Datasets:

dipanjanS

/

codeparrot-train-subset

like 0

kmike/scikit-learn

sklearn/utils/__init__.py

3

10094

""" The :mod:`sklearn.utils` module includes various utilites. """ from collections import Sequence import numpy as np from scipy.sparse import issparse import warnings from .murmurhash import murmurhash3_32 from .validation import (as_float_array, check_arrays, safe_asarray, assert_all_finite, array2d, atleast2d_or_csc, atleast2d_or_csr, warn_if_not_float, check_random_state) from .class_weight import compute_class_weight __all__ = ["murmurhash3_32", "as_float_array", "check_arrays", "safe_asarray", "assert_all_finite", "array2d", "atleast2d_or_csc", "atleast2d_or_csr", "warn_if_not_float", "check_random_state", "compute_class_weight"] # Make sure that DeprecationWarning get printed warnings.simplefilter("always", DeprecationWarning) class deprecated(object): """Decorator to mark a function or class as deprecated. Issue a warning when the function is called/the class is instantiated and adds a warning to the docstring. The optional extra argument will be appended to the deprecation message and the docstring. Note: to use this with the default value for extra, put in an empty of parentheses: >>> from sklearn.utils import deprecated >>> deprecated() # doctest: +ELLIPSIS <sklearn.utils.deprecated object at ...> >>> @deprecated() ... def some_function(): pass """ # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, # but with many changes. def __init__(self, extra=''): """ Parameters ---------- extra: string to be added to the deprecation messages """ self.extra = extra def __call__(self, obj): if isinstance(obj, type): return self._decorate_class(obj) else: return self._decorate_fun(obj) def _decorate_class(self, cls): msg = "Class %s is deprecated" % cls.__name__ if self.extra: msg += "; %s" % self.extra # FIXME: we should probably reset __new__ for full generality init = cls.__init__ def wrapped(*args, **kwargs): warnings.warn(msg, category=DeprecationWarning) return init(*args, **kwargs) cls.__init__ = wrapped wrapped.__name__ = '__init__' wrapped.__doc__ = self._update_doc(init.__doc__) wrapped.deprecated_original = init return cls def _decorate_fun(self, fun): """Decorate function fun""" msg = "Function %s is deprecated" % fun.__name__ if self.extra: msg += "; %s" % self.extra def wrapped(*args, **kwargs): warnings.warn(msg, category=DeprecationWarning) return fun(*args, **kwargs) wrapped.__name__ = fun.__name__ wrapped.__dict__ = fun.__dict__ wrapped.__doc__ = self._update_doc(fun.__doc__) return wrapped def _update_doc(self, olddoc): newdoc = "DEPRECATED" if self.extra: newdoc = "%s: %s" % (newdoc, self.extra) if olddoc: newdoc = "%s\n\n%s" % (newdoc, olddoc) return newdoc def safe_mask(X, mask): """Return a mask which is safe to use on X. Parameters ---------- X : {array-like, sparse matrix} Data on which to apply mask. mask: array Mask to be used on X. Returns ------- mask """ mask = np.asanyarray(mask) if np.issubdtype(mask.dtype, np.int): return mask if hasattr(X, "toarray"): ind = np.arange(mask.shape[0]) mask = ind[mask] return mask def resample(*arrays, **options): """Resample arrays or sparse matrices in a consistent way The default strategy implements one step of the bootstrapping procedure. Parameters ---------- `*arrays` : sequence of arrays or scipy.sparse matrices with same shape[0] replace : boolean, True by default Implements resampling with replacement. If False, this will implement (sliced) random permutations. n_samples : int, None by default Number of samples to generate. If left to None this is automatically set to the first dimension of the arrays. random_state : int or RandomState instance Control the shuffling for reproducible behavior. Returns ------- Sequence of resampled views of the collections. The original arrays are not impacted. Examples -------- It is possible to mix sparse and dense arrays in the same run:: >>> X = [[1., 0.], [2., 1.], [0., 0.]] >>> y = np.array([0, 1, 2]) >>> from scipy.sparse import coo_matrix >>> X_sparse = coo_matrix(X) >>> from sklearn.utils import resample >>> X, X_sparse, y = resample(X, X_sparse, y, random_state=0) >>> X array([[ 1., 0.], [ 2., 1.], [ 1., 0.]]) >>> X_sparse # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE <3x2 sparse matrix of type '<... 'numpy.float64'>' with 4 stored elements in Compressed Sparse Row format> >>> X_sparse.toarray() array([[ 1., 0.], [ 2., 1.], [ 1., 0.]]) >>> y array([0, 1, 0]) >>> resample(y, n_samples=2, random_state=0) array([0, 1]) See also -------- :class:`sklearn.cross_validation.Bootstrap` :func:`sklearn.utils.shuffle` """ random_state = check_random_state(options.pop('random_state', None)) replace = options.pop('replace', True) max_n_samples = options.pop('n_samples', None) if options: raise ValueError("Unexpected kw arguments: %r" % options.keys()) if len(arrays) == 0: return None first = arrays[0] n_samples = first.shape[0] if hasattr(first, 'shape') else len(first) if max_n_samples is None: max_n_samples = n_samples if max_n_samples > n_samples: raise ValueError("Cannot sample %d out of arrays with dim %d" % ( max_n_samples, n_samples)) arrays = check_arrays(*arrays, sparse_format='csr') if replace: indices = random_state.randint(0, n_samples, size=(max_n_samples,)) else: indices = np.arange(n_samples) random_state.shuffle(indices) indices = indices[:max_n_samples] resampled_arrays = [] for array in arrays: array = array[indices] resampled_arrays.append(array) if len(resampled_arrays) == 1: # syntactic sugar for the unit argument case return resampled_arrays[0] else: return resampled_arrays def shuffle(*arrays, **options): """Shuffle arrays or sparse matrices in a consistent way This is a convenience alias to ``resample(*arrays, replace=False)`` to do random permutations of the collections. Parameters ---------- `*arrays` : sequence of arrays or scipy.sparse matrices with same shape[0] random_state : int or RandomState instance Control the shuffling for reproducible behavior. n_samples : int, None by default Number of samples to generate. If left to None this is automatically set to the first dimension of the arrays. Returns ------- Sequence of shuffled views of the collections. The original arrays are not impacted. Examples -------- It is possible to mix sparse and dense arrays in the same run:: >>> X = [[1., 0.], [2., 1.], [0., 0.]] >>> y = np.array([0, 1, 2]) >>> from scipy.sparse import coo_matrix >>> X_sparse = coo_matrix(X) >>> from sklearn.utils import shuffle >>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0) >>> X array([[ 0., 0.], [ 2., 1.], [ 1., 0.]]) >>> X_sparse # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE <3x2 sparse matrix of type '<... 'numpy.float64'>' with 3 stored elements in Compressed Sparse Row format> >>> X_sparse.toarray() array([[ 0., 0.], [ 2., 1.], [ 1., 0.]]) >>> y array([2, 1, 0]) >>> shuffle(y, n_samples=2, random_state=0) array([0, 1]) See also -------- :func:`sklearn.utils.resample` """ options['replace'] = False return resample(*arrays, **options) def safe_sqr(X, copy=True): """Element wise squaring of array-likes and sparse matrices. Parameters ---------- X : array like, matrix, sparse matrix Returns ------- X ** 2 : element wise square """ X = safe_asarray(X) if issparse(X): if copy: X = X.copy() X.data **= 2 else: if copy: X = X ** 2 else: X **= 2 return X def gen_even_slices(n, n_packs): """Generator to create n_packs slices going up to n. Examples -------- >>> from sklearn.utils import gen_even_slices >>> list(gen_even_slices(10, 1)) [slice(0, 10, None)] >>> list(gen_even_slices(10, 10)) #doctest: +ELLIPSIS [slice(0, 1, None), slice(1, 2, None), ..., slice(9, 10, None)] >>> list(gen_even_slices(10, 5)) #doctest: +ELLIPSIS [slice(0, 2, None), slice(2, 4, None), ..., slice(8, 10, None)] >>> list(gen_even_slices(10, 3)) [slice(0, 4, None), slice(4, 7, None), slice(7, 10, None)] """ start = 0 for pack_num in range(n_packs): this_n = n // n_packs if pack_num < n % n_packs: this_n += 1 if this_n > 0: end = start + this_n yield slice(start, end, None) start = end def tosequence(x): """Cast iterable x to a Sequence, avoiding a copy if possible.""" if isinstance(x, np.ndarray): return np.asarray(x) elif isinstance(x, Sequence): return x else: return list(x) class ConvergenceWarning(Warning): "Custom warning to capture convergence problems"

