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Clyde-fare/scikit-learn | sklearn/utils/testing.py | 71 | 26178 | """Testing utilities."""
# Copyright (c) 2011, 2012
# Authors: Pietro Berkes,
# Andreas Muller
# Mathieu Blondel
# Olivier Grisel
# Arnaud Joly
# Denis Engemann
# License: BSD 3 clause
import os
import inspect
import pkgutil
import warnings
import sys
import re
import platform
import scipy as sp
import scipy.io
from functools import wraps
try:
# Python 2
from urllib2 import urlopen
from urllib2 import HTTPError
except ImportError:
# Python 3+
from urllib.request import urlopen
from urllib.error import HTTPError
import tempfile
import shutil
import os.path as op
import atexit
# WindowsError only exist on Windows
try:
WindowsError
except NameError:
WindowsError = None
import sklearn
from sklearn.base import BaseEstimator
from sklearn.externals import joblib
# Conveniently import all assertions in one place.
from nose.tools import assert_equal
from nose.tools import assert_not_equal
from nose.tools import assert_true
from nose.tools import assert_false
from nose.tools import assert_raises
from nose.tools import raises
from nose import SkipTest
from nose import with_setup
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_less
import numpy as np
from sklearn.base import (ClassifierMixin, RegressorMixin, TransformerMixin,
ClusterMixin)
__all__ = ["assert_equal", "assert_not_equal", "assert_raises",
"assert_raises_regexp", "raises", "with_setup", "assert_true",
"assert_false", "assert_almost_equal", "assert_array_equal",
"assert_array_almost_equal", "assert_array_less",
"assert_less", "assert_less_equal",
"assert_greater", "assert_greater_equal"]
try:
from nose.tools import assert_in, assert_not_in
except ImportError:
# Nose < 1.0.0
def assert_in(x, container):
assert_true(x in container, msg="%r in %r" % (x, container))
def assert_not_in(x, container):
assert_false(x in container, msg="%r in %r" % (x, container))
try:
from nose.tools import assert_raises_regex
except ImportError:
# for Python 2
def assert_raises_regex(expected_exception, expected_regexp,
callable_obj=None, *args, **kwargs):
"""Helper function to check for message patterns in exceptions"""
not_raised = False
try:
callable_obj(*args, **kwargs)
not_raised = True
except expected_exception as e:
error_message = str(e)
if not re.compile(expected_regexp).search(error_message):
raise AssertionError("Error message should match pattern "
"%r. %r does not." %
(expected_regexp, error_message))
if not_raised:
raise AssertionError("%s not raised by %s" %
(expected_exception.__name__,
callable_obj.__name__))
# assert_raises_regexp is deprecated in Python 3.4 in favor of
# assert_raises_regex but lets keep the bacward compat in scikit-learn with
# the old name for now
assert_raises_regexp = assert_raises_regex
def _assert_less(a, b, msg=None):
message = "%r is not lower than %r" % (a, b)
if msg is not None:
message += ": " + msg
assert a < b, message
def _assert_greater(a, b, msg=None):
message = "%r is not greater than %r" % (a, b)
if msg is not None:
message += ": " + msg
assert a > b, message
def assert_less_equal(a, b, msg=None):
message = "%r is not lower than or equal to %r" % (a, b)
if msg is not None:
message += ": " + msg
assert a <= b, message
def assert_greater_equal(a, b, msg=None):
message = "%r is not greater than or equal to %r" % (a, b)
if msg is not None:
message += ": " + msg
assert a >= b, message
def assert_warns(warning_class, func, *args, **kw):
"""Test that a certain warning occurs.
Parameters
----------
warning_class : the warning class
The class to test for, e.g. UserWarning.
func : callable
Calable object to trigger warnings.
*args : the positional arguments to `func`.
**kw : the keyword arguments to `func`
Returns
-------
result : the return value of `func`
"""
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger a warning.
result = func(*args, **kw)
if hasattr(np, 'VisibleDeprecationWarning'):
# Filter out numpy-specific warnings in numpy >= 1.9
w = [e for e in w
if e.category is not np.VisibleDeprecationWarning]
# Verify some things
if not len(w) > 0:
raise AssertionError("No warning raised when calling %s"
% func.__name__)
found = any(warning.category is warning_class for warning in w)
if not found:
raise AssertionError("%s did not give warning: %s( is %s)"
% (func.__name__, warning_class, w))
return result
def assert_warns_message(warning_class, message, func, *args, **kw):
# very important to avoid uncontrolled state propagation
"""Test that a certain warning occurs and with a certain message.
Parameters
----------
warning_class : the warning class
The class to test for, e.g. UserWarning.
message : str | callable
The entire message or a substring to test for. If callable,
it takes a string as argument and will trigger an assertion error
if it returns `False`.
func : callable
Calable object to trigger warnings.
*args : the positional arguments to `func`.
**kw : the keyword arguments to `func`.
Returns
-------
result : the return value of `func`
"""
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
if hasattr(np, 'VisibleDeprecationWarning'):
# Let's not catch the numpy internal DeprecationWarnings
warnings.simplefilter('ignore', np.VisibleDeprecationWarning)
# Trigger a warning.
result = func(*args, **kw)
# Verify some things
if not len(w) > 0:
raise AssertionError("No warning raised when calling %s"
% func.__name__)
found = [issubclass(warning.category, warning_class) for warning in w]
if not any(found):
raise AssertionError("No warning raised for %s with class "
"%s"
% (func.__name__, warning_class))
message_found = False
# Checks the message of all warnings belong to warning_class
for index in [i for i, x in enumerate(found) if x]:
# substring will match, the entire message with typo won't
msg = w[index].message # For Python 3 compatibility
msg = str(msg.args[0] if hasattr(msg, 'args') else msg)
if callable(message): # add support for certain tests
check_in_message = message
else:
check_in_message = lambda msg: message in msg
if check_in_message(msg):
message_found = True
break
if not message_found:
raise AssertionError("Did not receive the message you expected "
"('%s') for <%s>, got: '%s'"
% (message, func.__name__, msg))
return result
# To remove when we support numpy 1.7
def assert_no_warnings(func, *args, **kw):
# XXX: once we may depend on python >= 2.6, this can be replaced by the
# warnings module context manager.
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
result = func(*args, **kw)
if hasattr(np, 'VisibleDeprecationWarning'):
# Filter out numpy-specific warnings in numpy >= 1.9
w = [e for e in w
if e.category is not np.VisibleDeprecationWarning]
if len(w) > 0:
raise AssertionError("Got warnings when calling %s: %s"
% (func.__name__, w))
return result
def ignore_warnings(obj=None):
""" Context manager and decorator to ignore warnings
Note. Using this (in both variants) will clear all warnings
from all python modules loaded. In case you need to test
cross-module-warning-logging this is not your tool of choice.
Examples
--------
>>> with ignore_warnings():
... warnings.warn('buhuhuhu')
>>> def nasty_warn():
... warnings.warn('buhuhuhu')
... print(42)
>>> ignore_warnings(nasty_warn)()
42
"""
if callable(obj):
return _ignore_warnings(obj)
else:
return _IgnoreWarnings()
def _ignore_warnings(fn):
"""Decorator to catch and hide warnings without visual nesting"""
@wraps(fn)
def wrapper(*args, **kwargs):
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
return fn(*args, **kwargs)
w[:] = []
return wrapper
class _IgnoreWarnings(object):
"""Improved and simplified Python warnings context manager
Copied from Python 2.7.5 and modified as required.
"""
def __init__(self):
"""
Parameters
==========
category : warning class
The category to filter. Defaults to Warning. If None,
all categories will be muted.
"""
self._record = True
self._module = sys.modules['warnings']
self._entered = False
self.log = []
def __repr__(self):
args = []
if self._record:
args.append("record=True")
if self._module is not sys.modules['warnings']:
args.append("module=%r" % self._module)
name = type(self).__name__
return "%s(%s)" % (name, ", ".join(args))
def __enter__(self):
clean_warning_registry() # be safe and not propagate state + chaos
warnings.simplefilter('always')
if self._entered:
raise RuntimeError("Cannot enter %r twice" % self)
self._entered = True
self._filters = self._module.filters
self._module.filters = self._filters[:]
self._showwarning = self._module.showwarning
if self._record:
self.log = []
def showwarning(*args, **kwargs):
self.log.append(warnings.WarningMessage(*args, **kwargs))
self._module.showwarning = showwarning
return self.log
else:
return None
def __exit__(self, *exc_info):
if not self._entered:
raise RuntimeError("Cannot exit %r without entering first" % self)
self._module.filters = self._filters
self._module.showwarning = self._showwarning
self.log[:] = []
clean_warning_registry() # be safe and not propagate state + chaos
try:
from nose.tools import assert_less
except ImportError:
assert_less = _assert_less
try:
from nose.tools import assert_greater
except ImportError:
assert_greater = _assert_greater
def _assert_allclose(actual, desired, rtol=1e-7, atol=0,
err_msg='', verbose=True):
actual, desired = np.asanyarray(actual), np.asanyarray(desired)
if np.allclose(actual, desired, rtol=rtol, atol=atol):
return
msg = ('Array not equal to tolerance rtol=%g, atol=%g: '
'actual %s, desired %s') % (rtol, atol, actual, desired)
raise AssertionError(msg)
if hasattr(np.testing, 'assert_allclose'):
assert_allclose = np.testing.assert_allclose
else:
assert_allclose = _assert_allclose
def assert_raise_message(exceptions, message, function, *args, **kwargs):
"""Helper function to test error messages in exceptions
Parameters
----------
exceptions : exception or tuple of exception
Name of the estimator
func : callable
Calable object to raise error
*args : the positional arguments to `func`.
**kw : the keyword arguments to `func`
"""
try:
function(*args, **kwargs)
except exceptions as e:
error_message = str(e)
if message not in error_message:
raise AssertionError("Error message does not include the expected"
" string: %r. Observed error message: %r" %
(message, error_message))
else:
# concatenate exception names
if isinstance(exceptions, tuple):
names = " or ".join(e.__name__ for e in exceptions)
else:
names = exceptions.__name__
raise AssertionError("%s not raised by %s" %
(names, function.__name__))
def fake_mldata(columns_dict, dataname, matfile, ordering=None):
"""Create a fake mldata data set.
Parameters
----------
columns_dict : dict, keys=str, values=ndarray
Contains data as columns_dict[column_name] = array of data.
dataname : string
Name of data set.
matfile : string or file object
The file name string or the file-like object of the output file.
ordering : list, default None
List of column_names, determines the ordering in the data set.
Notes
-----
This function transposes all arrays, while fetch_mldata only transposes
'data', keep that into account in the tests.
"""
datasets = dict(columns_dict)
# transpose all variables
for name in datasets:
datasets[name] = datasets[name].T
if ordering is None:
ordering = sorted(list(datasets.keys()))
# NOTE: setting up this array is tricky, because of the way Matlab
# re-packages 1D arrays
datasets['mldata_descr_ordering'] = sp.empty((1, len(ordering)),
dtype='object')
for i, name in enumerate(ordering):
datasets['mldata_descr_ordering'][0, i] = name
scipy.io.savemat(matfile, datasets, oned_as='column')
class mock_mldata_urlopen(object):
def __init__(self, mock_datasets):
"""Object that mocks the urlopen function to fake requests to mldata.
`mock_datasets` is a dictionary of {dataset_name: data_dict}, or
{dataset_name: (data_dict, ordering).
`data_dict` itself is a dictionary of {column_name: data_array},
and `ordering` is a list of column_names to determine the ordering
in the data set (see `fake_mldata` for details).
When requesting a dataset with a name that is in mock_datasets,
this object creates a fake dataset in a StringIO object and
returns it. Otherwise, it raises an HTTPError.
"""
self.mock_datasets = mock_datasets
def __call__(self, urlname):
dataset_name = urlname.split('/')[-1]
if dataset_name in self.mock_datasets:
resource_name = '_' + dataset_name
from io import BytesIO
matfile = BytesIO()
dataset = self.mock_datasets[dataset_name]
ordering = None
if isinstance(dataset, tuple):
dataset, ordering = dataset
fake_mldata(dataset, resource_name, matfile, ordering)
matfile.seek(0)
return matfile
else:
raise HTTPError(urlname, 404, dataset_name + " is not available",
[], None)
def install_mldata_mock(mock_datasets):
# Lazy import to avoid mutually recursive imports
from sklearn import datasets
datasets.mldata.urlopen = mock_mldata_urlopen(mock_datasets)
def uninstall_mldata_mock():
# Lazy import to avoid mutually recursive imports
from sklearn import datasets
datasets.mldata.urlopen = urlopen
# Meta estimators need another estimator to be instantiated.
META_ESTIMATORS = ["OneVsOneClassifier",
"OutputCodeClassifier", "OneVsRestClassifier", "RFE",
"RFECV", "BaseEnsemble"]
# estimators that there is no way to default-construct sensibly
OTHER = ["Pipeline", "FeatureUnion", "GridSearchCV",
"RandomizedSearchCV"]
# some trange ones
DONT_TEST = ['SparseCoder', 'EllipticEnvelope', 'DictVectorizer',
'LabelBinarizer', 'LabelEncoder',
'MultiLabelBinarizer', 'TfidfTransformer',
'TfidfVectorizer', 'IsotonicRegression',
'OneHotEncoder', 'RandomTreesEmbedding',
'FeatureHasher', 'DummyClassifier', 'DummyRegressor',
'TruncatedSVD', 'PolynomialFeatures',
'GaussianRandomProjectionHash', 'HashingVectorizer',
'CheckingClassifier', 'PatchExtractor', 'CountVectorizer',
# GradientBoosting base estimators, maybe should
# exclude them in another way
'ZeroEstimator', 'ScaledLogOddsEstimator',
'QuantileEstimator', 'MeanEstimator',
'LogOddsEstimator', 'PriorProbabilityEstimator',
'_SigmoidCalibration', 'VotingClassifier']
def all_estimators(include_meta_estimators=False,
include_other=False, type_filter=None,
include_dont_test=False):
"""Get a list of all estimators from sklearn.
This function crawls the module and gets all classes that inherit
from BaseEstimator. Classes that are defined in test-modules are not
included.
By default meta_estimators such as GridSearchCV are also not included.
Parameters
----------
include_meta_estimators : boolean, default=False
Whether to include meta-estimators that can be constructed using
an estimator as their first argument. These are currently
BaseEnsemble, OneVsOneClassifier, OutputCodeClassifier,
OneVsRestClassifier, RFE, RFECV.
include_other : boolean, default=False
Wether to include meta-estimators that are somehow special and can
not be default-constructed sensibly. These are currently
Pipeline, FeatureUnion and GridSearchCV
include_dont_test : boolean, default=False
Whether to include "special" label estimator or test processors.
type_filter : string, list of string, or None, default=None
Which kind of estimators should be returned. If None, no filter is
applied and all estimators are returned. Possible values are
'classifier', 'regressor', 'cluster' and 'transformer' to get
estimators only of these specific types, or a list of these to
get the estimators that fit at least one of the types.
Returns
-------
estimators : list of tuples
List of (name, class), where ``name`` is the class name as string
and ``class`` is the actuall type of the class.
"""
def is_abstract(c):
if not(hasattr(c, '__abstractmethods__')):
return False
if not len(c.__abstractmethods__):
return False
return True
all_classes = []
# get parent folder
path = sklearn.__path__
for importer, modname, ispkg in pkgutil.walk_packages(
path=path, prefix='sklearn.', onerror=lambda x: None):
if ".tests." in modname:
continue
module = __import__(modname, fromlist="dummy")
classes = inspect.getmembers(module, inspect.isclass)
all_classes.extend(classes)
all_classes = set(all_classes)
estimators = [c for c in all_classes
if (issubclass(c[1], BaseEstimator)
and c[0] != 'BaseEstimator')]
# get rid of abstract base classes
estimators = [c for c in estimators if not is_abstract(c[1])]
if not include_dont_test:
estimators = [c for c in estimators if not c[0] in DONT_TEST]
if not include_other:
estimators = [c for c in estimators if not c[0] in OTHER]
# possibly get rid of meta estimators
if not include_meta_estimators:
estimators = [c for c in estimators if not c[0] in META_ESTIMATORS]
if type_filter is not None:
if not isinstance(type_filter, list):
type_filter = [type_filter]
else:
type_filter = list(type_filter) # copy
filtered_estimators = []
filters = {'classifier': ClassifierMixin,
'regressor': RegressorMixin,
'transformer': TransformerMixin,
'cluster': ClusterMixin}
for name, mixin in filters.items():
if name in type_filter:
type_filter.remove(name)
filtered_estimators.extend([est for est in estimators
if issubclass(est[1], mixin)])
estimators = filtered_estimators
if type_filter:
raise ValueError("Parameter type_filter must be 'classifier', "
"'regressor', 'transformer', 'cluster' or None, got"
" %s." % repr(type_filter))
# drop duplicates, sort for reproducibility
return sorted(set(estimators))
def set_random_state(estimator, random_state=0):
if "random_state" in estimator.get_params().keys():
estimator.set_params(random_state=random_state)
def if_matplotlib(func):
"""Test decorator that skips test if matplotlib not installed. """
@wraps(func)
def run_test(*args, **kwargs):
try:
import matplotlib
matplotlib.use('Agg', warn=False)
# this fails if no $DISPLAY specified
import matplotlib.pyplot as plt
plt.figure()
except ImportError:
raise SkipTest('Matplotlib not available.')
else:
return func(*args, **kwargs)
return run_test
def if_not_mac_os(versions=('10.7', '10.8', '10.9'),
message='Multi-process bug in Mac OS X >= 10.7 '
'(see issue #636)'):
"""Test decorator that skips test if OS is Mac OS X and its
major version is one of ``versions``.
"""
warnings.warn("if_not_mac_os is deprecated in 0.17 and will be removed"
" in 0.19: use the safer and more generic"
" if_safe_multiprocessing_with_blas instead",
DeprecationWarning)
mac_version, _, _ = platform.mac_ver()
skip = '.'.join(mac_version.split('.')[:2]) in versions
def decorator(func):
if skip:
@wraps(func)
def func(*args, **kwargs):
raise SkipTest(message)
return func
return decorator
def if_safe_multiprocessing_with_blas(func):
"""Decorator for tests involving both BLAS calls and multiprocessing
Under Python < 3.4 and POSIX (e.g. Linux or OSX), using multiprocessing in
conjunction with some implementation of BLAS (or other libraries that
manage an internal posix thread pool) can cause a crash or a freeze of the
Python process.
Under Python 3.4 and later, joblib uses the forkserver mode of
multiprocessing which does not trigger this problem.
In practice all known packaged distributions (from Linux distros or
Anaconda) of BLAS under Linux seems to be safe. So we this problem seems to
only impact OSX users.
This wrapper makes it possible to skip tests that can possibly cause
this crash under OSX with.
"""
@wraps(func)
def run_test(*args, **kwargs):
if sys.platform == 'darwin' and sys.version_info[:2] < (3, 4):
raise SkipTest(
"Possible multi-process bug with some BLAS under Python < 3.4")
return func(*args, **kwargs)
return run_test
def clean_warning_registry():
"""Safe way to reset warnings """
warnings.resetwarnings()
reg = "__warningregistry__"
for mod_name, mod in list(sys.modules.items()):
if 'six.moves' in mod_name:
continue
if hasattr(mod, reg):
getattr(mod, reg).clear()
def check_skip_network():
if int(os.environ.get('SKLEARN_SKIP_NETWORK_TESTS', 0)):
raise SkipTest("Text tutorial requires large dataset download")
def check_skip_travis():
"""Skip test if being run on Travis."""
if os.environ.get('TRAVIS') == "true":
raise SkipTest("This test needs to be skipped on Travis")
def _delete_folder(folder_path, warn=False):
"""Utility function to cleanup a temporary folder if still existing.
Copy from joblib.pool (for independance)"""
try:
if os.path.exists(folder_path):
# This can fail under windows,
# but will succeed when called by atexit
shutil.rmtree(folder_path)
except WindowsError:
if warn:
warnings.warn("Could not delete temporary folder %s" % folder_path)
class TempMemmap(object):
def __init__(self, data, mmap_mode='r'):
self.temp_folder = tempfile.mkdtemp(prefix='sklearn_testing_')
self.mmap_mode = mmap_mode
self.data = data
def __enter__(self):
fpath = op.join(self.temp_folder, 'data.pkl')
joblib.dump(self.data, fpath)
data_read_only = joblib.load(fpath, mmap_mode=self.mmap_mode)
atexit.register(lambda: _delete_folder(self.temp_folder, warn=True))
return data_read_only
def __exit__(self, exc_type, exc_val, exc_tb):
_delete_folder(self.temp_folder)
with_network = with_setup(check_skip_network)
with_travis = with_setup(check_skip_travis)
| bsd-3-clause |
jorge2703/scikit-learn | sklearn/datasets/mlcomp.py | 286 | 3855 | # Copyright (c) 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
"""Glue code to load http://mlcomp.org data as a scikit.learn dataset"""
import os
import numbers
from sklearn.datasets.base import load_files
def _load_document_classification(dataset_path, metadata, set_=None, **kwargs):
if set_ is not None:
dataset_path = os.path.join(dataset_path, set_)
return load_files(dataset_path, metadata.get('description'), **kwargs)
LOADERS = {
'DocumentClassification': _load_document_classification,
# TODO: implement the remaining domain formats
}
def load_mlcomp(name_or_id, set_="raw", mlcomp_root=None, **kwargs):
"""Load a datasets as downloaded from http://mlcomp.org
Parameters
----------
name_or_id : the integer id or the string name metadata of the MLComp
dataset to load
set_ : select the portion to load: 'train', 'test' or 'raw'
mlcomp_root : the filesystem path to the root folder where MLComp datasets
are stored, if mlcomp_root is None, the MLCOMP_DATASETS_HOME
environment variable is looked up instead.
**kwargs : domain specific kwargs to be passed to the dataset loader.
Read more in the :ref:`User Guide <datasets>`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'filenames', the files holding the raw to learn, 'target', the
classification labels (integer index), 'target_names',
the meaning of the labels, and 'DESCR', the full description of the
dataset.
Note on the lookup process: depending on the type of name_or_id,
will choose between integer id lookup or metadata name lookup by
looking at the unzipped archives and metadata file.
TODO: implement zip dataset loading too
"""
if mlcomp_root is None:
try:
mlcomp_root = os.environ['MLCOMP_DATASETS_HOME']
except KeyError:
raise ValueError("MLCOMP_DATASETS_HOME env variable is undefined")
mlcomp_root = os.path.expanduser(mlcomp_root)
mlcomp_root = os.path.abspath(mlcomp_root)
mlcomp_root = os.path.normpath(mlcomp_root)
if not os.path.exists(mlcomp_root):
raise ValueError("Could not find folder: " + mlcomp_root)
# dataset lookup
if isinstance(name_or_id, numbers.Integral):
# id lookup
dataset_path = os.path.join(mlcomp_root, str(name_or_id))
else:
# assume name based lookup
dataset_path = None
expected_name_line = "name: " + name_or_id
for dataset in os.listdir(mlcomp_root):
metadata_file = os.path.join(mlcomp_root, dataset, 'metadata')
if not os.path.exists(metadata_file):
continue
with open(metadata_file) as f:
for line in f:
if line.strip() == expected_name_line:
dataset_path = os.path.join(mlcomp_root, dataset)
break
if dataset_path is None:
raise ValueError("Could not find dataset with metadata line: " +
expected_name_line)
# loading the dataset metadata
metadata = dict()
metadata_file = os.path.join(dataset_path, 'metadata')
if not os.path.exists(metadata_file):
raise ValueError(dataset_path + ' is not a valid MLComp dataset')
with open(metadata_file) as f:
for line in f:
if ":" in line:
key, value = line.split(":", 1)
metadata[key.strip()] = value.strip()
format = metadata.get('format', 'unknow')
loader = LOADERS.get(format)
if loader is None:
raise ValueError("No loader implemented for format: " + format)
return loader(dataset_path, metadata, set_=set_, **kwargs)
| bsd-3-clause |
a-doumoulakis/tensorflow | tensorflow/contrib/learn/python/learn/estimators/_sklearn.py | 151 | 6723 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""sklearn cross-support."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os
import numpy as np
import six
def _pprint(d):
return ', '.join(['%s=%s' % (key, str(value)) for key, value in d.items()])
class _BaseEstimator(object):
"""This is a cross-import when sklearn is not available.
Adopted from sklearn.BaseEstimator implementation.
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py
"""
def get_params(self, deep=True):
"""Get parameters for this estimator.
Args:
deep: boolean, optional
If `True`, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns:
params : mapping of string to any
Parameter names mapped to their values.
"""
out = dict()
param_names = [name for name in self.__dict__ if not name.startswith('_')]
for key in param_names:
value = getattr(self, key, None)
if isinstance(value, collections.Callable):
continue
# XXX: should we rather test if instance of estimator?
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
def set_params(self, **params):
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
``<component>__<parameter>`` so that it's possible to update each
component of a nested object.
Args:
**params: Parameters.
Returns:
self
Raises:
ValueError: If params contain invalid names.
"""
if not params:
# Simple optimisation to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
for key, value in six.iteritems(params):
split = key.split('__', 1)
if len(split) > 1:
# nested objects case
name, sub_name = split
if name not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(name, self))
sub_object = valid_params[name]
sub_object.set_params(**{sub_name: value})
else:
# simple objects case
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self.__class__.__name__))
setattr(self, key, value)
return self
def __repr__(self):
class_name = self.__class__.__name__
return '%s(%s)' % (class_name,
_pprint(self.get_params(deep=False)),)
# pylint: disable=old-style-class
class _ClassifierMixin():
"""Mixin class for all classifiers."""
pass
class _RegressorMixin():
"""Mixin class for all regression estimators."""
pass
class _TransformerMixin():
"""Mixin class for all transformer estimators."""
class NotFittedError(ValueError, AttributeError):
"""Exception class to raise if estimator is used before fitting.
This class inherits from both ValueError and AttributeError to help with
exception handling and backward compatibility.
Examples:
>>> from sklearn.svm import LinearSVC
>>> from sklearn.exceptions import NotFittedError
>>> try:
... LinearSVC().predict([[1, 2], [2, 3], [3, 4]])
... except NotFittedError as e:
... print(repr(e))
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
NotFittedError('This LinearSVC instance is not fitted yet',)
Copied from
https://github.com/scikit-learn/scikit-learn/master/sklearn/exceptions.py
"""
# pylint: enable=old-style-class
def _accuracy_score(y_true, y_pred):
score = y_true == y_pred
return np.average(score)
def _mean_squared_error(y_true, y_pred):
if len(y_true.shape) > 1:
y_true = np.squeeze(y_true)
if len(y_pred.shape) > 1:
y_pred = np.squeeze(y_pred)
return np.average((y_true - y_pred)**2)
def _train_test_split(*args, **options):
# pylint: disable=missing-docstring
test_size = options.pop('test_size', None)
train_size = options.pop('train_size', None)
random_state = options.pop('random_state', None)
if test_size is None and train_size is None:
train_size = 0.75
elif train_size is None:
train_size = 1 - test_size
train_size = int(train_size * args[0].shape[0])
np.random.seed(random_state)
indices = np.random.permutation(args[0].shape[0])
train_idx, test_idx = indices[:train_size], indices[train_size:]
result = []
for x in args:
result += [x.take(train_idx, axis=0), x.take(test_idx, axis=0)]
return tuple(result)
# If "TENSORFLOW_SKLEARN" flag is defined then try to import from sklearn.
TRY_IMPORT_SKLEARN = os.environ.get('TENSORFLOW_SKLEARN', False)
if TRY_IMPORT_SKLEARN:
# pylint: disable=g-import-not-at-top,g-multiple-import,unused-import
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin
from sklearn.metrics import accuracy_score, log_loss, mean_squared_error
from sklearn.cross_validation import train_test_split
try:
from sklearn.exceptions import NotFittedError
except ImportError:
try:
from sklearn.utils.validation import NotFittedError
except ImportError:
pass
else:
# Naive implementations of sklearn classes and functions.
BaseEstimator = _BaseEstimator
ClassifierMixin = _ClassifierMixin
RegressorMixin = _RegressorMixin
TransformerMixin = _TransformerMixin
accuracy_score = _accuracy_score
log_loss = None
mean_squared_error = _mean_squared_error
train_test_split = _train_test_split
| apache-2.0 |
jorge2703/scikit-learn | sklearn/tree/export.py | 53 | 15772 | """
This module defines export functions for decision trees.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Trevor Stephens <trev.stephens@gmail.com>
# Licence: BSD 3 clause
import numpy as np
from ..externals import six
from . import _tree
def _color_brew(n):
"""Generate n colors with equally spaced hues.
Parameters
----------
n : int
The number of colors required.
Returns
-------
color_list : list, length n
List of n tuples of form (R, G, B) being the components of each color.
"""
color_list = []
# Initialize saturation & value; calculate chroma & value shift
s, v = 0.75, 0.9
c = s * v
m = v - c
for h in np.arange(25, 385, 360. / n).astype(int):
# Calculate some intermediate values
h_bar = h / 60.
x = c * (1 - abs((h_bar % 2) - 1))
# Initialize RGB with same hue & chroma as our color
rgb = [(c, x, 0),
(x, c, 0),
(0, c, x),
(0, x, c),
(x, 0, c),
(c, 0, x),
(c, x, 0)]
r, g, b = rgb[int(h_bar)]
# Shift the initial RGB values to match value and store
rgb = [(int(255 * (r + m))),
(int(255 * (g + m))),
(int(255 * (b + m)))]
color_list.append(rgb)
return color_list
def export_graphviz(decision_tree, out_file="tree.dot", max_depth=None,
feature_names=None, class_names=None, label='all',
filled=False, leaves_parallel=False, impurity=True,
node_ids=False, proportion=False, rotate=False,
rounded=False, special_characters=False):
"""Export a decision tree in DOT format.
This function generates a GraphViz representation of the decision tree,
which is then written into `out_file`. Once exported, graphical renderings
can be generated using, for example::
$ dot -Tps tree.dot -o tree.ps (PostScript format)
$ dot -Tpng tree.dot -o tree.png (PNG format)
The sample counts that are shown are weighted with any sample_weights that
might be present.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
decision_tree : decision tree classifier
The decision tree to be exported to GraphViz.
out_file : file object or string, optional (default="tree.dot")
Handle or name of the output file.
max_depth : int, optional (default=None)
The maximum depth of the representation. If None, the tree is fully
generated.
feature_names : list of strings, optional (default=None)
Names of each of the features.
class_names : list of strings, bool or None, optional (default=None)
Names of each of the target classes in ascending numerical order.
Only relevant for classification and not supported for multi-output.
If ``True``, shows a symbolic representation of the class name.
label : {'all', 'root', 'none'}, optional (default='all')
Whether to show informative labels for impurity, etc.
Options include 'all' to show at every node, 'root' to show only at
the top root node, or 'none' to not show at any node.
filled : bool, optional (default=False)
When set to ``True``, paint nodes to indicate majority class for
classification, extremity of values for regression, or purity of node
for multi-output.
leaves_parallel : bool, optional (default=False)
When set to ``True``, draw all leaf nodes at the bottom of the tree.
impurity : bool, optional (default=True)
When set to ``True``, show the impurity at each node.
node_ids : bool, optional (default=False)
When set to ``True``, show the ID number on each node.
proportion : bool, optional (default=False)
When set to ``True``, change the display of 'values' and/or 'samples'
to be proportions and percentages respectively.
rotate : bool, optional (default=False)
When set to ``True``, orient tree left to right rather than top-down.
rounded : bool, optional (default=False)
When set to ``True``, draw node boxes with rounded corners and use
Helvetica fonts instead of Times-Roman.
special_characters : bool, optional (default=False)
When set to ``False``, ignore special characters for PostScript
compatibility.
Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> clf = tree.DecisionTreeClassifier()
>>> iris = load_iris()
>>> clf = clf.fit(iris.data, iris.target)
>>> tree.export_graphviz(clf,
... out_file='tree.dot') # doctest: +SKIP
"""
def get_color(value):
# Find the appropriate color & intensity for a node
if colors['bounds'] is None:
# Classification tree
color = list(colors['rgb'][np.argmax(value)])
sorted_values = sorted(value, reverse=True)
alpha = int(255 * (sorted_values[0] - sorted_values[1]) /
(1 - sorted_values[1]))
else:
# Regression tree or multi-output
color = list(colors['rgb'][0])
alpha = int(255 * ((value - colors['bounds'][0]) /
(colors['bounds'][1] - colors['bounds'][0])))
# Return html color code in #RRGGBBAA format
color.append(alpha)
hex_codes = [str(i) for i in range(10)]
hex_codes.extend(['a', 'b', 'c', 'd', 'e', 'f'])
color = [hex_codes[c // 16] + hex_codes[c % 16] for c in color]
return '#' + ''.join(color)
def node_to_str(tree, node_id, criterion):
# Generate the node content string
if tree.n_outputs == 1:
value = tree.value[node_id][0, :]
else:
value = tree.value[node_id]
# Should labels be shown?
labels = (label == 'root' and node_id == 0) or label == 'all'
# PostScript compatibility for special characters
if special_characters:
characters = ['#', '<SUB>', '</SUB>', '≤', '<br/>', '>']
node_string = '<'
else:
characters = ['#', '[', ']', '<=', '\\n', '"']
node_string = '"'
# Write node ID
if node_ids:
if labels:
node_string += 'node '
node_string += characters[0] + str(node_id) + characters[4]
# Write decision criteria
if tree.children_left[node_id] != _tree.TREE_LEAF:
# Always write node decision criteria, except for leaves
if feature_names is not None:
feature = feature_names[tree.feature[node_id]]
else:
feature = "X%s%s%s" % (characters[1],
tree.feature[node_id],
characters[2])
node_string += '%s %s %s%s' % (feature,
characters[3],
round(tree.threshold[node_id], 4),
characters[4])
# Write impurity
if impurity:
if isinstance(criterion, _tree.FriedmanMSE):
criterion = "friedman_mse"
elif not isinstance(criterion, six.string_types):
criterion = "impurity"
if labels:
node_string += '%s = ' % criterion
node_string += (str(round(tree.impurity[node_id], 4)) +
characters[4])
# Write node sample count
if labels:
node_string += 'samples = '
if proportion:
percent = (100. * tree.n_node_samples[node_id] /
float(tree.n_node_samples[0]))
node_string += (str(round(percent, 1)) + '%' +
characters[4])
else:
node_string += (str(tree.n_node_samples[node_id]) +
characters[4])
# Write node class distribution / regression value
if proportion and tree.n_classes[0] != 1:
# For classification this will show the proportion of samples
value = value / tree.weighted_n_node_samples[node_id]
if labels:
node_string += 'value = '
if tree.n_classes[0] == 1:
# Regression
value_text = np.around(value, 4)
elif proportion:
# Classification
value_text = np.around(value, 2)
elif np.all(np.equal(np.mod(value, 1), 0)):
# Classification without floating-point weights
value_text = value.astype(int)
else:
# Classification with floating-point weights
value_text = np.around(value, 4)
# Strip whitespace
value_text = str(value_text.astype('S32')).replace("b'", "'")
value_text = value_text.replace("' '", ", ").replace("'", "")
if tree.n_classes[0] == 1 and tree.n_outputs == 1:
value_text = value_text.replace("[", "").replace("]", "")
value_text = value_text.replace("\n ", characters[4])
node_string += value_text + characters[4]
# Write node majority class
if (class_names is not None and
tree.n_classes[0] != 1 and
tree.n_outputs == 1):
# Only done for single-output classification trees
if labels:
node_string += 'class = '
if class_names is not True:
class_name = class_names[np.argmax(value)]
else:
class_name = "y%s%s%s" % (characters[1],
np.argmax(value),
characters[2])
node_string += class_name
# Clean up any trailing newlines
if node_string[-2:] == '\\n':
node_string = node_string[:-2]
if node_string[-5:] == '<br/>':
node_string = node_string[:-5]
return node_string + characters[5]
def recurse(tree, node_id, criterion, parent=None, depth=0):
if node_id == _tree.TREE_LEAF:
raise ValueError("Invalid node_id %s" % _tree.TREE_LEAF)
left_child = tree.children_left[node_id]
right_child = tree.children_right[node_id]
# Add node with description
if max_depth is None or depth <= max_depth:
# Collect ranks for 'leaf' option in plot_options
if left_child == _tree.TREE_LEAF:
ranks['leaves'].append(str(node_id))
elif str(depth) not in ranks:
ranks[str(depth)] = [str(node_id)]
else:
ranks[str(depth)].append(str(node_id))
out_file.write('%d [label=%s'
% (node_id,
node_to_str(tree, node_id, criterion)))
if filled:
# Fetch appropriate color for node
if 'rgb' not in colors:
# Initialize colors and bounds if required
colors['rgb'] = _color_brew(tree.n_classes[0])
if tree.n_outputs != 1:
# Find max and min impurities for multi-output
colors['bounds'] = (np.min(-tree.impurity),
np.max(-tree.impurity))
elif tree.n_classes[0] == 1:
# Find max and min values in leaf nodes for regression
colors['bounds'] = (np.min(tree.value),
np.max(tree.value))
if tree.n_outputs == 1:
node_val = (tree.value[node_id][0, :] /
tree.weighted_n_node_samples[node_id])
if tree.n_classes[0] == 1:
# Regression
node_val = tree.value[node_id][0, :]
else:
# If multi-output color node by impurity
node_val = -tree.impurity[node_id]
out_file.write(', fillcolor="%s"' % get_color(node_val))
out_file.write('] ;\n')
if parent is not None:
# Add edge to parent
out_file.write('%d -> %d' % (parent, node_id))
if parent == 0:
# Draw True/False labels if parent is root node
angles = np.array([45, -45]) * ((rotate - .5) * -2)
out_file.write(' [labeldistance=2.5, labelangle=')
if node_id == 1:
out_file.write('%d, headlabel="True"]' % angles[0])
else:
out_file.write('%d, headlabel="False"]' % angles[1])
out_file.write(' ;\n')
if left_child != _tree.TREE_LEAF:
recurse(tree, left_child, criterion=criterion, parent=node_id,
depth=depth + 1)
recurse(tree, right_child, criterion=criterion, parent=node_id,
depth=depth + 1)
else:
ranks['leaves'].append(str(node_id))
out_file.write('%d [label="(...)"' % node_id)
if filled:
# color cropped nodes grey
out_file.write(', fillcolor="#C0C0C0"')
out_file.write('] ;\n' % node_id)
if parent is not None:
# Add edge to parent
out_file.write('%d -> %d ;\n' % (parent, node_id))
own_file = False
try:
if isinstance(out_file, six.string_types):
if six.PY3:
out_file = open(out_file, "w", encoding="utf-8")
else:
out_file = open(out_file, "wb")
own_file = True
# The depth of each node for plotting with 'leaf' option
ranks = {'leaves': []}
# The colors to render each node with
colors = {'bounds': None}
out_file.write('digraph Tree {\n')
# Specify node aesthetics
out_file.write('node [shape=box')
rounded_filled = []
if filled:
rounded_filled.append('filled')
if rounded:
rounded_filled.append('rounded')
if len(rounded_filled) > 0:
out_file.write(', style="%s", color="black"'
% ", ".join(rounded_filled))
if rounded:
out_file.write(', fontname=helvetica')
out_file.write('] ;\n')
# Specify graph & edge aesthetics
if leaves_parallel:
out_file.write('graph [ranksep=equally, splines=polyline] ;\n')
if rounded:
out_file.write('edge [fontname=helvetica] ;\n')
if rotate:
out_file.write('rankdir=LR ;\n')
# Now recurse the tree and add node & edge attributes
if isinstance(decision_tree, _tree.Tree):
recurse(decision_tree, 0, criterion="impurity")
else:
recurse(decision_tree.tree_, 0, criterion=decision_tree.criterion)
# If required, draw leaf nodes at same depth as each other
if leaves_parallel:
for rank in sorted(ranks):
out_file.write("{rank=same ; " +
"; ".join(r for r in ranks[rank]) + "} ;\n")
out_file.write("}")
finally:
if own_file:
out_file.close()
| bsd-3-clause |
kchodorow/tensorflow | tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_benchmark_test.py | 82 | 8976 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Regression test for DNNLinearCombinedEstimator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import tempfile
from tensorflow.contrib.layers.python.layers import feature_column
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined
from tensorflow.contrib.learn.python.learn.estimators import estimator_test_utils
from tensorflow.contrib.learn.python.learn.estimators import run_config
from tensorflow.contrib.learn.python.learn.estimators import test_data
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
from tensorflow.python.training import adagrad
from tensorflow.python.training import ftrl
from tensorflow.python.training import server_lib
# Desired training steps, reported in benchmark. Actual steps might be slightly
# more than this since supervisor training runs for a non-detrministic number of
# steps.
_ITERS = 100
_METRIC_KEYS = {
'accuracy',
'auc',
'accuracy/threshold_0.500000_mean',
'loss',
'precision/positive_threshold_0.500000_mean',
'recall/positive_threshold_0.500000_mean',
}
class DNNLinearCombinedClassifierBenchmark(test.Benchmark):
def _assertSingleClassMetrics(self, metrics):
estimator_test_utils.assert_in_range(0.9, 1.0, 'auc', metrics)
estimator_test_utils.assert_in_range(0.9, 1.0,
'accuracy/threshold_0.500000_mean',
metrics)
estimator_test_utils.assert_in_range(
0.9, 1.0, 'precision/positive_threshold_0.500000_mean', metrics)
estimator_test_utils.assert_in_range(
0.9, 1.0, 'recall/positive_threshold_0.500000_mean', metrics)
self._assertCommonMetrics(metrics)
def _assertCommonMetrics(self, metrics):
estimator_test_utils.assert_in_range(_ITERS, _ITERS + 5, 'global_step',
metrics)
estimator_test_utils.assert_in_range(0.9, 1.0, 'accuracy', metrics)
estimator_test_utils.assert_in_range(0.0, 0.2, 'loss', metrics)
self.report_benchmark(
iters=metrics['global_step'],
extras={k: v
for k, v in metrics.items() if k in _METRIC_KEYS})
def benchmarkMatrixData(self):
iris = test_data.prepare_iris_data_for_logistic_regression()
cont_feature = feature_column.real_valued_column('feature', dimension=4)
bucketized_feature = feature_column.bucketized_column(
cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
model_dir=tempfile.mkdtemp(),
linear_feature_columns=(bucketized_feature,),
dnn_feature_columns=(cont_feature,),
dnn_hidden_units=(3, 3))
input_fn = test_data.iris_input_logistic_fn
metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
input_fn=input_fn, steps=100)
self._assertSingleClassMetrics(metrics)
def benchmarkTensorData(self):
def _input_fn():
iris = test_data.prepare_iris_data_for_logistic_regression()
features = {}
for i in range(4):
# The following shows how to provide the Tensor data for
# RealValuedColumns.
features.update({
str(i):
array_ops.reshape(
constant_op.constant(
iris.data[:, i], dtype=dtypes.float32), (-1, 1))
})
# The following shows how to provide the SparseTensor data for
# a SparseColumn.
features['dummy_sparse_column'] = sparse_tensor.SparseTensor(
values=('en', 'fr', 'zh'),
indices=((0, 0), (0, 1), (60, 0)),
dense_shape=(len(iris.target), 2))
labels = array_ops.reshape(
constant_op.constant(
iris.target, dtype=dtypes.int32), (-1, 1))
return features, labels
iris = test_data.prepare_iris_data_for_logistic_regression()
cont_features = [
feature_column.real_valued_column(str(i)) for i in range(4)
]
linear_features = [
feature_column.bucketized_column(
cont_features[i],
test_data.get_quantile_based_buckets(iris.data[:, i], 10))
for i in range(4)
]
linear_features.append(
feature_column.sparse_column_with_hash_bucket(
'dummy_sparse_column', hash_bucket_size=100))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
model_dir=tempfile.mkdtemp(),
linear_feature_columns=linear_features,
dnn_feature_columns=cont_features,
dnn_hidden_units=(3, 3))
metrics = classifier.fit(input_fn=_input_fn, steps=_ITERS).evaluate(
input_fn=_input_fn, steps=100)
self._assertSingleClassMetrics(metrics)
def benchmarkCustomOptimizer(self):
iris = test_data.prepare_iris_data_for_logistic_regression()
cont_feature = feature_column.real_valued_column('feature', dimension=4)
bucketized_feature = feature_column.bucketized_column(
cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
model_dir=tempfile.mkdtemp(),
linear_feature_columns=(bucketized_feature,),
linear_optimizer=ftrl.FtrlOptimizer(learning_rate=0.1),
dnn_feature_columns=(cont_feature,),
dnn_hidden_units=(3, 3),
dnn_optimizer=adagrad.AdagradOptimizer(learning_rate=0.1))
input_fn = test_data.iris_input_logistic_fn
metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
input_fn=input_fn, steps=100)
self._assertSingleClassMetrics(metrics)
def benchmarkMultiClass(self):
iris = base.load_iris()
cont_feature = feature_column.real_valued_column('feature', dimension=4)
bucketized_feature = feature_column.bucketized_column(
cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
n_classes=3,
linear_feature_columns=(bucketized_feature,),
dnn_feature_columns=(cont_feature,),
dnn_hidden_units=(3, 3))
input_fn = test_data.iris_input_multiclass_fn
metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
input_fn=input_fn, steps=100)
self._assertCommonMetrics(metrics)
def benchmarkPartitionedVariables(self):
def _input_fn():
features = {
'language':
sparse_tensor.SparseTensor(
values=('en', 'fr', 'zh'),
indices=((0, 0), (0, 1), (2, 0)),
dense_shape=(3, 2))
}
labels = constant_op.constant(((1,), (0,), (0,)))
return features, labels
# The given hash_bucket_size results in variables larger than the
# default min_slice_size attribute, so the variables are partitioned.
sparse_feature = feature_column.sparse_column_with_hash_bucket(
'language', hash_bucket_size=2e7)
embedding_feature = feature_column.embedding_column(
sparse_feature, dimension=1)
tf_config = {
'cluster': {
run_config.TaskType.PS: ['fake_ps_0', 'fake_ps_1']
}
}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = run_config.RunConfig()
# Because we did not start a distributed cluster, we need to pass an
# empty ClusterSpec, otherwise the device_setter will look for
# distributed jobs, such as "/job:ps" which are not present.
config._cluster_spec = server_lib.ClusterSpec({})
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
linear_feature_columns=(sparse_feature,),
dnn_feature_columns=(embedding_feature,),
dnn_hidden_units=(3, 3),
config=config)
metrics = classifier.fit(input_fn=_input_fn, steps=_ITERS).evaluate(
input_fn=_input_fn, steps=100)
self._assertCommonMetrics(metrics)
if __name__ == '__main__':
test.main()
| apache-2.0 |
madjelan/CostSensitiveClassification | costcla/metrics/__init__.py | 1 | 2607 | """
The :mod:`costcla.metrics` module includes metrics to assess performance on cost-sensitive classification tasks given class prediction and cost-matrix
Functions named as ``*_score`` return a scalar value to maximize: the higher the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize: the lower the better
"""
from costs import cost_loss
from costs import savings_score
from costs import brier_score_loss
import numpy as np
from sklearn.utils import column_or_1d
from sklearn.metrics import roc_auc_score
__all__ = ['cost_loss',
'savings_score',
'brier_score_loss',
'binary_classification_metrics']
def binary_classification_metrics(y_true, y_pred, y_prob):
#TODO: update description
"""classification_metrics.
This function cal...
Parameters
----------
y_true : array-like
Ground truth (correct) labels.
y_pred : array-like
Predicted labels, as returned by a classifier.
y_prob : array-like
Predicted probabilities, as returned by a classifier.
Returns
-------
dict(tp, fp, fn, tn, accuracy, recall, precision, f1score, auc, brier_loss)
Examples
--------
>>> from costcla.metrics import binary_classification_metrics
>>> y_pred = [0, 1, 0, 0]
>>> y_true = [0, 1, 1, 0]
>>> y_prob = [0.2, 0.8, 0.4, 0.3]
>>> binary_classification_metrics(y_true, y_pred, y_prob)
{'accuracy': 0.75,
'auc': 0.75,
'brier_loss': 0.13249999999999998,
'f1score': 0.6666666666666666,
'fn': 1.0,
'fp': 0.0,
'precision': 1.0,
'recall': 0.5,
'tn': 2.0,
'tp': 1.0}
"""
y_true = column_or_1d(y_true)
y_pred = column_or_1d(y_pred)
y_prob = column_or_1d(y_prob)
n_samples = y_true.shape[0]
tp = float((y_pred * y_true).sum())
fp = float((y_pred[np.nonzero(y_true == 0)[0]]).sum())
fn = float((y_true[np.nonzero(y_pred == 0)[0]]).sum())
tn = float(n_samples - tp - fn - fp)
accuracy = (tp + tn) / n_samples
auc = roc_auc_score(y_true, y_pred)
brier_loss = brier_score_loss(y_true, y_prob)
if (tp + fp) == 0:
precision = 0
else:
precision = tp / (tp + fp)
if (tp + fn) == 0:
recall = 0
else:
recall = tp / (tp + fn)
if (recall + precision) == 0:
f1score = 0
else:
f1score = 2 * (precision * recall) / (precision + recall)
return dict(tp=tp, fp=fp, fn=fn, tn=tn, accuracy=accuracy, recall=recall,
precision=precision, f1score=f1score, auc=auc, brier_loss=brier_loss)
| bsd-3-clause |
EnsekiTT/ml_review | svm/svm.py | 1 | 6357 | # -*- coding:utf-8 -*-
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import inspect
# それがラムダ式か
def islambda(f):
return inspect.isfunction(f) and f.__name__ == (lambda: True).__name__
class SVM():
def __init__(self, kernel='liner', C=2, tol=0.01, eps=0.01):
print "hello this is SVM code."
self.kernel = kernel
self.tol = tol
self.C = C
self.eps = eps
def learn(self, target, point):
self.target = np.array(target)
self.target_len = len(self.target)
self.point = np.matrix(point)
self.alpha = np.zeros(self.target_len)
self.b = 0
self.error = -1*np.array(target, dtype=float)
self.SMO()
self.s = [i for i in range(len(self.target)) if self.eps < self.alpha[i]]
self.m = [i for i in range(len(self.target)) if self.eps < self.alpha[i] < self.C - self.eps]
self.b = 0.0
for i in self.m:
self.b += self.target[i]
for j in self.s:
self.b -= (self.alpha[j]*self.target[j]*self.setKernel(self.point[i], self.point[j]))
self.b /= len(self.m)
def takeStep(self, i1, i2):
if i1 == i2:
return 0
alph1 = self.alpha[i1]
alph2 = self.alpha[i2]
y1 = target[i1]
y2 = target[i2]
E1 = self.error[i1]
E2 = self.error[i2]
s = y1 * y2
if y1 != y2: # [Platt 1998] (13)
L = max([0, alph2 - alph1])
H = min([self.C, self.C + alph2 - alph1])
else: # [Platt 1998] (14)
L = max([0, alph2 + alph1 - self.C])
H = min([self.C, alph2 + alph1])
if L == H:
return 0
k11 = self.setKernel(self.point[i1], self.point[i1])
k12 = self.setKernel(self.point[i1], self.point[i2])
k22 = self.setKernel(self.point[i2], self.point[i2])
eta = 2 * k12 - k11 - k22
if eta > 0:
a2 = alph2 + y2 * (E1-E2) / eta
if a2 < L:
a2 = L
elif a2 > H:
a2 = H
else:
v1 = self.evalExample(i1) + self.b - alph1 * y1 * k11 - alph2 * y2 * k12
v2 = self.evalExample(i2) + self.b - alph1 * y1 * k12 - alph2 * y2 * k22
gamma = alph1 + s * alph2
Lobj = self.getObj(alph1, L, i1, i2, v1, v2, s)
Hobj = self.getObj(alph1, H, i1, i2, v1, v2, s)
if Lobj > Hobj + self.eps:
a2 = L
elif Lobj < Hobj - self.eps:
a2 = H
else:
a2 = alph2
if a2 < self.eps:
a2 = 0
elif a2 > self.C - self.eps:
a2 = self.C
if np.abs(a2-alph2) < self.eps*(a2+alph2+self.eps):
return 0
a1 = alph1 + s * (alph2 - a2)
b = self.b
b1 = E1 + y1 * (a1 - alph1) * k11 + y2 * (a2 - alph2) * k12 + self.b #[Platt 1998] (20)
b2 = E2 + y1 * (a1 - alph1) * k12 + y2 * (a2 - alph2) * k22 + self.b #[Platt 1998] (21)
self.b = (b1 + b2) / 2
for i, a in enumerate(self.alpha):
if not (a == 0 or a == self.C):
self.error[i] += y1 * (a1 - alph1) * self.setKernel(self.point[i1], self.point[i]) + b
self.error[i] += y2 * (a2 - alph2) * self.setKernel(self.point[i2], self.point[i]) - self.b
self.error[i1] = 0
self.error[i2] = 0
self.alpha[i1] = a1
self.alpha[i2] = a2
return 1
def getObj(self, alph1, O, i1, i2, v1, v2, s):
w = alph1 + O - 0.5 * alph1**2 * self.setKernel(self.point[i1], self.point[i1])
w += -0.5 * O**2 * self.setKernel(self.point[i2], self.point[i2])
w += -s * alph1 * O * self.setKernel(self.point[i1], self.point[i2])
w += -alph1 * self.target[i1] * v1
w += -O * self.target[i2] * v2
w += 0
return w
def evalExample(self, index):
ret = 0
for i, a in enumerate(self.alpha):
if a != 0:
ret += a * self.target[i] * self.setKernel(self.point[index], self.point[i])
return ret + self.b
def setKernel(self, k1, k2):
if islambda(self.kernel):
return self.kernel(k1, k2)
elif self.kernel == 'liner':
ret = np.dot(np.array(k1), np.array(k2).T)
else:
ret = np.dot(np.array(k1), np.array(k2).T)
return ret
def examineExample(self, i2):
y2 = self.target[i2]
alph2 = self.alpha[i2]
E2 = self.error[i2]
r2 = E2*y2
if (r2 < -self.tol and alph2 < self.C) or (r2 > self.tol and alph2 > 0):
nznc_alpha = [i for i in range(len(self.alpha)) if 0 != self.alpha[i] and self.C != self.alpha[i]]
if np.sum(nznc_alpha) > 1:
maxE = -np.inf
for i, E in enumerate(self.error):
if np.abs(E2 - E) > maxE:
maxE = np.abs(E2 - E)
i1 = i
if self.takeStep(i1, i2):
return 1
rnznc_alpha = nznc_alpha
np.random.shuffle(rnznc_alpha)
r_alpha = self.alpha
np.random.shuffle(r_alpha)
for i in rnznc_alpha:
i1 = int(i)
if self.takeStep(i1, i2):
return 1
for i in r_alpha:
i1 = int(i)
if self.takeStep(i1, i2):
return 1
return 0
def SMO(self):
numChanged = 0
examineAll = 1
while (numChanged > 0 or examineAll):
numChanged = 0
if examineAll:
for I in range(self.target_len):
numChanged += self.examineExample(I)
else:
for I in range(self.target_len):
if (self.eps < self.alpha[I] < self.C - self.eps):
numChanged += self.examineExample(I)
if examineAll == 1:
examineAll = 0
elif numChanged == 0:
examineAll = 1
def calc(self, x):
ret = self.b
for i in self.s:
ret += (self.alpha[i]*self.target[i]*
self.setKernel(x, self.point[i]))
return ret
def get_alpha(self):
return self.alpha
alpha = property(get_alpha)
if __name__ == "__main__":
datas = pd.read_csv('../datasets/iris.txt', delim_whitespace=True)
Label = []
for i, d in enumerate(datas['Species']):
if d == 'setosa':
Label.append(0)
elif d == 'versicolor':
Label.append(1)
elif d == 'virginica':
Label.append(2)
datas['Label'] = Label
target = list(datas['Label'][0:100]*2.0-1.0)
points = datas.as_matrix(columns=['Sepal.Length', 'Sepal.Width'])[0:100] #, 'Petal.Length', 'Petal.Width'
svm = SVM()
# print svm.setKernel(points[1], points[5])
svm.learn(target, points)
ans = [svm.calc(i) for i in points]
print ans | mit |
jorge2703/scikit-learn | sklearn/cluster/__init__.py | 359 | 1228 | """
The :mod:`sklearn.cluster` module gathers popular unsupervised clustering
algorithms.
"""
from .spectral import spectral_clustering, SpectralClustering
from .mean_shift_ import (mean_shift, MeanShift,
estimate_bandwidth, get_bin_seeds)
from .affinity_propagation_ import affinity_propagation, AffinityPropagation
from .hierarchical import (ward_tree, AgglomerativeClustering, linkage_tree,
FeatureAgglomeration)
from .k_means_ import k_means, KMeans, MiniBatchKMeans
from .dbscan_ import dbscan, DBSCAN
from .bicluster import SpectralBiclustering, SpectralCoclustering
from .birch import Birch
__all__ = ['AffinityPropagation',
'AgglomerativeClustering',
'Birch',
'DBSCAN',
'KMeans',
'FeatureAgglomeration',
'MeanShift',
'MiniBatchKMeans',
'SpectralClustering',
'affinity_propagation',
'dbscan',
'estimate_bandwidth',
'get_bin_seeds',
'k_means',
'linkage_tree',
'mean_shift',
'spectral_clustering',
'ward_tree',
'SpectralBiclustering',
'SpectralCoclustering']
| bsd-3-clause |
Clyde-fare/scikit-learn | sklearn/cluster/__init__.py | 359 | 1228 | """
The :mod:`sklearn.cluster` module gathers popular unsupervised clustering
algorithms.
"""
from .spectral import spectral_clustering, SpectralClustering
from .mean_shift_ import (mean_shift, MeanShift,
estimate_bandwidth, get_bin_seeds)
from .affinity_propagation_ import affinity_propagation, AffinityPropagation
from .hierarchical import (ward_tree, AgglomerativeClustering, linkage_tree,
FeatureAgglomeration)
from .k_means_ import k_means, KMeans, MiniBatchKMeans
from .dbscan_ import dbscan, DBSCAN
from .bicluster import SpectralBiclustering, SpectralCoclustering
from .birch import Birch
__all__ = ['AffinityPropagation',
'AgglomerativeClustering',
'Birch',
'DBSCAN',
'KMeans',
'FeatureAgglomeration',
'MeanShift',
'MiniBatchKMeans',
'SpectralClustering',
'affinity_propagation',
'dbscan',
'estimate_bandwidth',
'get_bin_seeds',
'k_means',
'linkage_tree',
'mean_shift',
'spectral_clustering',
'ward_tree',
'SpectralBiclustering',
'SpectralCoclustering']
| bsd-3-clause |
Clyde-fare/scikit-learn | sklearn/linear_model/tests/test_bayes.py | 296 | 1770 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.linear_model.bayes import BayesianRidge, ARDRegression
from sklearn import datasets
from sklearn.utils.testing import assert_array_almost_equal
def test_bayesian_on_diabetes():
# Test BayesianRidge on diabetes
raise SkipTest("XFailed Test")
diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target
clf = BayesianRidge(compute_score=True)
# Test with more samples than features
clf.fit(X, y)
# Test that scores are increasing at each iteration
assert_array_equal(np.diff(clf.scores_) > 0, True)
# Test with more features than samples
X = X[:5, :]
y = y[:5]
clf.fit(X, y)
# Test that scores are increasing at each iteration
assert_array_equal(np.diff(clf.scores_) > 0, True)
def test_toy_bayesian_ridge_object():
# Test BayesianRidge on toy
X = np.array([[1], [2], [6], [8], [10]])
Y = np.array([1, 2, 6, 8, 10])
clf = BayesianRidge(compute_score=True)
clf.fit(X, Y)
# Check that the model could approximately learn the identity function
test = [[1], [3], [4]]
assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
def test_toy_ard_object():
# Test BayesianRegression ARD classifier
X = np.array([[1], [2], [3]])
Y = np.array([1, 2, 3])
clf = ARDRegression(compute_score=True)
clf.fit(X, Y)
# Check that the model could approximately learn the identity function
test = [[1], [3], [4]]
assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
| bsd-3-clause |
yukisakurai/hhntup | hhskim.py | 1 | 44883 | # Author: Noel Dawe
import ROOT
import os
import math
import subprocess
import goodruns
import externaltools
# rootpy imports
from rootpy.plotting import Hist
from rootpy.tree.filtering import EventFilter, EventFilterList
from rootpy.tree import Tree, TreeChain, TreeModel, TreeBuffer
from rootpy.extern.argparse import ArgumentParser
from rootpy.io import root_open
from rootpy import stl
# Show stack traces for ROOT warning messages
#from rootpy import log
#import logging
#log["/ROOT"].show_stack(min_level=logging.WARNING)
# local imports
from higgstautau import hepmc
from higgstautau import tautools
from higgstautau import eventshapes
from higgstautau import datasets
from higgstautau import utils
from higgstautau.batch import ATLASStudent
from higgstautau.units import GeV
from higgstautau.mixins import *
from higgstautau.filters import *
from higgstautau.hadhad import branches as hhbranches
from higgstautau.hadhad.objects import define_objects
from higgstautau.hadhad.models import *
from higgstautau.hadhad.filters import *
from higgstautau import httcpana
from higgstautau import mass
from higgstautau.mass import is_MET_bisecting
from higgstautau.overlap import TauJetOverlapRemoval
from higgstautau.embedding import (
EmbeddingPileupPatch, EmbeddingIsolation, EmbeddingCorrections)
from higgstautau.systematics import Systematics
from higgstautau.met import METRecalculation
from higgstautau.jetcalibration import JetCalibration
from higgstautau.tauspinner import EmbeddingTauSpinner, TauSpinner
from higgstautau.patches import ElectronIDpatch, TauIDpatch
from higgstautau.trigger import update_trigger_config, get_trigger_config
from higgstautau.trigger.efficiency import TauTriggerEfficiency
from higgstautau.trigger.emulation import (
TauTriggerEmulation, update_trigger_trees)
#from higgstautau.trigger.matching import (
# TauTriggerMatchIndex, TauTriggerMatchThreshold)
from higgstautau.pileup import (
PileupTemplates, PileupReweight, get_pileup_reweighting_tool,
averageIntPerXingPatch, PileupScale)
from higgstautau.rand import RandomRunNumber, RandomSeed
from higgstautau import log; log = log[__name__]
from higgstautau.hadhad.bdtscore import BDTScore
class hhskim(ATLASStudent):
def __init__(self, options, **kwargs):
super(hhskim, self).__init__(**kwargs)
parser = ArgumentParser()
parser.add_argument('--local', action='store_true', default=False)
parser.add_argument('--syst-terms', default=None)
parser.add_argument('--student-verbose', action='store_true', default=False)
parser.add_argument('--student-very-verbose', action='store_true', default=False)
parser.add_argument('--redo-selection', action='store_true', default=False)
parser.add_argument('--nominal-values', action='store_true', default=False)
args = parser.parse_args(options)
self.args = args
if args.syst_terms is not None:
args.syst_terms = set([
eval('Systematics.%s' % term) for term in
args.syst_terms.split(',')])
if args.local:
def merge(inputs, output, metadata):
# merge output trees
root_output = output + '.root'
log.info("merging output trees")
subprocess.call(['hadd', root_output] + inputs)
if metadata.datatype == datasets.DATA:
# merge GRLs
log.info("merging GRL fragments")
grl = goodruns.GRL()
for input in inputs:
grl |= goodruns.GRL('%s:/lumi' % input)
grl.save('%s:/lumi' % root_output)
hhskim.merge = staticmethod(merge)
def work(self):
# get argument values
local = self.args.local
syst_terms = self.args.syst_terms
datatype = self.metadata.datatype
year = self.metadata.year
verbose = self.args.student_verbose
very_verbose = self.args.student_very_verbose
redo_selection = True #self.args.redo_selection
nominal_values = self.args.nominal_values
# get the dataset name
dsname = os.getenv('INPUT_DATASET_NAME', None)
if dsname is None:
# attempt to guess dsname from dirname
if self.files:
dsname = os.path.basename(os.path.dirname(self.files[0]))
# is this a signal sample?
# if so we will also keep some truth information in the output below
is_signal = datatype == datasets.MC and (
'_VBFH' in dsname or
'_ggH' in dsname or
'_ZH' in dsname or
'_WH' in dsname or
'_ttH' in dsname)
log.info("DATASET: {0}".format(dsname))
log.info("IS SIGNAL: {0}".format(is_signal))
# is this an inclusive signal sample for overlap studies?
is_inclusive_signal = is_signal and '_inclusive' in dsname
# is this a BCH-fixed sample? (temporary)
is_bch_sample = 'r5470_r4540_p1344' in dsname
if is_bch_sample:
log.warning("this is a BCH-fixed r5470 sample")
# onfilechange will contain a list of functions to be called as the
# chain rolls over to each new file
onfilechange = []
count_funcs = {}
if datatype != datasets.DATA:
# count the weighted number of events
if local:
def mc_weight_count(event):
return event.hh_mc_weight
else:
def mc_weight_count(event):
return event.mc_event_weight
count_funcs = {
'mc_weight': mc_weight_count,
}
# three instances of the pileup reweighting tool are created to write
# out the nominal, high and low pileup weights
pileup_tool = None
pileup_tool_high = None
pileup_tool_low = None
if local:
# local means running on the skims, the output of this script
# running on the grid
if datatype == datasets.DATA:
# merge the GRL fragments
merged_grl = goodruns.GRL()
def update_grl(student, grl, name, file, tree):
grl |= str(file.Get('Lumi/%s' % student.metadata.treename).GetString())
onfilechange.append((update_grl, (self, merged_grl,)))
if datatype == datasets.DATA:
merged_cutflow = Hist(1, 0, 1, name='cutflow', type='D')
else:
merged_cutflow = Hist(2, 0, 2, name='cutflow', type='D')
def update_cutflow(student, cutflow, name, file, tree):
# record a cut-flow
year = student.metadata.year
datatype = student.metadata.datatype
cutflow[1].value += file.cutflow_event[1].value
if datatype != datasets.DATA:
cutflow[2].value += file.cutflow_event_mc_weight[1].value
onfilechange.append((update_cutflow, (self, merged_cutflow,)))
else:
# get pileup reweighting tool
pileup_tool = get_pileup_reweighting_tool(
year=year,
use_defaults=True)
pileup_tool_high = get_pileup_reweighting_tool(
year=year,
use_defaults=True,
systematic='high')
pileup_tool_low = get_pileup_reweighting_tool(
year=year,
use_defaults=True,
systematic='low')
if datatype not in (datasets.EMBED, datasets.MCEMBED):
# merge TrigConfTrees
metadirname = '%sMeta' % self.metadata.treename
trigconfchain = ROOT.TChain('%s/TrigConfTree' % metadirname)
map(trigconfchain.Add, self.files)
metadir = self.output.mkdir(metadirname)
metadir.cd()
trigconfchain.Merge(self.output, -1, 'fast keep')
self.output.cd()
if datatype == datasets.DATA:
# merge GRL XML strings
merged_grl = goodruns.GRL()
for fname in self.files:
with root_open(fname) as f:
for key in f.Lumi.keys():
merged_grl |= goodruns.GRL(
str(key.ReadObj().GetString()),
from_string=True)
lumi_dir = self.output.mkdir('Lumi')
lumi_dir.cd()
xml_string= ROOT.TObjString(merged_grl.str())
xml_string.Write(self.metadata.treename)
self.output.cd()
self.output.cd()
# create the output tree
model = get_model(datatype, dsname,
prefix=None if local else 'hh_',
is_inclusive_signal=is_inclusive_signal)
log.info("Output Model:\n\n{0}\n\n".format(model))
outtree = Tree(name=self.metadata.treename,
model=model)
if local:
tree = outtree
else:
tree = outtree.define_object(name='tree', prefix='hh_')
tree.define_object(name='tau', prefix='tau_')
tree.define_object(name='tau1', prefix='tau1_')
tree.define_object(name='tau2', prefix='tau2_')
tree.define_object(name='truetau1', prefix='truetau1_')
tree.define_object(name='truetau2', prefix='truetau2_')
tree.define_object(name='jet1', prefix='jet1_')
tree.define_object(name='jet2', prefix='jet2_')
tree.define_object(name='jet3', prefix='jet3_')
mmc_objects = [
tree.define_object(name='mmc0', prefix='mmc0_'),
tree.define_object(name='mmc1', prefix='mmc1_'),
tree.define_object(name='mmc2', prefix='mmc2_'),
]
for mmc_obj in mmc_objects:
mmc_obj.define_object(name='resonance', prefix='resonance_')
mmc_obj.define_object(name='tau1', prefix='tau1_')
mmc_obj.define_object(name='tau2', prefix='tau2_')
mmc_obj.define_object(name='nu1', prefix='nu1_')
mmc_obj.define_object(name='nu2', prefix='nu2_')
mmc_obj.define_object(name='vistau1', prefix='vistau1_')
mmc_obj.define_object(name='vistau2', prefix='vistau2_')
trigger_emulation = TauTriggerEmulation(
year=year,
passthrough=local or datatype != datasets.MC or year > 2011,
count_funcs=count_funcs)
if not trigger_emulation.passthrough:
onfilechange.append(
(update_trigger_trees, (self, trigger_emulation,)))
trigger_config = None
if datatype not in (datasets.EMBED, datasets.MCEMBED):
# trigger config tool to read trigger info in the ntuples
trigger_config = get_trigger_config()
# update the trigger config maps on every file change
onfilechange.append((update_trigger_config, (trigger_config,)))
# define the list of event filters
if local and syst_terms is None and not redo_selection:
event_filters = None
else:
tau_ntrack_recounted_use_ntup = False
if year > 2011:
# peek at first tree to determine if the extended number of
# tracks is already stored
with root_open(self.files[0]) as test_file:
test_tree = test_file.Get(self.metadata.treename)
tau_ntrack_recounted_use_ntup = (
'tau_out_track_n_extended' in test_tree)
event_filters = EventFilterList([
GRLFilter(
self.grl,
passthrough=(
local or (
datatype not in (datasets.DATA, datasets.EMBED))),
count_funcs=count_funcs),
CoreFlags(
passthrough=local,
count_funcs=count_funcs),
EmbeddingPileupPatch(
passthrough=(
local or year > 2011 or datatype != datasets.EMBED),
count_funcs=count_funcs),
averageIntPerXingPatch(
passthrough=(
local or year < 2012 or datatype != datasets.MC),
count_funcs=count_funcs),
PileupTemplates(
year=year,
passthrough=(
local or is_bch_sample or datatype not in (
datasets.MC, datasets.MCEMBED)),
count_funcs=count_funcs),
RandomSeed(
datatype=datatype,
count_funcs=count_funcs),
BCHSampleRunNumber(
passthrough=not is_bch_sample,
count_funcs=count_funcs),
RandomRunNumber(
tree=tree,
datatype=datatype,
pileup_tool=pileup_tool,
passthrough=local,
count_funcs=count_funcs),
trigger_emulation,
Triggers(
year=year,
tree=tree,
datatype=datatype,
passthrough=datatype in (datasets.EMBED, datasets.MCEMBED),
count_funcs=count_funcs),
PileupReweight(
year=year,
tool=pileup_tool,
tool_high=pileup_tool_high,
tool_low=pileup_tool_low,
tree=tree,
passthrough=(
local or (
datatype not in (datasets.MC, datasets.MCEMBED))),
count_funcs=count_funcs),
PriVertex(
passthrough=local,
count_funcs=count_funcs),
LArError(
passthrough=local,
count_funcs=count_funcs),
TileError(
passthrough=local,
count_funcs=count_funcs),
TileTrips(
passthrough=(
local or datatype in (datasets.MC, datasets.MCEMBED)),
count_funcs=count_funcs),
JetCopy(
tree=tree,
passthrough=local,
count_funcs=count_funcs),
# IMPORTANT!
# JetCalibration MUST COME BEFORE ANYTHING THAT REFERS TO
# jet.fourvect since jet.fourvect IS CACHED!
JetCalibration(
datatype=datatype,
year=year,
verbose=very_verbose,
passthrough=local or nominal_values,
count_funcs=count_funcs),
# in situ TES shift for 2012 data
TauEnergyShift(
passthrough=(
local or datatype != datasets.DATA
or year < 2012 or nominal_values),
count_funcs=count_funcs),
# truth matching must come before systematics due to
# TES_TRUE/FAKE
TruthMatching(
passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
NvtxJets(
tree=tree,
count_funcs=count_funcs),
# PUT THE SYSTEMATICS "FILTER" BEFORE
# ANY FILTERS THAT REFER TO OBJECTS
# BUT AFTER CALIBRATIONS
# Systematics must also come before anything that refers to
# thing.fourvect since fourvect is cached!
Systematics(
terms=syst_terms,
year=year,
datatype=datatype,
tree=tree,
verbose=verbose,
passthrough=not syst_terms,
count_funcs=count_funcs),
JetIsPileup(
passthrough=(
local or year < 2012 or
datatype not in (datasets.MC, datasets.MCEMBED)),
count_funcs=count_funcs),
LArHole(
tree=tree,
passthrough=year > 2011,
count_funcs=count_funcs),
JetCleaning(
datatype=datatype,
year=year,
count_funcs=count_funcs),
ElectronVeto(
count_funcs=count_funcs),
MuonVeto(
year=year,
count_funcs=count_funcs),
TauPT(2,
thresh=20 * GeV,
count_funcs=count_funcs),
TauHasTrack(2,
count_funcs=count_funcs),
TauEta(2,
count_funcs=count_funcs),
TauElectronVeto(2,
count_funcs=count_funcs),
TauMuonVeto(2,
count_funcs=count_funcs),
TauAuthor(2,
count_funcs=count_funcs),
TauCrack(2,
count_funcs=count_funcs),
TauLArHole(2,
tree=tree,
passthrough=year > 2011,
count_funcs=count_funcs),
# before selecting the leading and subleading taus
# be sure to only consider good candidates
TauIDMedium(2,
count_funcs=count_funcs),
#TauTriggerMatchIndex(
# config=trigger_config,
# year=year,
# datatype=datatype,
# passthrough=datatype == datasets.EMBED,
# count_funcs=count_funcs),
# Select two leading taus at this point
# 25 and 35 for data
# 20 and 30 for MC to leave room for TES uncertainty
TauLeadSublead(
lead=(
35 * GeV if datatype == datasets.DATA or local
else 30 * GeV),
sublead=(
25 * GeV if datatype == datasets.DATA or local
else 20 * GeV),
count_funcs=count_funcs),
# taus are sorted (in decreasing order) by pT from here on
TauIDSelection(
tree=tree,
count_funcs=count_funcs),
TaudR(3.2,
count_funcs=count_funcs),
#TauTriggerMatchThreshold(
# datatype=datatype,
# tree=tree,
# count_funcs=count_funcs),
TauTriggerEfficiency(
year=year,
datatype=datatype,
tree=tree,
tes_systematic=self.args.syst_terms and (
Systematics.TES_TERMS & self.args.syst_terms),
passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
PileupScale(
tree=tree,
year=year,
datatype=datatype,
passthrough=local,
count_funcs=count_funcs),
TauIDScaleFactors(
year=year,
passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
TauFakeRateScaleFactors(
year=year,
datatype=datatype,
tree=tree,
tes_up=(self.args.syst_terms is not None and
(Systematics.TES_FAKE_TOTAL_UP in self.args.syst_terms or
Systematics.TES_FAKE_FINAL_UP in self.args.syst_terms)),
tes_down=(self.args.syst_terms is not None and
(Systematics.TES_FAKE_TOTAL_DOWN in self.args.syst_terms or
Systematics.TES_FAKE_FINAL_DOWN in self.args.syst_terms)),
passthrough=datatype in (datasets.DATA, datasets.EMBED),
count_funcs=count_funcs),
HiggsPT(
year=year,
tree=tree,
passthrough=not is_signal or local,
count_funcs=count_funcs),
TauTrackRecounting(
year=year,
use_ntup_value=tau_ntrack_recounted_use_ntup,
passthrough=local,
count_funcs=count_funcs),
MCWeight(
datatype=datatype,
tree=tree,
passthrough=local or datatype == datasets.DATA,
count_funcs=count_funcs),
EmbeddingIsolation(
tree=tree,
passthrough=(
local or year < 2012 or
datatype not in (datasets.EMBED, datasets.MCEMBED)),
count_funcs=count_funcs),
EmbeddingCorrections(
tree=tree,
year=year,
passthrough=(
local or
datatype not in (datasets.EMBED, datasets.MCEMBED)),
count_funcs=count_funcs),
TauSpinner(
year=year,
tree=tree,
passthrough=(
local or
not (is_signal or
datatype in (datasets.EMBED, datasets.MCEMBED))),
count_funcs=count_funcs),
# put MET recalculation after tau selection but before tau-jet
# overlap removal and jet selection because of the RefAntiTau
# MET correction
METRecalculation(
terms=syst_terms,
year=year,
tree=tree,
refantitau=not nominal_values,
verbose=verbose,
very_verbose=very_verbose,
count_funcs=count_funcs),
TauJetOverlapRemoval(
count_funcs=count_funcs),
JetPreselection(
count_funcs=count_funcs),
NonIsolatedJet(
tree=tree,
count_funcs=count_funcs),
JetSelection(
year=year,
count_funcs=count_funcs),
RecoJetTrueTauMatching(
passthrough=datatype == datasets.DATA or local,
count_funcs=count_funcs),
BCHCleaning(
tree=tree,
passthrough=year == 2011 or local,
datatype=datatype,
count_funcs=count_funcs),
ClassifyInclusiveHiggsSample(
tree=tree,
passthrough=not is_inclusive_signal,
count_funcs=count_funcs),
])
# set the event filters
self.filters['event'] = event_filters
# peek at first tree to determine which branches to exclude
with root_open(self.files[0]) as test_file:
test_tree = test_file.Get(self.metadata.treename)
ignore_branches = test_tree.glob(
hhbranches.REMOVE,
exclude=hhbranches.KEEP)
ignore_branches_output = test_tree.glob(
hhbranches.REMOVE_OUTPUT,
exclude=hhbranches.KEEP_OUTPUT)
# initialize the TreeChain of all input files
chain = TreeChain(
self.metadata.treename,
files=self.files,
ignore_branches=ignore_branches,
events=self.events,
onfilechange=onfilechange,
filters=event_filters,
cache=True,
cache_size=50000000,
learn_entries=100)
if local:
copied = [
'EventNumber',
]
hh_buffer = TreeBuffer()
buffer = TreeBuffer()
for name, value in chain._buffer.items():
if name.startswith('hh_'):
hh_buffer[name[3:]] = value
elif name in copied:
buffer[name] = value
outtree.set_buffer(
hh_buffer,
create_branches=False,
visible=True)
outtree.set_buffer(
buffer,
create_branches=True,
visible=False)
else:
# additional decorations on existing objects
if year > 2011 and datatype in (datasets.MC, datasets.MCEMBED):
class Decorations(TreeModel):
jet_ispileup = stl.vector('bool')
chain.set_buffer(Decorations(), create_branches=True)
# include the branches in the input chain in the output tree
# set branches to be removed in ignore_branches
outtree.set_buffer(
chain._buffer,
ignore_branches=ignore_branches + ignore_branches_output,
create_branches=True,
ignore_duplicates=True,
transfer_objects=True,
visible=False)
# define tree objects
define_objects(chain, year)
# create the BDT classifier
bdt = BDTScore(year=year)
# create the HttCP
cp = httcpana.HttCP()
# create the MMC
mmc = mass.MMC(year=year)
# report which packages have been loaded
externaltools.report()
self.output.cd()
# The main event loop
# the event filters above are automatically run for each event and only
# the surviving events are looped on
for event in chain:
if local and syst_terms is None and not redo_selection:
outtree.Fill()
continue
# sort taus and jets in decreasing order by pT
event.taus.sort(key=lambda tau: tau.pt, reverse=True)
event.jets.sort(key=lambda jet: jet.pt, reverse=True)
# tau1 is the leading tau
# tau2 is the subleading tau
tau1, tau2 = event.taus
jets = list(event.jets)
jet1, jet2, jet3 = None, None, None
beta = None
# leading track
tau1_lead_track_tlv = ROOT.TLorentzVector()
tau1_lead_track_qoverp = -1E10
tau1_lead_track_d0 = -1E10
tau1_lead_track_z0 = -1E10
tau1_lead_track_tlv, tau1_lead_track_qoverp, tau1_lead_track_d0, tau1_lead_track_z0 = cp.find_lead_track ( tau1 )
tau1.lead_track_pt = tau1_lead_track_tlv.Pt()
tau1.lead_track_eta = tau1_lead_track_tlv.Eta()
tau1.lead_track_phi = tau1_lead_track_tlv.Phi()
tau1_lead_track_qoverp = tau1.lead_track_qoverp
tau1_lead_track_d0 = tau1.lead_track_d0
tau1_lead_track_z0 = tau1.lead_track_z0
tau2_lead_track_tlv = ROOT.TLorentzVector()
tau2_lead_track_qoverp = -1E10
tau2_lead_track_d0 = -1E10
tau2_lead_track_z0 = -1E10
tau2_lead_track_tlv, tau2_lead_track_qoverp, tau2_lead_track_d0, tau2_lead_track_z0 = cp.find_lead_track ( tau2 )
tau2.lead_track_pt = tau2_lead_track_tlv.Pt()
tau2.lead_track_eta = tau2_lead_track_tlv.Eta()
tau2.lead_track_phi = tau2_lead_track_tlv.Phi()
tau2_lead_track_qoverp = tau2.lead_track_qoverp
tau2_lead_track_d0 = tau2.lead_track_d0
tau2_lead_track_z0 = tau2.lead_track_z0
if len(jets) >= 2:
jet1, jet2 = jets[:2]
# determine boost of system
# determine jet CoM frame
beta = (jet1.fourvect + jet2.fourvect).BoostVector()
tree.jet_beta.copy_from(beta)
jet1.fourvect_boosted.copy_from(jet1.fourvect)
jet2.fourvect_boosted.copy_from(jet2.fourvect)
jet1.fourvect_boosted.Boost(beta * -1)
jet2.fourvect_boosted.Boost(beta * -1)
tau1.fourvect_boosted.copy_from(tau1.fourvect)
tau2.fourvect_boosted.copy_from(tau2.fourvect)
tau1.fourvect_boosted.Boost(beta * -1)
tau2.fourvect_boosted.Boost(beta * -1)
tau1.min_dr_jet = min(
tau1.fourvect.DeltaR(jet1.fourvect),
tau1.fourvect.DeltaR(jet2.fourvect))
tau2.min_dr_jet = min(
tau2.fourvect.DeltaR(jet1.fourvect),
tau2.fourvect.DeltaR(jet2.fourvect))
#sphericity, aplanarity = eventshapes.sphericity_aplanarity(
# [tau1.fourvect,
# tau2.fourvect,
# jet1.fourvect,
# jet2.fourvect])
# sphericity
#tree.sphericity = sphericity
# aplanarity
#tree.aplanarity = aplanarity
#sphericity_boosted, aplanarity_boosted = eventshapes.sphericity_aplanarity(
# [tau1.fourvect_boosted,
# tau2.fourvect_boosted,
# jet1.fourvect_boosted,
# jet2.fourvect_boosted])
# sphericity
#tree.sphericity_boosted = sphericity_boosted
# aplanarity
#tree.aplanarity_boosted = aplanarity_boosted
# tau centrality (degree to which they are between the two jets)
tau1.centrality = eventshapes.eta_centrality(
tau1.fourvect.Eta(),
jet1.fourvect.Eta(),
jet2.fourvect.Eta())
tau2.centrality = eventshapes.eta_centrality(
tau2.fourvect.Eta(),
jet1.fourvect.Eta(),
jet2.fourvect.Eta())
# boosted tau centrality
tau1.centrality_boosted = eventshapes.eta_centrality(
tau1.fourvect_boosted.Eta(),
jet1.fourvect_boosted.Eta(),
jet2.fourvect_boosted.Eta())
tau2.centrality_boosted = eventshapes.eta_centrality(
tau2.fourvect_boosted.Eta(),
jet1.fourvect_boosted.Eta(),
jet2.fourvect_boosted.Eta())
# 3rd leading jet
if len(jets) >= 3:
jet3 = jets[2]
jet3.fourvect_boosted.copy_from(jet3.fourvect)
jet3.fourvect_boosted.Boost(beta * -1)
elif len(jets) == 1:
jet1 = jets[0]
tau1.min_dr_jet = tau1.fourvect.DeltaR(jet1.fourvect)
tau2.min_dr_jet = tau2.fourvect.DeltaR(jet1.fourvect)
#sphericity, aplanarity = eventshapes.sphericity_aplanarity(
# [tau1.fourvect,
# tau2.fourvect,
# jet1.fourvect])
# sphericity
#tree.sphericity = sphericity
# aplanarity
#tree.aplanarity = aplanarity
RecoJetBlock.set(tree, jet1, jet2, jet3, local=local)
# mass of ditau + leading jet system
if jet1 is not None:
tree.mass_tau1_tau2_jet1 = (
tau1.fourvect + tau2.fourvect + jet1.fourvect).M()
# full sphericity and aplanarity
#sphericity_full, aplanarity_full = eventshapes.sphericity_aplanarity(
# [tau1.fourvect, tau2.fourvect] + [jet.fourvect for jet in jets])
#tree.sphericity_full = sphericity_full
#tree.aplanarity_full = aplanarity_full
#####################################
# number of tracks from PV minus taus
#####################################
ntrack_pv = 0
ntrack_nontau_pv = 0
privtx = ROOT.TVector3()
for vxp in event.vertices:
# primary vertex
if vxp.type == 1:
ntrack_pv = vxp.nTracks
ntrack_nontau_pv = ntrack_pv - tau1.numTrack - tau2.numTrack
break
if vpx.type == 1 and vxp.nTrack >= 4:
privtx.SetXYZ(vxp.x,vxp.y,vxp.z)
break
tree.ntrack_pv = ntrack_pv
tree.ntrack_nontau_pv = ntrack_nontau_pv
#########################
# MET variables
#########################
METx = event.MET.etx
METy = event.MET.ety
MET = event.MET.et
MET_vect = Vector2(METx, METy)
MET_4vect = LorentzVector()
MET_4vect.SetPxPyPzE(METx, METy, 0., MET)
MET_4vect_boosted = LorentzVector()
MET_4vect_boosted.copy_from(MET_4vect)
if beta is not None:
MET_4vect_boosted.Boost(beta * -1)
tree.MET_et = MET
tree.MET_etx = METx
tree.MET_ety = METy
tree.MET_phi = event.MET.phi
dPhi_tau1_tau2 = abs(tau1.fourvect.DeltaPhi(tau2.fourvect))
dPhi_tau1_MET = abs(tau1.fourvect.DeltaPhi(MET_4vect))
dPhi_tau2_MET = abs(tau2.fourvect.DeltaPhi(MET_4vect))
tree.dPhi_tau1_tau2 = dPhi_tau1_tau2
tree.dPhi_tau1_MET = dPhi_tau1_MET
tree.dPhi_tau2_MET = dPhi_tau2_MET
tree.dPhi_min_tau_MET = min(dPhi_tau1_MET, dPhi_tau2_MET)
tree.MET_bisecting = is_MET_bisecting(
dPhi_tau1_tau2,
dPhi_tau1_MET,
dPhi_tau2_MET)
sumET = event.MET.sumet
tree.MET_sumet = sumET
if sumET != 0:
tree.MET_sig = ((2. * MET / GeV) /
(utils.sign(sumET) * sqrt(abs(sumET / GeV))))
else:
tree.MET_sig = -1.
tree.MET_centrality = eventshapes.phi_centrality(
tau1.fourvect,
tau2.fourvect,
MET_vect)
tree.MET_centrality_boosted = eventshapes.phi_centrality(
tau1.fourvect_boosted,
tau2.fourvect_boosted,
MET_4vect_boosted)
tree.number_of_good_vertices = len(event.vertices)
##########################
# Jet and sum pt variables
##########################
tree.numJets = len(event.jets)
# sum pT with only the two leading jets
tree.sum_pt = sum(
[tau1.pt, tau2.pt] +
[jet.pt for jet in jets[:2]])
# sum pT with all selected jets
tree.sum_pt_full = sum(
[tau1.pt, tau2.pt] +
[jet.pt for jet in jets])
# vector sum pT with two leading jets and MET
tree.vector_sum_pt = sum(
[tau1.fourvect, tau2.fourvect] +
[jet.fourvect for jet in jets[:2]] +
[MET_4vect]).Pt()
# vector sum pT with all selected jets and MET
tree.vector_sum_pt_full = sum(
[tau1.fourvect, tau2.fourvect] +
[jet.fourvect for jet in jets] +
[MET_4vect]).Pt()
# resonance pT
tree.resonance_pt = sum(
[tau1.fourvect, tau2.fourvect, MET_4vect]).Pt()
#############################
# tau <-> vertex association
#############################
tree.tau_same_vertex = (
tau1.privtx_x == tau2.privtx_x and
tau1.privtx_y == tau2.privtx_y and
tau1.privtx_z == tau2.privtx_z)
tau1.vertex_prob = ROOT.TMath.Prob(
tau1.privtx_chiSquared,
int(tau1.privtx_numberDoF))
tau2.vertex_prob = ROOT.TMath.Prob(
tau2.privtx_chiSquared,
int(tau2.privtx_numberDoF))
##########################
# MMC Mass
##########################
mmc_result = mmc.mass(
tau1, tau2,
METx, METy, sumET,
njets=len(event.jets),
EventNumber=event.EventNumber)
mmc1_mass = 0.0
for mmc_method, mmc_object in enumerate(mmc_objects):
mmc_mass, mmc_resonance, mmc_met, \
mmc_tau1, mmc_tau2, \
mmc_nu1, mmc_nu2, \
mmc_vistau1, mmc_vistau2 = mmc_result[mmc_method]
if verbose:
log.info("MMC (method %d): %f" % (mmc_method, mmc_mass))
if mmc_method == 1:
mmc1_mass = mmc_mass
mmc_object.mass = mmc_mass
mmc_object.MET_et = mmc_met.Mod()
mmc_object.MET_etx = mmc_met.X()
mmc_object.MET_ety = mmc_met.Y()
mmc_object.MET_phi = math.pi - mmc_met.Phi()
if mmc_mass > 0:
FourMomentum.set(mmc_object.resonance, mmc_resonance)
FourMomentum.set(mmc_object.tau1, mmc_tau1)
FourMomentum.set(mmc_object.tau2, mmc_tau2)
FourMomentum.set(mmc_object.nu1, mmc_nu1)
FourMomentum.set(mmc_object.nu2, mmc_nu2)
FourMomentum.set(mmc_object.vistau1, mmc_vistau1)
FourMomentum.set(mmc_object.vistau2, mmc_vistau2)
############################
# collinear and visible mass
############################
vis_mass, collin_mass, tau1_x, tau2_x = mass.collinearmass(
tau1, tau2, METx, METy)
tree.mass_vis_tau1_tau2 = vis_mass
tree.mass_collinear_tau1_tau2 = collin_mass
tau1.collinear_momentum_fraction = tau1_x
tau2.collinear_momentum_fraction = tau2_x
###########################
# Match jets to VBF partons
###########################
#if datatype == datasets.MC and 'VBF' in dsname and year == 2011:
# # get partons (already sorted by eta in hepmc) FIXME!!!
# parton1, parton2 = hepmc.get_VBF_partons(event)
# tree.mass_true_quark1_quark2 = (parton1.fourvect + parton2.fourvect).M()
# # order here needs to be revised since jets are no longer
# # sorted by eta but instead by pT
# PartonBlock.set(tree, parton1, parton2)
# if len(jets) >= 2:
# jet1, jet2 = jets[:2]
# for i, jet in zip((1, 2), (jet1, jet2)):
# for parton in (parton1, parton2):
# if utils.dR(jet.eta, jet.phi, parton.eta, parton.phi) < .8:
# setattr(tree, 'jet%i_matched' % i, True)
############################
# MVA BDTScore
############################
# calculate BDTScore
inputs_vbf = [mmc1_mass,
tree.dEta_jets,
tree.eta_product_jets,
tree.mass_jet1_jet2,
tree.tau1_centrality,
tree.tau2_centrality,
tree.dR_tau1_tau2,
tree.MET_centrality,
tree.vector_sum_pt]
inputs_boosted = [mmc1_mass,
tree.dR_tau1_tau2,
tree.MET_centrality,
tree.sum_pt_full,
tree.tau_pt_ratio]
tree.BDTScore_vbf_0 = bdt.get_score(event.EventNumber, 0, 'VBF', inputs_vbf)
tree.BDTScore_boosted_0 = bdt.get_score(event.EventNumber, 0, 'Boosted', inputs_boosted)
tree.BDTScore_vbf_1 = bdt.get_score(event.EventNumber, 1, 'VBF', inputs_vbf)
tree.BDTScore_boosted_1 = bdt.get_score(event.EventNumber, 1, 'Boosted', inputs_boosted)
############################
# CP angle variables
############################
# calculate Acoplanarity angles
# for pi-pi decay
tree.Acoplanarity_IP = cp.Acoplanarity_IP( tau1, tau2, event.vertices )
# for pi-rho , rho-rho decay : leadtrk
tree.Acoplanarity_tau1_IP_tau2_rho_leadtrk = \
cp.Acoplanarity_tau1_IP_tau2_rho_leadtrk( tau1, tau2, event.vertices )
tree.Acoplanarity_tau2_IP_tau1_rho_leadtrk = \
cp.Acoplanarity_tau2_IP_tau1_rho_leadtrk( tau1, tau2, event.vertices )
tree.Acoplanarity_rho_leadtrk = cp.Acoplanarity_rho_leadtrk( tau1, tau2 )
if year > 2011:
# for pi-rho , rho-rho decay : cluster
tree.Acoplanarity_tau1_IP_tau2_rho_cluster = \
cp.Acoplanarity_tau1_IP_tau2_rho_cluster( tau1, tau2, event.vertices )
tree.Acoplanarity_tau2_IP_tau1_rho_cluster = \
cp.Acoplanarity_tau2_IP_tau1_rho_cluster( tau1, tau2, event.vertices )
tree.Acoplanarity_rho_cluster = cp.Acoplanarity_rho_cluster( tau1, tau2 )
# use mmc output information
for mmc_method, mmc_object in enumerate(mmc_objects):
if mmc_method == 0 :
tree.Acoplanarity_mmc0 = \
cp.Acoplanarity_mmc(mmc_object.vistau1,
mmc_object.nu1,
mmc_object.vistau2,
mmc_object.nu2)
tree.Acoplanarity_mmc0_visboost = \
cp.Acoplanarity_mmc_visboost(mmc_object.vistau1,
mmc_object.nu1,
mmc_object.vistau2,
mmc_object.nu2)
if mmc_method == 1 :
tree.Acoplanarity_mmc1 = \
cp.Acoplanarity_mmc(mmc_object.vistau1,
mmc_object.nu1,
mmc_object.vistau2,
mmc_object.nu2)
tree.Acoplanarity_mmc1_visboost = \
cp.Acoplanarity_mmc_visboost(mmc_object.vistau1,
mmc_object.nu1,
mmc_object.vistau2,
mmc_object.nu2)
if mmc_method == 2 :
tree.Acoplanarity_mmc2 = \
cp.Acoplanarity_mmc(mmc_object.vistau1,
mmc_object.nu1,
mmc_object.vistau2,
mmc_object.nu2)
tree.Acoplanarity_mmc2_visboost = \
cp.Acoplanarity_mmc_visboost(mmc_object.vistau1,
mmc_object.nu1,
mmc_object.vistau2,
mmc_object.nu2)
# Fill the tau block
# This must come after the RecoJetBlock is filled since
# that sets the jet_beta for boosting the taus
RecoTauBlock.set(event, tree, datatype, tau1, tau2, local=local)
if datatype != datasets.DATA:
TrueTauBlock.set(tree, tau1, tau2)
# fill the output tree
outtree.Fill(reset=True)
externaltools.report()
# flush any baskets remaining in memory to disk
self.output.cd()
outtree.FlushBaskets()
outtree.Write()
if local:
if datatype == datasets.DATA:
xml_string = ROOT.TObjString(merged_grl.str())
xml_string.Write('lumi')
merged_cutflow.Write()
| gpl-3.0 |
guorendong/iridium-browser-ubuntu | third_party/jinja2/visitor.py | 1402 | 3316 | # -*- coding: utf-8 -*-
"""
jinja2.visitor
~~~~~~~~~~~~~~
This module implements a visitor for the nodes.
:copyright: (c) 2010 by the Jinja Team.
:license: BSD.
"""
from jinja2.nodes import Node
class NodeVisitor(object):
"""Walks the abstract syntax tree and call visitor functions for every
node found. The visitor functions may return values which will be
forwarded by the `visit` method.
Per default the visitor functions for the nodes are ``'visit_'`` +
class name of the node. So a `TryFinally` node visit function would
be `visit_TryFinally`. This behavior can be changed by overriding
the `get_visitor` function. If no visitor function exists for a node
(return value `None`) the `generic_visit` visitor is used instead.
"""
def get_visitor(self, node):
"""Return the visitor function for this node or `None` if no visitor
exists for this node. In that case the generic visit function is
used instead.
"""
method = 'visit_' + node.__class__.__name__
return getattr(self, method, None)
def visit(self, node, *args, **kwargs):
"""Visit a node."""
f = self.get_visitor(node)
if f is not None:
return f(node, *args, **kwargs)
return self.generic_visit(node, *args, **kwargs)
def generic_visit(self, node, *args, **kwargs):
"""Called if no explicit visitor function exists for a node."""
for node in node.iter_child_nodes():
self.visit(node, *args, **kwargs)
class NodeTransformer(NodeVisitor):
"""Walks the abstract syntax tree and allows modifications of nodes.
The `NodeTransformer` will walk the AST and use the return value of the
visitor functions to replace or remove the old node. If the return
value of the visitor function is `None` the node will be removed
from the previous location otherwise it's replaced with the return
value. The return value may be the original node in which case no
replacement takes place.
"""
def generic_visit(self, node, *args, **kwargs):
for field, old_value in node.iter_fields():
if isinstance(old_value, list):
new_values = []
for value in old_value:
if isinstance(value, Node):
value = self.visit(value, *args, **kwargs)
if value is None:
continue
elif not isinstance(value, Node):
new_values.extend(value)
continue
new_values.append(value)
old_value[:] = new_values
elif isinstance(old_value, Node):
new_node = self.visit(old_value, *args, **kwargs)
if new_node is None:
delattr(node, field)
else:
setattr(node, field, new_node)
return node
def visit_list(self, node, *args, **kwargs):
"""As transformers may return lists in some places this method
can be used to enforce a list as return value.
"""
rv = self.visit(node, *args, **kwargs)
if not isinstance(rv, list):
rv = [rv]
return rv
| bsd-3-clause |
mgupta011235/TweetSafe | tfidf/gridsearch_xgb.py | 1 | 7330 | from sklearn.feature_extraction.text import TfidfVectorizer
import xgboost as xgb
import cPickle as pickle
from string import punctuation
from nltk import word_tokenize
from nltk.stem import snowball
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from nltk.tokenize import PunktSentenceTokenizer
import multiprocessing as mp
import time
stemmer = snowball.SnowballStemmer("english")
###############################################################################
#OHS tokenization code
def load_data(filename):
'''
Load data into a data frame for use in running model
'''
return pickle.load(open(filename, 'rb'))
def stem_tokens(tokens, stemmer):
'''Stem the tokens.'''
stemmed = []
for item in tokens:
stemmed.append(stemmer.stem(item))
return stemmed
def OHStokenize(text):
'''Tokenize & stem. Stems automatically for now.
Leaving "stemmer" out of function call, so it works with TfidfVectorizer'''
tokens = word_tokenize(text)
stems = stem_tokens(tokens, stemmer)
return stems
###########################################################################
# tokenization code
def seperatePunct(incomingString):
'''
Input:str,
Output: str with all puncuations seperated by spaces
'''
outstr = ''
characters = set(['!','@','#','$',"%","^","&","*",":","\\",
"(",")","+","=","?","\'","\"",";","/",
"{","}","[","]","<",">","~","`","|"])
for char in incomingString:
if char in characters:
outstr = outstr + ' ' + char + ' '
else:
outstr = outstr + char
return outstr
def hasNumbers(inputString):
'''
Input: str
Output: returns a 1 if the string contains a number
'''
return any(char.isdigit() for char in inputString)
def text_cleaner(wordList):
'''
INPUT: List of words to be tokenized
OUTPUT: List of tokenized words
'''
tokenziedList = []
for word in wordList:
#remove these substrings from the word
word = word.replace('[deleted]','')
word = word.replace('>','')
#if link, replace with linktag
if 'http' in word:
tokenziedList.append('LINK_TAG')
continue
#if reference to subreddit, replace with reddittag
if '/r/' in word:
tokenziedList.append('SUBREDDIT_TAG')
continue
#if reference to reddit user, replace with usertag
if '/u/' in word:
tokenziedList.append('USER_TAG')
continue
#if reference to twitter user, replace with usertag
if '@' in word:
tokenziedList.append('USER_TAG')
continue
#if number, replace with numtag
#m8 is a word, 5'10" and 54-59, 56:48 are numbers
if hasNumbers(word) and not any(char.isalpha() for char in word):
tokenziedList.append('NUM_TAG')
continue
#seperate puncuations and add to tokenizedList
newwords = seperatePunct(word).split(" ")
tokenziedList.extend(newwords)
return tokenziedList
def mytokenize(comment):
'''
Input: takes in a reddit comment as a str or unicode and tokenizes it
Output: a tokenized list
'''
tokenizer = PunktSentenceTokenizer()
sentenceList = tokenizer.tokenize(comment)
wordList = []
for sentence in sentenceList:
wordList.extend(sentence.split(" "))
return text_cleaner(wordList)
##############################################################################
#main
def search(xg_train,xg_cv,paramlist,i):
'''Input: traing set, eval set, xgboost parameter list, index
Output: returns a list with the parameters and the results of
evaluating the auc of the model on the eval set
'''
print "start i: ", i
print ""
#parameters
param = {'max_depth':paramlist[1],
'eta':paramlist[2],
'silent':1,
'objective':'binary:logistic',
'eval_metric':'auc'
}
num_round = paramlist[0]
watchlist = [(xg_train, 'train'), (xg_cv, 'eval')]
#create dictionary to store eval results
results = dict()
#train model
model = xgb.train(param,
xg_train,
num_round,
watchlist,
evals_result=results,
verbose_eval=False)
modelfilename = 'xgbfinal3_{}.model'.format(i)
model.save_model(modelfilename)
print "finish i: ", i
print ""
return [paramlist[0],paramlist[1],paramlist[2],results]
if __name__ == '__main__':
'''This script runs gridsearch to determine the optimal parameters
for xgboost on the reddit corpus'''
print "entering main..."
# path = '../../data/labeledRedditComments2.p'
# cvpath = '../../data/twitter_cross_val.csv'
#
# load_tstart = time.time()
# print 'loading data...'
# df = load_data(path)
# dfcv = pd.read_csv(cvpath)
# load_tstop = time.time()
#
# #take a subset of the data for testing this code
# randNums = np.random.randint(low=0,high=len(df.index),size=(100,1))
# rowList = [int(row) for row in randNums]
# dfsmall = df.ix[rowList,:]
#
# nf = dfsmall
#
# #create training set and labels
# X = nf.body
# y = nf.label
#
# Xcv = dfcv['tweet_text'].values
# ycv = dfcv['label'].values
#
# vect_tstart = time.time()
# print "vectorizing..."
# vect = TfidfVectorizer(stop_words='english', decode_error='ignore',
# tokenizer=OHStokenize)
#
# # fit & transform comments matrix
# tfidf_X = vect.fit_transform(X)
# tfidf_Xcv = vect.transform(Xcv)
# vect_tstop = time.time()
#
#
#
# print "converting data..."
# #convert to dense so that DMatrix doesn't drop cols with all zeros
# tfidf_Xcvd = tfidf_Xcv.todense()
#
# #data conversion to DMatrix
# xg_train = xgb.DMatrix(tfidf_X, label=y)
# xg_cv = xgb.DMatrix(tfidf_Xcvd, label=ycv)
print "loading vectorizer..."
vect = pickle.load(open('vect.p', 'rb'))
cvpath = 'twitter_cross_val.csv'
dfcv = pd.read_csv(cvpath)
Xcv = dfcv['tweet_text'].values
ycv = dfcv['label'].values
print "transforming cross val data..."
tfidf_Xcv = vect.transform(Xcv)
tfidf_Xcvd = tfidf_Xcv.todense()
xg_cv = xgb.DMatrix(tfidf_Xcvd, label=ycv)
print "loading training data..."
xg_train = xgb.DMatrix('xg_train2.buffer')
print 'gridsearching...'
grid_tstart = time.time()
results = []
i = 0
for eta in [0.3,0.6,0.9]:
for max_depth in [3,4,5]:
for num_rounds in [100,300,600,900]:
params = [num_rounds,max_depth,eta]
results.append(search(xg_train,xg_cv,params,i))
i+=1
grid_tstop = time.time()
#save data to dataframe
labels = ['num_rounds','max_depth','eta','eval_results']
df = pd.DataFrame(data=results,columns=labels)
df.to_csv('gridsearch3_xgb.csv')
# print "vect: {}".format(vect_tstop - vect_tstart)
print "gridsearch: {}".format(grid_tstop - grid_tstart)
| gpl-3.0 |
jundongl/scikit-feast | skfeature/example/test_JMI.py | 3 | 1482 | import scipy.io
from sklearn.metrics import accuracy_score
from sklearn import cross_validation
from sklearn import svm
from skfeature.function.information_theoretical_based import JMI
def main():
# load data
mat = scipy.io.loadmat('../data/colon.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
n_samples, n_features = X.shape # number of samples and number of features
# split data into 10 folds
ss = cross_validation.KFold(n_samples, n_folds=10, shuffle=True)
# perform evaluation on classification task
num_fea = 10 # number of selected features
clf = svm.LinearSVC() # linear SVM
correct = 0
for train, test in ss:
# obtain the index of each feature on the training set
idx,_,_ = JMI.jmi(X[train], y[train], n_selected_features=num_fea)
# obtain the dataset on the selected features
features = X[:, idx[0:num_fea]]
# train a classification model with the selected features on the training dataset
clf.fit(features[train], y[train])
# predict the class labels of test data
y_predict = clf.predict(features[test])
# obtain the classification accuracy on the test data
acc = accuracy_score(y[test], y_predict)
correct = correct + acc
# output the average classification accuracy over all 10 folds
print 'Accuracy:', float(correct)/10
if __name__ == '__main__':
main()
| gpl-2.0 |
jorge2703/scikit-learn | doc/sphinxext/gen_rst.py | 141 | 40026 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
import glob
import sys
import gzip
import posixpath
import subprocess
import warnings
from sklearn.externals import six
# Try Python 2 first, otherwise load from Python 3
try:
from StringIO import StringIO
import cPickle as pickle
import urllib2 as urllib
from urllib2 import HTTPError, URLError
except ImportError:
from io import StringIO
import pickle
import urllib.request
import urllib.error
import urllib.parse
from urllib.error import HTTPError, URLError
try:
# Python 2 built-in
execfile
except NameError:
def execfile(filename, global_vars=None, local_vars=None):
with open(filename, encoding='utf-8') as f:
code = compile(f.read(), filename, 'exec')
exec(code, global_vars, local_vars)
try:
basestring
except NameError:
basestring = str
import token
import tokenize
import numpy as np
try:
# make sure that the Agg backend is set before importing any
# matplotlib
import matplotlib
matplotlib.use('Agg')
except ImportError:
# this script can be imported by nosetest to find tests to run: we should not
# impose the matplotlib requirement in that case.
pass
from sklearn.externals import joblib
###############################################################################
# A tee object to redict streams to multiple outputs
class Tee(object):
def __init__(self, file1, file2):
self.file1 = file1
self.file2 = file2
def write(self, data):
self.file1.write(data)
self.file2.write(data)
def flush(self):
self.file1.flush()
self.file2.flush()
###############################################################################
# Documentation link resolver objects
def _get_data(url):
"""Helper function to get data over http or from a local file"""
if url.startswith('http://'):
# Try Python 2, use Python 3 on exception
try:
resp = urllib.urlopen(url)
encoding = resp.headers.dict.get('content-encoding', 'plain')
except AttributeError:
resp = urllib.request.urlopen(url)
encoding = resp.headers.get('content-encoding', 'plain')
data = resp.read()
if encoding == 'plain':
pass
elif encoding == 'gzip':
data = StringIO(data)
data = gzip.GzipFile(fileobj=data).read()
else:
raise RuntimeError('unknown encoding')
else:
with open(url, 'r') as fid:
data = fid.read()
fid.close()
return data
mem = joblib.Memory(cachedir='_build')
get_data = mem.cache(_get_data)
def parse_sphinx_searchindex(searchindex):
"""Parse a Sphinx search index
Parameters
----------
searchindex : str
The Sphinx search index (contents of searchindex.js)
Returns
-------
filenames : list of str
The file names parsed from the search index.
objects : dict
The objects parsed from the search index.
