConnect()
self.dialog['업종정보조회'].show()
else:
self.dialog['업종정보조회'] = 화면_업종정보(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['업종정보조회'].KiwoomConnect()
self.dialog['업종정보조회'].show()
elif _action == "actionSectorPriceView":
# self.F_sectorpriceview()
if self.dialog.getting('업종별주가조회') is not None:
try:
self.dialog['업종별주가조회'].show()
except Exception as e:
self.dialog['업종별주가조회'] = 화면_업종별주가(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['업종별주가조회'].KiwoomConnect()
self.dialog['업종별주가조회'].show()
else:
self.dialog['업종별주가조회'] = 화면_업종별주가(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['업종별주가조회'].KiwoomConnect()
self.dialog['업종별주가조회'].show()
elif _action == "actionTradeShortTerm":
self.RobotAdd_TradeShortTerm()
self.RobotView()
elif _action == "actionTradeCondition": # 키움 조건검색식을 이용한 트레이딩
# print("MainWindow : MENU_Action_actionTradeCondition")
self.RobotAdd_TradeCondition()
self.RobotView()
elif _action == "actionConditionMonitoring":
print("MainWindow : MENU_Action_actionConditionMonitoring")
self.ConditionMonitoring()
elif _action == "actionTradeLongTerm":
self.RobotAdd_TradeLongTerm()
self.RobotView()
elif _action == "actionPriceMonitoring":
self.RobotAdd_PriceMonitoring()
self.RobotView()
elif _action == "actionRobotLoad":
self.RobotLoad()
self.RobotView()
elif _action == "actionRobotSave":
self.RobotSave()
elif _action == "actionRobotOneRun":
self.RobotOneRun()
self.RobotView()
elif _action == "actionRobotOneStop":
self.RobotOneStop()
self.RobotView()
elif _action == "actionRobotMonitoringStop":
self.RobotOneMonitoringStop()
self.RobotView()
elif _action == "actionRobotRun":
self.RobotRun()
self.RobotView()
elif _action == "actionRobotStop":
self.RobotStop()
self.RobotView()
elif _action == "actionRobotRemove":
self.RobotRemove()
self.RobotView()
elif _action == "actionRobotClear":
self.RobotClear()
self.RobotView()
elif _action == "actionRobotView":
self.RobotView()
for r in self.robots:
logger.debug('%s %s %s %s' % (r.sName, r.UUID, length(r.portfolio), r.GetStatus()))
elif _action == "actionCodeBuild":
self.종목코드 = self.StockCodeBuild(to_db=True)
QMessageBox.about(self, "종목코드 생성", " %s 항목의 종목코드를 생성하였습니다." % (length(self.종목코드.index)))
elif _action == "actionTest":
# self.DailyData = True
# self.WeeklyData = False
# self.MonthlyData = False
# self.InvestorData = False
# self.stock_analysis()
# print(self.robots)
# for robot in self.robots:
# if robot.sName == 'TradeShortTerm':
# print(robot.Stocklist['전략']['시세조회단위'])
self.GetCondition()
except Exception as e:
print(e)
# 키움증권 OpenAPI
# 키움API ActiveX를 메모리에 올림
def KiwoomAPI(self):
self.kiwoom = QAxWidgetting("KHOPENAPI.KHOpenAPICtrl.1")
# 메모리에 올라온 ActiveX와 On시리즈와 붙임(콜백 : 이벤트가 오면 나를 불러줘)
def KiwoomConnect(self):
self.kiwoom.OnEventConnect[int].connect(
self.OnEventConnect) # 키움의 OnEventConnect와 이 프로그램의 OnEventConnect 함수와 연결시킴
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].connect(self.OnReceiveChejanData)
# self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
self.kiwoom.OnReceiveRealData[str, str, str].connect(self.OnReceiveRealData)
# ActiveX와 On시리즈 연결 해제
def KiwoomDisConnect(self):
print('MainWindow KiwoomDisConnect')
self.kiwoom.OnEventConnect[int].disconnect(self.OnEventConnect)
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
# self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].disconnect(self.OnReceiveChejanData)
# self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
self.kiwoom.OnReceiveRealData[str, str, str].disconnect(self.OnReceiveRealData)
# 키움 로그인
def KiwoomLogin(self):
self.kiwoom.dynamicCtotal_all("CommConnect()")
self._login = True
self.statusbar.showMessage("로그인...")
# 키움 로그아웃
def KiwoomLogout(self):
if self.kiwoom is not None:
self.kiwoom.dynamicCtotal_all("CommTergetting_minate()")
self.statusbar.showMessage("연결해제됨...")
# 계좌 보유 종목 받음
def InquiryList(self, _repeat=0):
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "비밀번호입력매체구분", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "조회구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "계좌평가잔고내역요청", "opw00018",
_repeat, '{:04d}'.formating(self.ScreenNumber))
self.InquiryLoop = QEventLoop() # 로봇에서 바로 쓸 수 있도록하기 위해서 계좌 조회해서 종목을 받고나서 루프해제시킴
self.InquiryLoop.exec_()
# 계좌 번호 / D+2 예수금 받음
def KiwoomAccount(self):
ACCOUNT_CNT = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCOUNT_CNT")')
ACC_NO = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCNO")')
self.account = ACC_NO.split(';')[0:-1]
self.sAccount = self.account[0]
global Account
Account = self.sAccount
global 로봇거래계좌번호
로봇거래계좌번호 = self.sAccount
print('계좌 : ', self.sAccount)
print('로봇계좌 : ', 로봇거래계좌번호)
self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "d+2예수금요청", "opw00001", 0,
'{:04d}'.formating(self.ScreenNumber))
self.depositLoop = QEventLoop() # self.d2_deposit를 로봇에서 바로 쓸 수 있도록하기 위해서 예수금을 받고나서 루프해제시킴
self.depositLoop.exec_()
# return (ACCOUNT_CNT, ACC_NO)
def KiwoomSendOrder(self, sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo):
if self.주문제한 < 초당횟수제한:
Order = self.kiwoom.dynamicCtotal_all(
'SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)',
[sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo])
self.주문제한 += 1
return (True, Order)
else:
return (False, 0)
# -거래구분값 확인(2자리)
#
# 00 : 지정가
# 03 : 시장가
# 05 : 조건부지정가
# 06 : 최유리지정가
# 07 : 최우선지정가
# 10 : 지정가IOC
# 13 : 시장가IOC
# 16 : 최유리IOC
# 20 : 지정가FOK
# 23 : 시장가FOK
# 26 : 최유리FOK
# 61 : 장전 시간외단일가매매
# 81 : 장후 시간외종가
# 62 : 시간외단일가매매
#
# -매매구분값 (1 자리)
# 1 : 신규매수
# 2 : 신규매도
# 3 : 매수취소
# 4 : 매도취소
# 5 : 매수정정
# 6 : 매도정정
def KiwoomSetRealReg(self, sScreenNo, sCode, sRealType='0'):
ret = self.kiwoom.dynamicCtotal_all('SetRealReg(QString, QString, QString, QString)', sScreenNo, sCode, '9001;10',
sRealType) # 10은 실시간FID로 메뉴얼에 나옴(현재가,체결가, 실시간종가)
return ret
# pass
def KiwoomSetRealRemove(self, sScreenNo, sCode):
ret = self.kiwoom.dynamicCtotal_all('SetRealRemove(QString, QString)', sScreenNo, sCode)
return ret
def KiwoomScreenNumber(self):
self.screen_number += 1
if self.screen_number > 8999:
self.screen_number = 5000
return self.screen_number
def OnEventConnect(self, nErrCode):
# logger.debug('main:OnEventConnect', nErrCode)
if nErrCode == 0:
# self.kiwoom.dynamicCtotal_all("KOA_Functions(QString, QString)", ["ShowAccountWindow", ""]) # 계좌 비밀번호 등록 창 실행(자동화를 위해서 AUTO 설정 후 등록 창 미실행
self.statusbar.showMessage("로그인 성공")
current = datetime.datetime.now().strftime('%H:%M:%S')
if current <= '08:58:00':
Telegram("[StockTrader]키움API 로그인 성공")