bsd-3-clause

mne-tools/mne-tools.github.io

0.20/_downloads/76822bb92a8465181ec2a7ee96ca8cf4/plot_decoding_csp_timefreq.py

1

6457

""" ============================================================================ Decoding in time-frequency space data using the Common Spatial Pattern (CSP) ============================================================================ The time-frequency decomposition is estimated by iterating over raw data that has been band-passed at different frequencies. This is used to compute a covariance matrix over each epoch or a rolling time-window and extract the CSP filtered signals. A linear discriminant classifier is then applied to these signals. """ # Authors: Laura Gwilliams <laura.gwilliams@nyu.edu> # Jean-Remi King <jeanremi.king@gmail.com> # Alex Barachant <alexandre.barachant@gmail.com> # Alexandre Gramfort <alexandre.gramfort@inria.fr> # # License: BSD (3-clause) import numpy as np import matplotlib.pyplot as plt from mne import Epochs, create_info, events_from_annotations from mne.io import concatenate_raws, read_raw_edf from mne.datasets import eegbci from mne.decoding import CSP from mne.time_frequency import AverageTFR from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.model_selection import StratifiedKFold, cross_val_score from sklearn.pipeline import make_pipeline from sklearn.preprocessing import LabelEncoder ############################################################################### # Set parameters and read data event_id = dict(hands=2, feet=3) # motor imagery: hands vs feet subject = 1 runs = [6, 10, 14] raw_fnames = eegbci.load_data(subject, runs) raw = concatenate_raws([read_raw_edf(f, preload=True) for f in raw_fnames]) # Extract information from the raw file sfreq = raw.info['sfreq'] events, _ = events_from_annotations(raw, event_id=dict(T1=2, T2=3)) raw.pick_types(meg=False, eeg=True, stim=False, eog=False, exclude='bads') # Assemble the classifier using scikit-learn pipeline clf = make_pipeline(CSP(n_components=4, reg=None, log=True, norm_trace=False), LinearDiscriminantAnalysis()) n_splits = 5 # how many folds to use for cross-validation cv = StratifiedKFold(n_splits=n_splits, shuffle=True) # Classification & Time-frequency parameters tmin, tmax = -.200, 2.000 n_cycles = 10. # how many complete cycles: used to define window size min_freq = 5. max_freq = 25. n_freqs = 8 # how many frequency bins to use # Assemble list of frequency range tuples freqs = np.linspace(min_freq, max_freq, n_freqs) # assemble frequencies freq_ranges = list(zip(freqs[:-1], freqs[1:])) # make freqs list of tuples # Infer window spacing from the max freq and number of cycles to avoid gaps window_spacing = (n_cycles / np.max(freqs) / 2.) centered_w_times = np.arange(tmin, tmax, window_spacing)[1:] n_windows = len(centered_w_times) # Instantiate label encoder le = LabelEncoder() ############################################################################### # Loop through frequencies, apply classifier and save scores # init scores freq_scores = np.zeros((n_freqs - 1,)) # Loop through each frequency range of interest for freq, (fmin, fmax) in enumerate(freq_ranges): # Infer window size based on the frequency being used w_size = n_cycles / ((fmax + fmin) / 2.) # in seconds # Apply band-pass filter to isolate the specified frequencies raw_filter = raw.copy().filter(fmin, fmax, n_jobs=1, fir_design='firwin', skip_by_annotation='edge') # Extract epochs from filtered data, padded by window size epochs = Epochs(raw_filter, events, event_id, tmin - w_size, tmax + w_size, proj=False, baseline=None, preload=True) epochs.drop_bad() y = le.fit_transform(epochs.events[:, 2]) X = epochs.get_data() # Save mean scores over folds for each frequency and time window freq_scores[freq] = np.mean(cross_val_score(estimator=clf, X=X, y=y, scoring='roc_auc', cv=cv, n_jobs=1), axis=0) ############################################################################### # Plot frequency results plt.bar(freqs[:-1], freq_scores, width=np.diff(freqs)[0], align='edge', edgecolor='black') plt.xticks(freqs) plt.ylim([0, 1]) plt.axhline(len(epochs['feet']) / len(epochs), color='k', linestyle='--', label='chance level') plt.legend() plt.xlabel('Frequency (Hz)') plt.ylabel('Decoding Scores') plt.title('Frequency Decoding Scores') ############################################################################### # Loop through frequencies and time, apply classifier and save scores # init scores tf_scores = np.zeros((n_freqs - 1, n_windows)) # Loop through each frequency range of interest for freq, (fmin, fmax) in enumerate(freq_ranges): # Infer window size based on the frequency being used w_size = n_cycles / ((fmax + fmin) / 2.) # in seconds # Apply band-pass filter to isolate the specified frequencies raw_filter = raw.copy().filter(fmin, fmax, n_jobs=1, fir_design='firwin', skip_by_annotation='edge') # Extract epochs from filtered data, padded by window size epochs = Epochs(raw_filter, events, event_id, tmin - w_size, tmax + w_size, proj=False, baseline=None, preload=True) epochs.drop_bad() y = le.fit_transform(epochs.events[:, 2]) # Roll covariance, csp and lda over time for t, w_time in enumerate(centered_w_times): # Center the min and max of the window w_tmin = w_time - w_size / 2. w_tmax = w_time + w_size / 2. # Crop data into time-window of interest X = epochs.copy().crop(w_tmin, w_tmax).get_data() # Save mean scores over folds for each frequency and time window tf_scores[freq, t] = np.mean(cross_val_score(estimator=clf, X=X, y=y, scoring='roc_auc', cv=cv, n_jobs=1), axis=0) ############################################################################### # Plot time-frequency results # Set up time frequency object av_tfr = AverageTFR(create_info(['freq'], sfreq), tf_scores[np.newaxis, :], centered_w_times, freqs[1:], 1) chance = np.mean(y) # set chance level to white in the plot av_tfr.plot([0], vmin=chance, title="Time-Frequency Decoding Scores", cmap=plt.cm.Reds)