"""
def _select_block(str_in, start_tag, end_tag):
"""Select first block delimited by start_tag and end_tag"""
start_pos = str_in.find(start_tag)
if start_pos < 0:
raise ValueError('start_tag not found')
depth = 0
for pos in range(start_pos, len(str_in)):
if str_in[pos] == start_tag:
depth += 1
elif str_in[pos] == end_tag:
depth -= 1
if depth == 0:
break
sel = str_in[start_pos + 1:pos]
return sel
def _parse_dict_recursive(dict_str):
"""Parse a dictionary from the search index"""
dict_out = dict()
pos_last = 0
pos = dict_str.find(':')
while pos >= 0:
key = dict_str[pos_last:pos]
if dict_str[pos + 1] == '[':
# value is a list
pos_tmp = dict_str.find(']', pos + 1)
if pos_tmp < 0:
raise RuntimeError('error when parsing dict')
value = dict_str[pos + 2: pos_tmp].split(',')
# try to convert elements to int
for i in range(len(value)):
try:
value[i] = int(value[i])
except ValueError:
pass
elif dict_str[pos + 1] == '{':
# value is another dictionary
subdict_str = _select_block(dict_str[pos:], '{', '}')
value = _parse_dict_recursive(subdict_str)
pos_tmp = pos + len(subdict_str)
else:
raise ValueError('error when parsing dict: unknown elem')
key = key.strip('"')
if len(key) > 0:
dict_out[key] = value
pos_last = dict_str.find(',', pos_tmp)
if pos_last < 0:
break
pos_last += 1
pos = dict_str.find(':', pos_last)
return dict_out
# Make sure searchindex uses UTF-8 encoding
if hasattr(searchindex, 'decode'):
searchindex = searchindex.decode('UTF-8')
# parse objects
query = 'objects:'
pos = searchindex.find(query)
if pos < 0:
raise ValueError('"objects:" not found in search index')
sel = _select_block(searchindex[pos:], '{', '}')
objects = _parse_dict_recursive(sel)
# parse filenames
query = 'filenames:'
pos = searchindex.find(query)
if pos < 0:
raise ValueError('"filenames:" not found in search index')
filenames = searchindex[pos + len(query) + 1:]
filenames = filenames[:filenames.find(']')]
filenames = [f.strip('"') for f in filenames.split(',')]
return filenames, objects
class SphinxDocLinkResolver(object):
""" Resolve documentation links using searchindex.js generated by Sphinx
Parameters
----------
doc_url : str
The base URL of the project website.
searchindex : str
Filename of searchindex, relative to doc_url.
extra_modules_test : list of str
List of extra module names to test.
relative : bool
Return relative links (only useful for links to documentation of this
package).
"""
def __init__(self, doc_url, searchindex='searchindex.js',
extra_modules_test=None, relative=False):
self.doc_url = doc_url
self.relative = relative
self._link_cache = {}
self.extra_modules_test = extra_modules_test
self._page_cache = {}
if doc_url.startswith('http://'):
if relative:
raise ValueError('Relative links are only supported for local '
'URLs (doc_url cannot start with "http://)"')
searchindex_url = doc_url + '/' + searchindex
else:
searchindex_url = os.path.join(doc_url, searchindex)
# detect if we are using relative links on a Windows system
if os.name.lower() == 'nt' and not doc_url.startswith('http://'):
if not relative:
raise ValueError('You have to use relative=True for the local'
' package on a Windows system.')
self._is_windows = True
else:
self._is_windows = False
# download and initialize the search index
sindex = get_data(searchindex_url)
filenames, objects = parse_sphinx_searchindex(sindex)
self._searchindex = dict(filenames=filenames, objects=objects)
def _get_link(self, cobj):
"""Get a valid link, False if not found"""
fname_idx = None
full_name = cobj['module_short'] + '.' + cobj['name']
if full_name in self._searchindex['objects']:
value = self._searchindex['objects'][full_name]
if isinstance(value, dict):
value = value[next(iter(value.keys()))]
fname_idx = value[0]
elif cobj['module_short'] in self._searchindex['objects']:
value = self._searchindex['objects'][cobj['module_short']]
if cobj['name'] in value.keys():
fname_idx = value[cobj['name']][0]
if fname_idx is not None:
fname = self._searchindex['filenames'][fname_idx] + '.html'
if self._is_windows:
fname = fname.replace('/', '\\')
link = os.path.join(self.doc_url, fname)
else:
link = posixpath.join(self.doc_url, fname)
if hasattr(link, 'decode'):
link = link.decode('utf-8', 'replace')
if link in self._page_cache:
html = self._page_cache[link]
else:
html = get_data(link)
self._page_cache[link] = html
# test if cobj appears in page
comb_names = [cobj['module_short'] + '.' + cobj['name']]
if self.extra_modules_test is not None:
for mod in self.extra_modules_test:
comb_names.append(mod + '.' + cobj['name'])
url = False
if hasattr(html, 'decode'):
# Decode bytes under Python 3
html = html.decode('utf-8', 'replace')
for comb_name in comb_names:
if hasattr(comb_name, 'decode'):
# Decode bytes under Python 3
comb_name = comb_name.decode('utf-8', 'replace')
if comb_name in html:
url = link + u'#' + comb_name
link = url
else:
link = False
return link
def resolve(self, cobj, this_url):
"""Resolve the link to the documentation, returns None if not found
Parameters
----------
cobj : dict
Dict with information about the "code object" for which we are
resolving a link.
cobi['name'] : function or class name (str)
cobj['module_short'] : shortened module name (str)
cobj['module'] : module name (str)
this_url: str
URL of the current page. Needed to construct relative URLs
(only used if relative=True in constructor).
Returns
-------
link : str | None
The link (URL) to the documentation.
"""
full_name = cobj['module_short'] + '.' + cobj['name']
link = self._link_cache.get(full_name, None)
if link is None:
# we don't have it cached
link = self._get_link(cobj)
# cache it for the future
self._link_cache[full_name] = link
if link is False or link is None:
# failed to resolve
return None
if self.relative:
link = os.path.relpath(link, start=this_url)
if self._is_windows:
# replace '\' with '/' so it on the web
link = link.replace('\\', '/')
# for some reason, the relative link goes one directory too high up
link = link[3:]
return link
###############################################################################
rst_template = """
.. _example_%(short_fname)s:
%(docstring)s
**Python source code:** :download:`%(fname)s <%(fname)s>`
.. literalinclude:: %(fname)s
:lines: %(end_row)s-
"""
plot_rst_template = """
.. _example_%(short_fname)s:
%(docstring)s
%(image_list)s
%(stdout)s
**Python source code:** :download:`%(fname)s <%(fname)s>`
.. literalinclude:: %(fname)s
:lines: %(end_row)s-
**Total running time of the example:** %(time_elapsed) .2f seconds
(%(time_m) .0f minutes %(time_s) .2f seconds)
"""
# The following strings are used when we have several pictures: we use
# an html div tag that our CSS uses to turn the lists into horizontal
# lists.
HLIST_HEADER = """
.. rst-class:: horizontal
"""
HLIST_IMAGE_TEMPLATE = """
*
.. image:: images/%s
:scale: 47
"""
SINGLE_IMAGE = """
.. image:: images/%s
:align: center
"""
# The following dictionary contains the information used to create the
# thumbnails for the front page of the scikit-learn home page.
# key: first image in set
# values: (number of plot in set, height of thumbnail)
carousel_thumbs = {'plot_classifier_comparison_001.png': (1, 600),
'plot_outlier_detection_001.png': (3, 372),
'plot_gp_regression_001.png': (2, 250),
'plot_adaboost_twoclass_001.png': (1, 372),
'plot_compare_methods_001.png': (1, 349)}
def extract_docstring(filename, ignore_heading=False):
""" Extract a module-level docstring, if any
"""
if six.PY2:
lines = open(filename).readlines()
else:
lines = open(filename, encoding='utf-8').readlines()
start_row = 0
if lines[0].startswith('#!'):
lines.pop(0)
start_row = 1
docstring = ''
first_par = ''
line_iterator = iter(lines)
tokens = tokenize.generate_tokens(lambda: next(line_iterator))
for tok_type, tok_content, _, (erow, _), _ in tokens:
tok_type = token.tok_name[tok_type]
if tok_type in ('NEWLINE', 'COMMENT', 'NL', 'INDENT', 'DEDENT'):
continue
elif tok_type == 'STRING':
docstring = eval(tok_content)
# If the docstring is formatted with several paragraphs, extract
# the first one:
paragraphs = '\n'.join(
line.rstrip() for line
in docstring.split('\n')).split('\n\n')
if paragraphs:
if ignore_heading:
if len(paragraphs) > 1:
first_par = re.sub('\n', ' ', paragraphs[1])
first_par = ((first_par[:95] + '...')
if len(first_par) > 95 else first_par)
else:
raise ValueError("Docstring not found by gallery.\n"
"Please check the layout of your"
" example file:\n {}\n and make sure"
" it's correct".format(filename))
else:
first_par = paragraphs[0]
break
return docstring, first_par, erow + 1 + start_row
def generate_example_rst(app):
""" Generate the list of examples, as well as the contents of
examples.
"""
root_dir = os.path.join(app.builder.srcdir, 'auto_examples')
example_dir = os.path.abspath(os.path.join(app.builder.srcdir, '..',
'examples'))
generated_dir = os.path.abspath(os.path.join(app.builder.srcdir,
'modules', 'generated'))
try:
plot_gallery = eval(app.builder.config.plot_gallery)
except TypeError:
plot_gallery = bool(app.builder.config.plot_gallery)
if not os.path.exists(example_dir):
os.makedirs(example_dir)
if not os.path.exists(root_dir):
os.makedirs(root_dir)
if not os.path.exists(generated_dir):
os.makedirs(generated_dir)
# we create an index.rst with all examples
fhindex = open(os.path.join(root_dir, 'index.rst'), 'w')
# Note: The sidebar button has been removed from the examples page for now
# due to how it messes up the layout. Will be fixed at a later point
fhindex.write("""\
.. raw:: html
<style type="text/css">
div#sidebarbutton {
/* hide the sidebar collapser, while ensuring vertical arrangement */
display: none;
}
</style>
.. _examples-index:
Examples
========
""")
# Here we don't use an os.walk, but we recurse only twice: flat is
# better than nested.
seen_backrefs = set()
generate_dir_rst('.', fhindex, example_dir, root_dir, plot_gallery, seen_backrefs)
for directory in sorted(os.listdir(example_dir)):
if os.path.isdir(os.path.join(example_dir, directory)):
generate_dir_rst(directory, fhindex, example_dir, root_dir, plot_gallery, seen_backrefs)
fhindex.flush()
def extract_line_count(filename, target_dir):
# Extract the line count of a file
example_file = os.path.join(target_dir, filename)
if six.PY2:
lines = open(example_file).readlines()
else:
lines = open(example_file, encoding='utf-8').readlines()
start_row = 0
if lines and lines[0].startswith('#!'):
lines.pop(0)
start_row = 1
line_iterator = iter(lines)
tokens = tokenize.generate_tokens(lambda: next(line_iterator))
check_docstring = True
erow_docstring = 0
for tok_type, _, _, (erow, _), _ in tokens:
tok_type = token.tok_name[tok_type]
if tok_type in ('NEWLINE', 'COMMENT', 'NL', 'INDENT', 'DEDENT'):
continue
elif (tok_type == 'STRING') and check_docstring:
erow_docstring = erow
check_docstring = False
return erow_docstring+1+start_row, erow+1+start_row
def line_count_sort(file_list, target_dir):
# Sort the list of examples by line-count
new_list = [x for x in file_list if x.endswith('.py')]
unsorted = np.zeros(shape=(len(new_list), 2))
unsorted = unsorted.astype(np.object)
for count, exmpl in enumerate(new_list):
docstr_lines, total_lines = extract_line_count(exmpl, target_dir)
unsorted[count][1] = total_lines - docstr_lines
unsorted[count][0] = exmpl
index = np.lexsort((unsorted[:, 0].astype(np.str),
unsorted[:, 1].astype(np.float)))
if not len(unsorted):
return []
return np.array(unsorted[index][:, 0]).tolist()
def _thumbnail_div(subdir, full_dir, fname, snippet):
"""Generates RST to place a thumbnail in a gallery"""
thumb = os.path.join(full_dir, 'images', 'thumb', fname[:-3] + '.png')
link_name = os.path.join(full_dir, fname).replace(os.path.sep, '_')
ref_name = os.path.join(subdir, fname).replace(os.path.sep, '_')
if ref_name.startswith('._'):
ref_name = ref_name[2:]
out = []
out.append("""
.. raw:: html
<div class="thumbnailContainer" tooltip="{}">
""".format(snippet))
out.append('.. figure:: %s\n' % thumb)
if link_name.startswith('._'):
link_name = link_name[2:]
if full_dir != '.':
out.append(' :target: ./%s/%s.html\n\n' % (full_dir, fname[:-3]))
else:
out.append(' :target: ./%s.html\n\n' % link_name[:-3])
out.append(""" :ref:`example_%s`
.. raw:: html
</div>
""" % (ref_name))
return ''.join(out)
def generate_dir_rst(directory, fhindex, example_dir, root_dir, plot_gallery, seen_backrefs):
""" Generate the rst file for an example directory.
"""
if not directory == '.':
target_dir = os.path.join(root_dir, directory)
src_dir = os.path.join(example_dir, directory)
else:
target_dir = root_dir
src_dir = example_dir
if not os.path.exists(os.path.join(src_dir, 'README.txt')):
raise ValueError('Example directory %s does not have a README.txt' %
src_dir)
fhindex.write("""
%s
""" % open(os.path.join(src_dir, 'README.txt')).read())
if not os.path.exists(target_dir):
os.makedirs(target_dir)
sorted_listdir = line_count_sort(os.listdir(src_dir),
src_dir)
if not os.path.exists(os.path.join(directory, 'images', 'thumb')):
os.makedirs(os.path.join(directory, 'images', 'thumb'))
for fname in sorted_listdir:
if fname.endswith('py'):
backrefs = generate_file_rst(fname, target_dir, src_dir, root_dir, plot_gallery)
new_fname = os.path.join(src_dir, fname)
_, snippet, _ = extract_docstring(new_fname, True)
fhindex.write(_thumbnail_div(directory, directory, fname, snippet))
fhindex.write("""
.. toctree::
:hidden:
%s/%s
""" % (directory, fname[:-3]))
for backref in backrefs:
include_path = os.path.join(root_dir, '../modules/generated/%s.examples' % backref)
seen = backref in seen_backrefs
with open(include_path, 'a' if seen else 'w') as ex_file:
if not seen:
# heading
print(file=ex_file)
print('Examples using ``%s``' % backref, file=ex_file)
print('-----------------%s--' % ('-' * len(backref)),
file=ex_file)
print(file=ex_file)
rel_dir = os.path.join('../../auto_examples', directory)
ex_file.write(_thumbnail_div(directory, rel_dir, fname, snippet))
seen_backrefs.add(backref)
fhindex.write("""
.. raw:: html
<div class="clearer"></div>
""") # clear at the end of the section
# modules for which we embed links into example code
DOCMODULES = ['sklearn', 'matplotlib', 'numpy', 'scipy']
def make_thumbnail(in_fname, out_fname, width, height):
"""Make a thumbnail with the same aspect ratio centered in an
image with a given width and height
"""
# local import to avoid testing dependency on PIL:
try:
from PIL import Image
except ImportError:
import Image
img = Image.open(in_fname)
width_in, height_in = img.size
scale_w = width / float(width_in)
scale_h = height / float(height_in)
if height_in * scale_w <= height:
scale = scale_w
else:
scale = scale_h
width_sc = int(round(scale * width_in))
height_sc = int(round(scale * height_in))
# resize the image
img.thumbnail((width_sc, height_sc), Image.ANTIALIAS)
# insert centered
thumb = Image.new('RGB', (width, height), (255, 255, 255))
pos_insert = ((width - width_sc) // 2, (height - height_sc) // 2)
thumb.paste(img, pos_insert)
thumb.save(out_fname)
# Use optipng to perform lossless compression on the resized image if
# software is installed
if os.environ.get('SKLEARN_DOC_OPTIPNG', False):
try:
subprocess.call(["optipng", "-quiet", "-o", "9", out_fname])
except Exception:
warnings.warn('Install optipng to reduce the size of the generated images')
def get_short_module_name(module_name, obj_name):
""" Get the shortest possible module name """
parts = module_name.split('.')
short_name = module_name
for i in range(len(parts) - 1, 0, -1):
short_name = '.'.join(parts[:i])
try:
exec('from %s import %s' % (short_name, obj_name))
except ImportError:
# get the last working module name
short_name = '.'.join(parts[:(i + 1)])
break
return short_name
class NameFinder(ast.NodeVisitor):
"""Finds the longest form of variable names and their imports in code
Only retains names from imported modules.
"""
def __init__(self):
super(NameFinder, self).__init__()
self.imported_names = {}
self.accessed_names = set()
def visit_Import(self, node, prefix=''):
for alias in node.names:
local_name = alias.asname or alias.name
self.imported_names[local_name] = prefix + alias.name
def visit_ImportFrom(self, node):
self.visit_Import(node, node.module + '.')
def visit_Name(self, node):
self.accessed_names.add(node.id)
def visit_Attribute(self, node):
attrs = []
while isinstance(node, ast.Attribute):
attrs.append(node.attr)
node = node.value
if isinstance(node, ast.Name):
# This is a.b, not e.g. a().b
attrs.append(node.id)
self.accessed_names.add('.'.join(reversed(attrs)))
else:
# need to get a in a().b
self.visit(node)
def get_mapping(self):
for name in self.accessed_names:
local_name = name.split('.', 1)[0]
remainder = name[len(local_name):]
if local_name in self.imported_names:
# Join import path to relative path
full_name = self.imported_names[local_name] + remainder
yield name, full_name
def identify_names(code):
"""Builds a codeobj summary by identifying and resovles used names
>>> code = '''
... from a.b import c
... import d as e
... print(c)
... e.HelloWorld().f.g
... '''
>>> for name, o in sorted(identify_names(code).items()):
... print(name, o['name'], o['module'], o['module_short'])
c c a.b a.b
e.HelloWorld HelloWorld d d
"""
finder = NameFinder()
finder.visit(ast.parse(code))
example_code_obj = {}
for name, full_name in finder.get_mapping():
# name is as written in file (e.g. np.asarray)
# full_name includes resolved import path (e.g. numpy.asarray)
module, attribute = full_name.rsplit('.', 1)
# get shortened module name
module_short = get_short_module_name(module, attribute)
cobj = {'name': attribute, 'module': module,
'module_short': module_short}
example_code_obj[name] = cobj
return example_code_obj
def generate_file_rst(fname, target_dir, src_dir, root_dir, plot_gallery):
""" Generate the rst file for a given example.
Returns the set of sklearn functions/classes imported in the example.
"""
base_image_name = os.path.splitext(fname)[0]
image_fname = '%s_%%03d.png' % base_image_name
this_template = rst_template
last_dir = os.path.split(src_dir)[-1]
# to avoid leading . in file names, and wrong names in links
if last_dir == '.' or last_dir == 'examples':
last_dir = ''
else:
last_dir += '_'
short_fname = last_dir + fname
src_file = os.path.join(src_dir, fname)
example_file = os.path.join(target_dir, fname)
shutil.copyfile(src_file, example_file)
# The following is a list containing all the figure names
figure_list = []
image_dir = os.path.join(target_dir, 'images')
thumb_dir = os.path.join(image_dir, 'thumb')
if not os.path.exists(image_dir):
os.makedirs(image_dir)
if not os.path.exists(thumb_dir):
os.makedirs(thumb_dir)
image_path = os.path.join(image_dir, image_fname)
stdout_path = os.path.join(image_dir,
'stdout_%s.txt' % base_image_name)
time_path = os.path.join(image_dir,
'time_%s.txt' % base_image_name)
thumb_file = os.path.join(thumb_dir, base_image_name + '.png')
time_elapsed = 0
if plot_gallery and fname.startswith('plot'):
# generate the plot as png image if file name
# starts with plot and if it is more recent than an
# existing image.
first_image_file = image_path % 1
if os.path.exists(stdout_path):
stdout = open(stdout_path).read()
else:
stdout = ''
if os.path.exists(time_path):
time_elapsed = float(open(time_path).read())
if not os.path.exists(first_image_file) or \
os.stat(first_image_file).st_mtime <= os.stat(src_file).st_mtime:
# We need to execute the code
print('plotting %s' % fname)
t0 = time()
import matplotlib.pyplot as plt
plt.close('all')
cwd = os.getcwd()
try:
# First CD in the original example dir, so that any file
# created by the example get created in this directory
orig_stdout = sys.stdout
os.chdir(os.path.dirname(src_file))
my_buffer = StringIO()
my_stdout = Tee(sys.stdout, my_buffer)
sys.stdout = my_stdout
my_globals = {'pl': plt}
execfile(os.path.basename(src_file), my_globals)
time_elapsed = time() - t0
sys.stdout = orig_stdout
my_stdout = my_buffer.getvalue()
if '__doc__' in my_globals:
# The __doc__ is often printed in the example, we
# don't with to echo it
my_stdout = my_stdout.replace(
my_globals['__doc__'],
'')
my_stdout = my_stdout.strip().expandtabs()
if my_stdout:
stdout = '**Script output**::\n\n %s\n\n' % (
'\n '.join(my_stdout.split('\n')))
open(stdout_path, 'w').write(stdout)
open(time_path, 'w').write('%f' % time_elapsed)
os.chdir(cwd)
# In order to save every figure we have two solutions :
# * iterate from 1 to infinity and call plt.fignum_exists(n)
# (this requires the figures to be numbered
# incrementally: 1, 2, 3 and not 1, 2, 5)
# * iterate over [fig_mngr.num for fig_mngr in
# matplotlib._pylab_helpers.Gcf.get_all_fig_managers()]
fig_managers = matplotlib._pylab_helpers.Gcf.get_all_fig_managers()
for fig_mngr in fig_managers:
# Set the fig_num figure as the current figure as we can't
# save a figure that's not the current figure.
fig = plt.figure(fig_mngr.num)
kwargs = {}
to_rgba = matplotlib.colors.colorConverter.to_rgba
for attr in ['facecolor', 'edgecolor']:
fig_attr = getattr(fig, 'get_' + attr)()
default_attr = matplotlib.rcParams['figure.' + attr]
if to_rgba(fig_attr) != to_rgba(default_attr):
kwargs[attr] = fig_attr
fig.savefig(image_path % fig_mngr.num, **kwargs)
figure_list.append(image_fname % fig_mngr.num)
except:
print(80 * '_')
print('%s is not compiling:' % fname)
traceback.print_exc()
print(80 * '_')
finally:
os.chdir(cwd)
sys.stdout = orig_stdout
print(" - time elapsed : %.2g sec" % time_elapsed)
else:
figure_list = [f[len(image_dir):]
for f in glob.glob(image_path.replace("%03d",
'[0-9][0-9][0-9]'))]
figure_list.sort()
# generate thumb file
this_template = plot_rst_template
car_thumb_path = os.path.join(os.path.split(root_dir)[0], '_build/html/stable/_images/')
# Note: normaly, make_thumbnail is used to write to the path contained in `thumb_file`
# which is within `auto_examples/../images/thumbs` depending on the example.
# Because the carousel has different dimensions than those of the examples gallery,
# I did not simply reuse them all as some contained whitespace due to their default gallery
# thumbnail size. Below, for a few cases, seperate thumbnails are created (the originals can't
# just be overwritten with the carousel dimensions as it messes up the examples gallery layout).
# The special carousel thumbnails are written directly to _build/html/stable/_images/,
# as for some reason unknown to me, Sphinx refuses to copy my 'extra' thumbnails from the
# auto examples gallery to the _build folder. This works fine as is, but it would be cleaner to
# have it happen with the rest. Ideally the should be written to 'thumb_file' as well, and then
# copied to the _images folder during the `Copying Downloadable Files` step like the rest.
if not os.path.exists(car_thumb_path):
os.makedirs(car_thumb_path)
if os.path.exists(first_image_file):
# We generate extra special thumbnails for the carousel
carousel_tfile = os.path.join(car_thumb_path, base_image_name + '_carousel.png')
first_img = image_fname % 1
if first_img in carousel_thumbs:
make_thumbnail((image_path % carousel_thumbs[first_img][0]),
carousel_tfile, carousel_thumbs[first_img][1], 190)
make_thumbnail(first_image_file, thumb_file, 400, 280)
if not os.path.exists(thumb_file):
# create something to replace the thumbnail
make_thumbnail('images/no_image.png', thumb_file, 200, 140)
docstring, short_desc, end_row = extract_docstring(example_file)
# Depending on whether we have one or more figures, we're using a
# horizontal list or a single rst call to 'image'.
if len(figure_list) == 1:
figure_name = figure_list[0]
image_list = SINGLE_IMAGE % figure_name.lstrip('/')
else:
image_list = HLIST_HEADER
for figure_name in figure_list:
image_list += HLIST_IMAGE_TEMPLATE % figure_name.lstrip('/')
time_m, time_s = divmod(time_elapsed, 60)
f = open(os.path.join(target_dir, base_image_name + '.rst'), 'w')
f.write(this_template % locals())
f.flush()
# save variables so we can later add links to the documentation
if six.PY2:
example_code_obj = identify_names(open(example_file).read())
else:
example_code_obj = \
identify_names(open(example_file, encoding='utf-8').read())
if example_code_obj:
codeobj_fname = example_file[:-3] + '_codeobj.pickle'
with open(codeobj_fname, 'wb') as fid:
pickle.dump(example_code_obj, fid, pickle.HIGHEST_PROTOCOL)
backrefs = set('{module_short}.{name}'.format(**entry)
for entry in example_code_obj.values()
if entry['module'].startswith('sklearn'))
return backrefs
def embed_code_links(app, exception):
"""Embed hyperlinks to documentation into example code"""
if exception is not None:
return
print('Embedding documentation hyperlinks in examples..')
if app.builder.name == 'latex':
# Don't embed hyperlinks when a latex builder is used.
return
# Add resolvers for the packages for which we want to show links
doc_resolvers = {}
doc_resolvers['sklearn'] = SphinxDocLinkResolver(app.builder.outdir,
relative=True)
resolver_urls = {
'matplotlib': 'http://matplotlib.org',
'numpy': 'http://docs.scipy.org/doc/numpy-1.6.0',
'scipy': 'http://docs.scipy.org/doc/scipy-0.11.0/reference',
}
for this_module, url in resolver_urls.items():
try:
doc_resolvers[this_module] = SphinxDocLinkResolver(url)
except HTTPError as e:
print("The following HTTP Error has occurred:\n")
print(e.code)
except URLError as e:
print("\n...\n"
"Warning: Embedding the documentation hyperlinks requires "
"internet access.\nPlease check your network connection.\n"
"Unable to continue embedding `{0}` links due to a URL "
"Error:\n".format(this_module))
print(e.args)
example_dir = os.path.join(app.builder.srcdir, 'auto_examples')
html_example_dir = os.path.abspath(os.path.join(app.builder.outdir,
'auto_examples'))
# patterns for replacement
link_pattern = '<a href="%s">%s</a>'
orig_pattern = '<span class="n">%s</span>'
period = '<span class="o">.</span>'
for dirpath, _, filenames in os.walk(html_example_dir):
for fname in filenames:
print('\tprocessing: %s' % fname)
full_fname = os.path.join(html_example_dir, dirpath, fname)
subpath = dirpath[len(html_example_dir) + 1:]
pickle_fname = os.path.join(example_dir, subpath,
fname[:-5] + '_codeobj.pickle')
if os.path.exists(pickle_fname):
# we have a pickle file with the objects to embed links for
with open(pickle_fname, 'rb') as fid:
example_code_obj = pickle.load(fid)
fid.close()
str_repl = {}
# generate replacement strings with the links
for name, cobj in example_code_obj.items():
this_module = cobj['module'].split('.')[0]
if this_module not in doc_resolvers:
continue
try:
link = doc_resolvers[this_module].resolve(cobj,
full_fname)
except (HTTPError, URLError) as e:
print("The following error has occurred:\n")
print(repr(e))
continue
if link is not None:
parts = name.split('.')
name_html = period.join(orig_pattern % part
for part in parts)
str_repl[name_html] = link_pattern % (link, name_html)
# do the replacement in the html file
# ensure greediness
names = sorted(str_repl, key=len, reverse=True)
expr = re.compile(r'(?<!\.)\b' + # don't follow . or word
'|'.join(re.escape(name)
for name in names))
def substitute_link(match):
return str_repl[match.group()]
if len(str_repl) > 0:
with open(full_fname, 'rb') as fid:
lines_in = fid.readlines()
with open(full_fname, 'wb') as fid:
for line in lines_in:
line = line.decode('utf-8')
line = expr.sub(substitute_link, line)
fid.write(line.encode('utf-8'))
print('[done]')
def setup(app):
app.connect('builder-inited', generate_example_rst)
app.add_config_value('plot_gallery', True, 'html')
# embed links after build is finished
app.connect('build-finished', embed_code_links)
# Sphinx hack: sphinx copies generated images to the build directory
# each time the docs are made. If the desired image name already
# exists, it appends a digit to prevent overwrites. The problem is,
# the directory is never cleared. This means that each time you build
# the docs, the number of images in the directory grows.
#
# This question has been asked on the sphinx development list, but there
# was no response: http://osdir.com/ml/sphinx-dev/2011-02/msg00123.html
#
# The following is a hack that prevents this behavior by clearing the
# image build directory each time the docs are built. If sphinx
# changes their layout between versions, this will not work (though
# it should probably not cause a crash). Tested successfully
# on Sphinx 1.0.7
build_image_dir = '_build/html/_images'
if os.path.exists(build_image_dir):
filelist = os.listdir(build_image_dir)
for filename in filelist:
if filename.endswith('png'):
os.remove(os.path.join(build_image_dir, filename))
def setup_module():
# HACK: Stop nosetests running setup() above
pass
| bsd-3-clause |
vigilv/scikit-learn | sklearn/neighbors/base.py | 71 | 31147 | """Base and mixin classes for nearest neighbors"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output support by Arnaud Joly <a.joly@ulg.ac.be>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
import warnings
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import csr_matrix, issparse
from .ball_tree import BallTree
from .kd_tree import KDTree
from ..base import BaseEstimator
from ..metrics import pairwise_distances
from ..metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS
from ..utils import check_X_y, check_array, _get_n_jobs, gen_even_slices
from ..utils.fixes import argpartition
from ..utils.validation import DataConversionWarning
from ..utils.validation import NotFittedError
from ..externals import six
from ..externals.joblib import Parallel, delayed
VALID_METRICS = dict(ball_tree=BallTree.valid_metrics,
kd_tree=KDTree.valid_metrics,
# The following list comes from the
# sklearn.metrics.pairwise doc string
brute=(list(PAIRWISE_DISTANCE_FUNCTIONS.keys()) +
['braycurtis', 'canberra', 'chebyshev',
'correlation', 'cosine', 'dice', 'hamming',
'jaccard', 'kulsinski', 'mahalanobis',
'matching', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener',
'sokalsneath', 'sqeuclidean',
'yule', 'wminkowski']))
VALID_METRICS_SPARSE = dict(ball_tree=[],
kd_tree=[],
brute=PAIRWISE_DISTANCE_FUNCTIONS.keys())
class NeighborsWarning(UserWarning):
pass
# Make sure that NeighborsWarning are displayed more than once
warnings.simplefilter("always", NeighborsWarning)
def _check_weights(weights):
"""Check to make sure weights are valid"""
if weights in (None, 'uniform', 'distance'):
return weights
elif callable(weights):
return weights
else:
raise ValueError("weights not recognized: should be 'uniform', "
"'distance', or a callable function")
def _get_weights(dist, weights):
"""Get the weights from an array of distances and a parameter ``weights``
Parameters
===========
dist: ndarray
The input distances
weights: {'uniform', 'distance' or a callable}
The kind of weighting used
Returns
========
weights_arr: array of the same shape as ``dist``
if ``weights == 'uniform'``, then returns None
"""
if weights in (None, 'uniform'):
return None
elif weights == 'distance':
# if user attempts to classify a point that was zero distance from one
# or more training points, those training points are weighted as 1.0
# and the other points as 0.0
if dist.dtype is np.dtype(object):
for point_dist_i, point_dist in enumerate(dist):
# check if point_dist is iterable
# (ex: RadiusNeighborClassifier.predict may set an element of
# dist to 1e-6 to represent an 'outlier')
if hasattr(point_dist, '__contains__') and 0. in point_dist:
dist[point_dist_i] = point_dist == 0.
else:
dist[point_dist_i] = 1. / point_dist
else:
with np.errstate(divide='ignore'):
dist = 1. / dist
inf_mask = np.isinf(dist)
inf_row = np.any(inf_mask, axis=1)
dist[inf_row] = inf_mask[inf_row]
return dist
elif callable(weights):
return weights(dist)
else:
raise ValueError("weights not recognized: should be 'uniform', "
"'distance', or a callable function")
class NeighborsBase(six.with_metaclass(ABCMeta, BaseEstimator)):
"""Base class for nearest neighbors estimators."""
@abstractmethod
def __init__(self):
pass
def _init_params(self, n_neighbors=None, radius=None,
algorithm='auto', leaf_size=30, metric='minkowski',
p=2, metric_params=None, n_jobs=1, **kwargs):
if kwargs:
warnings.warn("Passing additional arguments to the metric "
"function as **kwargs is deprecated "
"and will no longer be supported in 0.18. "
"Use metric_params instead.",
DeprecationWarning, stacklevel=3)
if metric_params is None:
metric_params = {}
metric_params.update(kwargs)
self.n_neighbors = n_neighbors
self.radius = radius
self.algorithm = algorithm
self.leaf_size = leaf_size
self.metric = metric
self.metric_params = metric_params
self.p = p
self.n_jobs = n_jobs
if algorithm not in ['auto', 'brute',
'kd_tree', 'ball_tree']:
raise ValueError("unrecognized algorithm: '%s'" % algorithm)
if algorithm == 'auto':
if metric == 'precomputed':
alg_check = 'brute'
else:
alg_check = 'ball_tree'
else:
alg_check = algorithm
if callable(metric):
if algorithm == 'kd_tree':
# callable metric is only valid for brute force and ball_tree
raise ValueError(
"kd_tree algorithm does not support callable metric '%s'"
% metric)
elif metric not in VALID_METRICS[alg_check]:
raise ValueError("Metric '%s' not valid for algorithm '%s'"
% (metric, algorithm))
if self.metric_params is not None and 'p' in self.metric_params:
warnings.warn("Parameter p is found in metric_params. "
"The corresponding parameter from __init__ "
"is ignored.", SyntaxWarning, stacklevel=3)
effective_p = metric_params['p']
else:
effective_p = self.p
if self.metric in ['wminkowski', 'minkowski'] and effective_p < 1:
raise ValueError("p must be greater than one for minkowski metric")
self._fit_X = None
self._tree = None
self._fit_method = None
def _fit(self, X):
if self.metric_params is None:
self.effective_metric_params_ = {}
else:
self.effective_metric_params_ = self.metric_params.copy()
effective_p = self.effective_metric_params_.get('p', self.p)
if self.metric in ['wminkowski', 'minkowski']:
self.effective_metric_params_['p'] = effective_p
self.effective_metric_ = self.metric
# For minkowski distance, use more efficient methods where available
if self.metric == 'minkowski':
p = self.effective_metric_params_.pop('p', 2)
if p < 1:
raise ValueError("p must be greater than one "
"for minkowski metric")
elif p == 1:
self.effective_metric_ = 'manhattan'
elif p == 2:
self.effective_metric_ = 'euclidean'
elif p == np.inf:
self.effective_metric_ = 'chebyshev'
else:
self.effective_metric_params_['p'] = p
if isinstance(X, NeighborsBase):
self._fit_X = X._fit_X
self._tree = X._tree
self._fit_method = X._fit_method
return self
elif isinstance(X, BallTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'ball_tree'
return self
elif isinstance(X, KDTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'kd_tree'
return self
X = check_array(X, accept_sparse='csr')
n_samples = X.shape[0]
if n_samples == 0:
raise ValueError("n_samples must be greater than 0")
if issparse(X):
if self.algorithm not in ('auto', 'brute'):
warnings.warn("cannot use tree with sparse input: "
"using brute force")
if self.effective_metric_ not in VALID_METRICS_SPARSE['brute']:
raise ValueError("metric '%s' not valid for sparse input"
% self.effective_metric_)
self._fit_X = X.copy()
self._tree = None
self._fit_method = 'brute'
return self
self._fit_method = self.algorithm
self._fit_X = X
if self._fit_method == 'auto':
# A tree approach is better for small number of neighbors,
# and KDTree is generally faster when available
if ((self.n_neighbors is None or
self.n_neighbors < self._fit_X.shape[0] // 2) and
self.metric != 'precomputed'):
if self.effective_metric_ in VALID_METRICS['kd_tree']:
self._fit_method = 'kd_tree'
else:
self._fit_method = 'ball_tree'
else:
self._fit_method = 'brute'
if self._fit_method == 'ball_tree':
self._tree = BallTree(X, self.leaf_size,
metric=self.effective_metric_,
**self.effective_metric_params_)
elif self._fit_method == 'kd_tree':
self._tree = KDTree(X, self.leaf_size,
metric=self.effective_metric_,
**self.effective_metric_params_)
elif self._fit_method == 'brute':
self._tree = None
else:
raise ValueError("algorithm = '%s' not recognized"
% self.algorithm)
if self.n_neighbors is not None:
if self.n_neighbors <= 0:
raise ValueError(
"Expected n_neighbors > 0. Got %d" %
self.n_neighbors
)
return self
@property
def _pairwise(self):
# For cross-validation routines to split data correctly
return self.metric == 'precomputed'
class KNeighborsMixin(object):
"""Mixin for k-neighbors searches"""
def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
"""Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters
----------
X : array-like, shape (n_query, n_features), \
or (n_query, n_indexed) if metric == 'precomputed'
The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.
n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns
-------
dist : array
Array representing the lengths to points, only present if
return_distance=True
ind : array
Indices of the nearest points in the population matrix.
Examples
--------
In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who's
the closest point to [1,1,1]
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=1)
>>> neigh.fit(samples) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> print(neigh.kneighbors([[1., 1., 1.]])) # doctest: +ELLIPSIS
(array([[ 0.5]]), array([[2]]...))
As you can see, it returns [[0.5]], and [[2]], which means that the
element is at distance 0.5 and is the third element of samples
(indexes start at 0). You can also query for multiple points:
>>> X = [[0., 1., 0.], [1., 0., 1.]]
>>> neigh.kneighbors(X, return_distance=False) # doctest: +ELLIPSIS
array([[1],
[2]]...)
"""
if self._fit_method is None:
raise NotFittedError("Must fit neighbors before querying.")
if n_neighbors is None:
n_neighbors = self.n_neighbors
if X is not None:
query_is_train = False
X = check_array(X, accept_sparse='csr')
else:
query_is_train = True
X = self._fit_X
# Include an extra neighbor to account for the sample itself being
# returned, which is removed later
n_neighbors += 1
train_size = self._fit_X.shape[0]
if n_neighbors > train_size:
raise ValueError(
"Expected n_neighbors <= n_samples, "
" but n_samples = %d, n_neighbors = %d" %
(train_size, n_neighbors)
)
n_samples, _ = X.shape
sample_range = np.arange(n_samples)[:, None]
n_jobs = _get_n_jobs(self.n_jobs)
if self._fit_method == 'brute':
# for efficiency, use squared euclidean distances
if self.effective_metric_ == 'euclidean':
dist = pairwise_distances(X, self._fit_X, 'euclidean',
n_jobs=n_jobs, squared=True)
else:
dist = pairwise_distances(
X, self._fit_X, self.effective_metric_, n_jobs=n_jobs,
**self.effective_metric_params_)
neigh_ind = argpartition(dist, n_neighbors - 1, axis=1)
neigh_ind = neigh_ind[:, :n_neighbors]
# argpartition doesn't guarantee sorted order, so we sort again
neigh_ind = neigh_ind[
sample_range, np.argsort(dist[sample_range, neigh_ind])]
if return_distance:
if self.effective_metric_ == 'euclidean':
result = np.sqrt(dist[sample_range, neigh_ind]), neigh_ind
else:
result = dist[sample_range, neigh_ind], neigh_ind
else:
result = neigh_ind
elif self._fit_method in ['ball_tree', 'kd_tree']:
if issparse(X):
raise ValueError(
"%s does not work with sparse matrices. Densify the data, "
"or set algorithm='brute'" % self._fit_method)
result = Parallel(n_jobs, backend='threading')(
delayed(self._tree.query, check_pickle=False)(
X[s], n_neighbors, return_distance)
for s in gen_even_slices(X.shape[0], n_jobs)
)
if return_distance:
dist, neigh_ind = tuple(zip(*result))
result = np.vstack(dist), np.vstack(neigh_ind)
else:
result = np.vstack(result)
else:
raise ValueError("internal: _fit_method not recognized")
if not query_is_train:
return result
else:
# If the query data is the same as the indexed data, we would like
# to ignore the first nearest neighbor of every sample, i.e
# the sample itself.
if return_distance:
dist, neigh_ind = result
else:
neigh_ind = result
sample_mask = neigh_ind != sample_range
# Corner case: When the number of duplicates are more
# than the number of neighbors, the first NN will not
# be the sample, but a duplicate.
# In that case mask the first duplicate.
dup_gr_nbrs = np.all(sample_mask, axis=1)
sample_mask[:, 0][dup_gr_nbrs] = False
neigh_ind = np.reshape(
neigh_ind[sample_mask], (n_samples, n_neighbors - 1))
if return_distance:
dist = np.reshape(
dist[sample_mask], (n_samples, n_neighbors - 1))
return dist, neigh_ind
return neigh_ind
def kneighbors_graph(self, X=None, n_neighbors=None,
mode='connectivity'):
"""Computes the (weighted) graph of k-Neighbors for points in X
Parameters
----------
X : array-like, shape (n_query, n_features), \
or (n_query, n_indexed) if metric == 'precomputed'
The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.
n_neighbors : int
Number of neighbors for each sample.
(default is value passed to the constructor).
mode : {'connectivity', 'distance'}, optional
Type of returned matrix: 'connectivity' will return the
connectivity matrix with ones and zeros, in 'distance' the
edges are Euclidean distance between points.
Returns
-------
A : sparse matrix in CSR format, shape = [n_samples, n_samples_fit]
n_samples_fit is the number of samples in the fitted data
A[i, j] is assigned the weight of edge that connects i to j.
Examples
--------
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=2)
>>> neigh.fit(X) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> A = neigh.kneighbors_graph(X)
>>> A.toarray()
array([[ 1., 0., 1.],
[ 0., 1., 1.],
[ 1., 0., 1.]])
See also
--------
NearestNeighbors.radius_neighbors_graph
"""
if n_neighbors is None:
n_neighbors = self.n_neighbors
# kneighbors does the None handling.
if X is not None:
X = check_array(X, accept_sparse='csr')
n_samples1 = X.shape[0]
else:
n_samples1 = self._fit_X.shape[0]
n_samples2 = self._fit_X.shape[0]
n_nonzero = n_samples1 * n_neighbors
A_indptr = np.arange(0, n_nonzero + 1, n_neighbors)
# construct CSR matrix representation of the k-NN graph
if mode == 'connectivity':
A_data = np.ones(n_samples1 * n_neighbors)
A_ind = self.kneighbors(X, n_neighbors, return_distance=False)
elif mode == 'distance':
A_data, A_ind = self.kneighbors(
X, n_neighbors, return_distance=True)
A_data = np.ravel(A_data)
else:
raise ValueError(
'Unsupported mode, must be one of "connectivity" '
'or "distance" but got "%s" instead' % mode)
kneighbors_graph = csr_matrix((A_data, A_ind.ravel(), A_indptr),
shape=(n_samples1, n_samples2))
return kneighbors_graph
class RadiusNeighborsMixin(object):
"""Mixin for radius-based neighbors searches"""
def radius_neighbors(self, X=None, radius=None, return_distance=True):
"""Finds the neighbors within a given radius of a point or points.
Return the indices and distances of each point from the dataset
lying in a ball with size ``radius`` around the points of the query
array. Points lying on the boundary are included in the results.
The result points are *not* necessarily sorted by distance to their
query point.
Parameters
----------
X : array-like, (n_samples, n_features), optional
The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.
radius : float
Limiting distance of neighbors to return.
(default is the value passed to the constructor).
return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns
-------
dist : array, shape (n_samples,) of arrays
Array representing the distances to each point, only present if
return_distance=True. The distance values are computed according
to the ``metric`` constructor parameter.
ind : array, shape (n_samples,) of arrays
An array of arrays of indices of the approximate nearest points
from the population matrix that lie within a ball of size
``radius`` around the query points.
Examples
--------
In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who's
the closest point to [1, 1, 1]:
>>> import numpy as np
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.6)
>>> neigh.fit(samples) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> rng = neigh.radius_neighbors([[1., 1., 1.]])
>>> print(np.asarray(rng[0][0])) # doctest: +ELLIPSIS
[ 1.5 0.5]
>>> print(np.asarray(rng[1][0])) # doctest: +ELLIPSIS
[1 2]
The first array returned contains the distances to all points which
are closer than 1.6, while the second array returned contains their
indices. In general, multiple points can be queried at the same time.
Notes
-----
Because the number of neighbors of each point is not necessarily
equal, the results for multiple query points cannot be fit in a
standard data array.
For efficiency, `radius_neighbors` returns arrays of objects, where
each object is a 1D array of indices or distances.
"""
if self._fit_method is None:
raise NotFittedError("Must fit neighbors before querying.")
if X is not None:
query_is_train = False
X = check_array(X, accept_sparse='csr')
else:
query_is_train = True
X = self._fit_X
if radius is None:
radius = self.radius
n_samples = X.shape[0]
if self._fit_method == 'brute':
# for efficiency, use squared euclidean distances
if self.effective_metric_ == 'euclidean':
dist = pairwise_distances(X, self._fit_X, 'euclidean',
squared=True)
radius *= radius
else:
dist = pairwise_distances(X, self._fit_X,
self.effective_metric_,
**self.effective_metric_params_)
neigh_ind_list = [np.where(d <= radius)[0] for d in dist]
# See https://github.com/numpy/numpy/issues/5456
# if you want to understand why this is initialized this way.
neigh_ind = np.empty(n_samples, dtype='object')
neigh_ind[:] = neigh_ind_list
if return_distance:
dist_array = np.empty(n_samples, dtype='object')
if self.effective_metric_ == 'euclidean':
dist_list = [np.sqrt(d[neigh_ind[i]])
for i, d in enumerate(dist)]
else:
dist_list = [d[neigh_ind[i]]
for i, d in enumerate(dist)]
dist_array[:] = dist_list
results = dist_array, neigh_ind
else:
results = neigh_ind
elif self._fit_method in ['ball_tree', 'kd_tree']:
if issparse(X):
raise ValueError(
"%s does not work with sparse matrices. Densify the data, "
"or set algorithm='brute'" % self._fit_method)
results = self._tree.query_radius(X, radius,
return_distance=return_distance)
if return_distance:
results = results[::-1]
else:
raise ValueError("internal: _fit_method not recognized")
if not query_is_train:
return results
else:
# If the query data is the same as the indexed data, we would like
# to ignore the first nearest neighbor of every sample, i.e
# the sample itself.
if return_distance:
dist, neigh_ind = results
else:
neigh_ind = results
for ind, ind_neighbor in enumerate(neigh_ind):
mask = ind_neighbor != ind
neigh_ind[ind] = ind_neighbor[mask]
if return_distance:
dist[ind] = dist[ind][mask]
if return_distance:
return dist, neigh_ind
return neigh_ind
def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity'):
"""Computes the (weighted) graph of Neighbors for points in X
Neighborhoods are restricted the points at a distance lower than
radius.
Parameters
----------
X : array-like, shape = [n_samples, n_features], optional
The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.
radius : float
Radius of neighborhoods.
(default is the value passed to the constructor).
mode : {'connectivity', 'distance'}, optional
Type of returned matrix: 'connectivity' will return the
connectivity matrix with ones and zeros, in 'distance' the
edges are Euclidean distance between points.
Returns
-------
A : sparse matrix in CSR format, shape = [n_samples, n_samples]
A[i, j] is assigned the weight of edge that connects i to j.
Examples
--------
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.5)
>>> neigh.fit(X) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> A = neigh.radius_neighbors_graph(X)
>>> A.toarray()
array([[ 1., 0., 1.],
[ 0., 1., 0.],
[ 1., 0., 1.]])
See also
--------
kneighbors_graph
"""
if X is not None:
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
n_samples2 = self._fit_X.shape[0]
if radius is None:
radius = self.radius
# construct CSR matrix representation of the NN graph
if mode == 'connectivity':
A_ind = self.radius_neighbors(X, radius,
return_distance=False)
A_data = None
elif mode == 'distance':
dist, A_ind = self.radius_neighbors(X, radius,
return_distance=True)
A_data = np.concatenate(list(dist))
else:
raise ValueError(
'Unsupported mode, must be one of "connectivity", '
'or "distance" but got %s instead' % mode)
n_samples1 = A_ind.shape[0]
n_neighbors = np.array([len(a) for a in A_ind])
A_ind = np.concatenate(list(A_ind))
if A_data is None:
A_data = np.ones(len(A_ind))
A_indptr = np.concatenate((np.zeros(1, dtype=int),
np.cumsum(n_neighbors)))
return csr_matrix((A_data, A_ind, A_indptr),
shape=(n_samples1, n_samples2))
class SupervisedFloatMixin(object):
def fit(self, X, y):
"""Fit the model using X as training data and y as target values
Parameters
----------
X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric='precomputed'.
y : {array-like, sparse matrix}
Target values, array of float values, shape = [n_samples]
or [n_samples, n_outputs]
"""
if not isinstance(X, (KDTree, BallTree)):
X, y = check_X_y(X, y, "csr", multi_output=True)
self._y = y
return self._fit(X)
class SupervisedIntegerMixin(object):
def fit(self, X, y):
"""Fit the model using X as training data and y as target values
Parameters
----------
X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric='precomputed'.
y : {array-like, sparse matrix}
Target values of shape = [n_samples] or [n_samples, n_outputs]
"""
if not isinstance(X, (KDTree, BallTree)):
X, y = check_X_y(X, y, "csr", multi_output=True)
if y.ndim == 1 or y.ndim == 2 and y.shape[1] == 1:
if y.ndim != 1:
warnings.warn("A column-vector y was passed when a 1d array "
"was expected. Please change the shape of y to "
"(n_samples, ), for example using ravel().",
DataConversionWarning, stacklevel=2)
self.outputs_2d_ = False
y = y.reshape((-1, 1))
else:
self.outputs_2d_ = True
self.classes_ = []
self._y = np.empty(y.shape, dtype=np.int)
for k in range(self._y.shape[1]):
classes, self._y[:, k] = np.unique(y[:, k], return_inverse=True)
self.classes_.append(classes)
if not self.outputs_2d_:
self.classes_ = self.classes_[0]
self._y = self._y.ravel()
return self._fit(X)
class UnsupervisedMixin(object):
def fit(self, X, y=None):
"""Fit the model using X as training data
Parameters
----------
X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric='precomputed'.
"""
return self._fit(X)
| bsd-3-clause |
Clyde-fare/scikit-learn | sklearn/neighbors/base.py | 71 | 31147 | """Base and mixin classes for nearest neighbors"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output support by Arnaud Joly <a.joly@ulg.ac.be>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
import warnings
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import csr_matrix, issparse
from .ball_tree import BallTree
from .kd_tree import KDTree
from ..base import BaseEstimator
from ..metrics import pairwise_distances
from ..metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS
from ..utils import check_X_y, check_array, _get_n_jobs, gen_even_slices
from ..utils.fixes import argpartition
from ..utils.validation import DataConversionWarning
from ..utils.validation import NotFittedError
from ..externals import six
from ..externals.joblib import Parallel, delayed
VALID_METRICS = dict(ball_tree=BallTree.valid_metrics,
kd_tree=KDTree.valid_metrics,
# The following list comes from the
# sklearn.metrics.pairwise doc string
brute=(list(PAIRWISE_DISTANCE_FUNCTIONS.keys()) +
['braycurtis', 'canberra', 'chebyshev',
'correlation', 'cosine', 'dice', 'hamming',
'jaccard', 'kulsinski', 'mahalanobis',
'matching', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener',
'sokalsneath', 'sqeuclidean',
'yule', 'wminkowski']))
VALID_METRICS_SPARSE = dict(ball_tree=[],
kd_tree=[],
brute=PAIRWISE_DISTANCE_FUNCTIONS.keys())
class NeighborsWarning(UserWarning):
pass
# Make sure that NeighborsWarning are displayed more than once
warnings.simplefilter("always", NeighborsWarning)
def _check_weights(weights):
"""Check to make sure weights are valid"""
if weights in (None, 'uniform', 'distance'):
return weights
elif callable(weights):
return weights
else:
raise ValueError("weights not recognized: should be 'uniform', "
"'distance', or a callable function")
def _get_weights(dist, weights):
"""Get the weights from an array of distances and a parameter ``weights``
Parameters
===========
dist: ndarray
The input distances
weights: {'uniform', 'distance' or a callable}
The kind of weighting used
Returns
========
weights_arr: array of the same shape as ``dist``
if ``weights == 'uniform'``, then returns None
"""
if weights in (None, 'uniform'):
return None
elif weights == 'distance':
# if user attempts to classify a point that was zero distance from one
# or more training points, those training points are weighted as 1.0
# and the other points as 0.0
if dist.dtype is np.dtype(object):
for point_dist_i, point_dist in enumerate(dist):
# check if point_dist is iterable
# (ex: RadiusNeighborClassifier.predict may set an element of
# dist to 1e-6 to represent an 'outlier')
if hasattr(point_dist, '__contains__') and 0. in point_dist:
dist[point_dist_i] = point_dist == 0.
else:
dist[point_dist_i] = 1. / point_dist
else:
with np.errstate(divide='ignore'):
dist = 1. / dist
inf_mask = np.isinf(dist)
inf_row = np.any(inf_mask, axis=1)
dist[inf_row] = inf_mask[inf_row]
return dist
elif callable(weights):
return weights(dist)
else:
raise ValueError("weights not recognized: should be 'uniform', "
"'distance', or a callable function")
class NeighborsBase(six.with_metaclass(ABCMeta, BaseEstimator)):
"""Base class for nearest neighbors estimators."""
@abstractmethod
def __init__(self):
pass
def _init_params(self, n_neighbors=None, radius=None,
algorithm='auto', leaf_size=30, metric='minkowski',
p=2, metric_params=None, n_jobs=1, **kwargs):
if kwargs:
warnings.warn("Passing additional arguments to the metric "
"function as **kwargs is deprecated "
"and will no longer be supported in 0.18. "
"Use metric_params instead.",
DeprecationWarning, stacklevel=3)
if metric_params is None:
metric_params = {}
metric_params.update(kwargs)
self.n_neighbors = n_neighbors
self.radius = radius
self.algorithm = algorithm
self.leaf_size = leaf_size
self.metric = metric
self.metric_params = metric_params
self.p = p
self.n_jobs = n_jobs
if algorithm not in ['auto', 'brute',
'kd_tree', 'ball_tree']:
raise ValueError("unrecognized algorithm: '%s'" % algorithm)
if algorithm == 'auto':
if metric == 'precomputed':
alg_check = 'brute'
else:
alg_check = 'ball_tree'
else:
alg_check = algorithm
if callable(metric):
if algorithm == 'kd_tree':
# callable metric is only valid for brute force and ball_tree
raise ValueError(
"kd_tree algorithm does not support callable metric '%s'"
% metric)
elif metric not in VALID_METRICS[alg_check]:
raise ValueError("Metric '%s' not valid for algorithm '%s'"
% (metric, algorithm))
if self.metric_params is not None and 'p' in self.metric_params:
warnings.warn("Parameter p is found in metric_params. "
"The corresponding parameter from __init__ "
"is ignored.", SyntaxWarning, stacklevel=3)
effective_p = metric_params['p']
else:
effective_p = self.p
if self.metric in ['wminkowski', 'minkowski'] and effective_p < 1:
raise ValueError("p must be greater than one for minkowski metric")
self._fit_X = None
self._tree = None
self._fit_method = None
def _fit(self, X):
if self.metric_params is None:
self.effective_metric_params_ = {}
else:
self.effective_metric_params_ = self.metric_params.copy()
effective_p = self.effective_metric_params_.get('p', self.p)
if self.metric in ['wminkowski', 'minkowski']:
self.effective_metric_params_['p'] = effective_p
self.effective_metric_ = self.metric
# For minkowski distance, use more efficient methods where available
if self.metric == 'minkowski':
p = self.effective_metric_params_.pop('p', 2)
if p < 1:
raise ValueError("p must be greater than one "
"for minkowski metric")
elif p == 1:
self.effective_metric_ = 'manhattan'
elif p == 2:
self.effective_metric_ = 'euclidean'
elif p == np.inf:
self.effective_metric_ = 'chebyshev'
else:
self.effective_metric_params_['p'] = p
if isinstance(X, NeighborsBase):
self._fit_X = X._fit_X
self._tree = X._tree
self._fit_method = X._fit_method
return self
elif isinstance(X, BallTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'ball_tree'
return self
elif isinstance(X, KDTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'kd_tree'
return self
X = check_array(X, accept_sparse='csr')
n_samples = X.shape[0]
if n_samples == 0:
raise ValueError("n_samples must be greater than 0")
if issparse(X):
if self.algorithm not in ('auto', 'brute'):
warnings.warn("cannot use tree with sparse input: "
"using brute force")
if self.effective_metric_ not in VALID_METRICS_SPARSE['brute']:
raise ValueError("metric '%s' not valid for sparse input"
% self.effective_metric_)
self._fit_X = X.copy()
self._tree = None
self._fit_method = 'brute'
return self
self._fit_method = self.algorithm
self._fit_X = X
if self._fit_method == 'auto':
# A tree approach is better for small number of neighbors,
# and KDTree is generally faster when available
if ((self.n_neighbors is None or
self.n_neighbors < self._fit_X.shape[0] // 2) and
self.metric != 'precomputed'):
if self.effective_metric_ in VALID_METRICS['kd_tree']:
self._fit_method = 'kd_tree'
else:
self._fit_method = 'ball_tree'
else:
self._fit_method = 'brute'
if self._fit_method == 'ball_tree':
self._tree = BallTree(X, self.leaf_size,
metric=self.effective_metric_,
**self.effective_metric_params_)
elif self._fit_method == 'kd_tree':
self._tree = KDTree(X, self.leaf_size,
metric=self.effective_metric_,
**self.effective_metric_params_)
elif self._fit_method == 'brute':
self._tree = None
else:
raise ValueError("algorithm = '%s' not recognized"
% self.algorithm)
if self.n_neighbors is not None:
if self.n_neighbors <= 0:
raise ValueError(
"Expected n_neighbors > 0. Got %d" %
self.n_neighbors
)
return self
@property
def _pairwise(self):
# For cross-validation routines to split data correctly
return self.metric == 'precomputed'
class KNeighborsMixin(object):
"""Mixin for k-neighbors searches"""
def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
"""Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters
----------
X : array-like, shape (n_query, n_features), \
or (n_query, n_indexed) if metric == 'precomputed'
The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.
n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns
-------
dist : array
Array representing the lengths to points, only present if
return_distance=True
ind : array
Indices of the nearest points in the population matrix.
Examples
--------
In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who's
the closest point to [1,1,1]
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=1)
>>> neigh.fit(samples) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> print(neigh.kneighbors([[1., 1., 1.]])) # doctest: +ELLIPSIS
(array([[ 0.5]]), array([[2]]...))
As you can see, it returns [[0.5]], and [[2]], which means that the
element is at distance 0.5 and is the third element of samples
(indexes start at 0). You can also query for multiple points:
>>> X = [[0., 1., 0.], [1., 0., 1.]]
>>> neigh.kneighbors(X, return_distance=False) # doctest: +ELLIPSIS
array([[1],
[2]]...)
"""
if self._fit_method is None:
raise NotFittedError("Must fit neighbors before querying.")
if n_neighbors is None:
n_neighbors = self.n_neighbors
if X is not None:
query_is_train = False
X = check_array(X, accept_sparse='csr')
else:
query_is_train = True
X = self._fit_X
# Include an extra neighbor to account for the sample itself being
# returned, which is removed later
n_neighbors += 1
train_size = self._fit_X.shape[0]
if n_neighbors > train_size:
raise ValueError(
"Expected n_neighbors <= n_samples, "
" but n_samples = %d, n_neighbors = %d" %
(train_size, n_neighbors)
)
n_samples, _ = X.shape
sample_range = np.arange(n_samples)[:, None]
n_jobs = _get_n_jobs(self.n_jobs)
if self._fit_method == 'brute':
# for efficiency, use squared euclidean distances
if self.effective_metric_ == 'euclidean':
dist = pairwise_distances(X, self._fit_X, 'euclidean',
n_jobs=n_jobs, squared=True)
else:
dist = pairwise_distances(
X, self._fit_X, self.effective_metric_, n_jobs=n_jobs,
**self.effective_metric_params_)
neigh_ind = argpartition(dist, n_neighbors - 1, axis=1)
neigh_ind = neigh_ind[:, :n_neighbors]
# argpartition doesn't guarantee sorted order, so we sort again
neigh_ind = neigh_ind[
sample_range, np.argsort(dist[sample_range, neigh_ind])]
if return_distance:
if self.effective_metric_ == 'euclidean':
result = np.sqrt(dist[sample_range, neigh_ind]), neigh_ind
else:
result = dist[sample_range, neigh_ind], neigh_ind
else:
result = neigh_ind
elif self._fit_method in ['ball_tree', 'kd_tree']:
if issparse(X):
raise ValueError(
"%s does not work with sparse matrices. Densify the data, "
"or set algorithm='brute'" % self._fit_method)
result = Parallel(n_jobs, backend='threading')(
delayed(self._tree.query, check_pickle=False)(
X[s], n_neighbors, return_distance)
for s in gen_even_slices(X.shape[0], n_jobs)
)
if return_distance:
dist, neigh_ind = tuple(zip(*result))
result = np.vstack(dist), np.vstack(neigh_ind)
else:
result = np.vstack(result)
else:
raise ValueError("internal: _fit_method not recognized")
if not query_is_train:
return result
else:
# If the query data is the same as the indexed data, we would like
# to ignore the first nearest neighbor of every sample, i.e
# the sample itself.
if return_distance:
dist, neigh_ind = result
else:
neigh_ind = result
sample_mask = neigh_ind != sample_range
# Corner case: When the number of duplicates are more
# than the number of neighbors, the first NN will not
# be the sample, but a duplicate.
# In that case mask the first duplicate.
dup_gr_nbrs = np.all(sample_mask, axis=1)
sample_mask[:, 0][dup_gr_nbrs] = False
neigh_ind = np.reshape(
neigh_ind[sample_mask], (n_samples, n_neighbors - 1))
if return_distance:
dist = np.reshape(
dist[sample_mask], (n_samples, n_neighbors - 1))
return dist, neigh_ind
return neigh_ind
def kneighbors_graph(self, X=None, n_neighbors=None,
mode='connectivity'):
"""Computes the (weighted) graph of k-Neighbors for points in X
Parameters
----------
X : array-like, shape (n_query, n_features), \
or (n_query, n_indexed) if metric == 'precomputed'
The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.
n_neighbors : int
Number of neighbors for each sample.
(default is value passed to the constructor).
mode : {'connectivity', 'distance'}, optional
Type of returned matrix: 'connectivity' will return the
connectivity matrix with ones and zeros, in 'distance' the
edges are Euclidean distance between points.
Returns
-------
A : sparse matrix in CSR format, shape = [n_samples, n_samples_fit]
n_samples_fit is the number of samples in the fitted data
A[i, j] is assigned the weight of edge that connects i to j.
Examples
--------
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=2)
>>> neigh.fit(X) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> A = neigh.kneighbors_graph(X)
>>> A.toarray()
array([[ 1., 0., 1.],
[ 0., 1., 1.],
[ 1., 0., 1.]])
See also
--------
NearestNeighbors.radius_neighbors_graph
"""
if n_neighbors is None:
n_neighbors = self.n_neighbors
# kneighbors does the None handling.
if X is not None:
X = check_array(X, accept_sparse='csr')
n_samples1 = X.shape[0]
else:
n_samples1 = self._fit_X.shape[0]
n_samples2 = self._fit_X.shape[0]
n_nonzero = n_samples1 * n_neighbors
A_indptr = np.arange(0, n_nonzero + 1, n_neighbors)
# construct CSR matrix representation of the k-NN graph
if mode == 'connectivity':
A_data = np.ones(n_samples1 * n_neighbors)
A_ind = self.kneighbors(X, n_neighbors, return_distance=False)
elif mode == 'distance':
A_data, A_ind = self.kneighbors(
X, n_neighbors, return_distance=True)
A_data = np.ravel(A_data)
else:
raise ValueError(
'Unsupported mode, must be one of "connectivity" '
'or "distance" but got "%s" instead' % mode)
kneighbors_graph = csr_matrix((A_data, A_ind.ravel(), A_indptr),
shape=(n_samples1, n_samples2))
return kneighbors_graph
class RadiusNeighborsMixin(object):
"""Mixin for radius-based neighbors searches"""
def radius_neighbors(self, X=None, radius=None, return_distance=True):
"""Finds the neighbors within a given radius of a point or points.
Return the indices and distances of each point from the dataset
lying in a ball with size ``radius`` around the points of the query
array. Points lying on the boundary are included in the results.
The result points are *not* necessarily sorted by distance to their
query point.
Parameters
----------
X : array-like, (n_samples, n_features), optional
The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.
radius : float
Limiting distance of neighbors to return.
(default is the value passed to the constructor).
return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns
-------
dist : array, shape (n_samples,) of arrays
Array representing the distances to each point, only present if
return_distance=True. The distance values are computed according
to the ``metric`` constructor parameter.
ind : array, shape (n_samples,) of arrays
An array of arrays of indices of the approximate nearest points
from the population matrix that lie within a ball of size
``radius`` around the query points.
Examples
--------
In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who's
the closest point to [1, 1, 1]:
>>> import numpy as np
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.6)
>>> neigh.fit(samples) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> rng = neigh.radius_neighbors([[1., 1., 1.]])
>>> print(np.asarray(rng[0][0])) # doctest: +ELLIPSIS
[ 1.5 0.5]
>>> print(np.asarray(rng[1][0])) # doctest: +ELLIPSIS
[1 2]
The first array returned contains the distances to all points which
are closer than 1.6, while the second array returned contains their
indices. In general, multiple points can be queried at the same time.
Notes
-----
Because the number of neighbors of each point is not necessarily
equal, the results for multiple query points cannot be fit in a
standard data array.
For efficiency, `radius_neighbors` returns arrays of objects, where
each object is a 1D array of indices or distances.
"""
if self._fit_method is None:
raise NotFittedError("Must fit neighbors before querying.")
if X is not None:
query_is_train = False
X = check_array(X, accept_sparse='csr')
else:
query_is_train = True
X = self._fit_X
if radius is None:
radius = self.radius
n_samples = X.shape[0]
if self._fit_method == 'brute':
# for efficiency, use squared euclidean distances
if self.effective_metric_ == 'euclidean':
dist = pairwise_distances(X, self._fit_X, 'euclidean',
squared=True)
radius *= radius
else:
dist = pairwise_distances(X, self._fit_X,
self.effective_metric_,
**self.effective_metric_params_)
neigh_ind_list = [np.where(d <= radius)[0] for d in dist]
# See https://github.com/numpy/numpy/issues/5456
# if you want to understand why this is initialized this way.
neigh_ind = np.empty(n_samples, dtype='object')
neigh_ind[:] = neigh_ind_list
if return_distance:
dist_array = np.empty(n_samples, dtype='object')
if self.effective_metric_ == 'euclidean':
dist_list = [np.sqrt(d[neigh_ind[i]])
for i, d in enumerate(dist)]
else:
dist_list = [d[neigh_ind[i]]
for i, d in enumerate(dist)]
dist_array[:] = dist_list
results = dist_array, neigh_ind
else:
results = neigh_ind
elif self._fit_method in ['ball_tree', 'kd_tree']:
if issparse(X):
raise ValueError(
"%s does not work with sparse matrices. Densify the data, "
"or set algorithm='brute'" % self._fit_method)
results = self._tree.query_radius(X, radius,
return_distance=return_distance)
if return_distance:
results = results[::-1]
else:
raise ValueError("internal: _fit_method not recognized")
if not query_is_train:
return results
else:
# If the query data is the same as the indexed data, we would like
# to ignore the first nearest neighbor of every sample, i.e
# the sample itself.
if return_distance:
dist, neigh_ind = results
else:
neigh_ind = results
for ind, ind_neighbor in enumerate(neigh_ind):
mask = ind_neighbor != ind
neigh_ind[ind] = ind_neighbor[mask]
if return_distance:
dist[ind] = dist[ind][mask]
if return_distance:
return dist, neigh_ind
return neigh_ind
def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity'):
"""Computes the (weighted) graph of Neighbors for points in X
Neighborhoods are restricted the points at a distance lower than
radius.
Parameters
----------
X : array-like, shape = [n_samples, n_features], optional
The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.
radius : float
Radius of neighborhoods.
(default is the value passed to the constructor).
mode : {'connectivity', 'distance'}, optional
Type of returned matrix: 'connectivity' will return the
connectivity matrix with ones and zeros, in 'distance' the
edges are Euclidean distance between points.
Returns
-------
A : sparse matrix in CSR format, shape = [n_samples, n_samples]
A[i, j] is assigned the weight of edge that connects i to j.
Examples
--------
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.5)
>>> neigh.fit(X) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> A = neigh.radius_neighbors_graph(X)
>>> A.toarray()
array([[ 1., 0., 1.],
[ 0., 1., 0.],
[ 1., 0., 1.]])
See also
--------
kneighbors_graph
"""
if X is not None:
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
n_samples2 = self._fit_X.shape[0]
if radius is None:
radius = self.radius
# construct CSR matrix representation of the NN graph
if mode == 'connectivity':
A_ind = self.radius_neighbors(X, radius,
return_distance=False)
A_data = None
elif mode == 'distance':
dist, A_ind = self.radius_neighbors(X, radius,
return_distance=True)
A_data = np.concatenate(list(dist))
else:
raise ValueError(
'Unsupported mode, must be one of "connectivity", '
'or "distance" but got %s instead' % mode)
n_samples1 = A_ind.shape[0]
n_neighbors = np.array([len(a) for a in A_ind])
A_ind = np.concatenate(list(A_ind))
if A_data is None:
A_data = np.ones(len(A_ind))
A_indptr = np.concatenate((np.zeros(1, dtype=int),
np.cumsum(n_neighbors)))
return csr_matrix((A_data, A_ind, A_indptr),
shape=(n_samples1, n_samples2))
class SupervisedFloatMixin(object):
def fit(self, X, y):
"""Fit the model using X as training data and y as target values
Parameters
----------
X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric='precomputed'.
y : {array-like, sparse matrix}
Target values, array of float values, shape = [n_samples]
or [n_samples, n_outputs]
"""
if not isinstance(X, (KDTree, BallTree)):
X, y = check_X_y(X, y, "csr", multi_output=True)
self._y = y
return self._fit(X)
class SupervisedIntegerMixin(object):
def fit(self, X, y):
"""Fit the model using X as training data and y as target values
Parameters
----------
X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric='precomputed'.
y : {array-like, sparse matrix}
Target values of shape = [n_samples] or [n_samples, n_outputs]
"""
if not isinstance(X, (KDTree, BallTree)):
X, y = check_X_y(X, y, "csr", multi_output=True)
if y.ndim == 1 or y.ndim == 2 and y.shape[1] == 1:
if y.ndim != 1:
warnings.warn("A column-vector y was passed when a 1d array "
"was expected. Please change the shape of y to "
"(n_samples, ), for example using ravel().",
DataConversionWarning, stacklevel=2)
self.outputs_2d_ = False
y = y.reshape((-1, 1))
else:
self.outputs_2d_ = True
self.classes_ = []
self._y = np.empty(y.shape, dtype=np.int)
for k in range(self._y.shape[1]):
classes, self._y[:, k] = np.unique(y[:, k], return_inverse=True)
self.classes_.append(classes)
if not self.outputs_2d_:
self.classes_ = self.classes_[0]
self._y = self._y.ravel()
return self._fit(X)
class UnsupervisedMixin(object):
def fit(self, X, y=None):
"""Fit the model using X as training data
Parameters
----------
X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric='precomputed'.
"""
return self._fit(X)
| bsd-3-clause |
faneshion/MatchZoo | matchzoo/__init__.py | 1 | 1785 | from pathlib import Path
USER_DIR = Path.expanduser(Path('~')).joinpath('.matchzoo')
if not USER_DIR.exists():
USER_DIR.mkdir()
USER_DATA_DIR = USER_DIR.joinpath('datasets')
if not USER_DATA_DIR.exists():
USER_DATA_DIR.mkdir()
USER_TUNED_MODELS_DIR = USER_DIR.joinpath('tuned_models')
from .version import __version__
from .data_pack import DataPack
from .data_pack import pack
from .data_pack import load_data_pack
from . import metrics
from . import tasks
from . import preprocessors
from . import data_generator
from .data_generator import DataGenerator
from .data_generator import DataGeneratorBuilder
from .preprocessors.chain_transform import chain_transform
from . import metrics
from . import losses
from . import engine
from . import models
from . import embedding
from . import datasets
from . import layers
from . import auto
from . import contrib
from .engine import hyper_spaces
from .engine.base_model import load_model
from .engine.base_preprocessor import load_preprocessor
from .engine import callbacks
from .engine.param import Param
from .engine.param_table import ParamTable
from .embedding.embedding import Embedding
from .utils import one_hot, make_keras_optimizer_picklable
from .preprocessors.build_unit_from_data_pack import build_unit_from_data_pack
from .preprocessors.build_vocab_unit import build_vocab_unit
# deprecated, should be removed in v2.2
from .contrib.legacy_data_generator import DPoolDataGenerator
from .contrib.legacy_data_generator import DPoolPairDataGenerator
from .contrib.legacy_data_generator import HistogramDataGenerator
from .contrib.legacy_data_generator import HistogramPairDataGenerator
from .contrib.legacy_data_generator import DynamicDataGenerator
from .contrib.legacy_data_generator import PairDataGenerator
| apache-2.0 |
analysiscenter/dataset | batchflow/models/torch/losses/binary.py | 1 | 6582 | """ Losses for binary predictions. """
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class BCE(nn.Module):
""" Binary cross-entropy that allows int/float for `pos_weight`, unlike native PyTorch implementation.
Parameters
----------
pos_weight : number
A weight for positive examples.
"""
def __init__(self, pos_weight=1, reduction='mean'):
super().__init__()
self.pos_weight = pos_weight
self.reduction = reduction
def forward(self, prediction, target):
pos_weight = self.pos_weight * torch.ones(1, device=prediction.device)
loss = F.binary_cross_entropy_with_logits(prediction, target, pos_weight=pos_weight,
reduction=self.reduction)
return loss
class TopK(nn.Module):
""" Binary cross entropy computed only over k% worst elements.
Parameters
----------
pos_weight : number
A weight for positive examples.
k : number
Percent of worst examples to include in loss computation.
"""
def __init__(self, pos_weight=1, k=10):
super().__init__()
self.pos_weight = pos_weight
self.k = k
def forward(self, prediction, target):
pos_weight = self.pos_weight * torch.ones(1, device=prediction.device)
loss = F.binary_cross_entropy_with_logits(prediction, target,
pos_weight=pos_weight,
reduction='none')
n = np.prod(loss.shape)
loss, _ = torch.topk(loss.view(-1), int(n * self.k) // 100, sorted=False)
return loss.mean()
class Dice(nn.Module):
""" Sørensen-Dice Coefficient as a loss function. Sudre C. et al. "`Generalised Dice overlap as a deep
learning loss function for highly unbalanced segmentations <https://arxiv.org/abs/1707.03237>`_".
Predictions are passed through a sigmoid function to obtain probabilities.
"""
def __init__(self, eps=1e-7):
super().__init__()
self.eps = eps
def forward(self, prediction, target):
prediction = torch.sigmoid(prediction)
dice_coeff = 2. * (prediction * target).sum() / (prediction.sum() + target.sum() + self.eps)
return 1 - dice_coeff
class PenaltyDice(Dice):
""" Modification of a Dice loss with additional weight towards false positives and false negatives.
Yang Su et al. "` Major Vessel Segmentation on X-ray Coronary Angiography using Deep Networks with a
Novel Penalty Loss Function <https://openreview.net/forum?id=H1lTh8unKN>`_".
Parameters
----------
k : number
Penalty coefficient: the bigger, the more weight is put on false positives and negatives.
"""
def __init__(self, k=3):
super().__init__()
self.k = k
def forward(self, prediction, target):
dice_loss = super().forward(prediction, target)
return dice_loss / (1 + self.k*(1 - dice_loss))
class LogDice(Dice):
""" Modification of a Dice loss with additional emphasis on smaller objects.
Wong K. et al. "` 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced
Object Sizes <https://arxiv.org/abs/1809.00076>`_".
Parameters
----------
gamma : number
Modulating factor. The bigger, the more weight is put on less accurate predictions.
"""
def __init__(self, gamma=2.):
super().__init__()
self.gamma = gamma
def forward(self, prediction, target):
dice_loss = super().forward(prediction, target)
dice_coeff = 1 - dice_loss
return torch.pow(-torch.log(dice_coeff), self.gamma)
class Tversky(nn.Module):
""" Generalization of Dice loss with finer control over false positives and negatives weights.
Salehi S. et al. "` Tversky loss function for image segmentation using 3D fully convolutional
deep networks <https://arxiv.org/abs/1706.05721>`_".
If `alpha` and `beta` equal 0.5, identical to Dice.
If `alpha` and `beta` equal 1, identical to Jaccard loss.
If `alpha` > `beta`, put more weight on false positives, and vice versa.
Parameters
----------
alpha : number
Weight for false positive examples.
beta : number
Weight for false negative examples.
"""
def __init__(self, alpha=1., beta=1., eps=1e-7):
super().__init__()
self.alpha, self.beta = alpha, beta
self.eps = eps
def forward(self, prediction, target):
prediction = torch.sigmoid(prediction)
intersection = (prediction * target).sum()
false_positive = (prediction * (1 - target)).sum()
false_negative = ((1 - prediction) * target).sum()
tversky_coeff = intersection / (intersection + self.alpha * false_positive
+ self.beta * false_negative + self.eps)
return 1 - tversky_coeff
class FocalTversky(Tversky):
""" Modification of Tversky loss with additional emphasis on harder examples.
Abraham N. et al. "` A Novel Focal Tversky loss function with improved Attention U-Net for
lesion segmentation <https://arxiv.org/abs/1810.07842>`_".
Parameters
----------
gamma : number
Modulating factor. The bigger, the more weight is put on harder examples.
"""
def __init__(self, alpha=1., beta=1., gamma=1.3):
super().__init__(alpha=alpha, beta=beta)
self.gamma = gamma
def forward(self, prediction, target):
tversky_loss = super().forward(prediction, target)
return torch.pow(tversky_loss, self.gamma)
class SSLoss(nn.Module):
""" Sensitivity-specificity loss.
Sensitivity is error over trues, specificity is error over falses: essentially, they correspond to
`how good are we doing where we should predict True` and `how bad are we doing where we should predict False`.
Parameters
----------
r : number
Weight for specificity; weight for sensitivity is 1 - `r`.
"""
def __init__(self, r=0.1, eps=1e-7):
super().__init__()
self.r = r
self.eps = eps
def forward(self, prediction, target):
prediction = torch.sigmoid(prediction)
inverse = 1 - target
squared_error = (target - prediction)**2
specificity = (squared_error * target).sum() / (target.sum() + self.eps)
sensitivity = (squared_error * inverse).sum() / (inverse.sum() + self.eps)
return self.r * specificity + (1 - self.r) * sensitivity
| apache-2.0 |
a-doumoulakis/tensorflow | tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py | 108 | 5314 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import numpy as np
from tensorflow.python.platform import test
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.datasets import synthetic
class SyntheticTest(test.TestCase):
"""Test synthetic dataset generation"""
def test_make_dataset(self):
"""Test if the synthetic routine wrapper complains about the name"""
self.assertRaises(ValueError, datasets.make_dataset, name='_non_existing_name')
def test_all_datasets_callable(self):
"""Test if all methods inside the `SYNTHETIC` are callable"""
self.assertIsInstance(datasets.SYNTHETIC, dict)
if len(datasets.SYNTHETIC) > 0:
for name, method in six.iteritems(datasets.SYNTHETIC):
self.assertTrue(callable(method))
def test_circles(self):
"""Test if the circles are generated correctly
Tests:
- return type is `Dataset`
- returned `data` shape is (n_samples, n_features)
- returned `target` shape is (n_samples,)
- set of unique classes range is [0, n_classes)
TODO:
- all points have the same radius, if no `noise` specified
"""
n_samples = 100
n_classes = 2
circ = synthetic.circles(n_samples = n_samples, noise = None, n_classes = n_classes)
self.assertIsInstance(circ, datasets.base.Dataset)
self.assertTupleEqual(circ.data.shape, (n_samples,2))
self.assertTupleEqual(circ.target.shape, (n_samples,))
self.assertSetEqual(set(circ.target), set(range(n_classes)))
def test_circles_replicable(self):
"""Test if the data generation is replicable with a specified `seed`
Tests:
- return the same value if raised with the same seed
- return different values if noise or seed is different
"""
seed = 42
noise = 0.1
circ0 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed)
circ1 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed)
np.testing.assert_array_equal(circ0.data, circ1.data)
np.testing.assert_array_equal(circ0.target, circ1.target)
circ1 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed+1)
self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, circ1.data)
self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.target, circ1.target)
circ1 = synthetic.circles(n_samples = 100, noise = noise/2., n_classes = 2, seed = seed)
self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, circ1.data)
def test_spirals(self):
"""Test if the circles are generated correctly
Tests:
- if mode is unknown, ValueError is raised
- return type is `Dataset`
- returned `data` shape is (n_samples, n_features)
- returned `target` shape is (n_samples,)
- set of unique classes range is [0, n_classes)
"""
self.assertRaises(ValueError, synthetic.spirals, mode='_unknown_mode_spiral_')
n_samples = 100
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir = synthetic.spirals(n_samples = n_samples, noise = None, mode = mode)
self.assertIsInstance(spir, datasets.base.Dataset)
self.assertTupleEqual(spir.data.shape, (n_samples,2))
self.assertTupleEqual(spir.target.shape, (n_samples,))
self.assertSetEqual(set(spir.target), set(range(2)))
def test_spirals_replicable(self):
"""Test if the data generation is replicable with a specified `seed`
Tests:
- return the same value if raised with the same seed
- return different values if noise or seed is different
"""
seed = 42
noise = 0.1
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir0 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed)
spir1 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed)
np.testing.assert_array_equal(spir0.data, spir1.data)
np.testing.assert_array_equal(spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed+1)
self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data)
self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples = 1000, noise = noise/2., seed = seed)
self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data)
if __name__ == "__main__":
test.main()
| apache-2.0 |
kchodorow/tensorflow | tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py | 108 | 5314 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import numpy as np
from tensorflow.python.platform import test
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.datasets import synthetic
class SyntheticTest(test.TestCase):
"""Test synthetic dataset generation"""
def test_make_dataset(self):
"""Test if the synthetic routine wrapper complains about the name"""
self.assertRaises(ValueError, datasets.make_dataset, name='_non_existing_name')
def test_all_datasets_callable(self):
"""Test if all methods inside the `SYNTHETIC` are callable"""
self.assertIsInstance(datasets.SYNTHETIC, dict)
if len(datasets.SYNTHETIC) > 0:
for name, method in six.iteritems(datasets.SYNTHETIC):
self.assertTrue(callable(method))
def test_circles(self):
"""Test if the circles are generated correctly
Tests:
- return type is `Dataset`
- returned `data` shape is (n_samples, n_features)
- returned `target` shape is (n_samples,)
- set of unique classes range is [0, n_classes)
TODO:
- all points have the same radius, if no `noise` specified
"""
n_samples = 100
n_classes = 2
circ = synthetic.circles(n_samples = n_samples, noise = None, n_classes = n_classes)
self.assertIsInstance(circ, datasets.base.Dataset)
self.assertTupleEqual(circ.data.shape, (n_samples,2))
self.assertTupleEqual(circ.target.shape, (n_samples,))
self.assertSetEqual(set(circ.target), set(range(n_classes)))
def test_circles_replicable(self):
"""Test if the data generation is replicable with a specified `seed`
Tests:
- return the same value if raised with the same seed
- return different values if noise or seed is different
"""
seed = 42
noise = 0.1
circ0 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed)
circ1 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed)
np.testing.assert_array_equal(circ0.data, circ1.data)
np.testing.assert_array_equal(circ0.target, circ1.target)
circ1 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed+1)
self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, circ1.data)
self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.target, circ1.target)
circ1 = synthetic.circles(n_samples = 100, noise = noise/2., n_classes = 2, seed = seed)
self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, circ1.data)
def test_spirals(self):
"""Test if the circles are generated correctly
Tests:
- if mode is unknown, ValueError is raised
- return type is `Dataset`
- returned `data` shape is (n_samples, n_features)
- returned `target` shape is (n_samples,)
- set of unique classes range is [0, n_classes)
"""
self.assertRaises(ValueError, synthetic.spirals, mode='_unknown_mode_spiral_')
n_samples = 100
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir = synthetic.spirals(n_samples = n_samples, noise = None, mode = mode)
self.assertIsInstance(spir, datasets.base.Dataset)
self.assertTupleEqual(spir.data.shape, (n_samples,2))
self.assertTupleEqual(spir.target.shape, (n_samples,))
self.assertSetEqual(set(spir.target), set(range(2)))
def test_spirals_replicable(self):
"""Test if the data generation is replicable with a specified `seed`
Tests:
- return the same value if raised with the same seed
- return different values if noise or seed is different
"""
seed = 42
noise = 0.1
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir0 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed)
spir1 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed)
np.testing.assert_array_equal(spir0.data, spir1.data)
np.testing.assert_array_equal(spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed+1)
self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data)
self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples = 1000, noise = noise/2., seed = seed)
self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data)
if __name__ == "__main__":
test.main()
| apache-2.0 |
Clyde-fare/scikit-learn | examples/linear_model/plot_polynomial_interpolation.py | 250 | 1895 | #!/usr/bin/env python
"""
========================
Polynomial interpolation
========================
This example demonstrates how to approximate a function with a polynomial of
degree n_degree by using ridge regression. Concretely, from n_samples 1d
points, it suffices to build the Vandermonde matrix, which is n_samples x
n_degree+1 and has the following form:
[[1, x_1, x_1 ** 2, x_1 ** 3, ...],
[1, x_2, x_2 ** 2, x_2 ** 3, ...],
...]
Intuitively, this matrix can be interpreted as a matrix of pseudo features (the
points raised to some power). The matrix is akin to (but different from) the
matrix induced by a polynomial kernel.
This example shows that you can do non-linear regression with a linear model,
using a pipeline to add non-linear features. Kernel methods extend this idea
and can induce very high (even infinite) dimensional feature spaces.
"""
print(__doc__)
# Author: Mathieu Blondel
# Jake Vanderplas
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
def f(x):
""" function to approximate by polynomial interpolation"""
return x * np.sin(x)
# generate points used to plot
x_plot = np.linspace(0, 10, 100)
# generate points and keep a subset of them
x = np.linspace(0, 10, 100)
rng = np.random.RandomState(0)
rng.shuffle(x)
x = np.sort(x[:20])
y = f(x)
# create matrix versions of these arrays
X = x[:, np.newaxis]
X_plot = x_plot[:, np.newaxis]
plt.plot(x_plot, f(x_plot), label="ground truth")
plt.scatter(x, y, label="training points")
for degree in [3, 4, 5]:
model = make_pipeline(PolynomialFeatures(degree), Ridge())
model.fit(X, y)
y_plot = model.predict(X_plot)
plt.plot(x_plot, y_plot, label="degree %d" % degree)
plt.legend(loc='lower left')
plt.show()
| bsd-3-clause |
Clyde-fare/scikit-learn | examples/bicluster/plot_spectral_coclustering.py | 274 | 1736 | """
==============================================
A demo of the Spectral Co-Clustering algorithm
==============================================
This example demonstrates how to generate a dataset and bicluster it
using the the Spectral Co-Clustering algorithm.
The dataset is generated using the ``make_biclusters`` function, which
creates a matrix of small values and implants bicluster with large
values. The rows and columns are then shuffled and passed to the
Spectral Co-Clustering algorithm. Rearranging the shuffled matrix to
make biclusters contiguous shows how accurately the algorithm found
the biclusters.
"""
print(__doc__)
# Author: Kemal Eren <kemal@kemaleren.com>
# License: BSD 3 clause
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import make_biclusters
from sklearn.datasets import samples_generator as sg
from sklearn.cluster.bicluster import SpectralCoclustering
from sklearn.metrics import consensus_score
data, rows, columns = make_biclusters(
shape=(300, 300), n_clusters=5, noise=5,
shuffle=False, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")
data, row_idx, col_idx = sg._shuffle(data, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")
model = SpectralCoclustering(n_clusters=5, random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))
print("consensus score: {:.3f}".format(score))
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
plt.show()
| bsd-3-clause |
fducau/AAE_pytorch | script/aae_pytorch_basic.py | 1 | 8635 | import argparse
import torch
import pickle
import numpy as np
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# Training settings
parser = argparse.ArgumentParser(description='PyTorch semi-supervised MNIST')
parser.add_argument('--batch-size', type=int, default=100, metavar='N',
help='input batch size for training (default: 100)')
parser.add_argument('--epochs', type=int, default=500, metavar='N',
help='number of epochs to train (default: 10)')
args = parser.parse_args()
cuda = torch.cuda.is_available()
seed = 10
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
n_classes = 10
z_dim = 2
X_dim = 784
y_dim = 10
train_batch_size = args.batch_size
valid_batch_size = args.batch_size
N = 1000
epochs = args.epochs
##################################
# Load data and create Data loaders
##################################
def load_data(data_path='../data/'):
print('loading data!')
trainset_labeled = pickle.load(open(data_path + "train_labeled.p", "rb"))
trainset_unlabeled = pickle.load(open(data_path + "train_unlabeled.p", "rb"))
# Set -1 as labels for unlabeled data
trainset_unlabeled.train_labels = torch.from_numpy(np.array([-1] * 47000))
validset = pickle.load(open(data_path + "validation.p", "rb"))
train_labeled_loader = torch.utils.data.DataLoader(trainset_labeled,
batch_size=train_batch_size,
shuffle=True, **kwargs)
train_unlabeled_loader = torch.utils.data.DataLoader(trainset_unlabeled,
batch_size=train_batch_size,
shuffle=True, **kwargs)
valid_loader = torch.utils.data.DataLoader(validset, batch_size=valid_batch_size, shuffle=True)
return train_labeled_loader, train_unlabeled_loader, valid_loader
##################################
# Define Networks
##################################
# Encoder
class Q_net(nn.Module):
def __init__(self):
super(Q_net, self).__init__()
self.lin1 = nn.Linear(X_dim, N)
self.lin2 = nn.Linear(N, N)
# Gaussian code (z)
self.lin3gauss = nn.Linear(N, z_dim)
def forward(self, x):
x = F.dropout(self.lin1(x), p=0.2, training=self.training)
x = F.relu(x)
x = F.dropout(self.lin2(x), p=0.2, training=self.training)
x = F.relu(x)
xgauss = self.lin3gauss(x)
return xgauss
# Decoder
class P_net(nn.Module):
def __init__(self):
super(P_net, self).__init__()
self.lin1 = nn.Linear(z_dim, N)
self.lin2 = nn.Linear(N, N)
self.lin3 = nn.Linear(N, X_dim)
def forward(self, x):
x = self.lin1(x)
x = F.dropout(x, p=0.2, training=self.training)
x = F.relu(x)
x = self.lin2(x)
x = F.dropout(x, p=0.2, training=self.training)
x = self.lin3(x)
return F.sigmoid(x)
class D_net_gauss(nn.Module):
def __init__(self):
super(D_net_gauss, self).__init__()
self.lin1 = nn.Linear(z_dim, N)
self.lin2 = nn.Linear(N, N)
self.lin3 = nn.Linear(N, 1)
def forward(self, x):
x = F.dropout(self.lin1(x), p=0.2, training=self.training)
x = F.relu(x)
x = F.dropout(self.lin2(x), p=0.2, training=self.training)
x = F.relu(x)
return F.sigmoid(self.lin3(x))
####################
# Utility functions
####################
def save_model(model, filename):
print('Best model so far, saving it...')
torch.save(model.state_dict(), filename)
def report_loss(epoch, D_loss_gauss, G_loss, recon_loss):
'''
Print loss
'''
print('Epoch-{}; D_loss_gauss: {:.4}; G_loss: {:.4}; recon_loss: {:.4}'.format(epoch,
D_loss_gauss.data[0],
G_loss.data[0],
recon_loss.data[0]))
def create_latent(Q, loader):
'''
Creates the latent representation for the samples in loader
return:
z_values: numpy array with the latent representations
labels: the labels corresponding to the latent representations
'''
Q.eval()
labels = []
for batch_idx, (X, target) in enumerate(loader):
X = X * 0.3081 + 0.1307
# X.resize_(loader.batch_size, X_dim)
X, target = Variable(X), Variable(target)
labels.extend(target.data.tolist())
if cuda:
X, target = X.cuda(), target.cuda()
# Reconstruction phase
z_sample = Q(X)
if batch_idx > 0:
z_values = np.concatenate((z_values, np.array(z_sample.data.tolist())))
else:
z_values = np.array(z_sample.data.tolist())
labels = np.array(labels)
return z_values, labels
####################
# Train procedure
####################
def train(P, Q, D_gauss, P_decoder, Q_encoder, Q_generator, D_gauss_solver, data_loader):
'''
Train procedure for one epoch.
'''
TINY = 1e-15
# Set the networks in train mode (apply dropout when needed)
Q.train()
P.train()
D_gauss.train()
# Loop through the labeled and unlabeled dataset getting one batch of samples from each
# The batch size has to be a divisor of the size of the dataset or it will return
# invalid samples
for X, target in data_loader:
# Load batch and normalize samples to be between 0 and 1
X = X * 0.3081 + 0.1307
X.resize_(train_batch_size, X_dim)
X, target = Variable(X), Variable(target)
if cuda:
X, target = X.cuda(), target.cuda()
# Init gradients
P.zero_grad()
Q.zero_grad()
D_gauss.zero_grad()
#######################
# Reconstruction phase
#######################
z_sample = Q(X)
X_sample = P(z_sample)
recon_loss = F.binary_cross_entropy(X_sample + TINY, X.resize(train_batch_size, X_dim) + TINY)
recon_loss.backward()
P_decoder.step()
Q_encoder.step()
P.zero_grad()
Q.zero_grad()
D_gauss.zero_grad()
#######################
# Regularization phase
#######################
# Discriminator
Q.eval()
z_real_gauss = Variable(torch.randn(train_batch_size, z_dim) * 5.)
if cuda:
z_real_gauss = z_real_gauss.cuda()
z_fake_gauss = Q(X)
D_real_gauss = D_gauss(z_real_gauss)
D_fake_gauss = D_gauss(z_fake_gauss)
D_loss = -torch.mean(torch.log(D_real_gauss + TINY) + torch.log(1 - D_fake_gauss + TINY))
D_loss.backward()
D_gauss_solver.step()
P.zero_grad()
Q.zero_grad()
D_gauss.zero_grad()
# Generator
Q.train()
z_fake_gauss = Q(X)
D_fake_gauss = D_gauss(z_fake_gauss)
G_loss = -torch.mean(torch.log(D_fake_gauss + TINY))
G_loss.backward()
Q_generator.step()
P.zero_grad()
Q.zero_grad()
D_gauss.zero_grad()
return D_loss, G_loss, recon_loss
def generate_model(train_labeled_loader, train_unlabeled_loader, valid_loader):
torch.manual_seed(10)
if cuda:
Q = Q_net().cuda()
P = P_net().cuda()
D_gauss = D_net_gauss().cuda()
else:
Q = Q_net()
P = P_net()
D_gauss = D_net_gauss()
# Set learning rates
gen_lr = 0.0001
reg_lr = 0.00005
# Set optimizators
P_decoder = optim.Adam(P.parameters(), lr=gen_lr)
Q_encoder = optim.Adam(Q.parameters(), lr=gen_lr)
Q_generator = optim.Adam(Q.parameters(), lr=reg_lr)
D_gauss_solver = optim.Adam(D_gauss.parameters(), lr=reg_lr)
for epoch in range(epochs):
D_loss_gauss, G_loss, recon_loss = train(P, Q, D_gauss, P_decoder, Q_encoder,
Q_generator,
D_gauss_solver,
train_unlabeled_loader)
if epoch % 10 == 0:
report_loss(epoch, D_loss_gauss, G_loss, recon_loss)
return Q, P
if __name__ == '__main__':
train_labeled_loader, train_unlabeled_loader, valid_loader = load_data()
Q, P = generate_model(train_labeled_loader, train_unlabeled_loader, valid_loader)
| gpl-3.0 |
yugangw-msft/azure-cli | src/azure-cli/azure/cli/command_modules/storage/operations/fs_directory.py | 1 | 5712 | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
"""Custom operations for storage file datalake"""
from datetime import datetime
from azure.cli.core.profiles import ResourceType
from knack.util import todict
from ..util import get_datetime_from_string
def exists(cmd, client, timeout=None):
from azure.core.exceptions import HttpResponseError
try:
client.get_directory_properties(timeout=timeout)
return True
except HttpResponseError as ex:
from azure.cli.command_modules.storage.track2_util import _dont_fail_on_exist
StorageErrorCode = cmd.get_models("_shared.models#StorageErrorCode",
resource_type=ResourceType.DATA_STORAGE_FILEDATALAKE)
_dont_fail_on_exist(ex, StorageErrorCode.blob_not_found)
return False
def list_fs_directories(client, path=None, recursive=True, num_results=None, timeout=None):
generator = client.get_paths(path=path, recursive=recursive, timeout=timeout, max_results=num_results)
return list(f for f in generator if f.is_directory)
def get_directory_properties(client, timeout=None):
from .._transformers import transform_fs_access_output
prop = todict(client.get_directory_properties(timeout=timeout))
acl = transform_fs_access_output(client.get_access_control(timeout=timeout))
result = dict(prop, **acl)
return result
def remove_access_control_recursive(client, acl, **kwargs):
failed_entries = []
# the progress callback is invoked each time a batch is completed
def progress_callback(acl_changes):
# keep track of failed entries if there are any
if acl_changes.batch_failures:
failed_entries.extend(acl_changes.batch_failures)
result = client.remove_access_control_recursive(acl=acl, progress_hook=progress_callback, **kwargs)
result = todict(result)
result['failedEntries'] = failed_entries
return result
def set_access_control_recursive(client, acl, **kwargs):
failed_entries = []
# the progress callback is invoked each time a batch is completed
def progress_callback(acl_changes):
# keep track of failed entries if there are any
if acl_changes.batch_failures:
failed_entries.extend(acl_changes.batch_failures)
result = client.set_access_control_recursive(acl=acl, progress_hook=progress_callback, **kwargs)
result = todict(result)
result['failedEntries'] = failed_entries
return result
def update_access_control_recursive(client, acl, **kwargs):
failed_entries = []
# the progress callback is invoked each time a batch is completed
def progress_callback(acl_changes):
# keep track of failed entries if there are any
if acl_changes.batch_failures:
failed_entries.extend(acl_changes.batch_failures)
result = client.update_access_control_recursive(acl=acl, progress_hook=progress_callback, **kwargs)
result = todict(result)
result['failedEntries'] = failed_entries
return result
def fix_url_path(url):
# Change https://xx.dfs.core.windows.net/test/dir1%2Fdir2 to https://xx.dfs.core.windows.net/test/dir1/dir2
from urllib.parse import urlparse, urlunparse, unquote, quote
url_parts = urlparse(url)
fixed_path = quote(unquote(url_parts.path), '/()$=\',~')
return urlunparse(url_parts[:2] + (fixed_path,) + url_parts[3:])
def generate_sas_directory_uri(client, cmd, file_system_name, directory_name, permission=None,
expiry=None, start=None, id=None, ip=None, # pylint: disable=redefined-builtin
protocol=None, cache_control=None, content_disposition=None,
content_encoding=None, content_language=None,
content_type=None, full_uri=False, as_user=False, ):
from urllib.parse import quote
generate_directory_sas = cmd.get_models('_shared_access_signature#generate_directory_sas')
sas_kwargs = {}
if as_user:
user_delegation_key = client.get_user_delegation_key(
get_datetime_from_string(start) if start else datetime.utcnow(),
get_datetime_from_string(expiry))
sas_token = generate_directory_sas(account_name=client.account_name, file_system_name=file_system_name,
directory_name=directory_name,
credential=user_delegation_key if as_user else client.credential.account_key,
permission=permission, expiry=expiry, start=start, policy_id=id,
ip=ip, protocol=protocol,
cache_control=cache_control, content_disposition=content_disposition,
content_encoding=content_encoding, content_language=content_language,
content_type=content_type, **sas_kwargs)
sas_token = quote(sas_token, safe='&%()$=\',~')
if full_uri:
t_directory_client = cmd.get_models('_data_lake_directory_client#DataLakeDirectoryClient')
directory_client = t_directory_client(account_url=client.url, file_system_name=file_system_name,
directory_name=directory_name, credential=sas_token)
return fix_url_path(directory_client.url)
return sas_token
| mit |
williamFalcon/pytorch-lightning | tests/accelerators/test_dp.py | 1 | 6332 | # Copyright The PyTorch Lightning team.
#
# 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 pytest
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import tests.helpers.pipelines as tpipes
import tests.helpers.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities import memory
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.helpers import BoringModel, RandomDataset
from tests.helpers.datamodules import ClassifDataModule
from tests.helpers.runif import RunIf
from tests.helpers.simple_models import ClassificationModel
class CustomClassificationModelDP(ClassificationModel):
def _step(self, batch, batch_idx):
x, y = batch
logits = self(x)
return {"logits": logits, "y": y}
def training_step(self, batch, batch_idx):
out = self._step(batch, batch_idx)
loss = F.cross_entropy(out["logits"], out["y"])
return loss
def validation_step(self, batch, batch_idx):
return self._step(batch, batch_idx)
def test_step(self, batch, batch_idx):
return self._step(batch, batch_idx)
def validation_step_end(self, outputs):
self.log("val_acc", self.valid_acc(outputs["logits"], outputs["y"]))
def test_step_end(self, outputs):
self.log("test_acc", self.test_acc(outputs["logits"], outputs["y"]))
@RunIf(min_gpus=2)
def test_multi_gpu_early_stop_dp(tmpdir):
"""Make sure DDP works. with early stopping"""
tutils.set_random_master_port()
dm = ClassifDataModule()
model = CustomClassificationModelDP()
trainer_options = dict(
default_root_dir=tmpdir,
callbacks=[EarlyStopping(monitor="val_acc")],
max_epochs=50,
limit_train_batches=10,
limit_val_batches=10,
gpus=[0, 1],
accelerator="dp",
)
tpipes.run_model_test(trainer_options, model, dm)
@RunIf(min_gpus=2)
def test_multi_gpu_model_dp(tmpdir):
tutils.set_random_master_port()
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=10,
limit_val_batches=10,
gpus=[0, 1],
accelerator="dp",
progress_bar_refresh_rate=0,
)
model = BoringModel()
tpipes.run_model_test(trainer_options, model)
# test memory helper functions
memory.get_memory_profile("min_max")
class ReductionTestModel(BoringModel):
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=2)
def val_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=2)
def test_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=2)
def add_outputs(self, output, device):
output.update(
{
"reduce_int": torch.tensor(device.index, dtype=torch.int, device=device),
"reduce_float": torch.tensor(device.index, dtype=torch.float, device=device),
}
)
def training_step(self, batch, batch_idx):
output = super().training_step(batch, batch_idx)
self.add_outputs(output, batch.device)
return output
def validation_step(self, batch, batch_idx):
output = super().validation_step(batch, batch_idx)
self.add_outputs(output, batch.device)
return output
def test_step(self, batch, batch_idx):
output = super().test_step(batch, batch_idx)
self.add_outputs(output, batch.device)
return output
def training_epoch_end(self, outputs):
assert outputs[0]["loss"].shape == torch.Size([])
assert outputs[0]["reduce_int"].item() == 0 # mean([0, 1]) = 0
assert outputs[0]["reduce_float"].item() == 0.5 # mean([0., 1.]) = 0.5
def test_dp_raise_exception_with_batch_transfer_hooks(tmpdir, monkeypatch):
"""
Test that an exception is raised when overriding batch_transfer_hooks in DP model.