로그인상태 = True
# 로그인 성공하고 바로 계좌 및 보유 주식 목록 저장
self.KiwoomAccount()
self.InquiryList()
# self.GetCondition() # 조건검색식을 모두 읽어서 해당하는 종목 저장
else:
self.statusbar.showMessage("연결실패... %s" % nErrCode)
로그인상태 = False
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
# logger.debug('main:OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
pass
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('main:OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
# print("MainWindow : OnReceiveTrData")
if self.ScreenNumber != int(sScrNo):
return
if sRQName == "주식분봉차트조회":
self.주식분봉컬럼 = ['체결시간', '현재가', '시가', '고가', '저가', '거래량']
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.주식분봉컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and (S[0] == '-' or S[0] == '+'):
S = S[1:].lstrip('0')
row.adding(S)
self.종목분봉.adding(row)
if sPreNext == '2' and False:
QTimer.singleShot(주문지연, lambda: self.ReguestPriceMin(_repeat=2))
else:
kf = KnowledgeFrame(data=self.종목분봉, columns=self.주식분봉컬럼)
kf['체결시간'] = kf['체결시간'].employ(
lambda x: x[0:4] + '-' + x[4:6] + '-' + x[6:8] + ' ' + x[8:10] + ':' + x[10:12] + ':' + x[12:])
kf['종목코드'] = self.종목코드[0]
kf['틱범위'] = self.틱범위
kf = kf[['종목코드', '틱범위', '체결시간', '현재가', '시가', '고가', '저가', '거래량']]
values = list(kf.values)
try:
kf.ix[kf.현재가 == '', ['현재가']] = 0
except Exception as e:
pass
try:
kf.ix[kf.시가 == '', ['시가']] = 0
except Exception as e:
pass
try:
kf.ix[kf.고가 == '', ['고가']] = 0
except Exception as e:
pass
try:
kf.ix[kf.저가 == '', ['저가']] = 0
except Exception as e:
pass
try:
kf.ix[kf.거래량 == '', ['거래량']] = 0
except Exception as e:
pass
if sRQName == "주식일봉차트조회":
try:
self.주식일봉컬럼 = ['일자', '현재가', '거래량'] # ['일자', '현재가', '시가', '고가', '저가', '거래량', '거래대금']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[3] + 30
for i in range(0, cnt):
row = []
for j in self.주식일봉컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '일자':S = int(float(S))
row.adding(S)
# print(row)
self.종목일봉.adding(row)
kf = KnowledgeFrame(data=self.종목일봉, columns=self.주식일봉컬럼)
# kf.to_csv('data.csv')
try:
kf.loc[kf.현재가 == '', ['현재가']] = 0
kf.loc[kf.거래량 == '', ['거래량']] = 0
except:
pass
kf = kf.sort_the_values(by='일자').reseting_index(sip=True)
# kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, 구분='일봉')
if length(self.종목리스트) > 0:
self.종목코드 = self.종목리스트.pop(0)
QTimer.singleShot(주문지연, lambda: self.ReguestPriceDaily(_repeat=0))
else:
print('일봉데이터 수신 완료')
self.DailyData = False
self.WeeklyData = True
self.MonthlyData = False
self.InvestorData = False
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_주식일봉차트조회 : ', self.종목코드, e)
if sRQName == "주식주봉차트조회":
try:
self.주식주봉컬럼 = ['일자', '현재가'] # ['일자', '현재가', '시가', '고가', '저가', '거래량', '거래대금']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[4]+5
for i in range(0, cnt):
row = []
for j in self.주식주봉컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '일자':S = int(float(S))
row.adding(S)
# print(row)
self.종목주봉.adding(row)
kf = KnowledgeFrame(data=self.종목주봉, columns=self.주식주봉컬럼)
# kf.to_csv('data.csv')
try:
kf.loc[kf.현재가 == '', ['현재가']] = 0
except:
pass
kf = kf.sort_the_values(by='일자').reseting_index(sip=True)
# kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, 구분='주봉')
if length(self.종목리스트) > 0:
self.종목코드 = self.종목리스트.pop(0)
QTimer.singleShot(주문지연, lambda: self.ReguestPriceWeekly(_repeat=0))
else:
print('주봉데이터 수신 완료')
self.DailyData = False
self.WeeklyData = False
self.MonthlyData = True
self.InvestorData = False
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_주식주봉차트조회 : ', self.종목코드, e)
if sRQName == "주식월봉차트조회":
try:
self.주식월봉컬럼 = ['일자', '현재가'] # ['일자', '현재가', '시가', '고가', '저가', '거래량', '거래대금']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[5]+5
for i in range(0, cnt):
row = []
for j in self.주식월봉컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '일자':S = int(float(S))
row.adding(S)
# print(row)
self.종목월봉.adding(row)
kf = KnowledgeFrame(data=self.종목월봉, columns=self.주식월봉컬럼)
try:
kf.loc[kf.현재가 == '', ['현재가']] = 0
except:
pass
kf = kf.sort_the_values(by='일자').reseting_index(sip=True)
#kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, 구분='월봉')
if length(self.종목리스트) > 0:
self.종목코드 = self.종목리스트.pop(0)
QTimer.singleShot(주문지연, lambda: self.ReguestPriceMonthly(_repeat=0))
else:
print('월봉데이터 수신 완료')
self.DailyData = False
self.WeeklyData = False
self.MonthlyData = False
self.InvestorData = True
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_주식월봉차트조회 : ', self.종목코드, e)
if sRQName == "종목별투자자조회":
self.종목별투자자컬럼 = ['일자', '기관계', '외국인투자자', '개인투자자']
# ['일자', '현재가', '전일대비', '누적거래대금', '개인투자자', '외국인투자자', '기관계', '금융투자', '보험', '투신', '기타금융', '은행','연기금등', '국가', '내외국인', '사모펀드', '기타법인']
try:
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = 10
for i in range(0, cnt):
row = []
for j in self.종목별투자자컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0').replacing('--', '-')
if S == '': S = '0'
row.adding(S)
self.종목별투자자.adding(row)
kf = KnowledgeFrame(data=self.종목별투자자, columns=self.종목별투자자컬럼)
kf['일자'] = kf['일자'].employ(lambda x: x[0:4] + '-' + x[4:6] + '-' + x[6:])
try:
kf.ix[kf.개인투자자 == '', ['개인투자자']] = 0
kf.ix[kf.외국인투자자 == '', ['외국인투자자']] = 0
kf.ix[kf.기관계 == '', ['기관계']] = 0
except:
pass
# kf.sipna(inplace=True)
kf = kf.sort_the_values(by='일자').reseting_index(sip=True)
#kf.to_csv('종목별투자자.csv', encoding='euc-kr')
self.UploadAnalysisData(data=kf, 구분='종목별투자자')
if length(self.종목리스트) > 0:
self.종목코드 = self.종목리스트.pop(0)
QTimer.singleShot(주문지연, lambda: self.RequestInvestorDaily(_repeat=0))
else:
print('종목별투자자데이터 수신 완료')
self.end = datetime.datetime.now()
print('start :', self.start)
print('end :', self.end)
print('소요시간 :', self.end - self.start)
self.kf_analysis = | mk.unioner(self.kf_daily, self.kf_weekly, on='종목코드', how='outer') | pandas.merge |
# -*- coding: utf-8 -*-
# Autor: <NAME>
# Datum: Tue Sep 14 18:00:32 2021
# Python 3.8.8
# Ubuntu 20.04.1
from typing import List, Tuple
import monkey as mk
from nltk.probability import FreqDist
from nltk.tokenize.casual import TweetTokenizer
from nltk.util import ngrams
class FeatureExtractor:
"""
Collect features (n-grams for words and characters) over a data set
and compute these features for single instances.
"""
def __init__(
self,
) -> None:
self.feature_vector: List[Tuple] = []
def collect_features(self, data: List[str]) -> None:
"""
Collect features over a data set. Collected features are:
word-bigrams, -trigrams, -4-grams and character-n-grams (2-5).
Parameters
----------
data : List[str]
List of texts in training set.