bsd-3-clause

bijanfallah/OI_CCLM

src/RMSE_MAPS_INGO.py

1

2007

# Program to show the maps of RMSE averaged over time import matplotlib.pyplot as plt from sklearn.metrics import mean_squared_error import os from netCDF4 import Dataset as NetCDFFile import numpy as np from CCLM_OUTS import Plot_CCLM # option == 1 -> shift 4 with default cclm domain and nboundlines = 3 # option == 2 -> shift 4 with smaller cclm domain and nboundlines = 3 # option == 3 -> shift 4 with smaller cclm domain and nboundlines = 6 # option == 4 -> shift 4 with corrected smaller cclm domain and nboundlines = 3 # option == 5 -> shift 4 with corrected smaller cclm domain and nboundlines = 4 # option == 6 -> shift 4 with corrected smaller cclm domain and nboundlines = 6 # option == 7 -> shift 4 with corrected smaller cclm domain and nboundlines = 9 # option == 8 -> shift 4 with corrected bigger cclm domain and nboundlines = 3 from CCLM_OUTS import Plot_CCLM #def f(x): # if x==-9999: # return float('NaN') # else: # return x def read_data_from_mistral(dir='/work/bb1029/b324045/work1/work/member/post/',name='member_T_2M_ts_seasmean.nc',var='T_2M'): # type: (object, object, object) -> object #a function to read the data from mistral work """ :rtype: object """ #CMD = 'scp $mistral:' + dir + name + ' ./' CMD = 'wget users.met.fu-berlin.de/~BijanFallah/' + dir + name os.system(CMD) nc = NetCDFFile(name) # for name2, variable in nc.variables.items(): # for attrname in variable.ncattrs(): # print(name2, variable, '-----------------',attrname) # #print("{} -- {}".format(attrname, getattr(variable, attrname))) os.remove(name) lats = nc.variables['lat'][:] lons = nc.variables['lon'][:] t = nc.variables[var][:].squeeze() rlats = nc.variables['rlat'][:] # extract/copy the data rlons = nc.variables['rlon'][:] #f2 = np.vectorize(f) #t= f2(t) #t=t.data t=t.squeeze() #print() nc.close() return(t, lats, lons, rlats, rlons)

mit

hsu/chrono

src/demos/trackVehicle/validationPlots_test_M113.py

5

4229

# -*- coding: utf-8 -*- """ Created on Wed May 06 11:00:53 2015 @author: newJustin """ import ChronoTrack_pandas as CT import pylab as py if __name__ == '__main__': # logger import logging as lg lg.basicConfig(fileName = 'logFile.log', level=lg.WARN, format='%(message)s') # default font size import matplotlib font = {'size' : 14} matplotlib.rc('font', **font) # ********************************************************************** # =============== USER INPUT ======================================= # data dir, end w/ '/' # data_dir = 'D:/Chrono_github_Build/bin/outdata_M113/' data_dir = 'E:/Chrono_github_Build/bin/outdata_M113/' ''' # list of data files to plot chassis = 'M113_chassis.csv' gearSubsys = 'M113_Side0_gear.csv' idlerSubsys = 'M113_Side0_idler.csv' # ptrainSubsys = 'test_driveChain_ptrain.csv' shoe0 = 'M113_Side0_shoe0.csv' ''' chassis = 'M113_400_200__chassis.csv' gearSubsys = 'M113_400_200__Side0_gear.csv' idlerSubsys = 'M113_400_200__Side0_idler.csv' # ptrainSubsys = 'test_driveChain_ptrain.csv' shoe0 = 'M113_400_200__Side0_shoe0.csv' data_files = [data_dir + chassis, data_dir + gearSubsys, data_dir + idlerSubsys, data_dir + shoe0] handle_list = ['chassis','gear','idler','shoe0'] # handle_list = ['Gear','idler','ptrain','shoe0','gearCV','idlerCV','rollerCV','gearContact','shoeGearContact'] ''' gearCV = 'test_driveChain_GearCV.csv' idlerCV = 'test_driveChain_idler0CV.csv' rollerCV = 'test_driveChain_roller0CV.csv' gearContact = 'test_driveChain_gearContact.csv' shoeGearContact = 'test_driveChain_shoe0GearContact.csv' ''' # data_files = [data_dir + gearSubsys, data_dir + idlerSubsys, data_dir + ptrainSubsys, data_dir + shoe0, data_dir + gearCV, data_dir + idlerCV, data_dir + rollerCV, data_dir + gearContact, data_dir+shoeGearContact] # handle_list = ['Gear','idler','ptrain','shoe0','gearCV','idlerCV','rollerCV','gearContact','shoeGearContact'] # list of data files for gear/pin comparison plots # Primitive gear geometry ''' gear = 'driveChain_P_gear.csv' gearContact = 'driveChain_P_gearContact.csv' shoe = 'driveChain_P_shoe0.csv' shoeContact = 'driveChain_P_shoe0GearContact.csv' ptrain = 'driveChain_P_ptrain.csv' # Collision Callback gear geometry gear = 'driveChain_CC_gear.csv' gearContact = 'driveChain_CC_gearContact.csv' shoe = 'driveChain_CC_shoe0.csv' shoeContact = 'driveChain_CC_shoe0GearContact.csv' ptrain = 'driveChain_CC_ptrain.csv' data_files = [data_dir+gear, data_dir+gearContact, data_dir+shoe, data_dir+shoeContact, data_dir+ptrain] handle_list = ['Gear','gearContact','shoe0','shoeGearContact','ptrain'] ''' # construct the panda class for the DriveChain, file list and list of legend M113_Chain0 = CT.ChronoTrack_pandas(data_files, handle_list) # set the time limits. tmin = -1 will plot the entire time range tmin = 1.0 tmax = 8.0 #0) plot the chassis M113_Chain0.plot_chassis(tmin, tmax) # 1) plot the gear body info M113_Chain0.plot_gear(tmin, tmax) # 2) plot idler body info, tensioner force M113_Chain0.plot_idler(tmin,tmax) ''' # 3) plot powertrain info M113_Chain0.plot_ptrain() ''' # 4) plot shoe 0 body info, and pin 0 force/torque M113_Chain0.plot_shoe(tmin,tmax) ''' # 5) plot gear Constraint Violations M113_Chain0.plot_gearCV(tmin,tmax) # 6) plot idler Constraint Violations M113_Chain0.plot_idlerCV(tmin,tmax) # 7) plot roller Constraint Violations M113_Chain0.plot_rollerCV(tmin,tmax) # 8) from the contact report callback function, gear contact info M113_Chain0.plot_gearContactInfo(tmin,tmax) # 9) from shoe-gear report callback function, contact info M113_Chain0.plot_shoeGearContactInfo(tmin,tmax) ''' # 10) track shoe trajectory: rel-X vs. rel-Y M113_Chain0.plot_trajectory(tmin,tmax) py.show()