"""
monkeypatch.setattr("torch.cuda.device_count", lambda: 2)
class CustomModel(BoringModel):
def transfer_batch_to_device(self, batch, device):
batch = batch.to(device)
return batch
trainer_options = dict(default_root_dir=tmpdir, max_steps=7, gpus=[0, 1], accelerator="dp")
trainer = Trainer(**trainer_options)
model = CustomModel()
with pytest.raises(MisconfigurationException, match=r"Overriding `transfer_batch_to_device` is not .* in DP"):
trainer.fit(model)
class CustomModel(BoringModel):
def on_before_batch_transfer(self, batch, dataloader_idx):
batch += 1
return batch
trainer = Trainer(**trainer_options)
model = CustomModel()
with pytest.raises(MisconfigurationException, match=r"Overriding `on_before_batch_transfer` is not .* in DP"):
trainer.fit(model)
class CustomModel(BoringModel):
def on_after_batch_transfer(self, batch, dataloader_idx):
batch += 1
return batch
trainer = Trainer(**trainer_options)
model = CustomModel()
with pytest.raises(MisconfigurationException, match=r"Overriding `on_after_batch_transfer` is not .* in DP"):
trainer.fit(model)
@RunIf(min_gpus=2)
def test_dp_training_step_dict(tmpdir):
"""This test verifies that dp properly reduces dictionaries"""
model = ReductionTestModel()
model.training_step_end = None
model.validation_step_end = None
model.test_step_end = None
trainer = pl.Trainer(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=1,
limit_val_batches=1,
limit_test_batches=1,
gpus=2,
accelerator="dp",
)
trainer.fit(model)
| apache-2.0 |
Clyde-fare/scikit-learn | examples/linear_model/plot_omp.py | 379 | 2263 | """
===========================
Orthogonal Matching Pursuit
===========================
Using orthogonal matching pursuit for recovering a sparse signal from a noisy
measurement encoded with a dictionary
"""
print(__doc__)
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import OrthogonalMatchingPursuit
from sklearn.linear_model import OrthogonalMatchingPursuitCV
from sklearn.datasets import make_sparse_coded_signal
n_components, n_features = 512, 100
n_nonzero_coefs = 17
# generate the data
###################
# y = Xw
# |x|_0 = n_nonzero_coefs
y, X, w = make_sparse_coded_signal(n_samples=1,
n_components=n_components,
n_features=n_features,
n_nonzero_coefs=n_nonzero_coefs,
random_state=0)
idx, = w.nonzero()
# distort the clean signal
##########################
y_noisy = y + 0.05 * np.random.randn(len(y))
# plot the sparse signal
########################
plt.figure(figsize=(7, 7))
plt.subplot(4, 1, 1)
plt.xlim(0, 512)
plt.title("Sparse signal")
plt.stem(idx, w[idx])
# plot the noise-free reconstruction
####################################
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
omp.fit(X, y)
coef = omp.coef_
idx_r, = coef.nonzero()
plt.subplot(4, 1, 2)
plt.xlim(0, 512)
plt.title("Recovered signal from noise-free measurements")
plt.stem(idx_r, coef[idx_r])
# plot the noisy reconstruction
###############################
omp.fit(X, y_noisy)
coef = omp.coef_
idx_r, = coef.nonzero()
plt.subplot(4, 1, 3)
plt.xlim(0, 512)
plt.title("Recovered signal from noisy measurements")
plt.stem(idx_r, coef[idx_r])
# plot the noisy reconstruction with number of non-zeros set by CV
##################################################################
omp_cv = OrthogonalMatchingPursuitCV()
omp_cv.fit(X, y_noisy)
coef = omp_cv.coef_
idx_r, = coef.nonzero()
plt.subplot(4, 1, 4)
plt.xlim(0, 512)
plt.title("Recovered signal from noisy measurements with CV")
plt.stem(idx_r, coef[idx_r])
plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)
plt.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit',
fontsize=16)
plt.show()
| bsd-3-clause |
williamFalcon/pytorch-lightning | tests/accelerators/ddp_model.py | 1 | 2010 | # Copyright The PyTorch Lightning team.
#
# 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.
"""
Runs either `.fit()` or `.test()` on a single node across multiple gpus.
"""
import os
from argparse import ArgumentParser
import torch
from pytorch_lightning import seed_everything, Trainer
from tests.helpers.datamodules import ClassifDataModule
from tests.helpers.simple_models import ClassificationModel
def main():
seed_everything(4321)
parser = ArgumentParser(add_help=False)
parser = Trainer.add_argparse_args(parser)
parser.add_argument("--trainer_method", default="fit")
parser.add_argument("--tmpdir")
parser.add_argument("--workdir")
parser.set_defaults(gpus=2)
parser.set_defaults(accelerator="ddp")
args = parser.parse_args()
dm = ClassifDataModule()
model = ClassificationModel()
trainer = Trainer.from_argparse_args(args)
if args.trainer_method == "fit":
trainer.fit(model, datamodule=dm)
result = None
elif args.trainer_method == "test":
result = trainer.test(model, datamodule=dm)
elif args.trainer_method == "fit_test":
trainer.fit(model, datamodule=dm)
result = trainer.test(model, datamodule=dm)
else:
raise ValueError(f"Unsupported: {args.trainer_method}")
result_ext = {"status": "complete", "method": args.trainer_method, "result": result}
file_path = os.path.join(args.tmpdir, "ddp.result")
torch.save(result_ext, file_path)
if __name__ == "__main__":
main()
| apache-2.0 |
jorge2703/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 352 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluated
on a binary classification task where the target Y is a non-linear function
of 10 input features.
Discrete SAMME AdaBoost adapts based on errors in predicted class labels
whereas real SAMME.R uses the predicted class probabilities.
.. [1] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical
Learning Ed. 2", Springer, 2009.
.. [2] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>,
# Noel Dawe <noel.dawe@gmail.com>
#
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import zero_one_loss
from sklearn.ensemble import AdaBoostClassifier
n_estimators = 400
# A learning rate of 1. may not be optimal for both SAMME and SAMME.R
learning_rate = 1.
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
X_test, y_test = X[2000:], y[2000:]
X_train, y_train = X[:2000], y[:2000]
dt_stump = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
dt_stump.fit(X_train, y_train)
dt_stump_err = 1.0 - dt_stump.score(X_test, y_test)
dt = DecisionTreeClassifier(max_depth=9, min_samples_leaf=1)
dt.fit(X_train, y_train)
dt_err = 1.0 - dt.score(X_test, y_test)
ada_discrete = AdaBoostClassifier(
base_estimator=dt_stump,
learning_rate=learning_rate,
n_estimators=n_estimators,
algorithm="SAMME")
ada_discrete.fit(X_train, y_train)
ada_real = AdaBoostClassifier(
base_estimator=dt_stump,
learning_rate=learning_rate,
n_estimators=n_estimators,
algorithm="SAMME.R")
ada_real.fit(X_train, y_train)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1, n_estimators], [dt_stump_err] * 2, 'k-',
label='Decision Stump Error')
ax.plot([1, n_estimators], [dt_err] * 2, 'k--',
label='Decision Tree Error')
ada_discrete_err = np.zeros((n_estimators,))
for i, y_pred in enumerate(ada_discrete.staged_predict(X_test)):
ada_discrete_err[i] = zero_one_loss(y_pred, y_test)
ada_discrete_err_train = np.zeros((n_estimators,))
for i, y_pred in enumerate(ada_discrete.staged_predict(X_train)):
ada_discrete_err_train[i] = zero_one_loss(y_pred, y_train)
ada_real_err = np.zeros((n_estimators,))
for i, y_pred in enumerate(ada_real.staged_predict(X_test)):
ada_real_err[i] = zero_one_loss(y_pred, y_test)
ada_real_err_train = np.zeros((n_estimators,))
for i, y_pred in enumerate(ada_real.staged_predict(X_train)):
ada_real_err_train[i] = zero_one_loss(y_pred, y_train)
ax.plot(np.arange(n_estimators) + 1, ada_discrete_err,
label='Discrete AdaBoost Test Error',
color='red')
ax.plot(np.arange(n_estimators) + 1, ada_discrete_err_train,
label='Discrete AdaBoost Train Error',
color='blue')
ax.plot(np.arange(n_estimators) + 1, ada_real_err,
label='Real AdaBoost Test Error',
color='orange')
ax.plot(np.arange(n_estimators) + 1, ada_real_err_train,
label='Real AdaBoost Train Error',
color='green')
ax.set_ylim((0.0, 0.5))
ax.set_xlabel('n_estimators')
ax.set_ylabel('error rate')
leg = ax.legend(loc='upper right', fancybox=True)
leg.get_frame().set_alpha(0.7)
plt.show()
| bsd-3-clause |
williamFalcon/pytorch-lightning | tests/core/test_lightning_module.py | 1 | 12519 | # Copyright The PyTorch Lightning team.
#
# 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.
from unittest.mock import Mock
import pytest
import torch
import torch.distributed as dist
from torch import nn
from torch.optim import Adam, SGD
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.utilities import _TORCH_SHARDED_TENSOR_AVAILABLE
from tests.helpers import BoringModel
from tests.helpers.runif import RunIf
def test_property_current_epoch():
"""Test that the current_epoch in LightningModule is accessible via the Trainer."""
model = BoringModel()
assert model.current_epoch == 0
trainer = Mock(current_epoch=123)
model.trainer = trainer
assert model.current_epoch == 123
def test_property_global_step():
"""Test that the global_step in LightningModule is accessible via the Trainer."""
model = BoringModel()
assert model.global_step == 0
trainer = Mock(global_step=123)
model.trainer = trainer
assert model.global_step == 123
def test_property_global_rank():
"""Test that the global rank in LightningModule is accessible via the Trainer."""
model = BoringModel()
assert model.global_rank == 0
trainer = Mock(global_rank=123)
model.trainer = trainer
assert model.global_rank == 123
def test_property_local_rank():
"""Test that the local rank in LightningModule is accessible via the Trainer."""
model = BoringModel()
assert model.local_rank == 0
trainer = Mock(local_rank=123)
model.trainer = trainer
assert model.local_rank == 123
def test_property_logger(tmpdir):
"""Test that the logger in LightningModule is accessible via the Trainer."""
model = BoringModel()
assert model.logger is None
logger = TensorBoardLogger(tmpdir)
trainer = Mock(logger=logger)
model.trainer = trainer
assert model.logger == logger
def test_params_groups_and_state_are_accessible(tmpdir):
class TestModel(BoringModel):
def training_step(self, batch, batch_idx, optimizer_idx):
output = self.layer(batch)
loss = self.loss(batch, output)
return {"loss": loss}
def configure_optimizers(self):
optimizer = SGD(self.layer.parameters(), lr=0.1)
optimizer_2 = Adam(self.layer.parameters(), lr=0.1)
return [optimizer, optimizer_2]
def optimizer_step(
self,
epoch,
batch_idx,
optimizer,
optimizer_idx,
optimizer_closure,
on_tpu=False,
using_native_amp=False,
using_lbfgs=False,
):
# warm up lr
if self.trainer.global_step < 500:
lr_scale = min(1.0, float(self.trainer.global_step + 1) / 500.0)
for pg in optimizer.param_groups:
pg["lr"] = lr_scale * 0.01
optimizer.step(closure=optimizer_closure)
model = TestModel()
model.training_epoch_end = None
trainer = Trainer(
max_epochs=1, default_root_dir=tmpdir, limit_train_batches=8, limit_val_batches=1, accumulate_grad_batches=1
)
trainer.fit(model)
def test_toggle_untoggle_2_optimizers_no_shared_parameters(tmpdir):
class TestModel(BoringModel):
def training_step(self, batch, batch_idx, optimizer_idx=None):
return super().training_step(batch, batch_idx)
def __init__(self):
super().__init__()
self.layer_1 = nn.Sequential(nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32))
self.layer_2 = nn.Sequential(
nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 2)
)
# set some weights to False to check untoggle works as expected.
self.layer_1[2].weight.requires_grad = False
self.layer_1[4].weight.requires_grad = False
self.layer_2[1].weight.requires_grad = False
self.layer_2[3].weight.requires_grad = False
def configure_optimizers(self):
optimizer = SGD(self.layer_1.parameters(), lr=0.1)
optimizer_2 = Adam(self.layer_2.parameters(), lr=0.1)
return [optimizer, optimizer_2]
def optimizer_step(
self,
current_epoch,
batch_nb,
optimizer,
optimizer_idx,
closure,
on_tpu=False,
using_native_amp=False,
using_lbfgs=False,
):
if optimizer_idx == 0:
assert self.layer_1[0].weight.requires_grad is True
assert self.layer_1[2].weight.requires_grad is False
assert self.layer_1[4].weight.requires_grad is False
assert self.layer_2[1].weight.requires_grad is False
assert self.layer_2[3].weight.requires_grad is False
assert self.layer_2[5].weight.requires_grad is False
if optimizer_idx == 1:
assert self.layer_1[0].weight.requires_grad is False
assert self.layer_1[2].weight.requires_grad is False
assert self.layer_1[4].weight.requires_grad is False
assert self.layer_2[1].weight.requires_grad is False
assert self.layer_2[3].weight.requires_grad is False
assert self.layer_2[5].weight.requires_grad is True
optimizer.step(closure=closure)
model = TestModel()
model.training_epoch_end = None
trainer = Trainer(
max_epochs=1, default_root_dir=tmpdir, limit_train_batches=8, accumulate_grad_batches=2, limit_val_batches=0
)
trainer.fit(model)
def test_toggle_untoggle_3_optimizers_shared_parameters(tmpdir):
class TestModel(BoringModel):
def __init__(self):
super().__init__()
self.layer_1 = nn.Sequential(nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32))
self.layer_2 = nn.Sequential(
nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 2)
)
self.layer_3 = nn.Sequential(
nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 2)
)
# set some weights to False to check untoggle works as expected.
self.layer_1[2].weight.requires_grad = False
self.layer_1[4].weight.requires_grad = False
self.layer_2[1].weight.requires_grad = False
self.layer_2[3].weight.requires_grad = False
self.layer_3[1].weight.requires_grad = False
self.layer_3[5].weight.requires_grad = False
def optimizer_step(
self,
current_epoch,
batch_nb,
optimizer,
optimizer_idx,
closure,
on_tpu=False,
using_native_amp=False,
using_lbfgs=False,
):
if optimizer_idx == 0:
assert self.layer_1[0].weight.requires_grad is True
assert self.layer_1[2].weight.requires_grad is False
assert self.layer_1[4].weight.requires_grad is False
assert self.layer_2[1].weight.requires_grad is False
assert self.layer_2[3].weight.requires_grad is False
assert self.layer_2[5].weight.requires_grad is True
assert self.layer_3[1].weight.requires_grad is False
assert self.layer_3[3].weight.requires_grad is False
assert self.layer_3[5].weight.requires_grad is False
if optimizer_idx == 1:
assert self.layer_1[0].weight.requires_grad is False
assert self.layer_1[2].weight.requires_grad is False
assert self.layer_1[4].weight.requires_grad is False
assert self.layer_2[1].weight.requires_grad is False
assert self.layer_2[3].weight.requires_grad is False
assert self.layer_2[5].weight.requires_grad is True
assert self.layer_3[1].weight.requires_grad is False
assert self.layer_3[3].weight.requires_grad is True
assert self.layer_3[5].weight.requires_grad is False
if optimizer_idx == 2:
assert self.layer_1[0].weight.requires_grad is True
assert self.layer_1[2].weight.requires_grad is False
assert self.layer_1[4].weight.requires_grad is False
assert self.layer_2[1].weight.requires_grad is False
assert self.layer_2[3].weight.requires_grad is False
assert self.layer_2[5].weight.requires_grad is False
assert self.layer_3[1].weight.requires_grad is False
assert self.layer_3[3].weight.requires_grad is True
assert self.layer_3[5].weight.requires_grad is False
optimizer.step(closure=closure)
def training_step(self, batch, batch_idx, optimizer_idx=None):
loss = super().training_step(batch, batch_idx)
# make sure the model is untoggle when returning None
return loss if batch_idx % 2 == 0 else None
@staticmethod
def combine_generators(gen_1, gen_2):
yield from gen_1
yield from gen_2
def configure_optimizers(self):
optimizer_1 = SGD(self.combine_generators(self.layer_1.parameters(), self.layer_2.parameters()), lr=0.1)
optimizer_2 = Adam(self.combine_generators(self.layer_2.parameters(), self.layer_3.parameters()), lr=0.1)
optimizer_3 = SGD(self.combine_generators(self.layer_3.parameters(), self.layer_1.parameters()), lr=0.1)
return [optimizer_1, optimizer_2, optimizer_3]
model = TestModel()
model.training_epoch_end = None
trainer = Trainer(max_epochs=1, default_root_dir=tmpdir, limit_train_batches=8, accumulate_grad_batches=2)
trainer.fit(model)
@RunIf(min_gpus=1)
def test_device_placement(tmpdir):
model = BoringModel()
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=1)
trainer.fit(model)
def assert_device(device: torch.device) -> None:
assert model.device == device
for p in model.parameters():
assert p.device == device
assert_device(torch.device("cpu"))
model.to(torch.device("cuda:0"))
assert_device(torch.device("cuda:0"))
trainer.test(model)
assert_device(torch.device("cpu"))
trainer.predict(model, dataloaders=model.train_dataloader())
assert_device(torch.device("cpu"))
class BoringModelWithShardedTensor(BoringModel):
def __init__(self, spec):
super().__init__()
self.sharded_tensor = dist._sharded_tensor.empty(spec, 10, 20)
self.sharded_tensor.local_shards()[0].tensor.fill_(0)
@pytest.mark.skipif(
not _TORCH_SHARDED_TENSOR_AVAILABLE, reason="Test requires the torch version to support `ShardedTensor`"
)
def test_sharded_tensor_state_dict(tmpdir, single_process_pg):
spec = dist._sharding_spec.ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cpu",
],
)
m_0 = BoringModelWithShardedTensor(spec)
m_0.sharded_tensor.local_shards()[0].tensor.fill_(1)
assert "sharded_tensor" in m_0.state_dict(), 'Expect "sharded_tensor" to appear in the state dict'
m_1 = BoringModelWithShardedTensor(spec)
assert not torch.allclose(
m_1.sharded_tensor.local_shards()[0].tensor, m_0.sharded_tensor.local_shards()[0].tensor
), "Expect the shards to be different before `m_1` loading `m_0`'s state dict"
m_1.load_state_dict(m_0.state_dict(), strict=False)
assert torch.allclose(
m_1.sharded_tensor.local_shards()[0].tensor, m_0.sharded_tensor.local_shards()[0].tensor
), "Expect the shards to be same after `m_1` loading `m_0`'s state dict"
| apache-2.0 |
Impactstory/oadoi | queue_separate_table.py | 2 | 25621 | import os
import argparse
from time import time
from time import sleep
from sqlalchemy import sql
from sqlalchemy import exc
from subprocess import call
import heroku3
import boto.ec2
from boto.manage.cmdshell import sshclient_from_instance
from pprint import pprint
import datetime
from app import db
from app import logger
from jobs import update_registry
import jobs_defs # needs to be imported so the definitions get loaded into the registry
from util import elapsed
from util import run_sql
from util import get_sql_answer
from util import get_sql_answers
from util import clean_doi
from app import HEROKU_APP_NAME
from pub import Pub
# to get the clarivate dois in
# date; grep "WOS:" DOI_Output.txt | sed 's:\\:\\\\:g' | psql postgres://uc1l3d6vod6nsk:p5f54c0e9c8bb4067420ab6e6eb78a4a93234db67fbd3eede893a9a86781a484d@ec2-34-204-251-168.compute-1.amazonaws.com:5432/dds97qbhb1bu4i?ssl=true -c "copy dois_from_wos (wos_id) from STDIN;"; date;
def monitor_till_done(job_type):
logger.info(u"collecting data. will have some stats soon...")
logger.info(u"\n\n")
num_total = number_total_on_queue(job_type)
print "num_total", num_total
num_unfinished = number_unfinished(job_type)
print "num_unfinished", num_unfinished
loop_thresholds = {"short": 30, "long": 10*60, "medium": 60}
loop_unfinished = {"short": num_unfinished, "long": num_unfinished}
loop_start_time = {"short": time(), "long": time()}
# print_idle_dynos(job_type)
while all(loop_unfinished.values()):
for loop in ["short", "long"]:
if elapsed(loop_start_time[loop]) > loop_thresholds[loop]:
if loop in ["short", "long"]:
num_unfinished_now = number_unfinished(job_type)
num_finished_this_loop = loop_unfinished[loop] - num_unfinished_now
loop_unfinished[loop] = num_unfinished_now
# if loop=="long":
# logger.info(u"\n****"),
logger.info(u" {} finished in the last {} seconds, {} of {} are now finished ({}%). ".format(
num_finished_this_loop, loop_thresholds[loop],
num_total - num_unfinished_now,
num_total,
int(100*float(num_total - num_unfinished_now)/num_total)
)), # comma so the next part will stay on the same line
if num_finished_this_loop:
minutes_left = float(num_unfinished_now) / num_finished_this_loop * loop_thresholds[loop] / 60
logger.info(u"{} estimate: done in {} mins, which is {} hours".format(
loop, round(minutes_left, 1), round(minutes_left/60, 1)))
else:
print
loop_start_time[loop] = time()
# print_idle_dynos(job_type)
print".",
sleep(3)
logger.info(u"everything is done. turning off all the dynos")
scale_dyno(0, job_type)
def number_total_on_queue(job_type):
num = get_sql_answer(db, "select count(*) from {}".format(table_name(job_type)))
return num
def number_waiting_on_queue(job_type):
num = get_sql_answer(db, "select count(*) from {} where started is null".format(table_name(job_type)))
return num
def number_unfinished(job_type):
num = get_sql_answer(db, "select count(*) from {} where finished is null".format(table_name(job_type)))
return num
def print_status(job_type):
num_dois = number_total_on_queue(job_type)
num_waiting = number_waiting_on_queue(job_type)
if num_dois:
logger.info(u"There are {} dois in the queue, of which {} ({}%) are waiting to run".format(
num_dois, num_waiting, int(100*float(num_waiting)/num_dois)))
def kick(job_type):
q = u"""update {table_name} set started=null, finished=null
where finished is null""".format(
table_name=table_name(job_type))
run_sql(db, q)
print_status(job_type)
def reset_enqueued(job_type):
q = u"update {} set started=null, finished=null".format(table_name(job_type))
run_sql(db, q)
def truncate(job_type):
q = "truncate table {}".format(table_name(job_type))
run_sql(db, q)
def table_name(job_type):
table_name = "doi_queue"
if job_type=="hybrid":
table_name += "_with_hybrid"
elif job_type=="dates":
table_name += "_dates"
return table_name
def process_name(job_type):
# process_name = "run" # formation name is from Procfile
process_name = "update" # formation name is from Procfile
if job_type=="hybrid":
process_name += "_with_hybrid"
elif job_type=="dates":
process_name += "_dates"
return process_name
def num_dynos(job_type):
heroku_conn = heroku3.from_key(os.getenv("HEROKU_API_KEY"))
num_dynos = 0
try:
dynos = heroku_conn.apps()[HEROKU_APP_NAME].dynos()[process_name(job_type)]
num_dynos = len(dynos)
except (KeyError, TypeError) as e:
pass
return num_dynos
def print_idle_dynos(job_type):
heroku_conn = heroku3.from_key(os.getenv("HEROKU_API_KEY"))
app = heroku_conn.apps()[HEROKU_APP_NAME]
running_dynos = []
try:
running_dynos = [dyno for dyno in app.dynos() if dyno.name.startswith(process_name(job_type))]
except (KeyError, TypeError) as e:
pass
dynos_still_working = get_sql_answers(db, "select dyno from {} where started is not null and finished is null".format(table_name(job_type)))
dynos_still_working_names = [n for n in dynos_still_working]
logger.info(u"dynos still running: {}".format([d.name for d in running_dynos if d.name in dynos_still_working_names]))
# logger.info(u"dynos stopped:", [d.name for d in running_dynos if d.name not in dynos_still_working_names])
# kill_list = [d.kill() for d in running_dynos if d.name not in dynos_still_working_names]
def scale_dyno(n, job_type):
logger.info(u"starting with {} dynos".format(num_dynos(job_type)))
logger.info(u"setting to {} dynos".format(n))
heroku_conn = heroku3.from_key(os.getenv("HEROKU_API_KEY"))
app = heroku_conn.apps()[HEROKU_APP_NAME]
app.process_formation()[process_name(job_type)].scale(n)
logger.info(u"sleeping for 2 seconds while it kicks in")
sleep(2)
logger.info(u"verifying: now at {} dynos".format(num_dynos(job_type)))
def login_to_aws():
logger.info(u"logging in to aws")
conn = boto.ec2.connect_to_region('us-west-2')
# instance = conn.get_all_instances()[0].instances[0]
ssh_client = None
for reservation in conn.get_all_instances():
instance = reservation.instances[0]
try:
if not ssh_client:
ssh_client = sshclient_from_instance(instance, "data/key.pem", user_name="ec2-user")
print u"this instance worked: {}".format(instance)
except Exception:
pass
return (conn, ssh_client)
# clarivate
# python queue_separate_table.py --export_with_versions --week
# or, run this on aws
# create table export_main_changed_with_versions_20170118 as (select * from export_main_changed_with_versions where last_changed_date > '2018-01-10'::timestamp)
# it takes about 45 minutes
# then this
# python queue_separate_table.py --export --view=export_main_changed_with_versions_20180118
# which takes about 5 minutes
# mv all_dois*.csv datasets_for_clarivate
# mv all_dois*.csv.gz datasets_for_clarivate
def export_with_versions(do_all=False, job_type="normal", filename=None, view=None, week=False, json=False):
# ssh -i /Users/hpiwowar/Dropbox/ti/certificates/aws-data-export.pem ec2-user@ec2-13-59-23-54.us-east-2.compute.amazonaws.com
# aws s3 cp test.txt s3://mpr-ims-harvestor/mpr-ims-dev/harvestor_staging_bigBatch/OA/test.txt
# connect to our bucket
(conn, ssh_client) = login_to_aws()
# to connect to clarivate's bucket
# clarivate_conn = boto.ec2.connect_to_region('us-east-2')
# clarivate_instance = clarivate_conn.get_all_instances()[0].instances[0]
# clarivate_ssh_client = sshclient_from_instance(clarivate_instance, "/Users/hpiwowar/Dropbox/ti/certificates/aws-data-export.pem", user_name="ec2-user")
logger.info(u"log in done")
now_timestamp = datetime.datetime.utcnow().isoformat()[0:19].replace(":", "")
if not filename:
filename = "all_dois_{}.csv".format(now_timestamp)
today = datetime.datetime.utcnow()
if week:
last_week = today - datetime.timedelta(days=9)
view = "export_main_changed_with_versions where last_changed_date >= '{}'::timestamp and updated > '1043-01-01'::timestamp".format(last_week.isoformat()[0:19])
filename = "changed_dois_with_versions_{}_to_{}.csv".format(last_week.isoformat()[0:19], today.isoformat()[0:19]).replace(":", "")
else:
filename = "dois_with_versions_{}.csv".format(today.isoformat()[0:19]).replace(":", "")
if not view:
view = "export_main_changed_with_versions"
command = """psql {}?ssl=true -c "\copy (select * from {}) to '{}' WITH (FORMAT CSV, HEADER);" """.format(
os.getenv("DATABASE_URL"), view, filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
command = """gzip -c {} > {}.gz;""".format(
filename, filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
command = """date -r {}.gz;""".format(
filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
gz_modified = stdout.strip()
# command = """aws s3 cp {}.gz s3://mpr-ims-harvestor/mpr-ims-dev/harvestor_staging_bigBatch/OA/{}.gz --acl public-read --metadata "modifiedtimestamp='{}'";""".format(
# filename, filename, gz_modified)
command = """aws s3 cp {}.gz s3://oadoi-for-clarivate/{}.gz --acl public-read --metadata "modifiedtimestamp='{}'";""".format(
filename, filename, gz_modified)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
# also make a .DONE file
# how to calculate a checksum http://www.heatware.net/linux-unix/how-to-create-md5-checksums-and-validate-a-file-in-linux/
command = """md5sum {}.gz > {}.gz.DONE;""".format(
filename, filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
command = """date -r {}.gz;""".format(
filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
gz_done_modified = stdout.strip()
# copy up the .DONE file
# command = """aws s3 cp {}.gz.DONE s3://mpr-ims-harvestor/mpr-ims-dev/harvestor_staging_bigBatch/OA/{}.gz.DONE --acl public-read --metadata "modifiedtimestamp='{}'";""".format(
# filename, filename, gz_done_modified)
command = """aws s3 cp {}.gz.DONE s3://oadoi-for-clarivate/{}.gz.DONE --acl public-read --metadata "modifiedtimestamp='{}'";""".format(
filename, filename, gz_done_modified)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
# logger.info(u"now go to *** https://console.aws.amazon.com/s3/object/mpr-ims-harvestor/mpr-ims-dev/harvestor_staging_bigBatch/OA/{}.gz?region=us-east-1&tab=overview ***".format(
# filename))
logger.info(u"public link is at *** https://s3-us-west-2.amazonaws.com/oadoi-for-clarivate/{}.gz ***".format(
filename))
conn.close()
# for weekly update
# python queue_separate_table.py --export_no_versions --week --json
# 2 steps
# this step took 5.5 hours for a table of 93540542 rows
# on aws: create table export_main_no_versions_20180116 as (select * from export_main_no_versions)
# then from my prompt
# python queue_separate_table.py --export_no_versions --view export_main_no_versions_20180116
# aws s3 cp all_dois_20180122T165326.csv.gz s3://oadoi-datasets/oa_status_by_doi.csv.gz --acl public-read; date;
# or, for just the changed one
# python queue_separate_table.py --export_no_versions --view="export_main_changed_no_versions where last_changed_date >= '2018-01-21'::timestamp"
def export_no_versions(do_all=False, job_type="normal", filename=None, view="export_main_no_versions", week=False, json=False):
(conn, ssh_client) = login_to_aws()
logger.info(u"log in done")
today = datetime.datetime.utcnow()
if week:
last_week = today - datetime.timedelta(days=9)
if json:
view = "pub where last_changed_date >= '{}'::timestamp and updated > '1043-01-01'::timestamp".format(last_week.isoformat()[0:19])
filename = "changed_dois_{}_to_{}.jsonl".format(last_week.isoformat()[0:19], today.isoformat()[0:19]).replace(":", "")
else:
view = "export_main_changed_no_versions where last_changed_date >= '{}'::timestamp and updated > '1043-01-01'::timestamp".format(last_week.isoformat()[0:19])
filename = "changed_dois_{}_to_{}.csv".format(last_week.isoformat()[0:19], today.isoformat()[0:19]).replace(":", "")
else:
if json:
filename = "full_dois_{}.jsonl".format(today.isoformat()[0:19]).replace(":", "")
else:
filename = "full_dois_{}.csv".format(today.isoformat()[0:19]).replace(":", "")
if json:
command = """psql {}?ssl=true -c "\copy (select response_jsonb from {}) to '{}';" """.format(
os.getenv("DATABASE_URL"), view, filename)
else:
command = """psql {}?ssl=true -c "\copy (select * from {}) to '{}' WITH (FORMAT CSV, HEADER);" """.format(
os.getenv("DATABASE_URL"), view, filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
if json:
command = """sed -i 's/"publishedVersion"/null/g; s/"submittedVersion"/null/g; s/"acceptedVersion"/null/g' {}""".format(filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
command = """gzip -c {} > {}.gz; date;""".format(
filename, filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
command = """aws s3 cp {}.gz s3://unpaywall-data-updates/{}.gz --acl public-read; date; """.format(
filename, filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
# also make a .DONE file
# how to calculate a checksum http://www.heatware.net/linux-unix/how-to-create-md5-checksums-and-validate-a-file-in-linux/
command = """md5sum {}.gz > {}.gz.DONE; date;""".format(
filename, filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
# copy up the .DONE file
command = """aws s3 cp {}.gz.DONE s3://unpaywall-data-updates/{}.gz.DONE --acl public-read; date;""".format(
filename, filename)
logger.info(command)
status, stdout, stderr = ssh_client.run(command)
logger.info(u"{} {} {}".format(status, stdout, stderr))
logger.info(u"now go to *** https://console.aws.amazon.com/s3/object/unpaywall-data-updates/{}.gz?region=us-east-1&tab=overview ***".format(
filename))
logger.info(u"public link is at *** https://s3-us-west-2.amazonaws.com/unpaywall-data-updates/{}.gz ***".format(
filename))
conn.close()
def print_logs(job_type):
command = "heroku logs -t --dyno={}".format(process_name(job_type))
call(command, shell=True)
def add_dois_to_queue_from_file(filename, job_type):
start = time()
command = """psql `heroku config:get DATABASE_URL`?ssl=true -c "\copy {table_name} (id) FROM '{filename}' WITH CSV DELIMITER E'|';" """.format(
table_name=table_name(job_type), filename=filename)
call(command, shell=True)
q = "update {} set id=lower(id)".format(table_name(job_type))
run_sql(db, q)
logger.info(u"add_dois_to_queue_from_file done in {} seconds".format(elapsed(start, 1)))
print_status(job_type)
def add_dois_to_queue_from_query(where, job_type):
logger.info(u"adding all dois, this may take a while")
start = time()
table_name = "doi_queue"
# run_sql(db, "drop table {} cascade".format(table_name(job_type)))
# create_table_command = "CREATE TABLE {} as (select id, random() as rand, null::timestamp as finished, null::timestamp as started, null::text as dyno from crossref)".format(
# table_name(job_type))
create_table_command = "CREATE TABLE {} as (select id, random() as rand, null::timestamp as finished, null::timestamp as started from pub);".format(
table_name)
if where:
create_table_command = create_table_command.replace("from pub)", "from pub where {})".format(where))
run_sql(db, create_table_command)
create_table_command += """
alter table {table_name} alter column rand set default random();
CREATE INDEX {table_name}_id_idx ON {table_name} USING btree (id);
CREATE INDEX {table_name}_finished_null_rand_idx on {table_name} (rand) where finished is null;
CREATE INDEX {table_name}_started_null_rand_idx ON {table_name} USING btree (rand, started) WHERE started is null;
-- from https://lob.com/blog/supercharge-your-postgresql-performance
-- vacuums and analyzes every ten million rows
ALTER TABLE {table_name} SET (autovacuum_vacuum_scale_factor = 0.0);
ALTER TABLE {table_name} SET (autovacuum_vacuum_threshold = 10000000);
ALTER TABLE {table_name} SET (autovacuum_analyze_scale_factor = 0.0);
ALTER TABLE {table_name} SET (autovacuum_analyze_threshold = 10000000);
""".format(
table_name=table_name)
for command in create_table_command.split(";"):
run_sql(db, command)
command = """create or replace view export_queue as
SELECT id AS doi,
updated AS updated,
response_jsonb->>'evidence' AS evidence,
response_jsonb->>'oa_status' AS oa_color,
response_jsonb->>'free_fulltext_url' AS best_open_url,
response_jsonb->>'year' AS year,
response_jsonb->>'found_hybrid' AS found_hybrid,
response_jsonb->>'found_green' AS found_green,
response_jsonb->>'error' AS error,
response_jsonb->>'is_boai_license' AS is_boai_license,
replace(api->'_source'->>'journal', '
', '') AS journal,
replace(api->'_source'->>'publisher', '
', '') AS publisher,
api->'_source'->>'title' AS title,
api->'_source'->>'subject' AS subject,
response_jsonb->>'license' AS license
FROM pub where id in (select id from {table_name})""".format(
table_name=table_name(job_type))
# if job_type:
# command_with_hybrid = command.replace("response_jsonb", "response_with_hybrid").replace("export_queue", "export_queue_with_hybrid")
run_sql(db, command)
# they are already lowercased
logger.info(u"add_dois_to_queue_from_query done in {} seconds".format(elapsed(start, 1)))
print_status(job_type)
def run(parsed_args, job_type):
start = time()
if job_type in ("normal", "hybrid"):
update = update_registry.get("Pub."+process_name(job_type))
if parsed_args.doi:
parsed_args.id = clean_doi(parsed_args.doi)
parsed_args.doi = None
else:
update = update_registry.get("DateRange.get_unpaywall_events")
# update = update_registry.get("DateRange.get_pmh_events")
update.run(**vars(parsed_args))
logger.info(u"finished update in {} seconds".format(elapsed(start)))
resp = None
if job_type in ("normal", "hybrid"):
my_pub = Pub.query.get(parsed_args.id)
resp = my_pub.response_jsonb
pprint(resp)
return resp
# python doi_queue.py --hybrid --filename=data/dois_juan_accuracy.csv --dynos=40 --soup
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run stuff.")
parser.add_argument('--id', nargs="?", type=str, help="id of the one thing you want to update (case sensitive)")
parser.add_argument('--doi', nargs="?", type=str, help="id of the one thing you want to update (case insensitive)")
parser.add_argument('--filename', nargs="?", type=str, help="filename with dois, one per line")
parser.add_argument('--addall', default=False, action='store_true', help="add everything")
parser.add_argument('--where', nargs="?", type=str, default=None, help="""where string for addall (eg --where="response_jsonb->>'oa_status'='green'")""")
parser.add_argument('--hybrid', default=False, action='store_true', help="if hybrid, else don't include")
parser.add_argument('--dates', default=False, action='store_true', help="use date queue")
parser.add_argument('--all', default=False, action='store_true', help="do everything")
parser.add_argument('--week', default=False, action='store_true', help="for the last week")
parser.add_argument('--json', default=False, action='store_true', help="as json not csv")
parser.add_argument('--view', nargs="?", type=str, default=None, help="view name to export from")
parser.add_argument('--reset', default=False, action='store_true', help="do you want to just reset?")
parser.add_argument('--run', default=False, action='store_true', help="to run the queue")
parser.add_argument('--status', default=False, action='store_true', help="to logger.info(the status")
parser.add_argument('--dynos', default=None, type=int, help="scale to this many dynos")
parser.add_argument('--export_with_versions', default=False, action='store_true', help="export the results")
parser.add_argument('--export_no_versions', default=False, action='store_true', help="export the results")
parser.add_argument('--logs', default=False, action='store_true', help="logger.info(out logs")
parser.add_argument('--monitor', default=False, action='store_true', help="monitor till done, then turn off dynos")
parser.add_argument('--soup', default=False, action='store_true', help="soup to nuts")
parser.add_argument('--kick', default=False, action='store_true', help="put started but unfinished dois back to unstarted so they are retried")
parsed_args = parser.parse_args()
job_type = "normal"
if parsed_args.hybrid:
job_type = "hybrid"
if parsed_args.dates:
job_type = "dates"
if parsed_args.filename:
if num_dynos(job_type) > 0:
scale_dyno(0, job_type)
truncate(job_type)
add_dois_to_queue_from_file(parsed_args.filename, job_type)
if parsed_args.addall or parsed_args.where:
if num_dynos(job_type) > 0:
scale_dyno(0, job_type)
add_dois_to_queue_from_query(parsed_args.where, job_type)
if parsed_args.soup:
if num_dynos(job_type) > 0:
scale_dyno(0, job_type)
if parsed_args.dynos:
scale_dyno(parsed_args.dynos, job_type)
else:
logger.info(u"no number of dynos specified, so setting 1")
scale_dyno(1, job_type)
monitor_till_done(job_type)
scale_dyno(0, job_type)
export_with_versions(parsed_args.all, job_type, parsed_args.filename, parsed_args.view)
else:
if parsed_args.dynos != None: # to tell the difference from setting to 0
scale_dyno(parsed_args.dynos, job_type)
# if parsed_args.dynos > 0:
# print_logs(job_type)
if parsed_args.reset:
reset_enqueued(job_type)
if parsed_args.status:
print_status(job_type)
if parsed_args.monitor:
monitor_till_done(job_type)
scale_dyno(0, job_type)
if parsed_args.logs:
print_logs(job_type)
if parsed_args.export_with_versions:
export_with_versions(parsed_args.all, job_type, parsed_args.filename, parsed_args.view, parsed_args.week, parsed_args.json)
if parsed_args.export_no_versions:
export_no_versions(parsed_args.all, job_type, parsed_args.filename, parsed_args.view, parsed_args.week, parsed_args.json)
if parsed_args.kick:
kick(job_type)
if parsed_args.id or parsed_args.doi or parsed_args.run:
run(parsed_args, job_type)
# takes 4 hours
# \copy (select response_jsonb from pub) to 'jsonb_export_20180329_113154.jsonl
# takes 10 mins each
# date; sed -i 's/"publishedVersion"/null/g' jsonb_export_20180329_113154.jsonl; date
# date; sed -i 's/"submittedVersion"/null/g' jsonb_export_20180329_113154.jsonl; date
# date; sed -i 's/"acceptedVersion"/null/g' jsonb_export_20180329_113154.jsonl; date
# date; sed -i 's/\\\\/\\/g' jsonb_export_20180329_113154.jsonl; date
# date; sed -i 's/\n\n/\n/g' jsonb_export_20180329_113154.jsonl; date
# takes 40 minutes
# date; gzip -c jsonb_export_20180329_113154.jsonl > jsonb_export_20180329_113154.jsonl.gz; date;
# date; aws s3 cp jsonb_export_20180329_113154.jsonl.gz s3://unpaywall-data-updates/jsonb_export_20180329_113154.jsonl.gz --acl public-read; date; | mit |
electronicvisions/deep-spike | lib/baseline.py | 1 | 1652 | from sklearn.datasets import load_digits
from sklearn import cross_validation
from sklearn import preprocessing
import tensorflow as tf
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
h_1 = tf.nn.relu(tf.matmul(x, w_1) + b_1)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test}))
if __name__ == '__main__':
main()
| gpl-2.0 |
Clyde-fare/scikit-learn | sklearn/metrics/regression.py | 174 | 16953 | """Metrics to assess performance on regression task
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Arnaud Joly <a.joly@ulg.ac.be>
# Jochen Wersdorfer <jochen@wersdoerfer.de>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Joel Nothman <joel.nothman@gmail.com>
# Noel Dawe <noel@dawe.me>
# Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Michael Eickenberg <michael.eickenberg@gmail.com>
# Konstantin Shmelkov <konstantin.shmelkov@polytechnique.edu>
# License: BSD 3 clause
from __future__ import division
import numpy as np
from ..utils.validation import check_array, check_consistent_length
from ..utils.validation import column_or_1d
import warnings
__ALL__ = [
"mean_absolute_error",
"mean_squared_error",
"median_absolute_error",
"r2_score",
"explained_variance_score"
]
def _check_reg_targets(y_true, y_pred, multioutput):
"""Check that y_true and y_pred belong to the same regression task
Parameters
----------
y_true : array-like,
y_pred : array-like,
multioutput : array-like or string in ['raw_values', uniform_average',
'variance_weighted'] or None
None is accepted due to backward compatibility of r2_score().
Returns
-------
type_true : one of {'continuous', continuous-multioutput'}
The type of the true target data, as output by
'utils.multiclass.type_of_target'
y_true : array-like of shape = (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples, n_outputs)
Estimated target values.
multioutput : array-like of shape = (n_outputs) or string in ['raw_values',
uniform_average', 'variance_weighted'] or None
Custom output weights if ``multioutput`` is array-like or
just the corresponding argument if ``multioutput`` is a
correct keyword.
"""
check_consistent_length(y_true, y_pred)
y_true = check_array(y_true, ensure_2d=False)
y_pred = check_array(y_pred, ensure_2d=False)
if y_true.ndim == 1:
y_true = y_true.reshape((-1, 1))
if y_pred.ndim == 1:
y_pred = y_pred.reshape((-1, 1))
if y_true.shape[1] != y_pred.shape[1]:
raise ValueError("y_true and y_pred have different number of output "
"({0}!={1})".format(y_true.shape[1], y_pred.shape[1]))
n_outputs = y_true.shape[1]
multioutput_options = (None, 'raw_values', 'uniform_average',
'variance_weighted')
if multioutput not in multioutput_options:
multioutput = check_array(multioutput, ensure_2d=False)
if n_outputs == 1:
raise ValueError("Custom weights are useful only in "
"multi-output cases.")
elif n_outputs != len(multioutput):
raise ValueError(("There must be equally many custom weights "
"(%d) as outputs (%d).") %
(len(multioutput), n_outputs))
y_type = 'continuous' if n_outputs == 1 else 'continuous-multioutput'
return y_type, y_true, y_pred, multioutput
def mean_absolute_error(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Mean absolute error regression loss
Read more in the :ref:`User Guide <mean_absolute_error>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average']
or array-like of shape (n_outputs)
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
If multioutput is 'raw_values', then mean absolute error is returned
for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
MAE output is non-negative floating point. The best value is 0.0.
Examples
--------
>>> from sklearn.metrics import mean_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_absolute_error(y_true, y_pred)
0.5
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_error(y_true, y_pred)
0.75
>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
array([ 0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.849...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
output_errors = np.average(np.abs(y_pred - y_true),
weights=sample_weight, axis=0)
if multioutput == 'raw_values':
return output_errors
elif multioutput == 'uniform_average':
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
def mean_squared_error(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Mean squared error regression loss
Read more in the :ref:`User Guide <mean_squared_error>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average']
or array-like of shape (n_outputs)
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
A non-negative floating point value (the best value is 0.0), or an
array of floating point values, one for each individual target.
Examples
--------
>>> from sklearn.metrics import mean_squared_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_squared_error(y_true, y_pred)
0.375
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> mean_squared_error(y_true, y_pred) # doctest: +ELLIPSIS
0.708...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
... # doctest: +ELLIPSIS
array([ 0.416..., 1. ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.824...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
output_errors = np.average((y_true - y_pred) ** 2, axis=0,
weights=sample_weight)
if multioutput == 'raw_values':
return output_errors
elif multioutput == 'uniform_average':
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
def median_absolute_error(y_true, y_pred):
"""Median absolute error regression loss
Read more in the :ref:`User Guide <median_absolute_error>`.
Parameters
----------
y_true : array-like of shape = (n_samples)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples)
Estimated target values.
Returns
-------
loss : float
A positive floating point value (the best value is 0.0).
Examples
--------
>>> from sklearn.metrics import median_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> median_absolute_error(y_true, y_pred)
0.5
"""
y_type, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred,
'uniform_average')
if y_type == 'continuous-multioutput':
raise ValueError("Multioutput not supported in median_absolute_error")
return np.median(np.abs(y_pred - y_true))
def explained_variance_score(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Explained variance regression score function
Best possible score is 1.0, lower values are worse.
Read more in the :ref:`User Guide <explained_variance_score>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average', \
'variance_weighted'] or array-like of shape (n_outputs)
Defines aggregating of multiple output scores.
Array-like value defines weights used to average scores.
'raw_values' :
Returns a full set of scores in case of multioutput input.
'uniform_average' :
Scores of all outputs are averaged with uniform weight.
'variance_weighted' :
Scores of all outputs are averaged, weighted by the variances
of each individual output.
Returns
-------
score : float or ndarray of floats
The explained variance or ndarray if 'multioutput' is 'raw_values'.
Notes
-----
This is not a symmetric function.
Examples
--------
>>> from sklearn.metrics import explained_variance_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> explained_variance_score(y_true, y_pred) # doctest: +ELLIPSIS
0.957...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> explained_variance_score(y_true, y_pred, multioutput='uniform_average')
... # doctest: +ELLIPSIS
0.983...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
y_diff_avg = np.average(y_true - y_pred, weights=sample_weight, axis=0)
numerator = np.average((y_true - y_pred - y_diff_avg) ** 2,
weights=sample_weight, axis=0)
y_true_avg = np.average(y_true, weights=sample_weight, axis=0)
denominator = np.average((y_true - y_true_avg) ** 2,
weights=sample_weight, axis=0)
nonzero_numerator = numerator != 0
nonzero_denominator = denominator != 0
valid_score = nonzero_numerator & nonzero_denominator
output_scores = np.ones(y_true.shape[1])
output_scores[valid_score] = 1 - (numerator[valid_score] /
denominator[valid_score])
output_scores[nonzero_numerator & ~nonzero_denominator] = 0.
if multioutput == 'raw_values':
# return scores individually
return output_scores
elif multioutput == 'uniform_average':
# passing to np.average() None as weights results is uniform mean
avg_weights = None
elif multioutput == 'variance_weighted':
avg_weights = denominator
else:
avg_weights = multioutput
return np.average(output_scores, weights=avg_weights)
def r2_score(y_true, y_pred,
sample_weight=None,
multioutput=None):
"""R^2 (coefficient of determination) regression score function.
Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.
Read more in the :ref:`User Guide <r2_score>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average',
'variance_weighted'] or None or array-like of shape (n_outputs)
Defines aggregating of multiple output scores.
Array-like value defines weights used to average scores.
Default value correponds to 'variance_weighted', but
will be changed to 'uniform_average' in next versions.
'raw_values' :
Returns a full set of scores in case of multioutput input.
'uniform_average' :
Scores of all outputs are averaged with uniform weight.
'variance_weighted' :
Scores of all outputs are averaged, weighted by the variances
of each individual output.
Returns
-------
z : float or ndarray of floats
The R^2 score or ndarray of scores if 'multioutput' is
'raw_values'.
Notes
-----
This is not a symmetric function.
Unlike most other scores, R^2 score may be negative (it need not actually
be the square of a quantity R).
References
----------
.. [1] `Wikipedia entry on the Coefficient of determination
<http://en.wikipedia.org/wiki/Coefficient_of_determination>`_
Examples
--------
>>> from sklearn.metrics import r2_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> r2_score(y_true, y_pred) # doctest: +ELLIPSIS
0.948...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> r2_score(y_true, y_pred, multioutput='variance_weighted') # doctest: +ELLIPSIS
0.938...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
if sample_weight is not None:
sample_weight = column_or_1d(sample_weight)
weight = sample_weight[:, np.newaxis]
else:
weight = 1.
numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0,
dtype=np.float64)
denominator = (weight * (y_true - np.average(
y_true, axis=0, weights=sample_weight)) ** 2).sum(axis=0,
dtype=np.float64)
nonzero_denominator = denominator != 0
nonzero_numerator = numerator != 0
valid_score = nonzero_denominator & nonzero_numerator
output_scores = np.ones([y_true.shape[1]])
output_scores[valid_score] = 1 - (numerator[valid_score] /
denominator[valid_score])
# arbitrary set to zero to avoid -inf scores, having a constant
# y_true is not interesting for scoring a regression anyway
output_scores[nonzero_numerator & ~nonzero_denominator] = 0.
if multioutput is None and y_true.shape[1] != 1:
# @FIXME change in 0.18
warnings.warn("Default 'multioutput' behavior now corresponds to "
"'variance_weighted' value, it will be changed "
"to 'uniform_average' in 0.18.",
DeprecationWarning)
multioutput = 'variance_weighted'
if multioutput == 'raw_values':
# return scores individually
return output_scores
elif multioutput == 'uniform_average':
# passing None as weights results is uniform mean
avg_weights = None
elif multioutput == 'variance_weighted':
avg_weights = denominator
# avoid fail on constant y or one-element arrays
if not np.any(nonzero_denominator):
if not np.any(nonzero_numerator):
return 1.0
else:
return 0.0
else:
avg_weights = multioutput
return np.average(output_scores, weights=avg_weights)
| bsd-3-clause |
Clyde-fare/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 326 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the separating hyperplane with automatically correction for
unbalanced classes.
.. currentmodule:: sklearn.linear_model
.. note::
This example will also work by replacing ``SVC(kernel="linear")``
with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter
of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour
such as that of a SVC with a linear kernel.
For example try instead of the ``SVC``::
clf = SGDClassifier(n_iter=100, alpha=0.01)
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
#from sklearn.linear_model import SGDClassifier
# we create 40 separable points
rng = np.random.RandomState(0)
n_samples_1 = 1000
n_samples_2 = 100
X = np.r_[1.5 * rng.randn(n_samples_1, 2),
0.5 * rng.randn(n_samples_2, 2) + [2, 2]]
y = [0] * (n_samples_1) + [1] * (n_samples_2)
# fit the model and get the separating hyperplane
clf = svm.SVC(kernel='linear', C=1.0)
clf.fit(X, y)
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - clf.intercept_[0] / w[1]
# get the separating hyperplane using weighted classes
wclf = svm.SVC(kernel='linear', class_weight={1: 10})
wclf.fit(X, y)
ww = wclf.coef_[0]
wa = -ww[0] / ww[1]
wyy = wa * xx - wclf.intercept_[0] / ww[1]
# plot separating hyperplanes and samples
h0 = plt.plot(xx, yy, 'k-', label='no weights')
h1 = plt.plot(xx, wyy, 'k--', label='with weights')
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.legend()
plt.axis('tight')
plt.show()
| bsd-3-clause |
predicador37/pyjstat | pyjstat/pyjstat.py | 1 | 36310 | # -*- coding: utf-8 -*-
"""pyjstat is a python module for JSON-stat formatted data manipulation.
This module allows reading and writing JSON-stat [1]_ format with python,
using data frame structures provided by the widely accepted
pandas library [2]_. The JSON-stat format is a simple lightweight JSON format
for data dissemination. Pyjstat is inspired in rjstat [3]_, a library to read
and write JSON-stat with R, by ajschumacher.
pyjstat is written and maintained by `Miguel Expósito Martín
<https://twitter.com/predicador37>`_ and is distributed under the Apache 2.0
License (see LICENSE file).
.. [1] http://json-stat.org/ for JSON-stat information
.. [2] http://pandas.pydata.org for Python Data Analysis Library information
.. [3] https://github.com/ajschumacher/rjstat for rjstat library information
Example:
Importing a JSON-stat file into a pandas data frame can be done as follows::
import urllib2
import json
import pyjstat
results = pyjstat.from_json_stat(json.load(urllib2.urlopen(
'http://json-stat.org/samples/oecd-canada.json')))
print results
"""
import inspect
import json
import logging
import warnings
from collections import OrderedDict
from datetime import datetime
import numpy as np
import pandas as pd
import requests
LOGGER = logging.getLogger(__name__)
LOGGER.setLevel(logging.INFO)
try:
basestring
except NameError:
basestring = str
class NumpyEncoder(json.JSONEncoder):
"""Custom JSON encoder class for Numpy data types."""
def default(self, obj):
"""Encode by default."""
if isinstance(obj, np.integer) or isinstance(obj, np.int64):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(NumpyEncoder, self).default(obj)
def to_int(variable):
"""Convert variable to integer or string depending on the case.
Args:
variable (string): a string containing a real string or an integer.
Returns:
variable(int, string): an integer or a string, depending on the content
of variable.
"""
try:
return int(variable)
except ValueError:
return variable
def to_str(variable):
"""Convert variable to integer or string depending on the case.
Args:
variable (string): a string containing a real string or an integer.
Returns:
variable(int, string): an integer or a string, depending on the content
of variable.
"""
try:
int(variable)
return str(variable)
except ValueError:
return variable
def check_version_2(dataset):
"""Check for json-stat version.
Check if json-stat version attribute exists and is equal or greater
than 2.0 for a given dataset.
Args:
dataset (OrderedDict): data in JSON-stat format, previously
deserialized to a python object by
json.load() or json.loads(),
Returns:
bool: True if version exists and is equal or greater than 2.0,
False otherwise. For datasets without the version attribute,
always return False.
"""
if float(dataset.get('version')) >= 2.0 \
if dataset.get('version') else False:
if 'id' not in dataset:
# Some datasets claim to be 2.0, but don't have version 2 structure.
return False
return True
else:
return False
def unnest_collection(collection, df_list):
"""Unnest collection extracting its datasets and converting them to df.
Args:
collection (OrderedDict): data in JSON-stat format, previously
deserialized to a python object by
json.load() or json.loads(),
df_list (list): list variable which will contain the converted
datasets.
Returns:
Nothing.
"""
for item in collection['link']['item']:
if item['class'] == 'dataset':
df_list.append(Dataset.read(item['href']).write('dataframe'))
elif item['class'] == 'collection':
nested_collection = request(item['href'])
unnest_collection(nested_collection, df_list)
def check_input(naming):
"""Check and validate input params.
Args:
naming (string): a string containing the naming type (label or id).
Returns:
Nothing
Raises:
ValueError: if the parameter is not in the allowed list.
"""
if naming not in ['label', 'id']:
raise ValueError('naming must be "label" or "id"')
def get_dimensions(js_dict, naming):
"""Get dimensions from input data.
Args:
js_dict (dict): dictionary containing dataset data and metadata.
naming (string, optional): dimension naming. Possible values: 'label'
or 'id'.
Returns:
dimensions (list): list of pandas data frames with dimension
category data.
dim_names (list): list of strings with dimension names.
"""
dimensions = []
dim_names = []
if check_version_2(js_dict):
dimension_dict = js_dict
else:
dimension_dict = js_dict['dimension']
for dim in dimension_dict['id']:
if 'label' in js_dict['dimension'][dim]:
dim_name = js_dict['dimension'][dim]['label']
else:
# All datasets don't have labels for dimensions
dim_name = dim
if not dim_name:
dim_name = dim
if naming == 'label':
dim_label = get_dim_label(js_dict, dim)
dimensions.append(dim_label)
dim_names.append(dim_name)
else:
dim_index = get_dim_index(js_dict, dim)
dimensions.append(dim_index)
dim_names.append(dim)
return dimensions, dim_names
def get_dim_label(js_dict, dim, dim_input="dataset"):
"""Get label from a given dimension.
Args:
js_dict (dict): dictionary containing dataset data and metadata.
dim (string): dimension name obtained from JSON file.
Returns:
dim_label(pandas.DataFrame): DataFrame with label-based dimension data.
"""
if dim_input == 'dataset':
dim_input = js_dict['dimension'][dim]
label_col = 'label'
elif dim_input == 'dimension':
label_col = js_dict['label']
dim_input = js_dict
else:
raise ValueError
try:
dim_label = dim_input['category']['label']
except KeyError:
dim_index = get_dim_index(js_dict, dim)
dim_label = pd.concat([dim_index['id'],
dim_index['id']],
axis=1)
dim_label.columns = ['id', 'label']
else:
dim_label = pd.DataFrame(list(zip(dim_label.keys(),
dim_label.values())),
index=dim_label.keys(),
columns=['id', label_col])
# index must be added to dim label so that it can be sorted
try:
dim_index = dim_input['category']['index']
except KeyError:
dim_index = pd.DataFrame(list(zip([dim_label['id'][0]], [0])),
index=[0],
columns=['id', 'index'])
else:
if type(dim_index) is list:
dim_index = pd.DataFrame(list(zip(dim_index,
range(0, len(dim_index)))),
index=dim_index, columns=['id', 'index'])
else:
dim_index = pd.DataFrame(list(zip(dim_index.keys(),
dim_index.values())),
index=dim_index.keys(),
columns=['id', 'index'])
dim_label = pd.merge(dim_label, dim_index, on='id').sort_values(by='index')
return dim_label
def get_dim_index(js_dict, dim):
"""Get index from a given dimension.
Args:
js_dict (dict): dictionary containing dataset data and metadata.
dim (string): dimension name obtained from JSON file.
Returns:
dim_index (pandas.DataFrame): DataFrame with index-based dimension data.
"""
try:
dim_index = js_dict['dimension'][dim]['category']['index']
except KeyError:
dim_label = get_dim_label(js_dict, dim)
dim_index = pd.DataFrame(list(zip([dim_label['id'][0]], [0])),
index=[0],
columns=['id', 'index'])
else:
if type(dim_index) is list:
dim_index = pd.DataFrame(list(zip(dim_index,
range(0, len(dim_index)))),
index=dim_index, columns=['id', 'index'])
else:
dim_index = pd.DataFrame(list(zip(dim_index.keys(),
dim_index.values())),
index=dim_index.keys(),
columns=['id', 'index'])
dim_index = dim_index.sort_values(by='index')
return dim_index
def get_values(js_dict, value='value'):
"""Get values from input data.
Args:
js_dict (dict): dictionary containing dataset data and metadata.
value (string, optional): name of the value column. Defaults to 'value'.
Returns:
values (list): list of dataset values.
"""
values = js_dict[value]
if type(values) is list:
if type(values[0]) is not dict or tuple:
return values
# being not a list of dicts or tuples leaves us with a dict...
values = {int(key): value for (key, value) in values.items()}
if js_dict.get('size'):
max_val = np.prod(np.array((js_dict['size'])))
else:
max_val = np.prod(np.array((js_dict['dimension']['size'])))
vals = max_val * [None]
for (key, value) in values.items():
vals[key] = value
values = vals
return values
def get_df_row(dimensions, naming='label', i=0, record=None):
"""Generate row dimension values for a pandas dataframe.
Args:
dimensions (list): list of pandas dataframes with dimension labels
generated by get_dim_label or get_dim_index methods.
naming (string, optional): dimension naming. Possible values: 'label'
or 'id'.
i (int): dimension list iteration index. Default is 0, it's used in the
recursive calls to the method.
record (list): list of values representing a pandas dataframe row,
except for the value column. Default is empty, it's used
in the recursive calls to the method.
Yields:
list: list with pandas dataframe column values except for value column
"""
check_input(naming)
if i == 0 or record is None:
record = []
for dimension in dimensions[i][naming]:
record.append(dimension)
if len(record) == len(dimensions):
yield record
if i + 1 < len(dimensions):
for row in get_df_row(dimensions, naming, i + 1, record):
yield row
if len(record) == i + 1:
record.pop()
def uniquify(seq):
"""Return unique values in a list in the original order.
See: http://www.peterbe.com/plog/uniqifiers-benchmark
Args:
seq (list): original list.
Returns:
list: list without duplicates preserving original order.
"""
seen = set()
seen_add = seen.add
return [x for x in seq if x not in seen and not seen_add(x)]
def generate_df(js_dict, naming, value="value"):
"""Decode JSON-stat dict into pandas.DataFrame object.
Helper method that should be called inside from_json_stat().
Args:
js_dict(OrderedDict): OrderedDict with data in JSON-stat format,
previously deserialized into a python object by
json.load() or json.loads(), for example.
naming(string): dimension naming. Possible values: 'label' or 'id.'
value (string, optional): name of the value column. Defaults to 'value'.
Returns:
output(DataFrame): pandas.DataFrame with converted data.
"""
values = []
dimensions, dim_names = get_dimensions(js_dict, naming)
values = get_values(js_dict, value=value)
output = pd.DataFrame([category + [values[i]]
for i, category in
enumerate(get_df_row(dimensions, naming))])
output.columns = dim_names + [value]
output.index = range(0, len(values))
return output
def from_json_stat(datasets, naming='label', value='value'):
"""Decode JSON-stat formatted data into pandas.DataFrame object.
Args:
datasets(OrderedDict, list): data in JSON-stat format, previously
deserialized to a python object by
json.load() or json.loads(), for example.
Both List and OrderedDict are accepted
as inputs.
naming(string, optional): dimension naming. Possible values: 'label'
or 'id'.Defaults to 'label'.
value (string, optional): name of the value column. Defaults to 'value'.
Returns:
results(list): list of pandas.DataFrame with imported data.
"""
warnings.warn(
"Shouldn't use this function anymore! Now use read() methods of"
"Dataset, Collection or Dimension.",
DeprecationWarning
)
check_input(naming)
results = []
if type(datasets) is list:
for idx, element in enumerate(datasets):
for dataset in element:
js_dict = datasets[idx][dataset]
results.append(generate_df(js_dict, naming, value))
elif isinstance(datasets, OrderedDict) or type(datasets) is dict or \
isinstance(datasets, Dataset):
if 'class' in datasets:
if datasets['class'] == 'dataset':
js_dict = datasets
results.append(generate_df(js_dict, naming, value))
else: # 1.00 bundle type
for dataset in datasets:
js_dict = datasets[dataset]
results.append(generate_df(js_dict, naming, value))
return results
def _round_decimals(dataframe, units, roles, value):
"""Round values according to units definition."""
if not isinstance(dataframe, pd.DataFrame):
return dataframe
if not isinstance(units, dict):
return dataframe
if not isinstance(roles, dict):
return dataframe
if not isinstance(value, str):
return dataframe
df = dataframe.copy(deep=True)
for unit in units.keys():
if unit in roles['metric']:
if '*' in units[unit].keys():
decimals = units[unit]['*'].get('decimals')
if decimals is not None:
df[value] = df[value].round(decimals)
else:
for label in units[unit].keys():
decimals = units[unit][label].get('decimals')
if decimals is not None:
for idx, row in df.iterrows():
if row[unit] == label:
df.at[
idx, value] = df.at[
idx, value].round(decimals)
return df
def _add_units_to_categories(categories, units, roles):
"""Add units to categories according to units definition."""
if not isinstance(categories, list):
return categories
if not isinstance(units, dict):
return categories
if not isinstance(roles, dict):
return categories
for unit in units.keys():
if unit in roles['metric']:
for idx, category in enumerate(categories):
if unit == list(category)[0]:
if '*' in units[unit].keys():
units_updated = {}
for c in categories[idx][unit]['category']['label']:
units_updated.update(
{c: {
'label': units[
unit]['*'].get('label'),
'decimals': units[
unit]['*'].get('decimals')
}}
)
categories[idx][unit]['category'].update(
{"unit": units_updated})
else:
categories[idx][unit]['category'].update(
{"unit": units[unit]})
return categories
def _get_categories(data, unit=None, role=None, value='value'):
"""Return a list of categories according to dimensions.
When unit, role and value are included in parameters,
decimals are rounded, also adds units dictionary to result.
Args:
data(pandas.Dataframe): must have exactly one value column.
value (string, optional): name of the value column. Defaults to 'value'.
role(dict, optional): roles for dimensions.
unit(dict, optional): unit for variables, if there is only one
element named '*' it will repeated for all.
"""
dims = data.filter([item for item in data.columns.values
if item not in value])
if isinstance(unit, dict) and isinstance(role, dict):
data = _round_decimals(data, unit, role, value)
categories_without_units = [
{to_int(i):
{"label": to_str(i),
"category":
{"index":
OrderedDict([(to_str(j), to_int(k))
for k, j in
enumerate(uniquify(dims[i]))]),
"label":
OrderedDict([(to_str(j), to_str(j))
for k, j in
enumerate(uniquify(dims[i]))])
}}}
for i in dims.columns.values]
categories = _add_units_to_categories(
categories_without_units, unit, role)
else:
categories = [{to_int(i):
{"label": to_str(i),
"category":
{"index":
OrderedDict([(to_str(j), to_int(k))
for k, j in
enumerate(uniquify(dims[i]))]),
"label":
OrderedDict([(to_str(j), to_str(j))
for k, j in
enumerate(uniquify(dims[i]))])}}}
for i in dims.columns.values]
return categories
def to_json_stat(input_df, value='value',
output='list', version='1.3',
updated=datetime.today(), source='Self-elaboration',
note=None, role=None, unit=None):
"""Encode pandas.DataFrame object into JSON-stat format.
The DataFrames must have exactly one value column.
Args:
df(pandas.DataFrame): pandas data frame (or list of data frames) to
encode.
value (string, optional): name of the value column. Defaults to 'value'.
output(string): accepts two values: 'list' or 'dict'. Produce list of
dicts or dict of dicts as output.
version(string): desired json-stat version. 2.0 is preferred now.
Apart from this, only older 1.3 format is accepted,
which is the default parameter in order to preserve
backwards compatibility.
updated(datetime): updated metadata in JSON-stat standard. Must be a
datetime in ISO format.
source(string, optional): data source in JSON-stat standard.
note(string, optional): information for metadata.
role(dict, optional): roles for dimensions.
unit(dict, optional): unit for variables, if there is only one
element named '*' it will repeated for all.
Returns:
output(string): String with JSON-stat object.
"""
warnings.warn(
"Shouldn't use this function anymore! Now use write() methods of"
"Dataset, Collection or Dimension.",
DeprecationWarning
)
data = []
if output == 'list':
result = []
elif output == 'dict':
result = OrderedDict({})
if isinstance(input_df, pd.DataFrame):
data.append(input_df)
else:
data = input_df
for row, dataframe in enumerate(data):
dims = data[row].filter([item for item in data[row].columns.values
if item not in value])
if len(dims.columns.values) != len(set(dims.columns.values)):
raise ValueError('Non-value columns must constitute a unique ID')
dim_names = list(dims)
if isinstance(role, dict):
for metric in role.get('metric'):
uniques_dimensions = dataframe[metric].unique()
for dimension in uniques_dimensions:
tmp_df = dataframe.query(metric+" == '"+dimension+"'")
time_column = tmp_df.loc[:, role.get('time')[0]]
if time_column.duplicated().any():
warnings.warn(
"Data duplicated in time dimension."
"Doesn't exist unique values for each dimension. ",
UserWarning
)
categories = _get_categories(dataframe, unit, role, value)
if float(version) >= 2.0:
dataset = {"dimension": OrderedDict(),
value: [None if pd.isnull(x) else x
for x in dataframe[value].values]}
dataset["version"] = version
dataset["class"] = "dataset"
dataset["updated"] = updated.isoformat()
dataset["source"] = source
if isinstance(role, dict):
dataset["role"] = role
if isinstance(note, str):
dataset["note"] = [note]
for category in categories:
dataset["dimension"].update(category)
dataset.update({"id": dim_names})
dataset.update({"size": [len(dims[i].unique())
for i in dims.columns.values]})
for category in categories:
dataset["dimension"].update(category)
else:
dataset = {"dataset" +
str(row + 1):
{"dimension": OrderedDict(),
value: [None if pd.isnull(x) else x
for x in dataframe[value].values]}}
for category in categories:
dataset["dataset" + str(row + 1)][
"dimension"].update(category)
dataset["dataset" + str(row + 1)][
"dimension"].update({"id": dim_names})
dataset["dataset" + str(row + 1)][
"dimension"].update({"size": [len(dims[i].unique())
for i in dims.columns.values]})
for category in categories:
dataset["dataset" + str(row + 1)][
"dimension"].update(category)
if output == 'list':
result.append(dataset)
elif output == 'dict':
result.update(dataset)
else:
result = None
return json.dumps(result, cls=NumpyEncoder)
def request(path, verify=True):
"""Send a request to a given URL accepting JSON format.
Args:
path (str): The URI to be requested.
Returns:
response: Deserialized JSON Python object.
Raises:
HTTPError: the HTTP error returned by the requested server.
InvalidURL: an invalid URL has been requested.
Exception: generic exception.
"""
headers = {'Accept': 'application/json'}
try:
requested_object = requests.get(path, headers=headers, verify=verify)
requested_object.raise_for_status()
except requests.exceptions.HTTPError as exception:
LOGGER.error((inspect.stack()[0][3]) + ': HTTPError = ' +
str(exception.response.status_code) + ' ' +
str(exception.response.reason) + ' ' + str(path))
raise
except requests.exceptions.InvalidURL as exception:
LOGGER.error('URLError = ' + str(exception.reason) + ' ' + str(path))
raise
except Exception:
import traceback
LOGGER.error('Generic exception: ' + traceback.format_exc())
raise
else:
response = requested_object.json()
return response
class Dataset(OrderedDict):
"""A class representing a JSONstat dataset."""
def __init__(self, *args, **kwargs):
"""Initialize object."""
super(Dataset, self).__init__(*args, **kwargs)
@ classmethod
def read(cls, data, verify=True, **kwargs):
"""Read data from URL, Dataframe, JSON string/file or OrderedDict.
Args:
data: can be a Pandas Dataframe, a JSON file, a JSON string,
an OrderedDict or a URL pointing to a JSONstat file.
verify: whether to host's SSL certificate.
kwargs: optional arguments for to_json_stat().
Returns:
An object of class Dataset populated with data.
"""
if isinstance(data, pd.DataFrame):
return cls((json.loads(
to_json_stat(data, output='dict', version='2.0', **kwargs),
object_pairs_hook=OrderedDict)))
elif isinstance(data, OrderedDict):
return cls(data)
elif (isinstance(data, basestring)
and data.startswith(("http://", "https://",
"ftp://", "ftps://"))):
# requests will do the rest...
return cls(request(data, verify=verify))
elif isinstance(data, basestring):
try:
json_dict = json.loads(data, object_pairs_hook=OrderedDict)
return cls(json_dict)
except ValueError:
raise
else:
try:
json_dict = json.load(data, object_pairs_hook=OrderedDict)
return cls(json_dict)
except ValueError:
raise
def write(self, output='jsonstat', naming="label", value='value'):
"""Write data from a Dataset object to JSONstat or Pandas Dataframe.
Args:
output(string): can accept 'jsonstat' or 'dataframe'. Default to
'jsonstat'.
naming (string): optional, ingored if output = 'jsonstat'.
Dimension naming.
Possible values: 'label' or 'id'. Defaults to 'label'.
value (string): optional, ignored if output = 'jsonstat'.
Name of value column.
Defaults to 'value'.
Returns:
Serialized JSONstat or a Pandas Dataframe,depending on the
'output' parameter.
"""
if output == 'jsonstat':
return json.dumps(OrderedDict(self), cls=NumpyEncoder)
elif output == 'dataframe':
return from_json_stat(self, naming=naming, value=value)[0]
else:
raise ValueError("Allowed arguments are 'jsonstat' or 'dataframe'")
def get_dimension_index(self, name, value):
"""Get a dimension index from its name.
Convert a dimension ID string and a category ID string into the
numeric index of that category in that dimension
Args:
name(string): ID string of the dimension.
value(string): ID string of the category.
Returns:
ndx[value](int): index of the category in the dimension.
"""
if 'index' not in self.get('dimension', {}). \
get(name, {}).get('category', {}):
return 0
ndx = self['dimension'][name]['category']['index']
if isinstance(ndx, list):
return ndx.index(value)
else:
return ndx[value]
def get_dimension_indices(self, query):
"""Get dimension indices.
Converts a dimension/category list of dicts into a list of
dimension indices.
Args:
query(list): dimension/category list of dicts.
Returns:
indices(list): list of dimensions' indices.
"""
ids = self['id'] if self.get('id') else self['dimension']['id']
indices = []
for idx, ident in enumerate(ids):
indices.append(self.get_dimension_index(
ident, [d.get(ident) for d in query if ident in d][0]))
return indices
def get_value_index(self, indices):
"""Convert a list of dimension indices into a numeric value index.
Args:
indices(list): list of dimension's indices.
Returns:
num(int): numeric value index.
"""
size = self['size'] if self.get('size') else self['dimension']['size']
ndims = len(size)
mult = 1
num = 0
for idx, dim in enumerate(size):
mult *= size[ndims - idx] if (idx > 0) else 1
num += mult * indices[ndims - idx - 1]
return num
def get_value_by_index(self, index):
"""Convert a numeric value index into its data value.
Args:
index(int): numeric value index.
Returns:
self['value'][index](float): Numeric data value.
"""
return self['value'][index]
def get_value(self, query):
"""Get data value.
Convert a dimension/category list of dicts into a data value
in three steps.
Args:
query(list): list of dicts with the desired query.
Returns:
value(float): numeric data value.
"""
indices = self.get_dimension_indices(query)
index = self.get_value_index(indices)
value = self.get_value_by_index(index)
return value
class Dimension(OrderedDict):
"""A class representing a JSONstat dimension."""
def __init__(self, *args, **kwargs):
"""Initialize object."""
super(Dimension, self).__init__(*args, **kwargs)
@ classmethod
def read(cls, data):
"""Read data from URL, Dataframe, JSON string/file or OrderedDict.
Args:
data: can be a Pandas Dataframe, a JSON string, a JSON file,
an OrderedDict or a URL pointing to a JSONstat file.
Returns:
An object of class Dimension populated with data.
"""
if isinstance(data, pd.DataFrame):
output = OrderedDict({})
output['version'] = '2.0'
output['class'] = 'dimension'
[label] = [x for x in list(data.columns.values) if
x not in ['id', 'index']]
output['label'] = label
output['category'] = OrderedDict({})
output['category']['index'] = data.id.tolist()
output['category']['label'] = OrderedDict(
zip(data.id.values, data[label].values))
return cls(output)
elif isinstance(data, OrderedDict):
return cls(data)
elif isinstance(data, basestring) and data.startswith(("http://",
"https://",
"ftp://",
"ftps://")):
return cls(request(data))
elif isinstance(data, basestring):
try:
json_dict = json.loads(data, object_pairs_hook=OrderedDict)
return cls(json_dict)
except ValueError:
raise
else:
try:
json_dict = json.load(data, object_pairs_hook=OrderedDict)
return cls(json_dict)
except ValueError:
raise
def write(self, output='jsonstat'):
"""Write data from a Dataset object to JSONstat or Pandas Dataframe.
Args:
output(string): can accept 'jsonstat' or 'dataframe'
Returns:
Serialized JSONstat or a Pandas Dataframe,depending on the
'output' parameter.
"""
if output == 'jsonstat':
return json.dumps(OrderedDict(self), cls=NumpyEncoder)
elif output == 'dataframe':
return get_dim_label(self, self['label'], 'dimension')
else:
raise ValueError("Allowed arguments are 'jsonstat' or 'dataframe'")
class Collection(OrderedDict):
"""A class representing a JSONstat collection."""
def __init__(self, *args, **kwargs):
"""Initialize object."""
super(Collection, self).__init__(*args, **kwargs)
@ classmethod
def read(cls, data):
"""Read data from URL or OrderedDict.
Args:
data: can be a URL pointing to a JSONstat file, a JSON string
or an OrderedDict.
Returns:
An object of class Collection populated with data.
"""
if isinstance(data, OrderedDict):
return cls(data)
elif isinstance(data, basestring) and data.startswith(("http://",
"https://",
"ftp://",
"ftps://")):
return cls(request(data))
elif isinstance(data, basestring):
try:
json_dict = json.loads(data, object_pairs_hook=OrderedDict)
return cls(json_dict)
except ValueError:
raise
else:
try:
json_dict = json.load(data, object_pairs_hook=OrderedDict)
return cls(json_dict)
except ValueError:
raise
def write(self, output='jsonstat'):
"""Write to JSON-stat or list of df.
Writes data from a Collection object to JSONstat or list of Pandas
Dataframes.
Args:
output(string): can accept 'jsonstat' or 'dataframe_list'
Returns:
Serialized JSONstat or a list of Pandas Dataframes,depending on
the 'output' parameter.
"""
if output == 'jsonstat':
return json.dumps(self)
elif output == 'dataframe_list':
df_list = []
unnest_collection(self, df_list)
return df_list
else:
raise ValueError(
"Allowed arguments are 'jsonstat' or 'dataframe_list'")
def get(self, element):
"""Get element from collection.
Get ith element from a collection in an object of the corresponding
class.
Args:
output(string): can accept 'jsonstat' or 'dataframe_list'
Returns:
Serialized JSONstat or a list of Pandas Dataframes,depending on
the 'output' parameter.
"""
if self['link']['item'][element]['class'] == 'dataset':
return Dataset.read(self['link']['item'][element]['href'])
elif self['link']['item'][element]['class'] == 'collection':
return Collection.read(self['link']['item'][element]['href'])
elif self['link']['item'][element]['class'] == 'dimension':
return Dimension.read(self['link']['item'][element]['href'])
else:
raise ValueError(
"Class not allowed. Please use dataset, collection or "
"dimension'")
| apache-2.0 |
yukisakurai/hhntup | higgstautau/jetcalibration.py | 1 | 4797 | """
See instructions here:
https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/JetCalibrationToolsForPhysicsAnalyses
https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/ApplyJetCalibration2011
https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/ApplyJetCalibration2012
"""
from rootpy.tree.filtering import EventFilter
from rootpy import ROOTError
from externaltools import ApplyJetCalibration
from ROOT import JetCalibrationTool
from . import log; log = log[__name__]
from . import datasets
class JetCalibration(EventFilter):
"""
This class applies jet calibrations
"""
def __init__(self, datatype, year,
verbose=False,
passthrough=False,
**kwargs):
super(JetCalibration, self).__init__(
passthrough=passthrough,
**kwargs)
if not passthrough:
self.year = year
self.datatype = datatype
self.isdata = datatype in (datasets.DATA, datasets.EMBED)
self.verbose = verbose
self.algo = 'AntiKt4LCTopo'
if year == 2011:
config = 'InsituJES_2011_Preliminary.config'
# use Rel17_JES_AFII.config for fastsim MC
elif year == 2012:
config = 'JES_Full2012dataset_Preliminary_Jan13.config'
# use JES_Full2012dataset_Preliminary_AFII_Jan13.config for fastsim MC
else:
raise ValueError(
"No JES calibration defined for year %d" % year)
log.info("Using JES config %s" % config)
self.config_file = ApplyJetCalibration.get_resource(
'CalibrationConfigs/%s' % config)
self.jes_tool = JetCalibrationTool(
self.algo,
self.config_file,
self.isdata)
def passes(self, event):
# For the pile-up correction, we need mu and NPV(2+ tracks)
mu = event.averageIntPerXing
# count the number of vertices with 2 or more tracks
NPV = 0
for vertex in event.vertices:
if vertex.nTracks >= 2:
NPV += 1
if self.verbose:
print "JETS BEFORE RECALIBRATION"
self.print_jets(event)
if self.year == 2011:
for jet in event.jets:
Eraw = jet.constscale_E
eta_det = jet.constscale_eta
eta = jet.EtaOrigin
phi = jet.PhiOrigin
m = jet.MOrigin
calib_jet = self.jes_tool.ApplyOffsetEtaJES(
Eraw, eta_det, eta, phi, m, mu, NPV)
jet.E = calib_jet.E()
jet.m = calib_jet.M()
jet.pt = calib_jet.Pt()
jet.eta = calib_jet.Eta()
jet.phi = calib_jet.Phi()
elif self.year == 2012:
rho = event.Eventshape_rhoKt4LC
for jet in event.jets:
Eraw = jet.constscale_E
eta = jet.constscale_eta
phi = jet.constscale_phi
m = jet.constscale_m
Ax = jet.ActiveAreaPx
Ay = jet.ActiveAreaPy
Az = jet.ActiveAreaPz
Ae = jet.ActiveAreaE
try:
calib_jet = self.jes_tool.ApplyJetAreaOffsetEtaJES(
Eraw, eta, phi, m, Ax, Ay, Az, Ae, rho, mu, NPV)
jet.E = calib_jet.E()
jet.m = calib_jet.M()
jet.pt = calib_jet.Pt()
jet.eta = calib_jet.Eta()
jet.phi = calib_jet.Phi()
except ROOTError as e:
print "JET ERROR"
print "Run: ", event.RunNumber
print "Event: ", event.EventNumber
print "Eraw", Eraw
print "eta", eta
print "phi", phi
print "m", m
print "Ax", Ax
print "Ay", Ay
print "Az", Az
print "Ae", Ae
raise e
else:
raise ValueError('Invalid year in jet calibration: %d' % self.year)
if self.verbose:
print "JETS AFTER RECALIBRATION"
self.print_jets(event)
return True
def print_jets(self, event):
for i in xrange(event.jet_n):
# use raw access to D3PD branches as an extra check
print "E: %.3f pT: %.3f eta: %.5f phi: %.5f M: %.3f" % (
event.jet_E[i],
event.jet_pt[i],
event.jet_eta[i],
event.jet_phi[i],
event.jet_m[i])
| gpl-3.0 |
Ventrosky/python-scripts | nlp-scripts/project2.py | 1 | 18812 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Progetto di Linguistica Computazionale - A.A. 2016/2017
# Programma 2
# Salvatore Ventrone - 539872
import sys, codecs, nltk, re, math
from nltk import bigrams, trigrams
from decimal import Decimal, ROUND_DOWN
def usage():
print '-'*80
print "Progetto di Linguistica Computazionale - Programma 2"
print
print "Usage: project2.py text_file1 text_file2"
print
print "Estraete ed ordinate in ordine di frequenza decrescente, indicando la relativa frequenza:"
print "- le 10 PoS (Part-of-Speech) più frequenti;"
print "- i 20 token più frequenti escludendo la punteggiatura;"
print "- i 20 bigrammi di token più frequenti che non contengono punteggiatura, articoli e congiunzioni;"
print "- i 20 trigrammi di token più frequenti che non contengono punteggiatura, articoli e congiunzioni;"
print "Estraete ed ordinate i 20 bigrammi (Aggettivo, Sostantivo) dove ogni frequenza(token) > 2:"
print "- con probabilità congiunta massima, indicandone la probabilità;"
print "- con probabilità condizionata massima, indicandone la probabilità;"
print "- con forza associativa (Local Mutual Information) massima, indicandone forza associativa;"
print "Le due frasi con probabilità più alta:"
print "- prima frase calcolata attraverso un modello di Markov di ordine 0;"
print "- seconda con un modello di Markov di ordine 1;"
print "- i due modelli devono usare le statistiche estratte dal corpus che contiene le frasi;"
print "- frasi lunghe almeno 10 token, ogni token deve avere una frequenza maggiore di 2;"
print "Dopo aver individuato e classificato le Entità Nominate (NE) presenti nel testo, estraete:"
print "- i 20 nomi propri di persona più frequenti (tipi), ordinati per frequenza;"
print "- i 20 nomi propri di luogo più frequenti (tipi), ordinati per frequenza."
print
print "Examples: "
print "python project2.py clinton.txt trump.txt"
print "./project2.py clinton.txt trump.txt > output2.txt"
print ""
print '-'*80
sys.exit(0)
# tutti tokens con almeno freq. 2
def frasiPerMarkov(frasi, tokensTOT):
distrFreq = nltk.FreqDist(tokensTOT)
frasiMarkov = []
for frase in frasi: # frasi gia di len >= 10
freq = list(map(lambda x: distrFreq[x], frase)) # creo vettore di frequenze dei token
if not 1 in freq: # controllo se nessun token appare solo 1 volta nel testo
frasiMarkov.append(frase)
return frasiMarkov
# tokenizza frasi
def estraiTokens(frasi):
tokensTOT = []
frasiLunghe = [] # vettore frasi almeno 10 token
for frase in frasi:
tokens = nltk.word_tokenize(frase)
if len(tokens) > 9:
frasiLunghe.append(tokens)
tokensTOT = tokensTOT + tokens # costruisco vettore tokens
frasiMarkov = frasiPerMarkov(frasiLunghe, tokensTOT) # ogni token deve avere una frequenza maggiore di 2
return tokensTOT, frasiMarkov
# apre e tokenizza file di testo, restituisce tokens
def openTextFile(file1):
fileInput = codecs.open(file1, "r", "utf-8")
raw = fileInput.read()
sent_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
frasi = sent_tokenizer.tokenize(raw)
tokensTesto, frasiMarkov = estraiTokens(frasi) # creo il vettore con tokens, restituisco frasi per esercizio Markov
return tokensTesto, frasiMarkov, frasi
#inserisce elementi POS in una lista
def estraiSeqPOS(testoPOS):
listaPOS = []
for bigramma in testoPOS:
listaPOS.append(bigramma[1])
return listaPOS
# analisi morfosintattica ed Entità Nominate (NE)
def analisiLing(frasi):
namedEntityDict = { "PERSON" : [], "GPE" : []}
tokensPOStot=[]
for frase in frasi:
tokens = nltk.word_tokenize(frase)
tokensPOS = nltk.pos_tag(tokens)
analisi = nltk.ne_chunk(tokensPOS)
for nodo in analisi: # ciclo albero scorrendo nodi
NE = ''
if hasattr(nodo, 'label'): #controlla se chunk è un nodo intermedio
if nodo.label() in ["PERSON", "GPE"]: # nomi propri di persona o luogo
for partNE in nodo.leaves(): # ciclo foglie nodo selezionato
NE = NE+' '+partNE[0]
namedEntityDict[nodo.label()].append(NE)
tokensPOStot = tokensPOStot + tokensPOS
return tokensPOStot, namedEntityDict
# 10 PoS (Part-of-Speech) più frequenti;"
def top10POS(tokensPOS):
seqPOS = estraiSeqPOS(tokensPOS)
freqDist = nltk.FreqDist(seqPOS)
return freqDist.most_common(10)
# restituisce i bigrammi
def getBigrams(tokens):
bigrams1 = list(bigrams(tokens))
return bigrams1
# restituisce i trigrammi
def getTrigrams(tokens):
trigrams1 = list(trigrams(tokens))
return trigrams1
# 20 token più frequenti
def top20Tokens(tokens1):
esclusi = [',','.',':',';'] #non contenenti punteggiatura
nonPunteggiatura = [ a[0] for (a, b) in tokens1 if not (a[1] in esclusi)]
fdist = nltk.FreqDist(nonPunteggiatura)
return fdist.most_common(20)
# 20 bigrammi di token più frequenti
def top20Bigrams(bigrams1):
esclusi = [',','.',':',';','DT','CC'] #non contenenti punteggiatura, articoli e congiunzioni
nonPunteggiatura = [(a[0], b[0]) for (a, b) in bigrams1 if not (b[1] in esclusi) and not (a[1] in esclusi)]
fdist = nltk.FreqDist(nonPunteggiatura)
return fdist.most_common(20)
# 20 trigrammi di token più frequenti
def top20Trigrams(trigrams1):
esclusi = [',','.',':',';','DT','CC'] #non contenenti punteggiatura, articoli e congiunzioni
nonPunteggiatura = [(a[0], b[0], c[0]) for (a, b, c) in trigrams1 if not (b[1] in esclusi) and not (a[1] in esclusi) and not (c[1] in esclusi)]
fdist = nltk.FreqDist(nonPunteggiatura)
return fdist.most_common(20)
# restituisce vero se il PoS tag si riferisce ad aggettivo e sostantivo
def isAggAndSos(tag1, tag2):
return (re.search(r'^JJ', tag1) and re.search(r'^NN', tag2))
# bigrammi aggettivo e sostantivo, frequenza(token) > 2
def bigrammiAggSos(tokensPOS):
bigramsAggSos = [(a[0], b[0]) for (a, b) in tokensPOS if isAggAndSos(a[1],b[1])]
return bigramsAggSos
# dizionario {"token" : (freq_as, freq_rel)}
def dictVocabFreq(vocab, t_tokens):
v = {}
c = len(t_tokens)
freq = 0
for tok in vocab:
freq=t_tokens.count(tok)
v[tok] = (freq, (freq *1.0 / c *1.0))
return v
# dato dizionario ordina per valore
def ordinaDict(dictFreq):
return sorted(dictFreq.items(), key = lambda x: x[1], reverse = True)
# dato dizionario ordina per valore v[4] # P(u,v)
def ordinaProbCongiunta(dictFreq):
return sorted(dictFreq.items(), key = lambda x: x[1][4], reverse = True)
# dato dizionario ordina per valore v[3] # P(v|u)
def ordinaProbCondizionata(dictFreq):
return sorted(dictFreq.items(), key = lambda x: x[1][3], reverse = True)
# crea dizionario {"bigramma" : (F(u,v), F(u), F(v), P(v|u), P(u,v))}
def infoBigrams(bigrams1, setBigramsAggSost,vocabFreq):
b_infos = {}
freq = 0
for b in setBigramsAggSost:
freq = bigrams1.count(b)
u = b[0]
v = b[1]
f_u = vocabFreq[u][0] #freq. assoluta di u
f_u_r = vocabFreq[u][1] #freq. relativa di u
f_v = vocabFreq[v][0]
p_uv1 = (freq *1.0 / f_u *1.0) # P(v|u) probabilità condizionata
p_uv2 = (f_u_r * p_uv1) # P(u,v) probabilità congiunta
b_infos[b] = (freq, f_u, f_v, p_uv1, p_uv2) # memorizzo F(u,v), F(u), F(v), P(v|u), P(u,v)
return b_infos
# calcola Local Mutual Information dei bigrammi
def getLocalMutualInformation(bigramsDict, vocabFreq):
mi = {}
for k,v in bigramsDict.items():
u1 = k[0]
u2 = k[1]
p_uv = v[4] # P(u,v)
mi1 = (p_uv / (vocabFreq[u1][1] * vocabFreq[u2][1])) # mi1 = P(u,v)/(P(u)*P(v))
mi[k] = math.log( mi1, 2) * v[0] # log2(mi1) * f(<u,v>) = Local Mutual Information
return mi
# Calcola Probabilita Frase Markov 0
def probabilitaMarkov0(lenCorpus, distrFreq, frase):
probabilita = 1.0
for tok in frase:
probabilitaToken = (distrFreq[tok]*1.0 / lenCorpus*1.0)
probabilita = probabilita * probabilitaToken # P(w1, w2,...,wn)=P(w1)*P(w2)*...*P(wn)
return probabilita
# restituisce la frase Markov 0 con probabilità più alta
def maxProbMarkov0(lenCorpus, distrFreq, frasi):
probFraseMax = 0
topFrase = []
for frase in frasi:
p = probabilitaMarkov0(lenCorpus, distrFreq, frase) # Probabilita Frase Markov 0
if p > probFraseMax:
probFraseMax = p
topFrase = frase
return topFrase, probFraseMax
# Calcola Probabilita Frase Markov 1; bigramInfos = { bigramma : (F(u,v), F(u), F(v), P(v|u), P(u,v))}
def probabilitaMarkov1(lenCorpus, frase, bigramInfos):
probabilita = 1
w1 = frase[0]
primaParola = True
for tok in frase[1:]:
w2 = tok
if primaParola:
probabilita = ( bigramInfos[(w1,w2)][2] *1.0 / lenCorpus*1.0) #calcolo prob prima parola
primaParola = False
probabilitaBigramma = bigramInfos[(w1,w2)][3] # P(v|u)
probabilita = probabilita * probabilitaBigramma #P(w1, w2,...,wn)=P(w1)*P(w2|w1)*P(w3|w2)*...*P(wn|wn-1)
w1 = w2
return probabilita
# restituisce la frase Markov 1 con probabilità più alta
def maxProbMarkov1(lenCorpus, frasi, bigramInfos):
probFraseMax = 0
topFrase = []
for frase in frasi:
p = probabilitaMarkov1(lenCorpus, frase, bigramInfos) # Probabilita Frase Markov 1
if p > probFraseMax:
probFraseMax = p
topFrase = frase
return topFrase, probFraseMax
def main():
if not len(sys.argv[1:]):
usage()
# nomi dei file passati come argomenti
file1 = sys.argv[1]
file2 = sys.argv[2]
print '-'*80
print " Progetto di Linguistica Computazionale"
print " Programma 2 "
print '-'*80
tokensText1, frasiMarkov1, frasi1 = openTextFile(file1) # tokens del testo, frasi per es. Markov
tokensText2, frasiMarkov2, frasi2 = openTextFile(file2)
tokensPOS1, namedEntityDict1 = analisiLing(frasi1) # processo di annotazione ed NE
tokensPOS2, namedEntityDict2 = analisiLing(frasi2)
top10POS1 = top10POS(tokensPOS1) #10 PoS più frequenti
top10POS2 = top10POS(tokensPOS2)
print '-'*80
print " 10 PoS (Part-of-Speech) più frequenti"
print '-'*80
print ' Testo:', file1,' '*12, '| Testo:', file2
print '-'*80
for i in range(min(len(top10POS1),len(top10POS2))): # stampa il confronto
pos1, freq1 = top10POS1[i]
pos2, freq2 = top10POS2[i]
print " {: <20}{: <12}| {: <20}{: <12}".format(pos1, freq1, pos2, freq2)
print '-'*80
print " 20 Token più frequenti"
print '-'*80
print ' Testo:', file1,' '*12, '| Testo:', file2
print '-'*80
topTokens1 = top20Tokens(getBigrams(tokensPOS1)) #20 Token più frequenti
topTokens2 = top20Tokens(getBigrams(tokensPOS2))
for i in range(min(len(topTokens1),len(topTokens2))): # stampa il confronto
tok1, freq1 = topTokens1[i]
tok2, freq2 = topTokens2[i]
print " {: <25}{: <7}| {: <25}{: <7}".format(tok1.encode('utf-8'), freq1, tok2.encode('utf-8'), freq2)
print '-'*80
print '-'*80
print " 20 Bigrammi più frequenti"
print '-'*80
print ' Testo:', file1,' '*12, '| Testo:', file2
print '-'*80
bigrammi1 = getBigrams(tokensPOS1) # Bigrammi del testo annotati
bigrammi2 = getBigrams(tokensPOS2)
topBigrams1 = top20Bigrams(bigrammi1) #20 Bigrammi più frequenti
topBigrams2 = top20Bigrams(bigrammi2)
for i in range(min(len(topBigrams1),len(topBigrams2))): # stampa il confronto
big1, freq1 = topBigrams1[i]
big2, freq2 = topBigrams2[i]
print " {: <25}{: <7}| {: <25}{: <7}".format(big1[0].encode('utf-8') + " " +big1[1].encode('utf-8'), freq1, big2[0].encode('utf-8') + " " +big2[1].encode('utf-8'), freq2)
print '-'*80
print " 20 Trigrammi più frequenti"
print '-'*80
print ' Testo:', file1,' '*18, '| Testo:', file2
print '-'*80
topTrigrams1 = top20Trigrams(getTrigrams(tokensPOS1)) #20 Trigrammi più frequenti
topTrigrams2 = top20Trigrams(getTrigrams(tokensPOS2))
for i in range(min(len(topTrigrams1),len(topTrigrams2))): # stampa il confronto
tri1, freq1 = topTrigrams1[i]
tri2, freq2 = topTrigrams2[i]
print " {: <35}{: <3}| {: <35}{: <3}".format(tri1[0].encode('utf-8')+" "+tri1[1].encode('utf-8')+" "+tri1[2].encode('utf-8'), freq1, tri2[0].encode('utf-8') +" "+tri2[1].encode('utf-8')+" "+tri2[2].encode('utf-8'), freq2)
print '-'*80
print '-'*80
print " 20 Bigrammi - Aggettivo e Sostantivo"
print '-'*80
setBigramsAggSost1 = bigrammiAggSos(bigrammi1) # Set di Bigrammi: aggettivo , sostantivo
setBigramsAggSost2 = bigrammiAggSos(bigrammi2)
vocabFreq1 = dictVocabFreq(set(tokensText1), tokensText1) # dizionario con tokens e frequenze
vocabFreq2 = dictVocabFreq(set(tokensText2), tokensText2)
bigrams1 = getBigrams(tokensText1) # Bigrammi del testo non annotati
bigrams2 = getBigrams(tokensText2)
dictBigrammi1 = infoBigrams(bigrams1, setBigramsAggSost1, vocabFreq1) # dizionario con info bigrammi_AggSost : (F(u,v), F(u), F(v), P(v|u), P(u,v))
dictBigrammi2 = infoBigrams(bigrams2, setBigramsAggSost2, vocabFreq2)
forzaAssoc1 = getLocalMutualInformation(dictBigrammi1, vocabFreq1) # Local Mutual Information
forzaAssoc2 = getLocalMutualInformation(dictBigrammi2, vocabFreq2)
print " Con probabilità congiunta massima P(u,v):"
print ' Testo:', file1,' '*12, '| Testo:', file2
print '-'*80
probCongiunta1 = ordinaProbCongiunta(dictBigrammi1)[:20]
probCongiunta2 = ordinaProbCongiunta(dictBigrammi2)[:20]
for i in range(min(len(probCongiunta1), len(probCongiunta2))): # stampa il confronto
big1, p1 = probCongiunta1[i]
big2, p2 = probCongiunta2[i]
p1 = Decimal(str(p1[4])).quantize(Decimal('.00001'), rounding=ROUND_DOWN)
p2 = Decimal(str(p2[4])).quantize(Decimal('.00001'), rounding=ROUND_DOWN)
print " {: <25}{: <7}| {: <25}{: <7}".format(big1[0].encode('utf-8') + " " +big1[1].encode('utf-8'), p1, big2[0].encode('utf-8') + " " +big2[1].encode('utf-8'), p2)
print '-'*80
print " Con probabilità condizionata massima P(v|u):"
print ' Testo:', file1,' '*12, '| Testo:', file2
print '-'*80
probCondizionata1 = ordinaProbCondizionata(dictBigrammi1)[:20]
probCondizionata2 = ordinaProbCondizionata(dictBigrammi2)[:20]
for i in range(min(len(probCondizionata1),len(probCondizionata2))): # stampa il confronto
big1, p1 = probCondizionata1[i]
big2, p2 = probCondizionata2[i]
p1 = Decimal(str(p1[3])).quantize(Decimal('.0001'), rounding=ROUND_DOWN)
p2 = Decimal(str(p2[3])).quantize(Decimal('.0001'), rounding=ROUND_DOWN)
print " {: <25}{: <7}| {: <25}{: <7}".format(big1[0].encode('utf-8') + " " +big1[1].encode('utf-8'), p1, big2[0].encode('utf-8') + " " +big2[1].encode('utf-8'), p2)
print '-'*80
print " Con forza associativa massima (LMI):"
print ' Testo:', file1,' '*12, '| Testo:', file2
print '-'*80
topLMI1 = ordinaDict(forzaAssoc1)[:20]
topLMI2 = ordinaDict(forzaAssoc2)[:20]
for i in range(min(len(topLMI1),len(topLMI2))): # stampa il confronto
big1, lmi1 = topLMI1[i]
big2, lmi2 = topLMI2[i]
lmi1 = Decimal(str(lmi1)).quantize(Decimal('.001'), rounding=ROUND_DOWN)
lmi2 = Decimal(str(lmi2)).quantize(Decimal('.001'), rounding=ROUND_DOWN)
print " {: <25}{: <7}| {: <25}{: <7}".format(big1[0].encode('utf-8') + " " +big1[1].encode('utf-8'), lmi1, big2[0].encode('utf-8') + " " +big2[1].encode('utf-8'), lmi2)
print '-'*80
print '-'*80
print " Le due frasi con probabilità più alta"
print '-'*80
distrFreq1 = nltk.FreqDist(tokensText1)
topFrase1, probFraseMax1 = maxProbMarkov0(len(tokensText1),distrFreq1, frasiMarkov1) # frase Markov 0 con probabilità più alta
print " 1° Frase calcolata attraverso un modello di Markov di ordine 0:"
print '-'*80
print ' Testo:', file1
print ' "'," ".join(topFrase1).encode('utf-8'),'"'
print " Probabilità:", probFraseMax1
print
distrFreq2 = nltk.FreqDist(tokensText2)
topFrase2, probFraseMax2 = maxProbMarkov0(len(tokensText2),distrFreq2, frasiMarkov2) # frase Markov 0 con probabilità più alta
print ' Testo:', file2
print ' "'," ".join(topFrase2).encode('utf-8'),'"'
print " Probabilità:", probFraseMax2
print '-'*80
infoBigrammi1 = infoBigrams(bigrams1, set(bigrams1), vocabFreq1) # dizionario con info bigrammi : (F(u,v), F(u), F(v), P(v|u), P(u,v))
infoBigrammi2 = infoBigrams(bigrams2, set(bigrams2), vocabFreq2)
topFrase1, probFraseMax1 = maxProbMarkov1(len(tokensText2),frasiMarkov1, infoBigrammi1) # frase Markov 1 con probabilità più alta
print " 2° Frase calcolata attraverso un modello di Markov di ordine 1:"
print '-'*80
print ' Testo:', file1
print ' "'," ".join(topFrase1).encode('utf-8'),'"'
print " Probabilità:", probFraseMax1
print
distrFreq2 = nltk.FreqDist(tokensText2)
topFrase2, probFraseMax2 = maxProbMarkov1(len(tokensText2),frasiMarkov2, infoBigrammi2) # frase Markov 1 con probabilità più alta
print ' Testo:', file2
print ' "'," ".join(topFrase2).encode('utf-8'),'"'
print " Probabilità:", probFraseMax2
print '-'*80
print '-'*80
print " 20 nomi propri di persona più frequenti"
print ' Testo:', file1,' '*18, '| Testo:', file2
print '-'*80
topPerson1 = nltk.FreqDist(namedEntityDict1["PERSON"]).most_common(20) #i 20 nomi propri di persona più frequenti
topPerson2 = nltk.FreqDist(namedEntityDict2["PERSON"]).most_common(20)
for i in range(min(len(topPerson1),len(topPerson2))): # stampa il confronto
tok1, freq1 = topPerson1[i]
tok2, freq2 = topPerson2[i]
print " {: <35}{: <3}| {: <35}{: <3}".format(tok1.encode('utf-8'), freq1, tok2.encode('utf-8'), freq2)
print '-'*80
print " 20 nomi propri di luogo più frequenti"
print ' Testo:', file1,' '*18, '| Testo:', file2
print '-'*80
topGpe1 = nltk.FreqDist(namedEntityDict1["GPE"]).most_common(20) #i 20 nomi propri di luogo più frequenti
topGpe2 = nltk.FreqDist(namedEntityDict2["GPE"]).most_common(20)
for i in range(min(len(topGpe1),len(topGpe2))): # stampa il confronto
tok1, freq1 = topGpe1[i]
tok2, freq2 = topGpe2[i]
print " {: <35}{: <3}| {: <35}{: <3}".format(tok1.encode('utf-8'), freq1, tok2.encode('utf-8'), freq2)
print '-'*80
sys.exit(2)
main()
| gpl-3.0 |
savelov/radar-wms | get_single.py | 1 | 5612 | #!/usr/bin/env python3
# script fetches FMI Open data and imports it to DB
from tendo import singleton
me = singleton.SingleInstance() # will sys.exit(-1) if other instance is running
from db_setup import *
from cleaner import *
import configparser
from configurator import *
import os.path
tiff_dir_base = config.get("locations","wms_data_dir")
api_key = config.get( "settings","fmi_api_key")
# set logger
logger = set_logger( "fmi_open" )
from datetime import datetime,timedelta
import os
from urllib.request import urlopen,urlparse
from xml.etree import ElementTree
gml_namespace = "http://www.opengis.net/gml/3.2"
# sections in config file must match with layer names
wfs_layers = {
'fmi_radar_single_dbz':
'http://opendata.fmi.fi/wfs?request=GetFeature&storedquery_id=fmi::radar::single::dbz',
'fmi_radar_single_vrad':
'http://opendata.fmi.fi/wfs?request=GetFeature&storedquery_id=fmi::radar::single::vrad',
'fmi_radar_single_etop_20':
'http://opendata.fmi.fi/wfs?request=GetFeature&storedquery_id=fmi::radar::single::etop_20',
'fmi_radar_single_hclass':
'http://opendata.fmi.fi/wfs?request=GetFeature&storedquery_id=fmi::radar::single::hclass'
}
config_dataset_names,config_sections = get_sections_from_config()
def update():
add_datasets = []
return_datasets = []
logger.debug( "Start updating of DB" )
for layer in wfs_layers:
if not layer in config_dataset_names:
continue
else:
logger.debug( "Found layer %s from config" % layer )
section = config_sections[config_dataset_names.index(layer)]
# check that tiff directory exists
tiff_dir = "%s/%s" % (tiff_dir_base,layer)
if not os.path.exists(tiff_dir):
logger.debug( "GeoTIFF directory does not exist. Create it." )
os.makedirs(tiff_dir)
# get WFS to get WMS urls
try:
wfs_url = wfs_layers[layer].replace("{key}",api_key)
response = urlopen( wfs_url )
logger.debug( "Data from url %s fetched" % wfs_url )
# ignore network related problems
except:
logger.error( "Network error occurred while fetching url %s" % wfs_url )
continue
logger.debug( "Parse WFS response for layer %s" % layer )
wfs_response = ElementTree.fromstring(response.read())
file_references = wfs_response.findall('.//{%s}fileReference' % gml_namespace)
for ref in file_references:
url = ref.text
query = urlparse(url).query
query = query.split("&")
for q in query:
if q[0:4].lower()=="time":
time_value = q.split("=")[-1]
elif q[0:3].lower()=="srs":
projdef = q.split("=")[-1]
elif q[0:4].lower()=="bbox":
bbox = q.split("=")[-1]
elif q[0:6].lower()=="layers":
radar_name = q.split("=")[-1]
elif q[0:9].lower()=="elevation":
radar_elevation = q.split("=")[-1]
timestamp = datetime.strptime(time_value,\
"%Y-%m-%dT%H:%M:%SZ")
# search if dataset already exists
if (radar_name.split("_")[0] != "Radar:kesalahti") :
continue
if (layer!='fmi_radar_single_etop_20' and float(radar_elevation) > 0.5) :
continue
output_filename = tiff_dir + "/" + radar_name.replace("Radar:","")+ time_value.replace(":","")\
.replace("-","") + "_"+radar_elevation+ ".tif"
# skip file fetching if it already exists
if os.path.isfile(output_filename):
continue
# save file to disk
try:
response = urlopen( url )
logger.debug( "Fetched data from url %s" % url )
# ignore network related problems
except:
logger.error( "Network error or invalid api-key occurred while fetching url %s" % url )
continue
f = open(output_filename,"wb")
f.write( response.read() )
f.close()
# import dataset to db
logger.info( "Add new dataset: %s:%s to DB." % (layer,str(timestamp)) )
add_datasets.append(
RadarDataset(
name = layer,
timestamp = timestamp,
geotiff_path = output_filename,
projdef = projdef,
bbox_original = bbox,
dataset_type = config.get(section,"dataset_type"),
unit = config.get(section,"unit"),
style = config.get(section,"style"),
title = config.get(section,"title")
)
)
return_datasets.append({"name": layer,
"timestamp": timestamp })
# finally add new datasets to DB
if (len(add_datasets)>0):
session.add_all(add_datasets)
session.commit()
logger.info ( "Added %i results." % len(add_datasets) )
logger.debug( "Updating of DB finished" )
session.close()
return return_datasets
if __name__ == '__main__':
update()
# delete old
for layer in wfs_layers:
if not layer in config_dataset_names:
continue
else:
section = config_sections[config_dataset_names.index(layer)]
clean_up(config.get(section,"name"),
int( config.get(section,"cleanup_time") ))
| lgpl-3.0 |
ipashchenko/ml4vs | ml4vs/xgb.py | 1 | 10817 | import os
import numpy as np
from sklearn.metrics import (accuracy_score, f1_score, precision_recall_curve,
precision_score, recall_score)
from xgboost import XGBClassifier
from sklearn.preprocessing import Imputer
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import StratifiedKFold, cross_val_score, \
train_test_split, StratifiedShuffleSplit
from matplotlib import pyplot
from data_load import load_data, load_data_tgt
import xgboost as xgb
import pandas as pd
# load data
data_dir = '/home/ilya/code/ml4vs/data/dataset_OGLE/indexes_normalized'
file_1 = 'vast_lightcurve_statistics_normalized_variables_only.log'
file_0 = 'vast_lightcurve_statistics_normalized_constant_only.log'
file_0 = os.path.join(data_dir, file_0)
file_1 = os.path.join(data_dir, file_1)
names = ['Magnitude', 'clipped_sigma', 'meaningless_1', 'meaningless_2',
'star_ID', 'weighted_sigma', 'skew', 'kurt', 'I', 'J', 'K', 'L',
'Npts', 'MAD', 'lag1', 'RoMS', 'rCh2', 'Isgn', 'Vp2p', 'Jclp', 'Lclp',
'Jtim', 'Ltim', 'CSSD', 'Ex', 'inv_eta', 'E_A', 'S_B', 'NXS', 'IQR']
names_to_delete = ['Magnitude', 'meaningless_1', 'meaningless_2', 'star_ID',
'Npts']
X, y, df, feature_names, delta = load_data([file_0, file_1], names, names_to_delete)
# names_to_delete = ['Magnitude', 'meaningless_1', 'meaningless_2', 'star_ID',
# 'Npts']
# X_c, y, df, feature_names3, delta = load_data([file_0, file_1], names, names_to_delete)
imp = Imputer(missing_values='NaN', strategy='median', axis=0, verbose=2)
steps = [('imputation', imp),
('classification', XGBClassifier(max_depth=5, min_child_weight=1,
gamma=0, subsample=0.8,
colsample_bytree=0.8,
scale_pos_weight=150,
learning_rate=0.03,
n_estimators=1000))]
pipeline = Pipeline(steps)
# steps2 = [('imputation', imp),
# ('classification', XGBClassifier(max_depth=5, min_child_weight=1,
# gamma=0, subsample=0.8,
# colsample_bytree=0.8,
# scale_pos_weight=1,
# learning_rate=0.03,
# n_estimators=250))]
#
# pipeline2 = Pipeline(steps2)
imp3 = Imputer(missing_values='NaN', strategy='median', axis=0, verbose=2)
steps3 = [('imputation', imp3),
('classification', XGBClassifier(max_depth=5, min_child_weight=10,
gamma=0, subsample=0.8,
colsample_bytree=0.8,
colsample_bylevel=1.,
scale_pos_weight=150,
learning_rate=0.03,
n_estimators=950))]
pipeline3 = Pipeline(steps3)
##########################################################
# First check default parameters and check learning curves
sss = StratifiedShuffleSplit(y, n_iter=1, test_size=0.25, random_state=7)
for train_index, test_index in sss:
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# X_train_c, X_test_c = X_c[train_index], X_c[test_index]
# FIXME: Problem with evaluating pipeline
imp.fit(X_train)
X_trained_scaled = imp.transform(X_train)
# Use the same transformation & imputation for testing data!
X_test_scaled = imp.transform(X_test)
eval_set = [(X_trained_scaled, y_train), (X_test_scaled, y_test)]
# imp3.fit(X_train_c)
# X_trained_scaled_c = imp3.transform(X_train_c)
# # Use the same transformation & imputation for testing data!
# X_test_scaled_c = imp3.transform(X_test_c)
# eval_set3 = [(X_trained_scaled_c, y_train), (X_test_scaled_c, y_test)]
model = pipeline.named_steps['classification']
model.fit(X_trained_scaled, y_train, eval_metric=["error", "auc", "logloss"],
eval_set=eval_set, verbose=True, early_stopping_rounds=50)
# model2 = pipeline2.named_steps['classification']
# model2.fit(X_trained_scaled, y_train, eval_metric=["error", "logloss", "auc"],
# eval_set=eval_set, verbose=False)
# model3 = pipeline3.named_steps['classification']
# model3.fit(X_trained_scaled_c, y_train, eval_metric=["auc", "error", "logloss"],
# eval_set=eval_set3, verbose=False, early_stopping_rounds=30)
# make predictions for test data - now using pipeline
y_pred = pipeline.predict(X_test)
y_probapred = pipeline.predict_proba(X_test)
predictions = [round(value) for value in y_pred]
# y_pred2 = pipeline2.predict(X_test)
# y_probapred2 = pipeline2.predict_proba(X_test)
# predictions2 = [round(value) for value in y_pred2]
# y_pred3 = pipeline3.predict(X_test_c)
# y_probapred3 = pipeline3.predict_proba(X_test_c)
# predictions3 = [round(value) for value in y_pred3]
# evaluate predictions
accuracy = accuracy_score(y_test, predictions)
print("Model baseline")
print("Accuracy: %.2f%%" % (accuracy * 100.0))
f1 = f1_score(y_test, predictions)
print("F1-score: %.2f%%" % (f1 * 100.0))
precision = precision_score(y_test, y_pred)
print("Precision: %.2f%%" % (precision * 100.0))
recall = recall_score(y_test, predictions)
print("Recall: %.2f%%" % (recall * 100.0))
# print("Model with modification")
# accuracy3 = accuracy_score(y_test, predictions3)
# print("Accuracy: %.2f%%" % (accuracy3 * 100.0))
# f13 = f1_score(y_test, predictions3)
# print("F1-score: %.2f%%" % (f13 * 100.0))
# precision3 = precision_score(y_test, y_pred3)
# print("Precision: %.2f%%" % (precision3 * 100.0))
# recall3 = recall_score(y_test, predictions3)
# print("Recall: %.2f%%" % (recall3 * 100.0))
# print("Model with scale_pos_weight=1")
# accuracy2 = accuracy_score(y_test, predictions2)
# print("Accuracy: %.2f%%" % (accuracy2 * 100.0))
# f12 = f1_score(y_test, predictions2)
# print("F1-score: %.2f%%" % (f12 * 100.0))
# precision2 = precision_score(y_test, y_pred2)
# print("Precision: %.2f%%" % (precision2 * 100.0))
# recall2 = recall_score(y_test, predictions2)
# print("Recall: %.2f%%" % (recall2 * 100.0))
precisions, recalls, _ = precision_recall_curve(y_test, y_probapred[:, 1])
# precisions2, recalls2, _ = precision_recall_curve(y_test, y_probapred2[:, 1])
# precisions3, recalls3, _ = precision_recall_curve(y_test, y_probapred3[:, 1])
# Plot Precision-Recall curve
fig, ax = pyplot.subplots()
ax.plot(recalls, precisions, label='Baseline')
# ax.plot(recalls3, precisions3, label='With modifications')
# ax.plot(recalls2, precisions2, label='scale_pos_weight=1')
ax.set_xlabel('Recall')
ax.set_ylabel('Precision')
ax.set_ylim([0.0, 1.05])
ax.set_xlim([0.0, 1.0])
ax.set_title('XGBoost P-R curve')
ax.legend(loc='lower left')
fig.show()
# retrieve performance metrics
results = pipeline.named_steps['classification'].evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
pyplot.ylabel('Log Loss')
pyplot.title('XGBoost Log Loss')
pyplot.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
pyplot.ylabel('Classification Error')
pyplot.title('XGBoost Classification Error')
pyplot.show()
# plot auc
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['auc'], label='Train')
ax.plot(x_axis, results['validation_1']['auc'], label='Test')
ax.legend(loc='lower left')
pyplot.ylabel('AUC')
pyplot.title('XGBoost AUC')
pyplot.show()
scores_dict = model.booster().get_fscore()
scores_dict_ = dict()
for i, feature in enumerate(feature_names):
scores_dict_[feature] = scores_dict['f{}'.format(i)]
feat_imp = pd.Series(scores_dict_).sort_values(ascending=False)
feat_imp.plot(kind='bar', title='Feature Importances')
pyplot.ylabel('Feature Importance Score')
pyplot.show()
###########################################################
# kfold = StratifiedKFold(y, n_folds=4, shuffle=True, random_state=7)
# scores = cross_val_score(pipeline, X, y, cv=kfold, scoring='f1', n_jobs=-1)
# print("CV scores: ", scores)
model = XGBClassifier(max_depth=5, min_child_weight=1, gamma=0, subsample=0.8,
colsample_bytree=0.8, scale_pos_weight=1,
learning_rate=0.03, n_estimators=450)
imp = Imputer(missing_values='NaN', strategy='median', axis=0, verbose=2)
imp.fit(X)
X_scaled = imp.transform(X)
model.fit(X_scaled, y, verbose=True, eval_metric='auc')
file_tgt = 'LMC_SC19_PSF_Pgood98__vast_lightcurve_statistics_normalized.log'
file_tgt = os.path.join(data_dir, file_tgt)
X_tgt, feature_names, df = load_data_tgt(file_tgt, names, names_to_delete, delta)
X_tgt_scaled = imp.transform(X_tgt)
y_pred = model.predict(X_tgt_scaled)
proba_pred = model.predict_proba(X_tgt_scaled)
idx = proba_pred[:, 1] > 0.25
print("Found {} variables".format(np.count_nonzero(idx)))
with open('first_results.txt', 'w') as fo:
for line in list(df['star_ID'][idx]):
fo.write(line + '\n')
# kfold = StratifiedKFold(y, n_folds=4, shuffle=True, random_state=7)
# scores = cross_val_score(pipeline, X, y, cv=kfold, scoring='f1', n_jobs=-1)
# print("CV scores: ", scores)
#
#
# param_grid = {'classification__n_estimators': [5, 100, 150, 200],
# 'classification__max_depth': [2, 4, 6, 8]}
#
# print("Searching best parameters...")
# result = GridSearchCV(pipeline, param_grid, cv=kfold, n_jobs=-1, verbose=1,
# scoring='f1').fit(X, y)
# print("The best parameters are %s with a score of %0.2f"
# % (result.best_params_, result.best_score_))
#
# # summarize results
# print("Best: %f using %s" % (result.best_score_, result.best_params_))
# means, stdevs = [], []
# for params, mean_score, scores in result.grid_scores_:
# stdev = scores.std()
# means.append(mean_score)
# stdevs.append(stdev)
# print("%f (%f) with: %r" % (mean_score, stdev, params))
# # plot results
# scores = [x[1] for x in result.grid_scores_]
# max_depth = param_grid['classification__max_depth']
# n_estimators = param_grid['classification__n_estimators']
# scores = np.array(scores).reshape(len(max_depth), len(n_estimators))
# for i, value in enumerate(max_depth):
# pyplot.plot(n_estimators, scores[i], label='depth: ' + str(value))
# pyplot.legend()
# pyplot.xlabel('n_estimators')
# pyplot.ylabel('F1-score')
# pyplot.show()
| mit |
hfp/libxsmm | samples/deeplearning/sparse_training/fairseq/fairseq/models/fconv_self_att.py | 1 | 24306 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
FairseqDropout,
DownsampledMultiHeadAttention,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
from fairseq.incremental_decoding_utils import with_incremental_state
logger = logging.getLogger(__name__)
@register_model('fconv_self_att')
class FConvModelSelfAtt(FairseqEncoderDecoderModel):
@classmethod
def hub_models(cls):
return {
'conv.stories.pretrained': {
'path': 'https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz',
'checkpoint_file': 'pretrained_checkpoint.pt',
'tokenizer': 'nltk',
},
'conv.stories': {
'path': 'https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz',
'checkpoint_file': 'fusion_checkpoint.pt',
'tokenizer': 'nltk',
'pretrained': 'True',
'pretrained_checkpoint': './pretrained_checkpoint.pt',
},
# Test set containing dictionaries
'data.stories': 'https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2',
}
def __init__(self, encoder, decoder, pretrained_encoder=None):
super().__init__(encoder, decoder)
self.encoder.num_attention_layers = sum(layer is not None for layer in decoder.attention)
self.pretrained_encoder = pretrained_encoder
if self.pretrained_encoder is None:
encoders = {'encoder': encoder}
else:
encoders = {'encoder': encoder, 'pretrained': self.pretrained_encoder}
# for fusion model, CompositeEncoder contains both pretrained and training encoders
# these are forwarded and then combined in the decoder
self.encoder = CompositeEncoder(encoders)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-layers', type=str, metavar='EXPR',
help='encoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-layers', type=str, metavar='EXPR',
help='decoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
help='decoder attention [True, ...]')