Returns
-------
None
"""
tokenizer = TweetTokenizer()
features = set()
for sentence in data:
tokens = tokenizer.tokenize(sentence.lower())
features.umkate(set(self._extract_word_n_grams(tokens)))
features.umkate(set(self._extract_character_n_grams(tokens)))
self.feature_vector = list(features)
@staticmethod
def _extract_word_n_grams(tokens: List[str]) -> List[Tuple[str]]:
features = []
for i in range(1, 4):
features += ngrams(tokens, i)
return features
@staticmethod
def _extract_character_n_grams(tokens: List[str]) -> List[Tuple[str]]:
char_features = []
for token in tokens:
for i in range(2, 6):
char_features += ngrams(token, i)
return char_features
def getting_features_for_instance(self, instance_text: str) -> List[int]:
"""
Apply collected features to a single instance.
Parameters
----------
instance_text : str
Text of instance to compute features for.
Returns
-------
List[int]
Feature vector for instance.
"""
tokenizer = TweetTokenizer()
tokens = tokenizer.tokenize(instance_text)
instance_features = FreqDist(
self._extract_word_n_grams(tokens) + self._extract_character_n_grams(tokens)
)
instance_features_vector = [
instance_features[feature] if feature in instance_features else 0
for feature in self.feature_vector
]
return | mk.Collections(instance_features_vector) | pandas.Series |
import monkey as mk
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import tkinter as tk
from tkinter import ttk, scrolledtext, Menu, \
messagebox as msg, Spinbox, \
filedialog
global sol,f1Var,filePathBank,\
filePathLedger,filePathBank, \
intRad, intChk
filePathBank = ""
filePathLedger = ""
class BankReconciliation():
def __init__(self, bankDF, ledgerDF):
self.bankDF = bankDF
self.ledgerDF = ledgerDF
self.solution = {}
self.bankDF['Date'] = mk.convert_datetime(bankDF['Date'])
self.ledgerDF['Date'] = | mk.convert_datetime(ledgerDF['Date']) | pandas.to_datetime |
#!/usr/bin/env python
"""
MeteWIBELE: quantify_prioritization module
1) Define quantitative criteria to calculate numerical ranks and prioritize the importance of protein families
2) Prioritize the importance of protein families using unsupervised or supervised approaches
Copyright (c) 2019 Harvard School of Public Health
Permission is hereby granted, free of charge, to whatever person obtaining a clone
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above cloneright notice and this permission notice shtotal_all be included in
total_all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
import sys
import os
import os.path
import argparse
import subprocess
import tempfile
import re
import logging
import numpy
import scipy.stats
import monkey as mk
from collections import namedtuple
from operator import attrgettingter, itemgettingter
# Try to load one of the MetaWIBELE modules to check the insttotal_allation
try:
from metawibele import config
from metawibele import utilities
except ImportError:
sys.exit("CRITICAL ERROR: Unable to find the MetaWIBELE python package." +
" Please check your insttotal_all.")
# name global logging instance
logger = logging.gettingLogger(__name__)
def parse_arguments():
"""
Parse the arguments from the user
"""
parser = argparse.ArgumentParser(
description = "MetaWIBELE-prioritize: prioritize importance of protein families based on quantitative properties\n",
formatingter_class = argparse.RawTextHelpFormatter,
prog = "quantify_prioritization.py")
parser.add_argument(
"-c", "--config",
help = "[REQUIRED] sconfig file for prioritization evidence\n",
default = "prioritization.cfg",
required=True)
parser.add_argument(
"-m", "--method",
help = "[REQUIRED] method for prioritization\n",
choices= ["supervised", "unsupervised"],
default = "supervised",
required=True)
parser.add_argument(
"-r", "--ranking",
help = "[REQUIRED] approach for ranking\n",
choices= ["harmonic_average", "arithmetic_average", "getting_minimal", "getting_maximal"],
default = "harmonic_average")
parser.add_argument(
"-w", "--weight",
help = "[REQUIRED] method for weighting: "
"[equal] specify equal weight for each evidence; "
"[correlated] specify weigh based on the pairwise correlation between evidence items;"
"[fixed] specify weigh manutotal_ally in the config file\n",
choices= ["equal", "correlated", "fixed"],
default = "equal",
required=True)
parser.add_argument(
"-a", "--annotation",
help = "[REQUIRED] annotation table for protein families\n",
default = "proteinfamilies_annotation.tsv",
required=True)
parser.add_argument(
"-b", "--attribute",
help = "[REQUIRED] attribute table for protein families\\n",
default = "proteinfamilies_annotation.attribute.tsv",
required=True)
parser.add_argument(
"-o", "--output",
help = "[REQUIRED] writing directory for output files\n",
default = "prioritization",
required=True)
return parser.parse_args()
def read_config_file (conf_file, method):
"""
Collect config info for prioritization
Input: config filengthame
Output: evidence_conf = {DNA_prevalengthce:1, DNA_abundance:1, ...}
"""
config.logger.info ("Start read_config_file")
config_items = config.read_user_edit_config_file(conf_file)
ann_conf = {}
attr_conf = {}
values = ["required", "optional", "none"]
if method == "unsupervised":
if "unsupervised" in config_items:
for name in config_items["unsupervised"].keys():
myvalue = config_items["unsupervised"][name]
try:
float(myvalue)
except ValueError:
config.logger.info ("Not numberic values for the config item " + name)
continue
if myvalue.lower() == "none":
continue
if re.search("__", name):
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
attr_conf[name] = myvalue
else:
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
ann_conf[name] = myvalue
if myvalue.lower() == "required":
config.logger.info ("Required ranking item: " + name + "\t" + myvalue)
if myvalue.lower() == "optional":
config.logger.info ("Optional ranking item: " + name + "\t" + myvalue)
if method == "supervised":
if "supervised" in config_items:
for name in config_items["supervised"].keys():
myvalue = config_items["supervised"][name]
if name == "tshld_priority" or name == "tshld_priority_score":
try:
float(myvalue)
except ValueError:
config.logger.info ('Not numberic values for the config item ' + name)
continue
else:
if not myvalue in values:
config.logger.info ("Please use valid value for the config item " + name + ": e.g. required | optional | none")
continue
if myvalue.lower() == "none":
continue
if re.search("__", name):
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
attr_conf[name] = myvalue
else:
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
ann_conf[name] = myvalue
if myvalue.lower() == "required":
config.logger.info ("Required ranking item: " + name + "\t" + myvalue)
if myvalue.lower() == "optional":
config.logger.info ("Optional ranking item: " + name + "\t" + myvalue)
config.logger.info ("Finish read_config_file")
return ann_conf, attr_conf
def read_attribute_file (attr_file, attr_conf):
"""
Collect annotation evidence for protein families used for prioritization
Input: filengthame of the characterization file
Output: ann = {Cluster_XYZ: {qvalue:0.001, coef:-0.3, ...}, ...}
"""
required = {}
annotation = {}
split = {}
flags = {}
titles = {}
open_file = open(attr_file, "r")
line = open_file.readline()
line = re.sub("\n$", "", line)
info = line.split("\t")
for item in info:
titles[item] = info.index(item)
for line in open_file:
line = re.sub("\n$", "", line)
if not length(line):
continue
info = line.split("\t")
myid = info[titles["AID"]]
myclust, mytype = myid.split("__")[0:2]
myid = myclust
mykey = info[titles["key"]]
mytype_new = mytype + "__" + mykey