bsd-3-clause

lancezlin/ml_template_py

lib/python2.7/site-packages/sklearn/metrics/tests/test_score_objects.py

15

17443

import pickle import tempfile import shutil import os import numbers import numpy as np from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_raises_regexp from sklearn.utils.testing import assert_true from sklearn.utils.testing import ignore_warnings from sklearn.utils.testing import assert_not_equal from sklearn.utils.testing import assert_warns_message from sklearn.base import BaseEstimator from sklearn.metrics import (f1_score, r2_score, roc_auc_score, fbeta_score, log_loss, precision_score, recall_score) from sklearn.metrics.cluster import adjusted_rand_score from sklearn.metrics.scorer import (check_scoring, _PredictScorer, _passthrough_scorer) from sklearn.metrics import make_scorer, get_scorer, SCORERS from sklearn.svm import LinearSVC from sklearn.pipeline import make_pipeline from sklearn.cluster import KMeans from sklearn.dummy import DummyRegressor from sklearn.linear_model import Ridge, LogisticRegression from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from sklearn.datasets import make_blobs from sklearn.datasets import make_classification from sklearn.datasets import make_multilabel_classification from sklearn.datasets import load_diabetes from sklearn.model_selection import train_test_split, cross_val_score from sklearn.model_selection import GridSearchCV from sklearn.multiclass import OneVsRestClassifier from sklearn.externals import joblib REGRESSION_SCORERS = ['r2', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_median_absolute_error', 'mean_absolute_error', 'mean_squared_error', 'median_absolute_error'] CLF_SCORERS = ['accuracy', 'f1', 'f1_weighted', 'f1_macro', 'f1_micro', 'roc_auc', 'average_precision', 'precision', 'precision_weighted', 'precision_macro', 'precision_micro', 'recall', 'recall_weighted', 'recall_macro', 'recall_micro', 'neg_log_loss', 'log_loss', 'adjusted_rand_score' # not really, but works ] MULTILABEL_ONLY_SCORERS = ['precision_samples', 'recall_samples', 'f1_samples'] def _make_estimators(X_train, y_train, y_ml_train): # Make estimators that make sense to test various scoring methods sensible_regr = DummyRegressor(strategy='median') sensible_regr.fit(X_train, y_train) sensible_clf = DecisionTreeClassifier(random_state=0) sensible_clf.fit(X_train, y_train) sensible_ml_clf = DecisionTreeClassifier(random_state=0) sensible_ml_clf.fit(X_train, y_ml_train) return dict( [(name, sensible_regr) for name in REGRESSION_SCORERS] + [(name, sensible_clf) for name in CLF_SCORERS] + [(name, sensible_ml_clf) for name in MULTILABEL_ONLY_SCORERS] ) X_mm, y_mm, y_ml_mm = None, None, None ESTIMATORS = None TEMP_FOLDER = None def setup_module(): # Create some memory mapped data global X_mm, y_mm, y_ml_mm, TEMP_FOLDER, ESTIMATORS TEMP_FOLDER = tempfile.mkdtemp(prefix='sklearn_test_score_objects_') X, y = make_classification(n_samples=30, n_features=5, random_state=0) _, y_ml = make_multilabel_classification(n_samples=X.shape[0], random_state=0) filename = os.path.join(TEMP_FOLDER, 'test_data.pkl') joblib.dump((X, y, y_ml), filename) X_mm, y_mm, y_ml_mm = joblib.load(filename, mmap_mode='r') ESTIMATORS = _make_estimators(X_mm, y_mm, y_ml_mm) def teardown_module(): global X_mm, y_mm, y_ml_mm, TEMP_FOLDER, ESTIMATORS # GC closes the mmap file descriptors X_mm, y_mm, y_ml_mm, ESTIMATORS = None, None, None, None shutil.rmtree(TEMP_FOLDER) class EstimatorWithoutFit(object): """Dummy estimator to test check_scoring""" pass class EstimatorWithFit(BaseEstimator): """Dummy estimator to test check_scoring""" def fit(self, X, y): return self class EstimatorWithFitAndScore(object): """Dummy estimator to test check_scoring""" def fit(self, X, y): return self def score(self, X, y): return 1.0 class EstimatorWithFitAndPredict(object): """Dummy estimator to test check_scoring""" def fit(self, X, y): self.y = y return self def predict(self, X): return self.y class DummyScorer(object): """Dummy scorer that always returns 1.""" def __call__(self, est, X, y): return 1 def test_all_scorers_repr(): # Test that all scorers have a working repr for name, scorer in SCORERS.items(): repr(scorer) def test_check_scoring(): # Test all branches of check_scoring estimator = EstimatorWithoutFit() pattern = (r"estimator should be an estimator implementing 'fit' method," r" .* was passed") assert_raises_regexp(TypeError, pattern, check_scoring, estimator) estimator = EstimatorWithFitAndScore() estimator.fit([[1]], [1]) scorer = check_scoring(estimator) assert_true(scorer is _passthrough_scorer) assert_almost_equal(scorer(estimator, [[1]], [1]), 1.0) estimator = EstimatorWithFitAndPredict() estimator.fit([[1]], [1]) pattern = (r"If no scoring is specified, the estimator passed should have" r" a 'score' method\. The estimator .* does not\.") assert_raises_regexp(TypeError, pattern, check_scoring, estimator) scorer = check_scoring(estimator, "accuracy") assert_almost_equal(scorer(estimator, [[1]], [1]), 1.0) estimator = EstimatorWithFit() scorer = check_scoring(estimator, "accuracy") assert_true(isinstance(scorer, _PredictScorer)) estimator = EstimatorWithFit() scorer = check_scoring(estimator, allow_none=True) assert_true(scorer is None) def test_check_scoring_gridsearchcv(): # test that check_scoring works on GridSearchCV and pipeline. # slightly redundant non-regression test. grid = GridSearchCV(LinearSVC(), param_grid={'C': [.1, 1]}) scorer = check_scoring(grid, "f1") assert_true(isinstance(scorer, _PredictScorer)) pipe = make_pipeline(LinearSVC()) scorer = check_scoring(pipe, "f1") assert_true(isinstance(scorer, _PredictScorer)) # check that cross_val_score definitely calls the scorer # and doesn't make any assumptions about the estimator apart from having a # fit. scores = cross_val_score(EstimatorWithFit(), [[1], [2], [3]], [1, 0, 1], scoring=DummyScorer()) assert_array_equal(scores, 1) def test_make_scorer(): # Sanity check on the make_scorer factory function. f = lambda *args: 0 assert_raises(ValueError, make_scorer, f, needs_threshold=True, needs_proba=True) def test_classification_scores(): # Test classification scorers. X, y = make_blobs(random_state=0, centers=2) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = LinearSVC(random_state=0) clf.fit(X_train, y_train) for prefix, metric in [('f1', f1_score), ('precision', precision_score), ('recall', recall_score)]: score1 = get_scorer('%s_weighted' % prefix)(clf, X_test, y_test) score2 = metric(y_test, clf.predict(X_test), pos_label=None, average='weighted') assert_almost_equal(score1, score2) score1 = get_scorer('%s_macro' % prefix)(clf, X_test, y_test) score2 = metric(y_test, clf.predict(X_test), pos_label=None, average='macro') assert_almost_equal(score1, score2) score1 = get_scorer('%s_micro' % prefix)(clf, X_test, y_test) score2 = metric(y_test, clf.predict(X_test), pos_label=None, average='micro') assert_almost_equal(score1, score2) score1 = get_scorer('%s' % prefix)(clf, X_test, y_test) score2 = metric(y_test, clf.predict(X_test), pos_label=1) assert_almost_equal(score1, score2) # test fbeta score that takes an argument scorer = make_scorer(fbeta_score, beta=2) score1 = scorer(clf, X_test, y_test) score2 = fbeta_score(y_test, clf.predict(X_test), beta=2) assert_almost_equal(score1, score2) # test that custom scorer can be pickled unpickled_scorer = pickle.loads(pickle.dumps(scorer)) score3 = unpickled_scorer(clf, X_test, y_test) assert_almost_equal(score1, score3) # smoke test the repr: repr(fbeta_score) def test_regression_scorers(): # Test regression scorers. diabetes = load_diabetes() X, y = diabetes.data, diabetes.