parser.add_argument('--self-attention', type=str, metavar='EXPR',
help='decoder self-attention layers, ex: [True] + [False]*5')
parser.add_argument('--multihead-attention-nheads', type=int,
help='Number of heads to use in attention')
parser.add_argument('--multihead-self-attention-nheads', type=int,
help='Number of heads to use in self-attention')
parser.add_argument('--encoder-attention', type=str, metavar='EXPR',
help='encoder attention [True, ...]')
parser.add_argument('--encoder-attention-nheads', type=int,
help='Number of heads to use in encoder attention')
parser.add_argument('--project-input', type=str, metavar='EXPR',
help='Use projections in self-attention [True, ...]')
parser.add_argument('--gated-attention', type=str, metavar='EXPR',
help='Use GLU layers in self-attention projections [True, ...]')
parser.add_argument('--downsample', type=str, metavar='EXPR',
help='Use downsampling in self-attention [True, ...]')
parser.add_argument('--pretrained-checkpoint', metavar='DIR',
help='path to load checkpoint from pretrained model')
parser.add_argument('--pretrained', type=str, metavar='EXPR',
help='use pretrained model when training [True, ...]')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
trained_encoder, trained_decoder = None, None
pretrained = eval(args.pretrained)
if pretrained:
logger.info('loading pretrained model')
if not os.path.exists(args.pretrained_checkpoint):
new_pretrained_checkpoint = os.path.join(args.data, args.pretrained_checkpoint)
if os.path.exists(new_pretrained_checkpoint):
args.pretrained_checkpoint = new_pretrained_checkpoint
trained_model = checkpoint_utils.load_model_ensemble(
filenames=[args.pretrained_checkpoint],
task=task,
)[0][0]
trained_decoder = list(trained_model.children())[1]
trained_encoder = list(trained_model.children())[0]
# freeze pretrained model
for param in trained_decoder.parameters():
param.requires_grad = False
for param in trained_encoder.parameters():
param.requires_grad = False
encoder = FConvEncoder(
task.source_dictionary,
embed_dim=args.encoder_embed_dim,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
attention=eval(args.encoder_attention),
attention_nheads=args.encoder_attention_nheads,
)
decoder = FConvDecoder(
task.target_dictionary,
embed_dim=args.decoder_embed_dim,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
selfattention=eval(args.self_attention),
attention_nheads=args.multihead_attention_nheads,
selfattention_nheads=args.multihead_self_attention_nheads,
project_input=eval(args.project_input),
gated_attention=eval(args.gated_attention),
downsample=eval(args.downsample),
pretrained=pretrained,
trained_decoder=trained_decoder,
)
model = FConvModelSelfAtt(encoder, decoder, trained_encoder)
return model
@property
def pretrained(self):
return self.pretrained_encoder is not None
class FConvEncoder(FairseqEncoder):
"""Convolutional encoder"""
def __init__(
self, dictionary, embed_dim=512, max_positions=1024,
convolutions=((512, 3),) * 20, dropout=0.1, attention=False,
attention_nheads=1,
):
super().__init__(dictionary)
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.num_attention_layers = None
num_embeddings = len(dictionary)
self.padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
self.padding_idx,
)
def expand_bool_array(val):
if isinstance(val, bool):
# expand True into [True, True, ...] and do the same with False
return [val] * len(convolutions)
return val
attention = expand_bool_array(attention)
in_channels = convolutions[0][0]
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.attention = nn.ModuleList()
self.attproj = nn.ModuleList()
for i, (out_channels, kernel_size) in enumerate(convolutions):
self.projections.append(
Linear(in_channels, out_channels) if in_channels != out_channels else None
)
self.convolutions.append(
ConvTBC(in_channels, out_channels * 2, kernel_size, dropout=dropout)
)
self.attention.append(
SelfAttention(out_channels, embed_dim, attention_nheads) if attention[i] else None
)
in_channels = out_channels
self.fc2 = Linear(in_channels, embed_dim)
def forward(self, src_tokens, src_lengths):
# embed tokens and positions
x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens)
x = self.dropout_module(x)
input_embedding = x.transpose(0, 1)
# project to size of convolution
x = self.fc1(x)
encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B
if not encoder_padding_mask.any():
encoder_padding_mask = None
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# temporal convolutions
for proj, conv, attention in zip(self.projections, self.convolutions, self.attention):
residual = x if proj is None else proj(x)
if encoder_padding_mask is not None:
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
x = self.dropout_module(x)
padding_l = (conv.kernel_size[0] - 1) // 2
padding_r = conv.kernel_size[0] // 2
x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
x = conv(x)
x = F.glu(x, dim=2)
if attention is not None:
x = attention(x)
x = (x + residual) * math.sqrt(0.5)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
# project back to size of embedding
x = self.fc2(x)
if encoder_padding_mask is not None:
encoder_padding_mask = encoder_padding_mask.t() # -> B x T
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
# scale gradients (this only affects backward, not forward)
x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers))
# add output to input embedding for attention
y = (x + input_embedding.transpose(0, 1)) * math.sqrt(0.5)
return {
'encoder_out': (x, y),
'encoder_padding_mask': encoder_padding_mask, # B x T
}
def reorder_encoder_out(self, encoder_out, new_order):
encoder_out['encoder_out'] = tuple(
eo.index_select(0, new_order) for eo in encoder_out['encoder_out']
)
if encoder_out['encoder_padding_mask'] is not None:
encoder_out['encoder_padding_mask'] = \
encoder_out['encoder_padding_mask'].index_select(0, new_order)
if 'pretrained' in encoder_out:
encoder_out['pretrained']['encoder_out'] = tuple(
eo.index_select(0, new_order)
for eo in encoder_out['pretrained']['encoder_out']
)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return self.embed_positions.max_positions
@with_incremental_state
class FConvDecoder(FairseqDecoder):
"""Convolutional decoder"""
def __init__(
self, dictionary, embed_dim=512, out_embed_dim=256, max_positions=1024,
convolutions=((512, 3),) * 8, attention=True, dropout=0.1,
selfattention=False, attention_nheads=1, selfattention_nheads=1,
project_input=False, gated_attention=False, downsample=False,
pretrained=False, trained_decoder=None,
):
super().__init__(dictionary)
self.register_buffer('version', torch.Tensor([2]))
self.pretrained = pretrained
self.pretrained_decoder = trained_decoder
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.need_attn = True
in_channels = convolutions[0][0]
def expand_bool_array(val):
if isinstance(val, bool):
# expand True into [True, True, ...] and do the same with False
return [val] * len(convolutions)
return val
attention = expand_bool_array(attention)
selfattention = expand_bool_array(selfattention)
if not isinstance(attention, list) or len(attention) != len(convolutions):
raise ValueError('Attention is expected to be a list of booleans of '
'length equal to the number of layers.')
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
padding_idx,
)
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.attention = nn.ModuleList()
self.selfattention = nn.ModuleList()
self.attproj = nn.ModuleList()
for i, (out_channels, kernel_size) in enumerate(convolutions):
self.projections.append(
Linear(in_channels, out_channels) if in_channels != out_channels else None
)
self.convolutions.append(
LinearizedConv1d(
in_channels, out_channels * 2, kernel_size,
padding=(kernel_size - 1), dropout=dropout,
)
)
self.attention.append(
DownsampledMultiHeadAttention(
out_channels, embed_dim, attention_nheads,
project_input=project_input, gated=False, downsample=False,
) if attention[i] else None
)
self.attproj.append(
Linear(out_channels, embed_dim, dropout=dropout) if attention[i] else None
)
self.selfattention.append(
SelfAttention(
out_channels, embed_dim, selfattention_nheads,
project_input=project_input, gated=gated_attention,
downsample=downsample,
) if selfattention[i] else None
)
in_channels = out_channels
self.fc2 = Linear(in_channels, out_embed_dim)
self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)
# model fusion
if self.pretrained:
# independent gates are learned from the concatenated input
self.gate1 = nn.Sequential(Linear(out_embed_dim*2, out_embed_dim), nn.Sigmoid())
self.gate2 = nn.Sequential(Linear(out_embed_dim*2, out_embed_dim), nn.Sigmoid())
# pretrained and trained models are joined
self.joining = nn.Sequential(
Linear(out_embed_dim*2, out_embed_dim*2),
LayerNorm(out_embed_dim*2),
nn.GLU(),
Linear(out_embed_dim, out_embed_dim*2),
LayerNorm(out_embed_dim*2),
nn.GLU(),
Linear(out_embed_dim, out_embed_dim),
LayerNorm(out_embed_dim)
)
# pretrained model contains an output layer that is nhid -> vocab size
# but the models are combined in their hidden state
# the hook stores the output of the pretrained model forward
self.pretrained_outputs = {}
def save_output():
def hook(a, b, output):
self.pretrained_outputs["out"] = output
return hook
self.pretrained_decoder.fc2.register_forward_hook(save_output())
def forward(self, prev_output_tokens, encoder_out):
trained_encoder_out = encoder_out['pretrained'] if self.pretrained else None
encoder_out = encoder_out['encoder']['encoder_out']
encoder_a, encoder_b = self._split_encoder_out(encoder_out)
# embed positions
positions = self.embed_positions(prev_output_tokens)
# embed tokens and positions
x = self.embed_tokens(prev_output_tokens) + positions
x = self.dropout_module(x)
target_embedding = x.transpose(0, 1)
# project to size of convolution
x = self.fc1(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# temporal convolutions
avg_attn_scores = None
for proj, conv, attention, selfattention, attproj in zip(
self.projections, self.convolutions, self.attention, self.selfattention, self.attproj
):
residual = x if proj is None else proj(x)
x = self.dropout_module(x)
x = conv(x)
x = F.glu(x, dim=2)
# attention
if attention is not None:
r = x
x, attn_scores = attention(attproj(x) + target_embedding, encoder_a, encoder_b)
x = x + r
if not self.training and self.need_attn:
if avg_attn_scores is None:
avg_attn_scores = attn_scores
else:
avg_attn_scores.add_(attn_scores)
if selfattention is not None:
x = selfattention(x)
x = (x + residual) * math.sqrt(0.5)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
# project back to size of vocabulary
x = self.fc2(x)
x = self.dropout_module(x)
if not self.pretrained:
x = self.fc3(x)
# fusion gating
if self.pretrained:
trained_x, _ = self.pretrained_decoder.forward(prev_output_tokens, trained_encoder_out)
y = torch.cat([x, self.pretrained_outputs["out"]], dim=-1)
gate1 = self.gate1(y)
gate2 = self.gate2(y)
gated_x1 = gate1 * x
gated_x2 = gate2 * self.pretrained_outputs["out"]
fusion = torch.cat([gated_x1, gated_x2], dim=-1)
fusion = self.joining(fusion)
fusion_output = self.fc3(fusion)
return fusion_output, avg_attn_scores
else:
return x, avg_attn_scores
def max_positions(self):
"""Maximum output length supported by the decoder."""
return self.embed_positions.max_positions
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def _split_encoder_out(self, encoder_out):
"""Split and transpose encoder outputs."""
# transpose only once to speed up attention layers
encoder_a, encoder_b = encoder_out
encoder_a = encoder_a.transpose(0, 1).contiguous()
encoder_b = encoder_b.transpose(0, 1).contiguous()
result = (encoder_a, encoder_b)
return result
class SelfAttention(nn.Module):
def __init__(self, out_channels, embed_dim, num_heads, project_input=False, gated=False, downsample=False):
super().__init__()
self.attention = DownsampledMultiHeadAttention(
out_channels, embed_dim, num_heads, dropout=0, bias=True,
project_input=project_input, gated=gated, downsample=downsample,
)
self.in_proj_q = Linear(out_channels, embed_dim)
self.in_proj_k = Linear(out_channels, embed_dim)
self.in_proj_v = Linear(out_channels, embed_dim)
self.ln = LayerNorm(out_channels)
def forward(self, x):
residual = x
query = self.in_proj_q(x)
key = self.in_proj_k(x)
value = self.in_proj_v(x)
x, _ = self.attention(query, key, value, mask_future_timesteps=True, use_scalar_bias=True)
return self.ln(x + residual)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
m.weight.data.normal_(0, 0.1)
return m
def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
m.weight.data.normal_(0, 0.1)
return m
def Linear(in_features, out_features, dropout=0.):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return m
def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0., **kwargs):
"""Weight-normalized Conv1d layer optimized for decoding"""
m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return m
def ConvTBC(in_channels, out_channels, kernel_size, dropout=0., **kwargs):
"""Weight-normalized Conv1d layer"""
from fairseq.modules import ConvTBC
m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return m
@register_model_architecture('fconv_self_att', 'fconv_self_att')
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 3')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 8')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_attention = getattr(args, 'decoder_attention', 'True')
args.self_attention = getattr(args, 'self_attention', 'False')
args.encoder_attention = getattr(args, 'encoder_attention', 'False')
args.multihead_attention_nheads = getattr(args, 'multihead_attention_nheads', 1)
args.multihead_self_attention_nheads = getattr(args, 'multihead_self_attention_nheads', 1)
args.encoder_attention_nheads = getattr(args, 'encoder_attention_nheads', 1)
args.project_input = getattr(args, 'project_input', 'False')
args.gated_attention = getattr(args, 'gated_attention', 'False')
args.downsample = getattr(args, 'downsample', 'False')
args.pretrained_checkpoint = getattr(args, 'pretrained_checkpoint', '')
args.pretrained = getattr(args, 'pretrained', 'False')
@register_model_architecture('fconv_self_att', 'fconv_self_att_wp')
def fconv_self_att_wp(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
args.encoder_layers = getattr(args, 'encoder_layers', '[(128, 3)] * 2 + [(512,3)] * 1')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.self_attention = getattr(args, 'self_attention', 'True')
args.multihead_self_attention_nheads = getattr(args, 'multihead_self_attention_nheads', 4)
args.project_input = getattr(args, 'project_input', 'True')
args.gated_attention = getattr(args, 'gated_attention', 'True')
args.downsample = getattr(args, 'downsample', 'True')
base_architecture(args)
| bsd-3-clause |
thomasantony/CarND-Projects | Exercises/Term1/alexnet-feature-extraction/caffe_classes.py | 24 | 21706 | class_names = '''tench, Tinca tinca
goldfish, Carassius auratus
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
tiger shark, Galeocerdo cuvieri
hammerhead, hammerhead shark
electric ray, crampfish, numbfish, torpedo
stingray
cock
hen
ostrich, Struthio camelus
brambling, Fringilla montifringilla
goldfinch, Carduelis carduelis
house finch, linnet, Carpodacus mexicanus
junco, snowbird
indigo bunting, indigo finch, indigo bird, Passerina cyanea
robin, American robin, Turdus migratorius
bulbul
jay
magpie
chickadee
water ouzel, dipper
kite
bald eagle, American eagle, Haliaeetus leucocephalus
vulture
great grey owl, great gray owl, Strix nebulosa
European fire salamander, Salamandra salamandra
common newt, Triturus vulgaris
eft
spotted salamander, Ambystoma maculatum
axolotl, mud puppy, Ambystoma mexicanum
bullfrog, Rana catesbeiana
tree frog, tree-frog
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
loggerhead, loggerhead turtle, Caretta caretta
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
mud turtle
terrapin
box turtle, box tortoise
banded gecko
common iguana, iguana, Iguana iguana
American chameleon, anole, Anolis carolinensis
whiptail, whiptail lizard
agama
frilled lizard, Chlamydosaurus kingi
alligator lizard
Gila monster, Heloderma suspectum
green lizard, Lacerta viridis
African chameleon, Chamaeleo chamaeleon
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
African crocodile, Nile crocodile, Crocodylus niloticus
American alligator, Alligator mississipiensis
triceratops
thunder snake, worm snake, Carphophis amoenus
ringneck snake, ring-necked snake, ring snake
hognose snake, puff adder, sand viper
green snake, grass snake
king snake, kingsnake
garter snake, grass snake
water snake
vine snake
night snake, Hypsiglena torquata
boa constrictor, Constrictor constrictor
rock python, rock snake, Python sebae
Indian cobra, Naja naja
green mamba
sea snake
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
diamondback, diamondback rattlesnake, Crotalus adamanteus
sidewinder, horned rattlesnake, Crotalus cerastes
trilobite
harvestman, daddy longlegs, Phalangium opilio
scorpion
black and gold garden spider, Argiope aurantia
barn spider, Araneus cavaticus
garden spider, Aranea diademata
black widow, Latrodectus mactans
tarantula
wolf spider, hunting spider
tick
centipede
black grouse
ptarmigan
ruffed grouse, partridge, Bonasa umbellus
prairie chicken, prairie grouse, prairie fowl
peacock
quail
partridge
African grey, African gray, Psittacus erithacus
macaw
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
lorikeet
coucal
bee eater
hornbill
hummingbird
jacamar
toucan
drake
red-breasted merganser, Mergus serrator
goose
black swan, Cygnus atratus
tusker
echidna, spiny anteater, anteater
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
wallaby, brush kangaroo
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
wombat
jellyfish
sea anemone, anemone
brain coral
flatworm, platyhelminth
nematode, nematode worm, roundworm
conch
snail
slug
sea slug, nudibranch
chiton, coat-of-mail shell, sea cradle, polyplacophore
chambered nautilus, pearly nautilus, nautilus
Dungeness crab, Cancer magister
rock crab, Cancer irroratus
fiddler crab
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
American lobster, Northern lobster, Maine lobster, Homarus americanus
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
crayfish, crawfish, crawdad, crawdaddy
hermit crab
isopod
white stork, Ciconia ciconia
black stork, Ciconia nigra
spoonbill
flamingo
little blue heron, Egretta caerulea
American egret, great white heron, Egretta albus
bittern
crane
limpkin, Aramus pictus
European gallinule, Porphyrio porphyrio
American coot, marsh hen, mud hen, water hen, Fulica americana
bustard
ruddy turnstone, Arenaria interpres
red-backed sandpiper, dunlin, Erolia alpina
redshank, Tringa totanus
dowitcher
oystercatcher, oyster catcher
pelican
king penguin, Aptenodytes patagonica
albatross, mollymawk
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
killer whale, killer, orca, grampus, sea wolf, Orcinus orca
dugong, Dugong dugon
sea lion
Chihuahua
Japanese spaniel
Maltese dog, Maltese terrier, Maltese
Pekinese, Pekingese, Peke
Shih-Tzu
Blenheim spaniel
papillon
toy terrier
Rhodesian ridgeback
Afghan hound, Afghan
basset, basset hound
beagle
bloodhound, sleuthhound
bluetick
black-and-tan coonhound
Walker hound, Walker foxhound
English foxhound
redbone
borzoi, Russian wolfhound
Irish wolfhound
Italian greyhound
whippet
Ibizan hound, Ibizan Podenco
Norwegian elkhound, elkhound
otterhound, otter hound
Saluki, gazelle hound
Scottish deerhound, deerhound
Weimaraner
Staffordshire bullterrier, Staffordshire bull terrier
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
Bedlington terrier
Border terrier
Kerry blue terrier
Irish terrier
Norfolk terrier
Norwich terrier
Yorkshire terrier
wire-haired fox terrier
Lakeland terrier
Sealyham terrier, Sealyham
Airedale, Airedale terrier
cairn, cairn terrier
Australian terrier
Dandie Dinmont, Dandie Dinmont terrier
Boston bull, Boston terrier
miniature schnauzer
giant schnauzer
standard schnauzer
Scotch terrier, Scottish terrier, Scottie
Tibetan terrier, chrysanthemum dog
silky terrier, Sydney silky
soft-coated wheaten terrier
West Highland white terrier
Lhasa, Lhasa apso
flat-coated retriever
curly-coated retriever
golden retriever
Labrador retriever
Chesapeake Bay retriever
German short-haired pointer
vizsla, Hungarian pointer
English setter
Irish setter, red setter
Gordon setter
Brittany spaniel
clumber, clumber spaniel
English springer, English springer spaniel
Welsh springer spaniel
cocker spaniel, English cocker spaniel, cocker
Sussex spaniel
Irish water spaniel
kuvasz
schipperke
groenendael
malinois
briard
kelpie
komondor
Old English sheepdog, bobtail
Shetland sheepdog, Shetland sheep dog, Shetland
collie
Border collie
Bouvier des Flandres, Bouviers des Flandres
Rottweiler
German shepherd, German shepherd dog, German police dog, alsatian
Doberman, Doberman pinscher
miniature pinscher
Greater Swiss Mountain dog
Bernese mountain dog
Appenzeller
EntleBucher
boxer
bull mastiff
Tibetan mastiff
French bulldog
Great Dane
Saint Bernard, St Bernard
Eskimo dog, husky
malamute, malemute, Alaskan malamute
Siberian husky
dalmatian, coach dog, carriage dog
affenpinscher, monkey pinscher, monkey dog
basenji
pug, pug-dog
Leonberg
Newfoundland, Newfoundland dog
Great Pyrenees
Samoyed, Samoyede
Pomeranian
chow, chow chow
keeshond
Brabancon griffon
Pembroke, Pembroke Welsh corgi
Cardigan, Cardigan Welsh corgi
toy poodle
miniature poodle
standard poodle
Mexican hairless
timber wolf, grey wolf, gray wolf, Canis lupus
white wolf, Arctic wolf, Canis lupus tundrarum
red wolf, maned wolf, Canis rufus, Canis niger
coyote, prairie wolf, brush wolf, Canis latrans
dingo, warrigal, warragal, Canis dingo
dhole, Cuon alpinus
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
hyena, hyaena
red fox, Vulpes vulpes
kit fox, Vulpes macrotis
Arctic fox, white fox, Alopex lagopus
grey fox, gray fox, Urocyon cinereoargenteus
tabby, tabby cat
tiger cat
Persian cat
Siamese cat, Siamese
Egyptian cat
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
lynx, catamount
leopard, Panthera pardus
snow leopard, ounce, Panthera uncia
jaguar, panther, Panthera onca, Felis onca
lion, king of beasts, Panthera leo
tiger, Panthera tigris
cheetah, chetah, Acinonyx jubatus
brown bear, bruin, Ursus arctos
American black bear, black bear, Ursus americanus, Euarctos americanus
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
sloth bear, Melursus ursinus, Ursus ursinus
mongoose
meerkat, mierkat
tiger beetle
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
ground beetle, carabid beetle
long-horned beetle, longicorn, longicorn beetle
leaf beetle, chrysomelid
dung beetle
rhinoceros beetle
weevil
fly
bee
ant, emmet, pismire
grasshopper, hopper
cricket
walking stick, walkingstick, stick insect
cockroach, roach
mantis, mantid
cicada, cicala
leafhopper
lacewing, lacewing fly
dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
damselfly
admiral
ringlet, ringlet butterfly
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
cabbage butterfly
sulphur butterfly, sulfur butterfly
lycaenid, lycaenid butterfly
starfish, sea star
sea urchin
sea cucumber, holothurian
wood rabbit, cottontail, cottontail rabbit
hare
Angora, Angora rabbit
hamster
porcupine, hedgehog
fox squirrel, eastern fox squirrel, Sciurus niger
marmot
beaver
guinea pig, Cavia cobaya
sorrel
zebra
hog, pig, grunter, squealer, Sus scrofa
wild boar, boar, Sus scrofa
warthog
hippopotamus, hippo, river horse, Hippopotamus amphibius
ox
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
bison
ram, tup
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
ibex, Capra ibex
hartebeest
impala, Aepyceros melampus
gazelle
Arabian camel, dromedary, Camelus dromedarius
llama
weasel
mink
polecat, fitch, foulmart, foumart, Mustela putorius
black-footed ferret, ferret, Mustela nigripes
otter
skunk, polecat, wood pussy
badger
armadillo
three-toed sloth, ai, Bradypus tridactylus
orangutan, orang, orangutang, Pongo pygmaeus
gorilla, Gorilla gorilla
chimpanzee, chimp, Pan troglodytes
gibbon, Hylobates lar
siamang, Hylobates syndactylus, Symphalangus syndactylus
guenon, guenon monkey
patas, hussar monkey, Erythrocebus patas
baboon
macaque
langur
colobus, colobus monkey
proboscis monkey, Nasalis larvatus
marmoset
capuchin, ringtail, Cebus capucinus
howler monkey, howler
titi, titi monkey
spider monkey, Ateles geoffroyi
squirrel monkey, Saimiri sciureus
Madagascar cat, ring-tailed lemur, Lemur catta
indri, indris, Indri indri, Indri brevicaudatus
Indian elephant, Elephas maximus
African elephant, Loxodonta africana
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
barracouta, snoek
eel
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
rock beauty, Holocanthus tricolor
anemone fish
sturgeon
gar, garfish, garpike, billfish, Lepisosteus osseus
lionfish
puffer, pufferfish, blowfish, globefish
abacus
abaya
academic gown, academic robe, judge's robe
accordion, piano accordion, squeeze box
acoustic guitar
aircraft carrier, carrier, flattop, attack aircraft carrier
airliner
airship, dirigible
altar
ambulance
amphibian, amphibious vehicle
analog clock
apiary, bee house
apron
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
assault rifle, assault gun
backpack, back pack, knapsack, packsack, rucksack, haversack
bakery, bakeshop, bakehouse
balance beam, beam
balloon
ballpoint, ballpoint pen, ballpen, Biro
Band Aid
banjo
bannister, banister, balustrade, balusters, handrail
barbell
barber chair
barbershop
barn
barometer
barrel, cask
barrow, garden cart, lawn cart, wheelbarrow
baseball
basketball
bassinet
bassoon
bathing cap, swimming cap
bath towel
bathtub, bathing tub, bath, tub
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
beacon, lighthouse, beacon light, pharos
beaker
bearskin, busby, shako
beer bottle
beer glass
bell cote, bell cot
bib
bicycle-built-for-two, tandem bicycle, tandem
bikini, two-piece
binder, ring-binder
binoculars, field glasses, opera glasses
birdhouse
boathouse
bobsled, bobsleigh, bob
bolo tie, bolo, bola tie, bola
bonnet, poke bonnet
bookcase
bookshop, bookstore, bookstall
bottlecap
bow
bow tie, bow-tie, bowtie
brass, memorial tablet, plaque
brassiere, bra, bandeau
breakwater, groin, groyne, mole, bulwark, seawall, jetty
breastplate, aegis, egis
broom
bucket, pail
buckle
bulletproof vest
bullet train, bullet
butcher shop, meat market
cab, hack, taxi, taxicab
caldron, cauldron
candle, taper, wax light
cannon
canoe
can opener, tin opener
cardigan
car mirror
carousel, carrousel, merry-go-round, roundabout, whirligig
carpenter's kit, tool kit
carton
car wheel
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
cassette
cassette player
castle
catamaran
CD player
cello, violoncello
cellular telephone, cellular phone, cellphone, cell, mobile phone
chain
chainlink fence
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
chain saw, chainsaw
chest
chiffonier, commode
chime, bell, gong
china cabinet, china closet
Christmas stocking
church, church building
cinema, movie theater, movie theatre, movie house, picture palace
cleaver, meat cleaver, chopper
cliff dwelling
cloak
clog, geta, patten, sabot
cocktail shaker
coffee mug
coffeepot
coil, spiral, volute, whorl, helix
combination lock
computer keyboard, keypad
confectionery, confectionary, candy store
container ship, containership, container vessel
convertible
corkscrew, bottle screw
cornet, horn, trumpet, trump
cowboy boot
cowboy hat, ten-gallon hat
cradle
crane
crash helmet
crate
crib, cot
Crock Pot
croquet ball
crutch
cuirass
dam, dike, dyke
desk
desktop computer
dial telephone, dial phone
diaper, nappy, napkin
digital clock
digital watch
dining table, board
dishrag, dishcloth
dishwasher, dish washer, dishwashing machine
disk brake, disc brake
dock, dockage, docking facility
dogsled, dog sled, dog sleigh
dome
doormat, welcome mat
drilling platform, offshore rig
drum, membranophone, tympan
drumstick
dumbbell
Dutch oven
electric fan, blower
electric guitar
electric locomotive
entertainment center
envelope
espresso maker
face powder
feather boa, boa
file, file cabinet, filing cabinet
fireboat
fire engine, fire truck
fire screen, fireguard
flagpole, flagstaff
flute, transverse flute
folding chair
football helmet
forklift
fountain
fountain pen
four-poster
freight car
French horn, horn
frying pan, frypan, skillet
fur coat
garbage truck, dustcart
gasmask, respirator, gas helmet
gas pump, gasoline pump, petrol pump, island dispenser
goblet
go-kart
golf ball
golfcart, golf cart
gondola
gong, tam-tam
gown
grand piano, grand
greenhouse, nursery, glasshouse
grille, radiator grille
grocery store, grocery, food market, market
guillotine
hair slide
hair spray
half track
hammer
hamper
hand blower, blow dryer, blow drier, hair dryer, hair drier
hand-held computer, hand-held microcomputer
handkerchief, hankie, hanky, hankey
hard disc, hard disk, fixed disk
harmonica, mouth organ, harp, mouth harp
harp
harvester, reaper
hatchet
holster
home theater, home theatre
honeycomb
hook, claw
hoopskirt, crinoline
horizontal bar, high bar
horse cart, horse-cart
hourglass
iPod
iron, smoothing iron
jack-o'-lantern
jean, blue jean, denim
jeep, landrover
jersey, T-shirt, tee shirt
jigsaw puzzle
jinrikisha, ricksha, rickshaw
joystick
kimono
knee pad
knot
lab coat, laboratory coat
ladle
lampshade, lamp shade
laptop, laptop computer
lawn mower, mower
lens cap, lens cover
letter opener, paper knife, paperknife
library
lifeboat
lighter, light, igniter, ignitor
limousine, limo
liner, ocean liner
lipstick, lip rouge
Loafer
lotion
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
loupe, jeweler's loupe
lumbermill, sawmill
magnetic compass
mailbag, postbag
mailbox, letter box
maillot
maillot, tank suit
manhole cover
maraca
marimba, xylophone
mask
matchstick
maypole
maze, labyrinth
measuring cup
medicine chest, medicine cabinet
megalith, megalithic structure
microphone, mike
microwave, microwave oven
military uniform
milk can
minibus
miniskirt, mini
minivan
missile
mitten
mixing bowl
mobile home, manufactured home
Model T
modem
monastery
monitor
moped
mortar
mortarboard
mosque
mosquito net
motor scooter, scooter
mountain bike, all-terrain bike, off-roader
mountain tent
mouse, computer mouse
mousetrap
moving van
muzzle
nail
neck brace
necklace
nipple
notebook, notebook computer
obelisk
oboe, hautboy, hautbois
ocarina, sweet potato
odometer, hodometer, mileometer, milometer
oil filter
organ, pipe organ
oscilloscope, scope, cathode-ray oscilloscope, CRO
overskirt
oxcart
oxygen mask
packet
paddle, boat paddle
paddlewheel, paddle wheel
padlock
paintbrush
pajama, pyjama, pj's, jammies
palace
panpipe, pandean pipe, syrinx
paper towel
parachute, chute
parallel bars, bars
park bench
parking meter
passenger car, coach, carriage
patio, terrace
pay-phone, pay-station
pedestal, plinth, footstall
pencil box, pencil case
pencil sharpener
perfume, essence
Petri dish
photocopier
pick, plectrum, plectron
pickelhaube
picket fence, paling
pickup, pickup truck
pier
piggy bank, penny bank
pill bottle
pillow
ping-pong ball
pinwheel
pirate, pirate ship
pitcher, ewer
plane, carpenter's plane, woodworking plane
planetarium
plastic bag
plate rack
plow, plough
plunger, plumber's helper
Polaroid camera, Polaroid Land camera
pole
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
poncho
pool table, billiard table, snooker table
pop bottle, soda bottle
pot, flowerpot
potter's wheel
power drill
prayer rug, prayer mat
printer
prison, prison house
projectile, missile
projector
puck, hockey puck
punching bag, punch bag, punching ball, punchball
purse
quill, quill pen
quilt, comforter, comfort, puff
racer, race car, racing car
racket, racquet
radiator
radio, wireless
radio telescope, radio reflector
rain barrel
recreational vehicle, RV, R.V.
reel
reflex camera
refrigerator, icebox
remote control, remote
restaurant, eating house, eating place, eatery
revolver, six-gun, six-shooter
rifle
rocking chair, rocker
rotisserie
rubber eraser, rubber, pencil eraser
rugby ball
rule, ruler
running shoe
safe
safety pin
saltshaker, salt shaker
sandal
sarong
sax, saxophone
scabbard
scale, weighing machine
school bus
schooner
scoreboard
screen, CRT screen
screw
screwdriver
seat belt, seatbelt
sewing machine
shield, buckler
shoe shop, shoe-shop, shoe store
shoji
shopping basket
shopping cart
shovel
shower cap
shower curtain
ski
ski mask
sleeping bag
slide rule, slipstick
sliding door
slot, one-armed bandit
snorkel
snowmobile
snowplow, snowplough
soap dispenser
soccer ball
sock
solar dish, solar collector, solar furnace
sombrero
soup bowl
space bar
space heater
space shuttle
spatula
speedboat
spider web, spider's web
spindle
sports car, sport car
spotlight, spot
stage
steam locomotive
steel arch bridge
steel drum
stethoscope
stole
stone wall
stopwatch, stop watch
stove
strainer
streetcar, tram, tramcar, trolley, trolley car
stretcher
studio couch, day bed
stupa, tope
submarine, pigboat, sub, U-boat
suit, suit of clothes
sundial
sunglass
sunglasses, dark glasses, shades
sunscreen, sunblock, sun blocker
suspension bridge
swab, swob, mop
sweatshirt
swimming trunks, bathing trunks
swing
switch, electric switch, electrical switch
syringe
table lamp
tank, army tank, armored combat vehicle, armoured combat vehicle
tape player
teapot
teddy, teddy bear
television, television system
tennis ball
thatch, thatched roof
theater curtain, theatre curtain
thimble
thresher, thrasher, threshing machine
throne
tile roof
toaster
tobacco shop, tobacconist shop, tobacconist
toilet seat
torch
totem pole
tow truck, tow car, wrecker
toyshop
tractor
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
tray
trench coat
tricycle, trike, velocipede
trimaran
tripod
triumphal arch
trolleybus, trolley coach, trackless trolley
trombone
tub, vat
turnstile
typewriter keyboard
umbrella
unicycle, monocycle
upright, upright piano
vacuum, vacuum cleaner
vase
vault
velvet
vending machine
vestment
viaduct
violin, fiddle
volleyball
waffle iron
wall clock
wallet, billfold, notecase, pocketbook
wardrobe, closet, press
warplane, military plane
washbasin, handbasin, washbowl, lavabo, wash-hand basin
washer, automatic washer, washing machine
water bottle
water jug
water tower
whiskey jug
whistle
wig
window screen
window shade
Windsor tie
wine bottle
wing
wok
wooden spoon
wool, woolen, woollen
worm fence, snake fence, snake-rail fence, Virginia fence
wreck
yawl
yurt
web site, website, internet site, site
comic book
crossword puzzle, crossword
street sign
traffic light, traffic signal, stoplight
book jacket, dust cover, dust jacket, dust wrapper
menu
plate
guacamole
consomme
hot pot, hotpot
trifle
ice cream, icecream
ice lolly, lolly, lollipop, popsicle
French loaf
bagel, beigel
pretzel
cheeseburger
hotdog, hot dog, red hot
mashed potato
head cabbage
broccoli
cauliflower
zucchini, courgette
spaghetti squash
acorn squash
butternut squash
cucumber, cuke
artichoke, globe artichoke
bell pepper
cardoon
mushroom
Granny Smith
strawberry
orange
lemon
fig
pineapple, ananas
banana
jackfruit, jak, jack
custard apple
pomegranate
hay
carbonara
chocolate sauce, chocolate syrup
dough
meat loaf, meatloaf
pizza, pizza pie
potpie
burrito
red wine
espresso
cup
eggnog
alp
bubble
cliff, drop, drop-off
coral reef
geyser
lakeside, lakeshore
promontory, headland, head, foreland
sandbar, sand bar
seashore, coast, seacoast, sea-coast
valley, vale
volcano
ballplayer, baseball player
groom, bridegroom
scuba diver
rapeseed
daisy
yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
corn
acorn
hip, rose hip, rosehip
buckeye, horse chestnut, conker
coral fungus
agaric
gyromitra
stinkhorn, carrion fungus
earthstar
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
bolete
ear, spike, capitulum
toilet tissue, toilet paper, bathroom tissue'''.split("\n") | mit |
microsoft/onnxruntime | orttraining/pytorch_frontend_examples/mnist_training.py | 1 | 6831 | ## This code is from https://github.com/pytorch/examples/blob/master/mnist/main.py
## with modification to do training using onnxruntime as backend on cuda device.
## A private PyTorch build from https://aiinfra.visualstudio.com/Lotus/_git/pytorch (ORTTraining branch) is needed to run the demo.
## Model testing is not complete.
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np
import os
from onnxruntime.capi.ort_trainer import IODescription, ModelDescription, ORTTrainer
from mpi4py import MPI
try:
from onnxruntime.capi._pybind_state import set_cuda_device_id
except ImportError:
pass
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
def my_loss(x, target):
return F.nll_loss(F.log_softmax(x, dim=1), target)
def train_with_trainer(args, trainer, device, train_loader, epoch):
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
data = data.reshape(data.shape[0], -1)
learning_rate = torch.tensor([args.lr])
loss = trainer.train_step(data, target, learning_rate)
# Since the output corresponds to [loss_desc, probability_desc], the first value is taken as loss.
if batch_idx % args.log_interval == 0:
print(
"Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
epoch,
batch_idx * len(data),
len(train_loader.dataset),
100.0 * batch_idx / len(train_loader),
loss[0],
)
)
# TODO: comple this once ORT training can do evaluation.
def test_with_trainer(args, trainer, device, test_loader):
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
data = data.reshape(data.shape[0], -1)
output = F.log_softmax(trainer.eval_step(data, fetches=["probability"]), dim=1)
test_loss += F.nll_loss(output, target, reduction="sum").item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print(
"\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)
)
)
def mnist_model_description():
input_desc = IODescription("input1", ["batch", 784], torch.float32)
label_desc = IODescription(
"label",
[
"batch",
],
torch.int64,
num_classes=10,
)
loss_desc = IODescription("loss", [], torch.float32)
probability_desc = IODescription("probability", ["batch", 10], torch.float32)
return ModelDescription([input_desc, label_desc], [loss_desc, probability_desc])
def main():
# Training settings
parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
parser.add_argument(
"--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
)
parser.add_argument(
"--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)"
)
parser.add_argument("--epochs", type=int, default=10, metavar="N", help="number of epochs to train (default: 10)")
parser.add_argument("--lr", type=float, default=0.01, metavar="LR", help="learning rate (default: 0.01)")
parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training")
parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
parser.add_argument(
"--log-interval",
type=int,
default=10,
metavar="N",
help="how many batches to wait before logging training status",
)
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
kwargs = {"num_workers": 0, "pin_memory": True}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(
"../data",
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]),
),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(
"../data",
train=False,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]),
),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs,
)
comm = MPI.COMM_WORLD
args.local_rank = (
int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) if ("OMPI_COMM_WORLD_LOCAL_RANK" in os.environ) else 0
)
args.world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) if ("OMPI_COMM_WORLD_RANK" in os.environ) else 0
args.world_size = comm.Get_size()
if use_cuda:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
args.n_gpu = 1
set_cuda_device_id(args.local_rank)
else:
device = torch.device("cpu")
input_size = 784
hidden_size = 500
num_classes = 10
model = NeuralNet(input_size, hidden_size, num_classes)
model_desc = mnist_model_description()
# use log_interval as gradient accumulate steps
trainer = ORTTrainer(
model,
my_loss,
model_desc,
"SGDOptimizer",
None,
IODescription(
"Learning_Rate",
[
1,
],
torch.float32,
),
device,
1,
args.world_rank,
args.world_size,
use_mixed_precision=False,
allreduce_post_accumulation=True,
)
print("\nBuild ort model done.")
for epoch in range(1, args.epochs + 1):
train_with_trainer(args, trainer, device, train_loader, epoch)
import pdb
test_with_trainer(args, trainer, device, test_loader)
if __name__ == "__main__":
main()
| mit |
hfp/libxsmm | samples/deeplearning/sparse_training/fairseq/fairseq/optim/fused_adam.py | 1 | 13372 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import types
import torch
def get_fused_adam_class():
"""
Look for the FusedAdam optimizer from apex. We first try to load the
"contrib" interface, which is a bit faster than the main interface,
but is technically deprecated.
"""
try:
# The "deprecated" interface in recent versions of apex is a bit
# faster than the main interface, since we don't use the apex
# optimizer. This can be installed by passing the
# `--deprecated_fused_adam` option when building apex.
global fused_adam_cuda
import importlib
fused_adam_cuda = importlib.import_module("fused_adam_cuda")
return FusedAdamV1
except ImportError:
try:
# fallback to the newer interface
from apex.optimizers import FusedAdam as _FusedAdam # noqa
from apex.multi_tensor_apply import multi_tensor_applier
if multi_tensor_applier.available:
return FusedAdamV2
except ImportError:
pass
return None
class FusedAdamV1(torch.optim.Optimizer):
"""
Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
``python setup.py install --cuda_ext --cpp_ext``.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Compared to the original version in Apex, the fairseq version casts grads
and params to FP32 internally to support ``--memory-efficient-fp16``.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups.
lr (float, optional): learning rate. (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square. (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability. (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False) NOT SUPPORTED in FusedAdam!
eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
adds eps to the bias-corrected second moment estimate before
evaluating square root instead of adding it to the square root of
second moment estimate as in the original paper. (default: False)
.. _Adam: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params,
lr=1e-3, bias_correction=True,
betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt=False,
weight_decay=0., max_grad_norm=0., amsgrad=False):
global fused_adam_cuda
import importlib
fused_adam_cuda = importlib.import_module("fused_adam_cuda")
if amsgrad:
raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
defaults = {
'lr': lr,
'bias_correction': bias_correction,
'betas': betas,
'eps': eps,
'weight_decay': weight_decay,
'max_grad_norm': max_grad_norm,
}
super().__init__(params, defaults)
self.eps_mode = 0 if eps_inside_sqrt else 1
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
@property
def supports_step_with_scale(self):
return True
def step(self, closure=None, grads=None, scale=1., grad_norms=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
grads (list of tensors, optional): weight gradient to use for the
optimizer update. If gradients have type torch.half, parameters
are expected to be in type torch.float. (default: None)
output params (list of tensors, optional): A reduced precision copy
of the updated weights written out in addition to the regular
updated weights. Have to be of same type as gradients. (default: None)
scale (float, optional): factor to divide gradient tensor values
by before applying to weights. (default: 1)
"""
loss = None
if closure is not None:
loss = closure()
if grads is None:
grads_group = [None] * len(self.param_groups)
# backward compatibility
# assuming a list/generator of parameter means single group
elif isinstance(grads, types.GeneratorType):
grads_group = [grads]
elif type(grads[0]) != list:
grads_group = [grads]
else:
grads_group = grads
if grad_norms is None:
grad_norms = [None]*len(self.param_groups)
for group, grads_this_group, grad_norm in zip(self.param_groups, grads_group, grad_norms):
if grads_this_group is None:
grads_this_group = [None]*len(group['params'])
# compute combined scale factor for this group
combined_scale = scale
if group.get('max_grad_norm', 0) > 0:
# norm is in fact norm*scale
clip = ((grad_norm / scale) + 1e-6) / group['max_grad_norm']
if clip > 1:
combined_scale = clip * scale
bias_correction = 1 if group.get('bias_correction', 1) else 0
for p, grad in zip(group['params'], grads_this_group):
# note: p.grad should not ever be set for correct
# operation of mixed precision optimizer that sometimes
# sends None gradients
if p.grad is None and grad is None:
continue
if grad is None:
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
'FusedAdam does not support sparse gradients, '
'please consider SparseAdam instead'
)
p_data_fp32 = p.data.float()
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p_data_fp32)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
else:
state['exp_avg'] = state['exp_avg'].to(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].to(p_data_fp32)
exp_avg = state['exp_avg']
exp_avg_sq = state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
out_p = p.data
with torch.cuda.device(p.device):
fused_adam_cuda.adam(p_data_fp32,
out_p,
exp_avg,
exp_avg_sq,
grad,
group['lr'],
beta1,
beta2,
group['eps'],
combined_scale,
state['step'],
self.eps_mode,
bias_correction,
group['weight_decay'])
return loss
try:
from apex.optimizers import FusedAdam
from apex.multi_tensor_apply import multi_tensor_applier
class FusedAdamV2(FusedAdam):
"""
Compared to the original version in Apex, the fairseq version casts grads
and params to FP32 internally to support ``--memory-efficient-fp16``.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if not hasattr(self, 'multi_tensor_adam'):
raise Exception('Apex installation is outdated. Please install an updated version of apex.')
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
def step(self, closure=None, grads=None, output_params=None, scale=None, grad_norms=None):
"""Performs a single optimization step."""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
bias_correction = 1 if group['bias_correction'] else 0
beta1, beta2 = group['betas']
# assume same step across group now to simplify things
# per parameter step can be easily support by making it tensor, or pass list into kernel
if 'step' in group:
group['step'] += 1
else:
group['step'] = 1
# create lists for multi-tensor apply
g_16, p_16, orig_p_16, m_16, v_16 = [], [], [], [], []
g_32, p_32, m_32, v_32 = [], [], [], []
for p in group['params']:
if p.grad is None:
continue
if p.grad.data.is_sparse:
raise RuntimeError(
'FusedAdam does not support sparse gradients, '
'please consider SparseAdam instead'
)
state = self.state[p]
# State initialization
if len(state) == 0:
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data, dtype=torch.float)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data, dtype=torch.float)
else:
state['exp_avg'] = state['exp_avg'].to(device=p.data.device, dtype=torch.float)
state['exp_avg_sq'] = state['exp_avg_sq'].to(device=p.data.device, dtype=torch.float)
if p.dtype == torch.float16:
g_16.append(p.grad.data.float())
p_16.append(p.data.float())
orig_p_16.append(p.data)
m_16.append(state['exp_avg'])
v_16.append(state['exp_avg_sq'])
elif p.dtype == torch.float32:
g_32.append(p.grad.data)
p_32.append(p.data)
m_32.append(state['exp_avg'])
v_32.append(state['exp_avg_sq'])
else:
raise RuntimeError('FusedAdam only support fp16 and fp32.')
with torch.cuda.device(p.device):
if(len(g_16) > 0):
multi_tensor_applier(self.multi_tensor_adam,
self._dummy_overflow_buf,
[g_16, p_16, m_16, v_16],
group['lr'],
beta1,
beta2,
group['eps'],
group['step'],
self.adam_w_mode,
bias_correction,
group['weight_decay'])
for orig_p, p in zip(orig_p_16, p_16):
orig_p.copy_(p.data)
if(len(g_32) > 0):
multi_tensor_applier(self.multi_tensor_adam,
self._dummy_overflow_buf,
[g_32, p_32, m_32, v_32],
group['lr'],
beta1,
beta2,
group['eps'],
group['step'],
self.adam_w_mode,
bias_correction,
group['weight_decay'])
return loss
except ImportError:
pass
| bsd-3-clause |
Clyde-fare/scikit-learn | sklearn/datasets/tests/test_base.py | 204 | 5878 | import os
import shutil
import tempfile
import warnings
import nose
import numpy
from pickle import loads
from pickle import dumps
from sklearn.datasets import get_data_home
from sklearn.datasets import clear_data_home
from sklearn.datasets import load_files
from sklearn.datasets import load_sample_images
from sklearn.datasets import load_sample_image
from sklearn.datasets import load_digits
from sklearn.datasets import load_diabetes
from sklearn.datasets import load_linnerud
from sklearn.datasets import load_iris
from sklearn.datasets import load_boston
from sklearn.datasets.base import Bunch
from sklearn.externals.six import b, u
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_raises
DATA_HOME = tempfile.mkdtemp(prefix="scikit_learn_data_home_test_")
LOAD_FILES_ROOT = tempfile.mkdtemp(prefix="scikit_learn_load_files_test_")
TEST_CATEGORY_DIR1 = ""
TEST_CATEGORY_DIR2 = ""
def _remove_dir(path):
if os.path.isdir(path):
shutil.rmtree(path)
def teardown_module():
"""Test fixture (clean up) run once after all tests of this module"""
for path in [DATA_HOME, LOAD_FILES_ROOT]:
_remove_dir(path)
def setup_load_files():
global TEST_CATEGORY_DIR1
global TEST_CATEGORY_DIR2
TEST_CATEGORY_DIR1 = tempfile.mkdtemp(dir=LOAD_FILES_ROOT)
TEST_CATEGORY_DIR2 = tempfile.mkdtemp(dir=LOAD_FILES_ROOT)
sample_file = tempfile.NamedTemporaryFile(dir=TEST_CATEGORY_DIR1,
delete=False)
sample_file.write(b("Hello World!\n"))
sample_file.close()
def teardown_load_files():
_remove_dir(TEST_CATEGORY_DIR1)
_remove_dir(TEST_CATEGORY_DIR2)
def test_data_home():
# get_data_home will point to a pre-existing folder
data_home = get_data_home(data_home=DATA_HOME)
assert_equal(data_home, DATA_HOME)
assert_true(os.path.exists(data_home))
# clear_data_home will delete both the content and the folder it-self
clear_data_home(data_home=data_home)
assert_false(os.path.exists(data_home))
# if the folder is missing it will be created again
data_home = get_data_home(data_home=DATA_HOME)
assert_true(os.path.exists(data_home))
def test_default_empty_load_files():
res = load_files(LOAD_FILES_ROOT)
assert_equal(len(res.filenames), 0)
assert_equal(len(res.target_names), 0)
assert_equal(res.DESCR, None)
@nose.tools.with_setup(setup_load_files, teardown_load_files)
def test_default_load_files():
res = load_files(LOAD_FILES_ROOT)
assert_equal(len(res.filenames), 1)
assert_equal(len(res.target_names), 2)
assert_equal(res.DESCR, None)
assert_equal(res.data, [b("Hello World!\n")])
@nose.tools.with_setup(setup_load_files, teardown_load_files)
def test_load_files_w_categories_desc_and_encoding():
category = os.path.abspath(TEST_CATEGORY_DIR1).split('/').pop()
res = load_files(LOAD_FILES_ROOT, description="test",
categories=category, encoding="utf-8")
assert_equal(len(res.filenames), 1)
assert_equal(len(res.target_names), 1)
assert_equal(res.DESCR, "test")
assert_equal(res.data, [u("Hello World!\n")])
@nose.tools.with_setup(setup_load_files, teardown_load_files)
def test_load_files_wo_load_content():
res = load_files(LOAD_FILES_ROOT, load_content=False)
assert_equal(len(res.filenames), 1)
assert_equal(len(res.target_names), 2)
assert_equal(res.DESCR, None)
assert_equal(res.get('data'), None)
def test_load_sample_images():
try:
res = load_sample_images()
assert_equal(len(res.images), 2)
assert_equal(len(res.filenames), 2)
assert_true(res.DESCR)
except ImportError:
warnings.warn("Could not load sample images, PIL is not available.")
def test_load_digits():
digits = load_digits()
assert_equal(digits.data.shape, (1797, 64))
assert_equal(numpy.unique(digits.target).size, 10)
def test_load_digits_n_class_lt_10():
digits = load_digits(9)
assert_equal(digits.data.shape, (1617, 64))
assert_equal(numpy.unique(digits.target).size, 9)
def test_load_sample_image():
try:
china = load_sample_image('china.jpg')
assert_equal(china.dtype, 'uint8')
assert_equal(china.shape, (427, 640, 3))
except ImportError:
warnings.warn("Could not load sample images, PIL is not available.")
def test_load_missing_sample_image_error():
have_PIL = True
try:
try:
from scipy.misc import imread
except ImportError:
from scipy.misc.pilutil import imread
except ImportError:
have_PIL = False
if have_PIL:
assert_raises(AttributeError, load_sample_image,
'blop.jpg')
else:
warnings.warn("Could not load sample images, PIL is not available.")
def test_load_diabetes():
res = load_diabetes()
assert_equal(res.data.shape, (442, 10))
assert_true(res.target.size, 442)
def test_load_linnerud():
res = load_linnerud()
assert_equal(res.data.shape, (20, 3))
assert_equal(res.target.shape, (20, 3))
assert_equal(len(res.target_names), 3)
assert_true(res.DESCR)
def test_load_iris():
res = load_iris()
assert_equal(res.data.shape, (150, 4))
assert_equal(res.target.size, 150)
assert_equal(res.target_names.size, 3)
assert_true(res.DESCR)
def test_load_boston():
res = load_boston()
assert_equal(res.data.shape, (506, 13))
assert_equal(res.target.size, 506)
assert_equal(res.feature_names.size, 13)
assert_true(res.DESCR)
def test_loads_dumps_bunch():
bunch = Bunch(x="x")
bunch_from_pkl = loads(dumps(bunch))
bunch_from_pkl.x = "y"
assert_equal(bunch_from_pkl['x'], bunch_from_pkl.x)
| bsd-3-clause |
williamFalcon/pytorch-lightning | pytorch_lightning/trainer/configuration_validator.py | 1 | 10445 | # Copyright The PyTorch Lightning team.
#
# 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 pytorch_lightning as pl
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
from pytorch_lightning.utilities.warnings import rank_zero_deprecation, rank_zero_warn
class ConfigValidator:
def __init__(self, trainer: "pl.Trainer") -> None:
self.trainer = trainer
def verify_loop_configurations(self, model: "pl.LightningModule") -> None:
r"""
Checks that the model is configured correctly before the run is started.
Args:
model: The model to check the configuration.
"""
if self.trainer.state.fn in (TrainerFn.FITTING, TrainerFn.TUNING):
self.__verify_train_loop_configuration(model)
self.__verify_eval_loop_configuration(model, "val")
self.__verify_manual_optimization_support(model)
self.__check_training_step_requires_dataloader_iter(model)
elif self.trainer.state.fn == TrainerFn.VALIDATING:
self.__verify_eval_loop_configuration(model, "val")
elif self.trainer.state.fn == TrainerFn.TESTING:
self.__verify_eval_loop_configuration(model, "test")
elif self.trainer.state.fn == TrainerFn.PREDICTING:
self.__verify_predict_loop_configuration(model)
self.__verify_dp_batch_transfer_support(model)
def __verify_train_loop_configuration(self, model: "pl.LightningModule") -> None:
# -----------------------------------
# verify model has a training step
# -----------------------------------
has_training_step = is_overridden("training_step", model)
if not has_training_step:
raise MisconfigurationException(
"No `training_step()` method defined. Lightning `Trainer` expects as minimum a"
" `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined."
)
# -----------------------------------
# verify model has a train dataloader
# -----------------------------------
has_train_dataloader = is_overridden("train_dataloader", model)
if not has_train_dataloader:
raise MisconfigurationException(
"No `train_dataloader()` method defined. Lightning `Trainer` expects as minimum a"
" `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined."
)
# -----------------------------------
# verify model has optimizer
# -----------------------------------
has_optimizers = is_overridden("configure_optimizers", model)
if not has_optimizers:
raise MisconfigurationException(
"No `configure_optimizers()` method defined. Lightning `Trainer` expects as minimum a"
" `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined."
)
# ----------------------------------------------
# verify model does not have
# - on_train_dataloader
# - on_val_dataloader
# ----------------------------------------------
has_on_train_dataloader = is_overridden("on_train_dataloader", model)
if has_on_train_dataloader:
rank_zero_deprecation(
"Method `on_train_dataloader` in DataHooks is deprecated and will be removed in v1.7.0."
" Please use `train_dataloader()` directly."
)
has_on_val_dataloader = is_overridden("on_val_dataloader", model)
if has_on_val_dataloader:
rank_zero_deprecation(
"Method `on_val_dataloader` in DataHooks is deprecated and will be removed in v1.7.0."
" Please use `val_dataloader()` directly."
)
trainer = self.trainer
trainer.overriden_optimizer_step = is_overridden("optimizer_step", model)
trainer.overriden_optimizer_zero_grad = is_overridden("optimizer_zero_grad", model)
automatic_optimization = model.automatic_optimization
going_to_accumulate_grad_batches = trainer.accumulation_scheduler.going_to_accumulate_grad_batches()
has_overriden_optimization_functions = trainer.overriden_optimizer_step or trainer.overriden_optimizer_zero_grad
if has_overriden_optimization_functions and going_to_accumulate_grad_batches and automatic_optimization:
rank_zero_warn(
"When using `Trainer(accumulate_grad_batches != 1)` and overriding"
"`LightningModule.optimizer_{step,zero_grad}`, the hooks will not be called on every batch"
"(rather, they are called on every optimization step)."
)
def __verify_eval_loop_configuration(self, model: "pl.LightningModule", stage: str) -> None:
loader_name = f"{stage}_dataloader"
step_name = "validation_step" if stage == "val" else "test_step"
has_loader = is_overridden(loader_name, model)
has_step = is_overridden(step_name, model)
if has_loader and not has_step:
rank_zero_warn(f"you passed in a {loader_name} but have no {step_name}. Skipping {stage} loop")
if has_step and not has_loader:
rank_zero_warn(f"you defined a {step_name} but have no {loader_name}. Skipping {stage} loop")
# ----------------------------------------------
# verify model does not have
# - on_val_dataloader
# - on_test_dataloader
# ----------------------------------------------
has_on_val_dataloader = is_overridden("on_val_dataloader", model)
if has_on_val_dataloader:
rank_zero_deprecation(
"Method `on_val_dataloader` in DataHooks is deprecated and will be removed in v1.7.0."
" Please use `val_dataloader()` directly."
)
has_on_test_dataloader = is_overridden("on_test_dataloader", model)
if has_on_test_dataloader:
rank_zero_deprecation(
"Method `on_test_dataloader` in DataHooks is deprecated and will be removed in v1.7.0."
" Please use `test_dataloader()` directly."
)
def __verify_predict_loop_configuration(self, model: "pl.LightningModule") -> None:
has_predict_dataloader = is_overridden("predict_dataloader", model)
if not has_predict_dataloader:
raise MisconfigurationException("Dataloader not found for `Trainer.predict`")
# ----------------------------------------------
# verify model does not have
# - on_predict_dataloader
# ----------------------------------------------
has_on_predict_dataloader = is_overridden("on_predict_dataloader", model)
if has_on_predict_dataloader:
rank_zero_deprecation(
"Method `on_predict_dataloader` in DataHooks is deprecated and will be removed in v1.7.0."
" Please use `predict_dataloader()` directly."
)
def __verify_dp_batch_transfer_support(self, model: "pl.LightningModule") -> None:
"""Raise Misconfiguration exception since these hooks are not supported in DP mode"""
# TODO: Remove this blocker once batch transfer to device is integrated in Lightning for DP mode.
batch_transfer_hooks = ("on_before_batch_transfer", "transfer_batch_to_device", "on_after_batch_transfer")
for hook in batch_transfer_hooks:
if self.trainer.accelerator_connector.use_dp and is_overridden(hook, model):
raise MisconfigurationException(f"Overriding `{hook}` is not supported in DP mode.")
def __verify_manual_optimization_support(self, model: "pl.LightningModule") -> None:
if model.automatic_optimization:
return
if self.trainer.gradient_clip_val > 0:
raise MisconfigurationException(
"Automatic gradient clipping is not supported for manual optimization."
f" Remove `Trainer(gradient_clip_val={self.trainer.gradient_clip_val})`"
" or switch to automatic optimization."
)
if self.trainer.accumulate_grad_batches != 1:
raise MisconfigurationException(
"Automatic gradient accumulation is not supported for manual optimization."
f" Remove `Trainer(accumulate_grad_batches={self.trainer.accumulate_grad_batches})`"
" or switch to automatic optimization."
)
def __check_training_step_requires_dataloader_iter(self, model: "pl.LightningModule"):
"""Check if the current `training_step` is requesting `dataloader_iter`."""
training_step_fx = getattr(model, "training_step")
if is_param_in_hook_signature(training_step_fx, "dataloader_iter", explicit=True):
if is_overridden("on_train_batch_start", model):
raise MisconfigurationException(
"The model hook `on_train_batch_start` is not compatible with "
"taking a `dataloader_iter` argument in your `training_step`."
)
if is_overridden("on_train_batch_end", model):
raise MisconfigurationException(
"The model hook `on_train_batch_end` is not compatible with "
"taking a `dataloader_iter` argument in your `training_step`."
)
if model.truncated_bptt_steps > 0:
raise MisconfigurationException(
"The model taking a `dataloader_iter` argument in your `training_step` "
"is incompatible with `truncated_bptt_steps > 0`."
)
| apache-2.0 |
williamFalcon/pytorch-lightning | pytorch_lightning/loops/base.py | 1 | 8803 | # Copyright The PyTorch Lightning team.
#
# 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.
from abc import ABC, abstractmethod
from typing import Any, Dict, Optional
from deprecate import void
from torchmetrics import Metric
import pytorch_lightning as pl
from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
from pytorch_lightning.trainer.progress import BaseProgress, Progress
from pytorch_lightning.utilities.apply_func import apply_to_collection
from pytorch_lightning.utilities.exceptions import MisconfigurationException
class Loop(ABC):
"""
Basic Loops interface. All classes derived from this must implement the following properties and methods:
* :attr:`done` (property): Condition to break the loop
* :attr:`reset` (method): Resets the internal state between multiple calls of :attr:`run`
* :attr:`advance` (method): Implements one step of the loop
This class implements the following loop structure:
.. codeblock:: python
on_run_start()
while not done:
on_advance_start()
advance()
on_advance_end()
on_run_end()
"""
def __init__(self) -> None:
self.restarting = False
self._trainer: Optional["pl.Trainer"] = None
@property
def trainer(self) -> Optional["pl.Trainer"]:
return self._trainer
@trainer.setter
def trainer(self, trainer: "pl.Trainer"):
"""Connects this loop's trainer and its children"""
if not isinstance(trainer, pl.Trainer):
raise MisconfigurationException(
f"Loop {self.__class__.__name__} should be connected to a `Trainer`, found: {trainer}."
)
self._trainer = trainer
for v in self.__dict__.values():
if isinstance(v, Loop):
v.trainer = trainer
@property
@abstractmethod
def done(self) -> bool:
"""Property indicating when loop is finished"""
@property
def skip(self) -> bool:
"""Determine whether to return immediately from the call to :meth:`run`."""
return False
def connect(self, **kwargs: "Loop") -> None:
"""Optionally connect one or multiple loops to this one. Linked loops should form a tree."""
def on_skip(self) -> Optional[Any]:
"""
The function to run when :meth:`run` should be skipped, determined by the condition in :attr:`skip`.
Returns:
the default output value of :meth:`on_run_end`
"""
def run(self, *args: Any, **kwargs: Any) -> Optional[Any]:
"""
The main entry point to the loop.
Will frequently check the :attr:`done` condition and calls :attr:`advance`
until :attr:`done` evaluates to ``True``.
Returns:
the output of :attr:`on_run_end` (often outputs collected from each step of the loop)
"""
if self.skip:
return self.on_skip()
self.reset()
self.on_run_start(*args, **kwargs)
while not self.done:
try:
self.on_advance_start(*args, **kwargs)
self.advance(*args, **kwargs)
self.on_advance_end()
self.restarting = False
except StopIteration:
break
output = self.on_run_end()
return output
@abstractmethod
def reset(self) -> None:
"""Resets the internal state of the loop at the beginning of each call to :attr:`run`."""
def on_run_start(self, *args: Any, **kwargs: Any) -> None:
"""
Hook to be called as the first thing after entering :attr:`run` (except the state reset).
Accepts all arguments passed to :attr:`run`.
"""
void(*args, **kwargs)
def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
"""
Hook to be called each time before :attr:`advance` is called. Accepts all arguments passed to :attr`run`.
"""
void(*args, **kwargs)
@abstractmethod
def advance(self, *args: Any, **kwargs: Any) -> None:
"""Performs a single step. Accepts all arguments passed to :attr:`run`."""
def on_advance_end(self) -> None:
"""Hook to be called each time after :attr:`advance` is called."""
def on_run_end(self) -> Any:
"""Hook to be called at the end of the run. Its return argument is returned from :attr:`run`."""
def teardown(self) -> None:
"""Use to release memory etc."""
def on_save_checkpoint(self) -> Dict:
"""
Called when saving a model checkpoint, use to persist loop state.
Returns:
The current loop state.
"""
return {}
def on_load_checkpoint(self, state_dict: Dict) -> None:
"""Called when loading a model checkpoint, use to reload loop state."""
def state_dict(self, destination: Optional[Dict] = None, prefix: Optional[str] = "") -> Dict:
"""
The state dict is determined by the state and progress of this loop and all its children.
Args:
destination: An existing dictionary to update with this loop's state. By default a new dictionary
is returned.
prefix: A prefix for each key in the state dictionary
"""
if destination is None:
destination = {}
destination[prefix + "state_dict"] = self.on_save_checkpoint()
for k, v in self.__dict__.items():
key = prefix + k
if isinstance(v, BaseProgress):
destination[key] = v.state_dict()
elif isinstance(v, Loop):
v.state_dict(destination, key + ".")
elif isinstance(v, ResultCollection):
# sync / unsync metrics
v.sync()
destination[key] = v.state_dict()
v.unsync()
return destination
def load_state_dict(
self,
state_dict: Dict,
prefix: str = "",
restart_progress: bool = True,
metrics: Optional[Dict[str, Metric]] = None,
) -> None:
"""Loads the state of this loop and all its children."""
self._load_from_state_dict(state_dict.copy(), prefix, restart_progress, metrics)
for k, v in self.__dict__.items():
if isinstance(v, Loop):
v.load_state_dict(state_dict.copy(), prefix + k + ".", restart_progress)
def _load_from_state_dict(
self, state_dict: Dict, prefix: str, restart_progress: bool, metrics: Optional[Dict[str, Metric]] = None
) -> None:
for k, v in self.__dict__.items():
key = prefix + k
if isinstance(v, BaseProgress):
v.load_state_dict(state_dict[key])
if restart_progress:
apply_to_collection(v, Progress, lambda p: p.current.reset_on_restart())
elif (
isinstance(v, ResultCollection)
and self.trainer is not None
and getattr(self.trainer, "lightning_module", None) is not None
):
metric_attributes = {
name: module
for name, module in self.trainer.lightning_module.named_modules()
if isinstance(module, Metric)
}
if metrics:
metric_attributes.update(metrics)
# The `ResultCollection` objects have 2 types of metrics: `Tensor` and `torchmetrics.Metric`.
# When creating a checkpoint, the `Metric`s are dropped from the loop `state_dict` to serialize only
# Python primitives. However, their states are saved with the model's `state_dict`.
# On reload, we need to re-attach the `Metric`s back to the `ResultCollection`.
# The references are provided through the `metric_attributes` dictionary.
v.load_state_dict(
state_dict[prefix + k], metrics=metric_attributes, sync_fn=self.trainer.training_type_plugin.reduce
)
if not self.trainer.is_global_zero:
v.reset(metrics=False)
self.on_load_checkpoint(state_dict[prefix + "state_dict"])
self.restarting = True
| apache-2.0 |
evansd/django | tests/generic_views/test_list.py | 29 | 12000 | import datetime
from django.core.exceptions import ImproperlyConfigured
from django.test import TestCase, override_settings
from django.views.generic.base import View
from .models import Artist, Author, Book, Page
@override_settings(ROOT_URLCONF='generic_views.urls')
class ListViewTests(TestCase):
@classmethod
def setUpTestData(cls):
cls.artist1 = Artist.objects.create(name='Rene Magritte')
cls.author1 = Author.objects.create(name='Roberto Bolaño', slug='roberto-bolano')
cls.author2 = Author.objects.create(name='Scott Rosenberg', slug='scott-rosenberg')
cls.book1 = Book.objects.create(name='2066', slug='2066', pages=800, pubdate=datetime.date(2008, 10, 1))
cls.book1.authors.add(cls.author1)
cls.book2 = Book.objects.create(
name='Dreaming in Code', slug='dreaming-in-code', pages=300, pubdate=datetime.date(2006, 5, 1)
)
cls.page1 = Page.objects.create(
content='I was once bitten by a moose.', template='generic_views/page_template.html'
)
def test_items(self):
res = self.client.get('/list/dict/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/list.html')
self.assertEqual(res.context['object_list'][0]['first'], 'John')
def test_queryset(self):
res = self.client.get('/list/authors/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertIsInstance(res.context['view'], View)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertIsNone(res.context['paginator'])
self.assertIsNone(res.context['page_obj'])
self.assertFalse(res.context['is_paginated'])
def test_paginated_queryset(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(len(res.context['object_list']), 30)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertTrue(res.context['is_paginated'])
self.assertEqual(res.context['page_obj'].number, 1)
self.assertEqual(res.context['paginator'].num_pages, 4)
self.assertEqual(res.context['author_list'][0].name, 'Author 00')
self.assertEqual(list(res.context['author_list'])[-1].name, 'Author 29')
def test_paginated_queryset_shortdata(self):
# Short datasets also result in a paginated view.
res = self.client.get('/list/authors/paginated/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['page_obj'].number, 1)
self.assertEqual(res.context['paginator'].num_pages, 1)
self.assertFalse(res.context['is_paginated'])
def test_paginated_get_page_by_query_string(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/', {'page': '2'})
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(len(res.context['object_list']), 30)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['author_list'][0].name, 'Author 30')
self.assertEqual(res.context['page_obj'].number, 2)
def test_paginated_get_last_page_by_query_string(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/', {'page': 'last'})
self.assertEqual(res.status_code, 200)
self.assertEqual(len(res.context['object_list']), 10)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['author_list'][0].name, 'Author 90')
self.assertEqual(res.context['page_obj'].number, 4)
def test_paginated_get_page_by_urlvar(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/3/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(len(res.context['object_list']), 30)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['author_list'][0].name, 'Author 60')
self.assertEqual(res.context['page_obj'].number, 3)
def test_paginated_page_out_of_range(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/42/')
self.assertEqual(res.status_code, 404)
def test_paginated_invalid_page(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/?page=frog')
self.assertEqual(res.status_code, 404)
def test_paginated_custom_paginator_class(self):
self._make_authors(7)
res = self.client.get('/list/authors/paginated/custom_class/')
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['paginator'].num_pages, 1)
# Custom pagination allows for 2 orphans on a page size of 5
self.assertEqual(len(res.context['object_list']), 7)
def test_paginated_custom_page_kwarg(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/custom_page_kwarg/', {'pagina': '2'})
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(len(res.context['object_list']), 30)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['author_list'][0].name, 'Author 30')
self.assertEqual(res.context['page_obj'].number, 2)
def test_paginated_custom_paginator_constructor(self):
self._make_authors(7)
res = self.client.get('/list/authors/paginated/custom_constructor/')
self.assertEqual(res.status_code, 200)
# Custom pagination allows for 2 orphans on a page size of 5
self.assertEqual(len(res.context['object_list']), 7)
def test_paginated_orphaned_queryset(self):
self._make_authors(92)
res = self.client.get('/list/authors/paginated-orphaned/')
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['page_obj'].number, 1)
res = self.client.get(
'/list/authors/paginated-orphaned/', {'page': 'last'})
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['page_obj'].number, 3)
res = self.client.get(
'/list/authors/paginated-orphaned/', {'page': '3'})
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['page_obj'].number, 3)
res = self.client.get(
'/list/authors/paginated-orphaned/', {'page': '4'})
self.assertEqual(res.status_code, 404)
def test_paginated_non_queryset(self):
res = self.client.get('/list/dict/paginated/')
self.assertEqual(res.status_code, 200)
self.assertEqual(len(res.context['object_list']), 1)
def test_verbose_name(self):
res = self.client.get('/list/artists/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/list.html')
self.assertEqual(list(res.context['object_list']), list(Artist.objects.all()))
self.assertIs(res.context['artist_list'], res.context['object_list'])
self.assertIsNone(res.context['paginator'])
self.assertIsNone(res.context['page_obj'])
self.assertFalse(res.context['is_paginated'])
def test_allow_empty_false(self):
res = self.client.get('/list/authors/notempty/')
self.assertEqual(res.status_code, 200)
Author.objects.all().delete()
res = self.client.get('/list/authors/notempty/')
self.assertEqual(res.status_code, 404)
def test_template_name(self):
res = self.client.get('/list/authors/template_name/')
self.assertEqual(res.status_code, 200)
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertTemplateUsed(res, 'generic_views/list.html')
def test_template_name_suffix(self):
res = self.client.get('/list/authors/template_name_suffix/')
self.assertEqual(res.status_code, 200)
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertTemplateUsed(res, 'generic_views/author_objects.html')
def test_context_object_name(self):
res = self.client.get('/list/authors/context_object_name/')
self.assertEqual(res.status_code, 200)
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertNotIn('authors', res.context)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertTemplateUsed(res, 'generic_views/author_list.html')
def test_duplicate_context_object_name(self):
res = self.client.get('/list/authors/dupe_context_object_name/')
self.assertEqual(res.status_code, 200)
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertNotIn('authors', res.context)
self.assertNotIn('author_list', res.context)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
def test_missing_items(self):
with self.assertRaises(ImproperlyConfigured):
self.client.get('/list/authors/invalid/')
def test_paginated_list_view_does_not_load_entire_table(self):
# Regression test for #17535
self._make_authors(3)
# 1 query for authors
with self.assertNumQueries(1):
self.client.get('/list/authors/notempty/')
# same as above + 1 query to test if authors exist + 1 query for pagination
with self.assertNumQueries(3):
self.client.get('/list/authors/notempty/paginated/')
def test_explicitly_ordered_list_view(self):
Book.objects.create(name="Zebras for Dummies", pages=800, pubdate=datetime.date(2006, 9, 1))
res = self.client.get('/list/books/sorted/')
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['object_list'][0].name, '2066')
self.assertEqual(res.context['object_list'][1].name, 'Dreaming in Code')
self.assertEqual(res.context['object_list'][2].name, 'Zebras for Dummies')
res = self.client.get('/list/books/sortedbypagesandnamedec/')
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['object_list'][0].name, 'Dreaming in Code')
self.assertEqual(res.context['object_list'][1].name, 'Zebras for Dummies')
self.assertEqual(res.context['object_list'][2].name, '2066')
@override_settings(DEBUG=True)
def test_paginated_list_view_returns_useful_message_on_invalid_page(self):
# test for #19240
# tests that source exception's message is included in page
self._make_authors(1)
res = self.client.get('/list/authors/paginated/2/')
self.assertEqual(res.status_code, 404)
self.assertEqual(res.context.get('reason'), "Invalid page (2): That page contains no results")
def _make_authors(self, n):
Author.objects.all().delete()
for i in range(n):
Author.objects.create(name='Author %02i' % i, slug='a%s' % i)
| bsd-3-clause |
hfp/libxsmm | samples/deeplearning/sparse_training/dlrm/test_roc.py | 1 | 1915 | import numpy as np
from bottleneck import rankdata
import time
from roc_auc_score import roc_auc_score
import sklearn.metrics
def roc_auc_score_bottleneck(actual, predicted, approx = False):
if approx: r = np.argsort(predicted)
else: r = rankdata(predicted)
n_pos = np.sum(actual)
n_neg = len(actual) - n_pos
sum1 = (np.sum(r[actual==1]) - n_pos*(n_pos+1)/2)
print(f"bottleneck nPos {n_pos} nNeg {n_neg}, sum {sum1}")
return sum1 / (n_pos*n_neg)
d = np.load('targets_scores.npz')
t = d['targets'].reshape([-1])
s = d['scores'].reshape([-1])
#t = t[:2000000]
#s = s[:2000000]
# t = np.array([0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0]).astype(np.float32)
# s = np.array([0.1, 0.3, 0.7, 0.2, 0.1, 0.3, 0.4, 0.9, 0.2, 0.7]).astype(np.float32)
# t = np.array([0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0]).astype(np.float32)
# s = np.array([0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.7, 0.7, 0.9]).astype(np.float32)
# #[1.5 1.5 3.5 3.5 5.5 5.5 7.0 8.5 8.5 10.0
# #[ 0 1 2 3 4
print(f"# pairs: {t.shape}")
roc = roc_auc_score(t, s)
t1 = time.time()
roc = roc_auc_score(t, s)
t2 = time.time()
print(f"{'C++:':12s} roc: {roc} Time: {t2-t1:.4f} sec")
t1 = time.time()
roc = roc_auc_score(t, s)
t2 = time.time()
print(f"{'C++:':12s} roc: {roc} Time: {t2-t1:.4f} sec")
t1 = time.time()
#roc = sklearn.metrics.roc_auc_score(t, s)
acc = sklearn.metrics.accuracy_score(t, np.round(s))
t2 = time.time()
#print(f"{'sklearn:':12s} roc: {roc:.7f} Time: {t2-t1:.4f} sec")
print(f"{'sklearn acc:':12s} acc: {acc:.7f} Time: {t2-t1:.4f} sec")
t1 = time.time()
loss = sklearn.metrics.log_loss(t, s)
t2 = time.time()
print(f"{'sklearn:':12s} loss: {loss:.7f} Time: {t2-t1:.4f} sec")
t1 = time.time()
roc = roc_auc_score_bottleneck(t, s)
t2 = time.time()
print(f"{'bottleneck:':12s} roc: {roc:.7f} Time: {t2-t1:.4f} sec")
| bsd-3-clause |
microsoft/onnxruntime | onnxruntime/python/tools/transformers/models/gpt2/gpt2_tester.py | 1 | 19606 | # -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
# This script helps evaluation of GPT-2 model.
import logging
import math
import os
import statistics
import sys
import timeit
import numpy
import torch
from gpt2_helper import Gpt2Helper, Gpt2Inputs
sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
from benchmark_helper import Precision
logger = logging.getLogger(__name__)
class Gpt2Metric:
def __init__(self, treatment_name, baseline_name="Torch", top_k=20):
assert top_k > 1 and top_k <= 100
self.baseline = baseline_name
self.treatment = treatment_name
self.name: str = f"{treatment_name} vs {baseline_name}"
self.top_k = top_k
self.top_1_error: int = 0
self.top_k_error: int = 0
self.total_samples: int = 0
self.max_logits_diff: float = 0 # for non-empty past state
self.max_logits_diff_no_past: float = 0 # for empty past state
self.batch_top1_error: torch.FloatTensor = None # top 1 error for current batch
self.batch_topk_error: torch.FloatTensor = None # top k error for current batch
self.seq_len_latency = {}
def print(self):
if self.baseline != self.treatment:
print("---")
print(f"Metrics for {self.treatment} (baseline={self.baseline}):")
if self.total_samples > 0:
top_1_error_rate = 100.0 * self.top_1_error / self.total_samples
top_k_error_rate = 100.0 * self.top_k_error / self.total_samples
print(
f"Total={self.total_samples} Top1Error={self.top_1_error} ({top_1_error_rate:.2f}%) Top{self.top_k}Error={self.top_k_error} ({top_k_error_rate:.2f}%)"
)
print("Max logits diffs:")
print(f"\twith past = {self.max_logits_diff:.6f}")
print(f"\tempty past = {self.max_logits_diff_no_past:.6f}")
else:
print(f"Metrics for {self.treatment} (baseline):")
if self.seq_len_latency:
print("Past sequence length range and average latency:")
total = 0
count = 0
for key in sorted(self.seq_len_latency.keys()):
average = statistics.mean(self.seq_len_latency[key]) * 1000.0
if key == 0:
print("\t{}: \t{:.2f} ms".format(key, average))
else:
print("\t[{}, {}]:\t{:.2f} ms".format(2**key, 2 ** (key + 1) - 1, average))
total += average * len(self.seq_len_latency[key])
count += len(self.seq_len_latency[key])
print("Average Latency: {:.2f} ms".format(total / count))
def diff_logits(self, baseline_logits, treatment_logits, is_empty_past: bool):
diff = (baseline_logits - treatment_logits).abs().max()
if is_empty_past:
self.max_logits_diff_no_past = max(self.max_logits_diff_no_past, diff)
else:
self.max_logits_diff = max(self.max_logits_diff, diff)
return diff
def start_batch(self, batch_size: int):
self.total_samples += batch_size
self.batch_top1_error = torch.zeros((batch_size, 1), dtype=torch.bool)
self.batch_topk_error = torch.zeros((batch_size, 1), dtype=torch.bool)
def eval_batch(self, baseline, treatment, past_seq_len, verbose=True):
self._eval_topk(baseline.top_1_tokens, treatment.top_1_tokens, 1, verbose)
self._eval_topk(baseline.top_k_tokens, treatment.top_k_tokens, self.top_k, verbose)
max_diff = self.diff_logits(baseline.logits, treatment.logits, past_seq_len == 0)
if verbose:
print(f"Max logits diffs of {self.name}: {max_diff}")
def _eval_topk(self, baseline_topk, treatment_topk, top_k, verbose=True):
if not torch.all(torch.eq(baseline_topk, treatment_topk)):
if top_k == 1:
if verbose:
print(f"Generated tokens not matched for {self.name}")
self.batch_top1_error |= torch.eq(baseline_topk, treatment_topk).logical_not()
else:
if verbose:
print(
f"Top {top_k} tokens not matched for {self.name}. This will lead to wrong beam search results"
)
self.batch_topk_error |= (
torch.eq(baseline_topk, treatment_topk).logical_not().sum(1).unsqueeze(dim=1) > 0
)
def end_batch(self):
self.top_1_error += self.batch_top1_error.sum()
self.top_k_error += self.batch_topk_error.sum()
def add_latency(self, past_seq_len, latency):
key = int(math.log2(past_seq_len)) + 1 if past_seq_len > 0 else 0
if key not in self.seq_len_latency:
self.seq_len_latency[key] = []
self.seq_len_latency[key].append(latency)
class Gpt2Tester:
def __init__(
self,
input_ids,
position_ids,
attention_mask,
num_attention_heads,
hidden_size,
num_layer,
device,
is_fp16=False,
top_k=20,
top_k_required_order=False,
):
self.batch_size = input_ids.shape[0]
self.input_length = input_ids.shape[1]
self.n_layer = num_layer
self.input_ids = input_ids
self.position_ids = position_ids
self.attention_mask = attention_mask
self.has_position_ids = position_ids is not None
self.has_attention_mask = attention_mask is not None
# Emtpy past state for first inference
self.past = []
past_shape = [
2,
self.batch_size,
num_attention_heads,
0,
hidden_size // num_attention_heads,
]
for i in range(num_layer):
empty_past = torch.empty(past_shape).type(torch.float16 if is_fp16 else torch.float32)
self.past.append(empty_past.to(device))
self.logits = None
self.top_1_tokens = None
self.top_k_tokens = None
self.top_k = top_k
self.top_k_required_order = top_k_required_order
def get_inputs(self) -> Gpt2Inputs:
return Gpt2Inputs(self.input_ids, self.position_ids, self.attention_mask, self.past)
def save_test_data(self, session, output, save_test_data_dir, test_case_id):
from onnx import numpy_helper
path = os.path.join(save_test_data_dir, "test_data_set_" + str(test_case_id))
if os.path.exists(path):
print(f"Directory {path} existed. Skip saving test data")
return
os.makedirs(path, exist_ok=True)
def add_tensor(input_tensors, torch_tensor, name):
input_tensors.append(numpy_helper.from_array(torch_tensor.clone().cpu().numpy(), name))
input_tensors = []
add_tensor(input_tensors, self.input_ids, "input_ids")
if self.has_position_ids:
add_tensor(input_tensors, self.position_ids, "position_ids")
if self.has_attention_mask:
add_tensor(input_tensors, self.attention_mask, "attention_mask")
for i in range(self.n_layer):
add_tensor(input_tensors, self.past[i], "past_" + str(i))
for i, tensor in enumerate(input_tensors):
with open(os.path.join(path, "input_{}.pb".format(i)), "wb") as f:
f.write(tensor.SerializeToString())
output_names = [output.name for output in session.get_outputs()]
for i, name in enumerate(output_names):
tensor = numpy_helper.from_array(
output[i] if isinstance(output[i], numpy.ndarray) else output[i].clone().cpu().numpy()
)
with open(os.path.join(path, "output_{}.pb".format(i)), "wb") as f:
f.write(tensor.SerializeToString())
print(f"Test data saved to directory {path}")
def update(self, output, step, device):
"""
Update the inputs for next inference.
"""
self.logits = (
torch.from_numpy(output[0]) if isinstance(output[0], numpy.ndarray) else output[0].clone().detach().cpu()
)
self.top_1_tokens = Gpt2Tester.predict_next_token(self.logits)
self.top_k_tokens = Gpt2Tester.predict_next_token(self.logits, self.top_k, self.top_k_required_order)
self.input_ids = self.top_1_tokens.clone().detach().reshape([self.batch_size, 1]).to(device)
if self.has_position_ids:
self.position_ids = (
torch.tensor([self.input_length + step - 1]).unsqueeze(0).repeat(self.batch_size, 1).to(device)
)
if self.has_attention_mask:
self.attention_mask = torch.cat(
[
self.attention_mask,
torch.ones([self.batch_size, 1]).type_as(self.attention_mask),
],
1,
).to(device)
self.past = []
if isinstance(output[1], tuple): # past in torch output is tuple
self.past = list(output[1])
else:
for i in range(self.n_layer):
past_i = (
torch.from_numpy(output[i + 1])
if isinstance(output[i + 1], numpy.ndarray)
else output[i + 1].clone().detach()
)
self.past.append(past_i.to(device))
def diff(self, baseline):
"""
Compare inputs and logits output.
"""
print("start diff...")
if self.logits is not None:
max_io_diff = (self.logits - baseline.logits).abs().max()
if max_io_diff > 1e-4:
print(f"Max logits difference is too large: {max_io_diff}")
if not torch.all(self.input_ids == baseline.input_ids):
print("Input_ids is different", self.input_ids, baseline.input_ids)
if self.has_position_ids:
if not torch.all(self.position_ids == baseline.position_ids):
print(
"position_ids is different",
self.position_ids,
baseline.position_ids,
)
if self.has_attention_mask:
if not torch.all(self.attention_mask == baseline.attention_mask):
print(
"attention_mask is different",
self.attention_mask,
baseline.attention_mask,
)
assert len(self.past) == len(baseline.past)
for i, past_i in enumerate(self.past):
assert past_i.shape == baseline.past[i].shape
if past_i.nelement() > 0:
max_past_diff = (past_i - baseline.past[i]).abs().max()
if max_past_diff > 1e-4:
print(f"max_past_diff[{i}]={max_past_diff}")
@staticmethod
def predict_next_token(logits, top_k=1, required_order=False):
"""
Get top k topkens based on logits.
"""
# logits has shape (batch_size, seq_len, vocab_size)
# last token logits has shape (batch_size, vocab_size)
lastTokenLogits = logits[:, -1]
if top_k == 1:
generatedTokens = torch.argmax(lastTokenLogits, 1, True)
return generatedTokens
else:
topk = torch.argsort(lastTokenLogits, -1, descending=True)[:, :top_k]
if not required_order:
sorted_topk, _ = topk.sort()
return sorted_topk
return topk
@staticmethod
def diff_present(onnx_output, onnx_io_output, n_layer):
"""
Compare the present outputs of two outputs from ONNX Runtime.
"""
present_diff_max = []
for i in range(n_layer):
onnx_present_i = (
torch.from_numpy(onnx_output[i + 1])
if isinstance(onnx_output[i + 1], numpy.ndarray)
else onnx_output[i + 1]
)
onnx_io_present_i = (
torch.from_numpy(onnx_io_output[i + 1])
if isinstance(onnx_io_output[i + 1], numpy.ndarray)
else onnx_io_output[i + 1]
)
max_diff = (onnx_present_i - onnx_io_present_i).abs().max()
present_diff_max.append(max_diff)
print(f"present_diff_max={present_diff_max}")
@staticmethod
def is_quantized_onnx_model(onnx_model_path):
"""
Returns True if the ONNX model is quantized.
"""
from onnx import load
model = load(onnx_model_path)
from onnxruntime.quantization.quantize import __producer__ as quantize_producer
return model.producer_name == quantize_producer
@staticmethod
def test_generation(
session,
model,
device,
test_inputs,
precision=Precision.FLOAT32,
model_class="Gpt2LMHeadModel",
top_k=20,
top_k_no_order=True,
max_steps=24,
max_inputs=0,
verbose=False,
save_test_data=0,
save_test_data_dir=".",
):
"""
Test Generation using greedy beam search (without sampling) to compare PyTorch and ONNX model.
It will print top 1 and top k errors on the given test inputs.