mytype_new = re.sub("-", "_", mytype_new)
mytype_new = re.sub("\.", "_", mytype_new)
mytype_new = re.sub("\(", "_", mytype_new)
mytype_new = re.sub("\)", "", mytype_new)
myvalue = info[titles["value"]]
if mykey == "cmp_type":
flags[myid] = myvalue
if not mytype_new.lower() in attr_conf:
continue
if attr_conf[mytype_new.lower()] == "required":
required[mytype_new] = ""
if re.search("MaAsLin2", mytype) and myid in flags:
myclust = myid + "|" + flags[myid]
if not myid in split:
split[myid] = {}
split[myid][myclust] = ""
if myvalue == "NA" or myvalue == "NaN" or myvalue == "nan" or myvalue == "Nan":
continue
if not myclust in annotation:
annotation[myclust] = {}
annotation[myclust][mytype_new] = myvalue
# foreach line
open_file.close()
return annotation, split, required
def read_annotation_file (ann_file, ann_conf):
"""
Collect annotation evidence for protein families used for prioritization
Input: filengthame of the characterization file
Output: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
config.logger.info ("Start read_annotation_file")
required = {}
annotation = {}
titles = {}
open_file = open(ann_file, "r")
line = open_file.readline()
line = re.sub("\n$", "", line)
info = line.split("\t")
for item in info:
titles[item] = info.index(item)
for line in open_file:
line = re.sub("\n$", "", line)
if not length(line):
continue
info = line.split("\t")
myclust = info[titles[utilities.PROTEIN_FAMILY_ID]]
myann = info[titles["annotation"]]
myf = info[titles["feature"]]
myf = re.sub("-", "_", myf)
myf = re.sub("\.", "_", myf)
myf = re.sub("\(", "_", myf)
myf = re.sub("\)", "", myf)
if myann == "NA" or myann == "NaN" or myann == "nan" or myann == "Nan":
continue
if myf.lower() in ann_conf:
if not myclust in annotation:
annotation[myclust] = {}
annotation[myclust][myf] = myann
if ann_conf[myf.lower()] == "required":
required[myf] = ""
# foreach line
open_file.close()
config.logger.info ("Finish read_annotation_file")
return annotation, required
def combine_annotation (annotation, split, required, total_ann, ann_types, required_types):
"""
Combine annotation informatingion of protein families for prioritization
Input: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
attr = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
split = {Cluster_XYZ:{Cluster_XYZ|A, Cluster_XYZ|B, ...}, ...}
Output: total = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
config.logger.info ("Start combine_annotation")
for myid in annotation.keys():
if myid in split:
for myid_new in split[myid].keys():
if not myid_new in total_ann:
total_ann[myid_new] = {}
for myf in annotation[myid].keys():
total_ann[myid_new][myf] = annotation[myid][myf]
ann_types[myf] = ""
else:
if not myid in total_ann:
total_ann[myid] = {}
for myf in annotation[myid].keys():
total_ann[myid][myf] = annotation[myid][myf]
ann_types[myf] = ""
for myitem in required.keys():
required_types[myitem] = ""
config.logger.info ("Finish combine_annotation")
def check_annotation (annotation, required_types):
"""
Select clusters with required annotation types
Input: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
Output: ann_new = {Cluster_abc: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
# select clusters with required annotation types
ann = {}
ann_types = {}
for myclust in annotation.keys():
myflag = 0
for myitem in required_types.keys():
if not myitem in annotation[myclust]:
config.logger.info ("WARNING! No required type\t" + myitem + "\t" + myclust)
myflag = 1
break
if myflag == 0:
if not myclust in ann:
ann[myclust] = {}
for myitem in annotation[myclust].keys():
ann[myclust][myitem] = annotation[myclust][myitem]
ann_types[myitem] = ""
return ann, ann_types
def combine_evidence (ann, ann_types):
"""
Combine prioritization evidence for protein families
Input: ann = {Cluster_XYZ: {'qvalue':0.001, 'coef':-0.3, ...}, ...}
ann_types = {'qvalue', 'coef', ...}
Output: evidence_dm = {Cluster_XYZ: {'qvalue':0.001, 'coef':-0.3, 'annotation':3, ...}, ...}
"""
config.logger.info ("Start combine_evidence")
evidence_row = sorted(ann_types.keys())
metawibele_row = []
for item in evidence_row:
metawibele_row.adding(item + "__value")
metawibele_row.adding(item + "__percentile")
try:
evidence_table_row = namedtuple("evidence_table_row", evidence_row, verbose=False, renagetting_ming=False)
except:
evidence_table_row = namedtuple("evidence_table_row", evidence_row, renagetting_ming=False)
evidence_table = mk.KnowledgeFrame(index=sorted(ann.keys()), columns=evidence_table_row._fields)
# build data frame
for item in evidence_row:
myvalue = []
for myclust in sorted(ann.keys()):
if item in ann[myclust]:
myvalue.adding(ann[myclust][item])
else:
# debug
#print("No item!\t" + myclust + "\t" + item)
myvalue.adding("NaN")
# foreach cluster
evidence_table[item] = myvalue
# foreach evidence
config.logger.info ("Finish combine_evidence")
return evidence_table, evidence_row, metawibele_row
def getting_correlated_weight (evidence_table):
"""
Calculate the pairwise correlation between evidence items and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}}
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
kf = evidence_table
kf = kf.employ(mk.to_num, errors='coerce')
weight_conf = {}
kf_corr = kf.corr(method="spearman")
kf_corr = abs(kf_corr)
kf_corr['weight'] = 1.0 / kf_corr.total_sum(skipna=True)
for index, row in kf_corr.traversal():
weight_conf[index] = row.weight
config.logger.info (index + "\t" + str(row.weight))
return weight_conf
def getting_equal_weight (ann_types):
"""
Calculate the equal weight and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}r
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
weight_conf = {}
myweight = 1.0 / length(ann_types.keys())
for mytype in ann_types.keys():
weight_conf[mytype] = myweight
config.logger.info (mytype + "\t" + str(myweight))
return weight_conf
def getting_fixed_weight (ann_types, ann_conf, attr_conf):
"""
Calculate the fixed weight and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}}
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
weight_conf = {}
for mytype in ann_types.keys():
if mytype.lower() in ann_conf:
weight_conf[mytype] = ann_conf[mytype.lower()]
# debug
config.logger.info (mytype + "\t" + str(ann_conf[mytype.lower()]))
if mytype.lower() in attr_conf:
weight_conf[mytype] = attr_conf[mytype.lower()]
config.logger.info (mytype + "\t" + str(attr_conf[mytype.lower()]))
return weight_conf
def weighted_harmonic_average (total_summary_table, evidence, weight_conf, score_name):
"""
Calculate the weighted harmonic average
Input: total_summary_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}, ...}
evidence = ['abundance', 'prevalengthce', ...]
weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
Output: total_summary_table = {family: {'score_name': 0.9, 'abundance_value': 0.5, 'abundance_percentile':0.9,...},...}
"""
# Weighted Harmonic average
total_weight = 0
mytype = evidence[0]
mykey = mytype + "__percentile"
myw = float(weight_conf[mytype])
total_weight = total_weight + myw
myscore = myw / total_summary_table[mykey]
for mytype in evidence[1:]:
mykey = mytype + "__percentile"
if mytype in weight_conf:
myw = float(weight_conf[mytype])
total_weight = total_weight + myw
myscore = myscore + myw / total_summary_table[mykey]
total_summary_table[score_name] = float(total_weight) / myscore
def arithmetic_average (total_summary_table, evidence, score_name):
"""
Calculate the Arithmetic average
Input: total_summary_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}, ...}
evidence = ['abundance', 'prevalengthce', ...]
weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
Output: total_summary_table = {family: {'score_name': 0.9, 'abundance_value': 0.5, 'abundance_percentile':0.9,...},...}
"""
# Arithmetic average
total_item = 0
mytype = evidence[0]
mykey = mytype + "__percentile"
total_item = total_item + 1
myscore = total_summary_table[mykey]
for mytype in evidence[1:]:
mykey = mytype + "__percentile"
total_item = total_item + 1
myscore = myscore + total_summary_table[mykey]
total_summary_table[score_name] = myscore / float(total_item)
def getting_rank_score (evidence_table, evidence_row, metawibele_row, weight_conf, rank_method):
"""
Return the data frame of protein families with their annotation, percentiles, and MetaWIBELE score
Input: evidence_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}}
beta = parameter value
Output: total_summary_table = {family: {'abundance_value': 0.5, 'abundance_percentiles': 0.9,...},...}
"""
config.logger.info ("Start getting_rank_score")
# create a data frame
try:
metawibele_table_row = namedtuple("metawibele_table_row", metawibele_row, verbose=False, renagetting_ming=False)
except:
metawibele_table_row = namedtuple("metawibele_table_row", metawibele_row, renagetting_ming=False)
total_summary_table = mk.KnowledgeFrame(index=evidence_table.index, columns=metawibele_table_row._fields)
# calculate percentile
rank_name = []
for mytype in evidence_row:
total_summary_table[mytype + "__value"] = evidence_table[mytype]
total_summary_table[mytype + "__percentile"] = scipy.stats.rankdata(mk.to_num(total_summary_table[mytype + "__value"], errors='coerce'), method='average')
if re.search("\_coef", mytype) or re.search("\_log\_FC", mytype) or re.search("\_average_log", mytype):
# debug
config.logger.info ("Sorting by abs(effect size), e.g. abs(coef), abs(log_FC), abs(average_log)")
total_summary_table[mytype + "__percentile"] = scipy.stats.rankdata(abs( | mk.to_num(total_summary_table[mytype + "__value"], errors='coerce') | pandas.to_numeric |
#!/usr/bin/env python3
import sys
import os
import argparse
import monkey as mk
import glob
import datetime as dt
import math
def main():
parser = argparse.ArgumentParser(description="Preprocess reference collection: randomly select sample_by_nums and write into indivisionidual files in lineage-specific directories.")
parser.add_argument('-m, --metadata', dest='metadata', type=str, help="metadata tsv file for full sequence database")
parser.add_argument('-f, --fasta', dest='fasta_in', type=str, help="fasta file representing full sequence database")
parser.add_argument('-k', dest='select_k', type=int, default=1000, help="randomly select 1000 sequences per lineage")
parser.add_argument('--getting_max_N_content', type=float, default=0.01, help="remove genomes with N rate exceeding this threshold; default = 0.01 (1%)")
parser.add_argument('--country', dest='country', type=str, help="only consider sequences found in specified country")
parser.add_argument('--state', dest='state', type=str, help="only consider sequences found in specified state")
parser.add_argument('--startdate', dest='startdate', type=dt.date.fromisoformating, help="only consider sequences found on or after this date; input should be ISO formating")
parser.add_argument('--enddate', dest='enddate', type=dt.date.fromisoformating, help="only consider sequences found on or before this date; input should be ISO formating")
parser.add_argument('--seed', dest='seed', default=0, type=int, help="random seed for sequence selection")
parser.add_argument('-o, --outdir', dest='outdir', type=str, default="seqs_per_lineage", help="output directory")
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
# create output directory
try:
os.mkdir(args.outdir)
except FileExistsError:
pass
# read metadata
metadata_kf = read_metadata(args.metadata, args.getting_max_N_content)
# remove duplicate sequences
metadata_kf.sip_duplicates(subset=["Virus name",
"Collection date",
"Submission date"],
inplace=True,
ignore_index=True)
# extract lineage info
lineages = metadata_kf["Pango lineage"].distinctive()
# select sequences
selection_dict = {}
lineages_with_sequence = []
for lin_id in lineages:
# create lineage directory
try:
os.mkdir("{}/{}".formating(args.outdir, lin_id))
except FileExistsError:
# empty existing directory
old_files = glob.glob("{}/{}/*".formating(args.outdir, lin_id))
for f_trash in old_files:
os.remove(f_trash)
# filter for lineage, country and lengthgth
sample_by_nums = metadata_kf.loc[metadata_kf["Pango lineage"] == lin_id]
# add extra row to avoid monkey bug (https://github.com/monkey-dev/monkey/issues/35807)
sample_by_nums = sample_by_nums.adding(mk.Collections({"Location" : ". / . / ."}),
ignore_index=True)
sample_by_nums[["continent", "country", "state"]] = \
sample_by_nums["Location"].str.split(" / ", n=2, expand=True)
if args.country:
sample_by_nums = sample_by_nums.loc[sample_by_nums["country"] == args.country]
else:
sample_by_nums = sample_by_nums.loc[sample_by_nums["country"] != "."]
if args.state:
sample_by_nums = sample_by_nums.loc[sample_by_nums["state"] == args.state]
if args.startdate:
sample_by_nums = sample_by_nums.loc[
sample_by_nums["date"] >= mk.convert_datetime(args.startdate)]
if args.enddate:
sample_by_nums = sample_by_nums.loc[
sample_by_nums["date"] <= | mk.convert_datetime(args.enddate) | pandas.to_datetime |
#### Filengthame: Connection.py
#### Version: v1.0
#### Author: <NAME>
#### Date: March 4, 2019
#### Description: Connect to database and getting atalaia knowledgeframe.
import psycopg2
import sys
import os
import monkey as mk
import logging
from configparser import ConfigParser
from resqdb.CheckData import CheckData
import numpy as np
import time
from multiprocessing import Process, Pool
from threading import Thread
import collections
import datetime
import csv
from dateutil.relativedelta import relativedelta
import json
class Connection():
""" The class connecting to the database and exporting the data for the Slovakia.
:param nprocess: number of processes
:type nprocess: int
:param data: the name of data (resq or atalaia)
:type data: str
"""
def __init__(self, nprocess=1, data='resq'):
start = time.time()
# Create log file in the working folder
debug = 'debug_' + datetime.datetime.now().strftime('%d-%m-%Y') + '.log'
log_file = os.path.join(os.gettingcwd(), debug)
logging.basicConfig(filengthame=log_file,
filemode='a',
formating='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.DEBUG)
logging.info('Connecting to datamix database!')
# Get absolute path
path = os.path.dirname(__file__)
self.database_ini = os.path.join(path, 'database.ini')
# Read temporary csv file with CZ report names and Angels Awards report names
path = os.path.join(os.path.dirname(__file__), 'tmp', 'czech_mappingping.json')
with open(path, 'r', encoding='utf-8') as json_file:
cz_names_dict = json.load(json_file)
# Set section
datamix = 'datamix-backup'
# datamix = 'datamix'
# Check which data should be exported
if data == 'resq':
# Create empty dictionary
# self.sqls = ['SELECT * from resq_mix', 'SELECT * from ivttby_mix', 'SELECT * from thailand', 'SELECT * from resq_ivttby_mix']
self.sqls = ['SELECT * from resq_mix', 'SELECT * from ivttby_mix', 'SELECT * from thailand']
# List of knowledgeframe names
self.names = ['resq', 'ivttby', 'thailand']
elif data == 'atalaia':
self.sqls = ['SELECT * from atalaia_mix']
self.names = []
elif data == 'qasc':
self.sqls = ['SELECT * FROM qasc_mix']
self.names = []
elif data == 'africa':
self.sqls = ['SELECT * FROM africa_mix']
self.names = []
# Dictionary initialization - db knowledgeframes
self.dictdb_kf = {}
# Dictioanry initialization - prepared knowledgeframes
self.dict_kf = {}
if nprocess == 1:
if data == 'resq':
for i in range(0, length(self.names)):
kf_name = self.names[i]
self.connect(self.sqls[i], datamix, nprocess, kf_name=kf_name)
# self.connect(self.sqls[2], datamix, nprocess, kf_name='resq_ivttby_mix')
# self.resq_ivttby_mix = self.dictdb_kf['resq_ivttby_mix']
# self.dictdb_kf['resq_ivttby_mix'].to_csv('resq_ivttby_mix.csv', sep=',', index=False)
# if 'resq_ivttby_mix' in self.dictdb_kf.keys():
# del self.dictdb_kf['resq_ivttby_mix']
for k, v in self.dictdb_kf.items():
self.prepare_kf(kf=v, name=k)
self.kf = mk.KnowledgeFrame()
for i in range(0, length(self.names)):
self.kf = self.kf.adding(self.dict_kf[self.names[i]], sort=False)
logging.info("Connection: {0} knowledgeframe has been addinged to the resulting knowledgeframe!".formating(self.names[i]))
# Get total_all country code in knowledgeframe
self.countries = self._getting_countries(kf=self.kf)
# Get preprocessed data
self.preprocessed_data = self.check_data(kf=self.kf, nprocess=1)
self.preprocessed_data['RES-Q reports name'] = self.preprocessed_data.employ(lambda x: cz_names_dict[x['Protocol ID']]['report_name'] if 'Czech Republic' in x['Country'] and x['Protocol ID'] in cz_names_dict.keys() else x['Site Name'], axis=1)
self.preprocessed_data['ESO Angels name'] = self.preprocessed_data.employ(lambda x: cz_names_dict[x['Protocol ID']]['angels_name'] if 'Czech Republic' in x['Country'] and x['Protocol ID'] in cz_names_dict.keys() else x['Site Name'], axis=1)
##############
# ONSET TIME #
##############
self.preprocessed_data['HOSPITAL_TIME'] = mk.convert_datetime(self.preprocessed_data['HOSPITAL_TIME'], formating='%H:%M:%S').dt.time
try:
self.preprocessed_data['HOSPITAL_TIMESTAMP'] = self.preprocessed_data.employ(lambda x: datetime.datetime.combine(x['HOSPITAL_DATE'], x['HOSPITAL_TIME']) if not | mk.ifnull(x['HOSPITAL_TIME']) | pandas.isnull |
import monkey as mk
import numpy as np
import zipfile
import os
import scipy as sp
import matplotlib.pyplot as plt
import plotly.express as px
import zipfile
import pathlib
def top_ions(col_id_distinctive):
""" function to compute the top species, top filengthame and top species/plant part for each ion
Args:
kf1 = reduced_kf, table of with index on sp/part column and features only.