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = Ridge() clf.fit(X_train, y_train) score1 = get_scorer('r2')(clf, X_test, y_test) score2 = r2_score(y_test, clf.predict(X_test)) assert_almost_equal(score1, score2) def test_thresholded_scorers(): # Test scorers that take thresholds. X, y = make_blobs(random_state=0, centers=2) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = LogisticRegression(random_state=0) clf.fit(X_train, y_train) score1 = get_scorer('roc_auc')(clf, X_test, y_test) score2 = roc_auc_score(y_test, clf.decision_function(X_test)) score3 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]) assert_almost_equal(score1, score2) assert_almost_equal(score1, score3) logscore = get_scorer('neg_log_loss')(clf, X_test, y_test) logloss = log_loss(y_test, clf.predict_proba(X_test)) assert_almost_equal(-logscore, logloss) # same for an estimator without decision_function clf = DecisionTreeClassifier() clf.fit(X_train, y_train) score1 = get_scorer('roc_auc')(clf, X_test, y_test) score2 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]) assert_almost_equal(score1, score2) # test with a regressor (no decision_function) reg = DecisionTreeRegressor() reg.fit(X_train, y_train) score1 = get_scorer('roc_auc')(reg, X_test, y_test) score2 = roc_auc_score(y_test, reg.predict(X_test)) assert_almost_equal(score1, score2) # Test that an exception is raised on more than two classes X, y = make_blobs(random_state=0, centers=3) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf.fit(X_train, y_train) assert_raises(ValueError, get_scorer('roc_auc'), clf, X_test, y_test) def test_thresholded_scorers_multilabel_indicator_data(): # Test that the scorer work with multilabel-indicator format # for multilabel and multi-output multi-class classifier X, y = make_multilabel_classification(allow_unlabeled=False, random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) # Multi-output multi-class predict_proba clf = DecisionTreeClassifier() clf.fit(X_train, y_train) y_proba = clf.predict_proba(X_test) score1 = get_scorer('roc_auc')(clf, X_test, y_test) score2 = roc_auc_score(y_test, np.vstack(p[:, -1] for p in y_proba).T) assert_almost_equal(score1, score2) # Multi-output multi-class decision_function # TODO Is there any yet? clf = DecisionTreeClassifier() clf.fit(X_train, y_train) clf._predict_proba = clf.predict_proba clf.predict_proba = None clf.decision_function = lambda X: [p[:, 1] for p in clf._predict_proba(X)] y_proba = clf.decision_function(X_test) score1 = get_scorer('roc_auc')(clf, X_test, y_test) score2 = roc_auc_score(y_test, np.vstack(p for p in y_proba).T) assert_almost_equal(score1, score2) # Multilabel predict_proba clf = OneVsRestClassifier(DecisionTreeClassifier()) clf.fit(X_train, y_train) score1 = get_scorer('roc_auc')(clf, X_test, y_test) score2 = roc_auc_score(y_test, clf.predict_proba(X_test)) assert_almost_equal(score1, score2) # Multilabel decision function clf = OneVsRestClassifier(LinearSVC(random_state=0)) clf.fit(X_train, y_train) score1 = get_scorer('roc_auc')(clf, X_test, y_test) score2 = roc_auc_score(y_test, clf.decision_function(X_test)) assert_almost_equal(score1, score2) def test_unsupervised_scorers(): # Test clustering scorers against gold standard labeling. # We don't have any real unsupervised Scorers yet. X, y = make_blobs(random_state=0, centers=2) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) km = KMeans(n_clusters=3) km.fit(X_train) score1 = get_scorer('adjusted_rand_score')(km, X_test, y_test) score2 = adjusted_rand_score(y_test, km.predict(X_test)) assert_almost_equal(score1, score2) @ignore_warnings def test_raises_on_score_list(): # Test that when a list of scores is returned, we raise proper errors. X, y = make_blobs(random_state=0) f1_scorer_no_average = make_scorer(f1_score, average=None) clf = DecisionTreeClassifier() assert_raises(ValueError, cross_val_score, clf, X, y, scoring=f1_scorer_no_average) grid_search = GridSearchCV(clf, scoring=f1_scorer_no_average, param_grid={'max_depth': [1, 2]}) assert_raises(ValueError, grid_search.fit, X, y) @ignore_warnings def test_scorer_sample_weight(): # Test that scorers support sample_weight or raise sensible errors # Unlike the metrics invariance test, in the scorer case it's harder # to ensure that, on the classifier output, weighted and unweighted # scores really should be unequal. X, y = make_classification(random_state=0) _, y_ml = make_multilabel_classification(n_samples=X.shape[0], random_state=0) split = train_test_split(X, y, y_ml, random_state=0) X_train, X_test, y_train, y_test, y_ml_train, y_ml_test = split sample_weight = np.ones_like(y_test) sample_weight[:10] = 0 # get sensible estimators for each metric estimator = _make_estimators(X_train, y_train, y_ml_train) for name, scorer in SCORERS.items(): if name in MULTILABEL_ONLY_SCORERS: target = y_ml_test else: target = y_test try: weighted = scorer(estimator[name], X_test, target, sample_weight=sample_weight) ignored = scorer(estimator[name], X_test[10:], target[10:]) unweighted = scorer(estimator[name], X_test, target) assert_not_equal(weighted, unweighted, msg="scorer {0} behaves identically when " "called with sample weights: {1} vs " "{2}".format(name, weighted, unweighted)) assert_almost_equal(weighted, ignored, err_msg="scorer {0} behaves differently when " "ignoring samples and setting sample_weight to" " 0: {1} vs {2}".format(name, weighted, ignored)) except TypeError as e: assert_true("sample_weight" in str(e), "scorer {0} raises unhelpful exception when called " "with sample weights: {1}".format(name, str(e))) @ignore_warnings # UndefinedMetricWarning for P / R scores def check_scorer_memmap(scorer_name): scorer, estimator = SCORERS[scorer_name], ESTIMATORS[scorer_name] if scorer_name in MULTILABEL_ONLY_SCORERS: score = scorer(estimator, X_mm, y_ml_mm) else: score = scorer(estimator, X_mm, y_mm) assert isinstance(score, numbers.Number), scorer_name def test_scorer_memmap_input(): # Non-regression test for #6147: some score functions would # return singleton memmap when computed on memmap data instead of scalar # float values. for name in SCORERS.keys(): yield check_scorer_memmap, name def test_deprecated_names(): X, y = make_blobs(random_state=0, centers=2) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = LogisticRegression(random_state=0) clf.fit(X_train, y_train) for name in ('mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'log_loss'): warning_msg = "Scoring method %s was renamed to" % name for scorer in (get_scorer(name), SCORERS[name]): assert_warns_message(DeprecationWarning, warning_msg, scorer, clf, X, y) assert_warns_message(DeprecationWarning, warning_msg, cross_val_score, clf, X, y, scoring=name) def test_scoring_is_not_metric(): assert_raises_regexp(ValueError, 'make_scorer', check_scoring, LogisticRegression(), f1_score) assert_raises_regexp(ValueError, 'make_scorer', check_scoring, LogisticRegression(), roc_auc_score) assert_raises_regexp(ValueError, 'make_scorer', check_scoring, Ridge(), r2_score) assert_raises_regexp(ValueError, 'make_scorer', check_scoring, KMeans(), adjusted_rand_score)