"""
print(
f"start test generation: (top_k={top_k} top_k_no_order={top_k_no_order} max_steps={max_steps} test_inputs={len(test_inputs)} max_inputs={max_inputs})"
)
n_layer = model.config.n_layer
n_head = model.config.n_head
n_embd = model.config.n_embd
eos_token_id = model.config.eos_token_id
test_data_saved = 0
is_float16 = precision == Precision.FLOAT16
if is_float16:
assert "float16" in session.get_outputs()[0].type
# We will still use fp32 torch model as baseline when onnx model if fp16
model.eval().to(device)
# Allocate initial buffers for IO Binding of ONNX Runtimne. The buffer size will automatically increase later.
init_output_shapes = Gpt2Helper.get_output_shapes(
batch_size=4,
past_sequence_length=128,
sequence_length=32,
config=model.config,
model_class=model_class,
)
output_buffers = Gpt2Helper.get_output_buffers(init_output_shapes, device, is_float16=is_float16)
baseline_name = "Torch"
treatment_name = "Quantized Onnx" if precision == Precision.INT8 else "Onnx"
torch_metric = Gpt2Metric(baseline_name, baseline_name, top_k)
onnx_metric = Gpt2Metric(treatment_name, baseline_name, top_k)
onnx_io_metric = Gpt2Metric(treatment_name + " with IO Binding", baseline_name, top_k)
for i, inputs in enumerate(test_inputs):
if max_inputs > 0 and i == max_inputs:
break
if i % 10 == 0:
print(f"{i}")
input_ids = inputs["input_ids"]
position_ids = inputs["position_ids"] if "position_ids" in inputs else None
attention_mask = inputs["attention_mask"] if "attention_mask" in inputs else None
onnx_runner = Gpt2Tester(
input_ids,
position_ids,
attention_mask,
n_head,
n_embd,
n_layer,
device,
is_float16,
top_k,
not top_k_no_order,
)
onnx_io_runner = Gpt2Tester(
input_ids,
position_ids,
attention_mask,
n_head,
n_embd,
n_layer,
device,
is_float16,
top_k,
not top_k_no_order,
)
torch_runner = Gpt2Tester(
input_ids,
position_ids,
attention_mask,
n_head,
n_embd,
n_layer,
device,
False,
top_k,
not top_k_no_order,
) # Torch model baseline is fp32
batch_size = torch_runner.batch_size
onnx_metric.start_batch(batch_size)
onnx_io_metric.start_batch(batch_size)
with torch.no_grad():
done = torch.zeros(batch_size, dtype=torch.bool)
for step in range(max_steps):
seq_len = list(onnx_runner.input_ids.size())[1]
past_seq_len = list(onnx_runner.past[0].size())[3]
start_time = timeit.default_timer()
pytorch_output = Gpt2Helper.pytorch_inference(model, torch_runner.get_inputs())
torch_metric.add_latency(past_seq_len, timeit.default_timer() - start_time)
torch_runner.update(pytorch_output, step, device)
onnx_output, avg_latency_ms = Gpt2Helper.onnxruntime_inference(
session, onnx_runner.get_inputs(), total_runs=1
)
onnx_metric.add_latency(past_seq_len, avg_latency_ms / 1000.0)
onnx_runner.update(onnx_output, step, device)
output_shapes = Gpt2Helper.get_output_shapes(
batch_size,
past_seq_len,
seq_len,
model.config,
model_class=model_class,
)
Gpt2Helper.auto_increase_buffer_size(output_buffers, output_shapes)
(onnx_io_output, avg_latency_ms,) = Gpt2Helper.onnxruntime_inference_with_binded_io(
session,
onnx_io_runner.get_inputs(),
output_buffers,
output_shapes,
total_runs=1,
return_numpy=False,
include_copy_output_latency=True,
)
onnx_io_metric.add_latency(past_seq_len, avg_latency_ms / 1000.0)
if test_data_saved < save_test_data:
onnx_io_runner.save_test_data(session, onnx_io_output, save_test_data_dir, test_data_saved)
test_data_saved += 1
onnx_io_runner.update(onnx_io_output, step, device)
if verbose:
onnx_runner.diff(onnx_io_runner)
Gpt2Tester.diff_present(onnx_output, onnx_io_output, n_layer)
print("Top 1 tokens:")
print("\tTorch", torch_runner.top_1_tokens)
print("\tONNX", onnx_runner.top_1_tokens)
print("\tONNX with IO binding", onnx_io_runner.top_1_tokens)
onnx_metric.eval_batch(torch_runner, onnx_runner, past_seq_len, verbose=verbose)
onnx_io_metric.eval_batch(torch_runner, onnx_io_runner, past_seq_len, verbose=verbose)
done = done | (torch_runner.top_1_tokens == eos_token_id).any()
if torch.all(done):
break
onnx_metric.end_batch()
onnx_io_metric.end_batch()
torch_metric.print()
onnx_metric.print()
onnx_io_metric.print()
| mit |
FGRibreau/mailchecker | platform/python/MailChecker.py | 1 | 970358 | import re
import sys
if sys.version_info[0] >= 3:
xrange = range
class MailChecker(object):
blacklist = { 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}
valid_matcher = re.compile(r"\A(?!(?:(?:\x22?\x5C[\x00-\x7E]\x22?)|(?:\x22?[^\x5C\x22]\x22?)){255,})(?!(?:(?:\x22?\x5C[\x00-\x7E]\x22?)|(?:\x22?[^\x5C\x22]\x22?)){65,}@)(?:(?:[\x21\x23-\x27\x2A\x2B\x2D\x2F-\x39\x3D\x3F\x5E-\x7E]+)|(?:\x22(?:[\x01-\x08\x0B\x0C\x0E-\x1F\x21\x23-\x5B\x5D-\x7F]|(?:\x5C[\x00-\x7F]))*\x22))(?:\.(?:(?:[\x21\x23-\x27\x2A\x2B\x2D\x2F-\x39\x3D\x3F\x5E-\x7E]+)|(?:\x22(?:[\x01-\x08\x0B\x0C\x0E-\x1F\x21\x23-\x5B\x5D-\x7F]|(?:\x5C[\x00-\x7F]))*\x22)))*@(?:(?:(?!.*[^.]{64,})(?:(?:(?:xn--)?[a-z0-9]+(?:-[a-z0-9]+)*\.){1,126}){1,}(?:(?:[a-z][a-z0-9]*)|(?:(?:xn--)[a-z0-9]+))(?:-[a-z0-9]+)*)|(?:\[(?:(?:IPv6:(?:(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){7})|(?:(?!(?:.*[a-f0-9][:\]]){7,})(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){0,5})?::(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){0,5})?)))|(?:(?:IPv6:(?:(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){5}:)|(?:(?!(?:.*[a-f0-9]:){5,})(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){0,3})?::(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){0,3}:)?)))?(?:(?:25[0-5])|(?:2[0-4][0-9])|(?:1[0-9]{2})|(?:[1-9]?[0-9]))(?:\.(?:(?:25[0-5])|(?:2[0-4][0-9])|(?:1[0-9]{2})|(?:[1-9]?[0-9]))){3}))\]))\Z")
@classmethod
def is_valid(cls, email):
email = email.lower()
return (cls.is_valid_email_format(email) and
not cls.is_blacklisted(email))
@classmethod
def all_domain_suffixes(cls, email):
domain = email.split("@")[-1]
components = domain.split(".")
return (".".join(components[n:]) for n in xrange(0, len(components)))
@classmethod
def is_blacklisted(cls, email):
all_domain_suffixes = cls.all_domain_suffixes(email)
return any(domain in cls.blacklist for domain in all_domain_suffixes)
@classmethod
def is_valid_email_format(cls, email):
return bool(email) and cls.valid_matcher.search(email) is not None
@classmethod
def add_custom_domains(cls, domains = []):
cls.blacklist.update(domains)
| mit |
polyaxon/polyaxon | traceml/traceml/processors/importance_processors.py | 1 | 3387 | #!/usr/bin/python
#
# Copyright 2018-2022 Polyaxon, 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 math
import numpy as np
import pandas as pd
from typing import Dict, List, Optional, Tuple, Union
from polyaxon.utils.np_utils import sanitize_np_types
def clean_duplicates(
params: pd.DataFrame, metrics: pd.DataFrame
) -> Optional[Tuple[pd.DataFrame, pd.DataFrame]]:
duplicate_ids = metrics.duplicated()
params_df = params[~duplicate_ids]
metrics_df = metrics[~duplicate_ids]
if params.empty or metrics.empty:
return None
params_df = pd.get_dummies(params_df)
params_df = params_df.loc[:, ~params_df.columns.duplicated()]
return params_df, metrics_df
def clean_values(
params: List[Dict], metrics: List[Union[int, float]]
) -> Optional[Tuple[pd.DataFrame, pd.DataFrame]]:
if not metrics or not params:
return None
for m in metrics:
if not isinstance(m, (int, float)):
return None
metrics_df = pd.DataFrame(metrics)
if metrics_df.isnull().values.any():
return None
params_df = pd.DataFrame.from_records(params).replace(r"^\s*$", np.nan, regex=True)
for col in params_df.columns:
if not params_df[col].isnull().sum() == len(params_df[col]):
if params_df[col].dtype == "object":
params_df[col].fillna("NAN", inplace=True)
params_df[col].fillna("NAN", inplace=True)
params_df[col] = params_df[col].astype("category")
elif params_df[col].dtype == "float64" or params_df[col].dtype == "int64":
params_df[col].fillna(params_df[col].mean(), inplace=True)
else:
print("Unexpected Column type: {}".format(params_df[col].dtype))
else:
if params_df[col].dtype == "object":
params_df[col] = "NAN"
elif params_df[col].dtype == "float64" or params_df[col].dtype == "int64":
params_df[col] = 0
return clean_duplicates(params_df, metrics_df)
def _get_value(x):
if x is None or math.isnan(x):
return None
return round(sanitize_np_types(x), 3)
def calculate_importance_correlation(
params: List[Dict], metrics: List[Union[int, float]]
):
values = clean_values(params, metrics)
if not values:
return None
params_df, metrics_df = values
corr_list = params_df.corrwith(metrics_df[0])
from sklearn.ensemble import ExtraTreesRegressor
forest = ExtraTreesRegressor(n_estimators=250, random_state=0)
forest.fit(params_df, metrics_df[0])
feature_importances = forest.feature_importances_
results = {}
for i, name in enumerate(params_df.columns):
results[name] = {
"importance": _get_value(feature_importances[i]),
"correlation": _get_value(corr_list[name]),
}
return results
| apache-2.0 |
rvraghav93/scikit-learn | examples/gaussian_process/plot_gpr_prior_posterior.py | 33 | 2900 | """
==========================================================================
Illustration of prior and posterior Gaussian process for different kernels
==========================================================================
This example illustrates the prior and posterior of a GPR with different
kernels. Mean, standard deviation, and 10 samples are shown for both prior
and posterior.
"""
print(__doc__)
# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import numpy as np
from matplotlib import pyplot as plt
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import (RBF, Matern, RationalQuadratic,
ExpSineSquared, DotProduct,
ConstantKernel)
kernels = [1.0 * RBF(length_scale=1.0, length_scale_bounds=(1e-1, 10.0)),
1.0 * RationalQuadratic(length_scale=1.0, alpha=0.1),
1.0 * ExpSineSquared(length_scale=1.0, periodicity=3.0,
length_scale_bounds=(0.1, 10.0),
periodicity_bounds=(1.0, 10.0)),
ConstantKernel(0.1, (0.01, 10.0))
* (DotProduct(sigma_0=1.0, sigma_0_bounds=(0.0, 10.0)) ** 2),
1.0 * Matern(length_scale=1.0, length_scale_bounds=(1e-1, 10.0),
nu=1.5)]
for fig_index, kernel in enumerate(kernels):
# Specify Gaussian Process
gp = GaussianProcessRegressor(kernel=kernel)
# Plot prior
plt.figure(fig_index, figsize=(8, 8))
plt.subplot(2, 1, 1)
X_ = np.linspace(0, 5, 100)
y_mean, y_std = gp.predict(X_[:, np.newaxis], return_std=True)
plt.plot(X_, y_mean, 'k', lw=3, zorder=9)
plt.fill_between(X_, y_mean - y_std, y_mean + y_std,
alpha=0.2, color='k')
y_samples = gp.sample_y(X_[:, np.newaxis], 10)
plt.plot(X_, y_samples, lw=1)
plt.xlim(0, 5)
plt.ylim(-3, 3)
plt.title("Prior (kernel: %s)" % kernel, fontsize=12)
# Generate data and fit GP
rng = np.random.RandomState(4)
X = rng.uniform(0, 5, 10)[:, np.newaxis]
y = np.sin((X[:, 0] - 2.5) ** 2)
gp.fit(X, y)
# Plot posterior
plt.subplot(2, 1, 2)
X_ = np.linspace(0, 5, 100)
y_mean, y_std = gp.predict(X_[:, np.newaxis], return_std=True)
plt.plot(X_, y_mean, 'k', lw=3, zorder=9)
plt.fill_between(X_, y_mean - y_std, y_mean + y_std,
alpha=0.2, color='k')
y_samples = gp.sample_y(X_[:, np.newaxis], 10)
plt.plot(X_, y_samples, lw=1)
plt.scatter(X[:, 0], y, c='r', s=50, zorder=10, edgecolors=(0, 0, 0))
plt.xlim(0, 5)
plt.ylim(-3, 3)
plt.title("Posterior (kernel: %s)\n Log-Likelihood: %.3f"
% (gp.kernel_, gp.log_marginal_likelihood(gp.kernel_.theta)),
fontsize=12)
plt.tight_layout()
plt.show()
| bsd-3-clause |
cmoutard/mne-python | examples/forward/plot_source_space_morphing.py | 19 | 2852 | """
=========================
Use source space morphing
=========================
This example shows how to use source space morphing (as opposed to
SourceEstimate morphing) to create data that can be compared between
subjects.
.. warning:: Source space morphing will likely lead to source spaces that are
less evenly sampled than source spaces created for individual
subjects. Use with caution and check effects on localization
before use.
"""
# Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Eric larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import mne
data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_trans = op.join(data_path, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
fname_bem = op.join(subjects_dir, 'sample', 'bem',
'sample-5120-bem-sol.fif')
fname_src_fs = op.join(subjects_dir, 'fsaverage', 'bem',
'fsaverage-ico-5-src.fif')
raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
# Get relevant channel information
info = mne.io.read_info(raw_fname)
info = mne.pick_info(info, mne.pick_types(info, meg=True, eeg=False,
exclude=[]))
# Morph fsaverage's source space to sample
src_fs = mne.read_source_spaces(fname_src_fs)
src_morph = mne.morph_source_spaces(src_fs, subject_to='sample',
subjects_dir=subjects_dir)
# Compute the forward with our morphed source space
fwd = mne.make_forward_solution(info, trans=fname_trans,
src=src_morph, bem=fname_bem)
# fwd = mne.convert_forward_solution(fwd, surf_ori=True, force_fixed=True)
mag_map = mne.sensitivity_map(fwd, ch_type='mag')
# Return this SourceEstimate (on sample's surfaces) to fsaverage's surfaces
mag_map_fs = mag_map.to_original_src(src_fs, subjects_dir=subjects_dir)
# Plot the result, which tracks the sulcal-gyral folding
# outliers may occur, we'll place the cutoff at 99 percent.
kwargs = dict(clim=dict(kind='percent', lims=[0, 50, 99]),
# no smoothing, let's see the dipoles on the cortex.
smoothing_steps=1, hemi='rh', views=['lat'])
# Now note that the dipoles on fsaverage are almost equidistant while
# morphing will distribute the dipoles unevenly across the given subject's
# cortical surface to achieve the closest approximation to the average brain.
# Our testing code suggests a correlation of higher than 0.99.
brain_subject = mag_map.plot( # plot forward in subject source space (morphed)
time_label=None, subjects_dir=subjects_dir, **kwargs)
brain_fs = mag_map_fs.plot( # plot forward in original source space (remapped)
time_label=None, subjects_dir=subjects_dir, **kwargs)
| bsd-3-clause |
segasai/astrolibpy | plotting/idlplot.py | 1 | 37177 | # Copyright (C) 2009-2010 Sergey Koposov
# This file is part of astrolibpy
#
# astrolibpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# astrolibpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with astrolibpy. If not, see <http://www.gnu.org/licenses/>.
import matplotlib.pyplot as plt
import numpy as np
import scipy
import scipy.stats
import scipy.ndimage.filters
import scipy.stats
from matplotlib.pyplot import draw_if_interactive
import matplotlib
import math
import copy
import warnings
# this module is by default in interactive regime
plt.ion()
def listToArr(x):
return np.asarray(x)
def listToArrFlat(x):
return np.asarray(x).flatten()
def get_marker(ps, linestyle):
"""
Wrapper for point markers which understand idl-like ps options
(e.g. ps=3 for the point, ps=4 for the diamond)
"""
outlinestyle = ' '
markerHash = {3: '.', 4: 'D', 7: '+', 2: '*', 6: 's'}
try:
marker = markerHash[ps]
except KeyError:
if isinstance(ps, str):
marker = ps
else:
marker = ' '
if linestyle is not None:
outlinestyle = linestyle
else:
outlinestyle = '-'
return (marker, outlinestyle)
def filter_epa(im, kernsize):
if not hasattr(kernsize, '__iter__'):
kernsize1, kernsize2 = kernsize, kernsize
else:
kernsize1, kernsize2 = kernsize[0:2]
k11 = math.ceil(kernsize1)
k12 = math.ceil(kernsize2)
xgrid, ygrid = np.mgrid[-k11:k11:1, -k12:k12:1]
r2 = (xgrid / kernsize1)**2 + (ygrid / kernsize2)**2
filt = (1 - r2) * (r2 <= 1)
filt = filt / filt.sum()
# IMPORTANT TRANSPOSITION, because the image first dimension is y
im1 = scipy.ndimage.filters.convolve(im, filt.T, mode='reflect')
return im1
def smoother(arr, smooth=None, kernel=None):
if smooth is not None:
if kernel == 'gau':
if hasattr(smooth, '__iter__'):
smooth = smooth[::-1]
# because gaussian_filter convention is second dimension is x
arr = scipy.ndimage.filters.gaussian_filter(arr * 1., smooth)
elif kernel == 'epa':
arr = filter_epa(arr * 1., smooth)
else:
raise Exception('Wrong kernel')
return arr
def exceptionDecorator(func):
def wrapper(*args, **kwargs):
try:
isInteractive = plt.isinteractive()
# switch to non-interactive mode
# matplotlib.interactive(False)
ret = func(*args, **kwargs)
matplotlib.interactive(isInteractive)
draw_if_interactive()
return ret
except Exception as exc: # noqa
# switch back
matplotlib.interactive(isInteractive)
raise
wrapper.__doc__ = func.__doc__
return wrapper
def __findKnuth(x, minv, maxv):
"""
Implement Knuth method for histogram bin selection
"""
N = ((x >= minv) & (x <= maxv)).sum()
def funcer(M):
hh, loc = scipy.histogram(x, bins=M, range=[minv, maxv])
return np.log(M) + 1. / N * (scipy.special.gammaln(M / 2.) -
M * scipy.special.gammaln(0.5) -
scipy.special.gammaln(N + M / 2.) +
scipy.special.gammaln(hh + 0.5).sum())
maxN = 1000
ns = np.arange(1, maxN + 1)
vals = ns * 0.
for i in range(len(ns)):
vals[i] = funcer(ns[i])
bestn = ns[np.argmax(vals)]
if bestn == maxN:
print('WARNING the best number of bins is > maxbin(%d)' % (maxN))
return bestn
@exceptionDecorator
def plothist(x,
bin=None,
nbins=None,
xrange=None,
yrange=None,
min=None,
max=None,
overplot=False,
color='black',
xlog=False,
ylog=False,
nan=False,
weights=None,
norm=False,
kernel=None,
retpoints=False,
adaptive=False,
adaptive_thresh=30,
adaptive_depth=[2, 10],
weight_norm=False,
apply_func=None,
statistic=None,
knuth=False,
cumulative=False,
**kw):
"""
Plot the 1D histogram
Example:
>> plothist(dat, bin=0.1, min=0, max=3)
Keyword parameters:
------------------
bin
the binsize(float)
nbins
number of bins(integer)
It cannot be specified together with the bin= parameter
xlog, ylog
log the appropriate axis
weights
the 1-D array of weights used in the histogram creation
nan
boolean flag to ignore nan's
norm
boolean flag to normalize the histogram by the peak value
min,max
range of data for which the histogram is constructed
retpoints
boolean parameter controlling whether to return or not the
computed histogram. If yes the tuple with two arrays
(bin centers, Number of points in bins) is returned
overplot
boolean parameter for overplotting
adaptive
boolean for turning on/off the adaptive regime of
histogramming (adaptive bin size).
If True weights, nbins, bin,kernel parameters are ignored
adaptive_thresh
the limiting number of points in the bin for the adaptive
histogramming (default 30)
adaptive_depth
the list of two integers for the detalisation levels of
adaptive histogramming (default [2,10])
weight_norm
if True the value in each bin is mean weight of points within
the bin
"""
if nan:
ind = np.isfinite(x)
if weights is not None:
ind = np.isfinite(weights) & ind
dat = x[ind]
if weights is not None:
weights = weights[ind]
else:
dat = x
maxNone = False
if min is None:
min = np.nanmin(dat)
if max is None:
maxNone = True
max = np.nanmax(dat)
if bin is None and nbins is None:
if not knuth:
nbins = 100
else:
nbins = __findKnuth(dat, min, max)
bin = (max - min) * 1. / nbins
elif nbins is None:
nbins = int(math.ceil((max - min) * 1. / bin))
if maxNone:
max = min + nbins * bin
elif bin is None:
bin = (max - min) * 1. / nbins
else:
warnings.warn(
'both bin= and nbins= keywords were specified in ' +
'the plothist call', RuntimeWarning)
pass
# if both nbins and bin are defined I don't do anything
# it may be non-intuitive if kernel option is used, because
# it uses both nbins and bin options
if cumulative:
if (kernel is not None or adaptive or weights is not None):
raise RuntimeError(
'cumulative is incompatible with weights, kernel ' +
'or adaptive options')
if kernel is None:
if not adaptive:
if np.isscalar(weights) and weights is not None:
weights = np.zeros_like(dat) + weights
if statistic is None:
hh, loc = np.histogram(dat,
range=(min, max),
bins=nbins,
weights=weights)
if weight_norm:
hh1, loc = np.histogram(dat,
range=(min, max),
bins=nbins,
weights=None)
hh = hh * 1. / hh1
else:
S = scipy.stats.binned_statistic(dat,
weights,
statistic,
range=[min, max],
bins=nbins)
hh = S.statistic
loc = S.bin_edges
else:
import adabinner
hh, loc = adabinner.hist(dat,
xmin=min,
xmax=max,
hi=adaptive_depth,
thresh=adaptive_thresh)
if cumulative:
hh = np.cumsum(hh)
hh1 = np.repeat(hh, 2)
loc1 = np.concatenate(([loc[0]], np.repeat(loc[1:-1], 2), [loc[-1]]))
else:
loc1 = np.linspace(min, max, nbins * 10)
import sklearn.neighbors
xind = np.isfinite(dat)
kde = sklearn.neighbors.KernelDensity(bandwidth=bin, kernel=kernel)
kde.fit(np.asarray(dat[xind]).flatten().reshape(-1, 1))
hh1 = np.exp(kde.score_samples(loc1.reshape(-1, 1)))
if weights is not None:
print('WARNING weights ignored for KDE !')
if overplot:
func = oplot
else:
func = plot
if norm:
hh1 = hh1 * 1. / hh1.max()
kw['ps'] = kw.get('ps') or 0
if 'yr' not in kw:
kw['yr'] = [np.nanmin(hh1), np.nanmax(hh1)]
if 'xr' not in kw:
kw['xr'] = [min, max]
if apply_func is not None:
hh1 = apply_func(loc1, hh1)
func(loc1, hh1, color=color, xlog=xlog, ylog=ylog, **kw)
if retpoints:
return 0.5 * (loc[1:] + loc[:-1]), hh
@exceptionDecorator
def plot(arg1,
arg2=None,
xrange=None,
yrange=None,
ps=0,
thick=None,
xtitle=None,
ytitle=None,
color='black',
noerase=False,
overplot=False,
position=None,
ylog=False,
xlog=False,
xr=None,
yr=None,
title=None,
label=None,
nodata=False,
linestyle=None,
markersize=None,
xaxis_formatter=None,
yaxis_formatter=None,
autoscalex=False,
autoscaley=False,
markerfacecolor=None,
markeredgecolor=None,
markeredgewidth=None,
axis=None,
pad_range=0,
transpose=False,
**kw):
""" Plot your data in an IDL-like way with a lot of options in one
command.
Example:
>> plot(x,y,xrange=[0,39],yrange=[-1,10],ps=4,xtitle="X",
color='black',position=[0.1,0.1,0.9,0.9], xlog=True)
"""
if arg2 is None:
y = listToArrFlat(arg1)
x = np.arange(len(y))
else:
x = listToArrFlat(arg1)
y = listToArrFlat(arg2)
if not noerase:
plt.gcf().clf()
if position is not None and axis is None:
mypos = position[:]
mypos[2] = position[2] - position[0]
mypos[3] = position[3] - position[1]
plt.axes(mypos)
if axis is None:
axis = plt.gca()
marker, linestyle = get_marker(ps, linestyle)
if xr is not None:
xrange = xr
if yr is not None:
yrange = yr
if xrange is None and yrange is None:
ind = np.isfinite(x)
if not ind.any():
xrange = [0, 1]
else:
xrange = [np.min(x[ind]), np.max(x[ind])]
ind = np.isfinite(y)
if not ind.any():
yrange = [0, 1]
else:
yrange = [np.min(y[ind]), np.max(y[ind])]
assert (pad_range >= 0)
xrange = [
xrange[0] - pad_range * (xrange[1] - xrange[0]),
xrange[1] + pad_range * (xrange[1] - xrange[0])
]
yrange = [
yrange[0] - pad_range * (yrange[1] - yrange[0]),
yrange[1] + pad_range * (yrange[1] - yrange[0])
]
del ind
elif xrange is None and yrange is not None:
ind = (y < max(yrange[1], yrange[0])) & (y > min(
yrange[0], yrange[1])) & np.isfinite(x)
if ind.any():
xrange = [np.min(x[ind]), np.max(x[ind])]
else:
xrange = [np.min(x), np.max(x)]
del ind
elif xrange is not None and yrange is None:
ind = (x < np.maximum(xrange[1], xrange[0])) & (x > np.minimum(
xrange[0], xrange[1])) & np.isfinite(y)
if ind.any():
yrange = [np.min(y[ind]), np.max(y[ind])]
else:
yrange = [np.min(y), np.max(y)]
del ind
if len(yrange) != 2 or len(xrange) != 2:
raise ValueError("Wrong xrange or yrange")
if not overplot:
axis.minorticks_on()
if xtitle is not None:
axis.set_xlabel(xtitle)
if ytitle is not None:
axis.set_ylabel(ytitle)
axis.set_autoscalex_on(autoscalex)
axis.set_autoscaley_on(autoscaley)
if transpose:
xrange, yrange = yrange, xrange
if not overplot:
axis.set_xlim(xrange)
axis.set_ylim(yrange)
if xlog:
axis.set_xscale('log', subs=[2, 3, 4, 5, 6, 7, 8, 9])
if ylog:
axis.set_yscale('log', subs=[2, 3, 4, 5, 6, 7, 8, 9])
if xaxis_formatter is not None:
axis.xaxis.set_major_formatter(xaxis_formatter)
if yaxis_formatter is not None:
axis.yaxis.set_major_formatter(yaxis_formatter)
if title is not None:
plt.title(title)
if not nodata:
if transpose:
x, y = y, x
if markersize is None:
axis.plot(x,
y,
marker=marker,
linestyle=linestyle,
linewidth=thick,
color=color,
label=label,
markerfacecolor=markerfacecolor,
markeredgecolor=markeredgecolor,
markeredgewidth=markeredgewidth,
**kw)
else:
axis.plot(x,
y,
marker=marker,
linestyle=linestyle,
linewidth=thick,
color=color,
label=label,
markersize=markersize,
markerfacecolor=markerfacecolor,
markeredgecolor=markeredgecolor,
markeredgewidth=markeredgewidth,
**kw)
def oplot(x, y=None, **kw):
"""
Overplot your data (just a simple wrapper around plot)
Example:
>> oplot(x,2+y/10.,ps=3,color='blue')
"""
plot(x, y, noerase=True, overplot=True, **kw)
@exceptionDecorator
def ploterror(x,
y,
err0,
err1=None,
color='black',
ps=0,
ecolor='black',
overplot=False,
noerase=False,
elinewidth=None,
capsize=None,
markersize=None,
markeredgecolor=None,
markerfacecolor=None,
autoscalex=False,
autoscaley=False,
label=None,
**kw):
"""
Plot the data with error-bars
Example:
>> ploterror(x,y,erry) # plot only Y error-bars
>> ploterror(x,y,errx,erry) # plot data with X and Y error-bars
Keyword parameters:
------------------
capsize
integer param controlling the size of hats/caps of the error-bars
ecolor
color of the error-bars (different from the main color)
"""
if overplot:
noerase = True
if err1 is None:
erry = listToArr(err0)
else:
erry = listToArr(err1)
errx = listToArr(err0)
x = listToArr(x)
y = listToArr(y)
kw0 = kw.copy()
if kw0.get('yr') is None:
kw0['yr'] = [np.nanmin(y - erry), np.nanmax(y + erry)]
if markersize is not None:
kw0['markersize'] = markersize
if markeredgecolor is not None:
kw0['markeredgecolor'] = markeredgecolor
if markerfacecolor is not None:
kw0['markerfacecolor'] = markerfacecolor
plot(x, y, color=color, ps=ps, overplot=overplot, noerase=noerase, **kw0)
(marker, outlinestyle) = get_marker(ps, None)
kw1 = {
'ecolor': ecolor,
'marker': marker,
'color': color,
'linestyle': outlinestyle,
'elinewidth': elinewidth,
'markeredgewidth': elinewidth
}
if 'alpha' in kw0:
kw1['alpha'] = kw0['alpha']
if 'zorder' in kw0:
kw1['zorder'] = kw0['zorder']
if label is not None:
kw1['label'] = label
if markersize is not None:
kw1['markersize'] = markersize
if markeredgecolor is not None:
kw1['markeredgecolor'] = markeredgecolor
if markerfacecolor is not None:
kw1['markerfacecolor'] = markerfacecolor
if capsize is not None:
kw1['capsize'] = capsize
plt.gca().set_autoscalex_on(autoscalex)
plt.gca().set_autoscaley_on(autoscaley)
if err1 is None:
plt.gca().errorbar(x, y, erry, **kw1)
else:
plt.gca().errorbar(x, y, xerr=errx, yerr=erry, **kw1)
@exceptionDecorator
def tvaxis(image,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
xtitle="",
ytitle="",
title="",
vmin=None,
vmax=None,
aspect="auto",
xlog=False,
ylog=False,
position=None,
noerase=False,
bar=False,
bar_label='',
bar_fraction=0.05,
zlog=False,
smooth=None,
vmaxfrac=None,
xflip=False,
yflip=False,
kernel='gau',
vminfrac=None,
**kw):
"""
Display the 2D image with proper axes (similar to plt.imshow)
Example:
>> tvaxis(im,-20,10,-40,50)
Keyword parameters:
------------------
xmin,xmax,ymin,ymax
the ranges for x,y where the histogram is constructed.
These params can be specified not only as keywords but also as normal
arguments
vmin,vmax
the ranges of intensities shown on the plot
smooth
if not None this parameter controls additional smoothing of the 2D
histogram
bar
boolean parameter for switching on/off the plotting of the color-bar
"""
if not noerase:
plt.clf()
if position is not None:
mypos = position[:]
mypos[2] = position[2] - position[0]
mypos[3] = position[3] - position[1]
plt.axes(mypos)
if xmin is None:
xmin = 0
if ymin is None:
ymin = 0
if xmax is None:
xmax = image.shape[0]
if ymax is None:
ymax = image.shape[1]
if image.ndim == 3:
im = np.transpose(image, axes=(1, 0, 2))
elif image.ndim == 2:
im = image.T
else:
raise ValueError('Wrong dimensions of the input array')
im = smoother(im, smooth=smooth, kernel=kernel)
if vminfrac is not None and vmin is None:
vmin = scipy.stats.scoreatpercentile(im[np.isfinite(im)],
100 * vminfrac)
if vmaxfrac is not None and vmax is None:
vmax = scipy.stats.scoreatpercentile(im[np.isfinite(im)],
100 * vmaxfrac)
if vmin is not None and vmax is not None and vmin >= vmax:
warnings.warn("vmin is >= vmax... Resetting their values",
RuntimeWarning)
vmin = None
vmax = None
if zlog:
norm = matplotlib.colors.LogNorm(vmin=vmin, vmax=vmax)
else:
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
if xflip:
im = np.fliplr(im)
if yflip:
im = np.flipud(im)
axim = plt.imshow(im,
extent=(xmin, xmax, ymin, ymax),
aspect=aspect,
norm=norm,
origin='lower',
**kw)
if xlog:
plt.gca().set_xscale('log')
if ylog:
plt.gca().set_yscale('log')
plt.gca().set_xlabel(xtitle)
plt.gca().set_ylabel(ytitle)
plt.gca().minorticks_on()
if title is not None:
plt.title(title)
if bar:
if int(''.join((matplotlib.__version__).split('.')[:2])) >= 11:
kw = {'use_gridspec': True}
else:
kw = {}
cb = plt.colorbar(fraction=bar_fraction, **kw)
cb.set_label(bar_label)
return axim
def __parse_limit(val, x, oper=None):
if isinstance(val, str):
if val[0] == 'p':
perc = float(val[1:])
return scipy.stats.scoreatpercentile(x[np.isfinite(x)], perc)
else:
raise ValueError('unsupported limit format %s' % (val))
if val is not None:
return val
else:
return oper(x)
@exceptionDecorator
def tvhist2d(x,
y,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
vmin=None,
vmax=None,
bins=[100, 100],
xtitle="",
ytitle="",
title=None,
noerase=False,
weights=None,
zlog=False,
xflip=False,
yflip=False,
smooth=None,
quick=True,
cmap='gray_r',
normx=None,
normy=None,
xlog=False,
ylog=False,
weight_norm=False,
vminfrac=None,
vmaxfrac=None,
position=None,
xaxis_formatter=None,
yaxis_formatter=None,
bar=False,
bar_label='',
bar_fraction=0.05,
bar_pad=0.05,
bar_ticks_locator=None,
bar_formatter=None,
apply_func=None,
zsqrt=False,
ret_hist=False,
interpolation='nearest',
scatter_thresh=None,
scatter_opt={},
subplot=None,
kernel='gau',
statistic=None,
**kw):
""" Plot the 2D histogram of the data
Example:
>> tvhist2d(xs,ys,bins=[30,30])
>> tvhist2d(xs,ys,0,10,-1,2,bins=[30,30])
Keyword arguments:
-----------------
xmin,xmax,ymin,ymax
the ranges for x,y where the histogram is constructed.
These params can be specified not only as keywords but also as
normal arguments
>> tvhist2d(xs,ys,xmin=0,ymin=10,ymin=-1,ymax=2,bins=[30,30])
>> tvhist2d(xs,ys,0,10,-1,2,bins=[30,30])
Also xmin,..ymax can be specified as percentiles as strings, i.e.
>> tvhist2d(xs,ys,'p1','p99','p5','p95') for 1/99/5/95% respectively
vmin,vmax
the ranges of intensities shown on the plot
bins
the list of two integers specifying how many bins in x,y you want
smooth
if not None this parameter controls additional smoothing of the 2D
histogram
xflip, yflip
boolean parameters allowing to flip x,y axes
normx, normy
params controlling the normalization of the histogram along X or Y axes
if normx=='sum' then the normalization is done in such way that the sum
is the same for each row/column
if normx=='max' then the normalization is don in such way that
the brightest pixel value will be the same for each row/column
weight_norm
if True the value in each bin is mean weight of points within
the bin
bar
boolean parameter for switching on/off the plotting of the color-bar
bar_pad, bar_fraction
padding and fraction for bar placement
bar_ticks_locator
locator for the tickmarks on the colorbar
"""
x1 = listToArrFlat(x)
y1 = listToArrFlat(y)
xmin = __parse_limit(xmin, x1, np.nanmin)
xmax = __parse_limit(xmax, x1, np.nanmax)
ymin = __parse_limit(ymin, y1, np.nanmin)
ymax = __parse_limit(ymax, y1, np.nanmax)
plot_range = (xmin, xmax, ymin, ymax)
hist_range = [[ymin, ymax], [xmin, xmax]]
hist_range_ordered = []
hist_range_reverse = []
for i in range(2):
left, right = hist_range[i]
if left > right:
hist_range_ordered.append([right, left])
hist_range_reverse.append(True)
else:
hist_range_ordered.append([left, right])
hist_range_reverse.append(False)
binsRev = bins[::-1]
if statistic is None:
if not quick:
hh, yedges, xedges = scipy.histogram2d(y1,
x1,
range=hist_range_ordered,
bins=binsRev,
weights=weights)
if weight_norm:
hh1, yedges, xedges = scipy.histogram2d(
y1,
x1,
range=hist_range_ordered,
bins=binsRev,
weights=None)
hh = hh * 1. / hh1
else:
import quick_hist
hh = quick_hist.quick_hist((y1, x1),
range=hist_range_ordered,
nbins=binsRev,
weights=weights)
if weight_norm:
hh1 = quick_hist.quick_hist((y1, x1),
range=hist_range_ordered,
nbins=binsRev)
hh = hh * 1. / hh1
else:
hh = scipy.stats.binned_statistic_2d(y1,
x1,
weights,
statistic,
range=hist_range_ordered,
bins=binsRev).statistic
# reverse some of the dimensions if the axis range is reverse
slices = []
for i in range(2):
if hist_range_reverse[i]:
slices.append(slice(None, None, -1))
else:
slices.append(slice(None, None, None))
hh = hh[tuple(slices)]
if apply_func is not None:
hh = apply_func(hh)
hh = smoother(hh, smooth=smooth, kernel=kernel)
if normx is not None:
if normx == 'sum':
hhs = hh.sum(axis=0)
hhs = hhs + (hhs == 0)
hh = hh * 1. / hhs[None, :]
elif normx == 'max':
hhs = np.max(hh, axis=0)
hhs = hhs + (hhs == 0)
hh = hh * 1. / hhs[None, :]
elif normx == 'median':
hhs = np.median(hh, axis=0)
hhs = hhs + (hhs == 0)
hh = hh * 1. / hhs[None, :]
else:
raise Exception('unknown normx mode')
if normy is not None:
if normy == 'sum':
hhs = hh.sum(axis=1)
hhs = hhs + (hhs == 0)
hh = hh * 1. / hhs[:, None]
elif normy == 'max':
hhs = np.max(hh, axis=1)
hhs = hhs + (hhs == 0)
hh = hh * 1. / hhs[:, None]
elif normy == 'median':
hhs = np.median(hh, axis=1)
hhs = hhs + (hhs == 0)
hh = hh * 1. / hhs[:, None]
else:
raise Exception('unknown normy mode')
if scatter_thresh is not None:
locx = np.linspace(xmin, xmax, bins[0] + 1, True)
locy = np.linspace(ymin, ymax, bins[1] + 1, True)
posx = np.digitize(x1, locx)
posy = np.digitize(y1, locy)
# select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh.T[posx[ind] - 1, posy[ind] -
1] # values of the histogram where the points are
x1 = x1[ind][hhsub < scatter_thresh] # low density points
y1 = y1[ind][hhsub < scatter_thresh]
hh = hh.astype(np.float)
hh[hh <
scatter_thresh] = np.nan # fill the areas with low density by NaNs
if xflip:
plot_range = tuple(plot_range[_] for _ in [1, 0, 2, 3])
hh = np.fliplr(hh)
if yflip:
plot_range = tuple(plot_range[_] for _ in [0, 1, 3, 2])
hh = np.flipud(hh)
if subplot is not None:
noerase = True
if hasattr(subplot, '__iter__'):
plt.subplot(*subplot)
else:
plt.subplot(subplot)
if not noerase:
plt.gcf().clf()
if position is not None:
mypos = position[:]
mypos[2] = position[2] - position[0]
mypos[3] = position[3] - position[1]
plt.axes(mypos)
axis = plt.gca()
axis.set_xlabel(xtitle)
axis.set_ylabel(ytitle)
axis.minorticks_on()
if xaxis_formatter is not None:
axis.xaxis.set_major_formatter(xaxis_formatter)
if yaxis_formatter is not None:
axis.yaxis.set_major_formatter(yaxis_formatter)
if vminfrac is not None and vmin is None:
vmin = scipy.stats.scoreatpercentile(hh[np.isfinite(hh)],
100 * vminfrac)
if vmaxfrac is not None and vmax is None:
vmax = scipy.stats.scoreatpercentile(hh[np.isfinite(hh)],
100 * vmaxfrac)
if vmin is not None and vmax is not None and vmin >= vmax:
warnings.warn("vmin is >= vmax... Resetting their values",
RuntimeWarning)
vmin = None
vmax = None
if zlog:
norm = matplotlib.colors.LogNorm(vmin=vmin, vmax=vmax)
elif zsqrt:
norm = matplotlib.colors.SqrtNorm(vmin=vmin, vmax=vmax)
else:
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
axim = plt.imshow(hh,
extent=plot_range,
aspect='auto',
interpolation=interpolation,
cmap=cmap,
norm=norm,
origin='lower',
**kw)
if scatter_thresh is not None:
if 'ps' not in scatter_opt:
scatter_opt = copy.copy(scatter_opt)
scatter_opt['ps'] = 3
oplot(x1, y1, **scatter_opt)
if bar:
if bar_formatter is None:
bar_formatter = matplotlib.ticker.ScalarFormatter()
if int(''.join((matplotlib.__version__).split('.')[:2])) >= 11:
kw = {'use_gridspec': True}
else:
kw = {}
cb = plt.colorbar(fraction=bar_fraction,
pad=bar_pad,
ax=axis,
format=bar_formatter,
ticks=bar_ticks_locator,
**kw)
cb.set_label(bar_label)
if xlog:
plt.gca().set_xscale('log')
if ylog:
plt.gca().set_yscale('log')
if title is not None:
plt.title(title)
if ret_hist:
return hh
return axim
@exceptionDecorator
def contour(z,
x=None,
y=None,
xrange=None,
yrange=None,
zrange=None,
xr=None,
yr=None,
zr=None,
title=None,
xtitle=None,
ytitle=None,
position=None,
xlog=False,
ylog=False,
zlog=False,
xticklabel=None,
yticklabel=None,
zticklabel=None,
levels=None,
nlevels=256,
c_color="black",
cm="jet",
c_line="solid",
c_levels=None,
c_charsize=12.0,
c_thick=1,
thick=1,
font="monospace",
weight="normal",
charsize=14.0,
bar=True,
fill=True,
overplot=False,
noerase=False,
c_label=False,
bar_fraction=0.05,
xaxis_formatter=None,
label=None,
yaxis_formatter=None):
"""
Plot the contours of the 2d array.
Example:
>> contour(z)
if you have the x and y coordinates of your array then you can use them
>> contour(z, x, y)
In that case x,y can be either 2D arrays with the same shape as z, or
1D arrays with the
appropriate dimensions.
Keyword arguments:
-----------------
xr, xrange, yr, yrange
plot ranges for x and y
fill
boolean parameter controlling whether to fill area between contours
or not
bar
boolean parameter for plotting of the color-bar
overplot
boolean parameter for overplotting
nlevels
integer number of number of contours
c_label
boolean parameter for labeling or not-labeling each contour with
its value
"""
# Initialize x and y if these are not provided:
if x is None or y is None:
if z.ndim != 2:
raise Exception("The 2D array is required")
x, y = np.mgrid[0:z.shape[0], 0:z.shape[1]]
else:
if x.ndim == 1:
x_new = x[:, np.newaxis] * (y * 0 + 1)
else:
x_new = x
if y.ndim == 1:
y_new = ((x * 0 + 1) * y[:, np.newaxis]).transpose()
else:
y_new = y
x = x_new
y = y_new
# Define position of this plot:
if not noerase and not overplot:
plt.gcf().clf()
if position is not None:
mypos = position[:]
mypos[2] = position[2] - position[0]
mypos[3] = position[3] - position[1]
plt.axes(mypos)
axis = plt.gca()
# Rescaling of the axes:
if xlog:
axis.set_xscale('log')
if ylog:
axis.set_yscale('log')
if zlog:
z = np.log10(z)
# Setup axis ranges:
if xr is not None:
xrange = xr
if yr is not None:
yrange = yr
if zr is not None:
zrange = zr
xrange = xrange or [x.min(), x.max()]
yrange = yrange or [y.min(), y.max()]
zrange = zrange or [z.min(), z.max()]
# Setup levels for contour plot:
if levels is None:
zmin = zrange[0]
zmax = zrange[1]
levels = np.linspace(zmin, zmax, nlevels)
# Setup frame thickness:
# axis.frame.set_linewidth(thick)
# Setup x-, y-titles and the main title:
if xtitle is not None:
axis.set_xlabel(xtitle)
if ytitle is not None:
axis.set_ylabel(ytitle)
if title is not None:
plt.title(title)
# Assume autoscale is off:
axis.set_autoscale_on(False)
# Setup format of the major ticks:
if xticklabel is not None:
xFormatter = matplotlib.ticker.FormatStrFormatter(xticklabel)
axis.xaxis.set_major_formatter(xFormatter)
if yticklabel is not None:
yFormatter = matplotlib.ticker.FormatStrFormatter(yticklabel)
axis.yaxis.set_major_formatter(yFormatter)
if xaxis_formatter is not None:
axis.xaxis.set_major_formatter(xaxis_formatter)
if yaxis_formatter is not None:
axis.yaxis.set_major_formatter(yaxis_formatter)
if not overplot:
plt.gca().minorticks_on()
# Make a filled contour plot:
if fill:
cset1 = axis.contourf(x,
y,
z,
levels,
cmap=plt.cm.get_cmap(cm, nlevels))
# Add contour lines:
if c_levels is None:
c_levels = levels # [0:len(levels):int(nlevels/12)]
cset2 = axis.contour(x,
y,
z,
c_levels,
colors=c_color,
linewidths=c_thick,
hold='on')
if label is not None:
cset2.collections[0].set_label(label)
# Do not display dashed contours for negative values:
for c in cset2.collections:
c.set_linestyle(c_line)
# Add value labels on contour lines:
if zticklabel is not None:
zFormatter = matplotlib.ticker.FormatStrFormatter(zticklabel)
else:
zFormatter = None
if c_label:
if zticklabel is not None:
args = {'fmt': zticklabel}
else:
args = {}
axis.clabel(cset2, c_levels, inline=1, fontsize=c_charsize, **args)
# Do we need a color bar?:
if fill & bar:
# matplotlib.rcParams['ytick.labelsize']=c_charsize
if int(''.join((matplotlib.__version__).split('.')[:2])) >= 11:
kw = {'use_gridspec': True}
else:
kw = {}
plt.colorbar(cset1,
ticks=[np.min(levels), np.max(levels)],
fraction=bar_fraction,
format=zFormatter,
**kw)
| gpl-3.0 |
dhruv13J/scikit-learn | sklearn/neighbors/approximate.py | 127 | 22351 | """Approximate nearest neighbor search"""
# Author: Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# Joel Nothman <joel.nothman@gmail.com>
import numpy as np
import warnings
from scipy import sparse
from .base import KNeighborsMixin, RadiusNeighborsMixin
from ..base import BaseEstimator
from ..utils.validation import check_array
from ..utils import check_random_state
from ..metrics.pairwise import pairwise_distances
from ..random_projection import GaussianRandomProjection
__all__ = ["LSHForest"]
HASH_DTYPE = '>u4'
MAX_HASH_SIZE = np.dtype(HASH_DTYPE).itemsize * 8
def _find_matching_indices(tree, bin_X, left_mask, right_mask):
"""Finds indices in sorted array of integers.
Most significant h bits in the binary representations of the
integers are matched with the items' most significant h bits.
"""
left_index = np.searchsorted(tree, bin_X & left_mask)
right_index = np.searchsorted(tree, bin_X | right_mask,
side='right')
return left_index, right_index
def _find_longest_prefix_match(tree, bin_X, hash_size,
left_masks, right_masks):
"""Find the longest prefix match in tree for each query in bin_X
Most significant bits are considered as the prefix.
"""
hi = np.empty_like(bin_X, dtype=np.intp)
hi.fill(hash_size)
lo = np.zeros_like(bin_X, dtype=np.intp)
res = np.empty_like(bin_X, dtype=np.intp)
left_idx, right_idx = _find_matching_indices(tree, bin_X,
left_masks[hi],
right_masks[hi])
found = right_idx > left_idx
res[found] = lo[found] = hash_size
r = np.arange(bin_X.shape[0])
kept = r[lo < hi] # indices remaining in bin_X mask
while kept.shape[0]:
mid = (lo.take(kept) + hi.take(kept)) // 2
left_idx, right_idx = _find_matching_indices(tree,
bin_X.take(kept),
left_masks[mid],
right_masks[mid])
found = right_idx > left_idx
mid_found = mid[found]
lo[kept[found]] = mid_found + 1
res[kept[found]] = mid_found
hi[kept[~found]] = mid[~found]
kept = r[lo < hi]
return res
class ProjectionToHashMixin(object):
"""Turn a transformed real-valued array into a hash"""
@staticmethod
def _to_hash(projected):
if projected.shape[1] % 8 != 0:
raise ValueError('Require reduced dimensionality to be a multiple '
'of 8 for hashing')
# XXX: perhaps non-copying operation better
out = np.packbits((projected > 0).astype(int)).view(dtype=HASH_DTYPE)
return out.reshape(projected.shape[0], -1)
def fit_transform(self, X, y=None):
self.fit(X)
return self.transform(X)
def transform(self, X, y=None):
return self._to_hash(super(ProjectionToHashMixin, self).transform(X))
class GaussianRandomProjectionHash(ProjectionToHashMixin,
GaussianRandomProjection):
"""Use GaussianRandomProjection to produce a cosine LSH fingerprint"""
def __init__(self,
n_components=8,
random_state=None):
super(GaussianRandomProjectionHash, self).__init__(
n_components=n_components,
random_state=random_state)
def _array_of_arrays(list_of_arrays):
"""Creates an array of array from list of arrays."""
out = np.empty(len(list_of_arrays), dtype=object)
out[:] = list_of_arrays
return out
class LSHForest(BaseEstimator, KNeighborsMixin, RadiusNeighborsMixin):
"""Performs approximate nearest neighbor search using LSH forest.
LSH Forest: Locality Sensitive Hashing forest [1] is an alternative
method for vanilla approximate nearest neighbor search methods.
LSH forest data structure has been implemented using sorted
arrays and binary search and 32 bit fixed-length hashes.
Random projection is used as the hash family which approximates
cosine distance.
The cosine distance is defined as ``1 - cosine_similarity``: the lowest
value is 0 (identical point) but it is bounded above by 2 for the farthest
points. Its value does not depend on the norm of the vector points but
only on their relative angles.
Read more in the :ref:`User Guide <approximate_nearest_neighbors>`.
Parameters
----------
n_estimators : int (default = 10)
Number of trees in the LSH Forest.
min_hash_match : int (default = 4)
lowest hash length to be searched when candidate selection is
performed for nearest neighbors.
n_candidates : int (default = 10)
Minimum number of candidates evaluated per estimator, assuming enough
items meet the `min_hash_match` constraint.
n_neighbors : int (default = 5)
Number of neighbors to be returned from query function when
it is not provided to the :meth:`kneighbors` method.
radius : float, optinal (default = 1.0)
Radius from the data point to its neighbors. This is the parameter
space to use by default for the :meth`radius_neighbors` queries.
radius_cutoff_ratio : float, optional (default = 0.9)
A value ranges from 0 to 1. Radius neighbors will be searched until
the ratio between total neighbors within the radius and the total
candidates becomes less than this value unless it is terminated by
hash length reaching `min_hash_match`.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Attributes
----------
hash_functions_ : list of GaussianRandomProjectionHash objects
Hash function g(p,x) for a tree is an array of 32 randomly generated
float arrays with the same dimenstion as the data set. This array is
stored in GaussianRandomProjectionHash object and can be obtained
from ``components_`` attribute.
trees_ : array, shape (n_estimators, n_samples)
Each tree (corresponding to a hash function) contains an array of
sorted hashed values. The array representation may change in future
versions.
original_indices_ : array, shape (n_estimators, n_samples)
Original indices of sorted hashed values in the fitted index.
References
----------
.. [1] M. Bawa, T. Condie and P. Ganesan, "LSH Forest: Self-Tuning
Indexes for Similarity Search", WWW '05 Proceedings of the
14th international conference on World Wide Web, 651-660,
2005.
Examples
--------
>>> from sklearn.neighbors import LSHForest
>>> X_train = [[5, 5, 2], [21, 5, 5], [1, 1, 1], [8, 9, 1], [6, 10, 2]]
>>> X_test = [[9, 1, 6], [3, 1, 10], [7, 10, 3]]
>>> lshf = LSHForest()
>>> lshf.fit(X_train) # doctest: +NORMALIZE_WHITESPACE
LSHForest(min_hash_match=4, n_candidates=50, n_estimators=10,
n_neighbors=5, radius=1.0, radius_cutoff_ratio=0.9,
random_state=None)
>>> distances, indices = lshf.kneighbors(X_test, n_neighbors=2)
>>> distances # doctest: +ELLIPSIS
array([[ 0.069..., 0.149...],
[ 0.229..., 0.481...],
[ 0.004..., 0.014...]])
>>> indices
array([[1, 2],
[2, 0],
[4, 0]])
"""
def __init__(self, n_estimators=10, radius=1.0, n_candidates=50,
n_neighbors=5, min_hash_match=4, radius_cutoff_ratio=.9,
random_state=None):
self.n_estimators = n_estimators
self.radius = radius
self.random_state = random_state
self.n_candidates = n_candidates
self.n_neighbors = n_neighbors
self.min_hash_match = min_hash_match
self.radius_cutoff_ratio = radius_cutoff_ratio
def _compute_distances(self, query, candidates):
"""Computes the cosine distance.
Distance is from the query to points in the candidates array.
Returns argsort of distances in the candidates
array and sorted distances.
"""
if candidates.shape == (0,):
# needed since _fit_X[np.array([])] doesn't work if _fit_X sparse
return np.empty(0, dtype=np.int), np.empty(0, dtype=float)
if sparse.issparse(self._fit_X):
candidate_X = self._fit_X[candidates]
else:
candidate_X = self._fit_X.take(candidates, axis=0, mode='clip')
distances = pairwise_distances(query, candidate_X,
metric='cosine')[0]
distance_positions = np.argsort(distances)
distances = distances.take(distance_positions, mode='clip', axis=0)
return distance_positions, distances
def _generate_masks(self):
"""Creates left and right masks for all hash lengths."""
tri_size = MAX_HASH_SIZE + 1
# Called once on fitting, output is independent of hashes
left_mask = np.tril(np.ones((tri_size, tri_size), dtype=int))[:, 1:]
right_mask = left_mask[::-1, ::-1]
self._left_mask = np.packbits(left_mask).view(dtype=HASH_DTYPE)
self._right_mask = np.packbits(right_mask).view(dtype=HASH_DTYPE)
def _get_candidates(self, query, max_depth, bin_queries, n_neighbors):
"""Performs the Synchronous ascending phase.
Returns an array of candidates, their distance ranks and
distances.
"""
index_size = self._fit_X.shape[0]
# Number of candidates considered including duplicates
# XXX: not sure whether this is being calculated correctly wrt
# duplicates from different iterations through a single tree
n_candidates = 0
candidate_set = set()
min_candidates = self.n_candidates * self.n_estimators
while (max_depth > self.min_hash_match and
(n_candidates < min_candidates or
len(candidate_set) < n_neighbors)):
left_mask = self._left_mask[max_depth]
right_mask = self._right_mask[max_depth]
for i in range(self.n_estimators):
start, stop = _find_matching_indices(self.trees_[i],
bin_queries[i],
left_mask, right_mask)
n_candidates += stop - start
candidate_set.update(
self.original_indices_[i][start:stop].tolist())
max_depth -= 1
candidates = np.fromiter(candidate_set, count=len(candidate_set),
dtype=np.intp)
# For insufficient candidates, candidates are filled.
# Candidates are filled from unselected indices uniformly.
if candidates.shape[0] < n_neighbors:
warnings.warn(
"Number of candidates is not sufficient to retrieve"
" %i neighbors with"
" min_hash_match = %i. Candidates are filled up"
" uniformly from unselected"
" indices." % (n_neighbors, self.min_hash_match))
remaining = np.setdiff1d(np.arange(0, index_size), candidates)
to_fill = n_neighbors - candidates.shape[0]
candidates = np.concatenate((candidates, remaining[:to_fill]))
ranks, distances = self._compute_distances(query,
candidates.astype(int))
return (candidates[ranks[:n_neighbors]],
distances[:n_neighbors])
def _get_radius_neighbors(self, query, max_depth, bin_queries, radius):
"""Finds radius neighbors from the candidates obtained.
Their distances from query are smaller than radius.
Returns radius neighbors and distances.
"""
ratio_within_radius = 1
threshold = 1 - self.radius_cutoff_ratio
total_candidates = np.array([], dtype=int)
total_neighbors = np.array([], dtype=int)
total_distances = np.array([], dtype=float)
while (max_depth > self.min_hash_match and
ratio_within_radius > threshold):
left_mask = self._left_mask[max_depth]
right_mask = self._right_mask[max_depth]
candidates = []
for i in range(self.n_estimators):
start, stop = _find_matching_indices(self.trees_[i],
bin_queries[i],
left_mask, right_mask)
candidates.extend(
self.original_indices_[i][start:stop].tolist())
candidates = np.setdiff1d(candidates, total_candidates)
total_candidates = np.append(total_candidates, candidates)
ranks, distances = self._compute_distances(query, candidates)
m = np.searchsorted(distances, radius, side='right')
positions = np.searchsorted(total_distances, distances[:m])
total_neighbors = np.insert(total_neighbors, positions,
candidates[ranks[:m]])
total_distances = np.insert(total_distances, positions,
distances[:m])
ratio_within_radius = (total_neighbors.shape[0] /
float(total_candidates.shape[0]))
max_depth = max_depth - 1
return total_neighbors, total_distances
def fit(self, X, y=None):
"""Fit the LSH forest on the data.
This creates binary hashes of input data points by getting the
dot product of input points and hash_function then
transforming the projection into a binary string array based
on the sign (positive/negative) of the projection.
A sorted array of binary hashes is created.
Parameters
----------
X : array_like or sparse (CSR) matrix, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
self : object
Returns self.
"""
self._fit_X = check_array(X, accept_sparse='csr')
# Creates a g(p,x) for each tree
self.hash_functions_ = []
self.trees_ = []
self.original_indices_ = []
rng = check_random_state(self.random_state)
int_max = np.iinfo(np.int32).max
for i in range(self.n_estimators):
# This is g(p,x) for a particular tree.
# Builds a single tree. Hashing is done on an array of data points.
# `GaussianRandomProjection` is used for hashing.
# `n_components=hash size and n_features=n_dim.
hasher = GaussianRandomProjectionHash(MAX_HASH_SIZE,
rng.randint(0, int_max))
hashes = hasher.fit_transform(self._fit_X)[:, 0]
original_index = np.argsort(hashes)
bin_hashes = hashes[original_index]
self.original_indices_.append(original_index)
self.trees_.append(bin_hashes)
self.hash_functions_.append(hasher)
self._generate_masks()
return self
def _query(self, X):
"""Performs descending phase to find maximum depth."""
# Calculate hashes of shape (n_samples, n_estimators, [hash_size])
bin_queries = np.asarray([hasher.transform(X)[:, 0]
for hasher in self.hash_functions_])
bin_queries = np.rollaxis(bin_queries, 1)
# descend phase
depths = [_find_longest_prefix_match(tree, tree_queries, MAX_HASH_SIZE,
self._left_mask, self._right_mask)
for tree, tree_queries in zip(self.trees_,
np.rollaxis(bin_queries, 1))]
return bin_queries, np.max(depths, axis=0)
def kneighbors(self, X, n_neighbors=None, return_distance=True):
"""Returns n_neighbors of approximate nearest neighbors.
Parameters
----------
X : array_like or sparse (CSR) matrix, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single query.
n_neighbors : int, opitonal (default = None)
Number of neighbors required. If not provided, this will
return the number specified at the initialization.
return_distance : boolean, optional (default = False)
Returns the distances of neighbors if set to True.
Returns
-------
dist : array, shape (n_samples, n_neighbors)
Array representing the cosine distances to each point,
only present if return_distance=True.
ind : array, shape (n_samples, n_neighbors)
Indices of the approximate nearest points in the population
matrix.
"""
if not hasattr(self, 'hash_functions_'):
raise ValueError("estimator should be fitted.")
if n_neighbors is None:
n_neighbors = self.n_neighbors
X = check_array(X, accept_sparse='csr')
neighbors, distances = [], []
bin_queries, max_depth = self._query(X)
for i in range(X.shape[0]):
neighs, dists = self._get_candidates(X[i], max_depth[i],
bin_queries[i],
n_neighbors)
neighbors.append(neighs)
distances.append(dists)
if return_distance:
return np.array(distances), np.array(neighbors)
else:
return np.array(neighbors)
def radius_neighbors(self, X, radius=None, return_distance=True):
"""Finds the neighbors within a given radius of a point or points.
Return the indices and distances of some points from the dataset
lying in a ball with size ``radius`` around the points of the query
array. Points lying on the boundary are included in the results.
The result points are *not* necessarily sorted by distance to their
query point.
LSH Forest being an approximate method, some true neighbors from the
indexed dataset might be missing from the results.
Parameters
----------
X : array_like or sparse (CSR) matrix, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single query.
radius : float
Limiting distance of neighbors to return.
(default is the value passed to the constructor).
return_distance : boolean, optional (default = False)
Returns the distances of neighbors if set to True.
Returns
-------
dist : array, shape (n_samples,) of arrays
Each element is an array representing the cosine distances
to some points found within ``radius`` of the respective query.
Only present if ``return_distance=True``.
ind : array, shape (n_samples,) of arrays
Each element is an array of indices for neighbors within ``radius``
of the respective query.
"""
if not hasattr(self, 'hash_functions_'):
raise ValueError("estimator should be fitted.")
if radius is None:
radius = self.radius
X = check_array(X, accept_sparse='csr')
neighbors, distances = [], []
bin_queries, max_depth = self._query(X)
for i in range(X.shape[0]):
neighs, dists = self._get_radius_neighbors(X[i], max_depth[i],
bin_queries[i], radius)
neighbors.append(neighs)
distances.append(dists)
if return_distance:
return _array_of_arrays(distances), _array_of_arrays(neighbors)
else:
return _array_of_arrays(neighbors)
def partial_fit(self, X, y=None):
"""
Inserts new data into the already fitted LSH Forest.
Cost is proportional to new total size, so additions
should be batched.
Parameters
----------
X : array_like or sparse (CSR) matrix, shape (n_samples, n_features)
New data point to be inserted into the LSH Forest.
"""
X = check_array(X, accept_sparse='csr')
if not hasattr(self, 'hash_functions_'):
return self.fit(X)
if X.shape[1] != self._fit_X.shape[1]:
raise ValueError("Number of features in X and"
" fitted array does not match.")
n_samples = X.shape[0]
n_indexed = self._fit_X.shape[0]
for i in range(self.n_estimators):
bin_X = self.hash_functions_[i].transform(X)[:, 0]
# gets the position to be added in the tree.
positions = self.trees_[i].searchsorted(bin_X)
# adds the hashed value into the tree.
self.trees_[i] = np.insert(self.trees_[i],
positions, bin_X)
# add the entry into the original_indices_.
self.original_indices_[i] = np.insert(self.original_indices_[i],
positions,
np.arange(n_indexed,
n_indexed +
n_samples))
# adds the entry into the input_array.
if sparse.issparse(X) or sparse.issparse(self._fit_X):
self._fit_X = sparse.vstack((self._fit_X, X))
else:
self._fit_X = np.row_stack((self._fit_X, X))
return self
| bsd-3-clause |
AnasGhrab/scikit-learn | examples/tree/plot_tree_regression.py | 205 | 1476 | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns local linear regressions approximating the sine curve.
We can see that if the maximum depth of the tree (controlled by the
`max_depth` parameter) is set too high, the decision trees learn too fine
details of the training data and learn from the noise, i.e. they overfit.
"""
print(__doc__)
# Import the necessary modules and libraries
import numpy as np
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
# Create a random dataset
rng = np.random.RandomState(1)
X = np.sort(5 * rng.rand(80, 1), axis=0)
y = np.sin(X).ravel()
y[::5] += 3 * (0.5 - rng.rand(16))
# Fit regression model
regr_1 = DecisionTreeRegressor(max_depth=2)
regr_2 = DecisionTreeRegressor(max_depth=5)
regr_1.fit(X, y)
regr_2.fit(X, y)
# Predict
X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
y_1 = regr_1.predict(X_test)
y_2 = regr_2.predict(X_test)
# Plot the results
plt.figure()
plt.scatter(X, y, c="k", label="data")
plt.plot(X_test, y_1, c="g", label="max_depth=2", linewidth=2)
plt.plot(X_test, y_2, c="r", label="max_depth=5", linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Decision Tree Regression")
plt.legend()
plt.show()
| bsd-3-clause |
rvraghav93/scikit-learn | sklearn/linear_model/tests/test_coordinate_descent.py | 3 | 30153 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from sys import version_info
import numpy as np
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regex
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import TempMemmap
from sklearn.linear_model.coordinate_descent import Lasso, \
LassoCV, ElasticNet, ElasticNetCV, MultiTaskLasso, MultiTaskElasticNet, \
MultiTaskElasticNetCV, MultiTaskLassoCV, lasso_path, enet_path
from sklearn.linear_model import LassoLarsCV, lars_path
from sklearn.utils import check_array
def test_lasso_zero():
# Check that the lasso can handle zero data without crashing
X = [[0], [0], [0]]
y = [0, 0, 0]
clf = Lasso(alpha=0.1).fit(X, y)
pred = clf.predict([[1], [2], [3]])
assert_array_almost_equal(clf.coef_, [0])
assert_array_almost_equal(pred, [0, 0, 0])
assert_almost_equal(clf.dual_gap_, 0)
def test_lasso_toy():
# Test Lasso on a toy example for various values of alpha.
# When validating this against glmnet notice that glmnet divides it
# against nobs.
X = [[-1], [0], [1]]
Y = [-1, 0, 1] # just a straight line
T = [[2], [3], [4]] # test sample
clf = Lasso(alpha=1e-8)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1])
assert_array_almost_equal(pred, [2, 3, 4])
assert_almost_equal(clf.dual_gap_, 0)
clf = Lasso(alpha=0.1)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.85])
assert_array_almost_equal(pred, [1.7, 2.55, 3.4])
assert_almost_equal(clf.dual_gap_, 0)
clf = Lasso(alpha=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.25])
assert_array_almost_equal(pred, [0.5, 0.75, 1.])
assert_almost_equal(clf.dual_gap_, 0)
clf = Lasso(alpha=1)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.0])
assert_array_almost_equal(pred, [0, 0, 0])
assert_almost_equal(clf.dual_gap_, 0)
def test_enet_toy():
# Test ElasticNet for various parameters of alpha and l1_ratio.
# Actually, the parameters alpha = 0 should not be allowed. However,
# we test it as a border case.
# ElasticNet is tested with and without precomputed Gram matrix
X = np.array([[-1.], [0.], [1.]])
Y = [-1, 0, 1] # just a straight line
T = [[2.], [3.], [4.]] # test sample
# this should be the same as lasso
clf = ElasticNet(alpha=1e-8, l1_ratio=1.0)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1])
assert_array_almost_equal(pred, [2, 3, 4])
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.3, max_iter=100,
precompute=False)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf.set_params(max_iter=100, precompute=True)
clf.fit(X, Y) # with Gram
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf.set_params(max_iter=100, precompute=np.dot(X.T, X))
clf.fit(X, Y) # with Gram
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.45454], 3)
assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3)
assert_almost_equal(clf.dual_gap_, 0)
def build_dataset(n_samples=50, n_features=200, n_informative_features=10,
n_targets=1):
"""
build an ill-posed linear regression problem with many noisy features and
comparatively few samples
"""
random_state = np.random.RandomState(0)
if n_targets > 1:
w = random_state.randn(n_features, n_targets)
else:
w = random_state.randn(n_features)
w[n_informative_features:] = 0.0
X = random_state.randn(n_samples, n_features)
y = np.dot(X, w)
X_test = random_state.randn(n_samples, n_features)
y_test = np.dot(X_test, w)
return X, y, X_test, y_test
def test_lasso_cv():
X, y, X_test, y_test = build_dataset()
max_iter = 150
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter).fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
# Check that the lars and the coordinate descent implementation
# select a similar alpha
lars = LassoLarsCV(normalize=False, max_iter=30).fit(X, y)
# for this we check that they don't fall in the grid of
# clf.alphas further than 1
assert_true(np.abs(
np.searchsorted(clf.alphas_[::-1], lars.alpha_) -
np.searchsorted(clf.alphas_[::-1], clf.alpha_)) <= 1)
# check that they also give a similar MSE
mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
np.testing.assert_approx_equal(mse_lars(clf.alphas_[5]).mean(),
clf.mse_path_[5].mean(), significant=2)
# test set
assert_greater(clf.score(X_test, y_test), 0.99)
def test_lasso_cv_with_some_model_selection():
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn import datasets
from sklearn.linear_model import LassoCV
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target
pipe = make_pipeline(
StandardScaler(),
LassoCV(cv=StratifiedKFold(n_splits=5))
)
pipe.fit(X, y)
def test_lasso_cv_positive_constraint():
X, y, X_test, y_test = build_dataset()
max_iter = 500
# Ensure the unconstrained fit has a negative coefficient
clf_unconstrained = LassoCV(n_alphas=3, eps=1e-1, max_iter=max_iter, cv=2,
n_jobs=1)
clf_unconstrained.fit(X, y)
assert_true(min(clf_unconstrained.coef_) < 0)
# On same data, constrained fit has non-negative coefficients
clf_constrained = LassoCV(n_alphas=3, eps=1e-1, max_iter=max_iter,
positive=True, cv=2, n_jobs=1)
clf_constrained.fit(X, y)
assert_true(min(clf_constrained.coef_) >= 0)
def test_lasso_path_return_models_vs_new_return_gives_same_coefficients():
# Test that lasso_path with lars_path style output gives the
# same result
# Some toy data
X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
y = np.array([1, 2, 3.1])
alphas = [5., 1., .5]
# Use lars_path and lasso_path(new output) with 1D linear interpolation
# to compute the same path
alphas_lars, _, coef_path_lars = lars_path(X, y, method='lasso')
coef_path_cont_lars = interpolate.interp1d(alphas_lars[::-1],
coef_path_lars[:, ::-1])
alphas_lasso2, coef_path_lasso2, _ = lasso_path(X, y, alphas=alphas,
return_models=False)
coef_path_cont_lasso = interpolate.interp1d(alphas_lasso2[::-1],
coef_path_lasso2[:, ::-1])
assert_array_almost_equal(
coef_path_cont_lasso(alphas), coef_path_cont_lars(alphas),
decimal=1)
def test_enet_path():
# We use a large number of samples and of informative features so that
# the l1_ratio selected is more toward ridge than lasso
X, y, X_test, y_test = build_dataset(n_samples=200, n_features=100,
n_informative_features=100)
max_iter = 150
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
clf = ElasticNetCV(alphas=[0.01, 0.05, 0.1], eps=2e-3,
l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter)
ignore_warnings(clf.fit)(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have selected an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
clf = ElasticNetCV(alphas=[0.01, 0.05, 0.1], eps=2e-3,
l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter, precompute=True)
ignore_warnings(clf.fit)(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have selected an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
# Multi-output/target case
X, y, X_test, y_test = build_dataset(n_features=10, n_targets=3)
clf = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7],
cv=3, max_iter=max_iter)
ignore_warnings(clf.fit)(X, y)
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
assert_equal(clf.coef_.shape, (3, 10))
# Mono-output should have same cross-validated alpha_ and l1_ratio_
# in both cases.
X, y, _, _ = build_dataset(n_features=10)
clf1 = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf1.fit(X, y)
clf2 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf2.fit(X, y[:, np.newaxis])
assert_almost_equal(clf1.l1_ratio_, clf2.l1_ratio_)
assert_almost_equal(clf1.alpha_, clf2.alpha_)
def test_path_parameters():
X, y, _, _ = build_dataset()
max_iter = 100
clf = ElasticNetCV(n_alphas=50, eps=1e-3, max_iter=max_iter,
l1_ratio=0.5, tol=1e-3)
clf.fit(X, y) # new params
assert_almost_equal(0.5, clf.l1_ratio)
assert_equal(50, clf.n_alphas)
assert_equal(50, len(clf.alphas_))
def test_warm_start():
X, y, _, _ = build_dataset()
clf = ElasticNet(alpha=0.1, max_iter=5, warm_start=True)
ignore_warnings(clf.fit)(X, y)
ignore_warnings(clf.fit)(X, y) # do a second round with 5 iterations
clf2 = ElasticNet(alpha=0.1, max_iter=10)
ignore_warnings(clf2.fit)(X, y)
assert_array_almost_equal(clf2.coef_, clf.coef_)
def test_lasso_alpha_warning():
X = [[-1], [0], [1]]
Y = [-1, 0, 1] # just a straight line
clf = Lasso(alpha=0)
assert_warns(UserWarning, clf.fit, X, Y)
def test_lasso_positive_constraint():
X = [[-1], [0], [1]]
y = [1, 0, -1] # just a straight line with negative slope
lasso = Lasso(alpha=0.1, max_iter=1000, positive=True)
lasso.fit(X, y)
assert_true(min(lasso.coef_) >= 0)
lasso = Lasso(alpha=0.1, max_iter=1000, precompute=True, positive=True)
lasso.fit(X, y)
assert_true(min(lasso.coef_) >= 0)
def test_enet_positive_constraint():
X = [[-1], [0], [1]]
y = [1, 0, -1] # just a straight line with negative slope
enet = ElasticNet(alpha=0.1, max_iter=1000, positive=True)
enet.fit(X, y)
assert_true(min(enet.coef_) >= 0)
def test_enet_cv_positive_constraint():
X, y, X_test, y_test = build_dataset()
max_iter = 500
# Ensure the unconstrained fit has a negative coefficient
enetcv_unconstrained = ElasticNetCV(n_alphas=3, eps=1e-1,
max_iter=max_iter,
cv=2, n_jobs=1)
enetcv_unconstrained.fit(X, y)
assert_true(min(enetcv_unconstrained.coef_) < 0)
# On same data, constrained fit has non-negative coefficients
enetcv_constrained = ElasticNetCV(n_alphas=3, eps=1e-1, max_iter=max_iter,
cv=2, positive=True, n_jobs=1)
enetcv_constrained.fit(X, y)
assert_true(min(enetcv_constrained.coef_) >= 0)
def test_uniform_targets():
enet = ElasticNetCV(fit_intercept=True, n_alphas=3)
m_enet = MultiTaskElasticNetCV(fit_intercept=True, n_alphas=3)
lasso = LassoCV(fit_intercept=True, n_alphas=3)
m_lasso = MultiTaskLassoCV(fit_intercept=True, n_alphas=3)
models_single_task = (enet, lasso)
models_multi_task = (m_enet, m_lasso)
rng = np.random.RandomState(0)
X_train = rng.random_sample(size=(10, 3))
X_test = rng.random_sample(size=(10, 3))
y1 = np.empty(10)
y2 = np.empty((10, 2))
for model in models_single_task:
for y_values in (0, 5):
y1.fill(y_values)
assert_array_equal(model.fit(X_train, y1).predict(X_test), y1)
assert_array_equal(model.alphas_, [np.finfo(float).resolution]*3)
for model in models_multi_task:
for y_values in (0, 5):
y2[:, 0].fill(y_values)
y2[:, 1].fill(2 * y_values)
assert_array_equal(model.fit(X_train, y2).predict(X_test), y2)
assert_array_equal(model.alphas_, [np.finfo(float).resolution]*3)
def test_multi_task_lasso_and_enet():
X, y, X_test, y_test = build_dataset()
Y = np.c_[y, y]
# Y_test = np.c_[y_test, y_test]
clf = MultiTaskLasso(alpha=1, tol=1e-8).fit(X, Y)
assert_true(0 < clf.dual_gap_ < 1e-5)
assert_array_almost_equal(clf.coef_[0], clf.coef_[1])
clf = MultiTaskElasticNet(alpha=1, tol=1e-8).fit(X, Y)
assert_true(0 < clf.dual_gap_ < 1e-5)
assert_array_almost_equal(clf.coef_[0], clf.coef_[1])
clf = MultiTaskElasticNet(alpha=1.0, tol=1e-8, max_iter=1)
assert_warns_message(ConvergenceWarning, 'did not converge', clf.fit, X, Y)
def test_lasso_readonly_data():
X = np.array([[-1], [0], [1]])
Y = np.array([-1, 0, 1]) # just a straight line
T = np.array([[2], [3], [4]]) # test sample
with TempMemmap((X, Y)) as (X, Y):
clf = Lasso(alpha=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.25])
assert_array_almost_equal(pred, [0.5, 0.75, 1.])
assert_almost_equal(clf.dual_gap_, 0)
def test_multi_task_lasso_readonly_data():
X, y, X_test, y_test = build_dataset()
Y = np.c_[y, y]
with TempMemmap((X, Y)) as (X, Y):
Y = np.c_[y, y]
clf = MultiTaskLasso(alpha=1, tol=1e-8).fit(X, Y)
assert_true(0 < clf.dual_gap_ < 1e-5)
assert_array_almost_equal(clf.coef_[0], clf.coef_[1])
def test_enet_multitarget():
n_targets = 3
X, y, _, _ = build_dataset(n_samples=10, n_features=8,
n_informative_features=10, n_targets=n_targets)
estimator = ElasticNet(alpha=0.01, fit_intercept=True)
estimator.fit(X, y)
coef, intercept, dual_gap = (estimator.coef_, estimator.intercept_,
estimator.dual_gap_)
for k in range(n_targets):
estimator.fit(X, y[:, k])
assert_array_almost_equal(coef[k, :], estimator.coef_)
assert_array_almost_equal(intercept[k], estimator.intercept_)
assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)
def test_multioutput_enetcv_error():
rng = np.random.RandomState(0)
X = rng.randn(10, 2)
y = rng.randn(10, 2)
clf = ElasticNetCV()
assert_raises(ValueError, clf.fit, X, y)
def test_multitask_enet_and_lasso_cv():
X, y, _, _ = build_dataset(n_features=50, n_targets=3)
clf = MultiTaskElasticNetCV().fit(X, y)
assert_almost_equal(clf.alpha_, 0.00556, 3)
clf = MultiTaskLassoCV().fit(X, y)
assert_almost_equal(clf.alpha_, 0.00278, 3)
X, y, _, _ = build_dataset(n_targets=3)
clf = MultiTaskElasticNetCV(n_alphas=10, eps=1e-3, max_iter=100,
l1_ratio=[0.3, 0.5], tol=1e-3)
clf.fit(X, y)
assert_equal(0.5, clf.l1_ratio_)
assert_equal((3, X.shape[1]), clf.coef_.shape)
assert_equal((3, ), clf.intercept_.shape)
assert_equal((2, 10, 3), clf.mse_path_.shape)
assert_equal((2, 10), clf.alphas_.shape)
X, y, _, _ = build_dataset(n_targets=3)
clf = MultiTaskLassoCV(n_alphas=10, eps=1e-3, max_iter=100, tol=1e-3)
clf.fit(X, y)
assert_equal((3, X.shape[1]), clf.coef_.shape)
assert_equal((3, ), clf.intercept_.shape)
assert_equal((10, 3), clf.mse_path_.shape)
assert_equal(10, len(clf.alphas_))
def test_1d_multioutput_enet_and_multitask_enet_cv():
X, y, _, _ = build_dataset(n_features=10)
y = y[:, np.newaxis]
clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf.fit(X, y[:, 0])
clf1 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf1.fit(X, y)
assert_almost_equal(clf.l1_ratio_, clf1.l1_ratio_)
assert_almost_equal(clf.alpha_, clf1.alpha_)
assert_almost_equal(clf.coef_, clf1.coef_[0])
assert_almost_equal(clf.intercept_, clf1.intercept_[0])
def test_1d_multioutput_lasso_and_multitask_lasso_cv():
X, y, _, _ = build_dataset(n_features=10)
y = y[:, np.newaxis]
clf = LassoCV(n_alphas=5, eps=2e-3)
clf.fit(X, y[:, 0])
clf1 = MultiTaskLassoCV(n_alphas=5, eps=2e-3)
clf1.fit(X, y)
assert_almost_equal(clf.alpha_, clf1.alpha_)
assert_almost_equal(clf.coef_, clf1.coef_[0])
assert_almost_equal(clf.intercept_, clf1.intercept_[0])
def test_sparse_input_dtype_enet_and_lassocv():
X, y, _, _ = build_dataset(n_features=10)
clf = ElasticNetCV(n_alphas=5)
clf.fit(sparse.csr_matrix(X), y)
clf1 = ElasticNetCV(n_alphas=5)
clf1.fit(sparse.csr_matrix(X, dtype=np.float32), y)
assert_almost_equal(clf.alpha_, clf1.alpha_, decimal=6)
assert_almost_equal(clf.coef_, clf1.coef_, decimal=6)
clf = LassoCV(n_alphas=5)
clf.fit(sparse.csr_matrix(X), y)
clf1 = LassoCV(n_alphas=5)
clf1.fit(sparse.csr_matrix(X, dtype=np.float32), y)
assert_almost_equal(clf.alpha_, clf1.alpha_, decimal=6)
assert_almost_equal(clf.coef_, clf1.coef_, decimal=6)
def test_precompute_invalid_argument():
X, y, _, _ = build_dataset()
for clf in [ElasticNetCV(precompute="invalid"),
LassoCV(precompute="invalid")]:
assert_raises_regex(ValueError, ".*should be.*True.*False.*auto.*"
"array-like.*Got 'invalid'", clf.fit, X, y)
# Precompute = 'auto' is not supported for ElasticNet
assert_raises_regex(ValueError, ".*should be.*True.*False.*array-like.*"
"Got 'auto'", ElasticNet(precompute='auto').fit, X, y)
def test_warm_start_convergence():
X, y, _, _ = build_dataset()
model = ElasticNet(alpha=1e-3, tol=1e-3).fit(X, y)
n_iter_reference = model.n_iter_
# This dataset is not trivial enough for the model to converge in one pass.
assert_greater(n_iter_reference, 2)
# Check that n_iter_ is invariant to multiple calls to fit
# when warm_start=False, all else being equal.
model.fit(X, y)
n_iter_cold_start = model.n_iter_
assert_equal(n_iter_cold_start, n_iter_reference)
# Fit the same model again, using a warm start: the optimizer just performs
# a single pass before checking that it has already converged
model.set_params(warm_start=True)
model.fit(X, y)
n_iter_warm_start = model.n_iter_
assert_equal(n_iter_warm_start, 1)
def test_warm_start_convergence_with_regularizer_decrement():
boston = load_boston()
X, y = boston.data, boston.target
# Train a model to converge on a lightly regularized problem
final_alpha = 1e-5
low_reg_model = ElasticNet(alpha=final_alpha).fit(X, y)
# Fitting a new model on a more regularized version of the same problem.
# Fitting with high regularization is easier it should converge faster
# in general.
high_reg_model = ElasticNet(alpha=final_alpha * 10).fit(X, y)
assert_greater(low_reg_model.n_iter_, high_reg_model.n_iter_)
# Fit the solution to the original, less regularized version of the
# problem but from the solution of the highly regularized variant of
# the problem as a better starting point. This should also converge
# faster than the original model that starts from zero.
warm_low_reg_model = deepcopy(high_reg_model)
warm_low_reg_model.set_params(warm_start=True, alpha=final_alpha)
warm_low_reg_model.fit(X, y)
assert_greater(low_reg_model.n_iter_, warm_low_reg_model.n_iter_)
def test_random_descent():
# Test that both random and cyclic selection give the same results.
# Ensure that the test models fully converge and check a wide
# range of conditions.
# This uses the coordinate descent algo using the gram trick.
X, y, _, _ = build_dataset(n_samples=50, n_features=20)
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X, y)
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(X, y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# This uses the descent algo without the gram trick
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X.T, y[:20])
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(X.T, y[:20])
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Sparse Case
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(sparse.csr_matrix(X), y)
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(sparse.csr_matrix(X), y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Multioutput case.
new_y = np.hstack((y[:, np.newaxis], y[:, np.newaxis]))
clf_cyclic = MultiTaskElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X, new_y)
clf_random = MultiTaskElasticNet(selection='random', tol=1e-8,
random_state=42)
clf_random.fit(X, new_y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Raise error when selection is not in cyclic or random.
clf_random = ElasticNet(selection='invalid')
assert_raises(ValueError, clf_random.fit, X, y)
def test_enet_path_positive():
# Test positive parameter
X, Y, _, _ = build_dataset(n_samples=50, n_features=50, n_targets=2)
# For mono output
# Test that the coefs returned by positive=True in enet_path are positive
for path in [enet_path, lasso_path]:
pos_path_coef = path(X, Y[:, 0], positive=True)[1]
assert_true(np.all(pos_path_coef >= 0))
# For multi output, positive parameter is not allowed
# Test that an error is raised
for path in [enet_path, lasso_path]:
assert_raises(ValueError, path, X, Y, positive=True)
def test_sparse_dense_descent_paths():
# Test that dense and sparse input give the same input for descent paths.
X, y, _, _ = build_dataset(n_samples=50, n_features=20)
csr = sparse.csr_matrix(X)
for path in [enet_path, lasso_path]:
_, coefs, _ = path(X, y, fit_intercept=False)
_, sparse_coefs, _ = path(csr, y, fit_intercept=False)
assert_array_almost_equal(coefs, sparse_coefs)
def test_check_input_false():
X, y, _, _ = build_dataset(n_samples=20, n_features=10)
X = check_array(X, order='F', dtype='float64')
y = check_array(X, order='F', dtype='float64')
clf = ElasticNet(selection='cyclic', tol=1e-8)
# Check that no error is raised if data is provided in the right format
clf.fit(X, y, check_input=False)
# With check_input=False, an exhaustive check is not made on y but its
# dtype is still cast in _preprocess_data to X's dtype. So the test should
# pass anyway
X = check_array(X, order='F', dtype='float32')
clf.fit(X, y, check_input=False)
# With no input checking, providing X in C order should result in false
# computation
X = check_array(X, order='C', dtype='float64')
assert_raises(ValueError, clf.fit, X, y, check_input=False)
def test_overrided_gram_matrix():
X, y, _, _ = build_dataset(n_samples=20, n_features=10)
Gram = X.T.dot(X)
clf = ElasticNet(selection='cyclic', tol=1e-8, precompute=Gram,
fit_intercept=True)
assert_warns_message(UserWarning,
"Gram matrix was provided but X was centered"
" to fit intercept, "
"or X was normalized : recomputing Gram matrix.",
clf.fit, X, y)
def test_lasso_non_float_y():
X = [[0, 0], [1, 1], [-1, -1]]
y = [0, 1, 2]
y_float = [0.0, 1.0, 2.0]
for model in [ElasticNet, Lasso]:
clf = model(fit_intercept=False)
clf.fit(X, y)
clf_float = model(fit_intercept=False)
clf_float.fit(X, y_float)
assert_array_equal(clf.coef_, clf_float.coef_)
def test_enet_float_precision():
# Generate dataset
X, y, X_test, y_test = build_dataset(n_samples=20, n_features=10)
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
for normalize in [True, False]:
for fit_intercept in [True, False]:
coef = {}
intercept = {}
for dtype in [np.float64, np.float32]:
clf = ElasticNet(alpha=0.5, max_iter=100, precompute=False,
fit_intercept=fit_intercept,
normalize=normalize)
X = dtype(X)
y = dtype(y)
ignore_warnings(clf.fit)(X, y)
coef[('simple', dtype)] = clf.coef_
intercept[('simple', dtype)] = clf.intercept_
assert_equal(clf.coef_.dtype, dtype)
# test precompute Gram array
Gram = X.T.dot(X)
clf_precompute = ElasticNet(alpha=0.5, max_iter=100,
precompute=Gram,
fit_intercept=fit_intercept,
normalize=normalize)
ignore_warnings(clf_precompute.fit)(X, y)
assert_array_almost_equal(clf.coef_, clf_precompute.coef_)
assert_array_almost_equal(clf.intercept_,
clf_precompute.intercept_)
# test multi task enet
multi_y = np.hstack((y[:, np.newaxis], y[:, np.newaxis]))
clf_multioutput = MultiTaskElasticNet(
alpha=0.5, max_iter=100, fit_intercept=fit_intercept,
normalize=normalize)
clf_multioutput.fit(X, multi_y)
coef[('multi', dtype)] = clf_multioutput.coef_
intercept[('multi', dtype)] = clf_multioutput.intercept_
assert_equal(clf.coef_.dtype, dtype)
for v in ['simple', 'multi']:
assert_array_almost_equal(coef[(v, np.float32)],
coef[(v, np.float64)],
decimal=4)
assert_array_almost_equal(intercept[(v, np.float32)],
intercept[(v, np.float64)],
decimal=4)
def test_enet_l1_ratio():
# Test that an error message is raised if an estimator that
# uses _alpha_grid is called with l1_ratio=0
msg = ("Automatic alpha grid generation is not supported for l1_ratio=0. "
"Please supply a grid by providing your estimator with the "
"appropriate `alphas=` argument.")
X = np.array([[1, 2, 4, 5, 8], [3, 5, 7, 7, 8]]).T
y = np.array([12, 10, 11, 21, 5])
assert_raise_message(ValueError, msg, ElasticNetCV(
l1_ratio=0, random_state=42).fit, X, y)
assert_raise_message(ValueError, msg, MultiTaskElasticNetCV(
l1_ratio=0, random_state=42).fit, X, y[:, None])
# Test that l1_ratio=0 is allowed if we supply a grid manually
alphas = [0.1, 10]
estkwds = {'alphas': alphas, 'random_state': 42}
est_desired = ElasticNetCV(l1_ratio=0.00001, **estkwds)
est = ElasticNetCV(l1_ratio=0, **estkwds)
with ignore_warnings():
est_desired.fit(X, y)
est.fit(X, y)
assert_array_almost_equal(est.coef_, est_desired.coef_, decimal=5)
est_desired = MultiTaskElasticNetCV(l1_ratio=0.00001, **estkwds)
est = MultiTaskElasticNetCV(l1_ratio=0, **estkwds)
with ignore_warnings():
est.fit(X, y[:, None])
est_desired.fit(X, y[:, None])
assert_array_almost_equal(est.coef_, est_desired.coef_, decimal=5)
| bsd-3-clause |
dhruv13J/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 295 | 1247 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import pylab as pl
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_random_state(0)
one_core = []
multi_core = []
sample_sizes = range(1000, 6000, 1000)
for n_samples in sample_sizes:
X = random_state.rand(n_samples, 300)
start = time.time()
func(X, n_jobs=1)
one_core.append(time.time() - start)
start = time.time()
func(X, n_jobs=-1)
multi_core.append(time.time() - start)
pl.figure('scikit-learn parallel %s benchmark results' % func.__name__)
pl.plot(sample_sizes, one_core, label="one core")
pl.plot(sample_sizes, multi_core, label="multi core")
pl.xlabel('n_samples')
pl.ylabel('Time (s)')
pl.title('Parallel %s' % func.__name__)
pl.legend()
def euclidean_distances(X, n_jobs):
return pairwise_distances(X, metric="euclidean", n_jobs=n_jobs)
def rbf_kernels(X, n_jobs):
return pairwise_kernels(X, metric="rbf", n_jobs=n_jobs, gamma=0.1)
plot(euclidean_distances)
plot(rbf_kernels)
pl.show()
| bsd-3-clause |
AnasGhrab/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 295 | 1247 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import pylab as pl
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_random_state(0)
one_core = []
multi_core = []
sample_sizes = range(1000, 6000, 1000)
for n_samples in sample_sizes:
X = random_state.rand(n_samples, 300)
start = time.time()
func(X, n_jobs=1)
one_core.append(time.time() - start)
start = time.time()
func(X, n_jobs=-1)
multi_core.append(time.time() - start)
pl.figure('scikit-learn parallel %s benchmark results' % func.__name__)
pl.plot(sample_sizes, one_core, label="one core")
pl.plot(sample_sizes, multi_core, label="multi core")
pl.xlabel('n_samples')
pl.ylabel('Time (s)')
pl.title('Parallel %s' % func.__name__)
pl.legend()
def euclidean_distances(X, n_jobs):
return pairwise_distances(X, metric="euclidean", n_jobs=n_jobs)
def rbf_kernels(X, n_jobs):
return pairwise_kernels(X, metric="rbf", n_jobs=n_jobs, gamma=0.1)
plot(euclidean_distances)
plot(rbf_kernels)
pl.show()
| bsd-3-clause |
CKehl/pylearn2 | pylearn2/scripts/papers/maxout/svhn_preprocessing.py | 55 | 2006 | import os
import logging
import shutil
from theano import config
from pylearn2.datasets import preprocessing
from pylearn2.datasets.svhn import SVHN
from pylearn2.utils.string_utils import preprocess
orig_path = preprocess('${PYLEARN2_DATA_PATH}/SVHN/format2')
try:
local_path = preprocess('${SVHN_LOCAL_PATH}')
except ValueError:
raise ValueError("You need to define SVHN_LOCAL_PATH environment "
"variable.")
train_name ='h5/splitted_train_32x32.h5'
valid_name = 'h5/valid_32x32.h5'
test_name = 'h5/test_32x32.h5'
# copy data if don't exist
if not os.path.isdir(os.path.join(local_path, 'h5')):
os.makedirs(os.path.join(local_path, 'h5'))
for d_set in [train_name, valid_name, test_name]:
if not os.path.isfile(os.path.join(local_path, d_set)):
logging.info("Copying data from {0} to {1}".format(os.path.join(local_path, d_set), local_path))
shutil.copyfile(os.path.join(orig_path, d_set),
os.path.join(local_path, d_set))
def check_dtype(data):
if str(data.X.dtype) != config.floatX:
logging.warning("The dataset is saved as {}, changing theano's floatX "\
"to the same dtype".format(data.X.dtype))
config.floatX = str(data.X.dtype)
# Load train data
train = SVHN('splitted_train', path=local_path)
check_dtype(train)
# prepare preprocessing
pipeline = preprocessing.Pipeline()
# without batch_size there is a high chance that you might encounter memory error
# or pytables crashes
pipeline.items.append(preprocessing.GlobalContrastNormalization(batch_size=5000))
pipeline.items.append(preprocessing.LeCunLCN((32,32)))
# apply the preprocessings to train
train.apply_preprocessor(pipeline, can_fit=True)
del train
# load and preprocess valid
valid = SVHN('valid', path=local_path)
check_dtype(valid)
valid.apply_preprocessor(pipeline, can_fit=False)
# load and preprocess test
test = SVHN('test', path=local_path)
check_dtype(test)
test.apply_preprocessor(pipeline, can_fit=False)
| bsd-3-clause |
AnasGhrab/scikit-learn | benchmarks/bench_lasso.py | 295 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number of dimensions.
In both cases, only 10% of the features are informative.
"""
import gc
from time import time
import numpy as np
from sklearn.datasets.samples_generator import make_regression
def compute_bench(alpha, n_samples, n_features, precompute):
lasso_results = []
lars_lasso_results = []
it = 0
for ns in n_samples:
for nf in n_features:
it += 1
print('==================')
print('Iteration %s of %s' % (it, max(len(n_samples),
len(n_features))))
print('==================')
n_informative = nf // 10
X, Y, coef_ = make_regression(n_samples=ns, n_features=nf,
n_informative=n_informative,
noise=0.1, coef=True)
X /= np.sqrt(np.sum(X ** 2, axis=0)) # Normalize data
gc.collect()
print("- benchmarking Lasso")
clf = Lasso(alpha=alpha, fit_intercept=False,
precompute=precompute)
tstart = time()
clf.fit(X, Y)
lasso_results.append(time() - tstart)
gc.collect()
print("- benchmarking LassoLars")
clf = LassoLars(alpha=alpha, fit_intercept=False,
normalize=False, precompute=precompute)
tstart = time()
clf.fit(X, Y)
lars_lasso_results.append(time() - tstart)
return lasso_results, lars_lasso_results
if __name__ == '__main__':
from sklearn.linear_model import Lasso, LassoLars
import pylab as pl
alpha = 0.01 # regularization parameter
n_features = 10
list_n_samples = np.linspace(100, 1000000, 5).astype(np.int)
lasso_results, lars_lasso_results = compute_bench(alpha, list_n_samples,
[n_features], precompute=True)
pl.figure('scikit-learn LASSO benchmark results')
pl.subplot(211)
pl.plot(list_n_samples, lasso_results, 'b-',
label='Lasso')
pl.plot(list_n_samples, lars_lasso_results, 'r-',
label='LassoLars')
pl.title('precomputed Gram matrix, %d features, alpha=%s' % (n_features, alpha))
pl.legend(loc='upper left')
pl.xlabel('number of samples')
pl.ylabel('Time (s)')
pl.axis('tight')
n_samples = 2000
list_n_features = np.linspace(500, 3000, 5).astype(np.int)
lasso_results, lars_lasso_results = compute_bench(alpha, [n_samples],
list_n_features, precompute=False)
pl.subplot(212)
pl.plot(list_n_features, lasso_results, 'b-', label='Lasso')
pl.plot(list_n_features, lars_lasso_results, 'r-', label='LassoLars')
pl.title('%d samples, alpha=%s' % (n_samples, alpha))
pl.legend(loc='upper left')
pl.xlabel('number of features')
pl.ylabel('Time (s)')
pl.axis('tight')
pl.show()
| bsd-3-clause |
CKehl/pylearn2 | pylearn2/datasets/csv_dataset.py | 33 | 6458 | # -*- coding: utf-8 -*-
"""
A simple general csv dataset wrapper for pylearn2.
Can do automatic one-hot encoding based on labels present in a file.
"""
__authors__ = "Zygmunt Zając, Marco De Nadai"
__copyright__ = "Copyright 2013, Zygmunt Zając"
__credits__ = ["Zygmunt Zając", "Nicholas Dronen", "Marco De Nadai"]
__license__ = "3-clause BSD"
__maintainer__ = "?"
__email__ = "zygmunt@fastml.com"
import numpy as np
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from pylearn2.utils.string_utils import preprocess
class CSVDataset(DenseDesignMatrix):
"""
A generic class for accessing CSV files
labels, if present, should be in the first column
if there's no labels, set expect_labels to False
if there's no header line in your file, set expect_headers to False
Parameters
----------
path : str
The path to the CSV file.
task : str
The type of task in which the dataset will be used -- either
"classification" or "regression". The task determines the shape of the
target variable. For classification, it is a vector; for regression, a
matrix.
expect_labels : bool
Whether the CSV file contains a target variable in the first column.
expect_headers : bool
Whether the CSV file contains column headers.
delimiter : str
The CSV file's delimiter.
start : int
The first row of the CSV file to load.
stop : int
The last row of the CSV file to load.
start_fraction : float
The fraction of rows, starting at the beginning of the file, to load.
end_fraction : float
The fraction of rows, starting at the end of the file, to load.
num_outputs : int, optional
number of target variables. defaults to 1
"""
def __init__(self,
path='train.csv',
task='classification',
expect_labels=True,
expect_headers=True,
delimiter=',',
start=None,
stop=None,
start_fraction=None,
end_fraction=None,
num_outputs=1,
**kwargs):
self.path = path
self.task = task
self.expect_labels = expect_labels
self.expect_headers = expect_headers
self.delimiter = delimiter
self.start = start
self.stop = stop
self.start_fraction = start_fraction
self.end_fraction = end_fraction
self.num_outputs = num_outputs
self.view_converter = None
if task not in ['classification', 'regression']:
raise ValueError('task must be either "classification" or '
'"regression"; got ' + str(task))
if start_fraction is not None:
if end_fraction is not None:
raise ValueError("Use start_fraction or end_fraction, "
" not both.")
if start_fraction <= 0:
raise ValueError("start_fraction should be > 0")
if start_fraction >= 1:
raise ValueError("start_fraction should be < 1")
if end_fraction is not None:
if end_fraction <= 0:
raise ValueError("end_fraction should be > 0")
if end_fraction >= 1:
raise ValueError("end_fraction should be < 1")
if start is not None:
if start_fraction is not None or end_fraction is not None:
raise ValueError("Use start, start_fraction, or end_fraction,"
" just not together.")
if stop is not None:
if start_fraction is not None or end_fraction is not None:
raise ValueError("Use stop, start_fraction, or end_fraction,"
" just not together.")
# and go
self.path = preprocess(self.path)
X, y = self._load_data()
if self.task == 'regression':
super(CSVDataset, self).__init__(X=X, y=y, **kwargs)
else:
super(CSVDataset, self).__init__(X=X, y=y,
y_labels=np.max(y) + 1, **kwargs)
def _load_data(self):
"""
Loads the data from a CSV file (ending with a '.csv' filename).
Returns
-------
X : object
The features of the dataset.
y : object, optional
The target variable of the model.
"""
assert self.path.endswith('.csv')
if self.expect_headers:
data = np.loadtxt(self.path,
delimiter=self.delimiter,
skiprows=1)
else:
data = np.loadtxt(self.path, delimiter=self.delimiter)
def take_subset(X, y):
"""
Takes a subset of the dataset if the start_fraction,
stop_fraction or start/stop parameter of the class
is set.
Parameters
----------
X : object
The features of the dataset.
y : object, optional
The target variable of the model.
Returns
-------
X : object
The subset of the features of the dataset.
y : object, optional
The subset of the target variable of the model.
"""
if self.start_fraction is not None:
n = X.shape[0]
subset_end = int(self.start_fraction * n)
X = X[0:subset_end, :]
y = y[0:subset_end]
elif self.end_fraction is not None:
n = X.shape[0]
subset_start = int((1 - self.end_fraction) * n)
X = X[subset_start:, ]
y = y[subset_start:]
elif self.start is not None:
X = X[self.start:self.stop, ]
if y is not None:
y = y[self.start:self.stop]
return X, y
if self.expect_labels:
y = data[:, 0:self.num_outputs]
X = data[:, self.num_outputs:]
y = y.reshape((y.shape[0], self.num_outputs))
else:
X = data
y = None
X, y = take_subset(X, y)
return X, y
| bsd-3-clause |
ivannz/scikit-learn | build_tools/github/vendor.py | 13 | 5765 | """Embed vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll.
Note that vcruntime140_1.dll is only required (and available)
for 64-bit architectures.
"""
import os
import os.path as op
import shutil
import sys
import textwrap
TARGET_FOLDER = op.join("sklearn", ".libs")
DISTRIBUTOR_INIT = op.join("sklearn", "_distributor_init.py")
VCOMP140_SRC_PATH = "C:\\Windows\\System32\\vcomp140.dll"
VCRUNTIME140_SRC_PATH = "C:\\Windows\\System32\\vcruntime140.dll"
VCRUNTIME140_1_SRC_PATH = "C:\\Windows\\System32\\vcruntime140_1.dll"
def make_distributor_init_32_bits(
distributor_init, vcomp140_dll_filename, vcruntime140_dll_filename
):
"""Create a _distributor_init.py file for 32-bit architectures.
This file is imported first when importing the sklearn package
so as to pre-load the vendored vcomp140.dll and vcruntime140.dll.
"""
with open(distributor_init, "wt") as f:
f.write(
textwrap.dedent(
"""
'''Helper to preload vcomp140.dll and vcruntime140.dll to
prevent "not found" errors.
Once vcomp140.dll and vcruntime140.dll are preloaded, the
namespace is made available to any subsequent vcomp140.dll
and vcruntime140.dll. This is created as part of the scripts
that build the wheel.
'''
import os
import os.path as op
from ctypes import WinDLL
if os.name == "nt":
# Load vcomp140.dll and vcruntime140.dll
libs_path = op.join(op.dirname(__file__), ".libs")
vcomp140_dll_filename = op.join(libs_path, "{0}")
vcruntime140_dll_filename = op.join(libs_path, "{1}")
WinDLL(op.abspath(vcomp140_dll_filename))
WinDLL(op.abspath(vcruntime140_dll_filename))
""".format(
vcomp140_dll_filename, vcruntime140_dll_filename
)
)
)
def make_distributor_init_64_bits(
distributor_init,
vcomp140_dll_filename,
vcruntime140_dll_filename,
vcruntime140_1_dll_filename,
):
"""Create a _distributor_init.py file for 64-bit architectures.
This file is imported first when importing the sklearn package
so as to pre-load the vendored vcomp140.dll, vcruntime140.dll
and vcruntime140_1.dll.
"""
with open(distributor_init, "wt") as f:
f.write(
textwrap.dedent(
"""
'''Helper to preload vcomp140.dll, vcruntime140.dll and
vcruntime140_1.dll to prevent "not found" errors.
Once vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll are
preloaded, the namespace is made available to any subsequent
vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll. This is
created as part of the scripts that build the wheel.
'''
import os
import os.path as op
from ctypes import WinDLL
if os.name == "nt":
# Load vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll
libs_path = op.join(op.dirname(__file__), ".libs")
vcomp140_dll_filename = op.join(libs_path, "{0}")
vcruntime140_dll_filename = op.join(libs_path, "{1}")
vcruntime140_1_dll_filename = op.join(libs_path, "{2}")
WinDLL(op.abspath(vcomp140_dll_filename))
WinDLL(op.abspath(vcruntime140_dll_filename))
WinDLL(op.abspath(vcruntime140_1_dll_filename))
""".format(
vcomp140_dll_filename,
vcruntime140_dll_filename,
vcruntime140_1_dll_filename,
)
)
)
def main(wheel_dirname, bitness):
"""Embed vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll."""
if not op.exists(VCOMP140_SRC_PATH):
raise ValueError(f"Could not find {VCOMP140_SRC_PATH}.")
if not op.exists(VCRUNTIME140_SRC_PATH):
raise ValueError(f"Could not find {VCRUNTIME140_SRC_PATH}.")
if not op.exists(VCRUNTIME140_1_SRC_PATH) and bitness == "64":
raise ValueError(f"Could not find {VCRUNTIME140_1_SRC_PATH}.")
if not op.isdir(wheel_dirname):
raise RuntimeError(f"Could not find {wheel_dirname} file.")
vcomp140_dll_filename = op.basename(VCOMP140_SRC_PATH)
vcruntime140_dll_filename = op.basename(VCRUNTIME140_SRC_PATH)
vcruntime140_1_dll_filename = op.basename(VCRUNTIME140_1_SRC_PATH)
target_folder = op.join(wheel_dirname, TARGET_FOLDER)
distributor_init = op.join(wheel_dirname, DISTRIBUTOR_INIT)
# Create the "sklearn/.libs" subfolder
if not op.exists(target_folder):
os.mkdir(target_folder)
print(f"Copying {VCOMP140_SRC_PATH} to {target_folder}.")
shutil.copy2(VCOMP140_SRC_PATH, target_folder)
print(f"Copying {VCRUNTIME140_SRC_PATH} to {target_folder}.")
shutil.copy2(VCRUNTIME140_SRC_PATH, target_folder)
if bitness == "64":
print(f"Copying {VCRUNTIME140_1_SRC_PATH} to {target_folder}.")
shutil.copy2(VCRUNTIME140_1_SRC_PATH, target_folder)
# Generate the _distributor_init file in the source tree
print("Generating the '_distributor_init.py' file.")
if bitness == "32":
make_distributor_init_32_bits(
distributor_init, vcomp140_dll_filename, vcruntime140_dll_filename
)
else:
make_distributor_init_64_bits(
distributor_init,
vcomp140_dll_filename,
vcruntime140_dll_filename,
vcruntime140_1_dll_filename,
)
if __name__ == "__main__":
_, wheel_file, bitness = sys.argv
main(wheel_file, bitness)
| bsd-3-clause |
osmanbaskaya/mapping-impact | run/classifier.py | 1 | 3157 | #! /usr/bin/python
# -*- coding: utf-8 -*-
__author__ = "Osman Baskaya"
from sklearn import grid_search
from sklearn.svm import SVC
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.tree import DecisionTreeClassifier
#from statsmodels.discrete.discrete_model import MNLogit
import numpy as np
import sys
class ClassifierWrapper(object):
def __init__(self, name, classifier):
self.name = name
self.classifier = classifier
self.is_optimized = False
def optimize(self, X, y, cv, parameters=None):
"""Hyperparameter optimization"""
best_params = best_estimator = None
if parameters is None:
parameters = self.parameters
print >> sys.stderr, "Parameter space:{}".format(parameters)
try:
clf = grid_search.GridSearchCV(self.classifier, parameters, cv=cv, verbose=1,
n_jobs=20)
clf.fit(X, y)
print >> sys.stderr, clf.grid_scores_
best_params = clf.best_params_
best_estimator = clf.best_estimator_
except ValueError, e: # all instances belong to the same class
print >> sys.stderr, e
#FIXME: Why do we get index errors for book.v
except IndexError, e:
print >> sys.stderr, e
#try:
#msg = "Best_params:{}, Best Score:{}".format(best_params, clf.best_score_)
#print >> sys.stderr, msg
#except AttributeError:
#pass
return best_params, best_estimator, clf.best_score_
class SVCWrapper(ClassifierWrapper):
def __init__(self, name, **kwargs):
super(SVCWrapper, self).__init__(name, SVC(**kwargs))
gamma = 10.0 ** np.arange(-5, 4)
C = 10.0 ** np.arange(-2, 6)
#C = [0.003, 0.03, 0.3, 1, 9, 27, 81, 243, 729, 2000, 6000]
if self.classifier.kernel == "rbf":
self.parameters = {'C': C, 'gamma': gamma}
elif self.classifier.kernel == "linear":
self.parameters = {'C': C}
class MultinomialNBWrapper(ClassifierWrapper):
def __init__(self, name="MultinomialNB"):
super(MultinomialNBWrapper, self).__init__(name, MultinomialNB())
self.is_optimized = True
self.parameters = {'alpha': np.linspace(0,1,11)}
class BernoulliNBWrapper(ClassifierWrapper):
def __init__(self, name="BernoulliNBWrapper"):
super(BernoulliNBWrapper, self).__init__(name, BernoulliNB())
self.parameters = {'alpha': np.linspace(0,1,11)}
self.is_optimized = True
class DecisionTreeWrapper(ClassifierWrapper):
def __init__(self, name, criterion):
super(DecisionTreeWrapper, self).__init__(name,
DecisionTreeClassifier(criterion=criterion))
self.is_optimized = True
#class MNLogitWrapper(ClassifierWrapper):
#def __init__(self):
#raise NotImplementedError, "soon"
#super(MNLogitWrapper, self).__init__("MNLogitWrapper", MNLogitW())
#self.parameters = {}
#print self.name
def main():
pass
if __name__ == '__main__':
main()
| mit |
hitszxp/scikit-learn | sklearn/ensemble/partial_dependence.py | 36 | 14909 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.joblib import Parallel, delayed
from ..externals import six
from ..externals.six.moves import map, range, zip
from ..utils import check_array
from ..tree._tree import DTYPE
from ._gradient_boosting import _partial_dependence_tree
from .gradient_boosting import BaseGradientBoosting
def _grid_from_X(X, percentiles=(0.05, 0.95), grid_resolution=100):
"""Generate a grid of points based on the ``percentiles of ``X``.