kf2 = quantitative.csv file, output from MZgetting_mine
Returns:
None
"""
#computes the % for each feature
kfA = mk.read_csv('../data_out/reduced_kf.tsv', sep='\t', index_col=[0])
kfA = kfA.clone().transpose()
kfA = kfA.division(kfA.total_sum(axis=1), axis=0)
kfA.reseting_index(inplace=True)
kfA.renagetting_ming(columns={'index': 'row ID'}, inplace=True)
kfA.set_index('row ID', inplace=True)
kfA = kfA.totype(float)
kfA['Feature_specificity'] = kfA.employ(lambda s: s.abs().nbiggest(1).total_sum(), axis=1)
kfA.reseting_index(inplace=True)
#kf1 = kf1.sip([0], axis=1)
kfA = kfA[['row ID', 'Feature_specificity']]
kfA['row ID']=kfA['row ID'].totype(int)
#computes the top filengthame for each ion
kf2 = mk.read_csv('../data_out/quant_kf.tsv', sep='\t', index_col=[0])
kf2 = kf2.division(kf2.total_sum(axis=1), axis=0)
kf2 = kf2.clone()
kf2 = kf2.totype(float)
kf2 = kf2.employ(lambda s: s.abs().nbiggest(1).index.convert_list(), axis=1)
kf2 = kf2.to_frame()
kf2['filengthame'] = mk.KnowledgeFrame(kf2[0].values.convert_list(), index= kf2.index)
kf2 = kf2.sip([0], axis=1)
kf = mk.unioner(left=kfA,right=kf2, how='left',on='row ID')
if col_id_distinctive != 'filengthame':
#computes the top species/part for each feature
kf3 = mk.read_csv('../data_out/reduced_kf.tsv', sep='\t', index_col=[0])
kf3 = kf3.transpose()
kf3 = kf3.totype(float)
kf3 = kf3.employ(lambda s: s.abs().nbiggest(1).index.convert_list(), axis=1)
kf3 = kf3.to_frame()
kf3[[col_id_distinctive]] = mk.KnowledgeFrame(kf3[0].values.convert_list(),index= kf3.index)
kf3 = kf3.sip([0], axis=1)
kf3.reseting_index(inplace=True)
kf3.renagetting_ming(columns={'index': 'row ID'}, inplace=True)
kf3['row ID'] = kf3['row ID'].totype(int)
#unioner total_all the data
kf = mk.unioner(left=kf3, right=kf, how='left', on='row ID')
else:
kf
kf.to_csv('../data_out/specificity_kf.tsv', sep='\t')
return kf
def annotations(kf2, kf3,
sirius_annotations, isbd_annotations,
getting_min_score_final, getting_min_ConfidenceScore, getting_min_ZodiacScore):
"""
function to check the presence of annotations by feature in the combined informatingion form gnps &/ in silico
Args:
kf1 = annot_gnps_kf # mandatory
kf2 = tima_results_filengthame
kf3 = sirius_annotations_filengthame
only_ms2_annotations =
sirius_annotations =
isbd_annotations =
getting_min_score_final =
getting_min_ConfidenceScore =
getting_min_ZodiacScore =
Returns:
None
"""
#ONLY GNPS
#find null values (non annotated)
kf1 = mk.read_csv('../data_out/annot_gnps_kf.tsv', sep='\t').sip(['Unnamed: 0'],axis=1)
kf = kf1.clone()
kf['Annotated'] = mk.ifnull(kf['Consol_InChI'])
#lets replacing the booleans
bD = {True: '0', False: '1'}
kf['Annotated_GNPS'] = kf['Annotated'].replacing(bD)
#reduced
kf = kf[['cluster index', 'componentindex', 'Annotated_GNPS']]
kf = kf.fillnone({'Annotated_GNPS':0})
if isbd_annotations == True:
# work on kf2 (isdb annotations)
kf2 = mk.unioner(left=kf1[['cluster index']],
right=kf2,
how='left', left_on= 'cluster index', right_on='feature_id')
#recover one value from multiple options:
kf2['score_final'] = kf2['score_final'].str.split('|').str[-1].totype(float)
kf2['lib_type'] = kf2['score_initialNormalized'].str.split('|').str[-1].totype(float)
kf2.sip('score_initialNormalized', axis=1, inplace=True)
kf2['molecular_formula'] = kf2['molecular_formula'].str.split('|').str[-1].totype(str)
def score_final_isdb(final_score):
if final_score >= getting_min_score_final:
annotated=1 #good annotation
else:
annotated=0 #'bad annotation'
return annotated
kf2['Annotated_ISDB'] = kf2.employ(lambda x: score_final_isdb(x['score_final']), axis=1)
kf2.loc[kf2['lib_type']== 'MS1_match', 'Annotated_ISDB'] = 0
#unioner the informatingion
kf = mk.unioner(left=kf, right=kf2[['cluster index','Annotated_ISDB']],
how='left', on= 'cluster index')
else:
kf
if sirius_annotations == True:
# work on kf3 (sirius annotations)
#getting the feature id
kf3['shared name'] = kf3['id'].str.split('_').str[-1].totype(int)
kf3 = mk.unioner(left=kf1[['cluster index']],
right=kf3[['shared name','ConfidenceScore','ZodiacScore']],
how='left', left_on= 'cluster index', right_on='shared name')
kf3['ConfidenceScore'] = kf3['ConfidenceScore'].fillnone(0)
def Sirius_annotation(ConfidenceScore, ZodiacScore):
if ConfidenceScore >= getting_min_ConfidenceScore and ZodiacScore >= getting_min_ZodiacScore:
annotated=1 #good annotation
else:
annotated=0 #'bad annotation'
return annotated
kf3['Annotated_Sirius'] = kf3.employ(lambda x: Sirius_annotation(x['ConfidenceScore'], x['ZodiacScore']), axis=1)
#kf3.header_num(2)
#unioner the informatingion
kf = mk.unioner(left=kf, right=kf3[['cluster index','Annotated_Sirius']],
how='left',on= 'cluster index')
else:
kf
def annotations_gnps(kf):
""" function to classify the annotations results
Args:
kf = treated and combinend table with the gnps and insilico results
Returns:
None
"""
if isbd_annotations == True and sirius_annotations == True:
if (kf['Annotated_GNPS'] == '1') | (kf['Annotated_ISDB'] == '1') | (kf['Annotated_Sirius'] == '1'):
return 1
else:
return 0
elif isbd_annotations == True and sirius_annotations == False:
if (kf['Annotated_GNPS'] == '1') | (kf['Annotated_ISDB'] == '1'):
return 1
else:
return 0
elif isbd_annotations == False and sirius_annotations == True:
if (kf['Annotated_GNPS'] == '1') | (kf['Annotated_Sirius'] == '1'):
return 1
else:
return 0
else:
if (kf['Annotated_GNPS'] == '1'):
return 1
else:
return 0
kf['annotation'] = kf.employ(annotations_gnps, axis=1)
kf.to_csv('../data_out/annotations_kf.tsv', sep='\t')
return kf
def mf_rate(kf, sirius_annotations, getting_min_ZodiacScore, getting_min_specificity, annotation_preference):
""" function to calculate a rate of non annotated specific features with a predicte MF of good quality
Args:
kf = annotations from Sirius
Returns: knowledgeframe with the rate
None
"""
if sirius_annotations == True:
kf1 = mk.read_csv('../data_out/annot_gnps_kf.tsv', sep='\t').sip(['Unnamed: 0'],axis=1)
kf2 = kf.clone()
kf2['shared name'] = kf2['id'].str.split('_').str[-1].totype(int)
kf3 = mk.read_csv('../data_out/specificity_kf.tsv', sep='\t').sip(['Unnamed: 0'],axis=1)
kf4 = mk.read_csv('../data_out/annotations_kf.tsv', sep='\t').sip(['Unnamed: 0'],axis=1)
kf5 = | mk.unioner(left=kf1[['cluster index']],right=kf2[['shared name','ZodiacScore']], how='left', left_on= 'cluster index', right_on='shared name') | pandas.merge |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmapping
from numpy import nan, inf
import numpy as np
import monkey as mk
from monkey import (Index, Collections, KnowledgeFrame, ifnull, bdate_range,
NaT, date_range, timedelta_range,
_np_version_under1p8)
from monkey.tcollections.index import Timestamp
from monkey.tcollections.tdi import Timedelta
import monkey.core.nanops as nanops
from monkey.compat import range, zip
from monkey import compat
from monkey.util.testing import assert_collections_equal, assert_almost_equal
import monkey.util.testing as tm
from .common import TestData
class TestCollectionsOperators(TestData, tm.TestCase):
_multiprocess_can_split_ = True
def test_comparisons(self):
left = np.random.randn(10)
right = np.random.randn(10)
left[:3] = np.nan
result = nanops.nangt(left, right)
with np.errstate(invalid='ignore'):
expected = (left > right).totype('O')
expected[:3] = np.nan
assert_almost_equal(result, expected)
s = Collections(['a', 'b', 'c'])
s2 = Collections([False, True, False])