mit

wavelets/zipline

zipline/examples/dual_ema_talib.py

2

3230

#!/usr/bin/env python # # Copyright 2013 Quantopian, Inc. # # 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 copy 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 matplotlib.pyplot as plt from zipline.algorithm import TradingAlgorithm from zipline.utils.factory import load_from_yahoo # Import exponential moving average from talib wrapper from zipline.transforms.ta import EMA from datetime import datetime import pytz class DualEMATaLib(TradingAlgorithm): """Dual Moving Average Crossover algorithm. This algorithm buys apple once its short moving average crosses its long moving average (indicating upwards momentum) and sells its shares once the averages cross again (indicating downwards momentum). """ def initialize(self, short_window=20, long_window=40): # Add 2 mavg transforms, one with a long window, one # with a short window. self.short_ema_trans = EMA(timeperiod=short_window) self.long_ema_trans = EMA(timeperiod=long_window) # To keep track of whether we invested in the stock or not self.invested = False def handle_data(self, data): self.short_ema = self.short_ema_trans.handle_data(data) self.long_ema = self.long_ema_trans.handle_data(data) if self.short_ema is None or self.long_ema is None: return self.buy = False self.sell = False if (self.short_ema > self.long_ema).all() and not self.invested: self.order('AAPL', 100) self.invested = True self.buy = True elif (self.short_ema < self.long_ema).all() and self.invested: self.order('AAPL', -100) self.invested = False self.sell = True self.record(AAPL=data['AAPL'].price, short_ema=self.short_ema['AAPL'], long_ema=self.long_ema['AAPL'], buy=self.buy, sell=self.sell) if __name__ == '__main__': start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc) end = datetime(1991, 1, 1, 0, 0, 0, 0, pytz.utc) data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start, end=end) dma = DualEMATaLib() results = dma.run(data).dropna() fig = plt.figure() ax1 = fig.add_subplot(211, ylabel='portfolio value') results.portfolio_value.plot(ax=ax1) ax2 = fig.add_subplot(212) results[['AAPL', 'short_ema', 'long_ema']].plot(ax=ax2) ax2.plot(results.ix[results.buy].index, results.short_ema[results.buy], '^', markersize=10, color='m') ax2.plot(results.ix[results.sell].index, results.short_ema[results.sell], 'v', markersize=10, color='k') plt.legend(loc=0) plt.gcf().set_size_inches(18, 8)

apache-2.0

ChanChiChoi/scikit-learn

examples/model_selection/plot_roc.py

146

3697

""" ======================================= Receiver Operating Characteristic (ROC) ======================================= Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality. ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis. This means that the top left corner of the plot is the "ideal" point - a false positive rate of zero, and a true positive rate of one. This is not very realistic, but it does mean that a larger area under the curve (AUC) is usually better. The "steepness" of ROC curves is also important, since it is ideal to maximize the true positive rate while minimizing the false positive rate. ROC curves are typically used in binary classification to study the output of a classifier. In order to extend ROC curve and ROC area to multi-class or multi-label classification, it is necessary to binarize the output. One ROC curve can be drawn per label, but one can also draw a ROC curve by considering each element of the label indicator matrix as a binary prediction (micro-averaging). .. note:: See also :func:`sklearn.metrics.roc_auc_score`, :ref:`example_model_selection_plot_roc_crossval.py`. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.cross_validation import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier # Import some data to play with iris = datasets.load_iris() X = iris.data y = iris.target # Binarize the output y = label_binarize(y, classes=[0, 1, 2]) n_classes = y.shape[1] # Add noisy features to make the problem harder random_state = np.random.RandomState(0) n_samples, n_features = X.shape X = np.c_[X, random_state.randn(n_samples, 200 * n_features)] # shuffle and split training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0) # Learn to predict each class against the other classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True, random_state=random_state)) y_score = classifier.fit(X_train, y_train).decision_function(X_test) # Compute ROC curve and ROC area for each class fpr = dict() tpr = dict() roc_auc = dict() for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) # Plot of a ROC curve for a specific class plt.figure() plt.plot(fpr[2], tpr[2], label='ROC curve (area = %0.2f)' % roc_auc[2]) plt.plot([0, 1], [0, 1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic example') plt.legend(loc="lower right") plt.show() # Plot ROC curve plt.figure() plt.plot(fpr["micro"], tpr["micro"], label='micro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc["micro"])) for i in range(n_classes): plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Some extension of Receiver operating characteristic to multi-class') plt.legend(loc="lower right") plt.show()

bsd-3-clause

rcolasanti/CompaniesHouseScraper

DVLACompanyNmeMatchCoHoAPIFindMissing.py

1

5174

import requests import json import numpy as np import pandas as pd import CoHouseToken from difflib import SequenceMatcher # In[3]: def exactMatch(line1, line2): line1=line1.upper().rstrip() line2=line2.upper().rstrip() #print("|"+line1+"|"+line2+"|",line1==line2) return line1==line2 def aStopWord(word): return word.upper().replace("COMPANY","CO").replace("LIMITED","LTD").replace("&","AND").rstrip() def spaces(word): w = word.upper().replace("/"," ") w = w.replace("."," ").replace(","," ").replace("-"," ").rstrip() return w def removeAStopWord(word): w = word.upper().replace("LTD"," ").replace("CO"," ").replace("AND"," ").replace("("," ").replace("/"," ") w = w.replace(")"," ").replace("."," ").replace(","," ").replace("-"," ").rstrip() return w def removeABlank(word): w = word.replace(" ","") return w def removeABracket (line): flag = False word="" for a in line: if a=="(": flag = True a="" if a==")": a="" flag = False if flag: a="" word+=a return word def stopWord(line1, line2): line1=aStopWord(line1) line2=aStopWord(line2) #print("|"+line1+"|"+line2+"|",line1==line2) return line1==line2 def removeStopWord(line1, line2): line1=spaces(line1) line2=spaces(line2) line1=aStopWord(line1) line2=aStopWord(line2) line1=removeAStopWord(line1) line2=removeAStopWord(line2) #print("|"+line1+"|"+line2+"|",line1==line2) return line1==line2 def removeBlanks(line1, line2): line1=spaces(line1) line2=spaces(line2) line1=aStopWord(line1) line2=aStopWord(line2) line1=removeAStopWord(line1) line2=removeAStopWord(line2) line1=removeABlank(line1) line2=removeABlank(line2) return line1==line2 def removeBrackets(line1, line2): line1=removeABracket(line1) line2=removeABracket(line2) line1=spaces(line1) line2=spaces(line2) line1=aStopWord(line1) line2=aStopWord(line2) line1=removeAStopWord(line1) line2=removeAStopWord(line2) line1=removeABlank(line1) line2=removeABlank(line2) #print("|"+line1+"|"+line2+"|",line1==line2) return line1==line2 def strip(line1, line2): line1=removeABracket(line1) line2=removeABracket(line2) line1=spaces(line1) line2=spaces(line2) line1=aStopWord(line1) line2=aStopWord(line2) line1=removeAStopWord(line1) line2=removeAStopWord(line2) line1=removeABlank(line1) line2=removeABlank(line2) return line1,line2 def match(company,results): for i in results['items']: line = i['title'] number = i['company_number'] if(exactMatch(company,line)): return True,line,number for i in results['items']: line = i['title'] number = i['company_number'] if(stopWord(company,line)): return True,line,number for i in results['items']: line = i['title'] number = i['company_number'] if(removeStopWord(company,line)): return True,line,number for i in results['items']: line = i['title'] number = i['company_number'] if(removeBlanks(company,line)): return True,line,number for i in results['items']: line = i['title'] number = i['company_number'] if(removeBrackets(company,line)): return True,line,number #old_match(company,results) return False,"","" def main(args): print(args[0]) search_url ="https://api.companieshouse.gov.uk/search/companies?q=" token = CoHouseToken.getToken() pw = '' base_url = 'https://api.companieshouse.gov.uk' file = args[1] print(file) df = pd.read_csv(file,names=['Organisation']) companies = df.Organisation count=0 found = open("found2.csv",'w') missing = open("missing2.csv",'w') for c in companies: c =c.upper().replace("&","AND") c = c.split(" T/A ")[0] c = c.split("WAS ")[0] c= spaces(c) url=search_url+c results = json.loads(requests.get(url, auth=(token,pw)).text) for i , key in enumerate(results['items']): a,b = strip(c, key['title']) r = SequenceMatcher(None, a, b).ratio() print("%s \t %s\t %.2f \t %s \t %s"%(i,c,r,key['company_number'],key['title'])) v = input('type number or return to reject: ') if v =="": print("reject") missing.write("%s\n"%(c)) else: key = results['items'][int(v)] print("%s \t %s\t %.2f \t %s \t %s"%(v,c,r,key['company_number'],key['title'])) print("*************************") found.write("%s,%s,%s,\n"%(c,key['title'],key['company_number'])) print() #print(count/len(companies)) return 0 if __name__ == '__main__': import sys sys.exit(main(sys.argv))