The grid is generated by placing ``grid_resolution`` equally
spaced points between the ``percentiles`` of each column
of ``X``.
Parameters
----------
X : ndarray
The data
percentiles : tuple of floats
The percentiles which are used to construct the extreme
values of the grid axes.
grid_resolution : int
The number of equally spaced points that are placed
on the grid.
Returns
-------
grid : ndarray
All data points on the grid; ``grid.shape[1] == X.shape[1]``
and ``grid.shape[0] == grid_resolution * X.shape[1]``.
axes : seq of ndarray
The axes with which the grid has been created.
"""
if len(percentiles) != 2:
raise ValueError('percentile must be tuple of len 2')
if not all(0. <= x <= 1. for x in percentiles):
raise ValueError('percentile values must be in [0, 1]')
axes = []
for col in range(X.shape[1]):
uniques = np.unique(X[:, col])
if uniques.shape[0] < grid_resolution:
# feature has low resolution use unique vals
axis = uniques
else:
emp_percentiles = mquantiles(X, prob=percentiles, axis=0)
# create axis based on percentiles and grid resolution
axis = np.linspace(emp_percentiles[0, col],
emp_percentiles[1, col],
num=grid_resolution, endpoint=True)
axes.append(axis)
return cartesian(axes), axes
def partial_dependence(gbrt, target_variables, grid=None, X=None,
percentiles=(0.05, 0.95), grid_resolution=100):
"""Partial dependence of ``target_variables``.
Partial dependence plots show the dependence between the joint values
of the ``target_variables`` and the function represented
by the ``gbrt``.
Parameters
----------
gbrt : BaseGradientBoosting
A fitted gradient boosting model.
target_variables : array-like, dtype=int
The target features for which the partial dependecy should be
computed (size should be smaller than 3 for visual renderings).
grid : array-like, shape=(n_points, len(target_variables))
The grid of ``target_variables`` values for which the
partial dependecy should be evaluated (either ``grid`` or ``X``
must be specified).
X : array-like, shape=(n_samples, n_features)
The data on which ``gbrt`` was trained. It is used to generate
a ``grid`` for the ``target_variables``. The ``grid`` comprises
``grid_resolution`` equally spaced points between the two
``percentiles``.
percentiles : (low, high), default=(0.05, 0.95)
The lower and upper percentile used create the extreme values
for the ``grid``. Only if ``X`` is not None.
grid_resolution : int, default=100
The number of equally spaced points on the ``grid``.
Returns
-------
pdp : array, shape=(n_classes, n_points)
The partial dependence function evaluated on the ``grid``.
For regression and binary classification ``n_classes==1``.
axes : seq of ndarray or None
The axes with which the grid has been created or None if
the grid has been given.
Examples
--------
>>> samples = [[0, 0, 2], [1, 0, 0]]
>>> labels = [0, 1]
>>> from sklearn.ensemble import GradientBoostingClassifier
>>> gb = GradientBoostingClassifier(random_state=0).fit(samples, labels)
>>> kwargs = dict(X=samples, percentiles=(0, 1), grid_resolution=2)
>>> partial_dependence(gb, [0], **kwargs) # doctest: +SKIP
(array([[-4.52..., 4.52...]]), [array([ 0., 1.])])
"""
if not isinstance(gbrt, BaseGradientBoosting):
raise ValueError('gbrt has to be an instance of BaseGradientBoosting')
if gbrt.estimators_.shape[0] == 0:
raise ValueError('Call %s.fit before partial_dependence' %
gbrt.__class__.__name__)
if (grid is None and X is None) or (grid is not None and X is not None):
raise ValueError('Either grid or X must be specified')
target_variables = np.asarray(target_variables, dtype=np.int32,
order='C').ravel()
if any([not (0 <= fx < gbrt.n_features) for fx in target_variables]):
raise ValueError('target_variables must be in [0, %d]'
% (gbrt.n_features - 1))
if X is not None:
X = check_array(X, dtype=DTYPE, order='C')
grid, axes = _grid_from_X(X[:, target_variables], percentiles,
grid_resolution)
else:
assert grid is not None
# dont return axes if grid is given
axes = None
# grid must be 2d
if grid.ndim == 1:
grid = grid[:, np.newaxis]
if grid.ndim != 2:
raise ValueError('grid must be 2d but is %dd' % grid.ndim)
grid = np.asarray(grid, dtype=DTYPE, order='C')
assert grid.shape[1] == target_variables.shape[0]
n_trees_per_stage = gbrt.estimators_.shape[1]
n_estimators = gbrt.estimators_.shape[0]
pdp = np.zeros((n_trees_per_stage, grid.shape[0],), dtype=np.float64,
order='C')
for stage in range(n_estimators):
for k in range(n_trees_per_stage):
tree = gbrt.estimators_[stage, k].tree_
_partial_dependence_tree(tree, grid, target_variables,
gbrt.learning_rate, pdp[k])
return pdp, axes
def plot_partial_dependence(gbrt, X, features, feature_names=None,
label=None, n_cols=3, grid_resolution=100,
percentiles=(0.05, 0.95), n_jobs=1,
verbose=0, ax=None, line_kw=None,
contour_kw=None, **fig_kw):
"""Partial dependence plots for ``features``.
The ``len(features)`` plots are arranged in a grid with ``n_cols``
columns. Two-way partial dependence plots are plotted as contour
plots.
Parameters
----------
gbrt : BaseGradientBoosting
A fitted gradient boosting model.
X : array-like, shape=(n_samples, n_features)
The data on which ``gbrt`` was trained.
features : seq of tuples or ints
If seq[i] is an int or a tuple with one int value, a one-way
PDP is created; if seq[i] is a tuple of two ints, a two-way
PDP is created.
feature_names : seq of str
Name of each feature; feature_names[i] holds
the name of the feature with index i.
label : object
The class label for which the PDPs should be computed.
Only if gbrt is a multi-class model. Must be in ``gbrt.classes_``.
n_cols : int
The number of columns in the grid plot (default: 3).
percentiles : (low, high), default=(0.05, 0.95)
The lower and upper percentile used create the extreme values
for the PDP axes.
grid_resolution : int, default=100
The number of equally spaced points on the axes.
n_jobs : int
The number of CPUs to use to compute the PDs. -1 means 'all CPUs'.
Defaults to 1.
verbose : int
Verbose output during PD computations. Defaults to 0.
ax : Matplotlib axis object, default None
An axis object onto which the plots will be drawn.
line_kw : dict
Dict with keywords passed to the ``pylab.plot`` call.
For one-way partial dependence plots.
contour_kw : dict
Dict with keywords passed to the ``pylab.plot`` call.
For two-way partial dependence plots.
fig_kw : dict
Dict with keywords passed to the figure() call.
Note that all keywords not recognized above will be automatically
included here.
Returns
-------
fig : figure
The Matplotlib Figure object.
axs : seq of Axis objects
A seq of Axis objects, one for each subplot.
Examples
--------
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> X, y = make_friedman1()
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
>>> fig, axs = plot_partial_dependence(clf, X, [0, (0, 1)]) #doctest: +SKIP
...
"""
import matplotlib.pyplot as plt
from matplotlib import transforms
from matplotlib.ticker import MaxNLocator
from matplotlib.ticker import ScalarFormatter
if not isinstance(gbrt, BaseGradientBoosting):
raise ValueError('gbrt has to be an instance of BaseGradientBoosting')
if gbrt.estimators_.shape[0] == 0:
raise ValueError('Call %s.fit before partial_dependence' %
gbrt.__class__.__name__)
# set label_idx for multi-class GBRT
if hasattr(gbrt, 'classes_') and np.size(gbrt.classes_) > 2:
if label is None:
raise ValueError('label is not given for multi-class PDP')
label_idx = np.searchsorted(gbrt.classes_, label)
if gbrt.classes_[label_idx] != label:
raise ValueError('label %s not in ``gbrt.classes_``' % str(label))
else:
# regression and binary classification
label_idx = 0
X = check_array(X, dtype=DTYPE, order='C')
if gbrt.n_features != X.shape[1]:
raise ValueError('X.shape[1] does not match gbrt.n_features')
if line_kw is None:
line_kw = {'color': 'green'}
if contour_kw is None:
contour_kw = {}
# convert feature_names to list
if feature_names is None:
# if not feature_names use fx indices as name
feature_names = [str(i) for i in range(gbrt.n_features)]
elif isinstance(feature_names, np.ndarray):
feature_names = feature_names.tolist()
def convert_feature(fx):
if isinstance(fx, six.string_types):
try:
fx = feature_names.index(fx)
except ValueError:
raise ValueError('Feature %s not in feature_names' % fx)
return fx
# convert features into a seq of int tuples
tmp_features = []
for fxs in features:
if isinstance(fxs, (numbers.Integral,) + six.string_types):
fxs = (fxs,)
try:
fxs = np.array([convert_feature(fx) for fx in fxs], dtype=np.int32)
except TypeError:
raise ValueError('features must be either int, str, or tuple '
'of int/str')
if not (1 <= np.size(fxs) <= 2):
raise ValueError('target features must be either one or two')
tmp_features.append(fxs)
features = tmp_features
names = []
try:
for fxs in features:
l = []
# explicit loop so "i" is bound for exception below
for i in fxs:
l.append(feature_names[i])
names.append(l)
except IndexError:
raise ValueError('features[i] must be in [0, n_features) '
'but was %d' % i)
# compute PD functions
pd_result = Parallel(n_jobs=n_jobs, verbose=verbose)(
delayed(partial_dependence)(gbrt, fxs, X=X,
grid_resolution=grid_resolution)
for fxs in features)
# get global min and max values of PD grouped by plot type
pdp_lim = {}
for pdp, axes in pd_result:
min_pd, max_pd = pdp[label_idx].min(), pdp[label_idx].max()
n_fx = len(axes)
old_min_pd, old_max_pd = pdp_lim.get(n_fx, (min_pd, max_pd))
min_pd = min(min_pd, old_min_pd)
max_pd = max(max_pd, old_max_pd)
pdp_lim[n_fx] = (min_pd, max_pd)
# create contour levels for two-way plots
if 2 in pdp_lim:
Z_level = np.linspace(*pdp_lim[2], num=8)
if ax is None:
fig = plt.figure(**fig_kw)
else:
fig = ax.get_figure()
fig.clear()
n_cols = min(n_cols, len(features))
n_rows = int(np.ceil(len(features) / float(n_cols)))
axs = []
for i, fx, name, (pdp, axes) in zip(count(), features, names,
pd_result):
ax = fig.add_subplot(n_rows, n_cols, i + 1)
if len(axes) == 1:
ax.plot(axes[0], pdp[label_idx].ravel(), **line_kw)
else:
# make contour plot
assert len(axes) == 2
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[label_idx].reshape(list(map(np.size, axes))).T
CS = ax.contour(XX, YY, Z, levels=Z_level, linewidths=0.5,
colors='k')
ax.contourf(XX, YY, Z, levels=Z_level, vmax=Z_level[-1],
vmin=Z_level[0], alpha=0.75, **contour_kw)
ax.clabel(CS, fmt='%2.2f', colors='k', fontsize=10, inline=True)
# plot data deciles + axes labels
deciles = mquantiles(X[:, fx[0]], prob=np.arange(0.1, 1.0, 0.1))
trans = transforms.blended_transform_factory(ax.transData,
ax.transAxes)
ylim = ax.get_ylim()
ax.vlines(deciles, [0], 0.05, transform=trans, color='k')
ax.set_xlabel(name[0])
ax.set_ylim(ylim)
# prevent x-axis ticks from overlapping
ax.xaxis.set_major_locator(MaxNLocator(nbins=6, prune='lower'))
tick_formatter = ScalarFormatter()
tick_formatter.set_powerlimits((-3, 4))
ax.xaxis.set_major_formatter(tick_formatter)
if len(axes) > 1:
# two-way PDP - y-axis deciles + labels
deciles = mquantiles(X[:, fx[1]], prob=np.arange(0.1, 1.0, 0.1))
trans = transforms.blended_transform_factory(ax.transAxes,
ax.transData)
xlim = ax.get_xlim()
ax.hlines(deciles, [0], 0.05, transform=trans, color='k')
ax.set_ylabel(name[1])
# hline erases xlim
ax.set_xlim(xlim)
else:
ax.set_ylabel('Partial dependence')
if len(axes) == 1:
ax.set_ylim(pdp_lim[1])
axs.append(ax)
fig.subplots_adjust(bottom=0.15, top=0.7, left=0.1, right=0.95, wspace=0.4,
hspace=0.3)
return fig, axs
| bsd-3-clause |
oddt/oddt | oddt/scoring/__init__.py | 1 | 15331 | from os.path import dirname, join as path_join
import gzip
from itertools import chain
from functools import partial
import six
from six.moves import cPickle as pickle
import numpy as np
from scipy.sparse import vstack as sparse_vstack
import pandas as pd
from joblib import Parallel, delayed
from sklearn.model_selection import cross_val_score, KFold
from sklearn.base import is_classifier, is_regressor
from sklearn.metrics import accuracy_score, r2_score
import oddt
from oddt.utils import method_caller
from oddt.datasets import pdbbind
from oddt.fingerprints import sparse_to_csr_matrix, csr_matrix_to_sparse, fold
def cross_validate(model, cv_set, cv_target, n=10, shuffle=True, n_jobs=1):
"""Perform cross validation of model using provided data
Parameters
----------
model: object
Model to be tested
cv_set: array-like of shape = [n_samples, n_features]
Estimated target values.
cv_target: array-like of shape = [n_samples] or [n_samples, n_outputs]
Estimated target values.
n: integer (default = 10)
How many folds to be created from dataset
shuffle: bool (default = True)
Should data be shuffled before folding.
n_jobs: integer (default = 1)
How many CPUs to use during cross validation
Returns
-------
r2: array of shape = [n]
R^2 score for each of generated folds
"""
if shuffle:
cv = KFold(n_splits=n, shuffle=True)
else:
cv = n
return cross_val_score(model, cv_set, cv_target, cv=cv, n_jobs=n_jobs)
# FIX ### If possible make ensemble scorer lazy, for now it consumes all ligands
class scorer(object):
def __init__(self, model_instance, descriptor_generator_instance,
score_title='score'):
"""Scorer class is parent class for scoring functions.
Parameters
----------
model_instance: model
Medel compatible with sklearn API (fit, predict and score
methods)
descriptor_generator_instance: array of descriptors
Descriptor generator object
score_title: string
Title of score to be used.
"""
self.model = model_instance
self.descriptor_generator = descriptor_generator_instance
self.score_title = score_title
def _gen_pdbbind_desc(self,
pdbbind_dir,
pdbbind_versions=(2007, 2012, 2013, 2014, 2015, 2016),
desc_path=None,
include_general_set=False,
use_proteins=False,
**kwargs):
pdbbind_versions = sorted(pdbbind_versions)
opt = kwargs.get('opt', {})
# generate metadata
df = None
for pdbbind_version in pdbbind_versions:
p = pdbbind('%s/v%i/' % (pdbbind_dir, pdbbind_version),
version=pdbbind_version,
opt=opt)
# Core set
for set_name in p.pdbind_sets:
if set_name == 'general_PL':
dataset_key = '%i_general' % pdbbind_version
else:
dataset_key = '%i_%s' % (pdbbind_version, set_name)
tmp_df = pd.DataFrame({
'pdbid': list(p.sets[set_name].keys()),
dataset_key: list(p.sets[set_name].values())
})
if df is not None:
df = pd.merge(tmp_df, df, how='outer', on='pdbid')
else:
df = tmp_df
df.sort_values('pdbid', inplace=True)
tmp_act = df['%i_general' % pdbbind_versions[-1]].values
df = df.set_index('pdbid').notnull()
df['act'] = tmp_act
# take non-empty and core + refined set
df = df[df['act'].notnull() &
(df.filter(regex='.*_[refined,core]').any(axis=1) |
include_general_set)]
# build descriptos
pdbbind_db = pdbbind('%s/v%i/' % (pdbbind_dir, pdbbind_versions[-1]),
version=pdbbind_versions[-1])
if not desc_path:
desc_path = path_join(dirname(__file__) + 'descs.csv')
if self.n_jobs is None:
n_jobs = -1
else:
n_jobs = self.n_jobs
blacklist = []
if use_proteins:
# list of protein files that segfault OB 2.4.1
blacklist = pdbbind_db.protein_blacklist[oddt.toolkit.backend]
# check if PDBID exists or is blacklisted
desc_idx = [pid for pid in df.index.values
if (pid not in blacklist and
getattr(pdbbind_db[pid], 'protein'
if use_proteins
else 'pocket') is not None)]
result = Parallel(n_jobs=n_jobs, verbose=1)(
delayed(method_caller)(
self.descriptor_generator,
'build',
[pdbbind_db[pid].ligand],
protein=getattr(pdbbind_db[pid], 'protein' if use_proteins
else 'pocket'))
for pid in desc_idx)
# sparse descs may have different shapes, dense are stored np.array
sparse = (hasattr(self.descriptor_generator, 'sparse') and
self.descriptor_generator.sparse)
if not sparse:
result = np.vstack(result)
# create dataframe with descriptors with pdbids as index
df_desc = pd.DataFrame(result, index=desc_idx)
df_desc.index.rename('pdbid', inplace=True)
# for sparse features leave one column and cast explicitly to list
if sparse:
if len(df_desc.columns) > 1:
raise Exception('There are more than one column in the '
'sparse descriptor table.')
df_desc.columns = ['sparse']
df_desc['sparse'] = df_desc['sparse'].map(
lambda x: csr_matrix_to_sparse(x).tolist())
compression = None
if desc_path[-3:] == '.gz':
compression = 'gzip'
# DF are joined by index (pdbid) since some might be missing
df.join(df_desc, how='inner').to_csv(desc_path,
float_format='%.5g',
compression=compression)
def _load_pdbbind_desc(self, desc_path, pdbbind_version=2016,
train_set='refined', test_set='core',
train_blacklist=None, fold_size=None):
"""
TODO: write the docs
"""
df = pd.read_csv(desc_path, index_col='pdbid')
# generate dense representation of sparse descriptor in CSV
cols = list(map(str, range(len(self.descriptor_generator))))
if 'sparse' in df.columns:
# convert strings to np.arrays
df['sparse'] = df['sparse'].map(
lambda x: np.fromstring(x[1:-1], dtype=np.uint64, sep=','))
cols = 'sparse' # sparse array will have one column
# fold only if necessary
if fold_size:
df['sparse'] = df['sparse'].map(lambda x: fold(x, fold_size))
# convert to sparse csr_matrix
df['sparse'] = df['sparse'].map(
partial(sparse_to_csr_matrix,
size=len(self.descriptor_generator)))
if isinstance(train_set, six.string_types):
train_idx = df['%i_%s' % (pdbbind_version, train_set)]
else:
train_idx = df[['%i_%s' % (pdbbind_version, s)
for s in train_set]].any(axis=1)
if train_blacklist:
train_idx &= ~df.index.isin(train_blacklist)
train_idx &= ~df['%i_%s' % (pdbbind_version, test_set)]
# load sparse matrices as training is usually faster on them
if 'sparse' in df.columns:
self.train_descs = sparse_vstack(df.loc[train_idx, cols].values,
format='csr')
else:
self.train_descs = df.loc[train_idx, cols].values
self.train_target = df.loc[train_idx, 'act'].values
test_idx = df['%i_%s' % (pdbbind_version, test_set)]
if 'sparse' in df.columns:
self.test_descs = sparse_vstack(df.loc[test_idx, cols].values,
format='csr')
else:
self.test_descs = df.loc[test_idx, cols].values
self.test_target = df.loc[test_idx, 'act'].values
def fit(self, ligands, target, *args, **kwargs):
"""Trains model on supplied ligands and target values
Parameters
----------
ligands: array-like of ligands
Molecules to featurize and feed into the model
target: array-like of shape = [n_samples] or [n_samples, n_outputs]
Ground truth (correct) target values.
"""
self.train_descs = self.descriptor_generator.build(ligands)
return self.model.fit(self.train_descs, target, *args, **kwargs)
def predict(self, ligands, *args, **kwargs):
"""Predicts values (eg. affinity) for supplied ligands.
Parameters
----------
ligands: array-like of ligands
Molecules to featurize and feed into the model
Returns
-------
predicted: np.array or array of np.arrays of shape = [n_ligands]
Predicted scores for ligands
"""
descs = self.descriptor_generator.build(ligands)
return self.model.predict(descs)
def score(self, ligands, target, *args, **kwargs):
"""Methods estimates the quality of prediction using model's default
score (accuracy for classification or R^2 for regression)
Parameters
----------
ligands: array-like of ligands
Molecules to featurize and feed into the model
target: array-like of shape = [n_samples] or [n_samples, n_outputs]
Ground truth (correct) target values.
Returns
-------
s: float
Quality score (accuracy or R^2) for prediction
"""
descs = self.descriptor_generator.build(ligands)
return self.model.score(descs, target, *args, **kwargs)
def predict_ligand(self, ligand):
"""Local method to score one ligand and update it's scores.
Parameters
----------
ligand: oddt.toolkit.Molecule object
Ligand to be scored
Returns
-------
ligand: oddt.toolkit.Molecule object
Scored ligand with updated scores
"""
score = self.predict([ligand])[0]
ligand.data.update({self.score_title: score})
return ligand
def predict_ligands(self, ligands):
"""Method to score ligands in a lazy fashion.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecule objects
Ligands to be scored
Returns
-------
ligand: iterator of oddt.toolkit.Molecule objects
Scored ligands with updated scores
"""
return (self.predict_ligand(lig) for lig in ligands)
def set_protein(self, protein):
"""Proxy method to update protein in all relevant places.
Parameters
----------
protein: oddt.toolkit.Molecule object
New default protein
"""
self.protein = protein
if hasattr(self.descriptor_generator, 'set_protein'):
self.descriptor_generator.set_protein(protein)
else:
self.descriptor_generator.protein = protein
def save(self, filename):
"""Saves scoring function to a pickle file.
Parameters
----------
filename: string
Pickle filename
"""
# FIXME: re-set protein after pickling
self.set_protein(None)
# return joblib.dump(self, filename, compress=9)[0]
with gzip.open(filename, 'w+b', compresslevel=9) as f:
pickle.dump(self, f, protocol=2)
return filename
@classmethod
def load(self, filename):
"""Loads scoring function from a pickle file.
Parameters
----------
filename: string
Pickle filename
Returns
-------
sf: scorer-like object
Scoring function object loaded from a pickle
"""
# return joblib.load(filename)
kwargs = {'encoding': 'latin1'} if six.PY3 else {}
with gzip.open(filename, 'rb') as f:
out = pickle.load(f, **kwargs)
return out
class ensemble_model(object):
def __init__(self, models):
"""Proxy class to build an ensemble of models with an API as one
Parameters
----------
models: array
An array of models
"""
self._models = models if len(models) else None
if self._models is not None:
if is_classifier(self._models[0]):
check_type = is_classifier
self._scoring_fun = accuracy_score
elif is_regressor(self._models[0]):
check_type = is_regressor
self._scoring_fun = r2_score
else:
raise ValueError('Expected regressors or classifiers,'
' got %s instead' % type(self._models[0]))
for model in self._models:
if not check_type(model):
raise ValueError('Different types of models found, privide'
' either regressors or classifiers.')
def fit(self, X, y, *args, **kwargs):
for model in self._models:
model.fit(X, y, *args, **kwargs)
return self
def predict(self, X, *args, **kwargs):
return np.array([model.predict(X, *args, **kwargs)
for model in self._models]).mean(axis=0)
def score(self, X, y, *args, **kwargs):
return self._scoring_fun(y.flatten(),
self.predict(X, *args, **kwargs).flatten())
class ensemble_descriptor(object):
def __init__(self, descriptor_generators):
"""Proxy class to build an ensemble of destriptors with an API as one
Parameters
----------
models: array
An array of models
"""
self._desc_gens = (descriptor_generators if len(descriptor_generators)
else None)
self.titles = list(chain(*(desc_gen.titles
for desc_gen in self._desc_gens)))
def build(self, mols, *args, **kwargs):
desc = np.hstack(tuple(desc_gen.build(mols, *args, **kwargs)
for desc_gen in self._desc_gens))
return desc
def set_protein(self, protein):
for desc in self._desc_gens:
if hasattr(desc, 'set_protein'):
desc.set_protein(protein)
else:
desc.protein = protein
def __len__(self):
""" Returns the dimensions of descriptors """
return sum(len(desc) for desc in self._desc_gens)
def __reduce__(self):
return ensemble_descriptor, (self._desc_gens,)
| bsd-3-clause |
hitszxp/scikit-learn | sklearn/linear_model/tests/test_ridge.py | 10 | 22918 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings
from sklearn import datasets
from sklearn.metrics import mean_squared_error
from sklearn.metrics.scorer import SCORERS
from sklearn.metrics import make_scorer
from sklearn.linear_model.base import LinearRegression
from sklearn.linear_model.ridge import ridge_regression
from sklearn.linear_model.ridge import Ridge
from sklearn.linear_model.ridge import _RidgeGCV
from sklearn.linear_model.ridge import RidgeCV
from sklearn.linear_model.ridge import RidgeClassifier
from sklearn.linear_model.ridge import RidgeClassifierCV
from sklearn.linear_model.ridge import _solve_cholesky
from sklearn.linear_model.ridge import _solve_cholesky_kernel
from sklearn.cross_validation import KFold
diabetes = datasets.load_diabetes()
X_diabetes, y_diabetes = diabetes.data, diabetes.target
ind = np.arange(X_diabetes.shape[0])
rng = np.random.RandomState(0)
rng.shuffle(ind)
ind = ind[:200]
X_diabetes, y_diabetes = X_diabetes[ind], y_diabetes[ind]
iris = datasets.load_iris()
X_iris = sp.csr_matrix(iris.data)
y_iris = iris.target
DENSE_FILTER = lambda X: X
SPARSE_FILTER = lambda X: sp.csr_matrix(X)
def test_ridge():
"""Ridge regression convergence test using score
TODO: for this test to be robust, we should use a dataset instead
of np.random.
"""
rng = np.random.RandomState(0)
alpha = 1.0
for solver in ("svd", "sparse_cg", "cholesky", "lsqr"):
# With more samples than features
n_samples, n_features = 6, 5
y = rng.randn(n_samples)
X = rng.randn(n_samples, n_features)
ridge = Ridge(alpha=alpha, solver=solver)
ridge.fit(X, y)
assert_equal(ridge.coef_.shape, (X.shape[1], ))
assert_greater(ridge.score(X, y), 0.47)
if solver == "cholesky":
# Currently the only solver to support sample_weight.
ridge.fit(X, y, sample_weight=np.ones(n_samples))
assert_greater(ridge.score(X, y), 0.47)
# With more features than samples
n_samples, n_features = 5, 10
y = rng.randn(n_samples)
X = rng.randn(n_samples, n_features)
ridge = Ridge(alpha=alpha, solver=solver)
ridge.fit(X, y)
assert_greater(ridge.score(X, y), .9)
if solver == "cholesky":
# Currently the only solver to support sample_weight.
ridge.fit(X, y, sample_weight=np.ones(n_samples))
assert_greater(ridge.score(X, y), 0.9)
def test_primal_dual_relationship():
y = y_diabetes.reshape(-1, 1)
coef = _solve_cholesky(X_diabetes, y, alpha=[1e-2])
K = np.dot(X_diabetes, X_diabetes.T)
dual_coef = _solve_cholesky_kernel(K, y, alpha=[1e-2])
coef2 = np.dot(X_diabetes.T, dual_coef).T
assert_array_almost_equal(coef, coef2)
def test_ridge_singular():
# test on a singular matrix
rng = np.random.RandomState(0)
n_samples, n_features = 6, 6
y = rng.randn(n_samples // 2)
y = np.concatenate((y, y))
X = rng.randn(n_samples // 2, n_features)
X = np.concatenate((X, X), axis=0)
ridge = Ridge(alpha=0)
ridge.fit(X, y)
assert_greater(ridge.score(X, y), 0.9)
def test_ridge_sample_weights():
rng = np.random.RandomState(0)
for solver in ("cholesky", ):
for n_samples, n_features in ((6, 5), (5, 10)):
for alpha in (1.0, 1e-2):
y = rng.randn(n_samples)
X = rng.randn(n_samples, n_features)
sample_weight = 1 + rng.rand(n_samples)
coefs = ridge_regression(X, y,
alpha=alpha,
sample_weight=sample_weight,
solver=solver)
# Sample weight can be implemented via a simple rescaling
# for the square loss.
coefs2 = ridge_regression(
X * np.sqrt(sample_weight)[:, np.newaxis],
y * np.sqrt(sample_weight),
alpha=alpha, solver=solver)
assert_array_almost_equal(coefs, coefs2)
# Test for fit_intercept = True
est = Ridge(alpha=alpha, solver=solver)
est.fit(X, y, sample_weight=sample_weight)
# Check using Newton's Method
# Quadratic function should be solved in a single step.
# Initialize
sample_weight = np.sqrt(sample_weight)
X_weighted = sample_weight[:, np.newaxis] * (
np.column_stack((np.ones(n_samples), X)))
y_weighted = y * sample_weight
# Gradient is (X*coef-y)*X + alpha*coef_[1:]
# Remove coef since it is initialized to zero.
grad = -np.dot(y_weighted, X_weighted)
# Hessian is (X.T*X) + alpha*I except that the first
# diagonal element should be zero, since there is no
# penalization of intercept.
diag = alpha * np.ones(n_features + 1)
diag[0] = 0.
hess = np.dot(X_weighted.T, X_weighted)
hess.flat[::n_features + 2] += diag
coef_ = - np.dot(linalg.inv(hess), grad)
assert_almost_equal(coef_[0], est.intercept_)
assert_array_almost_equal(coef_[1:], est.coef_)
def test_ridge_shapes():
"""Test shape of coef_ and intercept_
"""
rng = np.random.RandomState(0)
n_samples, n_features = 5, 10
X = rng.randn(n_samples, n_features)
y = rng.randn(n_samples)
Y1 = y[:, np.newaxis]
Y = np.c_[y, 1 + y]
ridge = Ridge()
ridge.fit(X, y)
assert_equal(ridge.coef_.shape, (n_features,))
assert_equal(ridge.intercept_.shape, ())
ridge.fit(X, Y1)
assert_equal(ridge.coef_.shape, (1, n_features))
assert_equal(ridge.intercept_.shape, (1, ))
ridge.fit(X, Y)
assert_equal(ridge.coef_.shape, (2, n_features))
assert_equal(ridge.intercept_.shape, (2, ))
def test_ridge_intercept():
"""Test intercept with multiple targets GH issue #708
"""
rng = np.random.RandomState(0)
n_samples, n_features = 5, 10
X = rng.randn(n_samples, n_features)
y = rng.randn(n_samples)
Y = np.c_[y, 1. + y]
ridge = Ridge()
ridge.fit(X, y)
intercept = ridge.intercept_
ridge.fit(X, Y)
assert_almost_equal(ridge.intercept_[0], intercept)
assert_almost_equal(ridge.intercept_[1], intercept + 1.)
def test_toy_ridge_object():
"""Test BayesianRegression ridge classifier
TODO: test also n_samples > n_features
"""
X = np.array([[1], [2]])
Y = np.array([1, 2])
clf = Ridge(alpha=0.0)
clf.fit(X, Y)
X_test = [[1], [2], [3], [4]]
assert_almost_equal(clf.predict(X_test), [1., 2, 3, 4])
assert_equal(len(clf.coef_.shape), 1)
assert_equal(type(clf.intercept_), np.float64)
Y = np.vstack((Y, Y)).T
clf.fit(X, Y)
X_test = [[1], [2], [3], [4]]
assert_equal(len(clf.coef_.shape), 2)
assert_equal(type(clf.intercept_), np.ndarray)
def test_ridge_vs_lstsq():
"""On alpha=0., Ridge and OLS yield the same solution."""
rng = np.random.RandomState(0)
# we need more samples than features
n_samples, n_features = 5, 4
y = rng.randn(n_samples)
X = rng.randn(n_samples, n_features)
ridge = Ridge(alpha=0., fit_intercept=False)
ols = LinearRegression(fit_intercept=False)
ridge.fit(X, y)
ols.fit(X, y)
assert_almost_equal(ridge.coef_, ols.coef_)
ridge.fit(X, y)
ols.fit(X, y)
assert_almost_equal(ridge.coef_, ols.coef_)
def test_ridge_individual_penalties():
"""Tests the ridge object using individual penalties"""
rng = np.random.RandomState(42)
n_samples, n_features, n_targets = 20, 10, 5
X = rng.randn(n_samples, n_features)
y = rng.randn(n_samples, n_targets)
penalties = np.arange(n_targets)
coef_cholesky = np.array([
Ridge(alpha=alpha, solver="cholesky").fit(X, target).coef_
for alpha, target in zip(penalties, y.T)])
coefs_indiv_pen = [
Ridge(alpha=penalties, solver=solver, tol=1e-6).fit(X, y).coef_
for solver in ['svd', 'sparse_cg', 'lsqr', 'cholesky']]
for coef_indiv_pen in coefs_indiv_pen:
assert_array_almost_equal(coef_cholesky, coef_indiv_pen)
# Test error is raised when number of targets and penalties do not match.
ridge = Ridge(alpha=penalties[:3])
assert_raises(ValueError, ridge.fit, X, y)
def _test_ridge_loo(filter_):
# test that can work with both dense or sparse matrices
n_samples = X_diabetes.shape[0]
ret = []
ridge_gcv = _RidgeGCV(fit_intercept=False)
ridge = Ridge(alpha=1.0, fit_intercept=False)
# generalized cross-validation (efficient leave-one-out)
decomp = ridge_gcv._pre_compute(X_diabetes, y_diabetes)
errors, c = ridge_gcv._errors(1.0, y_diabetes, *decomp)
values, c = ridge_gcv._values(1.0, y_diabetes, *decomp)
# brute-force leave-one-out: remove one example at a time
errors2 = []
values2 = []
for i in range(n_samples):
sel = np.arange(n_samples) != i
X_new = X_diabetes[sel]
y_new = y_diabetes[sel]
ridge.fit(X_new, y_new)
value = ridge.predict([X_diabetes[i]])[0]
error = (y_diabetes[i] - value) ** 2
errors2.append(error)
values2.append(value)
# check that efficient and brute-force LOO give same results
assert_almost_equal(errors, errors2)
assert_almost_equal(values, values2)
# generalized cross-validation (efficient leave-one-out,
# SVD variation)
decomp = ridge_gcv._pre_compute_svd(X_diabetes, y_diabetes)
errors3, c = ridge_gcv._errors_svd(ridge.alpha, y_diabetes, *decomp)
values3, c = ridge_gcv._values_svd(ridge.alpha, y_diabetes, *decomp)
# check that efficient and SVD efficient LOO give same results
assert_almost_equal(errors, errors3)
assert_almost_equal(values, values3)
# check best alpha
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
alpha_ = ridge_gcv.alpha_
ret.append(alpha_)
# check that we get same best alpha with custom loss_func
f = ignore_warnings
scoring = make_scorer(mean_squared_error, greater_is_better=False)
ridge_gcv2 = RidgeCV(fit_intercept=False, scoring=scoring)
f(ridge_gcv2.fit)(filter_(X_diabetes), y_diabetes)
assert_equal(ridge_gcv2.alpha_, alpha_)
# check that we get same best alpha with custom score_func
func = lambda x, y: -mean_squared_error(x, y)
scoring = make_scorer(func)
ridge_gcv3 = RidgeCV(fit_intercept=False, scoring=scoring)
f(ridge_gcv3.fit)(filter_(X_diabetes), y_diabetes)
assert_equal(ridge_gcv3.alpha_, alpha_)
# check that we get same best alpha with a scorer
scorer = SCORERS['mean_squared_error']
ridge_gcv4 = RidgeCV(fit_intercept=False, scoring=scorer)
ridge_gcv4.fit(filter_(X_diabetes), y_diabetes)
assert_equal(ridge_gcv4.alpha_, alpha_)
# check that we get same best alpha with sample weights
ridge_gcv.fit(filter_(X_diabetes), y_diabetes,
sample_weight=np.ones(n_samples))
assert_equal(ridge_gcv.alpha_, alpha_)
# simulate several responses
Y = np.vstack((y_diabetes, y_diabetes)).T
ridge_gcv.fit(filter_(X_diabetes), Y)
Y_pred = ridge_gcv.predict(filter_(X_diabetes))
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
y_pred = ridge_gcv.predict(filter_(X_diabetes))
assert_array_almost_equal(np.vstack((y_pred, y_pred)).T,
Y_pred, decimal=5)
return ret
def _test_ridge_cv(filter_):
n_samples = X_diabetes.shape[0]
ridge_cv = RidgeCV()
ridge_cv.fit(filter_(X_diabetes), y_diabetes)
ridge_cv.predict(filter_(X_diabetes))
assert_equal(len(ridge_cv.coef_.shape), 1)
assert_equal(type(ridge_cv.intercept_), np.float64)
cv = KFold(n_samples, 5)
ridge_cv.set_params(cv=cv)
ridge_cv.fit(filter_(X_diabetes), y_diabetes)
ridge_cv.predict(filter_(X_diabetes))
assert_equal(len(ridge_cv.coef_.shape), 1)
assert_equal(type(ridge_cv.intercept_), np.float64)
def _test_ridge_diabetes(filter_):
ridge = Ridge(fit_intercept=False)
ridge.fit(filter_(X_diabetes), y_diabetes)
return np.round(ridge.score(filter_(X_diabetes), y_diabetes), 5)
def _test_multi_ridge_diabetes(filter_):
# simulate several responses
Y = np.vstack((y_diabetes, y_diabetes)).T
n_features = X_diabetes.shape[1]
ridge = Ridge(fit_intercept=False)
ridge.fit(filter_(X_diabetes), Y)
assert_equal(ridge.coef_.shape, (2, n_features))
Y_pred = ridge.predict(filter_(X_diabetes))
ridge.fit(filter_(X_diabetes), y_diabetes)
y_pred = ridge.predict(filter_(X_diabetes))
assert_array_almost_equal(np.vstack((y_pred, y_pred)).T,
Y_pred, decimal=3)
def _test_ridge_classifiers(filter_):
n_classes = np.unique(y_iris).shape[0]
n_features = X_iris.shape[1]
for clf in (RidgeClassifier(), RidgeClassifierCV()):
clf.fit(filter_(X_iris), y_iris)
assert_equal(clf.coef_.shape, (n_classes, n_features))
y_pred = clf.predict(filter_(X_iris))
assert_greater(np.mean(y_iris == y_pred), .79)
n_samples = X_iris.shape[0]
cv = KFold(n_samples, 5)
clf = RidgeClassifierCV(cv=cv)
clf.fit(filter_(X_iris), y_iris)
y_pred = clf.predict(filter_(X_iris))
assert_true(np.mean(y_iris == y_pred) >= 0.8)
def _test_tolerance(filter_):
ridge = Ridge(tol=1e-5)
ridge.fit(filter_(X_diabetes), y_diabetes)
score = ridge.score(filter_(X_diabetes), y_diabetes)
ridge2 = Ridge(tol=1e-3)
ridge2.fit(filter_(X_diabetes), y_diabetes)
score2 = ridge2.score(filter_(X_diabetes), y_diabetes)
assert_true(score >= score2)
def test_dense_sparse():
for test_func in (_test_ridge_loo,
_test_ridge_cv,
_test_ridge_diabetes,
_test_multi_ridge_diabetes,
_test_ridge_classifiers,
_test_tolerance):
# test dense matrix
ret_dense = test_func(DENSE_FILTER)
# test sparse matrix
ret_sparse = test_func(SPARSE_FILTER)
# test that the outputs are the same
if ret_dense is not None and ret_sparse is not None:
assert_array_almost_equal(ret_dense, ret_sparse, decimal=3)
def test_ridge_cv_sparse_svd():
X = sp.csr_matrix(X_diabetes)
ridge = RidgeCV(gcv_mode="svd")
assert_raises(TypeError, ridge.fit, X)
def test_ridge_sparse_svd():
X = sp.csc_matrix(rng.rand(100, 10))
y = rng.rand(100)
ridge = Ridge(solver='svd')
assert_raises(TypeError, ridge.fit, X, y)
def test_class_weights():
"""
Test class weights.
"""
X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y = [1, 1, 1, -1, -1]
clf = RidgeClassifier(class_weight=None)
clf.fit(X, y)
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))
# we give a small weights to class 1
clf = RidgeClassifier(class_weight={1: 0.001})
clf.fit(X, y)
# now the hyperplane should rotate clock-wise and
# the prediction on this point should shift
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))
# check if class_weight = 'auto' can handle negative labels.
clf = RidgeClassifier(class_weight='auto')
clf.fit(X, y)
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))
# class_weight = 'auto', and class_weight = None should return
# same values when y has equal number of all labels
X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0], [1.0, 1.0]])
y = [1, 1, -1, -1]
clf = RidgeClassifier(class_weight=None)
clf.fit(X, y)
clfa = RidgeClassifier(class_weight='auto')
clfa.fit(X, y)
assert_equal(len(clfa.classes_), 2)
assert_array_almost_equal(clf.coef_, clfa.coef_)
assert_array_almost_equal(clf.intercept_, clfa.intercept_)
def test_class_weights_cv():
"""
Test class weights for cross validated ridge classifier.
"""
X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y = [1, 1, 1, -1, -1]
clf = RidgeClassifierCV(class_weight=None, alphas=[.01, .1, 1])
clf.fit(X, y)
# we give a small weights to class 1
clf = RidgeClassifierCV(class_weight={1: 0.001}, alphas=[.01, .1, 1, 10])
clf.fit(X, y)
assert_array_equal(clf.predict([[-.2, 2]]), np.array([-1]))
def test_ridgecv_store_cv_values():
"""
Test _RidgeCV's store_cv_values attribute.
"""
rng = rng = np.random.RandomState(42)
n_samples = 8
n_features = 5
x = rng.randn(n_samples, n_features)
alphas = [1e-1, 1e0, 1e1]
n_alphas = len(alphas)
r = RidgeCV(alphas=alphas, store_cv_values=True)
# with len(y.shape) == 1
y = rng.randn(n_samples)
r.fit(x, y)
assert_equal(r.cv_values_.shape, (n_samples, n_alphas))
# with len(y.shape) == 2
n_responses = 3
y = rng.randn(n_samples, n_responses)
r.fit(x, y)
assert_equal(r.cv_values_.shape, (n_samples, n_responses, n_alphas))
def test_ridge_sample_weights_in_feature_space():
"""Check that Cholesky solver in feature space applies sample_weights
correctly.
"""
rng = np.random.RandomState(42)
n_samples_list = [5, 6, 7] * 2
n_features_list = [7, 6, 5] * 2
n_targets_list = [1, 1, 1, 2, 2, 2]
noise = 1.
alpha = 2.
alpha = np.atleast_1d(alpha)
for n_samples, n_features, n_targets in zip(n_samples_list,
n_features_list,
n_targets_list):
X = rng.randn(n_samples, n_features)
beta = rng.randn(n_features, n_targets)
Y = X.dot(beta)
Y_noisy = Y + rng.randn(*Y.shape) * np.sqrt((Y ** 2).sum(0)) * noise
K = X.dot(X.T)
sample_weights = 1. + (rng.randn(n_samples) ** 2) * 10
coef_sample_space = _solve_cholesky_kernel(K, Y_noisy, alpha,
sample_weight=sample_weights)
coef_feature_space = _solve_cholesky(X, Y_noisy, alpha,
sample_weight=sample_weights)
assert_array_almost_equal(X.T.dot(coef_sample_space),
coef_feature_space.T)
def test_raises_value_error_if_sample_weights_greater_than_1d():
"""Sample weights must be either scalar or 1D"""
n_sampless = [2, 3]
n_featuress = [3, 2]
rng = np.random.RandomState(42)
for n_samples, n_features in zip(n_sampless, n_featuress):
X = rng.randn(n_samples, n_features)
y = rng.randn(n_samples)
sample_weights_OK = rng.randn(n_samples) ** 2 + 1
sample_weights_OK_1 = 1.
sample_weights_OK_2 = 2.
sample_weights_not_OK = sample_weights_OK[:, np.newaxis]
sample_weights_not_OK_2 = sample_weights_OK[np.newaxis, :]
ridge = Ridge(alpha=1)
# make sure the "OK" sample weights actually work
ridge.fit(X, y, sample_weights_OK)
ridge.fit(X, y, sample_weights_OK_1)
ridge.fit(X, y, sample_weights_OK_2)
def fit_ridge_not_ok():
ridge.fit(X, y, sample_weights_not_OK)
def fit_ridge_not_ok_2():
ridge.fit(X, y, sample_weights_not_OK_2)
assert_raise_message(ValueError,
"Sample weights must be 1D array or scalar",
fit_ridge_not_ok)
assert_raise_message(ValueError,
"Sample weights must be 1D array or scalar",
fit_ridge_not_ok_2)
def test_sparse_design_with_sample_weights():
"""Sample weights must work with sparse matrices"""
n_sampless = [2, 3]
n_featuress = [3, 2]
rng = np.random.RandomState(42)
sparse_matrix_converters = [sp.coo_matrix,
sp.csr_matrix,
sp.csc_matrix,
sp.lil_matrix,
sp.dok_matrix
]
sparse_ridge = Ridge(alpha=1., fit_intercept=False)
dense_ridge = Ridge(alpha=1., fit_intercept=False)
for n_samples, n_features in zip(n_sampless, n_featuress):
X = rng.randn(n_samples, n_features)
y = rng.randn(n_samples)
sample_weights = rng.randn(n_samples) ** 2 + 1
for sparse_converter in sparse_matrix_converters:
X_sparse = sparse_converter(X)
sparse_ridge.fit(X_sparse, y, sample_weight=sample_weights)
dense_ridge.fit(X, y, sample_weight=sample_weights)
assert_array_almost_equal(sparse_ridge.coef_, dense_ridge.coef_,
decimal=6)
def test_deprecation_warning_dense_cholesky():
"""Tests if DeprecationWarning is raised at instantiation of estimators
and when ridge_regression is called"""
warning_class = DeprecationWarning
warning_message = ("The name 'dense_cholesky' is deprecated."
" Using 'cholesky' instead")
X = np.ones([2, 3])
y = np.ones(2)
func1 = lambda: Ridge(solver='dense_cholesky').fit(X, y)
func2 = lambda: RidgeClassifier(solver='dense_cholesky').fit(X, y)
X = np.ones([3, 2])
y = np.zeros(3)
func3 = lambda: ridge_regression(X, y, alpha=1, solver='dense_cholesky')
for func in [func1, func2, func3]:
assert_warns_message(warning_class, warning_message, func)
def test_raises_value_error_if_solver_not_supported():
"""Tests whether a ValueError is raised if a non-identified solver
is passed to ridge_regression"""
wrong_solver = "This is not a solver (MagritteSolveCV QuantumBitcoin)"
exception = ValueError
message = "Solver %s not understood" % wrong_solver
def func():
X = np.eye(3)
y = np.ones(3)
ridge_regression(X, y, alpha=1., solver=wrong_solver)
assert_raise_message(exception, message, func)
def test_sparse_cg_max_iter():
reg = Ridge(solver="sparse_cg", max_iter=1)
reg.fit(X_diabetes, y_diabetes)
assert_equal(reg.coef_.shape[0], X_diabetes.shape[1])
| bsd-3-clause |
kuza55/keras | keras/datasets/reuters.py | 5 | 3348 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from ..utils.data_utils import get_file
from six.moves import cPickle
from six.moves import zip
import numpy as np
import sys
def load_data(path='reuters.pkl', nb_words=None, skip_top=0,
maxlen=None, test_split=0.2, seed=113,
start_char=1, oov_char=2, index_from=3):
'''
# Arguments
path: where to store the data (in `/.keras/dataset`)
nb_words: max number of words to include. Words are ranked
by how often they occur (in the training set) and only
the most frequent words are kept
skip_top: skip the top N most frequently occuring words
(which may not be informative).
maxlen: truncate sequences after this length.
test_split: Fraction of the dataset to be used as test data.
seed: random seed for sample shuffling.
start_char: The start of a sequence will be marked with this character.
Set to 1 because 0 is usually the padding character.
oov_char: words that were cut out because of the `nb_words`
or `skip_top` limit will be replaced with this character.
index_from: index actual words with this index and higher.
Note that the 'out of vocabulary' character is only used for
words that were present in the training set but are not included
because they're not making the `nb_words` cut here.
Words that were not seen in the trining set but are in the test set
have simply been skipped.
'''
path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/reuters.pkl')
f = open(path, 'rb')
X, labels = cPickle.load(f)
f.close()
np.random.seed(seed)
np.random.shuffle(X)
np.random.seed(seed)
np.random.shuffle(labels)
if start_char is not None:
X = [[start_char] + [w + index_from for w in x] for x in X]
elif index_from:
X = [[w + index_from for w in x] for x in X]
if maxlen:
new_X = []
new_labels = []
for x, y in zip(X, labels):
if len(x) < maxlen:
new_X.append(x)
new_labels.append(y)
X = new_X
labels = new_labels
if not nb_words:
nb_words = max([max(x) for x in X])
# by convention, use 2 as OOV word
# reserve 'index_from' (=3 by default) characters: 0 (padding), 1 (start), 2 (OOV)
if oov_char is not None:
X = [[oov_char if (w >= nb_words or w < skip_top) else w for w in x] for x in X]
else:
nX = []
for x in X:
nx = []
for w in x:
if (w >= nb_words or w < skip_top):
nx.append(w)
nX.append(nx)
X = nX
X_train = X[:int(len(X) * (1 - test_split))]
y_train = labels[:int(len(X) * (1 - test_split))]
X_test = X[int(len(X) * (1 - test_split)):]
y_test = labels[int(len(X) * (1 - test_split)):]
return (X_train, y_train), (X_test, y_test)
def get_word_index(path='reuters_word_index.pkl'):
path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/reuters_word_index.pkl')
f = open(path, 'rb')
if sys.version_info < (3,):
data = cPickle.load(f)
else:
data = cPickle.load(f, encoding='latin1')
f.close()
return data
| mit |
Adai0808/BuildingMachineLearningSystemsWithPython | ch02/stump.py | 24 | 1604 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
from sklearn.datasets import load_iris
data = load_iris()
features = data.data
labels = data.target_names[data.target]
is_setosa = (labels == 'setosa')
features = features[~is_setosa]
labels = labels[~is_setosa]
is_virginica = (labels == 'virginica')
# Initialize to a value that is worse than any possible test
best_acc = -1.0
# Loop over all the features
for fi in range(features.shape[1]):
# Test every possible threshold value for feature fi
thresh = features[:, fi].copy()
# Test them in order
thresh.sort()
for t in thresh:
# Generate predictions using t as a threshold
pred = (features[:, fi] > t)
# Accuracy is the fraction of predictions that match reality
acc = (pred == is_virginica).mean()
# We test whether negating the test is a better threshold:
acc_neg = ((~pred) == is_virginica).mean()
if acc_neg > acc:
acc = acc_neg
negated = True
else:
negated = False
# If this is better than previous best, then this is now the new best:
if acc > best_acc:
best_acc = acc
best_fi = fi
best_t = t
best_is_negated = negated
print('Best threshold is {0} on feature {1} (index {2}), which achieves accuracy of {3:.1%}.'.format(
best_t, data.feature_names[best_fi], best_fi, best_acc))
| mit |
hitszxp/scikit-learn | examples/exercises/plot_cv_diabetes.py | 15 | 2528 | """
===============================================
Cross-validation on diabetes Dataset Exercise
===============================================
A tutorial excercise which uses cross-validation with linear models.
This exercise is used in the :ref:`cv_estimators_tut` part of the
:ref:`model_selection_tut` section of the :ref:`stat_learn_tut_index`.
"""
from __future__ import print_function
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cross_validation, datasets, linear_model
diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
lasso = linear_model.Lasso()
alphas = np.logspace(-4, -.5, 30)
scores = list()
scores_std = list()
for alpha in alphas:
lasso.alpha = alpha
this_scores = cross_validation.cross_val_score(lasso, X, y, n_jobs=1)
scores.append(np.mean(this_scores))
scores_std.append(np.std(this_scores))
plt.figure(figsize=(4, 3))
plt.semilogx(alphas, scores)
# plot error lines showing +/- std. errors of the scores
plt.semilogx(alphas, np.array(scores) + np.array(scores_std) / np.sqrt(len(X)),
'b--')
plt.semilogx(alphas, np.array(scores) - np.array(scores_std) / np.sqrt(len(X)),
'b--')
plt.ylabel('CV score')
plt.xlabel('alpha')
plt.axhline(np.max(scores), linestyle='--', color='.5')
##############################################################################
# Bonus: how much can you trust the selection of alpha?
# To answer this question we use the LassoCV object that sets its alpha
# parameter automatically from the data by internal cross-validation (i.e. it
# performs cross-validation on the training data it receives).
# We use external cross-validation to see how much the automatically obtained
# alphas differ across different cross-validation folds.
lasso_cv = linear_model.LassoCV(alphas=alphas)
k_fold = cross_validation.KFold(len(X), 3)
print("Answer to the bonus question:",
"how much can you trust the selection of alpha?")
print()
print("Alpha parameters maximising the generalization score on different")
print("subsets of the data:")
for k, (train, test) in enumerate(k_fold):
lasso_cv.fit(X[train], y[train])
print("[fold {0}] alpha: {1:.5f}, score: {2:.5f}".
format(k, lasso_cv.alpha_, lasso_cv.score(X[test], y[test])))
print()
print("Answer: Not very much since we obtained different alphas for different")
print("subsets of the data and moreover, the scores for these alphas differ")
print("quite substantially.")
plt.show()
| bsd-3-clause |
ToontownUprising/src | toontown/coghq/LawbotOfficeEntrance_Action00.py | 5 | 8383 | from toontown.coghq.SpecImports import *
import random
GlobalEntities = {1000: {'type': 'levelMgr',
'name': 'LevelMgr',
'comment': '',
'parentEntId': 0,
'cogLevel': 0,
'farPlaneDistance': 1500,
'modelFilename': 'phase_11/models/lawbotHQ/LB_Zone03a',
'wantDoors': 1},
1001: {'type': 'editMgr',
'name': 'EditMgr',
'parentEntId': 0,
'insertEntity': None,
'removeEntity': None,
'requestNewEntity': None,
'requestSave': None},
0: {'type': 'zone',
'name': 'UberZone',
'comment': '',
'parentEntId': 0,
'scale': 1,
'description': '',
'visibility': []},
10000: {'type': 'entrancePoint',
'name': '<unnamed>',
'comment': '',
'parentEntId': 0,
'pos': Point3(0, 6, 0),
'hpr': Vec3(0, 0, 0),
'scale': 1,
'entranceId': 0,
'radius': 15,
'theta': 20},
100000: {'type': 'model',
'name': '<unnamed>',
'comment': '',
'parentEntId': 10004,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': Point3(1, 1, 1),
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_torch_lampB'},
100001: {'type': 'model',
'name': 'copy of <unnamed>',
'comment': '',
'parentEntId': 100002,
'pos': Point3(0, 0, 0),
'hpr': Point3(0, 0, 180),
'scale': Point3(1, 1, 1),
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_torch_lampB'},
100004: {'type': 'model',
'name': '<unnamed>',
'comment': '',
'parentEntId': 100003,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': Point3(1, 1, 1),
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_torch_lampB'},
100006: {'type': 'model',
'name': 'copy of <unnamed>',
'comment': '',
'parentEntId': 100005,
'pos': Point3(0, 0, 0),
'hpr': Point3(0, 0, 180),
'scale': Point3(1, 1, 1),
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_torch_lampB'},
100008: {'type': 'model',
'name': '<unnamed>',
'comment': '',
'parentEntId': 100007,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': Point3(1.5, 1.5, 1.5),
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_paper_twist_stacks'},
100010: {'type': 'model',
'name': 'copy of <unnamed>',
'comment': '',
'parentEntId': 100009,
'pos': Point3(0, 0, 0),
'hpr': Point3(180, 0, 180),
'scale': Point3(1.5, 1.5, 1.5),
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_paper_twist_stacks'},
100012: {'type': 'model',
'name': '<unnamed>',
'comment': '',
'parentEntId': 100011,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': 1,
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_CardBoardBox'},
100013: {'type': 'model',
'name': 'copy of <unnamed>',
'comment': '',
'parentEntId': 100014,
'pos': Point3(0, 0, 0),
'hpr': Point3(0, 0, 180),
'scale': 1,
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_CardBoardBox'},
100016: {'type': 'model',
'name': '<unnamed>',
'comment': '',
'parentEntId': 100015,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': Point3(1.5, 1.5, 1.5),
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_couchA'},
100017: {'type': 'model',
'name': 'copy of <unnamed>',
'comment': '',
'parentEntId': 100018,
'pos': Point3(0, 0, 0),
'hpr': Point3(0, 0, 180),
'scale': Point3(1.5, 1.5, 1.5),
'collisionsOnly': 0,
'flattenType': 'light',
'loadType': 'loadModelCopy',
'modelPath': 'phase_11/models/lawbotHQ/LB_couchA'},
10002: {'type': 'nodepath',
'name': 'props',
'comment': '',
'parentEntId': 0,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': 1},
10004: {'type': 'nodepath',
'name': 'lamp',
'comment': '',
'parentEntId': 10002,
'pos': Point3(-10.1845, 16.3439, 0),
'hpr': Vec3(0, 0, 0),
'scale': Vec3(1, 1, 1)},
100003: {'type': 'nodepath',
'name': 'lamp2',
'comment': '',
'parentEntId': 10002,
'pos': Point3(10.3093, 14.7794, 0),
'hpr': Vec3(0, 0, 0),
'scale': Vec3(1, 1, 1)},
100007: {'type': 'nodepath',
'name': 'paper',
'comment': '',
'parentEntId': 10002,
'pos': Point3(14.3907, 11.4389, 0),
'hpr': Vec3(300.379, 0, 0),
'scale': Vec3(1, 1, 1)},
100011: {'type': 'nodepath',
'name': 'box',
'comment': '',
'parentEntId': 10002,
'pos': Point3(-18.5313, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': Vec3(1, 1, 1)},
100015: {'type': 'nodepath',
'name': 'couch',
'comment': '',
'parentEntId': 10002,
'pos': Point3(14.3585, -9.43671, 0),
'hpr': Vec3(270.699, 0, 0),
'scale': Vec3(1, 1, 1)},
100002: {'type': 'rendering',
'name': '<unnamed>',
'comment': '',
'parentEntId': 10004,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': 1,
'blending': 'Normal',
'colorA': 1.0,
'colorB': 1.0,
'colorG': 1.0,
'colorR': 1.0,
'fogOn': 0,
'renderBin': 'default'},
100005: {'type': 'rendering',
'name': '<unnamed>',
'comment': '',
'parentEntId': 100003,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': 1,
'blending': 'Normal',
'colorA': 1.0,
'colorB': 1.0,
'colorG': 1.0,
'colorR': 1.0,
'fogOn': 0,
'renderBin': 'default'},
100009: {'type': 'rendering',
'name': '<unnamed>',
'comment': '',
'parentEntId': 100007,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': 1,
'blending': 'Normal',
'colorA': 1.0,
'colorB': 1.0,
'colorG': 1.0,
'colorR': 1.0,
'fogOn': 0,
'renderBin': 'default'},
100014: {'type': 'rendering',
'name': '<unnamed>',
'comment': '',
'parentEntId': 100011,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': 1,
'blending': 'Normal',
'colorA': 1.0,
'colorB': 1.0,
'colorG': 1.0,
'colorR': 1.0,
'fogOn': 0,
'renderBin': 'default'},
100018: {'type': 'rendering',
'name': '<unnamed>',
'comment': '',
'parentEntId': 100015,
'pos': Point3(0, 0, 0),
'hpr': Vec3(0, 0, 0),
'scale': 1,
'blending': 'Normal',
'colorA': 1.0,
'colorB': 1.0,
'colorG': 1.0,
'colorR': 1.0,
'fogOn': 0,
'renderBin': 'default'}}
Scenario0 = {}
levelSpec = {'globalEntities': GlobalEntities,
'scenarios': [Scenario0],
'titleString': 'MemTag: LawbotOfficeEntrance_Action00 %s' % random.random()}
| mit |
cmoutard/mne-python | mne/datasets/eegbci/eegbci.py | 23 | 6469 | # Author: Martin Billinger <martin.billinger@tugraz.at>
# License: BSD Style.
import os
from os import path as op
from ..utils import _get_path, _do_path_update
from ...utils import _fetch_file, _url_to_local_path, verbose
EEGMI_URL = 'http://www.physionet.org/physiobank/database/eegmmidb/'
@verbose
def data_path(url, path=None, force_update=False, update_path=None,
verbose=None):
"""Get path to local copy of EEGMMI dataset URL
This is a low-level function useful for getting a local copy of a
remote EEGBCI dataet.
Parameters
----------
url : str
The dataset to use.
path : None | str
Location of where to look for the EEGBCI data storing location.
If None, the environment variable or config parameter
MNE_DATASETS_EEGBCI_PATH is used. If it doesn't exist, the
"mne-python/examples" directory is used. If the EEGBCI dataset
is not found under the given path (e.g., as
"mne-python/examples/MNE-eegbci-data"), the data
will be automatically downloaded to the specified folder.
force_update : bool
Force update of the dataset even if a local copy exists.
update_path : bool | None
If True, set the MNE_DATASETS_EEGBCI_PATH in mne-python
config to the given path. If None, the user is prompted.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
path : list of str
Local path to the given data file. This path is contained inside a list
of length one, for compatibility.
Notes
-----
For example, one could do:
>>> from mne.datasets import eegbci
>>> url = 'http://www.physionet.org/physiobank/database/eegmmidb/'
>>> eegbci.data_path(url, os.getenv('HOME') + '/datasets') # doctest:+SKIP
This would download the given EEGBCI data file to the 'datasets' folder,
and prompt the user to save the 'datasets' path to the mne-python config,
if it isn't there already.
The EEGBCI dataset is documented in the following publication:
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N.,
Wolpaw, J.R. (2004) BCI2000: A General-Purpose Brain-Computer Interface
(BCI) System. IEEE TBME 51(6):1034-1043
The data set is available at PhysioNet:
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG,
Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000) PhysioBank,
PhysioToolkit, and PhysioNet: Components of a New Research Resource for
Complex Physiologic Signals. Circulation 101(23):e215-e220
""" # noqa
key = 'MNE_DATASETS_EEGBCI_PATH'
name = 'EEGBCI'
path = _get_path(path, key, name)
destination = _url_to_local_path(url, op.join(path, 'MNE-eegbci-data'))
destinations = [destination]
# Fetch the file
if not op.isfile(destination) or force_update:
if op.isfile(destination):
os.remove(destination)
if not op.isdir(op.dirname(destination)):
os.makedirs(op.dirname(destination))
_fetch_file(url, destination, print_destination=False)
# Offer to update the path
_do_path_update(path, update_path, key, name)
return destinations
@verbose
def load_data(subject, runs, path=None, force_update=False, update_path=None,
base_url=EEGMI_URL, verbose=None):
"""Get paths to local copy of EEGBCI dataset files
Parameters
----------
subject : int
The subject to use. Can be in the range of 1-109 (inclusive).
runs : int | list of ints
The runs to use. Can be a list or a single number. The runs correspond
to the following tasks:
run | task
----------+-----------------------------------------
1 | Baseline, eyes open
2 | Baseline, eyes closed
3, 7, 11 | Motor execution: left vs right hand
4, 8, 12 | Motor imagery: left vs right hand
5, 9, 13 | Motor execution: hands vs feet
6, 10, 14 | Motor imagery: hands vs feet
path : None | str
Location of where to look for the EEGBCI data storing location.
If None, the environment variable or config parameter
MNE_DATASETS_EEGBCI_PATH is used. If it doesn't exist, the
"mne-python/examples" directory is used. If the EEGBCI dataset
is not found under the given path (e.g., as
"mne-python/examples/MEGSIM"), the data
will be automatically downloaded to the specified folder.
force_update : bool
Force update of the dataset even if a local copy exists.
update_path : bool | None
If True, set the MNE_DATASETS_EEGBCI_PATH in mne-python
config to the given path. If None, the user is prompted.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
paths : list
List of local data paths of the given type.
Notes
-----
For example, one could do:
>>> from mne.datasets import eegbci
>>> eegbci.load_data(1, [4, 10, 14],\
os.getenv('HOME') + '/datasets') # doctest:+SKIP
This would download runs 4, 10, and 14 (hand/foot motor imagery) runs from
subject 1 in the EEGBCI dataset to the 'datasets' folder, and prompt the
user to save the 'datasets' path to the mne-python config, if it isn't
there already.
The EEGBCI dataset is documented in the following publication:
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N.,
Wolpaw, J.R. (2004) BCI2000: A General-Purpose Brain-Computer Interface
(BCI) System. IEEE TBME 51(6):1034-1043
The data set is available at PhysioNet:
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG,
Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000) PhysioBank,
PhysioToolkit, and PhysioNet: Components of a New Research Resource for
Complex Physiologic Signals. Circulation 101(23):e215-e220
"""
if not hasattr(runs, '__iter__'):
runs = [runs]
data_paths = []
for r in runs:
url = '{u}S{s:03d}/S{s:03d}R{r:02d}.edf'.format(u=base_url,
s=subject, r=r)
data_paths.extend(data_path(url, path, force_update, update_path))
return data_paths
| bsd-3-clause |
davidlowryduda/hnrss | summarizer.py | 1 | 1484 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Produces an automated summary of the webpage.
Using the LSA Summarizer from sumy, this attempts to create an automated
summarizer of the content at a given url. There are very many unhandled
edge-cases, so use with some caution.
WARNING
-------
For some pages, this summarizer can hang. So this should be run with
some other memory management tools.
COPYRIGHT NOTICE
---------------
Copyright 2016-2017 David Lowry-Duda
You are free to redistribute and/or modify HNRSS under the
terms of the MIT License. A copy of this license should be made available with
the source.
I'm happy if you find HNRSS useful, but be advised that
it comes WITHOUT ANY WARRANTY; without even the implied warranty
of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
"""
from sumy.parsers.html import HtmlParser
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer as Summarizer
from sumy.nlp.stemmers import Stemmer
from sumy.utils import get_stop_words
LANGUAGE = "english"
stemmer = Stemmer(LANGUAGE)
summarizer = Summarizer(stemmer)
summarizer.stop_words = get_stop_words(LANGUAGE)
def summarize(url, sent_count=10):
"Produces `sent_cout` sentence summaries of `url`."
parser = HtmlParser.from_url(url, Tokenizer(LANGUAGE))
return " ".join([str(sentence) for sentence
in summarizer(parser.document, sent_count)])
| mit |
ivannz/scikit-learn | examples/cluster/plot_optics.py | 11 | 3572 | """
===================================
Demo of OPTICS clustering algorithm
===================================
.. currentmodule:: sklearn
Finds core samples of high density and expands clusters from them.
This example uses data that is generated so that the clusters have
different densities.
The :class:`~cluster.OPTICS` is first used with its Xi cluster detection
method, and then setting specific thresholds on the reachability, which
corresponds to :class:`~cluster.DBSCAN`. We can see that the different
clusters of OPTICS's Xi method can be recovered with different choices of
thresholds in DBSCAN.
"""
# Authors: Shane Grigsby <refuge@rocktalus.com>
# Adrin Jalali <adrin.jalali@gmail.com>
# License: BSD 3 clause
from sklearn.cluster import OPTICS, cluster_optics_dbscan
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
# Generate sample data
np.random.seed(0)
n_points_per_cluster = 250
C1 = [-5, -2] + 0.8 * np.random.randn(n_points_per_cluster, 2)
C2 = [4, -1] + 0.1 * np.random.randn(n_points_per_cluster, 2)
C3 = [1, -2] + 0.2 * np.random.randn(n_points_per_cluster, 2)
C4 = [-2, 3] + 0.3 * np.random.randn(n_points_per_cluster, 2)
C5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)
C6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2, C3, C4, C5, C6))
clust = OPTICS(min_samples=50, xi=0.05, min_cluster_size=0.05)
# Run the fit
clust.fit(X)
labels_050 = cluster_optics_dbscan(
reachability=clust.reachability_,
core_distances=clust.core_distances_,
ordering=clust.ordering_,
eps=0.5,
)
labels_200 = cluster_optics_dbscan(
reachability=clust.reachability_,
core_distances=clust.core_distances_,
ordering=clust.ordering_,
eps=2,
)
space = np.arange(len(X))
reachability = clust.reachability_[clust.ordering_]
labels = clust.labels_[clust.ordering_]
plt.figure(figsize=(10, 7))
G = gridspec.GridSpec(2, 3)
ax1 = plt.subplot(G[0, :])
ax2 = plt.subplot(G[1, 0])
ax3 = plt.subplot(G[1, 1])
ax4 = plt.subplot(G[1, 2])
# Reachability plot
colors = ["g.", "r.", "b.", "y.", "c."]
for klass, color in zip(range(0, 5), colors):
Xk = space[labels == klass]
Rk = reachability[labels == klass]
ax1.plot(Xk, Rk, color, alpha=0.3)
ax1.plot(space[labels == -1], reachability[labels == -1], "k.", alpha=0.3)
ax1.plot(space, np.full_like(space, 2.0, dtype=float), "k-", alpha=0.5)
ax1.plot(space, np.full_like(space, 0.5, dtype=float), "k-.", alpha=0.5)
ax1.set_ylabel("Reachability (epsilon distance)")
ax1.set_title("Reachability Plot")
# OPTICS
colors = ["g.", "r.", "b.", "y.", "c."]
for klass, color in zip(range(0, 5), colors):
Xk = X[clust.labels_ == klass]
ax2.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
ax2.plot(X[clust.labels_ == -1, 0], X[clust.labels_ == -1, 1], "k+", alpha=0.1)
ax2.set_title("Automatic Clustering\nOPTICS")
# DBSCAN at 0.5
colors = ["g", "greenyellow", "olive", "r", "b", "c"]
for klass, color in zip(range(0, 6), colors):
Xk = X[labels_050 == klass]
ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3, marker=".")
ax3.plot(X[labels_050 == -1, 0], X[labels_050 == -1, 1], "k+", alpha=0.1)
ax3.set_title("Clustering at 0.5 epsilon cut\nDBSCAN")
# DBSCAN at 2.
colors = ["g.", "m.", "y.", "c."]
for klass, color in zip(range(0, 4), colors):
Xk = X[labels_200 == klass]
ax4.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
ax4.plot(X[labels_200 == -1, 0], X[labels_200 == -1, 1], "k+", alpha=0.1)
ax4.set_title("Clustering at 2.0 epsilon cut\nDBSCAN")
plt.tight_layout()
plt.show()
| bsd-3-clause |
ivannz/scikit-learn | sklearn/tests/test_calibration.py | 9 | 39439 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import pytest
import numpy as np
from numpy.testing import assert_allclose
from scipy import sparse
from sklearn.base import BaseEstimator, clone
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import LeaveOneOut, train_test_split
from sklearn.utils._testing import (
assert_array_almost_equal,
assert_almost_equal,
assert_array_equal,
)
from sklearn.utils.extmath import softmax
from sklearn.exceptions import NotFittedError
from sklearn.datasets import make_classification, make_blobs, load_iris
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import KFold, cross_val_predict
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import (
RandomForestClassifier,
VotingClassifier,
)
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import LinearSVC
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.isotonic import IsotonicRegression
from sklearn.feature_extraction import DictVectorizer
from sklearn.impute import SimpleImputer
from sklearn.metrics import brier_score_loss
from sklearn.calibration import (
_CalibratedClassifier,
_SigmoidCalibration,
_sigmoid_calibration,
CalibratedClassifierCV,
CalibrationDisplay,
calibration_curve,
)
from sklearn.utils._mocking import CheckingClassifier
from sklearn.utils._testing import _convert_container
N_SAMPLES = 200
@pytest.fixture(scope="module")
def data():
X, y = make_classification(n_samples=N_SAMPLES, n_features=6, random_state=42)
return X, y
@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
@pytest.mark.parametrize("ensemble", [True, False])
def test_calibration(data, method, ensemble):
# Test calibration objects with isotonic and sigmoid
n_samples = N_SAMPLES // 2
X, y = data
sample_weight = np.random.RandomState(seed=42).uniform(size=y.size)
X -= X.min() # MultinomialNB only allows positive X
# split train and test
X_train, y_train, sw_train = X[:n_samples], y[:n_samples], sample_weight[:n_samples]
X_test, y_test = X[n_samples:], y[n_samples:]
# Naive-Bayes
clf = MultinomialNB(force_alpha=True).fit(X_train, y_train, sample_weight=sw_train)
prob_pos_clf = clf.predict_proba(X_test)[:, 1]
cal_clf = CalibratedClassifierCV(clf, cv=y.size + 1, ensemble=ensemble)
with pytest.raises(ValueError):
cal_clf.fit(X, y)
# Naive Bayes with calibration
for this_X_train, this_X_test in [
(X_train, X_test),
(sparse.csr_matrix(X_train), sparse.csr_matrix(X_test)),
]:
cal_clf = CalibratedClassifierCV(clf, method=method, cv=5, ensemble=ensemble)
# Note that this fit overwrites the fit on the entire training
# set
cal_clf.fit(this_X_train, y_train, sample_weight=sw_train)
prob_pos_cal_clf = cal_clf.predict_proba(this_X_test)[:, 1]
# Check that brier score has improved after calibration
assert brier_score_loss(y_test, prob_pos_clf) > brier_score_loss(
y_test, prob_pos_cal_clf
)
# Check invariance against relabeling [0, 1] -> [1, 2]
cal_clf.fit(this_X_train, y_train + 1, sample_weight=sw_train)
prob_pos_cal_clf_relabeled = cal_clf.predict_proba(this_X_test)[:, 1]
assert_array_almost_equal(prob_pos_cal_clf, prob_pos_cal_clf_relabeled)
# Check invariance against relabeling [0, 1] -> [-1, 1]
cal_clf.fit(this_X_train, 2 * y_train - 1, sample_weight=sw_train)
prob_pos_cal_clf_relabeled = cal_clf.predict_proba(this_X_test)[:, 1]
assert_array_almost_equal(prob_pos_cal_clf, prob_pos_cal_clf_relabeled)
# Check invariance against relabeling [0, 1] -> [1, 0]
cal_clf.fit(this_X_train, (y_train + 1) % 2, sample_weight=sw_train)
prob_pos_cal_clf_relabeled = cal_clf.predict_proba(this_X_test)[:, 1]
if method == "sigmoid":
assert_array_almost_equal(prob_pos_cal_clf, 1 - prob_pos_cal_clf_relabeled)
else:
# Isotonic calibration is not invariant against relabeling
# but should improve in both cases
assert brier_score_loss(y_test, prob_pos_clf) > brier_score_loss(
(y_test + 1) % 2, prob_pos_cal_clf_relabeled
)
def test_calibration_default_estimator(data):
# Check estimator default is LinearSVC
X, y = data
calib_clf = CalibratedClassifierCV(cv=2)
calib_clf.fit(X, y)
base_est = calib_clf.calibrated_classifiers_[0].estimator
assert isinstance(base_est, LinearSVC)
@pytest.mark.parametrize("ensemble", [True, False])
def test_calibration_cv_splitter(data, ensemble):
# Check when `cv` is a CV splitter
X, y = data
splits = 5
kfold = KFold(n_splits=splits)
calib_clf = CalibratedClassifierCV(cv=kfold, ensemble=ensemble)
assert isinstance(calib_clf.cv, KFold)
assert calib_clf.cv.n_splits == splits
calib_clf.fit(X, y)
expected_n_clf = splits if ensemble else 1
assert len(calib_clf.calibrated_classifiers_) == expected_n_clf
@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
@pytest.mark.parametrize("ensemble", [True, False])
def test_sample_weight(data, method, ensemble):
n_samples = N_SAMPLES // 2
X, y = data
sample_weight = np.random.RandomState(seed=42).uniform(size=len(y))
X_train, y_train, sw_train = X[:n_samples], y[:n_samples], sample_weight[:n_samples]
X_test = X[n_samples:]
estimator = LinearSVC(random_state=42)
calibrated_clf = CalibratedClassifierCV(estimator, method=method, ensemble=ensemble)
calibrated_clf.fit(X_train, y_train, sample_weight=sw_train)
probs_with_sw = calibrated_clf.predict_proba(X_test)
# As the weights are used for the calibration, they should still yield
# different predictions
calibrated_clf.fit(X_train, y_train)
probs_without_sw = calibrated_clf.predict_proba(X_test)
diff = np.linalg.norm(probs_with_sw - probs_without_sw)
assert diff > 0.1
@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
@pytest.mark.parametrize("ensemble", [True, False])
def test_parallel_execution(data, method, ensemble):
"""Test parallel calibration"""
X, y = data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
estimator = LinearSVC(random_state=42)
cal_clf_parallel = CalibratedClassifierCV(
estimator, method=method, n_jobs=2, ensemble=ensemble
)
cal_clf_parallel.fit(X_train, y_train)
probs_parallel = cal_clf_parallel.predict_proba(X_test)
cal_clf_sequential = CalibratedClassifierCV(
estimator, method=method, n_jobs=1, ensemble=ensemble
)
cal_clf_sequential.fit(X_train, y_train)
probs_sequential = cal_clf_sequential.predict_proba(X_test)
assert_allclose(probs_parallel, probs_sequential)
@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
@pytest.mark.parametrize("ensemble", [True, False])
# increase the number of RNG seeds to assess the statistical stability of this
# test:
@pytest.mark.parametrize("seed", range(2))
def test_calibration_multiclass(method, ensemble, seed):
def multiclass_brier(y_true, proba_pred, n_classes):
Y_onehot = np.eye(n_classes)[y_true]
return np.sum((Y_onehot - proba_pred) ** 2) / Y_onehot.shape[0]
# Test calibration for multiclass with classifier that implements
# only decision function.
clf = LinearSVC(random_state=7)
X, y = make_blobs(
n_samples=500, n_features=100, random_state=seed, centers=10, cluster_std=15.0
)
# Use an unbalanced dataset by collapsing 8 clusters into one class
# to make the naive calibration based on a softmax more unlikely
# to work.
y[y > 2] = 2
n_classes = np.unique(y).shape[0]
X_train, y_train = X[::2], y[::2]
X_test, y_test = X[1::2], y[1::2]
clf.fit(X_train, y_train)
cal_clf = CalibratedClassifierCV(clf, method=method, cv=5, ensemble=ensemble)
cal_clf.fit(X_train, y_train)
probas = cal_clf.predict_proba(X_test)
# Check probabilities sum to 1
assert_allclose(np.sum(probas, axis=1), np.ones(len(X_test)))
# Check that the dataset is not too trivial, otherwise it's hard
# to get interesting calibration data during the internal
# cross-validation loop.
assert 0.65 < clf.score(X_test, y_test) < 0.95
# Check that the accuracy of the calibrated model is never degraded
# too much compared to the original classifier.
assert cal_clf.score(X_test, y_test) > 0.95 * clf.score(X_test, y_test)
# Check that Brier loss of calibrated classifier is smaller than
# loss obtained by naively turning OvR decision function to
# probabilities via a softmax
uncalibrated_brier = multiclass_brier(
y_test, softmax(clf.decision_function(X_test)), n_classes=n_classes
)
calibrated_brier = multiclass_brier(y_test, probas, n_classes=n_classes)
assert calibrated_brier < 1.1 * uncalibrated_brier
# Test that calibration of a multiclass classifier decreases log-loss
# for RandomForestClassifier
clf = RandomForestClassifier(n_estimators=30, random_state=42)
clf.fit(X_train, y_train)
clf_probs = clf.predict_proba(X_test)
uncalibrated_brier = multiclass_brier(y_test, clf_probs, n_classes=n_classes)
cal_clf = CalibratedClassifierCV(clf, method=method, cv=5, ensemble=ensemble)
cal_clf.fit(X_train, y_train)
cal_clf_probs = cal_clf.predict_proba(X_test)
calibrated_brier = multiclass_brier(y_test, cal_clf_probs, n_classes=n_classes)
assert calibrated_brier < 1.1 * uncalibrated_brier
def test_calibration_zero_probability():
# Test an edge case where _CalibratedClassifier avoids numerical errors
# in the multiclass normalization step if all the calibrators output
# are zero all at once for a given sample and instead fallback to uniform
# probabilities.
class ZeroCalibrator:
# This function is called from _CalibratedClassifier.predict_proba.
def predict(self, X):
return np.zeros(X.shape[0])
X, y = make_blobs(
n_samples=50, n_features=10, random_state=7, centers=10, cluster_std=15.0
)
clf = DummyClassifier().fit(X, y)
calibrator = ZeroCalibrator()
cal_clf = _CalibratedClassifier(
estimator=clf, calibrators=[calibrator], classes=clf.classes_
)
probas = cal_clf.predict_proba(X)
# Check that all probabilities are uniformly 1. / clf.n_classes_
assert_allclose(probas, 1.0 / clf.n_classes_)
def test_calibration_prefit():
"""Test calibration for prefitted classifiers"""
n_samples = 50
X, y = make_classification(n_samples=3 * n_samples, n_features=6, random_state=42)
sample_weight = np.random.RandomState(seed=42).uniform(size=y.size)
X -= X.min() # MultinomialNB only allows positive X
# split train and test
X_train, y_train, sw_train = X[:n_samples], y[:n_samples], sample_weight[:n_samples]
X_calib, y_calib, sw_calib = (
X[n_samples : 2 * n_samples],
y[n_samples : 2 * n_samples],
sample_weight[n_samples : 2 * n_samples],
)
X_test, y_test = X[2 * n_samples :], y[2 * n_samples :]
# Naive-Bayes
clf = MultinomialNB(force_alpha=True)
# Check error if clf not prefit
unfit_clf = CalibratedClassifierCV(clf, cv="prefit")
with pytest.raises(NotFittedError):
unfit_clf.fit(X_calib, y_calib)
clf.fit(X_train, y_train, sw_train)
prob_pos_clf = clf.predict_proba(X_test)[:, 1]
# Naive Bayes with calibration
for this_X_calib, this_X_test in [
(X_calib, X_test),
(sparse.csr_matrix(X_calib), sparse.csr_matrix(X_test)),
]:
for method in ["isotonic", "sigmoid"]:
cal_clf = CalibratedClassifierCV(clf, method=method, cv="prefit")
for sw in [sw_calib, None]:
cal_clf.fit(this_X_calib, y_calib, sample_weight=sw)
y_prob = cal_clf.predict_proba(this_X_test)
y_pred = cal_clf.predict(this_X_test)
prob_pos_cal_clf = y_prob[:, 1]
assert_array_equal(y_pred, np.array([0, 1])[np.argmax(y_prob, axis=1)])
assert brier_score_loss(y_test, prob_pos_clf) > brier_score_loss(
y_test, prob_pos_cal_clf
)
@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
def test_calibration_ensemble_false(data, method):
# Test that `ensemble=False` is the same as using predictions from
# `cross_val_predict` to train calibrator.
X, y = data
clf = LinearSVC(random_state=7)
cal_clf = CalibratedClassifierCV(clf, method=method, cv=3, ensemble=False)
cal_clf.fit(X, y)
cal_probas = cal_clf.predict_proba(X)
# Get probas manually
unbiased_preds = cross_val_predict(clf, X, y, cv=3, method="decision_function")
if method == "isotonic":
calibrator = IsotonicRegression(out_of_bounds="clip")
else:
calibrator = _SigmoidCalibration()
calibrator.fit(unbiased_preds, y)
# Use `clf` fit on all data
clf.fit(X, y)
clf_df = clf.decision_function(X)
manual_probas = calibrator.predict(clf_df)
assert_allclose(cal_probas[:, 1], manual_probas)
def test_sigmoid_calibration():
"""Test calibration values with Platt sigmoid model"""
exF = np.array([5, -4, 1.0])
exY = np.array([1, -1, -1])
# computed from my python port of the C++ code in LibSVM
AB_lin_libsvm = np.array([-0.20261354391187855, 0.65236314980010512])
assert_array_almost_equal(AB_lin_libsvm, _sigmoid_calibration(exF, exY), 3)
lin_prob = 1.0 / (1.0 + np.exp(AB_lin_libsvm[0] * exF + AB_lin_libsvm[1]))
sk_prob = _SigmoidCalibration().fit(exF, exY).predict(exF)
assert_array_almost_equal(lin_prob, sk_prob, 6)
# check that _SigmoidCalibration().fit only accepts 1d array or 2d column
# arrays
with pytest.raises(ValueError):
_SigmoidCalibration().fit(np.vstack((exF, exF)), exY)
def test_calibration_curve():
"""Check calibration_curve function"""
y_true = np.array([0, 0, 0, 1, 1, 1])
y_pred = np.array([0.0, 0.1, 0.2, 0.8, 0.9, 1.0])
prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=2)
assert len(prob_true) == len(prob_pred)
assert len(prob_true) == 2
assert_almost_equal(prob_true, [0, 1])
assert_almost_equal(prob_pred, [0.1, 0.9])
# Probabilities outside [0, 1] should not be accepted at all.
with pytest.raises(ValueError):
calibration_curve([1], [-0.1])
# test that quantiles work as expected
y_true2 = np.array([0, 0, 0, 0, 1, 1])
y_pred2 = np.array([0.0, 0.1, 0.2, 0.5, 0.9, 1.0])
prob_true_quantile, prob_pred_quantile = calibration_curve(
y_true2, y_pred2, n_bins=2, strategy="quantile"
)
assert len(prob_true_quantile) == len(prob_pred_quantile)
assert len(prob_true_quantile) == 2
assert_almost_equal(prob_true_quantile, [0, 2 / 3])
assert_almost_equal(prob_pred_quantile, [0.1, 0.8])
# Check that error is raised when invalid strategy is selected
with pytest.raises(ValueError):
calibration_curve(y_true2, y_pred2, strategy="percentile")
# TODO(1.3): Remove this test.
def test_calibration_curve_with_unnormalized_proba():
"""Tests the `normalize` parameter of `calibration_curve`"""
y_true = np.array([0, 0, 0, 1, 1, 1])
y_pred = np.array([0.0, 0.1, 0.2, 0.8, 0.9, 1.0])
# Ensure `normalize` == False raises a FutureWarning.
with pytest.warns(FutureWarning):
calibration_curve(y_true, y_pred, n_bins=2, normalize=False)
# Ensure `normalize` == True raises a FutureWarning and behaves as expected.
with pytest.warns(FutureWarning):
prob_true_unnormalized, prob_pred_unnormalized = calibration_curve(
y_true, y_pred * 2, n_bins=2, normalize=True
)
prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=2)
assert_almost_equal(prob_true, prob_true_unnormalized)
assert_almost_equal(prob_pred, prob_pred_unnormalized)
@pytest.mark.parametrize("ensemble", [True, False])
def test_calibration_nan_imputer(ensemble):
"""Test that calibration can accept nan"""
X, y = make_classification(
n_samples=10, n_features=2, n_informative=2, n_redundant=0, random_state=42
)
X[0, 0] = np.nan
clf = Pipeline(
[("imputer", SimpleImputer()), ("rf", RandomForestClassifier(n_estimators=1))]
)
clf_c = CalibratedClassifierCV(clf, cv=2, method="isotonic", ensemble=ensemble)
clf_c.fit(X, y)
clf_c.predict(X)
@pytest.mark.parametrize("ensemble", [True, False])
def test_calibration_prob_sum(ensemble):
# Test that sum of probabilities is 1. A non-regression test for
# issue #7796
num_classes = 2
X, y = make_classification(n_samples=10, n_features=5, n_classes=num_classes)
clf = LinearSVC(C=1.0, random_state=7)
clf_prob = CalibratedClassifierCV(
clf, method="sigmoid", cv=LeaveOneOut(), ensemble=ensemble
)
clf_prob.fit(X, y)
probs = clf_prob.predict_proba(X)
assert_array_almost_equal(probs.sum(axis=1), np.ones(probs.shape[0]))
@pytest.mark.parametrize("ensemble", [True, False])
def test_calibration_less_classes(ensemble):
# Test to check calibration works fine when train set in a test-train
# split does not contain all classes
# Since this test uses LOO, at each iteration train set will not contain a
# class label
X = np.random.randn(10, 5)
y = np.arange(10)
clf = LinearSVC(C=1.0, random_state=7)
cal_clf = CalibratedClassifierCV(
clf, method="sigmoid", cv=LeaveOneOut(), ensemble=ensemble
)
cal_clf.fit(X, y)
for i, calibrated_classifier in enumerate(cal_clf.calibrated_classifiers_):
proba = calibrated_classifier.predict_proba(X)
if ensemble:
# Check that the unobserved class has proba=0
assert_array_equal(proba[:, i], np.zeros(len(y)))
# Check for all other classes proba>0
assert np.all(proba[:, :i] > 0)
assert np.all(proba[:, i + 1 :] > 0)
else:
# Check `proba` are all 1/n_classes
assert np.allclose(proba, 1 / proba.shape[0])
@pytest.mark.parametrize(
"X",
[
np.random.RandomState(42).randn(15, 5, 2),
np.random.RandomState(42).randn(15, 5, 2, 6),
],
)
def test_calibration_accepts_ndarray(X):
"""Test that calibration accepts n-dimensional arrays as input"""
y = [1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0]
class MockTensorClassifier(BaseEstimator):
"""A toy estimator that accepts tensor inputs"""
def fit(self, X, y):
self.classes_ = np.unique(y)
return self
def decision_function(self, X):
# toy decision function that just needs to have the right shape:
return X.reshape(X.shape[0], -1).sum(axis=1)
calibrated_clf = CalibratedClassifierCV(MockTensorClassifier())
# we should be able to fit this classifier with no error
calibrated_clf.fit(X, y)
@pytest.fixture
def dict_data():
dict_data = [
{"state": "NY", "age": "adult"},
{"state": "TX", "age": "adult"},
{"state": "VT", "age": "child"},
]
text_labels = [1, 0, 1]
return dict_data, text_labels
@pytest.fixture
def dict_data_pipeline(dict_data):
X, y = dict_data
pipeline_prefit = Pipeline(
[("vectorizer", DictVectorizer()), ("clf", RandomForestClassifier())]
)
return pipeline_prefit.fit(X, y)
def test_calibration_dict_pipeline(dict_data, dict_data_pipeline):
"""Test that calibration works in prefit pipeline with transformer
`X` is not array-like, sparse matrix or dataframe at the start.
See https://github.com/scikit-learn/scikit-learn/issues/8710
Also test it can predict without running into validation errors.
See https://github.com/scikit-learn/scikit-learn/issues/19637
"""
X, y = dict_data
clf = dict_data_pipeline
calib_clf = CalibratedClassifierCV(clf, cv="prefit")
calib_clf.fit(X, y)
# Check attributes are obtained from fitted estimator
assert_array_equal(calib_clf.classes_, clf.classes_)
# Neither the pipeline nor the calibration meta-estimator
# expose the n_features_in_ check on this kind of data.
assert not hasattr(clf, "n_features_in_")
assert not hasattr(calib_clf, "n_features_in_")
# Ensure that no error is thrown with predict and predict_proba
calib_clf.predict(X)
calib_clf.predict_proba(X)
@pytest.mark.parametrize(
"clf, cv",
[
pytest.param(LinearSVC(C=1), 2),
pytest.param(LinearSVC(C=1), "prefit"),
],
)
def test_calibration_attributes(clf, cv):
# Check that `n_features_in_` and `classes_` attributes created properly
X, y = make_classification(n_samples=10, n_features=5, n_classes=2, random_state=7)
if cv == "prefit":
clf = clf.fit(X, y)
calib_clf = CalibratedClassifierCV(clf, cv=cv)
calib_clf.fit(X, y)
if cv == "prefit":
assert_array_equal(calib_clf.classes_, clf.classes_)
assert calib_clf.n_features_in_ == clf.n_features_in_
else:
classes = LabelEncoder().fit(y).classes_
assert_array_equal(calib_clf.classes_, classes)
assert calib_clf.n_features_in_ == X.shape[1]
def test_calibration_inconsistent_prefit_n_features_in():
# Check that `n_features_in_` from prefit base estimator
# is consistent with training set
X, y = make_classification(n_samples=10, n_features=5, n_classes=2, random_state=7)
clf = LinearSVC(C=1).fit(X, y)
calib_clf = CalibratedClassifierCV(clf, cv="prefit")
msg = "X has 3 features, but LinearSVC is expecting 5 features as input."
with pytest.raises(ValueError, match=msg):
calib_clf.fit(X[:, :3], y)
def test_calibration_votingclassifier():
# Check that `CalibratedClassifier` works with `VotingClassifier`.
# The method `predict_proba` from `VotingClassifier` is dynamically
# defined via a property that only works when voting="soft".
X, y = make_classification(n_samples=10, n_features=5, n_classes=2, random_state=7)
vote = VotingClassifier(
estimators=[("lr" + str(i), LogisticRegression()) for i in range(3)],
voting="soft",
)
vote.fit(X, y)
calib_clf = CalibratedClassifierCV(estimator=vote, cv="prefit")
# smoke test: should not raise an error
calib_clf.fit(X, y)
@pytest.fixture(scope="module")
def iris_data():
return load_iris(return_X_y=True)
@pytest.fixture(scope="module")
def iris_data_binary(iris_data):
X, y = iris_data
return X[y < 2], y[y < 2]
def test_calibration_display_validation(pyplot, iris_data, iris_data_binary):
X, y = iris_data
X_binary, y_binary = iris_data_binary
reg = LinearRegression().fit(X, y)
msg = "'estimator' should be a fitted classifier"
with pytest.raises(ValueError, match=msg):
CalibrationDisplay.from_estimator(reg, X, y)
clf = LinearSVC().fit(X, y)
msg = "response method predict_proba is not defined in"
with pytest.raises(ValueError, match=msg):
CalibrationDisplay.from_estimator(clf, X, y)
clf = LogisticRegression()
with pytest.raises(NotFittedError):
CalibrationDisplay.from_estimator(clf, X, y)
@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
def test_calibration_display_non_binary(pyplot, iris_data, constructor_name):
X, y = iris_data
clf = DecisionTreeClassifier()
clf.fit(X, y)
y_prob = clf.predict_proba(X)
if constructor_name == "from_estimator":
msg = "to be a binary classifier, but got"
with pytest.raises(ValueError, match=msg):
CalibrationDisplay.from_estimator(clf, X, y)
else:
msg = "y should be a 1d array, got an array of shape"
with pytest.raises(ValueError, match=msg):
CalibrationDisplay.from_predictions(y, y_prob)
@pytest.mark.parametrize("n_bins", [5, 10])
@pytest.mark.parametrize("strategy", ["uniform", "quantile"])
def test_calibration_display_compute(pyplot, iris_data_binary, n_bins, strategy):
# Ensure `CalibrationDisplay.from_predictions` and `calibration_curve`
# compute the same results. Also checks attributes of the
# CalibrationDisplay object.
X, y = iris_data_binary
lr = LogisticRegression().fit(X, y)
viz = CalibrationDisplay.from_estimator(
lr, X, y, n_bins=n_bins, strategy=strategy, alpha=0.8
)
y_prob = lr.predict_proba(X)[:, 1]
prob_true, prob_pred = calibration_curve(
y, y_prob, n_bins=n_bins, strategy=strategy
)
assert_allclose(viz.prob_true, prob_true)
assert_allclose(viz.prob_pred, prob_pred)
assert_allclose(viz.y_prob, y_prob)
assert viz.estimator_name == "LogisticRegression"
# cannot fail thanks to pyplot fixture
import matplotlib as mpl # noqa
assert isinstance(viz.line_, mpl.lines.Line2D)
assert viz.line_.get_alpha() == 0.8
assert isinstance(viz.ax_, mpl.axes.Axes)
assert isinstance(viz.figure_, mpl.figure.Figure)
assert viz.ax_.get_xlabel() == "Mean predicted probability (Positive class: 1)"
assert viz.ax_.get_ylabel() == "Fraction of positives (Positive class: 1)"
expected_legend_labels = ["LogisticRegression", "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels
def test_plot_calibration_curve_pipeline(pyplot, iris_data_binary):
# Ensure pipelines are supported by CalibrationDisplay.from_estimator
X, y = iris_data_binary
clf = make_pipeline(StandardScaler(), LogisticRegression())
clf.fit(X, y)
viz = CalibrationDisplay.from_estimator(clf, X, y)
expected_legend_labels = [viz.estimator_name, "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels
@pytest.mark.parametrize(
"name, expected_label", [(None, "_line1"), ("my_est", "my_est")]
)
def test_calibration_display_default_labels(pyplot, name, expected_label):
prob_true = np.array([0, 1, 1, 0])
prob_pred = np.array([0.2, 0.8, 0.8, 0.4])
y_prob = np.array([])
viz = CalibrationDisplay(prob_true, prob_pred, y_prob, estimator_name=name)
viz.plot()
expected_legend_labels = [] if name is None else [name]
expected_legend_labels.append("Perfectly calibrated")
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels
def test_calibration_display_label_class_plot(pyplot):
# Checks that when instantiating `CalibrationDisplay` class then calling
# `plot`, `self.estimator_name` is the one given in `plot`
prob_true = np.array([0, 1, 1, 0])
prob_pred = np.array([0.2, 0.8, 0.8, 0.4])
y_prob = np.array([])
name = "name one"
viz = CalibrationDisplay(prob_true, prob_pred, y_prob, estimator_name=name)
assert viz.estimator_name == name
name = "name two"
viz.plot(name=name)
expected_legend_labels = [name, "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels
@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
def test_calibration_display_name_multiple_calls(
constructor_name, pyplot, iris_data_binary
):
# Check that the `name` used when calling
# `CalibrationDisplay.from_predictions` or
# `CalibrationDisplay.from_estimator` is used when multiple
# `CalibrationDisplay.viz.plot()` calls are made.
X, y = iris_data_binary
clf_name = "my hand-crafted name"
clf = LogisticRegression().fit(X, y)
y_prob = clf.predict_proba(X)[:, 1]
constructor = getattr(CalibrationDisplay, constructor_name)
params = (clf, X, y) if constructor_name == "from_estimator" else (y, y_prob)
viz = constructor(*params, name=clf_name)
assert viz.estimator_name == clf_name
pyplot.close("all")
viz.plot()
expected_legend_labels = [clf_name, "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels
pyplot.close("all")
clf_name = "another_name"
viz.plot(name=clf_name)
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels
def test_calibration_display_ref_line(pyplot, iris_data_binary):
# Check that `ref_line` only appears once
X, y = iris_data_binary
lr = LogisticRegression().fit(X, y)
dt = DecisionTreeClassifier().fit(X, y)
viz = CalibrationDisplay.from_estimator(lr, X, y)
viz2 = CalibrationDisplay.from_estimator(dt, X, y, ax=viz.ax_)
labels = viz2.ax_.get_legend_handles_labels()[1]
assert labels.count("Perfectly calibrated") == 1
@pytest.mark.parametrize("dtype_y_str", [str, object])
def test_calibration_curve_pos_label_error_str(dtype_y_str):
"""Check error message when a `pos_label` is not specified with `str` targets."""
rng = np.random.RandomState(42)
y1 = np.array(["spam"] * 3 + ["eggs"] * 2, dtype=dtype_y_str)
y2 = rng.randint(0, 2, size=y1.size)
err_msg = (
"y_true takes value in {'eggs', 'spam'} and pos_label is not "
"specified: either make y_true take value in {0, 1} or {-1, 1} or "
"pass pos_label explicitly"
)
with pytest.raises(ValueError, match=err_msg):
calibration_curve(y1, y2)
@pytest.mark.parametrize("dtype_y_str", [str, object])
def test_calibration_curve_pos_label(dtype_y_str):
"""Check the behaviour when passing explicitly `pos_label`."""
y_true = np.array([0, 0, 0, 1, 1, 1, 1, 1, 1])
classes = np.array(["spam", "egg"], dtype=dtype_y_str)
y_true_str = classes[y_true]
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.0])
# default case
prob_true, _ = calibration_curve(y_true, y_pred, n_bins=4)
assert_allclose(prob_true, [0, 0.5, 1, 1])
# if `y_true` contains `str`, then `pos_label` is required
prob_true, _ = calibration_curve(y_true_str, y_pred, n_bins=4, pos_label="egg")
assert_allclose(prob_true, [0, 0.5, 1, 1])
prob_true, _ = calibration_curve(y_true, 1 - y_pred, n_bins=4, pos_label=0)
assert_allclose(prob_true, [0, 0, 0.5, 1])
prob_true, _ = calibration_curve(y_true_str, 1 - y_pred, n_bins=4, pos_label="spam")
assert_allclose(prob_true, [0, 0, 0.5, 1])
@pytest.mark.parametrize("pos_label, expected_pos_label", [(None, 1), (0, 0), (1, 1)])
def test_calibration_display_pos_label(
pyplot, iris_data_binary, pos_label, expected_pos_label
):
"""Check the behaviour of `pos_label` in the `CalibrationDisplay`."""
X, y = iris_data_binary
lr = LogisticRegression().fit(X, y)
viz = CalibrationDisplay.from_estimator(lr, X, y, pos_label=pos_label)
y_prob = lr.predict_proba(X)[:, expected_pos_label]
prob_true, prob_pred = calibration_curve(y, y_prob, pos_label=pos_label)
assert_allclose(viz.prob_true, prob_true)
assert_allclose(viz.prob_pred, prob_pred)
assert_allclose(viz.y_prob, y_prob)
assert (
viz.ax_.get_xlabel()
== f"Mean predicted probability (Positive class: {expected_pos_label})"
)
assert (
viz.ax_.get_ylabel()
== f"Fraction of positives (Positive class: {expected_pos_label})"
)
expected_legend_labels = [lr.__class__.__name__, "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels
@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
@pytest.mark.parametrize("ensemble", [True, False])
def test_calibrated_classifier_cv_double_sample_weights_equivalence(method, ensemble):
"""Check that passing repeating twice the dataset `X` is equivalent to
passing a `sample_weight` with a factor 2."""
X, y = load_iris(return_X_y=True)
# Scale the data to avoid any convergence issue
X = StandardScaler().fit_transform(X)
# Only use 2 classes
X, y = X[:100], y[:100]
sample_weight = np.ones_like(y) * 2
# Interlace the data such that a 2-fold cross-validation will be equivalent
# to using the original dataset with a sample weights of 2
X_twice = np.zeros((X.shape[0] * 2, X.shape[1]), dtype=X.dtype)
X_twice[::2, :] = X
X_twice[1::2, :] = X
y_twice = np.zeros(y.shape[0] * 2, dtype=y.dtype)
y_twice[::2] = y
y_twice[1::2] = y
estimator = LogisticRegression()
calibrated_clf_without_weights = CalibratedClassifierCV(
estimator,
method=method,
ensemble=ensemble,
cv=2,
)
calibrated_clf_with_weights = clone(calibrated_clf_without_weights)
calibrated_clf_with_weights.fit(X, y, sample_weight=sample_weight)
calibrated_clf_without_weights.fit(X_twice, y_twice)
# Check that the underlying fitted estimators have the same coefficients
for est_with_weights, est_without_weights in zip(
calibrated_clf_with_weights.calibrated_classifiers_,
calibrated_clf_without_weights.calibrated_classifiers_,
):
assert_allclose(
est_with_weights.estimator.coef_,
est_without_weights.estimator.coef_,
)
# Check that the predictions are the same
y_pred_with_weights = calibrated_clf_with_weights.predict_proba(X)
y_pred_without_weights = calibrated_clf_without_weights.predict_proba(X)
assert_allclose(y_pred_with_weights, y_pred_without_weights)
@pytest.mark.parametrize("fit_params_type", ["list", "array"])
def test_calibration_with_fit_params(fit_params_type, data):
"""Tests that fit_params are passed to the underlying base estimator.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/12384
"""
X, y = data
fit_params = {
"a": _convert_container(y, fit_params_type),
"b": _convert_container(y, fit_params_type),
}
clf = CheckingClassifier(expected_fit_params=["a", "b"])
pc_clf = CalibratedClassifierCV(clf)
pc_clf.fit(X, y, **fit_params)
@pytest.mark.parametrize(
"sample_weight",
[
[1.0] * N_SAMPLES,
np.ones(N_SAMPLES),
],
)
def test_calibration_with_sample_weight_base_estimator(sample_weight, data):
"""Tests that sample_weight is passed to the underlying base
estimator.
"""
X, y = data
clf = CheckingClassifier(expected_sample_weight=True)
pc_clf = CalibratedClassifierCV(clf)
pc_clf.fit(X, y, sample_weight=sample_weight)
def test_calibration_without_sample_weight_base_estimator(data):
"""Check that even if the estimator doesn't support
sample_weight, fitting with sample_weight still works.
There should be a warning, since the sample_weight is not passed
on to the estimator.
"""
X, y = data
sample_weight = np.ones_like(y)
class ClfWithoutSampleWeight(CheckingClassifier):
def fit(self, X, y, **fit_params):
assert "sample_weight" not in fit_params
return super().fit(X, y, **fit_params)
clf = ClfWithoutSampleWeight()
pc_clf = CalibratedClassifierCV(clf)
with pytest.warns(UserWarning):
pc_clf.fit(X, y, sample_weight=sample_weight)
def test_calibration_with_fit_params_inconsistent_length(data):
"""fit_params having different length than data should raise the
correct error message.
"""
X, y = data
fit_params = {"a": y[:5]}
clf = CheckingClassifier(expected_fit_params=fit_params)
pc_clf = CalibratedClassifierCV(clf)
msg = (
r"Found input variables with inconsistent numbers of "
r"samples: \[" + str(N_SAMPLES) + r", 5\]"
)
with pytest.raises(ValueError, match=msg):
pc_clf.fit(X, y, **fit_params)
@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
@pytest.mark.parametrize("ensemble", [True, False])
def test_calibrated_classifier_cv_zeros_sample_weights_equivalence(method, ensemble):
"""Check that passing removing some sample from the dataset `X` is
equivalent to passing a `sample_weight` with a factor 0."""
X, y = load_iris(return_X_y=True)
# Scale the data to avoid any convergence issue
X = StandardScaler().fit_transform(X)
# Only use 2 classes and select samples such that 2-fold cross-validation
# split will lead to an equivalence with a `sample_weight` of 0
X = np.vstack((X[:40], X[50:90]))
y = np.hstack((y[:40], y[50:90]))
sample_weight = np.zeros_like(y)
sample_weight[::2] = 1
estimator = LogisticRegression()
calibrated_clf_without_weights = CalibratedClassifierCV(
estimator,
method=method,
ensemble=ensemble,
cv=2,
)
calibrated_clf_with_weights = clone(calibrated_clf_without_weights)
calibrated_clf_with_weights.fit(X, y, sample_weight=sample_weight)
calibrated_clf_without_weights.fit(X[::2], y[::2])
# Check that the underlying fitted estimators have the same coefficients
for est_with_weights, est_without_weights in zip(
calibrated_clf_with_weights.calibrated_classifiers_,
calibrated_clf_without_weights.calibrated_classifiers_,
):
assert_allclose(
est_with_weights.estimator.coef_,
est_without_weights.estimator.coef_,
)
# Check that the predictions are the same
y_pred_with_weights = calibrated_clf_with_weights.predict_proba(X)
y_pred_without_weights = calibrated_clf_without_weights.predict_proba(X)
assert_allclose(y_pred_with_weights, y_pred_without_weights)
# TODO(1.4): Remove
def test_calibrated_classifier_error_base_estimator(data):
"""Check that we raise an error is a user set both `base_estimator` and
`estimator`."""
calibrated_classifier = CalibratedClassifierCV(
base_estimator=LogisticRegression(), estimator=LogisticRegression()
)
with pytest.raises(ValueError, match="Both `base_estimator` and `estimator`"):
calibrated_classifier.fit(*data)
# TODO(1.4): Remove
def test_calibrated_classifier_deprecation_base_estimator(data):
"""Check that we raise a warning regarding the deprecation of
`base_estimator`."""
calibrated_classifier = CalibratedClassifierCV(base_estimator=LogisticRegression())
warn_msg = "`base_estimator` was renamed to `estimator`"
with pytest.warns(FutureWarning, match=warn_msg):
calibrated_classifier.fit(*data)
| bsd-3-clause |
AnasGhrab/scikit-learn | examples/ensemble/plot_gradient_boosting_regularization.py | 352 | 2843 | """
================================
Gradient Boosting regularization
================================
Illustration of the effect of different regularization strategies
for Gradient Boosting. The example is taken from Hastie et al 2009.