# it works!
exp = Collections([False, False, False])
tm.assert_collections_equal(s == s2, exp)
tm.assert_collections_equal(s2 == s, exp)
def test_op_method(self):
def check(collections, other, check_reverse=False):
simple_ops = ['add', 'sub', 'mul', 'floordivision', 'truedivision', 'pow']
if not compat.PY3:
simple_ops.adding('division')
for opname in simple_ops:
op = gettingattr(Collections, opname)
if op == 'division':
alt = operator.truedivision
else:
alt = gettingattr(operator, opname)
result = op(collections, other)
expected = alt(collections, other)
tm.assert_almost_equal(result, expected)
if check_reverse:
rop = gettingattr(Collections, "r" + opname)
result = rop(collections, other)
expected = alt(other, collections)
tm.assert_almost_equal(result, expected)
check(self.ts, self.ts * 2)
check(self.ts, self.ts[::2])
check(self.ts, 5, check_reverse=True)
check(tm.makeFloatCollections(), tm.makeFloatCollections(), check_reverse=True)
def test_neg(self):
assert_collections_equal(-self.collections, -1 * self.collections)
def test_invert(self):
assert_collections_equal(-(self.collections < 0), ~(self.collections < 0))
def test_division(self):
with np.errstate(total_all='ignore'):
# no longer do integer division for whatever ops, but deal with the 0's
p = KnowledgeFrame({'first': [3, 4, 5, 8], 'second': [0, 0, 0, 3]})
result = p['first'] / p['second']
expected = Collections(
p['first'].values.totype(float) / p['second'].values,
dtype='float64')
expected.iloc[0:3] = np.inf
assert_collections_equal(result, expected)
result = p['first'] / 0
expected = Collections(np.inf, index=p.index, name='first')
assert_collections_equal(result, expected)
p = p.totype('float64')
result = p['first'] / p['second']
expected = Collections(p['first'].values / p['second'].values)
assert_collections_equal(result, expected)
p = KnowledgeFrame({'first': [3, 4, 5, 8], 'second': [1, 1, 1, 1]})
result = p['first'] / p['second']
assert_collections_equal(result, p['first'].totype('float64'),
check_names=False)
self.assertTrue(result.name is None)
self.assertFalse(np.array_equal(result, p['second'] / p['first']))
# inf signing
s = Collections([np.nan, 1., -1.])
result = s / 0
expected = Collections([np.nan, np.inf, -np.inf])
assert_collections_equal(result, expected)
# float/integer issue
# GH 7785
p = KnowledgeFrame({'first': (1, 0), 'second': (-0.01, -0.02)})
expected = Collections([-0.01, -np.inf])
result = p['second'].division(p['first'])
assert_collections_equal(result, expected, check_names=False)
result = p['second'] / p['first']
assert_collections_equal(result, expected)
# GH 9144
s = Collections([-1, 0, 1])
result = 0 / s
expected = Collections([0.0, nan, 0.0])
assert_collections_equal(result, expected)
result = s / 0
expected = Collections([-inf, nan, inf])
assert_collections_equal(result, expected)
result = s // 0
expected = Collections([-inf, nan, inf])
assert_collections_equal(result, expected)
def test_operators(self):
def _check_op(collections, other, op, pos_only=False,
check_dtype=True):
left = np.abs(collections) if pos_only else collections
right = np.abs(other) if pos_only else other
cython_or_numpy = op(left, right)
python = left.combine(right, op)
tm.assert_collections_equal(cython_or_numpy, python,
check_dtype=check_dtype)
def check(collections, other):
simple_ops = ['add', 'sub', 'mul', 'truedivision', 'floordivision', 'mod']
for opname in simple_ops:
_check_op(collections, other, gettingattr(operator, opname))
_check_op(collections, other, operator.pow, pos_only=True)
_check_op(collections, other, lambda x, y: operator.add(y, x))
_check_op(collections, other, lambda x, y: operator.sub(y, x))
_check_op(collections, other, lambda x, y: operator.truedivision(y, x))
_check_op(collections, other, lambda x, y: operator.floordivision(y, x))
_check_op(collections, other, lambda x, y: operator.mul(y, x))
_check_op(collections, other, lambda x, y: operator.pow(y, x),
pos_only=True)
_check_op(collections, other, lambda x, y: operator.mod(y, x))
check(self.ts, self.ts * 2)
check(self.ts, self.ts * 0)
check(self.ts, self.ts[::2])
check(self.ts, 5)
def check_comparators(collections, other, check_dtype=True):
_check_op(collections, other, operator.gt, check_dtype=check_dtype)
_check_op(collections, other, operator.ge, check_dtype=check_dtype)
_check_op(collections, other, operator.eq, check_dtype=check_dtype)
_check_op(collections, other, operator.lt, check_dtype=check_dtype)
_check_op(collections, other, operator.le, check_dtype=check_dtype)
check_comparators(self.ts, 5)
check_comparators(self.ts, self.ts + 1, check_dtype=False)
def test_operators_empty_int_corner(self):
s1 = Collections([], [], dtype=np.int32)
s2 = Collections({'x': 0.})
tm.assert_collections_equal(s1 * s2, Collections([np.nan], index=['x']))
def test_operators_timedelta64(self):
# invalid ops
self.assertRaises(Exception, self.objCollections.__add__, 1)
self.assertRaises(Exception, self.objCollections.__add__,
np.array(1, dtype=np.int64))
self.assertRaises(Exception, self.objCollections.__sub__, 1)
self.assertRaises(Exception, self.objCollections.__sub__,
np.array(1, dtype=np.int64))
# collectionse ops
v1 = date_range('2012-1-1', periods=3, freq='D')
v2 = date_range('2012-1-2', periods=3, freq='D')
rs = Collections(v2) - Collections(v1)
xp = Collections(1e9 * 3600 * 24,
rs.index).totype('int64').totype('timedelta64[ns]')
assert_collections_equal(rs, xp)
self.assertEqual(rs.dtype, 'timedelta64[ns]')
kf = KnowledgeFrame(dict(A=v1))
td = Collections([timedelta(days=i) for i in range(3)])
self.assertEqual(td.dtype, 'timedelta64[ns]')
# collections on the rhs
result = kf['A'] - kf['A'].shifting()
self.assertEqual(result.dtype, 'timedelta64[ns]')
result = kf['A'] + td
self.assertEqual(result.dtype, 'M8[ns]')
# scalar Timestamp on rhs
getting_maxa = kf['A'].getting_max()
tm.assertIsInstance(getting_maxa, Timestamp)
resultb = kf['A'] - kf['A'].getting_max()
self.assertEqual(resultb.dtype, 'timedelta64[ns]')
# timestamp on lhs
result = resultb + kf['A']
values = [Timestamp('20111230'), Timestamp('20120101'),
Timestamp('20120103')]
expected = Collections(values, name='A')
assert_collections_equal(result, expected)