gpl-3.0

mclaughlin6464/pasta

pasta/ising.py

1

5474

''' This is a dummy file for me to get started making an Ising model. I'll get this 2-D Ising running, then generalize. ''' import argparse from itertools import izip import numpy as np from matplotlib import pyplot as plt import seaborn as sns sns.set() def run_ising(N, d, K, J,h, n_steps, plot = False): ''' :param N: :param d: :param K: :param J: :param h: :param n_steps: :param plot: :return: ''' if plot: try: assert d <= 2 except AssertionError: raise AssertionError("Can only plot in one or two dimensions.") #TODO wrap these better assert N >0 and N < 1000 assert d > 0 assert n_steps > 0 np.random.seed(0) size = tuple(N for i in xrange(d)) lattice = np.ones(size) #make a random initial state lattice-= np.random.randint(0,2, size =size)*2 # do different initialization E_0 = energy(lattice, potential, K, h) if plot: plt.ion() for step in xrange(n_steps): if step%1000 == 0: print step site = tuple(np.random.randint(0, N, size=d)) # consider flipping this site lattice[site] *= -1 E_f = energy(lattice, potential, K, h) # if E_F < E_0, keep # if E_F > E_0, keep randomly given change of energies if E_f >= E_0: keep = np.random.uniform() < np.exp(K / J * (E_0 - E_f)) else: keep = True if keep: E_0 = E_f else: lattice[site] *= -1 # fig = plt.figure() if plot and step % 100 == 0: if d == 1: plt.imshow(lattice.reshape((1, -1)),interpolation='none') else: plt.imshow(lattice, interpolation='none') plt.title(correlation(lattice, N/2)) plt.pause(0.01) plt.clf() return np.array([correlation(lattice, r) for r in xrange(1, N/2+1)]) def get_NN(site, N, d, r= 1): ''' The NN of the site. Will only return those UP in index (east, south, and down) to avoid double counting. Accounts for PBC :param site: (d,) array of coordinates in the lattice :param N: Size of one side of the lattice :param d: dimension of the lattice :return: dxd numpy array where each row corresponds to the nearest neighbors. ''' mult_sites = np.r_[ [site for i in xrange(d)]] adjustment = np.eye(d)*r return ((mult_sites+adjustment)%N).astype(int) def potential(s1, s2, K, h): ''' Basic Ising potential :param s1: First spin (-1 or 1) :param s2: Second spin :param K: Coupling constant :return: Energy of this particular bond ''' return -1*K*s1*s2 - h/2*(s1+s2)#should this be abstracted to call the NN function? def energy(lattice, potential, K, h = 0): ''' Calculate the energy of a lattice :param lattice: Lattice to calculate the energy on :param potential: Function defining the potential of a given site. :return: Energy of the lattice ''' N = lattice.shape[0] d = len(lattice.shape) dim_slices = np.meshgrid(*(xrange(N) for i in xrange(d)), indexing = 'ij') all_sites = izip(*[slice.flatten() for slice in dim_slices]) E = 0 for site in all_sites: nn = get_NN(site, N, d) for neighbor in nn: E+=potential(lattice[site], lattice[tuple(neighbor)],K = K, h = h) return E def magnetization(lattice): return lattice.mean() def correlation(lattice, r): ''' The average spin correlation at distance r. :param lattice: The lattice to calculate the statistic on. :param r: Distance to measure correlation :return: ''' N = lattice.shape[0] d = len(lattice.shape) dim_slices = np.meshgrid(*(xrange(N) for i in xrange(d)), indexing='ij') all_sites = izip(*[slice.flatten() for slice in dim_slices]) xi = 0 for site in all_sites: nn = get_NN(site, N, d, r) for neighbor in nn: xi += lattice[site]*lattice[tuple(neighbor)] return xi/((N**d)*d) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Simulate an ising model') parser.add_argument('N', type = int, help = 'Length of one side of the cube.') parser.add_argument('d', type = int, help = 'Number of dimensions of the cube.') #parser.add_argument('K', type = float, help ='Bond coupling strength.') parser.add_argument('J', type = float, default = 1.0, nargs = '?',\ help = 'Energy of bond strength. Optional, default is 1.') parser.add_argument('h', type = float, default=0.0, nargs = '?',\ help = 'Magnetic field strength. Optional, default is 0.') parser.add_argument('n_steps', type = int, default = 1000, nargs = '?',\ help = 'Number of steps to simulate. Default is 1e5') parser.add_argument('--plot', action = 'store_true',\ help = 'Whether or not to plot results. Only allowed with d = 1 or 2.') args = parser.parse_args() spins = [] Ks = [ 0.5,0.6,0.65, 0.7,0.8, 0.9] for K in Ks: print K spins.append(run_ising(K = K, **vars(args))) for K, spin in izip(Ks, spins): plt.plot(spin, label = K ) plt.legend(loc = 'best') plt.ylim([-0.1, 1.1]) plt.show()