The loss function used is binomial deviance. Regularization via
shrinkage (``learning_rate < 1.0``) improves performance considerably.
In combination with shrinkage, stochastic gradient boosting
(``subsample < 1.0``) can produce more accurate models by reducing the
variance via bagging.
Subsampling without shrinkage usually does poorly.
Another strategy to reduce the variance is by subsampling the features
analogous to the random splits in Random Forests
(via the ``max_features`` parameter).
.. [1] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical
Learning Ed. 2", Springer, 2009.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
#
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import ensemble
from sklearn import datasets
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
X = X.astype(np.float32)
# map labels from {-1, 1} to {0, 1}
labels, y = np.unique(y, return_inverse=True)
X_train, X_test = X[:2000], X[2000:]
y_train, y_test = y[:2000], y[2000:]
original_params = {'n_estimators': 1000, 'max_leaf_nodes': 4, 'max_depth': None, 'random_state': 2,
'min_samples_split': 5}
plt.figure()
for label, color, setting in [('No shrinkage', 'orange',
{'learning_rate': 1.0, 'subsample': 1.0}),
('learning_rate=0.1', 'turquoise',
{'learning_rate': 0.1, 'subsample': 1.0}),
('subsample=0.5', 'blue',
{'learning_rate': 1.0, 'subsample': 0.5}),
('learning_rate=0.1, subsample=0.5', 'gray',
{'learning_rate': 0.1, 'subsample': 0.5}),
('learning_rate=0.1, max_features=2', 'magenta',
{'learning_rate': 0.1, 'max_features': 2})]:
params = dict(original_params)
params.update(setting)
clf = ensemble.GradientBoostingClassifier(**params)
clf.fit(X_train, y_train)
# compute test set deviance
test_deviance = np.zeros((params['n_estimators'],), dtype=np.float64)
for i, y_pred in enumerate(clf.staged_decision_function(X_test)):
# clf.loss_ assumes that y_test[i] in {0, 1}
test_deviance[i] = clf.loss_(y_test, y_pred)
plt.plot((np.arange(test_deviance.shape[0]) + 1)[::5], test_deviance[::5],
'-', color=color, label=label)
plt.legend(loc='upper left')
plt.xlabel('Boosting Iterations')
plt.ylabel('Test Set Deviance')
plt.show()
| bsd-3-clause |
visualfabriq/portalytics | tests/test_multi_transformer.py | 1 | 3751 | import pandas as pd
from sklearn.model_selection import train_test_split
from tests.helpers import make_dataset
from vf_portalytics.multi_model import MultiTransformer
def test_multi_transformer_categorical_features():
total_x, total_y = make_dataset()
# make a copy of feature_0 to test if ordinals and nominals are being handled differently
total_x['feature_0_copy'] = total_x['feature_0']
# Declare basic parameters
target = 'target'
cat_feature = 'category'
feature_col_list = total_x.columns.drop(cat_feature)
clusters = total_x[cat_feature].unique()
# Split into train and test
train_index, test_index = train_test_split(total_x.index, test_size=0.33, random_state=5)
train_x, train_y = total_x.loc[train_index, :], total_y.loc[train_index]
test_x, test_y = total_x.loc[test_index, :], total_y.loc[test_index]
# keep all the features
selected_features = {}
for gp_key in clusters:
selected_features[gp_key] = feature_col_list
nominal_features = ['feature_0']
ordinal_features = ['feature_1', 'feature_0_copy']
# imitate params given from hyper optimization tuning
params = {
'A': {
'transformer_nominal': 'TargetEncoder',
'transformer_ordinal': 'OrdinalEncoder'
},
'B': {
'transformer_nominal': 'OneHotEncoder',
'transformer_ordinal': 'TargetEncoder'
},
'C': {
'transformer_nominal': 'OneHotEncoder',
'transformer_ordinal': 'JamesSteinEncoder'
},
'D': {
'transformer_nominal': 'JamesSteinEncoder',
'transformer_ordinal': 'OrdinalEncoder'
},
}
# Initiliaze transformer
transformer = MultiTransformer(
cat_feature,
clusters,
selected_features,
nominal_features,
ordinal_features,
params
)
transformer.fit(train_x, train_y)
transformed_test_x = transformer.transform(test_x)
# check shapes
assert test_x.shape[0] == transformed_test_x.shape[0]
assert transformed_test_x.shape[1] == 9
# check if OneHotEncoder did what expected
transformed_test_x = transformed_test_x.reindex(test_x.index)
assert (transformed_test_x['feature_0_1'].isna() == test_x[cat_feature].isin(['A', 'D'])).all()
# test if ordinals and nominals are being handled differently
assert transformed_test_x[test_x[cat_feature] == 'B']['feature_0'].isnull().values.all() # OneHotEncoder used
assert not transformed_test_x[test_x[cat_feature] == 'B']['feature_0_copy'].isnull().values.all() # TargetEncoder used
# check that non categoricals are not changed
assert transformed_test_x['feature_3'].equals(test_x['feature_3'])
def test_multi_transformer_non_categorical_features():
cat_feature = 'cat'
clusters = ['A']
selected_features = {'A': ['col_1', 'col_2']}
total_x = pd.DataFrame({'col_1': [50, 100, 200], 'col_2': [1., 2., 3.], 'cat_feature': clusters*3})
params = {
'A': {
'transformer_non_categorical': 'MinMaxScaler',
'potential_cat_feat': []
}
}
# Since MultiTransformer is built for having a transformer for each group of the input, these groups need to exist
total_x[cat_feature] = clusters[0]
# Initiliaze transformer
transformer = MultiTransformer(
cat_feature,
clusters,
selected_features,
nominals=[],
ordinals=[],
params=params
)
transformer.fit(total_x)
transformed_x = transformer.transform(total_x)
expected = pd.DataFrame({'col_1': [0., 0.333333, 1.], 'col_2': [0.0, 0.5, 1]})
pd.testing.assert_frame_equal(transformed_x, expected)
| gpl-3.0 |
AnasGhrab/scikit-learn | sklearn/tests/test_kernel_approximation.py | 242 | 7588 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn.utils.testing import assert_array_equal, assert_equal, assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_array_almost_equal, assert_raises
from sklearn.utils.testing import assert_less_equal
from sklearn.metrics.pairwise import kernel_metrics
from sklearn.kernel_approximation import RBFSampler
from sklearn.kernel_approximation import AdditiveChi2Sampler
from sklearn.kernel_approximation import SkewedChi2Sampler
from sklearn.kernel_approximation import Nystroem
from sklearn.metrics.pairwise import polynomial_kernel, rbf_kernel
# generate data
rng = np.random.RandomState(0)
X = rng.random_sample(size=(300, 50))
Y = rng.random_sample(size=(300, 50))
X /= X.sum(axis=1)[:, np.newaxis]
Y /= Y.sum(axis=1)[:, np.newaxis]
def test_additive_chi2_sampler():
# test that AdditiveChi2Sampler approximates kernel on random data
# compute exact kernel
# appreviations for easier formular
X_ = X[:, np.newaxis, :]
Y_ = Y[np.newaxis, :, :]
large_kernel = 2 * X_ * Y_ / (X_ + Y_)
# reduce to n_samples_x x n_samples_y by summing over features
kernel = (large_kernel.sum(axis=2))
# approximate kernel mapping
transform = AdditiveChi2Sampler(sample_steps=3)
X_trans = transform.fit_transform(X)
Y_trans = transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
assert_array_almost_equal(kernel, kernel_approx, 1)
X_sp_trans = transform.fit_transform(csr_matrix(X))
Y_sp_trans = transform.transform(csr_matrix(Y))
assert_array_equal(X_trans, X_sp_trans.A)
assert_array_equal(Y_trans, Y_sp_trans.A)
# test error is raised on negative input
Y_neg = Y.copy()
Y_neg[0, 0] = -1
assert_raises(ValueError, transform.transform, Y_neg)
# test error on invalid sample_steps
transform = AdditiveChi2Sampler(sample_steps=4)
assert_raises(ValueError, transform.fit, X)
# test that the sample interval is set correctly
sample_steps_available = [1, 2, 3]
for sample_steps in sample_steps_available:
# test that the sample_interval is initialized correctly
transform = AdditiveChi2Sampler(sample_steps=sample_steps)
assert_equal(transform.sample_interval, None)
# test that the sample_interval is changed in the fit method
transform.fit(X)
assert_not_equal(transform.sample_interval_, None)
# test that the sample_interval is set correctly
sample_interval = 0.3
transform = AdditiveChi2Sampler(sample_steps=4,
sample_interval=sample_interval)
assert_equal(transform.sample_interval, sample_interval)
transform.fit(X)
assert_equal(transform.sample_interval_, sample_interval)
def test_skewed_chi2_sampler():
# test that RBFSampler approximates kernel on random data
# compute exact kernel
c = 0.03
# appreviations for easier formular
X_c = (X + c)[:, np.newaxis, :]
Y_c = (Y + c)[np.newaxis, :, :]
# we do it in log-space in the hope that it's more stable
# this array is n_samples_x x n_samples_y big x n_features
log_kernel = ((np.log(X_c) / 2.) + (np.log(Y_c) / 2.) + np.log(2.) -
np.log(X_c + Y_c))
# reduce to n_samples_x x n_samples_y by summing over features in log-space
kernel = np.exp(log_kernel.sum(axis=2))
# approximate kernel mapping
transform = SkewedChi2Sampler(skewedness=c, n_components=1000,
random_state=42)
X_trans = transform.fit_transform(X)
Y_trans = transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
assert_array_almost_equal(kernel, kernel_approx, 1)
# test error is raised on negative input
Y_neg = Y.copy()
Y_neg[0, 0] = -1
assert_raises(ValueError, transform.transform, Y_neg)
def test_rbf_sampler():
# test that RBFSampler approximates kernel on random data
# compute exact kernel
gamma = 10.
kernel = rbf_kernel(X, Y, gamma=gamma)
# approximate kernel mapping
rbf_transform = RBFSampler(gamma=gamma, n_components=1000, random_state=42)
X_trans = rbf_transform.fit_transform(X)
Y_trans = rbf_transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
error = kernel - kernel_approx
assert_less_equal(np.abs(np.mean(error)), 0.01) # close to unbiased
np.abs(error, out=error)
assert_less_equal(np.max(error), 0.1) # nothing too far off
assert_less_equal(np.mean(error), 0.05) # mean is fairly close
def test_input_validation():
# Regression test: kernel approx. transformers should work on lists
# No assertions; the old versions would simply crash
X = [[1, 2], [3, 4], [5, 6]]
AdditiveChi2Sampler().fit(X).transform(X)
SkewedChi2Sampler().fit(X).transform(X)
RBFSampler().fit(X).transform(X)
X = csr_matrix(X)
RBFSampler().fit(X).transform(X)
def test_nystroem_approximation():
# some basic tests
rnd = np.random.RandomState(0)
X = rnd.uniform(size=(10, 4))
# With n_components = n_samples this is exact
X_transformed = Nystroem(n_components=X.shape[0]).fit_transform(X)
K = rbf_kernel(X)
assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
trans = Nystroem(n_components=2, random_state=rnd)
X_transformed = trans.fit(X).transform(X)
assert_equal(X_transformed.shape, (X.shape[0], 2))
# test callable kernel
linear_kernel = lambda X, Y: np.dot(X, Y.T)
trans = Nystroem(n_components=2, kernel=linear_kernel, random_state=rnd)
X_transformed = trans.fit(X).transform(X)
assert_equal(X_transformed.shape, (X.shape[0], 2))
# test that available kernels fit and transform
kernels_available = kernel_metrics()
for kern in kernels_available:
trans = Nystroem(n_components=2, kernel=kern, random_state=rnd)
X_transformed = trans.fit(X).transform(X)
assert_equal(X_transformed.shape, (X.shape[0], 2))
def test_nystroem_singular_kernel():
# test that nystroem works with singular kernel matrix
rng = np.random.RandomState(0)
X = rng.rand(10, 20)
X = np.vstack([X] * 2) # duplicate samples
gamma = 100
N = Nystroem(gamma=gamma, n_components=X.shape[0]).fit(X)
X_transformed = N.transform(X)
K = rbf_kernel(X, gamma=gamma)
assert_array_almost_equal(K, np.dot(X_transformed, X_transformed.T))
assert_true(np.all(np.isfinite(Y)))
def test_nystroem_poly_kernel_params():
# Non-regression: Nystroem should pass other parameters beside gamma.
rnd = np.random.RandomState(37)
X = rnd.uniform(size=(10, 4))
K = polynomial_kernel(X, degree=3.1, coef0=.1)
nystroem = Nystroem(kernel="polynomial", n_components=X.shape[0],
degree=3.1, coef0=.1)
X_transformed = nystroem.fit_transform(X)
assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
def test_nystroem_callable():
# Test Nystroem on a callable.
rnd = np.random.RandomState(42)
n_samples = 10
X = rnd.uniform(size=(n_samples, 4))
def logging_histogram_kernel(x, y, log):
"""Histogram kernel that writes to a log."""
log.append(1)
return np.minimum(x, y).sum()
kernel_log = []
X = list(X) # test input validation
Nystroem(kernel=logging_histogram_kernel,
n_components=(n_samples - 1),
kernel_params={'log': kernel_log}).fit(X)
assert_equal(len(kernel_log), n_samples * (n_samples - 1) / 2)
| bsd-3-clause |
AnasGhrab/scikit-learn | examples/linear_model/plot_sgd_weighted_samples.py | 341 | 1458 | """
=====================
SGD: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
# we create 20 points
np.random.seed(0)
X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
y = [1] * 10 + [-1] * 10
sample_weight = 100 * np.abs(np.random.randn(20))
# and assign a bigger weight to the last 10 samples
sample_weight[:10] *= 10
# plot the weighted data points
xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y, s=sample_weight, alpha=0.9,
cmap=plt.cm.bone)
## fit the unweighted model
clf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)
clf.fit(X, y)
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
no_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['solid'])
## fit the weighted model
clf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)
clf.fit(X, y, sample_weight=sample_weight)
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
samples_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['dashed'])
plt.legend([no_weights.collections[0], samples_weights.collections[0]],
["no weights", "with weights"], loc="lower left")
plt.xticks(())
plt.yticks(())
plt.show()
| bsd-3-clause |
st-tech/zr-obp | setup.py | 1 | 2029 | from obp.version import __version__
from setuptools import setup, find_packages
from os import path
import sys
here = path.abspath(path.dirname(__file__))
sys.path.insert(0, path.join(here, "obp"))
print("version")
print(__version__)
with open(path.join(here, "README.md"), encoding="utf-8") as f:
long_description = f.read()
package_data_list = ["obp/policy/conf/prior_bts.yaml", "obp/dataset/obd"]
setup(
name="obp",
version=__version__,
description="Open Bandit Pipeline: a python library for bandit algorithms and off-policy evaluation",
url="https://github.com/st-tech/zr-obp",
author="Yuta Saito",
author_email="open-bandit-project@googlegroups.com",
keywords=["bandit algorithms", "off-policy evaluation"],
long_description=long_description,
long_description_content_type="text/markdown",
install_requires=[
"matplotlib>=3.4.3",
"mypy-extensions>=0.4.3",
"numpy>=1.21.2",
"pandas>=1.3.2",
"pyyaml>=5.1",
"seaborn>=0.10.1",
"scikit-learn>=1.0.2",
"scipy>=1.7.3",
"torch>=1.9.0",
"tqdm>=4.62.2",
"pyieoe>=0.1.1",
"pingouin>=0.4.0",
],
license="Apache License",
packages=find_packages(
exclude=["benchmark", "docs", "examples", "obd", "tests", "slides"]
),
package_data={"obp": package_data_list},
include_package_data=True,
classifiers=[
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Mathematics",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development",
"Topic :: Software Development :: Libraries",
"Topic :: Software Development :: Libraries :: Python Modules",
"License :: OSI Approved :: Apache Software License",
],
)
| apache-2.0 |
google-research/xtreme | third_party/roberta.py | 1 | 34904 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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.
"""PyTorch RoBERTa model. """
import logging
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.configuration_roberta import RobertaConfig
from transformers.file_utils import add_start_docstrings
from transformers.modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
logger = logging.getLogger(__name__)
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
"roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin",
"roberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
"roberta-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
"distilroberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-pytorch_model.bin",
"roberta-base-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-pytorch_model.bin",
"roberta-large-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-pytorch_model.bin",
}
class RobertaEmbeddings(BertEmbeddings):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super(RobertaEmbeddings, self).__init__(config)
self.padding_idx = 1
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = self.create_position_ids_from_input_ids(input_ids).to(input_ids.device)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
return super(RobertaEmbeddings, self).forward(
input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds
)
def create_position_ids_from_input_ids(self, x):
""" Replace non-padding symbols with their position numbers. Position numbers begin at
padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
`utils.make_positions`.
:param torch.Tensor x:
:return torch.Tensor:
"""
mask = x.ne(self.padding_idx).long()
incremental_indicies = torch.cumsum(mask, dim=1) * mask
return incremental_indicies + self.padding_idx
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
""" We are provided embeddings directly. We cannot infer which are padded so just generate
sequential position ids.
:param torch.Tensor inputs_embeds:
:return torch.Tensor:
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
`RoBERTa: A Robustly Optimized BERT Pretraining Approach`_
by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
Veselin Stoyanov. It is based on Google's BERT model released in 2018.
It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
objective and training with much larger mini-batches and learning rates.
This implementation is the same as BertModel with a tiny embeddings tweak as well as a setup for Roberta pretrained
models.
This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
refer to the PyTorch documentation for all matter related to general usage and behavior.
.. _`RoBERTa: A Robustly Optimized BERT Pretraining Approach`:
https://arxiv.org/abs/1907.11692
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
ROBERTA_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
To match pre-training, RoBERTa input sequence should be formatted with <s> and </s> tokens as follows:
(a) For sequence pairs:
``tokens: <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
(b) For single sequences:
``tokens: <s> the dog is hairy . </s>``
Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with
the ``add_special_tokens`` parameter set to ``True``.
RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional` need to be trained) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Optional segment token indices to indicate first and second portions of the inputs.
This embedding matrice is not trained (not pretrained during RoBERTa pretraining), you will have to train it
during finetuning.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
**inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
"""
@add_start_docstrings(
"The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
ROBERTA_START_DOCSTRING,
ROBERTA_INPUTS_DOCSTRING,
)
class RobertaModel(BertModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the output of the last layer of the model.
**pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaModel, self).__init__(config)
self.embeddings = RobertaEmbeddings(config)
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
@add_start_docstrings(
"""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING
)
class RobertaForMaskedLM(BertPreTrainedModel):
r"""
**masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the masked language modeling loss.
Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Masked language modeling loss.
**prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForMaskedLM.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForMaskedLM, self).__init__(config)
self.roberta = RobertaModel(config)
self.lm_head = RobertaLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head.decoder
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
masked_lm_labels=None,
):
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
if masked_lm_labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
outputs = (masked_lm_loss,) + outputs
return outputs # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
class RobertaLMHead(nn.Module):
"""Roberta Head for masked language modeling."""
def __init__(self, config):
super(RobertaLMHead, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x) + self.bias
return x
@add_start_docstrings(
"""RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
on top of the pooled output) e.g. for GLUE tasks. """,
ROBERTA_START_DOCSTRING,
ROBERTA_INPUTS_DOCSTRING,
)
class RobertaForSequenceClassification(BertPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the sequence classification/regression loss.
Indices should be in ``[0, ..., config.num_labels]``.
If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForSequenceClassification.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
self.roberta = RobertaModel(config)
self.classifier = RobertaClassificationHead(config)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
):
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:]
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), logits, (hidden_states), (attentions)
@add_start_docstrings(
"""Roberta Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
ROBERTA_START_DOCSTRING,
ROBERTA_INPUTS_DOCSTRING,
)
class RobertaForMultipleChoice(BertPreTrainedModel):
r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
To match pre-training, RoBerta input sequence should be formatted with [CLS] and [SEP] tokens as follows:
(a) For sequence pairs:
``tokens: [CLS] is this jack ##son ##ville ? [SEP] [SEP] no it is not . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
(b) For single sequences:
``tokens: [CLS] the dog is hairy . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0``
Indices can be obtained using :class:`transformers.BertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Segment token indices to indicate first and second portions of the inputs.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Mask to avoid performing attention on padding token indices.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
**inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the multiple choice classification loss.
Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above)
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss.
**classification_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForMultipleChoice.from_pretrained('roberta-base')
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
labels = torch.tensor(1).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, classification_scores = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForMultipleChoice, self).__init__(config)
self.roberta = RobertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
self.init_weights()
def forward(
self,
input_ids=None,
token_type_ids=None,
attention_mask=None,
labels=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
):
num_choices = input_ids.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
outputs = self.roberta(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
outputs = (reshaped_logits,) + outputs[2:] # add hidden states and attention if they are here
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
outputs = (loss,) + outputs
return outputs # (loss), reshaped_logits, (hidden_states), (attentions)
@add_start_docstrings(
"""Roberta Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
ROBERTA_START_DOCSTRING,
ROBERTA_INPUTS_DOCSTRING,
)
class RobertaForTokenClassification(BertPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the token classification loss.
Indices should be in ``[0, ..., config.num_labels - 1]``.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss.
**scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForTokenClassification.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, scores = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForTokenClassification, self).__init__(config)
self.num_labels = config.num_labels
self.roberta = RobertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
):
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)[active_loss]
active_labels = labels.view(-1)[active_loss]
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), scores, (hidden_states), (attentions)
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super(RobertaClassificationHead, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
the hidden-states output to compute `span start logits` and `span end logits`). """,
ROBERTA_START_DOCSTRING,
ROBERTA_INPUTS_DOCSTRING,
)
class RobertaForQuestionAnswering(BertPreTrainedModel):
r"""
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
**end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
**start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-large')
model = RobertaForQuestionAnswering.from_pretrained('roberta-large')
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
input_ids = tokenizer.encode(question, text)
start_scores, end_scores = model(torch.tensor([input_ids]))
all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForQuestionAnswering, self).__init__(config)
self.num_labels = config.num_labels
self.roberta = RobertaModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
start_positions=None,
end_positions=None,
):
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
outputs = (start_logits, end_logits,) + outputs[2:]
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
outputs = (total_loss,) + outputs
return outputs # (loss), start_logits, end_logits, (hidden_states), (attentions) | apache-2.0 |
hitszxp/scikit-learn | examples/ensemble/plot_forest_iris.py | 332 | 6271 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
This plot compares the decision surfaces learned by a decision tree classifier
(first column), by a random forest classifier (second column), by an extra-
trees classifier (third column) and by an AdaBoost classifier (fourth column).
In the first row, the classifiers are built using the sepal width and the sepal
length features only, on the second row using the petal length and sepal length
only, and on the third row using the petal width and the petal length only.
In descending order of quality, when trained (outside of this example) on all
4 features using 30 estimators and scored using 10 fold cross validation, we see::
ExtraTreesClassifier() # 0.95 score
RandomForestClassifier() # 0.94 score
AdaBoost(DecisionTree(max_depth=3)) # 0.94 score
DecisionTree(max_depth=None) # 0.94 score
Increasing `max_depth` for AdaBoost lowers the standard deviation of the scores (but
the average score does not improve).
See the console's output for further details about each model.
In this example you might try to:
1) vary the ``max_depth`` for the ``DecisionTreeClassifier`` and
``AdaBoostClassifier``, perhaps try ``max_depth=3`` for the
``DecisionTreeClassifier`` or ``max_depth=None`` for ``AdaBoostClassifier``
2) vary ``n_estimators``
It is worth noting that RandomForests and ExtraTrees can be fitted in parallel
on many cores as each tree is built independently of the others. AdaBoost's
samples are built sequentially and so do not use multiple cores.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import clone
from sklearn.datasets import load_iris
from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,
AdaBoostClassifier)
from sklearn.externals.six.moves import xrange
from sklearn.tree import DecisionTreeClassifier
# Parameters
n_classes = 3
n_estimators = 30
plot_colors = "ryb"
cmap = plt.cm.RdYlBu
plot_step = 0.02 # fine step width for decision surface contours
plot_step_coarser = 0.5 # step widths for coarse classifier guesses
RANDOM_SEED = 13 # fix the seed on each iteration
# Load data
iris = load_iris()
plot_idx = 1
models = [DecisionTreeClassifier(max_depth=None),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),
n_estimators=n_estimators)]
for pair in ([0, 1], [0, 2], [2, 3]):
for model in models:
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Shuffle
idx = np.arange(X.shape[0])
np.random.seed(RANDOM_SEED)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
# Standardize
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
# Train
clf = clone(model)
clf = model.fit(X, y)
scores = clf.score(X, y)
# Create a title for each column and the console by using str() and
# slicing away useless parts of the string
model_title = str(type(model)).split(".")[-1][:-2][:-len("Classifier")]
model_details = model_title
if hasattr(model, "estimators_"):
model_details += " with {} estimators".format(len(model.estimators_))
print( model_details + " with features", pair, "has a score of", scores )
plt.subplot(3, 4, plot_idx)
if plot_idx <= len(models):
# Add a title at the top of each column
plt.title(model_title)
# Now plot the decision boundary using a fine mesh as input to a
# filled contour plot
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
np.arange(y_min, y_max, plot_step))
# Plot either a single DecisionTreeClassifier or alpha blend the
# decision surfaces of the ensemble of classifiers
if isinstance(model, DecisionTreeClassifier):
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=cmap)
else:
# Choose alpha blend level with respect to the number of estimators
# that are in use (noting that AdaBoost can use fewer estimators
# than its maximum if it achieves a good enough fit early on)
estimator_alpha = 1.0 / len(model.estimators_)
for tree in model.estimators_:
Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap)
# Build a coarser grid to plot a set of ensemble classifications
# to show how these are different to what we see in the decision
# surfaces. These points are regularly space and do not have a black outline
xx_coarser, yy_coarser = np.meshgrid(np.arange(x_min, x_max, plot_step_coarser),
np.arange(y_min, y_max, plot_step_coarser))
Z_points_coarser = model.predict(np.c_[xx_coarser.ravel(), yy_coarser.ravel()]).reshape(xx_coarser.shape)
cs_points = plt.scatter(xx_coarser, yy_coarser, s=15, c=Z_points_coarser, cmap=cmap, edgecolors="none")
# Plot the training points, these are clustered together and have a
# black outline
for i, c in zip(xrange(n_classes), plot_colors):
idx = np.where(y == i)
plt.scatter(X[idx, 0], X[idx, 1], c=c, label=iris.target_names[i],
cmap=cmap)
plot_idx += 1 # move on to the next plot in sequence
plt.suptitle("Classifiers on feature subsets of the Iris dataset")
plt.axis("tight")
plt.show()
| bsd-3-clause |
chamikaramj/beam | sdks/python/apache_beam/io/gcp/dicomio.py | 8 | 20660 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
#
"""DICOM IO connector
This module implements several tools to facilitate the interaction between
a Google Cloud Healthcare DICOM store and a Beam pipeline.
For more details on DICOM store and API:
https://cloud.google.com/healthcare/docs/how-tos/dicom
The DICOM IO connector can be used to search metadata or write DICOM files
to DICOM store.
When used together with Google Pubsub message connector, the
`FormatToQido` PTransform implemented in this module can be used
to convert Pubsub messages to search requests.
Since Traceability is crucial for healthcare
API users, every input or error message will be recorded in the output of
the DICOM IO connector. As a result, every PTransform in this module will
return a PCollection of dict that encodes results and detailed error messages.
Search instance's metadata (QIDO request)
===================================================
DicomSearch() wraps the QIDO request client and supports 3 levels of search.
Users should specify the level by setting the 'search_type' entry in the input
dict. They can also refine the search by adding tags to filter the results using
the 'params' entry. Here is a sample usage:
with Pipeline() as p:
input_dict = p | beam.Create(
[{'project_id': 'abc123', 'type': 'instances',...},
{'project_id': 'dicom_go', 'type': 'series',...}])
results = input_dict | io.gcp.DicomSearch()
results | 'print successful search' >> beam.Map(
lambda x: print(x['result'] if x['success'] else None))
results | 'print failed search' >> beam.Map(
lambda x: print(x['result'] if not x['success'] else None))
In the example above, successful qido search results and error messages for
failed requests are printed. When used in real life, user can choose to filter
those data and output them to wherever they want.
Convert DICOM Pubsub message to Qido search request
===================================================
Healthcare API users might read messages from Pubsub to monitor the store
operations (e.g. new file) in a DICOM storage. Pubsub message encode
DICOM as a web store path as well as instance ids. If users are interested in
getting new instance's metadata, they can use the `FormatToQido` transform
to convert the message into Qido Search dict then use the `DicomSearch`
transform. Here is a sample usage:
pipeline_options = PipelineOptions()
pipeline_options.view_as(StandardOptions).streaming = True
p = beam.Pipeline(options=pipeline_options)
pubsub = p | beam.io.ReadStringFromPubsub(subscription='a_dicom_store')
results = pubsub | FormatToQido()
success = results | 'filter message' >> beam.Filter(lambda x: x['success'])
qido_dict = success | 'get qido request' >> beam.Map(lambda x: x['result'])
metadata = qido_dict | DicomSearch()
In the example above, the pipeline is listening to a pubsub topic and waiting
for messages from DICOM API. When a new DICOM file comes into the storage, the
pipeline will receive a pubsub message, convert it to a Qido request dict and
feed it to DicomSearch() PTransform. As a result, users can get the metadata for
every new DICOM file. Note that not every pubsub message received is from DICOM
API, so we to filter the results first.
Store a DICOM file in a DICOM storage
===================================================
UploadToDicomStore() wraps store request API and users can use it to send a
DICOM file to a DICOM store. It supports two types of input: 1.file data in
byte[] 2.fileio object. Users should set the 'input_type' when initialzing
this PTransform. Here are the examples:
with Pipeline() as p:
input_dict = {'project_id': 'abc123', 'type': 'instances',...}
path = "gcs://bucketname/something/a.dcm"
match = p | fileio.MatchFiles(path)
fileio_obj = match | fileio.ReadAll()
results = fileio_obj | UploadToDicomStore(input_dict, 'fileio')
with Pipeline() as p:
input_dict = {'project_id': 'abc123', 'type': 'instances',...}
f = open("abc.dcm", "rb")
dcm_file = f.read()
byte_file = p | 'create byte file' >> beam.Create([dcm_file])
results = byte_file | UploadToDicomStore(input_dict, 'bytes')
The first example uses a PCollection of fileio objects as input.
UploadToDicomStore will read DICOM files from the objects and send them
to a DICOM storage.
The second example uses a PCollection of byte[] as input. UploadToDicomStore
will directly send those DICOM files to a DICOM storage.
Users can also get the operation results in the output PCollection if they want
to handle the failed store requests.
"""
# pytype: skip-file
from concurrent.futures import ThreadPoolExecutor
from concurrent.futures import as_completed
import apache_beam as beam
from apache_beam.io.gcp.dicomclient import DicomApiHttpClient
from apache_beam.transforms import PTransform
class DicomSearch(PTransform):
"""A PTransform used for retrieving DICOM instance metadata from Google
Cloud DICOM store. It takes a PCollection of dicts as input and return
a PCollection of dict as results:
INPUT:
The input dict represents DICOM web path parameters, which has the following
string keys and values:
{
'project_id': str,
'region': str,
'dataset_id': str,
'dicom_store_id': str,
'search_type': str,
'params': dict(str,str) (Optional),
}
Key-value pairs:
project_id: Id of the project in which the DICOM store is
located. (Required)
region: Region where the DICOM store resides. (Required)
dataset_id: Id of the dataset where DICOM store belongs to. (Required)
dicom_store_id: Id of the dicom store. (Required)
search_type: Which type of search it is, could only be one of the three
values: 'instances', 'series', or 'studies'. (Required)
params: A dict of str:str pairs used to refine QIDO search. (Optional)
Supported tags in three categories:
1.Studies:
* StudyInstanceUID,
* PatientName,
* PatientID,
* AccessionNumber,
* ReferringPhysicianName,
* StudyDate,
2.Series: all study level search terms and
* SeriesInstanceUID,
* Modality,
3.Instances: all study/series level search terms and
* SOPInstanceUID,
e.g. {"StudyInstanceUID":"1","SeriesInstanceUID":"2"}
OUTPUT:
The output dict wraps results as well as error messages:
{
'result': a list of dicts in JSON style.
'success': boolean value telling whether the operation is successful.
'input': detail ids and dicomweb path for this retrieval.
'status': status code from the server, used as error message.
}
"""
def __init__(
self, buffer_size=8, max_workers=5, client=None, credential=None):
"""Initializes DicomSearch.
Args:
buffer_size: # type: Int. Size of the request buffer.
max_workers: # type: Int. Maximum number of threads a worker can
create. If it is set to one, all the request will be processed
sequentially in a worker.
client: # type: object. If it is specified, all the Api calls will
made by this client instead of the default one (DicomApiHttpClient).
credential: # type: Google credential object, if it is specified, the
Http client will use it to create sessions instead of the default.
"""
self.buffer_size = buffer_size
self.max_workers = max_workers
self.client = client or DicomApiHttpClient()
self.credential = credential
def expand(self, pcoll):
return pcoll | beam.ParDo(
_QidoReadFn(
self.buffer_size, self.max_workers, self.client, self.credential))
class _QidoReadFn(beam.DoFn):
"""A DoFn for executing every qido query request."""
def __init__(self, buffer_size, max_workers, client, credential=None):
self.buffer_size = buffer_size
self.max_workers = max_workers
self.client = client
self.credential = credential
def start_bundle(self):
self.buffer = []
def finish_bundle(self):
for item in self._flush():
yield item
def validate_element(self, element):
# Check if all required keys present.
required_keys = [
'project_id', 'region', 'dataset_id', 'dicom_store_id', 'search_type'
]
for key in required_keys:
if key not in element:
error_message = 'Must have %s in the dict.' % (key)
return False, error_message
# Check if return type is correct.
if element['search_type'] in ['instances', "studies", "series"]:
return True, None
else:
error_message = (
'Search type can only be "studies", '
'"instances" or "series"')
return False, error_message
def process(
self,
element,
window=beam.DoFn.WindowParam,
timestamp=beam.DoFn.TimestampParam):
# Check if the element is valid
valid, error_message = self.validate_element(element)
if valid:
self.buffer.append((element, window, timestamp))
if len(self.buffer) >= self.buffer_size:
for item in self._flush():
yield item
else:
# Return this when the input dict dose not meet the requirements
out = {}
out['result'] = []
out['status'] = error_message
out['input'] = element
out['success'] = False
yield out
def make_request(self, element):
# Sending Qido request to DICOM Api
project_id = element['project_id']
region = element['region']
dataset_id = element['dataset_id']
dicom_store_id = element['dicom_store_id']
search_type = element['search_type']
params = element['params'] if 'params' in element else None
# Call qido search http client
result, status_code = self.client.qido_search(
project_id, region, dataset_id, dicom_store_id,
search_type, params, self.credential
)
out = {}
out['result'] = result
out['status'] = status_code
out['input'] = element
out['success'] = (status_code == 200)
return out
def process_buffer_element(self, buffer_element):
# Thread job runner - each thread makes a Qido search request
value = self.make_request(buffer_element[0])
windows = [buffer_element[1]]
timestamp = buffer_element[2]
return beam.utils.windowed_value.WindowedValue(
value=value, timestamp=timestamp, windows=windows)
def _flush(self):
# Create thread pool executor and process the buffered elements in paralllel
executor = ThreadPoolExecutor(max_workers=self.max_workers)
futures = [
executor.submit(self.process_buffer_element, ele) for ele in self.buffer
]
self.buffer = []
for f in as_completed(futures):
yield f.result()
class FormatToQido(PTransform):
"""A PTransform for converting pubsub messages into search input dict.
Takes PCollection of string as input and returns a PCollection of dict as
results. Note that some pubsub messages may not be from DICOM API, which
will be recorded as failed conversions.
INPUT:
The input are normally strings from Pubsub topic:
"projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/
dicomStores/DICOM_STORE_ID/dicomWeb/studies/STUDY_UID/
series/SERIES_UID/instances/INSTANCE_UID"
OUTPUT:
The output dict encodes results as well as error messages:
{
'result': a dict representing instance level qido search request.
'success': boolean value telling whether the conversion is successful.
'input': input pubsub message string.
}
"""
def __init__(self, credential=None):
"""Initializes FormatToQido.
Args:
credential: # type: Google credential object, if it is specified, the
Http client will use it instead of the default one.
"""
self.credential = credential
def expand(self, pcoll):
return pcoll | beam.ParDo(_ConvertStringToQido())
class _ConvertStringToQido(beam.DoFn):
"""A DoFn for converting pubsub string to qido search parameters."""
def process(self, element):
# Some constants for DICOM pubsub message
NUM_PUBSUB_STR_ENTRIES = 15
NUM_DICOM_WEBPATH_PARAMETERS = 5
NUM_TOTAL_PARAMETERS = 8
INDEX_PROJECT_ID = 1
INDEX_REGION = 3
INDEX_DATASET_ID = 5
INDEX_DICOMSTORE_ID = 7
INDEX_STUDY_ID = 10
INDEX_SERIE_ID = 12
INDEX_INSTANCE_ID = 14
entries = element.split('/')
# Output dict with error message, used when
# receiving invalid pubsub string.
error_dict = {}
error_dict['result'] = {}
error_dict['input'] = element
error_dict['success'] = False
if len(entries) != NUM_PUBSUB_STR_ENTRIES:
return [error_dict]
required_keys = [
'projects',
'locations',
'datasets',
'dicomStores',
'dicomWeb',
'studies',
'series',
'instances'
]
# Check if the required keys present and
# the positions of those keys are correct
for i in range(NUM_DICOM_WEBPATH_PARAMETERS):
if required_keys[i] != entries[i * 2]:
return [error_dict]
for i in range(NUM_DICOM_WEBPATH_PARAMETERS, NUM_TOTAL_PARAMETERS):
if required_keys[i] != entries[i * 2 - 1]:
return [error_dict]
# Compose dicom webpath parameters for qido search
qido_dict = {}
qido_dict['project_id'] = entries[INDEX_PROJECT_ID]
qido_dict['region'] = entries[INDEX_REGION]
qido_dict['dataset_id'] = entries[INDEX_DATASET_ID]
qido_dict['dicom_store_id'] = entries[INDEX_DICOMSTORE_ID]
qido_dict['search_type'] = 'instances'
# Compose instance level params for qido search
params = {}
params['StudyInstanceUID'] = entries[INDEX_STUDY_ID]
params['SeriesInstanceUID'] = entries[INDEX_SERIE_ID]
params['SOPInstanceUID'] = entries[INDEX_INSTANCE_ID]
qido_dict['params'] = params
out = {}
out['result'] = qido_dict
out['input'] = element
out['success'] = True
return [out]
class UploadToDicomStore(PTransform):
"""A PTransform for storing instances to a DICOM store.
Takes PCollection of byte[] as input and return a PCollection of dict as
results. The inputs are normally DICOM file in bytes or str filename.
INPUT:
This PTransform supports two types of input:
1. Byte[]: representing dicom file.
2. Fileio object: stream file object.
OUTPUT:
The output dict encodes status as well as error messages:
{
'success': boolean value telling whether the store is successful.
'input': undeliverable data. Exactly the same as the input,
only set if the operation is failed.
'status': status code from the server, used as error messages.
}
"""
def __init__(
self,
destination_dict,
input_type,
buffer_size=8,
max_workers=5,
client=None,
credential=None):
"""Initializes UploadToDicomStore.
Args:
destination_dict: # type: python dict, encodes DICOM endpoint information:
{
'project_id': str,
'region': str,
'dataset_id': str,
'dicom_store_id': str,
}
Key-value pairs:
* project_id: Id of the project in which DICOM store locates. (Required)
* region: Region where the DICOM store resides. (Required)
* dataset_id: Id of the dataset where DICOM store belongs to. (Required)
* dicom_store_id: Id of the dicom store. (Required)
input_type: # type: string, could only be 'bytes' or 'fileio'
buffer_size: # type: Int. Size of the request buffer.
max_workers: # type: Int. Maximum number of threads a worker can
create. If it is set to one, all the request will be processed
sequentially in a worker.
client: # type: object. If it is specified, all the Api calls will
made by this client instead of the default one (DicomApiHttpClient).
credential: # type: Google credential object, if it is specified, the
Http client will use it instead of the default one.
"""
self.destination_dict = destination_dict
# input_type pre-check
if input_type not in ['bytes', 'fileio']:
raise ValueError("input_type could only be 'bytes' or 'fileio'")
self.input_type = input_type
self.buffer_size = buffer_size
self.max_workers = max_workers
self.client = client
self.credential = credential
def expand(self, pcoll):
return pcoll | beam.ParDo(
_StoreInstance(
self.destination_dict,
self.input_type,
self.buffer_size,
self.max_workers,
self.client,
self.credential))
class _StoreInstance(beam.DoFn):
"""A DoFn read or fetch dicom files then push it to a dicom store."""
def __init__(
self,
destination_dict,
input_type,
buffer_size,
max_workers,
client,
credential=None):
# pre-check destination dict
required_keys = ['project_id', 'region', 'dataset_id', 'dicom_store_id']
for key in required_keys:
if key not in destination_dict:
raise ValueError('Must have %s in the dict.' % (key))
self.destination_dict = destination_dict
self.input_type = input_type
self.buffer_size = buffer_size
self.max_workers = max_workers
self.client = client
self.credential = credential
def start_bundle(self):
self.buffer = []
def finish_bundle(self):
for item in self._flush():
yield item
def process(
self,
element,
window=beam.DoFn.WindowParam,
timestamp=beam.DoFn.TimestampParam):
self.buffer.append((element, window, timestamp))
if len(self.buffer) >= self.buffer_size:
for item in self._flush():
yield item
def make_request(self, dicom_file):
# Send file to DICOM store and records the results.
project_id = self.destination_dict['project_id']
region = self.destination_dict['region']
dataset_id = self.destination_dict['dataset_id']
dicom_store_id = self.destination_dict['dicom_store_id']
# Feed the dicom file into store client
if self.client:
_, status_code = self.client.dicomweb_store_instance(
project_id, region, dataset_id, dicom_store_id, dicom_file,
self.credential
)
else:
_, status_code = DicomApiHttpClient().dicomweb_store_instance(
project_id, region, dataset_id, dicom_store_id, dicom_file,
self.credential
)
out = {}
out['status'] = status_code
out['success'] = (status_code == 200)
return out
def read_dicom_file(self, buffer_element):
# Read the file based on different input. If the read fails ,return
# an error dict which records input and error messages.
try:
if self.input_type == 'fileio':
f = buffer_element.open()
data = f.read()
f.close()
return True, data
else:
return True, buffer_element
except Exception as error_message:
error_out = {}
error_out['status'] = error_message
error_out['success'] = False
return False, error_out
def process_buffer_element(self, buffer_element):
# Thread job runner - each thread stores a DICOM file
success, read_result = self.read_dicom_file(buffer_element[0])
windows = [buffer_element[1]]
timestamp = buffer_element[2]
value = None
if success:
value = self.make_request(read_result)
else:
value = read_result
# save the undeliverable data
if not value['success']:
value['input'] = buffer_element[0]
return beam.utils.windowed_value.WindowedValue(
value=value, timestamp=timestamp, windows=windows)
def _flush(self):
# Create thread pool executor and process the buffered elements in paralllel
executor = ThreadPoolExecutor(max_workers=self.max_workers)
futures = [
executor.submit(self.process_buffer_element, ele) for ele in self.buffer
]
self.buffer = []
for f in as_completed(futures):
yield f.result()
| apache-2.0 |
nyu-mll/spinn | python/spinn/tests/test_stack.py | 1 | 4359 | import unittest
import numpy as np
import tempfile
import gflags
import sys
from spinn.spinn_core_model import BaseModel
from spinn.models.base import get_flags
import spinn.spinn_core_model
import spinn.rl_spinn
from spinn.util.trainer import ModelTrainer
# PyTorch
import torch
import torch.nn as nn
from spinn.util.test import MockModel, default_args, get_batch, compare_models
from spinn.util import afs_safe_logger
FLAGS = gflags.FLAGS
class SPINNTestCase(unittest.TestCase):
def test_save_load_model(self):
model_to_save = MockModel(BaseModel, default_args())
model_to_load = MockModel(BaseModel, default_args())
# Save to and load from temporary file.
temp = tempfile.NamedTemporaryFile()
torch.save(model_to_save.state_dict(), temp.name)
model_to_load.load_state_dict(torch.load(temp.name))
compare_models(model_to_save, model_to_load)
# Cleanup temporary file.
temp.close()
def test_save_sup_load_rl(self):
pass
model_to_save = MockModel(
spinn.spinn_core_model.BaseModel,
default_args())
# Parse command line flags.
get_flags()
FLAGS(sys.argv)
log_temp = tempfile.NamedTemporaryFile()
ckpt_temp = tempfile.NamedTemporaryFile()
logger = afs_safe_logger.ProtoLogger(log_temp.name)
FLAGS.ckpt_path = '.'
trainer_to_save = ModelTrainer(model_to_save, logger, FLAGS)
model_to_load = MockModel(spinn.rl_spinn.BaseModel, default_args())
trainer_to_load = ModelTrainer(model_to_load, logger, FLAGS)
# Save to and load from temporary file.
trainer_to_save.save(ckpt_temp.name)
trainer_to_load.load(ckpt_temp.name, cpu=True)
compare_models(model_to_save, model_to_load)
# Cleanup temporary file.
ckpt_temp.close()
log_temp.close()
def test_init_models(self):
MockModel(spinn.spinn_core_model.BaseModel, default_args())
MockModel(spinn.rl_spinn.BaseModel, default_args())
MockModel(
spinn.spinn_core_model.BaseModel,
default_args(
use_sentence_pair=True))
MockModel(
spinn.rl_spinn.BaseModel,
default_args(
use_sentence_pair=True))
def test_basic_stack(self):
model = MockModel(BaseModel, default_args())
X, transitions = get_batch()
class Projection(nn.Module):
def forward(self, x):
return x[:, :default_args()['model_dim']]
class Reduce(nn.Module):
def forward(self, lefts, rights, tracking):
batch_size = len(lefts)
return torch.chunk(
torch.cat(
lefts,
0) -
torch.cat(
rights,
0),
batch_size,
0)
model.encode = Projection()
model.spinn.reduce = Reduce()
model(X, transitions)
outputs = model.spinn_outp[0]
assert outputs[0][0].data[0] == (3 - (1 - (2 - 1)))
assert outputs[1][0].data[0] == ((3 - 2) - (4 - 5))
def test_validate_transitions_cantskip(self):
model = MockModel(BaseModel, default_args())
# To Test:
# 1. Cant SKIP
# 2. Cant SHIFT
# 3. Cant REDUCE
# 4. No change SHIFT
# 5. No change REDUCE
bufs = [
[None],
[],
[None],
[None],
[None],
[],
]
stacks = [
[None],
[None],
[None],
[],
[],
[None, None],
]
transitions = [
2, 1, 0, 0, 0, 1
]
preds = np.array([
0, 0, 1, 1, 0, 1
]).astype(np.int32)
ret, _ = model.spinn.validate(
transitions, preds, stacks, bufs, zero_padded=False)
expected = np.array([
2, 1, 0, 0, 0, 1
], dtype=np.int32)
assert all(
p == e for p, e in zip(
ret.ravel().tolist(), expected.ravel().tolist())), "gold: {}\npreds: {}".format(
expected, ret)
if __name__ == '__main__':
unittest.main()
| mit |
cmoutard/mne-python | mne/viz/tests/test_3d.py | 4 | 7511 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
# Mark Wronkiewicz <wronk.mark@gmail.com>
#
# License: Simplified BSD
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises, assert_equal
from mne import (make_field_map, pick_channels_evoked, read_evokeds,
read_trans, read_dipole, SourceEstimate)
from mne.viz import (plot_sparse_source_estimates, plot_source_estimates,
plot_trans)
from mne.utils import requires_mayavi, requires_pysurfer, run_tests_if_main
from mne.datasets import testing
from mne.source_space import read_source_spaces
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
data_dir = testing.data_path(download=False)
subjects_dir = op.join(data_dir, 'subjects')
trans_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_trunc-trans.fif')
src_fname = op.join(data_dir, 'subjects', 'sample', 'bem',
'sample-oct-6-src.fif')
dip_fname = op.join(data_dir, 'MEG', 'sample', 'sample_audvis_trunc_set1.dip')
base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
evoked_fname = op.join(base_dir, 'test-ave.fif')
warnings.simplefilter('always') # enable b/c these tests throw warnings
@testing.requires_testing_data
@requires_pysurfer
@requires_mayavi
def test_plot_sparse_source_estimates():
"""Test plotting of (sparse) source estimates
"""
sample_src = read_source_spaces(src_fname)
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_size = stc_data.size
stc_data[(np.random.rand(stc_size / 20) * stc_size).astype(int)] = \
np.random.RandomState(0).rand(stc_data.size / 20)
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
colormap = 'mne_analyze'
plot_source_estimates(stc, 'sample', colormap=colormap,
config_opts={'background': (1, 1, 0)},
subjects_dir=subjects_dir, colorbar=True,
clim='auto')
assert_raises(TypeError, plot_source_estimates, stc, 'sample',
figure='foo', hemi='both', clim='auto')
# now do sparse version
vertices = sample_src[0]['vertno']
inds = [111, 333]
stc_data = np.zeros((len(inds), n_time))
stc_data[0, 1] = 1.
stc_data[1, 4] = 2.
vertices = [vertices[inds], np.empty(0, dtype=np.int)]
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_sparse_source_estimates(sample_src, stc, bgcolor=(1, 1, 1),
opacity=0.5, high_resolution=False)
@testing.requires_testing_data
@requires_mayavi
def test_plot_evoked_field():
"""Test plotting evoked field
"""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1, ch_type=t)
evoked.plot_field(maps, time=0.1)
@testing.requires_testing_data
@requires_mayavi
def test_plot_trans():
"""Test plotting of -trans.fif files
"""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
plot_trans(evoked.info, trans_fname, subject='sample',
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_trans, evoked.info, trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
@testing.requires_testing_data
@requires_pysurfer
@requires_mayavi
def test_limits_to_control_points():
"""Test functionality for determing control points
"""
sample_src = read_source_spaces(src_fname)
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.random.RandomState(0).rand((n_verts * n_time))
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1, 'sample')
# Test for simple use cases
from mayavi import mlab
stc.plot(subjects_dir=subjects_dir)
stc.plot(clim=dict(pos_lims=(10, 50, 90)), subjects_dir=subjects_dir)
stc.plot(clim=dict(kind='value', lims=(10, 50, 90)), figure=99,
subjects_dir=subjects_dir)
stc.plot(colormap='hot', clim='auto', subjects_dir=subjects_dir)
stc.plot(colormap='mne', clim='auto', subjects_dir=subjects_dir)
figs = [mlab.figure(), mlab.figure()]
assert_raises(RuntimeError, stc.plot, clim='auto', figure=figs,
subjects_dir=subjects_dir)
# Test both types of incorrect limits key (lims/pos_lims)
assert_raises(KeyError, plot_source_estimates, stc, colormap='mne',
clim=dict(kind='value', lims=(5, 10, 15)),
subjects_dir=subjects_dir)
assert_raises(KeyError, plot_source_estimates, stc, colormap='hot',
clim=dict(kind='value', pos_lims=(5, 10, 15)),
subjects_dir=subjects_dir)
# Test for correct clim values
assert_raises(ValueError, stc.plot,
clim=dict(kind='value', pos_lims=[0, 1, 0]),
subjects_dir=subjects_dir)
assert_raises(ValueError, stc.plot, colormap='mne',
clim=dict(pos_lims=(5, 10, 15, 20)),
subjects_dir=subjects_dir)
assert_raises(ValueError, stc.plot,
clim=dict(pos_lims=(5, 10, 15), kind='foo'),
subjects_dir=subjects_dir)
assert_raises(ValueError, stc.plot, colormap='mne', clim='foo',
subjects_dir=subjects_dir)
assert_raises(ValueError, stc.plot, clim=(5, 10, 15),
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_source_estimates, 'foo', clim='auto',
subjects_dir=subjects_dir)
assert_raises(ValueError, stc.plot, hemi='foo', clim='auto',
subjects_dir=subjects_dir)
# Test handling of degenerate data
stc.plot(clim=dict(kind='value', lims=[0, 0, 1]),
subjects_dir=subjects_dir) # ok
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
# thresholded maps
stc._data.fill(1.)
plot_source_estimates(stc, subjects_dir=subjects_dir)
assert_equal(len(w), 0)
stc._data[0].fill(0.)
plot_source_estimates(stc, subjects_dir=subjects_dir)
assert_equal(len(w), 0)
stc._data.fill(0.)
plot_source_estimates(stc, subjects_dir=subjects_dir)
assert_equal(len(w), 1)
mlab.close()
@testing.requires_testing_data
@requires_mayavi
def test_plot_dipole_locations():
"""Test plotting dipole locations
"""
dipoles = read_dipole(dip_fname)
trans = read_trans(trans_fname)
dipoles.plot_locations(trans, 'sample', subjects_dir, fig_name='foo')
assert_raises(ValueError, dipoles.plot_locations, trans, 'sample',
subjects_dir, mode='foo')
run_tests_if_main()
| bsd-3-clause |
jlegendary/nupic | examples/opf/experiments/anomaly/temporal/simple/description.py | 8 | 14153 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
"""
Template file used by the OPF Experiment Generator to generate the actual
description.py file by replacing $XXXXXXXX tokens with desired values.
This description.py file was generated by:
'~/nupic/current/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/ExpGenerator.pyc'
"""
from nupic.frameworks.opf.expdescriptionapi import ExperimentDescriptionAPI
from nupic.frameworks.opf.expdescriptionhelpers import (
updateConfigFromSubConfig,
applyValueGettersToContainer,
DeferredDictLookup)
from nupic.frameworks.opf.clamodelcallbacks import *
from nupic.frameworks.opf.metrics import MetricSpec
from nupic.frameworks.opf.opfutils import (InferenceType,
InferenceElement)
from nupic.support import aggregationDivide
from nupic.frameworks.opf.opftaskdriver import (
IterationPhaseSpecLearnOnly,
IterationPhaseSpecInferOnly,
IterationPhaseSpecLearnAndInfer)
# Model Configuration Dictionary:
#
# Define the model parameters and adjust for any modifications if imported
# from a sub-experiment.
#
# These fields might be modified by a sub-experiment; this dict is passed
# between the sub-experiment and base experiment
#
#
# NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements
# within the config dictionary may be assigned futures derived from the
# ValueGetterBase class, such as DeferredDictLookup.
# This facility is particularly handy for enabling substitution of values in
# the config dictionary from other values in the config dictionary, which is
# needed by permutation.py-based experiments. These values will be resolved
# during the call to applyValueGettersToContainer(),
# which we call after the base experiment's config dictionary is updated from
# the sub-experiment. See ValueGetterBase and
# DeferredDictLookup for more details about value-getters.
#
# For each custom encoder parameter to be exposed to the sub-experiment/
# permutation overrides, define a variable in this section, using key names
# beginning with a single underscore character to avoid collisions with
# pre-defined keys (e.g., _dsEncoderFieldName2_N).
#
# Example:
# config = dict(
# _dsEncoderFieldName2_N = 70,
# _dsEncoderFieldName2_W = 5,
# dsEncoderSchema = [
# base=dict(
# fieldname='Name2', type='ScalarEncoder',
# name='Name2', minval=0, maxval=270, clipInput=True,
# n=DeferredDictLookup('_dsEncoderFieldName2_N'),
# w=DeferredDictLookup('_dsEncoderFieldName2_W')),
# ],
# )
# updateConfigFromSubConfig(config)
# applyValueGettersToContainer(config)
config = {
# Type of model that the rest of these parameters apply to.
'model': "CLA",
# Version that specifies the format of the config.
'version': 1,
# Intermediate variables used to compute fields in modelParams and also
# referenced from the control section.
'aggregationInfo': { 'days': 0,
'fields': [],
'hours': 0,
'microseconds': 0,
'milliseconds': 0,
'minutes': 0,
'months': 0,
'seconds': 0,
'weeks': 0,
'years': 0},
'predictAheadTime': None,
# Model parameter dictionary.
'modelParams': {
# The type of inference that this model will perform
'inferenceType': 'TemporalAnomaly',
'sensorParams': {
# Sensor diagnostic output verbosity control;
# if > 0: sensor region will print out on screen what it's sensing
# at each step 0: silent; >=1: some info; >=2: more info;
# >=3: even more info (see compute() in py/regions/RecordSensor.py)
'verbosity' : 0,
# Example:
# dsEncoderSchema = [
# DeferredDictLookup('__field_name_encoder'),
# ],
#
# (value generated from DS_ENCODER_SCHEMA)
'encoders': {
u'f': {
'clipInput': True,
'fieldname': u'f',
'n': 100,
'name': u'f',
'minval': 0,
'maxval': 5,
'type': 'ScalarEncoder',
'w': 21},
},
# A dictionary specifying the period for automatically-generated
# resets from a RecordSensor;
#
# None = disable automatically-generated resets (also disabled if
# all of the specified values evaluate to 0).
# Valid keys is the desired combination of the following:
# days, hours, minutes, seconds, milliseconds, microseconds, weeks
#
# Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12),
#
# (value generated from SENSOR_AUTO_RESET)
'sensorAutoReset' : None,
},
'spEnable': True,
'spParams': {
# SP diagnostic output verbosity control;
# 0: silent; >=1: some info; >=2: more info;
'spVerbosity' : 0,
'spatialImp' : 'cpp',
'globalInhibition': 1,
# Number of cell columns in the cortical region (same number for
# SP and TP)
# (see also tpNCellsPerCol)
'columnCount': 2048,
'inputWidth': 0,
# SP inhibition control (absolute value);
# Maximum number of active columns in the SP region's output (when
# there are more, the weaker ones are suppressed)
'numActiveColumnsPerInhArea': 40,
'seed': 1956,
# potentialPct
# What percent of the columns's receptive field is available
# for potential synapses. At initialization time, we will
# choose potentialPct * (2*potentialRadius+1)^2
'potentialPct': 0.5,
# The default connected threshold. Any synapse whose
# permanence value is above the connected threshold is
# a "connected synapse", meaning it can contribute to the
# cell's firing. Typical value is 0.10. Cells whose activity
# level before inhibition falls below minDutyCycleBeforeInh
# will have their own internal synPermConnectedCell
# threshold set below this default value.
# (This concept applies to both SP and TP and so 'cells'
# is correct here as opposed to 'columns')
'synPermConnected': 0.1,
'synPermActiveInc': 0.05,
'synPermInactiveDec': 0.008,
},
# Controls whether TP is enabled or disabled;
# TP is necessary for making temporal predictions, such as predicting
# the next inputs. Without TP, the model is only capable of
# reconstructing missing sensor inputs (via SP).
'tpEnable' : True,
'tpParams': {
# TP diagnostic output verbosity control;
# 0: silent; [1..6]: increasing levels of verbosity
# (see verbosity in nupic/trunk/py/nupic/research/TP.py and TP10X*.py)
'verbosity': 0,
# Number of cell columns in the cortical region (same number for
# SP and TP)
# (see also tpNCellsPerCol)
'columnCount': 2048,
# The number of cells (i.e., states), allocated per column.
'cellsPerColumn': 32,
'inputWidth': 2048,
'seed': 1960,
# Temporal Pooler implementation selector (see _getTPClass in
# CLARegion.py).
'temporalImp': 'cpp',
# New Synapse formation count
# NOTE: If None, use spNumActivePerInhArea
#
# TODO: need better explanation
'newSynapseCount': 20,
# Maximum number of synapses per segment
# > 0 for fixed-size CLA
# -1 for non-fixed-size CLA
#
# TODO: for Ron: once the appropriate value is placed in TP
# constructor, see if we should eliminate this parameter from
# description.py.
'maxSynapsesPerSegment': 32,
# Maximum number of segments per cell
# > 0 for fixed-size CLA
# -1 for non-fixed-size CLA
#
# TODO: for Ron: once the appropriate value is placed in TP
# constructor, see if we should eliminate this parameter from
# description.py.
'maxSegmentsPerCell': 128,
# Initial Permanence
# TODO: need better explanation
'initialPerm': 0.21,
# Permanence Increment
'permanenceInc': 0.1,
# Permanence Decrement
# If set to None, will automatically default to tpPermanenceInc
# value.
'permanenceDec' : 0.1,
'globalDecay': 0.0,
'maxAge': 0,
# Minimum number of active synapses for a segment to be considered
# during search for the best-matching segments.
# None=use default
# Replaces: tpMinThreshold
'minThreshold': 12,
# Segment activation threshold.
# A segment is active if it has >= tpSegmentActivationThreshold
# connected synapses that are active due to infActiveState
# None=use default
# Replaces: tpActivationThreshold
'activationThreshold': 16,
'outputType': 'normal',
# "Pay Attention Mode" length. This tells the TP how many new
# elements to append to the end of a learned sequence at a time.
# Smaller values are better for datasets with short sequences,
# higher values are better for datasets with long sequences.
'pamLength': 1,
},
'clParams': {
# Classifier implementation selection.
'implementation': 'cpp',
'regionName' : 'CLAClassifierRegion',
# Classifier diagnostic output verbosity control;
# 0: silent; [1..6]: increasing levels of verbosity
'clVerbosity' : 0,
# This controls how fast the classifier learns/forgets. Higher values
# make it adapt faster and forget older patterns faster.
'alpha': 0.001,
# This is set after the call to updateConfigFromSubConfig and is
# computed from the aggregationInfo and predictAheadTime.
'steps': '1',
},
'trainSPNetOnlyIfRequested': False,
},
}
# end of config dictionary
# Adjust base config dictionary for any modifications if imported from a
# sub-experiment
updateConfigFromSubConfig(config)
# Compute predictionSteps based on the predictAheadTime and the aggregation
# period, which may be permuted over.
if config['predictAheadTime'] is not None:
predictionSteps = int(round(aggregationDivide(
config['predictAheadTime'], config['aggregationInfo'])))
assert (predictionSteps >= 1)
config['modelParams']['clParams']['steps'] = str(predictionSteps)
# Adjust config by applying ValueGetterBase-derived
# futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order
# to support value-getter-based substitutions from the sub-experiment (if any)
applyValueGettersToContainer(config)
control = {
# The environment that the current model is being run in
"environment": 'nupic',
# Input stream specification per py/nupic/cluster/database/StreamDef.json.
#
'dataset' : { u'info': u'cerebro_dummy',
u'streams': [ { u'columns': [u'*'],
u'info': u'hotGym.csv',
u'source': u'file://'+os.path.join(os.path.dirname(__file__), 'data.csv')}],
u'version': 1},
# Iteration count: maximum number of iterations. Each iteration corresponds
# to one record from the (possibly aggregated) dataset. The task is
# terminated when either number of iterations reaches iterationCount or
# all records in the (possibly aggregated) database have been processed,
# whichever occurs first.
#
# iterationCount of -1 = iterate over the entire dataset
'iterationCount' : -1,
# A dictionary containing all the supplementary parameters for inference
"inferenceArgs":{u'predictedField': u'f', u'predictionSteps': [1]},
# Metrics: A list of MetricSpecs that instantiate the metrics that are
# computed for this experiment
'metrics':[
MetricSpec(field=u'f', metric='passThruPrediction', inferenceElement='anomalyScore', params={'window': 1000}),
],
# Logged Metrics: A sequence of regular expressions that specify which of
# the metrics from the Inference Specifications section MUST be logged for
# every prediction. The regex's correspond to the automatically generated
# metric labels. This is similar to the way the optimization metric is
# specified in permutations.py.
'loggedMetrics': ['.*'],
}
descriptionInterface = ExperimentDescriptionAPI(modelConfig=config,
control=control)
| gpl-3.0 |
rvraghav93/scikit-learn | sklearn/_build_utils/__init__.py | 76 | 2644 | """
Utilities useful during the build.
"""
# author: Andy Mueller, Gael Varoquaux
# license: BSD
from __future__ import division, print_function, absolute_import
import os
from distutils.version import LooseVersion
from numpy.distutils.system_info import get_info
DEFAULT_ROOT = 'sklearn'
CYTHON_MIN_VERSION = '0.23'
def get_blas_info():
def atlas_not_found(blas_info_):
def_macros = blas_info.get('define_macros', [])
for x in def_macros:
if x[0] == "NO_ATLAS_INFO":
# if x[1] != 1 we should have lapack
# how do we do that now?
return True
if x[0] == "ATLAS_INFO":
if "None" in x[1]:
# this one turned up on FreeBSD
return True
return False
blas_info = get_info('blas_opt', 0)
if (not blas_info) or atlas_not_found(blas_info):
cblas_libs = ['cblas']
blas_info.pop('libraries', None)
else:
cblas_libs = blas_info.pop('libraries', [])
return cblas_libs, blas_info
def build_from_c_and_cpp_files(extensions):
"""Modify the extensions to build from the .c and .cpp files.
This is useful for releases, this way cython is not required to
run python setup.py install.
"""
for extension in extensions:
sources = []
for sfile in extension.sources:
path, ext = os.path.splitext(sfile)
if ext in ('.pyx', '.py'):
if extension.language == 'c++':
ext = '.cpp'
else:
ext = '.c'
sfile = path + ext
sources.append(sfile)
extension.sources = sources
def maybe_cythonize_extensions(top_path, config):
"""Tweaks for building extensions between release and development mode."""
is_release = os.path.exists(os.path.join(top_path, 'PKG-INFO'))
if is_release:
build_from_c_and_cpp_files(config.ext_modules)
else:
message = ('Please install cython with a version >= {0} in order '
'to build a scikit-learn development version.').format(
CYTHON_MIN_VERSION)
try:
import Cython
if LooseVersion(Cython.__version__) < CYTHON_MIN_VERSION:
message += ' Your version of Cython was {0}.'.format(
Cython.__version__)
raise ValueError(message)
from Cython.Build import cythonize
except ImportError as exc:
exc.args += (message,)
raise
config.ext_modules = cythonize(config.ext_modules)
| bsd-3-clause |
aospan/linux-next-bcm4708-edgecore-ecw7220-l | scripts/analyze_suspend.py | 164 | 160875 | #!/usr/bin/python
#
# Tool for analyzing suspend/resume timing
# Copyright (c) 2013, Intel Corporation.
#
# This program is free software; you can redistribute it and/or modify it
# under the terms and conditions of the GNU General Public License,
# version 2, as published by the Free Software Foundation.
#
# This program is distributed in the hope it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
# more details.
#
# You should have received a copy of the GNU General Public License along with
# this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin St - Fifth Floor, Boston, MA 02110-1301 USA.
#
# Authors:
# Todd Brandt <todd.e.brandt@linux.intel.com>
#
# Links:
# Home Page
# https://01.org/suspendresume
# Source repo
# https://github.com/01org/suspendresume
# Documentation
# Getting Started
# https://01.org/suspendresume/documentation/getting-started
# Command List:
# https://01.org/suspendresume/documentation/command-list
#
# Description:
# This tool is designed to assist kernel and OS developers in optimizing
# their linux stack's suspend/resume time. Using a kernel image built
# with a few extra options enabled, the tool will execute a suspend and
# will capture dmesg and ftrace data until resume is complete. This data
# is transformed into a device timeline and a callgraph to give a quick
# and detailed view of which devices and callbacks are taking the most
# time in suspend/resume. The output is a single html file which can be
# viewed in firefox or chrome.
#
# The following kernel build options are required:
# CONFIG_PM_DEBUG=y
# CONFIG_PM_SLEEP_DEBUG=y
# CONFIG_FTRACE=y
# CONFIG_FUNCTION_TRACER=y
# CONFIG_FUNCTION_GRAPH_TRACER=y
# CONFIG_KPROBES=y
# CONFIG_KPROBES_ON_FTRACE=y
#
# For kernel versions older than 3.15:
# The following additional kernel parameters are required:
# (e.g. in file /etc/default/grub)
# GRUB_CMDLINE_LINUX_DEFAULT="... initcall_debug log_buf_len=16M ..."
#
# ----------------- LIBRARIES --------------------
import sys
import time
import os
import string
import re
import platform
from datetime import datetime
import struct
import ConfigParser
# ----------------- CLASSES --------------------
# Class: SystemValues
# Description:
# A global, single-instance container used to
# store system values and test parameters
class SystemValues:
ansi = False
version = '4.2'
verbose = False
addlogs = False
mindevlen = 0.001
mincglen = 1.0
srgap = 0
cgexp = False
outdir = ''
testdir = '.'
tpath = '/sys/kernel/debug/tracing/'
fpdtpath = '/sys/firmware/acpi/tables/FPDT'
epath = '/sys/kernel/debug/tracing/events/power/'
traceevents = [
'suspend_resume',
'device_pm_callback_end',
'device_pm_callback_start'
]
testcommand = ''
mempath = '/dev/mem'
powerfile = '/sys/power/state'
suspendmode = 'mem'
hostname = 'localhost'
prefix = 'test'
teststamp = ''
dmesgstart = 0.0
dmesgfile = ''
ftracefile = ''
htmlfile = ''
embedded = False
rtcwake = False
rtcwaketime = 10
rtcpath = ''
devicefilter = []
stamp = 0
execcount = 1
x2delay = 0
usecallgraph = False
usetraceevents = False
usetraceeventsonly = False
usetracemarkers = True
usekprobes = True
usedevsrc = False
notestrun = False
devprops = dict()
postresumetime = 0
devpropfmt = '# Device Properties: .*'
tracertypefmt = '# tracer: (?P<t>.*)'
firmwarefmt = '# fwsuspend (?P<s>[0-9]*) fwresume (?P<r>[0-9]*)$'
postresumefmt = '# post resume time (?P<t>[0-9]*)$'
stampfmt = '# suspend-(?P<m>[0-9]{2})(?P<d>[0-9]{2})(?P<y>[0-9]{2})-'+\
'(?P<H>[0-9]{2})(?P<M>[0-9]{2})(?P<S>[0-9]{2})'+\
' (?P<host>.*) (?P<mode>.*) (?P<kernel>.*)$'
kprobecolor = 'rgba(204,204,204,0.5)'
synccolor = 'rgba(204,204,204,0.5)'
debugfuncs = []
tracefuncs = {
'sys_sync': dict(),
'pm_prepare_console': dict(),
'pm_notifier_call_chain': dict(),
'freeze_processes': dict(),
'freeze_kernel_threads': dict(),
'pm_restrict_gfp_mask': dict(),
'acpi_suspend_begin': dict(),
'suspend_console': dict(),
'acpi_pm_prepare': dict(),
'syscore_suspend': dict(),
'arch_enable_nonboot_cpus_end': dict(),
'syscore_resume': dict(),
'acpi_pm_finish': dict(),
'resume_console': dict(),
'acpi_pm_end': dict(),
'pm_restore_gfp_mask': dict(),
'thaw_processes': dict(),
'pm_restore_console': dict(),
'CPU_OFF': {
'func':'_cpu_down',
'args_x86_64': {'cpu':'%di:s32'},
'format': 'CPU_OFF[{cpu}]',
'mask': 'CPU_.*_DOWN'
},
'CPU_ON': {
'func':'_cpu_up',
'args_x86_64': {'cpu':'%di:s32'},
'format': 'CPU_ON[{cpu}]',
'mask': 'CPU_.*_UP'
},
}
dev_tracefuncs = {
# general wait/delay/sleep
'msleep': { 'args_x86_64': {'time':'%di:s32'} },
'udelay': { 'func':'__const_udelay', 'args_x86_64': {'loops':'%di:s32'} },
'acpi_os_stall': dict(),
# ACPI
'acpi_resume_power_resources': dict(),
'acpi_ps_parse_aml': dict(),
# filesystem
'ext4_sync_fs': dict(),
# ATA
'ata_eh_recover': { 'args_x86_64': {'port':'+36(%di):s32'} },
# i915
'i915_gem_restore_gtt_mappings': dict(),
'intel_opregion_setup': dict(),
'intel_dp_detect': dict(),
'intel_hdmi_detect': dict(),
'intel_opregion_init': dict(),
}
kprobes_postresume = [
{
'name': 'ataportrst',
'func': 'ata_eh_recover',
'args': {'port':'+36(%di):s32'},
'format': 'ata{port}_port_reset',
'mask': 'ata.*_port_reset'
}
]
kprobes = dict()
timeformat = '%.3f'
def __init__(self):
# if this is a phoronix test run, set some default options
if('LOG_FILE' in os.environ and 'TEST_RESULTS_IDENTIFIER' in os.environ):
self.embedded = True
self.addlogs = True
self.htmlfile = os.environ['LOG_FILE']
self.hostname = platform.node()
if(self.hostname == ''):
self.hostname = 'localhost'
rtc = "rtc0"
if os.path.exists('/dev/rtc'):
rtc = os.readlink('/dev/rtc')
rtc = '/sys/class/rtc/'+rtc
if os.path.exists(rtc) and os.path.exists(rtc+'/date') and \
os.path.exists(rtc+'/time') and os.path.exists(rtc+'/wakealarm'):
self.rtcpath = rtc
if (hasattr(sys.stdout, 'isatty') and sys.stdout.isatty()):
self.ansi = True
def setPrecision(self, num):
if num < 0 or num > 6:
return
self.timeformat = '%.{0}f'.format(num)
def setOutputFile(self):
if((self.htmlfile == '') and (self.dmesgfile != '')):
m = re.match('(?P<name>.*)_dmesg\.txt$', self.dmesgfile)
if(m):
self.htmlfile = m.group('name')+'.html'
if((self.htmlfile == '') and (self.ftracefile != '')):
m = re.match('(?P<name>.*)_ftrace\.txt$', self.ftracefile)
if(m):
self.htmlfile = m.group('name')+'.html'
if(self.htmlfile == ''):
self.htmlfile = 'output.html'
def initTestOutput(self, subdir, testpath=''):
self.prefix = self.hostname
v = open('/proc/version', 'r').read().strip()
kver = string.split(v)[2]
n = datetime.now()
testtime = n.strftime('suspend-%m%d%y-%H%M%S')
if not testpath:
testpath = n.strftime('suspend-%y%m%d-%H%M%S')
if(subdir != "."):
self.testdir = subdir+"/"+testpath
else:
self.testdir = testpath
self.teststamp = \
'# '+testtime+' '+self.prefix+' '+self.suspendmode+' '+kver
if(self.embedded):
self.dmesgfile = \
'/tmp/'+testtime+'_'+self.suspendmode+'_dmesg.txt'
self.ftracefile = \
'/tmp/'+testtime+'_'+self.suspendmode+'_ftrace.txt'
return
self.dmesgfile = \
self.testdir+'/'+self.prefix+'_'+self.suspendmode+'_dmesg.txt'
self.ftracefile = \
self.testdir+'/'+self.prefix+'_'+self.suspendmode+'_ftrace.txt'
self.htmlfile = \
self.testdir+'/'+self.prefix+'_'+self.suspendmode+'.html'
if not os.path.isdir(self.testdir):
os.mkdir(self.testdir)
def setDeviceFilter(self, devnames):
self.devicefilter = string.split(devnames)
def rtcWakeAlarmOn(self):
os.system('echo 0 > '+self.rtcpath+'/wakealarm')
outD = open(self.rtcpath+'/date', 'r').read().strip()
outT = open(self.rtcpath+'/time', 'r').read().strip()
mD = re.match('^(?P<y>[0-9]*)-(?P<m>[0-9]*)-(?P<d>[0-9]*)', outD)
mT = re.match('^(?P<h>[0-9]*):(?P<m>[0-9]*):(?P<s>[0-9]*)', outT)
if(mD and mT):
# get the current time from hardware
utcoffset = int((datetime.now() - datetime.utcnow()).total_seconds())
dt = datetime(\
int(mD.group('y')), int(mD.group('m')), int(mD.group('d')),
int(mT.group('h')), int(mT.group('m')), int(mT.group('s')))
nowtime = int(dt.strftime('%s')) + utcoffset
else:
# if hardware time fails, use the software time
nowtime = int(datetime.now().strftime('%s'))
alarm = nowtime + self.rtcwaketime
os.system('echo %d > %s/wakealarm' % (alarm, self.rtcpath))
def rtcWakeAlarmOff(self):
os.system('echo 0 > %s/wakealarm' % self.rtcpath)
def initdmesg(self):
# get the latest time stamp from the dmesg log
fp = os.popen('dmesg')
ktime = '0'
for line in fp:
line = line.replace('\r\n', '')
idx = line.find('[')
if idx > 1:
line = line[idx:]
m = re.match('[ \t]*(\[ *)(?P<ktime>[0-9\.]*)(\]) (?P<msg>.*)', line)
if(m):
ktime = m.group('ktime')
fp.close()
self.dmesgstart = float(ktime)
def getdmesg(self):
# store all new dmesg lines since initdmesg was called
fp = os.popen('dmesg')
op = open(self.dmesgfile, 'a')
for line in fp:
line = line.replace('\r\n', '')
idx = line.find('[')
if idx > 1:
line = line[idx:]
m = re.match('[ \t]*(\[ *)(?P<ktime>[0-9\.]*)(\]) (?P<msg>.*)', line)
if(not m):
continue
ktime = float(m.group('ktime'))
if ktime > self.dmesgstart:
op.write(line)
fp.close()
op.close()
def addFtraceFilterFunctions(self, file):
fp = open(file)
list = fp.read().split('\n')
fp.close()
for i in list:
if len(i) < 2:
continue
self.tracefuncs[i] = dict()
def getFtraceFilterFunctions(self, current):
rootCheck(True)
if not current:
os.system('cat '+self.tpath+'available_filter_functions')
return
fp = open(self.tpath+'available_filter_functions')
master = fp.read().split('\n')
fp.close()
if len(self.debugfuncs) > 0:
for i in self.debugfuncs:
if i in master:
print i
else:
print self.colorText(i)
else:
for i in self.tracefuncs:
if 'func' in self.tracefuncs[i]:
i = self.tracefuncs[i]['func']
if i in master:
print i
else:
print self.colorText(i)
def setFtraceFilterFunctions(self, list):
fp = open(self.tpath+'available_filter_functions')
master = fp.read().split('\n')
fp.close()
flist = ''
for i in list:
if i not in master:
continue
if ' [' in i:
flist += i.split(' ')[0]+'\n'
else:
flist += i+'\n'
fp = open(self.tpath+'set_graph_function', 'w')
fp.write(flist)
fp.close()
def kprobeMatch(self, name, target):
if name not in self.kprobes:
return False
if re.match(self.kprobes[name]['mask'], target):
return True
return False
def basicKprobe(self, name):
self.kprobes[name] = {'name': name,'func': name,'args': dict(),'format': name,'mask': name}
def defaultKprobe(self, name, kdata):
k = kdata
for field in ['name', 'format', 'mask', 'func']:
if field not in k:
k[field] = name
archargs = 'args_'+platform.machine()
if archargs in k:
k['args'] = k[archargs]
else:
k['args'] = dict()
k['format'] = name
self.kprobes[name] = k
def kprobeColor(self, name):
if name not in self.kprobes or 'color' not in self.kprobes[name]:
return ''
return self.kprobes[name]['color']
def kprobeDisplayName(self, name, dataraw):
if name not in self.kprobes:
self.basicKprobe(name)
data = ''
quote=0
# first remvoe any spaces inside quotes, and the quotes
for c in dataraw:
if c == '"':
quote = (quote + 1) % 2
if quote and c == ' ':
data += '_'
elif c != '"':
data += c
fmt, args = self.kprobes[name]['format'], self.kprobes[name]['args']
arglist = dict()
# now process the args
for arg in sorted(args):
arglist[arg] = ''
m = re.match('.* '+arg+'=(?P<arg>.*) ', data);
if m:
arglist[arg] = m.group('arg')
else:
m = re.match('.* '+arg+'=(?P<arg>.*)', data);
if m:
arglist[arg] = m.group('arg')
out = fmt.format(**arglist)
out = out.replace(' ', '_').replace('"', '')
return out
def kprobeText(self, kprobe):
name, fmt, func, args = kprobe['name'], kprobe['format'], kprobe['func'], kprobe['args']
if re.findall('{(?P<n>[a-z,A-Z,0-9]*)}', func):
doError('Kprobe "%s" has format info in the function name "%s"' % (name, func), False)
for arg in re.findall('{(?P<n>[a-z,A-Z,0-9]*)}', fmt):
if arg not in args:
doError('Kprobe "%s" is missing argument "%s"' % (name, arg), False)
val = 'p:%s_cal %s' % (name, func)
for i in sorted(args):
val += ' %s=%s' % (i, args[i])
val += '\nr:%s_ret %s $retval\n' % (name, func)
return val
def addKprobes(self):
# first test each kprobe
print('INITIALIZING KPROBES...')
rejects = []
for name in sorted(self.kprobes):
if not self.testKprobe(self.kprobes[name]):
rejects.append(name)
# remove all failed ones from the list
for name in rejects:
vprint('Skipping KPROBE: %s' % name)
self.kprobes.pop(name)
self.fsetVal('', 'kprobe_events')
kprobeevents = ''
# set the kprobes all at once
for kp in self.kprobes:
val = self.kprobeText(self.kprobes[kp])
vprint('Adding KPROBE: %s\n%s' % (kp, val.strip()))
kprobeevents += self.kprobeText(self.kprobes[kp])
self.fsetVal(kprobeevents, 'kprobe_events')
# verify that the kprobes were set as ordered
check = self.fgetVal('kprobe_events')
linesout = len(kprobeevents.split('\n'))
linesack = len(check.split('\n'))
if linesack < linesout:
# if not, try appending the kprobes 1 by 1
for kp in self.kprobes:
kprobeevents = self.kprobeText(self.kprobes[kp])
self.fsetVal(kprobeevents, 'kprobe_events', 'a')
self.fsetVal('1', 'events/kprobes/enable')
def testKprobe(self, kprobe):
kprobeevents = self.kprobeText(kprobe)
if not kprobeevents:
return False
try:
self.fsetVal(kprobeevents, 'kprobe_events')
check = self.fgetVal('kprobe_events')
except:
return False
linesout = len(kprobeevents.split('\n'))
linesack = len(check.split('\n'))
if linesack < linesout:
return False
return True
def fsetVal(self, val, path, mode='w'):
file = self.tpath+path
if not os.path.exists(file):
return False
try:
fp = open(file, mode)
fp.write(val)
fp.close()
except:
pass
return True
def fgetVal(self, path):
file = self.tpath+path
res = ''
if not os.path.exists(file):
return res
try:
fp = open(file, 'r')
res = fp.read()
fp.close()
except:
pass
return res
def cleanupFtrace(self):
if(self.usecallgraph or self.usetraceevents):
self.fsetVal('0', 'events/kprobes/enable')
self.fsetVal('', 'kprobe_events')
def setupAllKprobes(self):
for name in self.tracefuncs:
self.defaultKprobe(name, self.tracefuncs[name])
for name in self.dev_tracefuncs:
self.defaultKprobe(name, self.dev_tracefuncs[name])
def isCallgraphFunc(self, name):
if len(self.debugfuncs) < 1 and self.suspendmode == 'command':
return True
if name in self.debugfuncs:
return True
funclist = []
for i in self.tracefuncs:
if 'func' in self.tracefuncs[i]:
funclist.append(self.tracefuncs[i]['func'])
else:
funclist.append(i)
if name in funclist:
return True
return False
def initFtrace(self, testing=False):
tp = self.tpath
print('INITIALIZING FTRACE...')
# turn trace off
self.fsetVal('0', 'tracing_on')
self.cleanupFtrace()
# set the trace clock to global
self.fsetVal('global', 'trace_clock')
# set trace buffer to a huge value
self.fsetVal('nop', 'current_tracer')
self.fsetVal('100000', 'buffer_size_kb')
# go no further if this is just a status check
if testing:
return
if self.usekprobes:
# add tracefunc kprobes so long as were not using full callgraph
if(not self.usecallgraph or len(self.debugfuncs) > 0):
for name in self.tracefuncs:
self.defaultKprobe(name, self.tracefuncs[name])
if self.usedevsrc:
for name in self.dev_tracefuncs:
self.defaultKprobe(name, self.dev_tracefuncs[name])
else:
self.usedevsrc = False
self.addKprobes()
# initialize the callgraph trace, unless this is an x2 run
if(self.usecallgraph):
# set trace type
self.fsetVal('function_graph', 'current_tracer')
self.fsetVal('', 'set_ftrace_filter')
# set trace format options
self.fsetVal('print-parent', 'trace_options')
self.fsetVal('funcgraph-abstime', 'trace_options')
self.fsetVal('funcgraph-cpu', 'trace_options')
self.fsetVal('funcgraph-duration', 'trace_options')
self.fsetVal('funcgraph-proc', 'trace_options')
self.fsetVal('funcgraph-tail', 'trace_options')
self.fsetVal('nofuncgraph-overhead', 'trace_options')
self.fsetVal('context-info', 'trace_options')
self.fsetVal('graph-time', 'trace_options')
self.fsetVal('0', 'max_graph_depth')
if len(self.debugfuncs) > 0:
self.setFtraceFilterFunctions(self.debugfuncs)
elif self.suspendmode == 'command':
self.fsetVal('', 'set_graph_function')
else:
cf = ['dpm_run_callback']
if(self.usetraceeventsonly):
cf += ['dpm_prepare', 'dpm_complete']
for fn in self.tracefuncs:
if 'func' in self.tracefuncs[fn]:
cf.append(self.tracefuncs[fn]['func'])
else:
cf.append(fn)
self.setFtraceFilterFunctions(cf)
if(self.usetraceevents):
# turn trace events on
events = iter(self.traceevents)
for e in events:
self.fsetVal('1', 'events/power/'+e+'/enable')
# clear the trace buffer
self.fsetVal('', 'trace')
def verifyFtrace(self):
# files needed for any trace data
files = ['buffer_size_kb', 'current_tracer', 'trace', 'trace_clock',
'trace_marker', 'trace_options', 'tracing_on']
# files needed for callgraph trace data
tp = self.tpath
if(self.usecallgraph):
files += [
'available_filter_functions',
'set_ftrace_filter',
'set_graph_function'
]
for f in files:
if(os.path.exists(tp+f) == False):
return False
return True
def verifyKprobes(self):
# files needed for kprobes to work
files = ['kprobe_events', 'events']
tp = self.tpath
for f in files:
if(os.path.exists(tp+f) == False):
return False
return True
def colorText(self, str):
if not self.ansi:
return str
return '\x1B[31;40m'+str+'\x1B[m'
sysvals = SystemValues()
# Class: DevProps
# Description:
# Simple class which holds property values collected
# for all the devices used in the timeline.
class DevProps:
syspath = ''
altname = ''
async = True
xtraclass = ''
xtrainfo = ''
def out(self, dev):
return '%s,%s,%d;' % (dev, self.altname, self.async)
def debug(self, dev):
print '%s:\n\taltname = %s\n\t async = %s' % (dev, self.altname, self.async)
def altName(self, dev):
if not self.altname or self.altname == dev:
return dev
return '%s [%s]' % (self.altname, dev)
def xtraClass(self):
if self.xtraclass:
return ' '+self.xtraclass
if not self.async:
return ' sync'
return ''
def xtraInfo(self):
if self.xtraclass:
return ' '+self.xtraclass
if self.async:
return ' async'
return ' sync'
# Class: DeviceNode
# Description:
# A container used to create a device hierachy, with a single root node
# and a tree of child nodes. Used by Data.deviceTopology()
class DeviceNode:
name = ''
children = 0
depth = 0
def __init__(self, nodename, nodedepth):
self.name = nodename
self.children = []
self.depth = nodedepth
# Class: Data
# Description:
# The primary container for suspend/resume test data. There is one for
# each test run. The data is organized into a cronological hierarchy:
# Data.dmesg {
# root structure, started as dmesg & ftrace, but now only ftrace
# contents: times for suspend start/end, resume start/end, fwdata
# phases {
# 10 sequential, non-overlapping phases of S/R
# contents: times for phase start/end, order/color data for html
# devlist {
# device callback or action list for this phase
# device {
# a single device callback or generic action
# contents: start/stop times, pid/cpu/driver info
# parents/children, html id for timeline/callgraph
# optionally includes an ftrace callgraph
# optionally includes intradev trace events
# }
# }
# }
# }
#
class Data:
dmesg = {} # root data structure
phases = [] # ordered list of phases
start = 0.0 # test start
end = 0.0 # test end
tSuspended = 0.0 # low-level suspend start
tResumed = 0.0 # low-level resume start
tLow = 0.0 # time spent in low-level suspend (standby/freeze)
fwValid = False # is firmware data available
fwSuspend = 0 # time spent in firmware suspend
fwResume = 0 # time spent in firmware resume
dmesgtext = [] # dmesg text file in memory
testnumber = 0
idstr = ''
html_device_id = 0
stamp = 0
outfile = ''
dev_ubiquitous = ['msleep', 'udelay']
def __init__(self, num):
idchar = 'abcdefghijklmnopqrstuvwxyz'
self.testnumber = num
self.idstr = idchar[num]
self.dmesgtext = []
self.phases = []
self.dmesg = { # fixed list of 10 phases
'suspend_prepare': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#CCFFCC', 'order': 0},
'suspend': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#88FF88', 'order': 1},
'suspend_late': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#00AA00', 'order': 2},
'suspend_noirq': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#008888', 'order': 3},
'suspend_machine': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#0000FF', 'order': 4},
'resume_machine': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#FF0000', 'order': 5},
'resume_noirq': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#FF9900', 'order': 6},
'resume_early': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#FFCC00', 'order': 7},
'resume': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#FFFF88', 'order': 8},
'resume_complete': {'list': dict(), 'start': -1.0, 'end': -1.0,
'row': 0, 'color': '#FFFFCC', 'order': 9}
}
self.phases = self.sortedPhases()
self.devicegroups = []
for phase in self.phases:
self.devicegroups.append([phase])
def getStart(self):
return self.dmesg[self.phases[0]]['start']
def setStart(self, time):
self.start = time
self.dmesg[self.phases[0]]['start'] = time
def getEnd(self):
return self.dmesg[self.phases[-1]]['end']
def setEnd(self, time):
self.end = time
self.dmesg[self.phases[-1]]['end'] = time
def isTraceEventOutsideDeviceCalls(self, pid, time):
for phase in self.phases:
list = self.dmesg[phase]['list']
for dev in list:
d = list[dev]
if(d['pid'] == pid and time >= d['start'] and
time < d['end']):
return False
return True
def targetDevice(self, phaselist, start, end, pid=-1):
tgtdev = ''
for phase in phaselist:
list = self.dmesg[phase]['list']
for devname in list:
dev = list[devname]
if(pid >= 0 and dev['pid'] != pid):
continue
devS = dev['start']
devE = dev['end']
if(start < devS or start >= devE or end <= devS or end > devE):
continue
tgtdev = dev
break
return tgtdev
def addDeviceFunctionCall(self, displayname, kprobename, proc, pid, start, end, cdata, rdata):
machstart = self.dmesg['suspend_machine']['start']
machend = self.dmesg['resume_machine']['end']
tgtdev = self.targetDevice(self.phases, start, end, pid)
if not tgtdev and start >= machstart and end < machend:
# device calls in machine phases should be serial
tgtdev = self.targetDevice(['suspend_machine', 'resume_machine'], start, end)
if not tgtdev:
if 'scsi_eh' in proc:
self.newActionGlobal(proc, start, end, pid)
self.addDeviceFunctionCall(displayname, kprobename, proc, pid, start, end, cdata, rdata)
else:
vprint('IGNORE: %s[%s](%d) [%f - %f] | %s | %s | %s' % (displayname, kprobename,
pid, start, end, cdata, rdata, proc))
return False
# detail block fits within tgtdev
if('src' not in tgtdev):
tgtdev['src'] = []
title = cdata+' '+rdata
mstr = '\(.*\) *(?P<args>.*) *\((?P<caller>.*)\+.* arg1=(?P<ret>.*)'
m = re.match(mstr, title)
if m:
c = m.group('caller')
a = m.group('args').strip()
r = m.group('ret')
if len(r) > 6:
r = ''
else:
r = 'ret=%s ' % r
l = '%0.3fms' % ((end - start) * 1000)
if kprobename in self.dev_ubiquitous:
title = '%s(%s) <- %s, %s(%s)' % (displayname, a, c, r, l)
else:
title = '%s(%s) %s(%s)' % (displayname, a, r, l)
e = TraceEvent(title, kprobename, start, end - start)
tgtdev['src'].append(e)
return True
def trimTimeVal(self, t, t0, dT, left):
if left:
if(t > t0):
if(t - dT < t0):
return t0
return t - dT
else:
return t
else:
if(t < t0 + dT):
if(t > t0):
return t0 + dT
return t + dT
else:
return t
def trimTime(self, t0, dT, left):
self.tSuspended = self.trimTimeVal(self.tSuspended, t0, dT, left)
self.tResumed = self.trimTimeVal(self.tResumed, t0, dT, left)
self.start = self.trimTimeVal(self.start, t0, dT, left)
self.end = self.trimTimeVal(self.end, t0, dT, left)
for phase in self.phases:
p = self.dmesg[phase]
p['start'] = self.trimTimeVal(p['start'], t0, dT, left)
p['end'] = self.trimTimeVal(p['end'], t0, dT, left)
list = p['list']
for name in list:
d = list[name]
d['start'] = self.trimTimeVal(d['start'], t0, dT, left)
d['end'] = self.trimTimeVal(d['end'], t0, dT, left)
if('ftrace' in d):
cg = d['ftrace']
cg.start = self.trimTimeVal(cg.start, t0, dT, left)
cg.end = self.trimTimeVal(cg.end, t0, dT, left)
for line in cg.list:
line.time = self.trimTimeVal(line.time, t0, dT, left)
if('src' in d):
for e in d['src']:
e.time = self.trimTimeVal(e.time, t0, dT, left)
def normalizeTime(self, tZero):
# trim out any standby or freeze clock time
if(self.tSuspended != self.tResumed):
if(self.tResumed > tZero):
self.trimTime(self.tSuspended, \
self.tResumed-self.tSuspended, True)
else:
self.trimTime(self.tSuspended, \
self.tResumed-self.tSuspended, False)
def newPhaseWithSingleAction(self, phasename, devname, start, end, color):
for phase in self.phases:
self.dmesg[phase]['order'] += 1
self.html_device_id += 1
devid = '%s%d' % (self.idstr, self.html_device_id)
list = dict()
list[devname] = \
{'start': start, 'end': end, 'pid': 0, 'par': '',
'length': (end-start), 'row': 0, 'id': devid, 'drv': '' };
self.dmesg[phasename] = \
{'list': list, 'start': start, 'end': end,
'row': 0, 'color': color, 'order': 0}
self.phases = self.sortedPhases()
def newPhase(self, phasename, start, end, color, order):
if(order < 0):
order = len(self.phases)
for phase in self.phases[order:]:
self.dmesg[phase]['order'] += 1
if(order > 0):
p = self.phases[order-1]
self.dmesg[p]['end'] = start
if(order < len(self.phases)):
p = self.phases[order]
self.dmesg[p]['start'] = end
list = dict()
self.dmesg[phasename] = \
{'list': list, 'start': start, 'end': end,
'row': 0, 'color': color, 'order': order}
self.phases = self.sortedPhases()
self.devicegroups.append([phasename])
def setPhase(self, phase, ktime, isbegin):
if(isbegin):
self.dmesg[phase]['start'] = ktime
else:
self.dmesg[phase]['end'] = ktime
def dmesgSortVal(self, phase):
return self.dmesg[phase]['order']
def sortedPhases(self):
return sorted(self.dmesg, key=self.dmesgSortVal)
def sortedDevices(self, phase):
list = self.dmesg[phase]['list']
slist = []
tmp = dict()
for devname in list:
dev = list[devname]
tmp[dev['start']] = devname
for t in sorted(tmp):
slist.append(tmp[t])
return slist
def fixupInitcalls(self, phase, end):
# if any calls never returned, clip them at system resume end
phaselist = self.dmesg[phase]['list']
for devname in phaselist:
dev = phaselist[devname]
if(dev['end'] < 0):
for p in self.phases:
if self.dmesg[p]['end'] > dev['start']:
dev['end'] = self.dmesg[p]['end']
break
vprint('%s (%s): callback didnt return' % (devname, phase))
def deviceFilter(self, devicefilter):
# remove all by the relatives of the filter devnames
filter = []
for phase in self.phases:
list = self.dmesg[phase]['list']
for name in devicefilter:
dev = name
while(dev in list):
if(dev not in filter):
filter.append(dev)
dev = list[dev]['par']
children = self.deviceDescendants(name, phase)
for dev in children:
if(dev not in filter):
filter.append(dev)
for phase in self.phases:
list = self.dmesg[phase]['list']
rmlist = []
for name in list:
pid = list[name]['pid']
if(name not in filter and pid >= 0):
rmlist.append(name)
for name in rmlist:
del list[name]
def fixupInitcallsThatDidntReturn(self):
# if any calls never returned, clip them at system resume end
for phase in self.phases:
self.fixupInitcalls(phase, self.getEnd())
def isInsideTimeline(self, start, end):
if(self.start <= start and self.end > start):
return True
return False
def phaseOverlap(self, phases):
rmgroups = []
newgroup = []
for group in self.devicegroups:
for phase in phases:
if phase not in group:
continue
for p in group:
if p not in newgroup:
newgroup.append(p)
if group not in rmgroups:
rmgroups.append(group)
for group in rmgroups:
self.devicegroups.remove(group)
self.devicegroups.append(newgroup)
def newActionGlobal(self, name, start, end, pid=-1, color=''):
# if event starts before timeline start, expand timeline
if(start < self.start):
self.setStart(start)
# if event ends after timeline end, expand the timeline
if(end > self.end):
self.setEnd(end)
# which phase is this device callback or action "in"
targetphase = "none"
htmlclass = ''
overlap = 0.0
phases = []
for phase in self.phases:
pstart = self.dmesg[phase]['start']
pend = self.dmesg[phase]['end']
o = max(0, min(end, pend) - max(start, pstart))
if o > 0:
phases.append(phase)
if o > overlap:
if overlap > 0 and phase == 'post_resume':
continue
targetphase = phase
overlap = o
if pid == -2:
htmlclass = ' bg'
if len(phases) > 1:
htmlclass = ' bg'
self.phaseOverlap(phases)
if targetphase in self.phases:
newname = self.newAction(targetphase, name, pid, '', start, end, '', htmlclass, color)
return (targetphase, newname)
return False
def newAction(self, phase, name, pid, parent, start, end, drv, htmlclass='', color=''):
# new device callback for a specific phase
self.html_device_id += 1
devid = '%s%d' % (self.idstr, self.html_device_id)
list = self.dmesg[phase]['list']
length = -1.0
if(start >= 0 and end >= 0):
length = end - start
if pid == -2:
i = 2
origname = name
while(name in list):
name = '%s[%d]' % (origname, i)
i += 1
list[name] = {'start': start, 'end': end, 'pid': pid, 'par': parent,
'length': length, 'row': 0, 'id': devid, 'drv': drv }
if htmlclass:
list[name]['htmlclass'] = htmlclass
if color:
list[name]['color'] = color
return name
def deviceIDs(self, devlist, phase):
idlist = []
list = self.dmesg[phase]['list']
for devname in list:
if devname in devlist:
idlist.append(list[devname]['id'])
return idlist
def deviceParentID(self, devname, phase):
pdev = ''
pdevid = ''
list = self.dmesg[phase]['list']
if devname in list:
pdev = list[devname]['par']
if pdev in list:
return list[pdev]['id']
return pdev
def deviceChildren(self, devname, phase):
devlist = []
list = self.dmesg[phase]['list']
for child in list:
if(list[child]['par'] == devname):
devlist.append(child)
return devlist
def deviceDescendants(self, devname, phase):
children = self.deviceChildren(devname, phase)
family = children
for child in children:
family += self.deviceDescendants(child, phase)
return family
def deviceChildrenIDs(self, devname, phase):
devlist = self.deviceChildren(devname, phase)
return self.deviceIDs(devlist, phase)
def printDetails(self):
vprint(' test start: %f' % self.start)
for phase in self.phases:
dc = len(self.dmesg[phase]['list'])
vprint(' %16s: %f - %f (%d devices)' % (phase, \
self.dmesg[phase]['start'], self.dmesg[phase]['end'], dc))
vprint(' test end: %f' % self.end)
def deviceChildrenAllPhases(self, devname):
devlist = []
for phase in self.phases:
list = self.deviceChildren(devname, phase)
for dev in list:
if dev not in devlist:
devlist.append(dev)
return devlist
def masterTopology(self, name, list, depth):
node = DeviceNode(name, depth)
for cname in list:
# avoid recursions
if name == cname:
continue
clist = self.deviceChildrenAllPhases(cname)
cnode = self.masterTopology(cname, clist, depth+1)
node.children.append(cnode)
return node
def printTopology(self, node):
html = ''
if node.name:
info = ''
drv = ''
for phase in self.phases:
list = self.dmesg[phase]['list']
if node.name in list:
s = list[node.name]['start']
e = list[node.name]['end']
if list[node.name]['drv']:
drv = ' {'+list[node.name]['drv']+'}'
info += ('<li>%s: %.3fms</li>' % (phase, (e-s)*1000))
html += '<li><b>'+node.name+drv+'</b>'
if info:
html += '<ul>'+info+'</ul>'
html += '</li>'
if len(node.children) > 0:
html += '<ul>'
for cnode in node.children:
html += self.printTopology(cnode)
html += '</ul>'
return html
def rootDeviceList(self):
# list of devices graphed
real = []
for phase in self.dmesg:
list = self.dmesg[phase]['list']
for dev in list:
if list[dev]['pid'] >= 0 and dev not in real:
real.append(dev)
# list of top-most root devices
rootlist = []
for phase in self.dmesg:
list = self.dmesg[phase]['list']
for dev in list:
pdev = list[dev]['par']
pid = list[dev]['pid']
if(pid < 0 or re.match('[0-9]*-[0-9]*\.[0-9]*[\.0-9]*\:[\.0-9]*$', pdev)):
continue
if pdev and pdev not in real and pdev not in rootlist:
rootlist.append(pdev)
return rootlist
def deviceTopology(self):
rootlist = self.rootDeviceList()
master = self.masterTopology('', rootlist, 0)
return self.printTopology(master)
def selectTimelineDevices(self, widfmt, tTotal, mindevlen):
# only select devices that will actually show up in html
self.tdevlist = dict()
for phase in self.dmesg:
devlist = []
list = self.dmesg[phase]['list']
for dev in list:
length = (list[dev]['end'] - list[dev]['start']) * 1000
width = widfmt % (((list[dev]['end']-list[dev]['start'])*100)/tTotal)
if width != '0.000000' and length >= mindevlen:
devlist.append(dev)
self.tdevlist[phase] = devlist
# Class: TraceEvent
# Description:
# A container for trace event data found in the ftrace file
class TraceEvent:
text = ''
time = 0.0
length = 0.0
title = ''
row = 0
def __init__(self, a, n, t, l):
self.title = a
self.text = n
self.time = t
self.length = l
# Class: FTraceLine
# Description:
# A container for a single line of ftrace data. There are six basic types:
# callgraph line:
# call: " dpm_run_callback() {"
# return: " }"
# leaf: " dpm_run_callback();"
# trace event:
# tracing_mark_write: SUSPEND START or RESUME COMPLETE
# suspend_resume: phase or custom exec block data
# device_pm_callback: device callback info
class FTraceLine:
time = 0.0
length = 0.0
fcall = False
freturn = False
fevent = False
fkprobe = False
depth = 0
name = ''
type = ''
def __init__(self, t, m='', d=''):
self.time = float(t)
if not m and not d:
return
# is this a trace event
if(d == 'traceevent' or re.match('^ *\/\* *(?P<msg>.*) \*\/ *$', m)):
if(d == 'traceevent'):
# nop format trace event
msg = m
else:
# function_graph format trace event
em = re.match('^ *\/\* *(?P<msg>.*) \*\/ *$', m)
msg = em.group('msg')
emm = re.match('^(?P<call>.*?): (?P<msg>.*)', msg)
if(emm):
self.name = emm.group('msg')
self.type = emm.group('call')
else:
self.name = msg
km = re.match('^(?P<n>.*)_cal$', self.type)
if km:
self.fcall = True
self.fkprobe = True
self.type = km.group('n')
return
km = re.match('^(?P<n>.*)_ret$', self.type)
if km:
self.freturn = True
self.fkprobe = True
self.type = km.group('n')
return
self.fevent = True
return
# convert the duration to seconds
if(d):
self.length = float(d)/1000000
# the indentation determines the depth
match = re.match('^(?P<d> *)(?P<o>.*)$', m)
if(not match):
return
self.depth = self.getDepth(match.group('d'))
m = match.group('o')
# function return
if(m[0] == '}'):
self.freturn = True
if(len(m) > 1):
# includes comment with function name
match = re.match('^} *\/\* *(?P<n>.*) *\*\/$', m)
if(match):
self.name = match.group('n').strip()
# function call
else:
self.fcall = True
# function call with children
if(m[-1] == '{'):
match = re.match('^(?P<n>.*) *\(.*', m)
if(match):
self.name = match.group('n').strip()
# function call with no children (leaf)
elif(m[-1] == ';'):
self.freturn = True
match = re.match('^(?P<n>.*) *\(.*', m)
if(match):
self.name = match.group('n').strip()
# something else (possibly a trace marker)
else:
self.name = m
def getDepth(self, str):
return len(str)/2
def debugPrint(self, dev=''):
if(self.freturn and self.fcall):
print('%s -- %f (%02d): %s(); (%.3f us)' % (dev, self.time, \
self.depth, self.name, self.length*1000000))
elif(self.freturn):
print('%s -- %f (%02d): %s} (%.3f us)' % (dev, self.time, \
self.depth, self.name, self.length*1000000))
else:
print('%s -- %f (%02d): %s() { (%.3f us)' % (dev, self.time, \
self.depth, self.name, self.length*1000000))
def startMarker(self):
global sysvals
# Is this the starting line of a suspend?
if not self.fevent:
return False
if sysvals.usetracemarkers:
if(self.name == 'SUSPEND START'):
return True
return False
else:
if(self.type == 'suspend_resume' and
re.match('suspend_enter\[.*\] begin', self.name)):
return True
return False
def endMarker(self):
# Is this the ending line of a resume?
if not self.fevent:
return False
if sysvals.usetracemarkers:
if(self.name == 'RESUME COMPLETE'):
return True
return False
else:
if(self.type == 'suspend_resume' and
re.match('thaw_processes\[.*\] end', self.name)):