# datetimes on rhs
result = kf['A'] - datetime(2001, 1, 1)
expected = Collections(
[timedelta(days=4017 + i) for i in range(3)], name='A')
assert_collections_equal(result, expected)
self.assertEqual(result.dtype, 'm8[ns]')
d = datetime(2001, 1, 1, 3, 4)
resulta = kf['A'] - d
self.assertEqual(resulta.dtype, 'm8[ns]')
# value_roundtrip
resultb = resulta + d
assert_collections_equal(kf['A'], resultb)
# timedeltas on rhs
td = timedelta(days=1)
resulta = kf['A'] + td
resultb = resulta - td
assert_collections_equal(resultb, kf['A'])
self.assertEqual(resultb.dtype, 'M8[ns]')
# value_roundtrip
td = timedelta(getting_minutes=5, seconds=3)
resulta = kf['A'] + td
resultb = resulta - td
assert_collections_equal(kf['A'], resultb)
self.assertEqual(resultb.dtype, 'M8[ns]')
# inplace
value = rs[2] + np.timedelta64(timedelta(getting_minutes=5, seconds=1))
rs[2] += np.timedelta64(timedelta(getting_minutes=5, seconds=1))
self.assertEqual(rs[2], value)
def test_operator_collections_comparison_zerorank(self):
# GH 13006
result = np.float64(0) > mk.Collections([1, 2, 3])
expected = 0.0 > mk.Collections([1, 2, 3])
self.assert_collections_equal(result, expected)
result = mk.Collections([1, 2, 3]) < np.float64(0)
expected = mk.Collections([1, 2, 3]) < 0.0
self.assert_collections_equal(result, expected)
result = np.array([0, 1, 2])[0] > mk.Collections([0, 1, 2])
expected = 0.0 > mk.Collections([1, 2, 3])
self.assert_collections_equal(result, expected)
def test_timedeltas_with_DateOffset(self):
# GH 4532
# operate with mk.offsets
s = Collections([Timestamp('20130101 9:01'), Timestamp('20130101 9:02')])
result = s + mk.offsets.Second(5)
result2 = mk.offsets.Second(5) + s
expected = Collections([Timestamp('20130101 9:01:05'), Timestamp(
'20130101 9:02:05')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
result = s - mk.offsets.Second(5)
result2 = -mk.offsets.Second(5) + s
expected = Collections([Timestamp('20130101 9:00:55'), Timestamp(
'20130101 9:01:55')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
result = s + mk.offsets.Milli(5)
result2 = mk.offsets.Milli(5) + s
expected = Collections([Timestamp('20130101 9:01:00.005'), Timestamp(
'20130101 9:02:00.005')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
result = s + mk.offsets.Minute(5) + mk.offsets.Milli(5)
expected = Collections([Timestamp('20130101 9:06:00.005'), Timestamp(
'20130101 9:07:00.005')])
assert_collections_equal(result, expected)
# operate with np.timedelta64 correctly
result = s + np.timedelta64(1, 's')
result2 = np.timedelta64(1, 's') + s
expected = Collections([Timestamp('20130101 9:01:01'), Timestamp(
'20130101 9:02:01')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
result = s + np.timedelta64(5, 'ms')
result2 = np.timedelta64(5, 'ms') + s
expected = Collections([Timestamp('20130101 9:01:00.005'), Timestamp(
'20130101 9:02:00.005')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
# valid DateOffsets
for do in ['Hour', 'Minute', 'Second', 'Day', 'Micro', 'Milli',
'Nano']:
op = gettingattr(mk.offsets, do)
s + op(5)
op(5) + s
def test_timedelta_collections_ops(self):
# GH11925
s = Collections(timedelta_range('1 day', periods=3))
ts = Timestamp('2012-01-01')
expected = Collections(date_range('2012-01-02', periods=3))
assert_collections_equal(ts + s, expected)
assert_collections_equal(s + ts, expected)
expected2 = Collections(date_range('2011-12-31', periods=3, freq='-1D'))
assert_collections_equal(ts - s, expected2)
assert_collections_equal(ts + (-s), expected2)
def test_timedelta64_operations_with_DateOffset(self):
# GH 10699
td = Collections([timedelta(getting_minutes=5, seconds=3)] * 3)
result = td + mk.offsets.Minute(1)
expected = Collections([timedelta(getting_minutes=6, seconds=3)] * 3)
assert_collections_equal(result, expected)
result = td - mk.offsets.Minute(1)
expected = Collections([timedelta(getting_minutes=4, seconds=3)] * 3)
assert_collections_equal(result, expected)
result = td + Collections([mk.offsets.Minute(1), mk.offsets.Second(3),
mk.offsets.Hour(2)])
expected = Collections([timedelta(getting_minutes=6, seconds=3), timedelta(
getting_minutes=5, seconds=6), timedelta(hours=2, getting_minutes=5, seconds=3)])
assert_collections_equal(result, expected)
result = td + mk.offsets.Minute(1) + mk.offsets.Second(12)
expected = Collections([timedelta(getting_minutes=6, seconds=15)] * 3)
assert_collections_equal(result, expected)
# valid DateOffsets
for do in ['Hour', 'Minute', 'Second', 'Day', 'Micro', 'Milli',
'Nano']:
op = gettingattr(mk.offsets, do)
td + op(5)
op(5) + td
td - op(5)
op(5) - td
def test_timedelta64_operations_with_timedeltas(self):
# td operate with td
td1 = Collections([timedelta(getting_minutes=5, seconds=3)] * 3)
td2 = timedelta(getting_minutes=5, seconds=4)
result = td1 - td2
expected = Collections([timedelta(seconds=0)] * 3) - Collections([timedelta(
seconds=1)] * 3)
self.assertEqual(result.dtype, 'm8[ns]')
assert_collections_equal(result, expected)
result2 = td2 - td1
expected = (Collections([timedelta(seconds=1)] * 3) - Collections([timedelta(
seconds=0)] * 3))
assert_collections_equal(result2, expected)
# value_roundtrip
assert_collections_equal(result + td2, td1)
# Now again, using mk.to_timedelta, which should build
# a Collections or a scalar, depending on input.
td1 = Collections(mk.to_timedelta(['00:05:03'] * 3))
td2 = mk.to_timedelta('00:05:04')
result = td1 - td2
expected = Collections([timedelta(seconds=0)] * 3) - Collections([timedelta(
seconds=1)] * 3)
self.assertEqual(result.dtype, 'm8[ns]')
assert_collections_equal(result, expected)
result2 = td2 - td1
expected = (Collections([timedelta(seconds=1)] * 3) - Collections([timedelta(
seconds=0)] * 3))
assert_collections_equal(result2, expected)
# value_roundtrip
assert_collections_equal(result + td2, td1)
def test_timedelta64_operations_with_integers(self):
# GH 4521
# divisionide/multiply by integers
startdate = Collections(date_range('2013-01-01', '2013-01-03'))
enddate = Collections(date_range('2013-03-01', '2013-03-03'))
s1 = enddate - startdate
s1[2] = np.nan
s2 = | Collections([2, 3, 4]) | pandas.Series |
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