mit

nicholaschris/landsatpy

stuff.py

1

1864

import cloud_detection_new as cloud_detection from matplotlib import pyplot as plt import views from skimage import exposure nir = cloud_detection.get_nir()[0:600,2000:2600] red = cloud_detection.get_red()[0:600,2000:2600] green = cloud_detection.get_green()[0:600,2000:2600] blue = cloud_detection.get_blue()[0:600,2000:2600] # or use coastal coastal = cloud_detection.get_coastal()[0:600,2000:2600] marine_shadow_index = (green-blue)/(green+blue) img = views.create_composite(red, green, blue) img_rescale = exposure.rescale_intensity(img, in_range=(0, 90)) plt.rcParams['savefig.facecolor'] = "0.8" vmin, vmax=0.0,0.1 def example_plot(ax, data, fontsize=12): ax.imshow(data, vmin=vmin, vmax=vmax) ax.locator_params(nbins=3) ax.set_xlabel('x-label', fontsize=fontsize) ax.set_ylabel('y-label', fontsize=fontsize) ax.set_title('Title', fontsize=fontsize) plt.close('all') fig = plt.figure ax1=plt.subplot(243) ax2=plt.subplot(244) ax3=plt.subplot(247) ax4=plt.subplot(248) ax5=plt.subplot(121) a_coastal = coastal[500:600, 500:600] a_blue = blue[500:600, 500:600] a_green = green[500:600, 500:600] a_red = red[500:600, 500:600] a_nir = nir[500:600, 500:600] a_img = img[500:600, 500:600] spec1 = [a_coastal[60, 60], a_blue[60, 60], a_green[60, 60], a_red[60, 60], a_nir[60, 60]] b_coastal = coastal[200:300, 100:200] b_blue = blue[200:300, 100:200] b_green = green[200:300, 100:200] b_red = red[200:300, 100:200] b_nir = nir[200:300, 100:200] b_img = img[200:300, 100:200] example_plot(ax1, coastal) example_plot(ax2, blue) example_plot(ax3, green) example_plot(ax4, red) ax5.imshow(img) # plt.tight_layout() plt.close('all') spec = [b_coastal[60, 60], b_blue[60, 60], b_green[60, 60], b_red[60, 60], b_nir[60, 60]] plt.plot(spec, 'k*-') plt.plot(spec1, 'k.-') plt.close('all') cbg = (coastal+blue+green)/3 plt.imshow(cbg/red)

mit

Monika319/EWEF-1

Cw2Rezonans/Karolina/Oscyloskop/OscyloskopZ5W2.py

1

1312

# -*- coding: utf-8 -*- """ Plot oscilloscope files from MultiSim """ import numpy as np import matplotlib.pyplot as plt import sys import os from matplotlib import rc rc('font',family="Consolas") files=["real_zad5_05f_p2.txt"] for NazwaPliku in files: print NazwaPliku Plik=open(NazwaPliku) #print DeltaT Dane=Plik.readlines()#[4:] DeltaT=float(Dane[2].split()[3].replace(",",".")) #M=len(Dane[4].split())/2 M=2 Dane=Dane[5:] Plik.close() print M Ys=[np.zeros(len(Dane)) for i in range(M)] for m in range(M): for i in range(len(Dane)): try: Ys[m][i]=float(Dane[i].split()[2+3*m].replace(",",".")) except: print m, i, 2+3*m, len(Dane[i].split()), Dane[i].split() #print i, Y[i] X=np.zeros_like(Ys[0]) for i in range(len(X)): X[i]=i*DeltaT for y in Ys: print max(y)-min(y) Opis=u"Układ szeregowy\nPołowa częstotliwości rezonansowej" Nazwa=u"Z5W2" plt.title(u"Przebieg napięciowy\n"+Opis) plt.xlabel(u"Czas t [s]") plt.ylabel(u"Napięcie [V]") plt.plot(X,Ys[0],label=u"Wejście") plt.plot(X,Ys[1],label=u"Wyjście") plt.grid() plt.legend(loc="best") plt.savefig(Nazwa + ".png", bbox_inches='tight') plt.show()

gpl-2.0

nddsg/TreeDecomps

xplodnTree/tdec/b2CliqueTreeRules.py

1

3569

#!/usr/bin/env python __author__ = 'saguinag' + '@' + 'nd.edu' __version__ = "0.1.0" ## ## fname "b2CliqueTreeRules.py" ## ## TODO: some todo list ## VersionLog: import net_metrics as metrics import pandas as pd import argparse, traceback import os, sys import networkx as nx import re from collections import deque, defaultdict, Counter import tree_decomposition as td import PHRG as phrg import probabilistic_cfg as pcfg import exact_phrg as xphrg import a1_hrg_cliq_tree as nfld from a1_hrg_cliq_tree import load_edgelist DEBUG = False def get_parser (): parser = argparse.ArgumentParser(description='b2CliqueTreeRules.py: given a tree derive grammar rules') parser.add_argument('-t', '--treedecomp', required=True, help='input tree decomposition (dimacs file format)') parser.add_argument('--version', action='version', version=__version__) return parser def dimacs_td_ct (tdfname): """ tree decomp to clique-tree """ print '... input file:', tdfname fname = tdfname graph_name = os.path.basename(fname) gname = graph_name.split('.')[0] gfname = "datasets/out." + gname tdh = os.path.basename(fname).split('.')[1] # tree decomp heuristic tfname = gname+"."+tdh G = load_edgelist(gfname) if DEBUG: print nx.info(G) print with open(fname, 'r') as f: # read tree decomp from inddgo lines = f.readlines() lines = [x.rstrip('\r\n') for x in lines] cbags = {} bags = [x.split() for x in lines if x.startswith('B')] for b in bags: cbags[int(b[1])] = [int(x) for x in b[3:]] # what to do with bag size? edges = [x.split()[1:] for x in lines if x.startswith('e')] edges = [[int(k) for k in x] for x in edges] tree = defaultdict(set) for s, t in edges: tree[frozenset(cbags[s])].add(frozenset(cbags[t])) if DEBUG: print '.. # of keys in `tree`:', len(tree.keys()) if DEBUG: print tree.keys() root = list(tree)[0] if DEBUG: print '.. Root:', root root = frozenset(cbags[1]) if DEBUG: print '.. Root:', root T = td.make_rooted(tree, root) if DEBUG: print '.. T rooted:', len(T) # nfld.unfold_2wide_tuple(T) # lets me display the tree's frozen sets T = phrg.binarize(T) prod_rules = {} td.new_visit(T, G, prod_rules) if DEBUG: print "--------------------" if DEBUG: print "- Production Rules -" if DEBUG: print "--------------------" for k in prod_rules.iterkeys(): if DEBUG: print k s = 0 for d in prod_rules[k]: s += prod_rules[k][d] for d in prod_rules[k]: prod_rules[k][d] = float(prod_rules[k][d]) / float(s) # normailization step to create probs not counts. if DEBUG: print '\t -> ', d, prod_rules[k][d] rules = [] id = 0 for k, v in prod_rules.iteritems(): sid = 0 for x in prod_rules[k]: rhs = re.findall("[^()]+", x) rules.append(("r%d.%d" % (id, sid), "%s" % re.findall("[^()]+", k)[0], rhs, prod_rules[k][x])) if DEBUG: print ("r%d.%d" % (id, sid), "%s" % re.findall("[^()]+", k)[0], rhs, prod_rules[k][x]) sid += 1 id += 1 df = pd.DataFrame(rules) outdf_fname = "./ProdRules/"+tfname+".prules" if not os.path.isfile(outdf_fname+".bz2"): print '...',outdf_fname, "written" df.to_csv(outdf_fname+".bz2", compression="bz2") else: print '...', outdf_fname, "file exists" return def main (): parser = get_parser() args = vars(parser.parse_args()) dimacs_td_ct(args['treedecomp']) # gen synth graph if __name__ == '__main__': try: main() except Exception, e: print str(e) traceback.print_exc() sys.exit(1) sys.exit(0)

mit