return True
return False
# Class: FTraceCallGraph
# Description:
# A container for the ftrace callgraph of a single recursive function.
# This can be a dpm_run_callback, dpm_prepare, or dpm_complete callgraph
# Each instance is tied to a single device in a single phase, and is
# comprised of an ordered list of FTraceLine objects
class FTraceCallGraph:
start = -1.0
end = -1.0
list = []
invalid = False
depth = 0
pid = 0
def __init__(self, pid):
self.start = -1.0
self.end = -1.0
self.list = []
self.depth = 0
self.pid = pid
def addLine(self, line, debug=False):
# if this is already invalid, just leave
if(self.invalid):
return False
# invalidate on too much data or bad depth
if(len(self.list) >= 1000000 or self.depth < 0):
self.invalidate(line)
return False
# compare current depth with this lines pre-call depth
prelinedep = line.depth
if(line.freturn and not line.fcall):
prelinedep += 1
last = 0
lasttime = line.time
virtualfname = 'execution_misalignment'
if len(self.list) > 0:
last = self.list[-1]
lasttime = last.time
# handle low misalignments by inserting returns
if prelinedep < self.depth:
if debug and last:
print '-------- task %d --------' % self.pid
last.debugPrint()
idx = 0
# add return calls to get the depth down
while prelinedep < self.depth:
if debug:
print 'MISALIGN LOW (add returns): C%d - eC%d' % (self.depth, prelinedep)
self.depth -= 1
if idx == 0 and last and last.fcall and not last.freturn:
# special case, turn last call into a leaf
last.depth = self.depth
last.freturn = True
last.length = line.time - last.time
if debug:
last.debugPrint()
else:
vline = FTraceLine(lasttime)
vline.depth = self.depth
vline.name = virtualfname
vline.freturn = True
self.list.append(vline)
if debug:
vline.debugPrint()
idx += 1
if debug:
line.debugPrint()
print ''
# handle high misalignments by inserting calls
elif prelinedep > self.depth:
if debug and last:
print '-------- task %d --------' % self.pid
last.debugPrint()
idx = 0
# add calls to get the depth up
while prelinedep > self.depth:
if debug:
print 'MISALIGN HIGH (add calls): C%d - eC%d' % (self.depth, prelinedep)
if idx == 0 and line.freturn and not line.fcall:
# special case, turn this return into a leaf
line.fcall = True
prelinedep -= 1
else:
vline = FTraceLine(lasttime)
vline.depth = self.depth
vline.name = virtualfname
vline.fcall = True
if debug:
vline.debugPrint()
self.list.append(vline)
self.depth += 1
if not last:
self.start = vline.time
idx += 1
if debug:
line.debugPrint()
print ''
# process the call and set the new depth
if(line.fcall and not line.freturn):
self.depth += 1
elif(line.freturn and not line.fcall):
self.depth -= 1
if len(self.list) < 1:
self.start = line.time
self.list.append(line)
if(line.depth == 0 and line.freturn):
if(self.start < 0):
self.start = line.time
self.end = line.time
if line.fcall:
self.end += line.length
if self.list[0].name == virtualfname:
self.invalid = True
return True
return False
def invalidate(self, line):
if(len(self.list) > 0):
first = self.list[0]
self.list = []
self.list.append(first)
self.invalid = True
id = 'task %s' % (self.pid)
window = '(%f - %f)' % (self.start, line.time)
if(self.depth < 0):
vprint('Too much data for '+id+\
' (buffer overflow), ignoring this callback')
else:
vprint('Too much data for '+id+\
' '+window+', ignoring this callback')
def slice(self, t0, tN):
minicg = FTraceCallGraph(0)
count = -1
firstdepth = 0
for l in self.list:
if(l.time < t0 or l.time > tN):
continue
if(count < 0):
if(not l.fcall or l.name == 'dev_driver_string'):
continue
firstdepth = l.depth
count = 0
l.depth -= firstdepth
minicg.addLine(l)
if((count == 0 and l.freturn and l.fcall) or
(count > 0 and l.depth <= 0)):
break
count += 1
return minicg
def repair(self, enddepth):
# bring the depth back to 0 with additional returns
fixed = False
last = self.list[-1]
for i in reversed(range(enddepth)):
t = FTraceLine(last.time)
t.depth = i
t.freturn = True
fixed = self.addLine(t)
if fixed:
self.end = last.time
return True
return False
def postProcess(self, debug=False):
stack = dict()
cnt = 0
for l in self.list:
if(l.fcall and not l.freturn):
stack[l.depth] = l
cnt += 1
elif(l.freturn and not l.fcall):
if(l.depth not in stack):
if debug:
print 'Post Process Error: Depth missing'
l.debugPrint()
return False
# transfer total time from return line to call line
stack[l.depth].length = l.length
stack.pop(l.depth)
l.length = 0
cnt -= 1
if(cnt == 0):
# trace caught the whole call tree
return True
elif(cnt < 0):
if debug:
print 'Post Process Error: Depth is less than 0'
return False
# trace ended before call tree finished
return self.repair(cnt)
def deviceMatch(self, pid, data):
found = False
# add the callgraph data to the device hierarchy
borderphase = {
'dpm_prepare': 'suspend_prepare',
'dpm_complete': 'resume_complete'
}
if(self.list[0].name in borderphase):
p = borderphase[self.list[0].name]
list = data.dmesg[p]['list']
for devname in list:
dev = list[devname]
if(pid == dev['pid'] and
self.start <= dev['start'] and
self.end >= dev['end']):
dev['ftrace'] = self.slice(dev['start'], dev['end'])
found = True
return found
for p in data.phases:
if(data.dmesg[p]['start'] <= self.start and
self.start <= data.dmesg[p]['end']):
list = data.dmesg[p]['list']
for devname in list:
dev = list[devname]
if(pid == dev['pid'] and
self.start <= dev['start'] and
self.end >= dev['end']):
dev['ftrace'] = self
found = True
break
break
return found
def newActionFromFunction(self, data):
name = self.list[0].name
if name in ['dpm_run_callback', 'dpm_prepare', 'dpm_complete']:
return
fs = self.start
fe = self.end
if fs < data.start or fe > data.end:
return
phase = ''
for p in data.phases:
if(data.dmesg[p]['start'] <= self.start and
self.start < data.dmesg[p]['end']):
phase = p
break
if not phase:
return
out = data.newActionGlobal(name, fs, fe, -2)
if out:
phase, myname = out
data.dmesg[phase]['list'][myname]['ftrace'] = self
def debugPrint(self):
print('[%f - %f] %s (%d)') % (self.start, self.end, self.list[0].name, self.pid)
for l in self.list:
if(l.freturn and l.fcall):
print('%f (%02d): %s(); (%.3f us)' % (l.time, \
l.depth, l.name, l.length*1000000))
elif(l.freturn):
print('%f (%02d): %s} (%.3f us)' % (l.time, \
l.depth, l.name, l.length*1000000))
else:
print('%f (%02d): %s() { (%.3f us)' % (l.time, \
l.depth, l.name, l.length*1000000))
print(' ')
# Class: Timeline
# Description:
# A container for a device timeline which calculates
# all the html properties to display it correctly
class Timeline:
html = {}
height = 0 # total timeline height
scaleH = 20 # timescale (top) row height
rowH = 30 # device row height
bodyH = 0 # body height
rows = 0 # total timeline rows
phases = []
rowmaxlines = dict()
rowcount = dict()
rowheight = dict()
def __init__(self, rowheight):
self.rowH = rowheight
self.html = {
'header': '',
'timeline': '',
'legend': '',
}
# Function: getDeviceRows
# Description:
# determine how may rows the device funcs will take
# Arguments:
# rawlist: the list of devices/actions for a single phase
# Output:
# The total number of rows needed to display this phase of the timeline
def getDeviceRows(self, rawlist):
# clear all rows and set them to undefined
lendict = dict()
for item in rawlist:
item.row = -1
lendict[item] = item.length
list = []
for i in sorted(lendict, key=lendict.get, reverse=True):
list.append(i)
remaining = len(list)
rowdata = dict()
row = 1
# try to pack each row with as many ranges as possible
while(remaining > 0):
if(row not in rowdata):
rowdata[row] = []
for i in list:
if(i.row >= 0):
continue
s = i.time
e = i.time + i.length
valid = True
for ritem in rowdata[row]:
rs = ritem.time
re = ritem.time + ritem.length
if(not (((s <= rs) and (e <= rs)) or
((s >= re) and (e >= re)))):
valid = False
break
if(valid):
rowdata[row].append(i)
i.row = row
remaining -= 1
row += 1
return row
# Function: getPhaseRows
# Description:
# Organize the timeline entries into the smallest
# number of rows possible, with no entry overlapping
# Arguments:
# list: the list of devices/actions for a single phase
# devlist: string list of device names to use
# Output:
# The total number of rows needed to display this phase of the timeline
def getPhaseRows(self, dmesg, devlist):
# clear all rows and set them to undefined
remaining = len(devlist)
rowdata = dict()
row = 0
lendict = dict()
myphases = []
for item in devlist:
if item[0] not in self.phases:
self.phases.append(item[0])
if item[0] not in myphases:
myphases.append(item[0])
self.rowmaxlines[item[0]] = dict()
self.rowheight[item[0]] = dict()
dev = dmesg[item[0]]['list'][item[1]]
dev['row'] = -1
lendict[item] = float(dev['end']) - float(dev['start'])
if 'src' in dev:
dev['devrows'] = self.getDeviceRows(dev['src'])
lenlist = []
for i in sorted(lendict, key=lendict.get, reverse=True):
lenlist.append(i)
orderedlist = []
for item in lenlist:
dev = dmesg[item[0]]['list'][item[1]]
if dev['pid'] == -2:
orderedlist.append(item)
for item in lenlist:
if item not in orderedlist:
orderedlist.append(item)
# try to pack each row with as many ranges as possible
while(remaining > 0):
rowheight = 1
if(row not in rowdata):
rowdata[row] = []
for item in orderedlist:
dev = dmesg[item[0]]['list'][item[1]]
if(dev['row'] < 0):
s = dev['start']
e = dev['end']
valid = True
for ritem in rowdata[row]:
rs = ritem['start']
re = ritem['end']
if(not (((s <= rs) and (e <= rs)) or
((s >= re) and (e >= re)))):
valid = False
break
if(valid):
rowdata[row].append(dev)
dev['row'] = row
remaining -= 1
if 'devrows' in dev and dev['devrows'] > rowheight:
rowheight = dev['devrows']
for phase in myphases:
self.rowmaxlines[phase][row] = rowheight
self.rowheight[phase][row] = rowheight * self.rowH
row += 1
if(row > self.rows):
self.rows = int(row)
for phase in myphases:
self.rowcount[phase] = row
return row
def phaseRowHeight(self, phase, row):
return self.rowheight[phase][row]
def phaseRowTop(self, phase, row):
top = 0
for i in sorted(self.rowheight[phase]):
if i >= row:
break
top += self.rowheight[phase][i]
return top
# Function: calcTotalRows
# Description:
# Calculate the heights and offsets for the header and rows
def calcTotalRows(self):
maxrows = 0
standardphases = []
for phase in self.phases:
total = 0
for i in sorted(self.rowmaxlines[phase]):
total += self.rowmaxlines[phase][i]
if total > maxrows:
maxrows = total
if total == self.rowcount[phase]:
standardphases.append(phase)
self.height = self.scaleH + (maxrows*self.rowH)
self.bodyH = self.height - self.scaleH
for phase in standardphases:
for i in sorted(self.rowheight[phase]):
self.rowheight[phase][i] = self.bodyH/self.rowcount[phase]
# Function: createTimeScale
# Description:
# Create the timescale for a timeline block
# Arguments:
# m0: start time (mode begin)
# mMax: end time (mode end)
# tTotal: total timeline time
# mode: suspend or resume
# Output:
# The html code needed to display the time scale
def createTimeScale(self, m0, mMax, tTotal, mode):
timescale = '<div class="t" style="right:{0}%">{1}</div>\n'
rline = '<div class="t" style="left:0;border-left:1px solid black;border-right:0;">Resume</div>\n'
output = '<div class="timescale">\n'
# set scale for timeline
mTotal = mMax - m0
tS = 0.1
if(tTotal <= 0):
return output+'</div>\n'
if(tTotal > 4):
tS = 1
divTotal = int(mTotal/tS) + 1
divEdge = (mTotal - tS*(divTotal-1))*100/mTotal
for i in range(divTotal):
htmlline = ''
if(mode == 'resume'):
pos = '%0.3f' % (100 - ((float(i)*tS*100)/mTotal))
val = '%0.fms' % (float(i)*tS*1000)
htmlline = timescale.format(pos, val)
if(i == 0):
htmlline = rline
else:
pos = '%0.3f' % (100 - ((float(i)*tS*100)/mTotal) - divEdge)
val = '%0.fms' % (float(i-divTotal+1)*tS*1000)
if(i == divTotal - 1):
val = 'Suspend'
htmlline = timescale.format(pos, val)
output += htmlline
output += '</div>\n'
return output
# Class: TestProps
# Description:
# A list of values describing the properties of these test runs
class TestProps:
stamp = ''
tracertype = ''
S0i3 = False
fwdata = []
ftrace_line_fmt_fg = \
'^ *(?P<time>[0-9\.]*) *\| *(?P<cpu>[0-9]*)\)'+\
' *(?P<proc>.*)-(?P<pid>[0-9]*) *\|'+\
'[ +!#\*@$]*(?P<dur>[0-9\.]*) .*\| (?P<msg>.*)'
ftrace_line_fmt_nop = \
' *(?P<proc>.*)-(?P<pid>[0-9]*) *\[(?P<cpu>[0-9]*)\] *'+\
'(?P<flags>.{4}) *(?P<time>[0-9\.]*): *'+\
'(?P<msg>.*)'
ftrace_line_fmt = ftrace_line_fmt_nop
cgformat = False
data = 0
ktemp = dict()
def __init__(self):
self.ktemp = dict()
def setTracerType(self, tracer):
self.tracertype = tracer
if(tracer == 'function_graph'):
self.cgformat = True
self.ftrace_line_fmt = self.ftrace_line_fmt_fg
elif(tracer == 'nop'):
self.ftrace_line_fmt = self.ftrace_line_fmt_nop
else:
doError('Invalid tracer format: [%s]' % tracer, False)
# Class: TestRun
# Description:
# A container for a suspend/resume test run. This is necessary as
# there could be more than one, and they need to be separate.
class TestRun:
ftemp = dict()
ttemp = dict()
data = 0
def __init__(self, dataobj):
self.data = dataobj
self.ftemp = dict()
self.ttemp = dict()
# ----------------- FUNCTIONS --------------------
# Function: vprint
# Description:
# verbose print (prints only with -verbose option)
# Arguments:
# msg: the debug/log message to print
def vprint(msg):
global sysvals
if(sysvals.verbose):
print(msg)
# Function: parseStamp
# Description:
# Pull in the stamp comment line from the data file(s),
# create the stamp, and add it to the global sysvals object
# Arguments:
# m: the valid re.match output for the stamp line
def parseStamp(line, data):
global sysvals
m = re.match(sysvals.stampfmt, line)
data.stamp = {'time': '', 'host': '', 'mode': ''}
dt = datetime(int(m.group('y'))+2000, int(m.group('m')),
int(m.group('d')), int(m.group('H')), int(m.group('M')),
int(m.group('S')))
data.stamp['time'] = dt.strftime('%B %d %Y, %I:%M:%S %p')
data.stamp['host'] = m.group('host')
data.stamp['mode'] = m.group('mode')
data.stamp['kernel'] = m.group('kernel')
sysvals.hostname = data.stamp['host']
sysvals.suspendmode = data.stamp['mode']
if not sysvals.stamp:
sysvals.stamp = data.stamp
# Function: diffStamp
# Description:
# compare the host, kernel, and mode fields in 3 stamps
# Arguments:
# stamp1: string array with mode, kernel, and host
# stamp2: string array with mode, kernel, and host
# Return:
# True if stamps differ, False if they're the same
def diffStamp(stamp1, stamp2):
if 'host' in stamp1 and 'host' in stamp2:
if stamp1['host'] != stamp2['host']:
return True
if 'kernel' in stamp1 and 'kernel' in stamp2:
if stamp1['kernel'] != stamp2['kernel']:
return True
if 'mode' in stamp1 and 'mode' in stamp2:
if stamp1['mode'] != stamp2['mode']:
return True
return False
# Function: doesTraceLogHaveTraceEvents
# Description:
# Quickly determine if the ftrace log has some or all of the trace events
# required for primary parsing. Set the usetraceevents and/or
# usetraceeventsonly flags in the global sysvals object
def doesTraceLogHaveTraceEvents():
global sysvals
# check for kprobes
sysvals.usekprobes = False
out = os.system('grep -q "_cal: (" '+sysvals.ftracefile)
if(out == 0):
sysvals.usekprobes = True
# check for callgraph data on trace event blocks
out = os.system('grep -q "_cpu_down()" '+sysvals.ftracefile)
if(out == 0):
sysvals.usekprobes = True
out = os.popen('head -1 '+sysvals.ftracefile).read().replace('\n', '')
m = re.match(sysvals.stampfmt, out)
if m and m.group('mode') == 'command':
sysvals.usetraceeventsonly = True
sysvals.usetraceevents = True
return
# figure out what level of trace events are supported
sysvals.usetraceeventsonly = True
sysvals.usetraceevents = False
for e in sysvals.traceevents:
out = os.system('grep -q "'+e+': " '+sysvals.ftracefile)
if(out != 0):
sysvals.usetraceeventsonly = False
if(e == 'suspend_resume' and out == 0):
sysvals.usetraceevents = True
# determine is this log is properly formatted
for e in ['SUSPEND START', 'RESUME COMPLETE']:
out = os.system('grep -q "'+e+'" '+sysvals.ftracefile)
if(out != 0):
sysvals.usetracemarkers = False
# Function: appendIncompleteTraceLog
# Description:
# [deprecated for kernel 3.15 or newer]
# Legacy support of ftrace outputs that lack the device_pm_callback
# and/or suspend_resume trace events. The primary data should be
# taken from dmesg, and this ftrace is used only for callgraph data
# or custom actions in the timeline. The data is appended to the Data
# objects provided.
# Arguments:
# testruns: the array of Data objects obtained from parseKernelLog
def appendIncompleteTraceLog(testruns):
global sysvals
# create TestRun vessels for ftrace parsing
testcnt = len(testruns)
testidx = 0
testrun = []
for data in testruns:
testrun.append(TestRun(data))
# extract the callgraph and traceevent data
vprint('Analyzing the ftrace data...')
tp = TestProps()
tf = open(sysvals.ftracefile, 'r')
data = 0
for line in tf:
# remove any latent carriage returns
line = line.replace('\r\n', '')
# grab the time stamp
m = re.match(sysvals.stampfmt, line)
if(m):
tp.stamp = line
continue
# determine the trace data type (required for further parsing)
m = re.match(sysvals.tracertypefmt, line)
if(m):
tp.setTracerType(m.group('t'))
continue
# device properties line
if(re.match(sysvals.devpropfmt, line)):
devProps(line)
continue
# parse only valid lines, if this is not one move on
m = re.match(tp.ftrace_line_fmt, line)
if(not m):
continue
# gather the basic message data from the line
m_time = m.group('time')
m_pid = m.group('pid')
m_msg = m.group('msg')
if(tp.cgformat):
m_param3 = m.group('dur')
else:
m_param3 = 'traceevent'
if(m_time and m_pid and m_msg):
t = FTraceLine(m_time, m_msg, m_param3)
pid = int(m_pid)
else:
continue
# the line should be a call, return, or event
if(not t.fcall and not t.freturn and not t.fevent):
continue
# look for the suspend start marker
if(t.startMarker()):
data = testrun[testidx].data
parseStamp(tp.stamp, data)
data.setStart(t.time)
continue
if(not data):
continue
# find the end of resume
if(t.endMarker()):
data.setEnd(t.time)
testidx += 1
if(testidx >= testcnt):
break
continue
# trace event processing
if(t.fevent):
# general trace events have two types, begin and end
if(re.match('(?P<name>.*) begin$', t.name)):
isbegin = True
elif(re.match('(?P<name>.*) end$', t.name)):
isbegin = False
else:
continue
m = re.match('(?P<name>.*)\[(?P<val>[0-9]*)\] .*', t.name)
if(m):
val = m.group('val')
if val == '0':
name = m.group('name')
else:
name = m.group('name')+'['+val+']'
else:
m = re.match('(?P<name>.*) .*', t.name)
name = m.group('name')
# special processing for trace events
if re.match('dpm_prepare\[.*', name):
continue
elif re.match('machine_suspend.*', name):
continue
elif re.match('suspend_enter\[.*', name):
if(not isbegin):
data.dmesg['suspend_prepare']['end'] = t.time
continue
elif re.match('dpm_suspend\[.*', name):
if(not isbegin):
data.dmesg['suspend']['end'] = t.time
continue
elif re.match('dpm_suspend_late\[.*', name):
if(isbegin):
data.dmesg['suspend_late']['start'] = t.time
else:
data.dmesg['suspend_late']['end'] = t.time
continue
elif re.match('dpm_suspend_noirq\[.*', name):
if(isbegin):
data.dmesg['suspend_noirq']['start'] = t.time
else:
data.dmesg['suspend_noirq']['end'] = t.time
continue
elif re.match('dpm_resume_noirq\[.*', name):
if(isbegin):
data.dmesg['resume_machine']['end'] = t.time
data.dmesg['resume_noirq']['start'] = t.time
else:
data.dmesg['resume_noirq']['end'] = t.time
continue
elif re.match('dpm_resume_early\[.*', name):
if(isbegin):
data.dmesg['resume_early']['start'] = t.time
else:
data.dmesg['resume_early']['end'] = t.time
continue
elif re.match('dpm_resume\[.*', name):
if(isbegin):
data.dmesg['resume']['start'] = t.time
else:
data.dmesg['resume']['end'] = t.time
continue
elif re.match('dpm_complete\[.*', name):
if(isbegin):
data.dmesg['resume_complete']['start'] = t.time
else:
data.dmesg['resume_complete']['end'] = t.time
continue
# skip trace events inside devices calls
if(not data.isTraceEventOutsideDeviceCalls(pid, t.time)):
continue
# global events (outside device calls) are simply graphed
if(isbegin):
# store each trace event in ttemp
if(name not in testrun[testidx].ttemp):
testrun[testidx].ttemp[name] = []
testrun[testidx].ttemp[name].append(\
{'begin': t.time, 'end': t.time})
else:
# finish off matching trace event in ttemp
if(name in testrun[testidx].ttemp):
testrun[testidx].ttemp[name][-1]['end'] = t.time
# call/return processing
elif sysvals.usecallgraph:
# create a callgraph object for the data
if(pid not in testrun[testidx].ftemp):
testrun[testidx].ftemp[pid] = []
testrun[testidx].ftemp[pid].append(FTraceCallGraph(pid))
# when the call is finished, see which device matches it
cg = testrun[testidx].ftemp[pid][-1]
if(cg.addLine(t)):
testrun[testidx].ftemp[pid].append(FTraceCallGraph(pid))
tf.close()
for test in testrun:
# add the traceevent data to the device hierarchy
if(sysvals.usetraceevents):
for name in test.ttemp:
for event in test.ttemp[name]:
test.data.newActionGlobal(name, event['begin'], event['end'])
# add the callgraph data to the device hierarchy
for pid in test.ftemp:
for cg in test.ftemp[pid]:
if len(cg.list) < 1 or cg.invalid:
continue
if(not cg.postProcess()):
id = 'task %s cpu %s' % (pid, m.group('cpu'))
vprint('Sanity check failed for '+\
id+', ignoring this callback')
continue
callstart = cg.start
callend = cg.end
for p in test.data.phases:
if(test.data.dmesg[p]['start'] <= callstart and
callstart <= test.data.dmesg[p]['end']):
list = test.data.dmesg[p]['list']
for devname in list:
dev = list[devname]
if(pid == dev['pid'] and
callstart <= dev['start'] and
callend >= dev['end']):
dev['ftrace'] = cg
break
if(sysvals.verbose):
test.data.printDetails()
# Function: parseTraceLog
# Description:
# Analyze an ftrace log output file generated from this app during
# the execution phase. Used when the ftrace log is the primary data source
# and includes the suspend_resume and device_pm_callback trace events
# The ftrace filename is taken from sysvals
# Output:
# An array of Data objects
def parseTraceLog():
global sysvals
vprint('Analyzing the ftrace data...')
if(os.path.exists(sysvals.ftracefile) == False):
doError('%s does not exist' % sysvals.ftracefile, False)
sysvals.setupAllKprobes()
tracewatch = ['suspend_enter']
if sysvals.usekprobes:
tracewatch += ['sync_filesystems', 'freeze_processes', 'syscore_suspend',
'syscore_resume', 'resume_console', 'thaw_processes', 'CPU_ON', 'CPU_OFF']
# extract the callgraph and traceevent data
tp = TestProps()
testruns = []
testdata = []
testrun = 0
data = 0
tf = open(sysvals.ftracefile, 'r')
phase = 'suspend_prepare'
for line in tf:
# remove any latent carriage returns
line = line.replace('\r\n', '')
# stamp line: each stamp means a new test run
m = re.match(sysvals.stampfmt, line)
if(m):
tp.stamp = line
continue
# firmware line: pull out any firmware data
m = re.match(sysvals.firmwarefmt, line)
if(m):
tp.fwdata.append((int(m.group('s')), int(m.group('r'))))
continue
# tracer type line: determine the trace data type
m = re.match(sysvals.tracertypefmt, line)
if(m):
tp.setTracerType(m.group('t'))
continue
# post resume time line: did this test run include post-resume data
m = re.match(sysvals.postresumefmt, line)
if(m):
t = int(m.group('t'))
if(t > 0):
sysvals.postresumetime = t
continue
# device properties line
if(re.match(sysvals.devpropfmt, line)):
devProps(line)
continue
# ftrace line: parse only valid lines
m = re.match(tp.ftrace_line_fmt, line)
if(not m):
continue
# gather the basic message data from the line
m_time = m.group('time')
m_proc = m.group('proc')
m_pid = m.group('pid')
m_msg = m.group('msg')
if(tp.cgformat):
m_param3 = m.group('dur')
else:
m_param3 = 'traceevent'
if(m_time and m_pid and m_msg):
t = FTraceLine(m_time, m_msg, m_param3)
pid = int(m_pid)
else:
continue
# the line should be a call, return, or event
if(not t.fcall and not t.freturn and not t.fevent):
continue
# find the start of suspend
if(t.startMarker()):
phase = 'suspend_prepare'
data = Data(len(testdata))
testdata.append(data)
testrun = TestRun(data)
testruns.append(testrun)
parseStamp(tp.stamp, data)
if len(tp.fwdata) > data.testnumber:
data.fwSuspend, data.fwResume = tp.fwdata[data.testnumber]
if(data.fwSuspend > 0 or data.fwResume > 0):
data.fwValid = True
data.setStart(t.time)
continue
if(not data):
continue
# find the end of resume
if(t.endMarker()):
if(sysvals.usetracemarkers and sysvals.postresumetime > 0):
phase = 'post_resume'
data.newPhase(phase, t.time, t.time, '#F0F0F0', -1)
data.setEnd(t.time)
if(not sysvals.usetracemarkers):
# no trace markers? then quit and be sure to finish recording
# the event we used to trigger resume end
if(len(testrun.ttemp['thaw_processes']) > 0):
# if an entry exists, assume this is its end
testrun.ttemp['thaw_processes'][-1]['end'] = t.time
break
continue
# trace event processing
if(t.fevent):
if(phase == 'post_resume'):
data.setEnd(t.time)
if(t.type == 'suspend_resume'):
# suspend_resume trace events have two types, begin and end
if(re.match('(?P<name>.*) begin$', t.name)):
isbegin = True
elif(re.match('(?P<name>.*) end$', t.name)):
isbegin = False
else:
continue
m = re.match('(?P<name>.*)\[(?P<val>[0-9]*)\] .*', t.name)
if(m):
val = m.group('val')
if val == '0':
name = m.group('name')
else:
name = m.group('name')+'['+val+']'
else:
m = re.match('(?P<name>.*) .*', t.name)
name = m.group('name')
# ignore these events
if(name.split('[')[0] in tracewatch):
continue
# -- phase changes --
# suspend_prepare start
if(re.match('dpm_prepare\[.*', t.name)):
phase = 'suspend_prepare'
if(not isbegin):
data.dmesg[phase]['end'] = t.time
continue
# suspend start
elif(re.match('dpm_suspend\[.*', t.name)):
phase = 'suspend'
data.setPhase(phase, t.time, isbegin)
continue
# suspend_late start
elif(re.match('dpm_suspend_late\[.*', t.name)):
phase = 'suspend_late'
data.setPhase(phase, t.time, isbegin)
continue
# suspend_noirq start
elif(re.match('dpm_suspend_noirq\[.*', t.name)):
phase = 'suspend_noirq'
data.setPhase(phase, t.time, isbegin)
if(not isbegin):
phase = 'suspend_machine'
data.dmesg[phase]['start'] = t.time
continue
# suspend_machine/resume_machine
elif(re.match('machine_suspend\[.*', t.name)):
if(isbegin):
phase = 'suspend_machine'
data.dmesg[phase]['end'] = t.time
data.tSuspended = t.time
else:
if(sysvals.suspendmode in ['mem', 'disk'] and not tp.S0i3):
data.dmesg['suspend_machine']['end'] = t.time
data.tSuspended = t.time
phase = 'resume_machine'
data.dmesg[phase]['start'] = t.time
data.tResumed = t.time
data.tLow = data.tResumed - data.tSuspended
continue
# acpi_suspend
elif(re.match('acpi_suspend\[.*', t.name)):
# acpi_suspend[0] S0i3
if(re.match('acpi_suspend\[0\] begin', t.name)):
if(sysvals.suspendmode == 'mem'):
tp.S0i3 = True
data.dmesg['suspend_machine']['end'] = t.time
data.tSuspended = t.time
continue
# resume_noirq start
elif(re.match('dpm_resume_noirq\[.*', t.name)):
phase = 'resume_noirq'
data.setPhase(phase, t.time, isbegin)
if(isbegin):
data.dmesg['resume_machine']['end'] = t.time
continue
# resume_early start
elif(re.match('dpm_resume_early\[.*', t.name)):
phase = 'resume_early'
data.setPhase(phase, t.time, isbegin)
continue
# resume start
elif(re.match('dpm_resume\[.*', t.name)):
phase = 'resume'
data.setPhase(phase, t.time, isbegin)
continue
# resume complete start
elif(re.match('dpm_complete\[.*', t.name)):
phase = 'resume_complete'
if(isbegin):
data.dmesg[phase]['start'] = t.time
continue
# skip trace events inside devices calls
if(not data.isTraceEventOutsideDeviceCalls(pid, t.time)):
continue
# global events (outside device calls) are graphed
if(name not in testrun.ttemp):
testrun.ttemp[name] = []
if(isbegin):
# create a new list entry
testrun.ttemp[name].append(\
{'begin': t.time, 'end': t.time, 'pid': pid})
else:
if(len(testrun.ttemp[name]) > 0):
# if an entry exists, assume this is its end
testrun.ttemp[name][-1]['end'] = t.time
elif(phase == 'post_resume'):
# post resume events can just have ends
testrun.ttemp[name].append({
'begin': data.dmesg[phase]['start'],
'end': t.time})
# device callback start
elif(t.type == 'device_pm_callback_start'):
m = re.match('(?P<drv>.*) (?P<d>.*), parent: *(?P<p>.*), .*',\
t.name);
if(not m):
continue
drv = m.group('drv')
n = m.group('d')
p = m.group('p')
if(n and p):
data.newAction(phase, n, pid, p, t.time, -1, drv)
# device callback finish
elif(t.type == 'device_pm_callback_end'):
m = re.match('(?P<drv>.*) (?P<d>.*), err.*', t.name);
if(not m):
continue
n = m.group('d')
list = data.dmesg[phase]['list']
if(n in list):
dev = list[n]
dev['length'] = t.time - dev['start']
dev['end'] = t.time
# kprobe event processing
elif(t.fkprobe):
kprobename = t.type
kprobedata = t.name
key = (kprobename, pid)
# displayname is generated from kprobe data
displayname = ''
if(t.fcall):
displayname = sysvals.kprobeDisplayName(kprobename, kprobedata)
if not displayname:
continue
if(key not in tp.ktemp):
tp.ktemp[key] = []
tp.ktemp[key].append({
'pid': pid,
'begin': t.time,
'end': t.time,
'name': displayname,
'cdata': kprobedata,
'proc': m_proc,
})
elif(t.freturn):
if(key not in tp.ktemp) or len(tp.ktemp[key]) < 1:
continue
e = tp.ktemp[key][-1]
if e['begin'] < 0.0 or t.time - e['begin'] < 0.000001:
tp.ktemp[key].pop()
else:
e['end'] = t.time
e['rdata'] = kprobedata
# callgraph processing
elif sysvals.usecallgraph:
# create a callgraph object for the data
key = (m_proc, pid)
if(key not in testrun.ftemp):
testrun.ftemp[key] = []
testrun.ftemp[key].append(FTraceCallGraph(pid))
# when the call is finished, see which device matches it
cg = testrun.ftemp[key][-1]
if(cg.addLine(t)):
testrun.ftemp[key].append(FTraceCallGraph(pid))
tf.close()
if sysvals.suspendmode == 'command':
for test in testruns:
for p in test.data.phases:
if p == 'resume_complete':
test.data.dmesg[p]['start'] = test.data.start
test.data.dmesg[p]['end'] = test.data.end
else:
test.data.dmesg[p]['start'] = test.data.start
test.data.dmesg[p]['end'] = test.data.start
test.data.tSuspended = test.data.start
test.data.tResumed = test.data.start
test.data.tLow = 0
test.data.fwValid = False
for test in testruns:
# add the traceevent data to the device hierarchy
if(sysvals.usetraceevents):
# add actual trace funcs
for name in test.ttemp:
for event in test.ttemp[name]:
test.data.newActionGlobal(name, event['begin'], event['end'], event['pid'])
# add the kprobe based virtual tracefuncs as actual devices
for key in tp.ktemp:
name, pid = key
if name not in sysvals.tracefuncs:
continue
for e in tp.ktemp[key]:
kb, ke = e['begin'], e['end']
if kb == ke or not test.data.isInsideTimeline(kb, ke):
continue
test.data.newActionGlobal(e['name'], kb, ke, pid)
# add config base kprobes and dev kprobes
for key in tp.ktemp:
name, pid = key
if name in sysvals.tracefuncs:
continue
for e in tp.ktemp[key]:
kb, ke = e['begin'], e['end']
if kb == ke or not test.data.isInsideTimeline(kb, ke):
continue
color = sysvals.kprobeColor(e['name'])
if name not in sysvals.dev_tracefuncs:
# config base kprobe
test.data.newActionGlobal(e['name'], kb, ke, -2, color)
elif sysvals.usedevsrc:
# dev kprobe
data.addDeviceFunctionCall(e['name'], name, e['proc'], pid, kb,
ke, e['cdata'], e['rdata'])
if sysvals.usecallgraph:
# add the callgraph data to the device hierarchy
sortlist = dict()
for key in test.ftemp:
proc, pid = key
for cg in test.ftemp[key]:
if len(cg.list) < 1 or cg.invalid:
continue
if(not cg.postProcess()):
id = 'task %s' % (pid)
vprint('Sanity check failed for '+\
id+', ignoring this callback')
continue
# match cg data to devices
if sysvals.suspendmode == 'command' or not cg.deviceMatch(pid, test.data):
sortkey = '%f%f%d' % (cg.start, cg.end, pid)
sortlist[sortkey] = cg
# create blocks for orphan cg data
for sortkey in sorted(sortlist):
cg = sortlist[sortkey]
name = cg.list[0].name
if sysvals.isCallgraphFunc(name):
vprint('Callgraph found for task %d: %.3fms, %s' % (cg.pid, (cg.end - cg.start)*1000, name))
cg.newActionFromFunction(test.data)
if sysvals.suspendmode == 'command':
if(sysvals.verbose):
for data in testdata:
data.printDetails()
return testdata
# fill in any missing phases
for data in testdata:
lp = data.phases[0]
for p in data.phases:
if(data.dmesg[p]['start'] < 0 and data.dmesg[p]['end'] < 0):
print('WARNING: phase "%s" is missing!' % p)
if(data.dmesg[p]['start'] < 0):
data.dmesg[p]['start'] = data.dmesg[lp]['end']
if(p == 'resume_machine'):
data.tSuspended = data.dmesg[lp]['end']
data.tResumed = data.dmesg[lp]['end']
data.tLow = 0
if(data.dmesg[p]['end'] < 0):
data.dmesg[p]['end'] = data.dmesg[p]['start']
lp = p
if(len(sysvals.devicefilter) > 0):
data.deviceFilter(sysvals.devicefilter)
data.fixupInitcallsThatDidntReturn()
if(sysvals.verbose):
data.printDetails()
return testdata
# Function: loadRawKernelLog
# Description:
# Load a raw kernel log that wasn't created by this tool, it might be
# possible to extract a valid suspend/resume log
def loadRawKernelLog(dmesgfile):
global sysvals
stamp = {'time': '', 'host': '', 'mode': 'mem', 'kernel': ''}
stamp['time'] = datetime.now().strftime('%B %d %Y, %I:%M:%S %p')
stamp['host'] = sysvals.hostname
testruns = []
data = 0
lf = open(dmesgfile, 'r')
for line in lf:
line = line.replace('\r\n', '')
idx = line.find('[')
if idx > 1:
line = line[idx:]
m = re.match('[ \t]*(\[ *)(?P<ktime>[0-9\.]*)(\]) (?P<msg>.*)', line)
if(not m):
continue
msg = m.group("msg")
m = re.match('PM: Syncing filesystems.*', msg)
if(m):
if(data):
testruns.append(data)
data = Data(len(testruns))
data.stamp = stamp
if(data):
m = re.match('.* *(?P<k>[0-9]\.[0-9]{2}\.[0-9]-.*) .*', msg)
if(m):
stamp['kernel'] = m.group('k')
m = re.match('PM: Preparing system for (?P<m>.*) sleep', msg)
if(m):
stamp['mode'] = m.group('m')
data.dmesgtext.append(line)
if(data):
testruns.append(data)
sysvals.stamp = stamp
sysvals.suspendmode = stamp['mode']
lf.close()
return testruns
# Function: loadKernelLog
# Description:
# [deprecated for kernel 3.15.0 or newer]
# load the dmesg file into memory and fix up any ordering issues
# The dmesg filename is taken from sysvals
# Output:
# An array of empty Data objects with only their dmesgtext attributes set
def loadKernelLog():
global sysvals
vprint('Analyzing the dmesg data...')
if(os.path.exists(sysvals.dmesgfile) == False):
doError('%s does not exist' % sysvals.dmesgfile, False)
# there can be multiple test runs in a single file
tp = TestProps()
testruns = []
data = 0
lf = open(sysvals.dmesgfile, 'r')
for line in lf:
line = line.replace('\r\n', '')
idx = line.find('[')
if idx > 1:
line = line[idx:]
m = re.match(sysvals.stampfmt, line)
if(m):
tp.stamp = line
continue
m = re.match(sysvals.firmwarefmt, line)
if(m):
tp.fwdata.append((int(m.group('s')), int(m.group('r'))))
continue
m = re.match('[ \t]*(\[ *)(?P<ktime>[0-9\.]*)(\]) (?P<msg>.*)', line)
if(not m):
continue
msg = m.group("msg")
if(re.match('PM: Syncing filesystems.*', msg)):
if(data):
testruns.append(data)
data = Data(len(testruns))
parseStamp(tp.stamp, data)
if len(tp.fwdata) > data.testnumber:
data.fwSuspend, data.fwResume = tp.fwdata[data.testnumber]
if(data.fwSuspend > 0 or data.fwResume > 0):
data.fwValid = True
if(re.match('ACPI: resume from mwait', msg)):
print('NOTE: This suspend appears to be freeze rather than'+\
' %s, it will be treated as such' % sysvals.suspendmode)
sysvals.suspendmode = 'freeze'
if(not data):
continue
data.dmesgtext.append(line)
if(data):
testruns.append(data)
lf.close()
if(len(testruns) < 1):
# bad log, but see if you can extract something meaningful anyway
testruns = loadRawKernelLog(sysvals.dmesgfile)
if(len(testruns) < 1):
doError(' dmesg log is completely unreadable: %s' \
% sysvals.dmesgfile, False)
# fix lines with same timestamp/function with the call and return swapped
for data in testruns:
last = ''
for line in data.dmesgtext:
mc = re.match('.*(\[ *)(?P<t>[0-9\.]*)(\]) calling '+\
'(?P<f>.*)\+ @ .*, parent: .*', line)
mr = re.match('.*(\[ *)(?P<t>[0-9\.]*)(\]) call '+\
'(?P<f>.*)\+ returned .* after (?P<dt>.*) usecs', last)
if(mc and mr and (mc.group('t') == mr.group('t')) and
(mc.group('f') == mr.group('f'))):
i = data.dmesgtext.index(last)
j = data.dmesgtext.index(line)
data.dmesgtext[i] = line
data.dmesgtext[j] = last
last = line
return testruns
# Function: parseKernelLog
# Description:
# [deprecated for kernel 3.15.0 or newer]
# Analyse a dmesg log output file generated from this app during
# the execution phase. Create a set of device structures in memory
# for subsequent formatting in the html output file
# This call is only for legacy support on kernels where the ftrace
# data lacks the suspend_resume or device_pm_callbacks trace events.
# Arguments:
# data: an empty Data object (with dmesgtext) obtained from loadKernelLog
# Output:
# The filled Data object
def parseKernelLog(data):
global sysvals
phase = 'suspend_runtime'
if(data.fwValid):
vprint('Firmware Suspend = %u ns, Firmware Resume = %u ns' % \
(data.fwSuspend, data.fwResume))
# dmesg phase match table
dm = {
'suspend_prepare': 'PM: Syncing filesystems.*',
'suspend': 'PM: Entering [a-z]* sleep.*',
'suspend_late': 'PM: suspend of devices complete after.*',
'suspend_noirq': 'PM: late suspend of devices complete after.*',
'suspend_machine': 'PM: noirq suspend of devices complete after.*',
'resume_machine': 'ACPI: Low-level resume complete.*',
'resume_noirq': 'ACPI: Waking up from system sleep state.*',
'resume_early': 'PM: noirq resume of devices complete after.*',
'resume': 'PM: early resume of devices complete after.*',
'resume_complete': 'PM: resume of devices complete after.*',
'post_resume': '.*Restarting tasks \.\.\..*',
}
if(sysvals.suspendmode == 'standby'):
dm['resume_machine'] = 'PM: Restoring platform NVS memory'
elif(sysvals.suspendmode == 'disk'):
dm['suspend_late'] = 'PM: freeze of devices complete after.*'
dm['suspend_noirq'] = 'PM: late freeze of devices complete after.*'
dm['suspend_machine'] = 'PM: noirq freeze of devices complete after.*'
dm['resume_machine'] = 'PM: Restoring platform NVS memory'
dm['resume_early'] = 'PM: noirq restore of devices complete after.*'
dm['resume'] = 'PM: early restore of devices complete after.*'
dm['resume_complete'] = 'PM: restore of devices complete after.*'
elif(sysvals.suspendmode == 'freeze'):
dm['resume_machine'] = 'ACPI: resume from mwait'
# action table (expected events that occur and show up in dmesg)
at = {
'sync_filesystems': {
'smsg': 'PM: Syncing filesystems.*',
'emsg': 'PM: Preparing system for mem sleep.*' },
'freeze_user_processes': {
'smsg': 'Freezing user space processes .*',
'emsg': 'Freezing remaining freezable tasks.*' },
'freeze_tasks': {
'smsg': 'Freezing remaining freezable tasks.*',
'emsg': 'PM: Entering (?P<mode>[a-z,A-Z]*) sleep.*' },
'ACPI prepare': {
'smsg': 'ACPI: Preparing to enter system sleep state.*',
'emsg': 'PM: Saving platform NVS memory.*' },
'PM vns': {
'smsg': 'PM: Saving platform NVS memory.*',
'emsg': 'Disabling non-boot CPUs .*' },
}
t0 = -1.0
cpu_start = -1.0
prevktime = -1.0
actions = dict()
for line in data.dmesgtext:
# -- preprocessing --
# parse each dmesg line into the time and message
m = re.match('[ \t]*(\[ *)(?P<ktime>[0-9\.]*)(\]) (?P<msg>.*)', line)
if(m):
val = m.group('ktime')
try:
ktime = float(val)
except:
doWarning('INVALID DMESG LINE: '+\
line.replace('\n', ''), 'dmesg')
continue
msg = m.group('msg')
# initialize data start to first line time
if t0 < 0:
data.setStart(ktime)
t0 = ktime
else:
continue
# hack for determining resume_machine end for freeze
if(not sysvals.usetraceevents and sysvals.suspendmode == 'freeze' \
and phase == 'resume_machine' and \
re.match('calling (?P<f>.*)\+ @ .*, parent: .*', msg)):
data.dmesg['resume_machine']['end'] = ktime
phase = 'resume_noirq'
data.dmesg[phase]['start'] = ktime
# -- phase changes --
# suspend start
if(re.match(dm['suspend_prepare'], msg)):
phase = 'suspend_prepare'
data.dmesg[phase]['start'] = ktime
data.setStart(ktime)
# suspend start
elif(re.match(dm['suspend'], msg)):
data.dmesg['suspend_prepare']['end'] = ktime
phase = 'suspend'
data.dmesg[phase]['start'] = ktime
# suspend_late start
elif(re.match(dm['suspend_late'], msg)):
data.dmesg['suspend']['end'] = ktime
phase = 'suspend_late'
data.dmesg[phase]['start'] = ktime
# suspend_noirq start
elif(re.match(dm['suspend_noirq'], msg)):
data.dmesg['suspend_late']['end'] = ktime
phase = 'suspend_noirq'
data.dmesg[phase]['start'] = ktime
# suspend_machine start
elif(re.match(dm['suspend_machine'], msg)):
data.dmesg['suspend_noirq']['end'] = ktime
phase = 'suspend_machine'
data.dmesg[phase]['start'] = ktime
# resume_machine start
elif(re.match(dm['resume_machine'], msg)):
if(sysvals.suspendmode in ['freeze', 'standby']):
data.tSuspended = prevktime
data.dmesg['suspend_machine']['end'] = prevktime
else:
data.tSuspended = ktime
data.dmesg['suspend_machine']['end'] = ktime
phase = 'resume_machine'
data.tResumed = ktime
data.tLow = data.tResumed - data.tSuspended
data.dmesg[phase]['start'] = ktime
# resume_noirq start
elif(re.match(dm['resume_noirq'], msg)):
data.dmesg['resume_machine']['end'] = ktime
phase = 'resume_noirq'
data.dmesg[phase]['start'] = ktime
# resume_early start
elif(re.match(dm['resume_early'], msg)):
data.dmesg['resume_noirq']['end'] = ktime
phase = 'resume_early'
data.dmesg[phase]['start'] = ktime
# resume start
elif(re.match(dm['resume'], msg)):
data.dmesg['resume_early']['end'] = ktime
phase = 'resume'
data.dmesg[phase]['start'] = ktime
# resume complete start
elif(re.match(dm['resume_complete'], msg)):
data.dmesg['resume']['end'] = ktime
phase = 'resume_complete'
data.dmesg[phase]['start'] = ktime
# post resume start
elif(re.match(dm['post_resume'], msg)):
data.dmesg['resume_complete']['end'] = ktime
data.setEnd(ktime)
phase = 'post_resume'
break
# -- device callbacks --
if(phase in data.phases):
# device init call
if(re.match('calling (?P<f>.*)\+ @ .*, parent: .*', msg)):
sm = re.match('calling (?P<f>.*)\+ @ '+\
'(?P<n>.*), parent: (?P<p>.*)', msg);
f = sm.group('f')
n = sm.group('n')
p = sm.group('p')
if(f and n and p):
data.newAction(phase, f, int(n), p, ktime, -1, '')
# device init return
elif(re.match('call (?P<f>.*)\+ returned .* after '+\
'(?P<t>.*) usecs', msg)):
sm = re.match('call (?P<f>.*)\+ returned .* after '+\
'(?P<t>.*) usecs(?P<a>.*)', msg);
f = sm.group('f')
t = sm.group('t')
list = data.dmesg[phase]['list']
if(f in list):
dev = list[f]
dev['length'] = int(t)
dev['end'] = ktime
# -- non-devicecallback actions --
# if trace events are not available, these are better than nothing
if(not sysvals.usetraceevents):
# look for known actions
for a in at:
if(re.match(at[a]['smsg'], msg)):
if(a not in actions):
actions[a] = []
actions[a].append({'begin': ktime, 'end': ktime})
if(re.match(at[a]['emsg'], msg)):
if(a in actions):
actions[a][-1]['end'] = ktime
# now look for CPU on/off events
if(re.match('Disabling non-boot CPUs .*', msg)):
# start of first cpu suspend
cpu_start = ktime
elif(re.match('Enabling non-boot CPUs .*', msg)):
# start of first cpu resume
cpu_start = ktime
elif(re.match('smpboot: CPU (?P<cpu>[0-9]*) is now offline', msg)):
# end of a cpu suspend, start of the next
m = re.match('smpboot: CPU (?P<cpu>[0-9]*) is now offline', msg)
cpu = 'CPU'+m.group('cpu')
if(cpu not in actions):
actions[cpu] = []
actions[cpu].append({'begin': cpu_start, 'end': ktime})
cpu_start = ktime
elif(re.match('CPU(?P<cpu>[0-9]*) is up', msg)):
# end of a cpu resume, start of the next
m = re.match('CPU(?P<cpu>[0-9]*) is up', msg)
cpu = 'CPU'+m.group('cpu')
if(cpu not in actions):
actions[cpu] = []
actions[cpu].append({'begin': cpu_start, 'end': ktime})
cpu_start = ktime
prevktime = ktime
# fill in any missing phases
lp = data.phases[0]
for p in data.phases:
if(data.dmesg[p]['start'] < 0 and data.dmesg[p]['end'] < 0):
print('WARNING: phase "%s" is missing, something went wrong!' % p)
print(' In %s, this dmesg line denotes the start of %s:' % \
(sysvals.suspendmode, p))
print(' "%s"' % dm[p])
if(data.dmesg[p]['start'] < 0):
data.dmesg[p]['start'] = data.dmesg[lp]['end']
if(p == 'resume_machine'):
data.tSuspended = data.dmesg[lp]['end']
data.tResumed = data.dmesg[lp]['end']
data.tLow = 0
if(data.dmesg[p]['end'] < 0):
data.dmesg[p]['end'] = data.dmesg[p]['start']
lp = p
# fill in any actions we've found
for name in actions:
for event in actions[name]:
data.newActionGlobal(name, event['begin'], event['end'])
if(sysvals.verbose):
data.printDetails()
if(len(sysvals.devicefilter) > 0):
data.deviceFilter(sysvals.devicefilter)
data.fixupInitcallsThatDidntReturn()
return True
# Function: createHTMLSummarySimple
# Description:
# Create summary html file for a series of tests
# Arguments:
# testruns: array of Data objects from parseTraceLog
def createHTMLSummarySimple(testruns, htmlfile):
global sysvals
# print out the basic summary of all the tests
hf = open(htmlfile, 'w')
# write the html header first (html head, css code, up to body start)
html = '<!DOCTYPE html>\n<html>\n<head>\n\
<meta http-equiv="content-type" content="text/html; charset=UTF-8">\n\
<title>AnalyzeSuspend Summary</title>\n\
<style type=\'text/css\'>\n\
body {overflow-y: scroll;}\n\
.stamp {width: 100%;text-align:center;background-color:#495E09;line-height:30px;color:white;font: 25px Arial;}\n\
table {width:100%;border-collapse: collapse;}\n\
.summary {font: 22px Arial;border:1px solid;}\n\
th {border: 1px solid black;background-color:#A7C942;color:white;}\n\
td {text-align: center;}\n\
tr.alt td {background-color:#EAF2D3;}\n\
tr.avg td {background-color:#BDE34C;}\n\
a:link {color: #90B521;}\n\
a:visited {color: #495E09;}\n\
a:hover {color: #B1DF28;}\n\
a:active {color: #FFFFFF;}\n\
</style>\n</head>\n<body>\n'
# group test header
count = len(testruns)
headline_stamp = '<div class="stamp">{0} {1} {2} {3} ({4} tests)</div>\n'
html += headline_stamp.format(sysvals.stamp['host'],
sysvals.stamp['kernel'], sysvals.stamp['mode'],
sysvals.stamp['time'], count)
# check to see if all the tests have the same value
stampcolumns = False
for data in testruns:
if diffStamp(sysvals.stamp, data.stamp):
stampcolumns = True
break
th = '\t<th>{0}</th>\n'
td = '\t<td>{0}</td>\n'
tdlink = '\t<td><a href="{0}">Click Here</a></td>\n'
# table header
html += '<table class="summary">\n<tr>\n'
html += th.format("Test #")
if stampcolumns:
html += th.format("Hostname")
html += th.format("Kernel Version")
html += th.format("Suspend Mode")
html += th.format("Test Time")
html += th.format("Suspend Time")
html += th.format("Resume Time")
html += th.format("Detail")
html += '</tr>\n'
# test data, 1 row per test
sTimeAvg = 0.0
rTimeAvg = 0.0
num = 1
for data in testruns:
# data.end is the end of post_resume
resumeEnd = data.dmesg['resume_complete']['end']
if num % 2 == 1:
html += '<tr class="alt">\n'
else:
html += '<tr>\n'
# test num
html += td.format("test %d" % num)
num += 1
if stampcolumns:
# host name
val = "unknown"
if('host' in data.stamp):
val = data.stamp['host']
html += td.format(val)
# host kernel
val = "unknown"
if('kernel' in data.stamp):
val = data.stamp['kernel']
html += td.format(val)
# suspend mode
val = "unknown"
if('mode' in data.stamp):
val = data.stamp['mode']
html += td.format(val)
# test time
val = "unknown"
if('time' in data.stamp):
val = data.stamp['time']
html += td.format(val)
# suspend time
sTime = (data.tSuspended - data.start)*1000
sTimeAvg += sTime
html += td.format("%3.3f ms" % sTime)
# resume time
rTime = (resumeEnd - data.tResumed)*1000
rTimeAvg += rTime
html += td.format("%3.3f ms" % rTime)
# link to the output html
html += tdlink.format(data.outfile)
html += '</tr>\n'
# last line: test average
if(count > 0):
sTimeAvg /= count
rTimeAvg /= count
html += '<tr class="avg">\n'
html += td.format('Average') # name
if stampcolumns:
html += td.format('') # host
html += td.format('') # kernel
html += td.format('') # mode
html += td.format('') # time
html += td.format("%3.3f ms" % sTimeAvg) # suspend time
html += td.format("%3.3f ms" % rTimeAvg) # resume time
html += td.format('') # output link
html += '</tr>\n'
# flush the data to file
hf.write(html+'</table>\n')
hf.write('</body>\n</html>\n')
hf.close()
def htmlTitle():
global sysvals
modename = {
'freeze': 'Freeze (S0)',
'standby': 'Standby (S1)',
'mem': 'Suspend (S3)',
'disk': 'Hibernate (S4)'
}
kernel = sysvals.stamp['kernel']
host = sysvals.hostname[0].upper()+sysvals.hostname[1:]
mode = sysvals.suspendmode
if sysvals.suspendmode in modename:
mode = modename[sysvals.suspendmode]
return host+' '+mode+' '+kernel
def ordinal(value):
suffix = 'th'
if value < 10 or value > 19:
if value % 10 == 1:
suffix = 'st'
elif value % 10 == 2:
suffix = 'nd'
elif value % 10 == 3:
suffix = 'rd'
return '%d%s' % (value, suffix)
# Function: createHTML
# Description:
# Create the output html file from the resident test data
# Arguments:
# testruns: array of Data objects from parseKernelLog or parseTraceLog
# Output:
# True if the html file was created, false if it failed
def createHTML(testruns):
global sysvals
if len(testruns) < 1:
print('ERROR: Not enough test data to build a timeline')
return
for data in testruns:
data.normalizeTime(testruns[-1].tSuspended)
x2changes = ['', 'absolute']
if len(testruns) > 1:
x2changes = ['1', 'relative']
# html function templates
headline_version = '<div class="version"><a href="https://01.org/suspendresume">AnalyzeSuspend v%s</a></div>' % sysvals.version
headline_stamp = '<div class="stamp">{0} {1} {2} {3}</div>\n'
html_devlist1 = '<button id="devlist1" class="devlist" style="float:left;">Device Detail%s</button>' % x2changes[0]
html_zoombox = '<center><button id="zoomin">ZOOM IN</button><button id="zoomout">ZOOM OUT</button><button id="zoomdef">ZOOM 1:1</button></center>\n'
html_devlist2 = '<button id="devlist2" class="devlist" style="float:right;">Device Detail2</button>\n'
html_timeline = '<div id="dmesgzoombox" class="zoombox">\n<div id="{0}" class="timeline" style="height:{1}px">\n'
html_tblock = '<div id="block{0}" class="tblock" style="left:{1}%;width:{2}%;">\n'
html_device = '<div id="{0}" title="{1}" class="thread{7}" style="left:{2}%;top:{3}px;height:{4}px;width:{5}%;{8}">{6}</div>\n'
html_traceevent = '<div title="{0}" class="traceevent" style="left:{1}%;top:{2}px;height:{3}px;width:{4}%;line-height:{3}px;">{5}</div>\n'
html_phase = '<div class="phase" style="left:{0}%;width:{1}%;top:{2}px;height:{3}px;background-color:{4}">{5}</div>\n'
html_phaselet = '<div id="{0}" class="phaselet" style="left:{1}%;width:{2}%;background-color:{3}"></div>\n'
html_legend = '<div id="p{3}" class="square" style="left:{0}%;background-color:{1}"> {2}</div>\n'
html_timetotal = '<table class="time1">\n<tr>'\
'<td class="green">{2} Suspend Time: <b>{0} ms</b></td>'\
'<td class="yellow">{2} Resume Time: <b>{1} ms</b></td>'\
'</tr>\n</table>\n'
html_timetotal2 = '<table class="time1">\n<tr>'\
'<td class="green">{3} Suspend Time: <b>{0} ms</b></td>'\
'<td class="gray">'+sysvals.suspendmode+' time: <b>{1} ms</b></td>'\
'<td class="yellow">{3} Resume Time: <b>{2} ms</b></td>'\
'</tr>\n</table>\n'
html_timetotal3 = '<table class="time1">\n<tr>'\
'<td class="green">Execution Time: <b>{0} ms</b></td>'\
'<td class="yellow">Command: <b>{1}</b></td>'\
'</tr>\n</table>\n'
html_timegroups = '<table class="time2">\n<tr>'\
'<td class="green">{4}Kernel Suspend: {0} ms</td>'\
'<td class="purple">{4}Firmware Suspend: {1} ms</td>'\
'<td class="purple">{4}Firmware Resume: {2} ms</td>'\
'<td class="yellow">{4}Kernel Resume: {3} ms</td>'\
'</tr>\n</table>\n'
# html format variables
rowheight = 30
devtextS = '14px'
devtextH = '30px'
hoverZ = 'z-index:10;'
if sysvals.usedevsrc:
hoverZ = ''
# device timeline
vprint('Creating Device Timeline...')
devtl = Timeline(rowheight)
# Generate the header for this timeline
for data in testruns:
tTotal = data.end - data.start
tEnd = data.dmesg['resume_complete']['end']
if(tTotal == 0):
print('ERROR: No timeline data')
sys.exit()
if(data.tLow > 0):
low_time = '%.0f'%(data.tLow*1000)
if sysvals.suspendmode == 'command':
run_time = '%.0f'%((data.end-data.start)*1000)
if sysvals.testcommand:
testdesc = sysvals.testcommand
else:
testdesc = 'unknown'
if(len(testruns) > 1):
testdesc = ordinal(data.testnumber+1)+' '+testdesc
thtml = html_timetotal3.format(run_time, testdesc)
devtl.html['header'] += thtml
elif data.fwValid:
suspend_time = '%.0f'%((data.tSuspended-data.start)*1000 + \
(data.fwSuspend/1000000.0))
resume_time = '%.0f'%((tEnd-data.tSuspended)*1000 + \
(data.fwResume/1000000.0))
testdesc1 = 'Total'
testdesc2 = ''
if(len(testruns) > 1):
testdesc1 = testdesc2 = ordinal(data.testnumber+1)
testdesc2 += ' '
if(data.tLow == 0):
thtml = html_timetotal.format(suspend_time, \
resume_time, testdesc1)
else:
thtml = html_timetotal2.format(suspend_time, low_time, \
resume_time, testdesc1)
devtl.html['header'] += thtml
sktime = '%.3f'%((data.dmesg['suspend_machine']['end'] - \
data.getStart())*1000)
sftime = '%.3f'%(data.fwSuspend / 1000000.0)
rftime = '%.3f'%(data.fwResume / 1000000.0)
rktime = '%.3f'%((data.dmesg['resume_complete']['end'] - \
data.dmesg['resume_machine']['start'])*1000)
devtl.html['header'] += html_timegroups.format(sktime, \
sftime, rftime, rktime, testdesc2)
else:
suspend_time = '%.0f'%((data.tSuspended-data.start)*1000)
resume_time = '%.0f'%((tEnd-data.tSuspended)*1000)
testdesc = 'Kernel'
if(len(testruns) > 1):
testdesc = ordinal(data.testnumber+1)+' '+testdesc
if(data.tLow == 0):
thtml = html_timetotal.format(suspend_time, \
resume_time, testdesc)
else:
thtml = html_timetotal2.format(suspend_time, low_time, \
resume_time, testdesc)
devtl.html['header'] += thtml
# time scale for potentially multiple datasets
t0 = testruns[0].start
tMax = testruns[-1].end
tSuspended = testruns[-1].tSuspended
tTotal = tMax - t0
# determine the maximum number of rows we need to draw
for data in testruns:
data.selectTimelineDevices('%f', tTotal, sysvals.mindevlen)
for group in data.devicegroups:
devlist = []
for phase in group:
for devname in data.tdevlist[phase]:
devlist.append((phase,devname))
devtl.getPhaseRows(data.dmesg, devlist)
devtl.calcTotalRows()
# create bounding box, add buttons
if sysvals.suspendmode != 'command':
devtl.html['timeline'] += html_devlist1
if len(testruns) > 1:
devtl.html['timeline'] += html_devlist2
devtl.html['timeline'] += html_zoombox
devtl.html['timeline'] += html_timeline.format('dmesg', devtl.height)
# draw the full timeline
phases = {'suspend':[],'resume':[]}
for phase in data.dmesg:
if 'resume' in phase:
phases['resume'].append(phase)
else:
phases['suspend'].append(phase)
# draw each test run chronologically
for data in testruns:
# if nore than one test, draw a block to represent user mode
if(data.testnumber > 0):
m0 = testruns[data.testnumber-1].end
mMax = testruns[data.testnumber].start
mTotal = mMax - m0
name = 'usermode%d' % data.testnumber
top = '%d' % devtl.scaleH
left = '%f' % (((m0-t0)*100.0)/tTotal)
width = '%f' % ((mTotal*100.0)/tTotal)
title = 'user mode (%0.3f ms) ' % (mTotal*1000)
devtl.html['timeline'] += html_device.format(name, \
title, left, top, '%d'%devtl.bodyH, width, '', '', '')
# now draw the actual timeline blocks
for dir in phases:
# draw suspend and resume blocks separately
bname = '%s%d' % (dir[0], data.testnumber)
if dir == 'suspend':
m0 = testruns[data.testnumber].start
mMax = testruns[data.testnumber].tSuspended
mTotal = mMax - m0
left = '%f' % (((m0-t0)*100.0)/tTotal)
else:
m0 = testruns[data.testnumber].tSuspended
mMax = testruns[data.testnumber].end
mTotal = mMax - m0
left = '%f' % ((((m0-t0)*100.0)+sysvals.srgap/2)/tTotal)
# if a timeline block is 0 length, skip altogether
if mTotal == 0:
continue
width = '%f' % (((mTotal*100.0)-sysvals.srgap/2)/tTotal)
devtl.html['timeline'] += html_tblock.format(bname, left, width)
for b in sorted(phases[dir]):
# draw the phase color background
phase = data.dmesg[b]
length = phase['end']-phase['start']
left = '%f' % (((phase['start']-m0)*100.0)/mTotal)
width = '%f' % ((length*100.0)/mTotal)
devtl.html['timeline'] += html_phase.format(left, width, \
'%.3f'%devtl.scaleH, '%.3f'%devtl.bodyH, \
data.dmesg[b]['color'], '')
# draw the devices for this phase
phaselist = data.dmesg[b]['list']
for d in data.tdevlist[b]:
name = d
drv = ''
dev = phaselist[d]
xtraclass = ''
xtrainfo = ''
xtrastyle = ''
if 'htmlclass' in dev:
xtraclass = dev['htmlclass']
xtrainfo = dev['htmlclass']
if 'color' in dev:
xtrastyle = 'background-color:%s;' % dev['color']
if(d in sysvals.devprops):
name = sysvals.devprops[d].altName(d)
xtraclass = sysvals.devprops[d].xtraClass()
xtrainfo = sysvals.devprops[d].xtraInfo()
if('drv' in dev and dev['drv']):
drv = ' {%s}' % dev['drv']
rowheight = devtl.phaseRowHeight(b, dev['row'])
rowtop = devtl.phaseRowTop(b, dev['row'])
top = '%.3f' % (rowtop + devtl.scaleH)
left = '%f' % (((dev['start']-m0)*100)/mTotal)
width = '%f' % (((dev['end']-dev['start'])*100)/mTotal)
length = ' (%0.3f ms) ' % ((dev['end']-dev['start'])*1000)
if sysvals.suspendmode == 'command':
title = name+drv+xtrainfo+length+'cmdexec'
else:
title = name+drv+xtrainfo+length+b
devtl.html['timeline'] += html_device.format(dev['id'], \
title, left, top, '%.3f'%rowheight, width, \
d+drv, xtraclass, xtrastyle)
if('src' not in dev):
continue
# draw any trace events for this device
vprint('Debug trace events found for device %s' % d)
vprint('%20s %20s %10s %8s' % ('title', \
'name', 'time(ms)', 'length(ms)'))
for e in dev['src']:
vprint('%20s %20s %10.3f %8.3f' % (e.title, \
e.text, e.time*1000, e.length*1000))
height = devtl.rowH
top = '%.3f' % (rowtop + devtl.scaleH + (e.row*devtl.rowH))
left = '%f' % (((e.time-m0)*100)/mTotal)
width = '%f' % (e.length*100/mTotal)
color = 'rgba(204,204,204,0.5)'
devtl.html['timeline'] += \
html_traceevent.format(e.title, \
left, top, '%.3f'%height, \
width, e.text)
# draw the time scale, try to make the number of labels readable
devtl.html['timeline'] += devtl.createTimeScale(m0, mMax, tTotal, dir)
devtl.html['timeline'] += '</div>\n'
# timeline is finished
devtl.html['timeline'] += '</div>\n</div>\n'
# draw a legend which describes the phases by color
if sysvals.suspendmode != 'command':
data = testruns[-1]
devtl.html['legend'] = '<div class="legend">\n'
pdelta = 100.0/len(data.phases)
pmargin = pdelta / 4.0
for phase in data.phases:
tmp = phase.split('_')
id = tmp[0][0]
if(len(tmp) > 1):
id += tmp[1][0]
order = '%.2f' % ((data.dmesg[phase]['order'] * pdelta) + pmargin)
name = string.replace(phase, '_', ' ')
devtl.html['legend'] += html_legend.format(order, \
data.dmesg[phase]['color'], name, id)
devtl.html['legend'] += '</div>\n'
hf = open(sysvals.htmlfile, 'w')
if not sysvals.cgexp:
cgchk = 'checked'
cgnchk = 'not(:checked)'
else:
cgchk = 'not(:checked)'
cgnchk = 'checked'
# write the html header first (html head, css code, up to body start)
html_header = '<!DOCTYPE html>\n<html>\n<head>\n\
<meta http-equiv="content-type" content="text/html; charset=UTF-8">\n\
<title>'+htmlTitle()+'</title>\n\
<style type=\'text/css\'>\n\
body {overflow-y:scroll;}\n\
.stamp {width:100%;text-align:center;background-color:gray;line-height:30px;color:white;font:25px Arial;}\n\
.callgraph {margin-top:30px;box-shadow:5px 5px 20px black;}\n\
.callgraph article * {padding-left:28px;}\n\
h1 {color:black;font:bold 30px Times;}\n\
t0 {color:black;font:bold 30px Times;}\n\
t1 {color:black;font:30px Times;}\n\
t2 {color:black;font:25px Times;}\n\
t3 {color:black;font:20px Times;white-space:nowrap;}\n\
t4 {color:black;font:bold 30px Times;line-height:60px;white-space:nowrap;}\n\
cS {color:blue;font:bold 11px Times;}\n\
cR {color:red;font:bold 11px Times;}\n\
table {width:100%;}\n\
.gray {background-color:rgba(80,80,80,0.1);}\n\
.green {background-color:rgba(204,255,204,0.4);}\n\
.purple {background-color:rgba(128,0,128,0.2);}\n\
.yellow {background-color:rgba(255,255,204,0.4);}\n\
.time1 {font:22px Arial;border:1px solid;}\n\
.time2 {font:15px Arial;border-bottom:1px solid;border-left:1px solid;border-right:1px solid;}\n\
td {text-align:center;}\n\
r {color:#500000;font:15px Tahoma;}\n\
n {color:#505050;font:15px Tahoma;}\n\
.tdhl {color:red;}\n\
.hide {display:none;}\n\
.pf {display:none;}\n\
.pf:'+cgchk+' + label {background:url(\'data:image/svg+xml;utf,<?xml version="1.0" standalone="no"?><svg xmlns="http://www.w3.org/2000/svg" height="18" width="18" version="1.1"><circle cx="9" cy="9" r="8" stroke="black" stroke-width="1" fill="white"/><rect x="4" y="8" width="10" height="2" style="fill:black;stroke-width:0"/><rect x="8" y="4" width="2" height="10" style="fill:black;stroke-width:0"/></svg>\') no-repeat left center;}\n\
.pf:'+cgnchk+' ~ label {background:url(\'data:image/svg+xml;utf,<?xml version="1.0" standalone="no"?><svg xmlns="http://www.w3.org/2000/svg" height="18" width="18" version="1.1"><circle cx="9" cy="9" r="8" stroke="black" stroke-width="1" fill="white"/><rect x="4" y="8" width="10" height="2" style="fill:black;stroke-width:0"/></svg>\') no-repeat left center;}\n\
.pf:'+cgchk+' ~ *:not(:nth-child(2)) {display:none;}\n\
.zoombox {position:relative;width:100%;overflow-x:scroll;}\n\
.timeline {position:relative;font-size:14px;cursor:pointer;width:100%; overflow:hidden;background:linear-gradient(#cccccc, white);}\n\
.thread {position:absolute;height:0%;overflow:hidden;line-height:'+devtextH+';font-size:'+devtextS+';border:1px solid;text-align:center;white-space:nowrap;background-color:rgba(204,204,204,0.5);}\n\
.thread.sync {background-color:'+sysvals.synccolor+';}\n\
.thread.bg {background-color:'+sysvals.kprobecolor+';}\n\
.thread:hover {background-color:white;border:1px solid red;'+hoverZ+'}\n\
.hover {background-color:white;border:1px solid red;'+hoverZ+'}\n\
.hover.sync {background-color:white;}\n\
.hover.bg {background-color:white;}\n\
.traceevent {position:absolute;font-size:10px;overflow:hidden;color:black;text-align:center;white-space:nowrap;border-radius:5px;border:1px solid black;background:linear-gradient(to bottom right,rgba(204,204,204,1),rgba(150,150,150,1));}\n\
.traceevent:hover {background:white;}\n\
.phase {position:absolute;overflow:hidden;border:0px;text-align:center;}\n\
.phaselet {position:absolute;overflow:hidden;border:0px;text-align:center;height:100px;font-size:24px;}\n\
.t {z-index:2;position:absolute;pointer-events:none;top:0%;height:100%;border-right:1px solid black;}\n\
.legend {position:relative; width:100%; height:40px; text-align:center;margin-bottom:20px}\n\
.legend .square {position:absolute;cursor:pointer;top:10px; width:0px;height:20px;border:1px solid;padding-left:20px;}\n\
button {height:40px;width:200px;margin-bottom:20px;margin-top:20px;font-size:24px;}\n\
.logbtn {position:relative;float:right;height:25px;width:50px;margin-top:3px;margin-bottom:0;font-size:10px;text-align:center;}\n\
.devlist {position:'+x2changes[1]+';width:190px;}\n\
a:link {color:white;text-decoration:none;}\n\
a:visited {color:white;}\n\
a:hover {color:white;}\n\
a:active {color:white;}\n\
.version {position:relative;float:left;color:white;font-size:10px;line-height:30px;margin-left:10px;}\n\
#devicedetail {height:100px;box-shadow:5px 5px 20px black;}\n\
.tblock {position:absolute;height:100%;}\n\
.bg {z-index:1;}\n\
</style>\n</head>\n<body>\n'
# no header or css if its embedded
if(sysvals.embedded):
hf.write('pass True tSus %.3f tRes %.3f tLow %.3f fwvalid %s tSus %.3f tRes %.3f\n' %
(data.tSuspended-data.start, data.end-data.tSuspended, data.tLow, data.fwValid, \
data.fwSuspend/1000000, data.fwResume/1000000))
else:
hf.write(html_header)
# write the test title and general info header
if(sysvals.stamp['time'] != ""):
hf.write(headline_version)
if sysvals.addlogs and sysvals.dmesgfile:
hf.write('<button id="showdmesg" class="logbtn">dmesg</button>')
if sysvals.addlogs and sysvals.ftracefile:
hf.write('<button id="showftrace" class="logbtn">ftrace</button>')
hf.write(headline_stamp.format(sysvals.stamp['host'],
sysvals.stamp['kernel'], sysvals.stamp['mode'], \
sysvals.stamp['time']))
# write the device timeline
hf.write(devtl.html['header'])
hf.write(devtl.html['timeline'])
hf.write(devtl.html['legend'])
hf.write('<div id="devicedetailtitle"></div>\n')
hf.write('<div id="devicedetail" style="display:none;">\n')
# draw the colored boxes for the device detail section
for data in testruns:
hf.write('<div id="devicedetail%d">\n' % data.testnumber)
for b in data.phases:
phase = data.dmesg[b]
length = phase['end']-phase['start']
left = '%.3f' % (((phase['start']-t0)*100.0)/tTotal)
width = '%.3f' % ((length*100.0)/tTotal)
hf.write(html_phaselet.format(b, left, width, \
data.dmesg[b]['color']))
if sysvals.suspendmode == 'command':
hf.write(html_phaselet.format('cmdexec', '0', '0', \
data.dmesg['resume_complete']['color']))
hf.write('</div>\n')
hf.write('</div>\n')
# write the ftrace data (callgraph)
data = testruns[-1]
if(sysvals.usecallgraph and not sysvals.embedded):
hf.write('<section id="callgraphs" class="callgraph">\n')
# write out the ftrace data converted to html
html_func_top = '<article id="{0}" class="atop" style="background-color:{1}">\n<input type="checkbox" class="pf" id="f{2}" checked/><label for="f{2}">{3} {4}</label>\n'
html_func_start = '<article>\n<input type="checkbox" class="pf" id="f{0}" checked/><label for="f{0}">{1} {2}</label>\n'
html_func_end = '</article>\n'
html_func_leaf = '<article>{0} {1}</article>\n'
num = 0
for p in data.phases:
list = data.dmesg[p]['list']
for devname in data.sortedDevices(p):
if('ftrace' not in list[devname]):
continue
devid = list[devname]['id']
cg = list[devname]['ftrace']
clen = (cg.end - cg.start) * 1000
if clen < sysvals.mincglen:
continue
fmt = '<r>(%.3f ms @ '+sysvals.timeformat+' to '+sysvals.timeformat+')</r>'
flen = fmt % (clen, cg.start, cg.end)
name = devname
if(devname in sysvals.devprops):
name = sysvals.devprops[devname].altName(devname)
if sysvals.suspendmode == 'command':
ftitle = name
else:
ftitle = name+' '+p
hf.write(html_func_top.format(devid, data.dmesg[p]['color'], \
num, ftitle, flen))
num += 1
for line in cg.list:
if(line.length < 0.000000001):
flen = ''
else:
fmt = '<n>(%.3f ms @ '+sysvals.timeformat+')</n>'
flen = fmt % (line.length*1000, line.time)
if(line.freturn and line.fcall):
hf.write(html_func_leaf.format(line.name, flen))
elif(line.freturn):
hf.write(html_func_end)
else:
hf.write(html_func_start.format(num, line.name, flen))
num += 1
hf.write(html_func_end)
hf.write('\n\n </section>\n')
# add the dmesg log as a hidden div
if sysvals.addlogs and sysvals.dmesgfile:
hf.write('<div id="dmesglog" style="display:none;">\n')
lf = open(sysvals.dmesgfile, 'r')
for line in lf:
hf.write(line)
lf.close()
hf.write('</div>\n')
# add the ftrace log as a hidden div
if sysvals.addlogs and sysvals.ftracefile:
hf.write('<div id="ftracelog" style="display:none;">\n')
lf = open(sysvals.ftracefile, 'r')
for line in lf:
hf.write(line)
lf.close()
hf.write('</div>\n')
if(not sysvals.embedded):
# write the footer and close
addScriptCode(hf, testruns)
hf.write('</body>\n</html>\n')
else:
# embedded out will be loaded in a page, skip the js
t0 = (testruns[0].start - testruns[-1].tSuspended) * 1000
tMax = (testruns[-1].end - testruns[-1].tSuspended) * 1000
# add js code in a div entry for later evaluation
detail = 'var bounds = [%f,%f];\n' % (t0, tMax)
detail += 'var devtable = [\n'
for data in testruns:
topo = data.deviceTopology()
detail += '\t"%s",\n' % (topo)
detail += '];\n'
hf.write('<div id=customcode style=display:none>\n'+detail+'</div>\n')
hf.close()
return True
# Function: addScriptCode
# Description:
# Adds the javascript code to the output html
# Arguments:
# hf: the open html file pointer
# testruns: array of Data objects from parseKernelLog or parseTraceLog
def addScriptCode(hf, testruns):
t0 = testruns[0].start * 1000
tMax = testruns[-1].end * 1000
# create an array in javascript memory with the device details
detail = ' var devtable = [];\n'
for data in testruns:
topo = data.deviceTopology()
detail += ' devtable[%d] = "%s";\n' % (data.testnumber, topo)
detail += ' var bounds = [%f,%f];\n' % (t0, tMax)
# add the code which will manipulate the data in the browser
script_code = \
'<script type="text/javascript">\n'+detail+\
' var resolution = -1;\n'\
' function redrawTimescale(t0, tMax, tS) {\n'\
' var rline = \'<div class="t" style="left:0;border-left:1px solid black;border-right:0;"><cR><-R</cR></div>\';\n'\
' var tTotal = tMax - t0;\n'\
' var list = document.getElementsByClassName("tblock");\n'\
' for (var i = 0; i < list.length; i++) {\n'\
' var timescale = list[i].getElementsByClassName("timescale")[0];\n'\
' var m0 = t0 + (tTotal*parseFloat(list[i].style.left)/100);\n'\
' var mTotal = tTotal*parseFloat(list[i].style.width)/100;\n'\
' var mMax = m0 + mTotal;\n'\
' var html = "";\n'\
' var divTotal = Math.floor(mTotal/tS) + 1;\n'\
' if(divTotal > 1000) continue;\n'\
' var divEdge = (mTotal - tS*(divTotal-1))*100/mTotal;\n'\
' var pos = 0.0, val = 0.0;\n'\
' for (var j = 0; j < divTotal; j++) {\n'\
' var htmlline = "";\n'\
' if(list[i].id[5] == "r") {\n'\
' pos = 100 - (((j)*tS*100)/mTotal);\n'\
' val = (j)*tS;\n'\
' htmlline = \'<div class="t" style="right:\'+pos+\'%">\'+val+\'ms</div>\';\n'\
' if(j == 0)\n'\
' htmlline = rline;\n'\
' } else {\n'\
' pos = 100 - (((j)*tS*100)/mTotal) - divEdge;\n'\
' val = (j-divTotal+1)*tS;\n'\
' if(j == divTotal - 1)\n'\
' htmlline = \'<div class="t" style="right:\'+pos+\'%"><cS>S-></cS></div>\';\n'\
' else\n'\
' htmlline = \'<div class="t" style="right:\'+pos+\'%">\'+val+\'ms</div>\';\n'\
' }\n'\
' html += htmlline;\n'\
' }\n'\
' timescale.innerHTML = html;\n'\
' }\n'\
' }\n'\
' function zoomTimeline() {\n'\
' var dmesg = document.getElementById("dmesg");\n'\
' var zoombox = document.getElementById("dmesgzoombox");\n'\
' var val = parseFloat(dmesg.style.width);\n'\
' var newval = 100;\n'\
' var sh = window.outerWidth / 2;\n'\
' if(this.id == "zoomin") {\n'\
' newval = val * 1.2;\n'\
' if(newval > 910034) newval = 910034;\n'\
' dmesg.style.width = newval+"%";\n'\
' zoombox.scrollLeft = ((zoombox.scrollLeft + sh) * newval / val) - sh;\n'\
' } else if (this.id == "zoomout") {\n'\
' newval = val / 1.2;\n'\
' if(newval < 100) newval = 100;\n'\
' dmesg.style.width = newval+"%";\n'\
' zoombox.scrollLeft = ((zoombox.scrollLeft + sh) * newval / val) - sh;\n'\
' } else {\n'\
' zoombox.scrollLeft = 0;\n'\
' dmesg.style.width = "100%";\n'\
' }\n'\
' var tS = [10000, 5000, 2000, 1000, 500, 200, 100, 50, 20, 10, 5, 2, 1];\n'\
' var t0 = bounds[0];\n'\
' var tMax = bounds[1];\n'\
' var tTotal = tMax - t0;\n'\
' var wTotal = tTotal * 100.0 / newval;\n'\
' var idx = 7*window.innerWidth/1100;\n'\
' for(var i = 0; (i < tS.length)&&((wTotal / tS[i]) < idx); i++);\n'\
' if(i >= tS.length) i = tS.length - 1;\n'\
' if(tS[i] == resolution) return;\n'\
' resolution = tS[i];\n'\
' redrawTimescale(t0, tMax, tS[i]);\n'\
' }\n'\
' function deviceHover() {\n'\
' var name = this.title.slice(0, this.title.indexOf(" ("));\n'\
' var dmesg = document.getElementById("dmesg");\n'\
' var dev = dmesg.getElementsByClassName("thread");\n'\
' var cpu = -1;\n'\
' if(name.match("CPU_ON\[[0-9]*\]"))\n'\
' cpu = parseInt(name.slice(7));\n'\
' else if(name.match("CPU_OFF\[[0-9]*\]"))\n'\
' cpu = parseInt(name.slice(8));\n'\
' for (var i = 0; i < dev.length; i++) {\n'\
' dname = dev[i].title.slice(0, dev[i].title.indexOf(" ("));\n'\
' var cname = dev[i].className.slice(dev[i].className.indexOf("thread"));\n'\
' if((cpu >= 0 && dname.match("CPU_O[NF]*\\\[*"+cpu+"\\\]")) ||\n'\
' (name == dname))\n'\
' {\n'\
' dev[i].className = "hover "+cname;\n'\
' } else {\n'\
' dev[i].className = cname;\n'\
' }\n'\
' }\n'\
' }\n'\
' function deviceUnhover() {\n'\
' var dmesg = document.getElementById("dmesg");\n'\
' var dev = dmesg.getElementsByClassName("thread");\n'\
' for (var i = 0; i < dev.length; i++) {\n'\
' dev[i].className = dev[i].className.slice(dev[i].className.indexOf("thread"));\n'\
' }\n'\
' }\n'\
' function deviceTitle(title, total, cpu) {\n'\
' var prefix = "Total";\n'\
' if(total.length > 3) {\n'\
' prefix = "Average";\n'\
' total[1] = (total[1]+total[3])/2;\n'\
' total[2] = (total[2]+total[4])/2;\n'\
' }\n'\
' var devtitle = document.getElementById("devicedetailtitle");\n'\
' var name = title.slice(0, title.indexOf(" ("));\n'\
' if(cpu >= 0) name = "CPU"+cpu;\n'\
' var driver = "";\n'\
' var tS = "<t2>(</t2>";\n'\
' var tR = "<t2>)</t2>";\n'\
' if(total[1] > 0)\n'\
' tS = "<t2>("+prefix+" Suspend:</t2><t0> "+total[1].toFixed(3)+" ms</t0> ";\n'\
' if(total[2] > 0)\n'\
' tR = " <t2>"+prefix+" Resume:</t2><t0> "+total[2].toFixed(3)+" ms<t2>)</t2></t0>";\n'\
' var s = title.indexOf("{");\n'\
' var e = title.indexOf("}");\n'\
' if((s >= 0) && (e >= 0))\n'\
' driver = title.slice(s+1, e) + " <t1>@</t1> ";\n'\
' if(total[1] > 0 && total[2] > 0)\n'\
' devtitle.innerHTML = "<t0>"+driver+name+"</t0> "+tS+tR;\n'\
' else\n'\
' devtitle.innerHTML = "<t0>"+title+"</t0>";\n'\
' return name;\n'\
' }\n'\
' function deviceDetail() {\n'\
' var devinfo = document.getElementById("devicedetail");\n'\
' devinfo.style.display = "block";\n'\
' var name = this.title.slice(0, this.title.indexOf(" ("));\n'\
' var cpu = -1;\n'\
' if(name.match("CPU_ON\[[0-9]*\]"))\n'\
' cpu = parseInt(name.slice(7));\n'\
' else if(name.match("CPU_OFF\[[0-9]*\]"))\n'\
' cpu = parseInt(name.slice(8));\n'\
' var dmesg = document.getElementById("dmesg");\n'\
' var dev = dmesg.getElementsByClassName("thread");\n'\
' var idlist = [];\n'\
' var pdata = [[]];\n'\
' if(document.getElementById("devicedetail1"))\n'\
' pdata = [[], []];\n'\
' var pd = pdata[0];\n'\
' var total = [0.0, 0.0, 0.0];\n'\
' for (var i = 0; i < dev.length; i++) {\n'\
' dname = dev[i].title.slice(0, dev[i].title.indexOf(" ("));\n'\
' if((cpu >= 0 && dname.match("CPU_O[NF]*\\\[*"+cpu+"\\\]")) ||\n'\
' (name == dname))\n'\
' {\n'\
' idlist[idlist.length] = dev[i].id;\n'\
' var tidx = 1;\n'\
' if(dev[i].id[0] == "a") {\n'\
' pd = pdata[0];\n'\
' } else {\n'\
' if(pdata.length == 1) pdata[1] = [];\n'\
' if(total.length == 3) total[3]=total[4]=0.0;\n'\
' pd = pdata[1];\n'\
' tidx = 3;\n'\
' }\n'\
' var info = dev[i].title.split(" ");\n'\
' var pname = info[info.length-1];\n'\
' pd[pname] = parseFloat(info[info.length-3].slice(1));\n'\
' total[0] += pd[pname];\n'\
' if(pname.indexOf("suspend") >= 0)\n'\
' total[tidx] += pd[pname];\n'\
' else\n'\
' total[tidx+1] += pd[pname];\n'\
' }\n'\
' }\n'\
' var devname = deviceTitle(this.title, total, cpu);\n'\
' var left = 0.0;\n'\
' for (var t = 0; t < pdata.length; t++) {\n'\
' pd = pdata[t];\n'\
' devinfo = document.getElementById("devicedetail"+t);\n'\
' var phases = devinfo.getElementsByClassName("phaselet");\n'\
' for (var i = 0; i < phases.length; i++) {\n'\
' if(phases[i].id in pd) {\n'\
' var w = 100.0*pd[phases[i].id]/total[0];\n'\
' var fs = 32;\n'\
' if(w < 8) fs = 4*w | 0;\n'\
' var fs2 = fs*3/4;\n'\
' phases[i].style.width = w+"%";\n'\
' phases[i].style.left = left+"%";\n'\
' phases[i].title = phases[i].id+" "+pd[phases[i].id]+" ms";\n'\
' left += w;\n'\
' var time = "<t4 style=\\"font-size:"+fs+"px\\">"+pd[phases[i].id]+" ms<br></t4>";\n'\
' var pname = "<t3 style=\\"font-size:"+fs2+"px\\">"+phases[i].id.replace("_", " ")+"</t3>";\n'\
' phases[i].innerHTML = time+pname;\n'\
' } else {\n'\
' phases[i].style.width = "0%";\n'\
' phases[i].style.left = left+"%";\n'\
' }\n'\
' }\n'\
' }\n'\
' var cglist = document.getElementById("callgraphs");\n'\
' if(!cglist) return;\n'\
' var cg = cglist.getElementsByClassName("atop");\n'\
' if(cg.length < 10) return;\n'\
' for (var i = 0; i < cg.length; i++) {\n'\
' if(idlist.indexOf(cg[i].id) >= 0) {\n'\
' cg[i].style.display = "block";\n'\
' } else {\n'\
' cg[i].style.display = "none";\n'\
' }\n'\
' }\n'\
' }\n'\
' function devListWindow(e) {\n'\
' var sx = e.clientX;\n'\
' if(sx > window.innerWidth - 440)\n'\
' sx = window.innerWidth - 440;\n'\
' var cfg="top="+e.screenY+", left="+sx+", width=440, height=720, scrollbars=yes";\n'\
' var win = window.open("", "_blank", cfg);\n'\
' if(window.chrome) win.moveBy(sx, 0);\n'\
' var html = "<title>"+e.target.innerHTML+"</title>"+\n'\
' "<style type=\\"text/css\\">"+\n'\
' " ul {list-style-type:circle;padding-left:10px;margin-left:10px;}"+\n'\
' "</style>"\n'\
' var dt = devtable[0];\n'\
' if(e.target.id != "devlist1")\n'\
' dt = devtable[1];\n'\
' win.document.write(html+dt);\n'\
' }\n'\
' function logWindow(e) {\n'\
' var name = e.target.id.slice(4);\n'\
' var win = window.open();\n'\
' var log = document.getElementById(name+"log");\n'\
' var title = "<title>"+document.title.split(" ")[0]+" "+name+" log</title>";\n'\
' win.document.write(title+"<pre>"+log.innerHTML+"</pre>");\n'\
' win.document.close();\n'\
' }\n'\
' function onClickPhase(e) {\n'\
' }\n'\
' window.addEventListener("resize", function () {zoomTimeline();});\n'\
' window.addEventListener("load", function () {\n'\
' var dmesg = document.getElementById("dmesg");\n'\
' dmesg.style.width = "100%"\n'\
' document.getElementById("zoomin").onclick = zoomTimeline;\n'\
' document.getElementById("zoomout").onclick = zoomTimeline;\n'\
' document.getElementById("zoomdef").onclick = zoomTimeline;\n'\
' var list = document.getElementsByClassName("square");\n'\
' for (var i = 0; i < list.length; i++)\n'\
' list[i].onclick = onClickPhase;\n'\
' var list = document.getElementsByClassName("logbtn");\n'\
' for (var i = 0; i < list.length; i++)\n'\
' list[i].onclick = logWindow;\n'\
' list = document.getElementsByClassName("devlist");\n'\
' for (var i = 0; i < list.length; i++)\n'\
' list[i].onclick = devListWindow;\n'\
' var dev = dmesg.getElementsByClassName("thread");\n'\
' for (var i = 0; i < dev.length; i++) {\n'\
' dev[i].onclick = deviceDetail;\n'\
' dev[i].onmouseover = deviceHover;\n'\
' dev[i].onmouseout = deviceUnhover;\n'\
' }\n'\
' zoomTimeline();\n'\
' });\n'\
'</script>\n'
hf.write(script_code);
# Function: executeSuspend
# Description:
# Execute system suspend through the sysfs interface, then copy the output
# dmesg and ftrace files to the test output directory.
def executeSuspend():
global sysvals
t0 = time.time()*1000
tp = sysvals.tpath
fwdata = []
# mark the start point in the kernel ring buffer just as we start
sysvals.initdmesg()
# start ftrace
if(sysvals.usecallgraph or sysvals.usetraceevents):
print('START TRACING')
sysvals.fsetVal('1', 'tracing_on')
# execute however many s/r runs requested
for count in range(1,sysvals.execcount+1):
# if this is test2 and there's a delay, start here
if(count > 1 and sysvals.x2delay > 0):
tN = time.time()*1000
while (tN - t0) < sysvals.x2delay:
tN = time.time()*1000
time.sleep(0.001)
# initiate suspend
if(sysvals.usecallgraph or sysvals.usetraceevents):
sysvals.fsetVal('SUSPEND START', 'trace_marker')
if sysvals.suspendmode == 'command':
print('COMMAND START')
if(sysvals.rtcwake):
print('will issue an rtcwake in %d seconds' % sysvals.rtcwaketime)
sysvals.rtcWakeAlarmOn()
os.system(sysvals.testcommand)
else:
if(sysvals.rtcwake):
print('SUSPEND START')
print('will autoresume in %d seconds' % sysvals.rtcwaketime)
sysvals.rtcWakeAlarmOn()
else:
print('SUSPEND START (press a key to resume)')
pf = open(sysvals.powerfile, 'w')
pf.write(sysvals.suspendmode)
# execution will pause here
try:
pf.close()
except:
pass
t0 = time.time()*1000
if(sysvals.rtcwake):
sysvals.rtcWakeAlarmOff()
# return from suspend
print('RESUME COMPLETE')
if(sysvals.usecallgraph or sysvals.usetraceevents):
sysvals.fsetVal('RESUME COMPLETE', 'trace_marker')
if(sysvals.suspendmode == 'mem'):
fwdata.append(getFPDT(False))
# look for post resume events after the last test run
t = sysvals.postresumetime
if(t > 0):
print('Waiting %d seconds for POST-RESUME trace events...' % t)
time.sleep(t)
# stop ftrace
if(sysvals.usecallgraph or sysvals.usetraceevents):
sysvals.fsetVal('0', 'tracing_on')
print('CAPTURING TRACE')
writeDatafileHeader(sysvals.ftracefile, fwdata)
os.system('cat '+tp+'trace >> '+sysvals.ftracefile)
sysvals.fsetVal('', 'trace')
devProps()
# grab a copy of the dmesg output
print('CAPTURING DMESG')
writeDatafileHeader(sysvals.dmesgfile, fwdata)
sysvals.getdmesg()
def writeDatafileHeader(filename, fwdata):
global sysvals
prt = sysvals.postresumetime
fp = open(filename, 'a')
fp.write(sysvals.teststamp+'\n')
if(sysvals.suspendmode == 'mem'):
for fw in fwdata:
if(fw):
fp.write('# fwsuspend %u fwresume %u\n' % (fw[0], fw[1]))
if(prt > 0):
fp.write('# post resume time %u\n' % prt)
fp.close()
# Function: setUSBDevicesAuto
# Description:
# Set the autosuspend control parameter of all USB devices to auto
# This can be dangerous, so use at your own risk, most devices are set
# to always-on since the kernel cant determine if the device can
# properly autosuspend
def setUSBDevicesAuto():
global sysvals
rootCheck(True)
for dirname, dirnames, filenames in os.walk('/sys/devices'):
if(re.match('.*/usb[0-9]*.*', dirname) and
'idVendor' in filenames and 'idProduct' in filenames):
os.system('echo auto > %s/power/control' % dirname)
name = dirname.split('/')[-1]
desc = os.popen('cat %s/product 2>/dev/null' % \
dirname).read().replace('\n', '')
ctrl = os.popen('cat %s/power/control 2>/dev/null' % \
dirname).read().replace('\n', '')
print('control is %s for %6s: %s' % (ctrl, name, desc))
# Function: yesno
# Description:
# Print out an equivalent Y or N for a set of known parameter values
# Output:
# 'Y', 'N', or ' ' if the value is unknown
def yesno(val):
yesvals = ['auto', 'enabled', 'active', '1']
novals = ['on', 'disabled', 'suspended', 'forbidden', 'unsupported']
if val in yesvals:
return 'Y'
elif val in novals:
return 'N'
return ' '
# Function: ms2nice
# Description:
# Print out a very concise time string in minutes and seconds
# Output:
# The time string, e.g. "1901m16s"
def ms2nice(val):
ms = 0
try:
ms = int(val)
except:
return 0.0
m = ms / 60000
s = (ms / 1000) - (m * 60)
return '%3dm%2ds' % (m, s)
# Function: detectUSB
# Description:
# Detect all the USB hosts and devices currently connected and add
# a list of USB device names to sysvals for better timeline readability
def detectUSB():
global sysvals
field = {'idVendor':'', 'idProduct':'', 'product':'', 'speed':''}
power = {'async':'', 'autosuspend':'', 'autosuspend_delay_ms':'',
'control':'', 'persist':'', 'runtime_enabled':'',
'runtime_status':'', 'runtime_usage':'',
'runtime_active_time':'',
'runtime_suspended_time':'',
'active_duration':'',
'connected_duration':''}
print('LEGEND')
print('---------------------------------------------------------------------------------------------')
print(' A = async/sync PM queue Y/N D = autosuspend delay (seconds)')
print(' S = autosuspend Y/N rACTIVE = runtime active (min/sec)')
print(' P = persist across suspend Y/N rSUSPEN = runtime suspend (min/sec)')
print(' E = runtime suspend enabled/forbidden Y/N ACTIVE = active duration (min/sec)')
print(' R = runtime status active/suspended Y/N CONNECT = connected duration (min/sec)')
print(' U = runtime usage count')
print('---------------------------------------------------------------------------------------------')
print(' NAME ID DESCRIPTION SPEED A S P E R U D rACTIVE rSUSPEN ACTIVE CONNECT')
print('---------------------------------------------------------------------------------------------')
for dirname, dirnames, filenames in os.walk('/sys/devices'):
if(re.match('.*/usb[0-9]*.*', dirname) and
'idVendor' in filenames and 'idProduct' in filenames):
for i in field:
field[i] = os.popen('cat %s/%s 2>/dev/null' % \
(dirname, i)).read().replace('\n', '')
name = dirname.split('/')[-1]
for i in power:
power[i] = os.popen('cat %s/power/%s 2>/dev/null' % \
(dirname, i)).read().replace('\n', '')
if(re.match('usb[0-9]*', name)):
first = '%-8s' % name
else:
first = '%8s' % name
print('%s [%s:%s] %-20s %-4s %1s %1s %1s %1s %1s %1s %1s %s %s %s %s' % \
(first, field['idVendor'], field['idProduct'], \
field['product'][0:20], field['speed'], \
yesno(power['async']), \
yesno(power['control']), \
yesno(power['persist']), \
yesno(power['runtime_enabled']), \
yesno(power['runtime_status']), \
power['runtime_usage'], \
power['autosuspend'], \
ms2nice(power['runtime_active_time']), \
ms2nice(power['runtime_suspended_time']), \
ms2nice(power['active_duration']), \
ms2nice(power['connected_duration'])))
# Function: devProps
# Description:
# Retrieve a list of properties for all devices in the trace log
def devProps(data=0):
global sysvals
props = dict()
if data:
idx = data.index(': ') + 2
if idx >= len(data):
return
devlist = data[idx:].split(';')
for dev in devlist:
f = dev.split(',')
if len(f) < 3:
continue
dev = f[0]
props[dev] = DevProps()
props[dev].altname = f[1]
if int(f[2]):
props[dev].async = True
else:
props[dev].async = False
sysvals.devprops = props
if sysvals.suspendmode == 'command' and 'testcommandstring' in props:
sysvals.testcommand = props['testcommandstring'].altname
return
if(os.path.exists(sysvals.ftracefile) == False):
doError('%s does not exist' % sysvals.ftracefile, False)
# first get the list of devices we need properties for
msghead = 'Additional data added by AnalyzeSuspend'
alreadystamped = False
tp = TestProps()
tf = open(sysvals.ftracefile, 'r')
for line in tf:
if msghead in line:
alreadystamped = True
continue
# determine the trace data type (required for further parsing)
m = re.match(sysvals.tracertypefmt, line)
if(m):
tp.setTracerType(m.group('t'))
continue
# parse only valid lines, if this is not one move on
m = re.match(tp.ftrace_line_fmt, line)
if(not m or 'device_pm_callback_start' not in line):
continue
m = re.match('.*: (?P<drv>.*) (?P<d>.*), parent: *(?P<p>.*), .*', m.group('msg'));
if(not m):
continue
drv, dev, par = m.group('drv'), m.group('d'), m.group('p')
if dev not in props:
props[dev] = DevProps()
tf.close()
if not alreadystamped and sysvals.suspendmode == 'command':
out = '#\n# '+msghead+'\n# Device Properties: '
out += 'testcommandstring,%s,0;' % (sysvals.testcommand)
with open(sysvals.ftracefile, 'a') as fp:
fp.write(out+'\n')
sysvals.devprops = props
return
# now get the syspath for each of our target devices
for dirname, dirnames, filenames in os.walk('/sys/devices'):
if(re.match('.*/power', dirname) and 'async' in filenames):
dev = dirname.split('/')[-2]
if dev in props and (not props[dev].syspath or len(dirname) < len(props[dev].syspath)):
props[dev].syspath = dirname[:-6]
# now fill in the properties for our target devices
for dev in props:
dirname = props[dev].syspath
if not dirname or not os.path.exists(dirname):
continue
with open(dirname+'/power/async') as fp:
text = fp.read()
props[dev].async = False
if 'enabled' in text:
props[dev].async = True
fields = os.listdir(dirname)
if 'product' in fields:
with open(dirname+'/product') as fp:
props[dev].altname = fp.read()
elif 'name' in fields:
with open(dirname+'/name') as fp:
props[dev].altname = fp.read()
elif 'model' in fields:
with open(dirname+'/model') as fp:
props[dev].altname = fp.read()
elif 'description' in fields:
with open(dirname+'/description') as fp:
props[dev].altname = fp.read()
elif 'id' in fields:
with open(dirname+'/id') as fp:
props[dev].altname = fp.read()
elif 'idVendor' in fields and 'idProduct' in fields:
idv, idp = '', ''
with open(dirname+'/idVendor') as fp:
idv = fp.read().strip()
with open(dirname+'/idProduct') as fp:
idp = fp.read().strip()
props[dev].altname = '%s:%s' % (idv, idp)
if props[dev].altname:
out = props[dev].altname.strip().replace('\n', ' ')
out = out.replace(',', ' ')
out = out.replace(';', ' ')
props[dev].altname = out
# and now write the data to the ftrace file
if not alreadystamped:
out = '#\n# '+msghead+'\n# Device Properties: '
for dev in sorted(props):
out += props[dev].out(dev)
with open(sysvals.ftracefile, 'a') as fp:
fp.write(out+'\n')
sysvals.devprops = props
# Function: getModes
# Description:
# Determine the supported power modes on this system
# Output:
# A string list of the available modes
def getModes():
global sysvals
modes = ''
if(os.path.exists(sysvals.powerfile)):
fp = open(sysvals.powerfile, 'r')
modes = string.split(fp.read())
fp.close()
return modes
# Function: getFPDT
# Description:
# Read the acpi bios tables and pull out FPDT, the firmware data
# Arguments:
# output: True to output the info to stdout, False otherwise
def getFPDT(output):
global sysvals
rectype = {}
rectype[0] = 'Firmware Basic Boot Performance Record'
rectype[1] = 'S3 Performance Table Record'
prectype = {}
prectype[0] = 'Basic S3 Resume Performance Record'
prectype[1] = 'Basic S3 Suspend Performance Record'
rootCheck(True)
if(not os.path.exists(sysvals.fpdtpath)):
if(output):
doError('file does not exist: %s' % sysvals.fpdtpath, False)
return False
if(not os.access(sysvals.fpdtpath, os.R_OK)):
if(output):
doError('file is not readable: %s' % sysvals.fpdtpath, False)
return False
if(not os.path.exists(sysvals.mempath)):
if(output):
doError('file does not exist: %s' % sysvals.mempath, False)
return False
if(not os.access(sysvals.mempath, os.R_OK)):
if(output):
doError('file is not readable: %s' % sysvals.mempath, False)
return False
fp = open(sysvals.fpdtpath, 'rb')
buf = fp.read()
fp.close()
if(len(buf) < 36):
if(output):
doError('Invalid FPDT table data, should '+\
'be at least 36 bytes', False)
return False
table = struct.unpack('4sIBB6s8sI4sI', buf[0:36])
if(output):
print('')
print('Firmware Performance Data Table (%s)' % table[0])
print(' Signature : %s' % table[0])
print(' Table Length : %u' % table[1])
print(' Revision : %u' % table[2])
print(' Checksum : 0x%x' % table[3])
print(' OEM ID : %s' % table[4])
print(' OEM Table ID : %s' % table[5])
print(' OEM Revision : %u' % table[6])
print(' Creator ID : %s' % table[7])
print(' Creator Revision : 0x%x' % table[8])
print('')
if(table[0] != 'FPDT'):
if(output):
doError('Invalid FPDT table')
return False
if(len(buf) <= 36):
return False
i = 0
fwData = [0, 0]
records = buf[36:]
fp = open(sysvals.mempath, 'rb')
while(i < len(records)):
header = struct.unpack('HBB', records[i:i+4])
if(header[0] not in rectype):
i += header[1]
continue
if(header[1] != 16):
i += header[1]
continue
addr = struct.unpack('Q', records[i+8:i+16])[0]
try:
fp.seek(addr)
first = fp.read(8)
except:
if(output):
print('Bad address 0x%x in %s' % (addr, sysvals.mempath))
return [0, 0]
rechead = struct.unpack('4sI', first)
recdata = fp.read(rechead[1]-8)
if(rechead[0] == 'FBPT'):
record = struct.unpack('HBBIQQQQQ', recdata)
if(output):
print('%s (%s)' % (rectype[header[0]], rechead[0]))
print(' Reset END : %u ns' % record[4])
print(' OS Loader LoadImage Start : %u ns' % record[5])
print(' OS Loader StartImage Start : %u ns' % record[6])
print(' ExitBootServices Entry : %u ns' % record[7])
print(' ExitBootServices Exit : %u ns' % record[8])
elif(rechead[0] == 'S3PT'):
if(output):
print('%s (%s)' % (rectype[header[0]], rechead[0]))
j = 0
while(j < len(recdata)):
prechead = struct.unpack('HBB', recdata[j:j+4])
if(prechead[0] not in prectype):
continue
if(prechead[0] == 0):
record = struct.unpack('IIQQ', recdata[j:j+prechead[1]])
fwData[1] = record[2]
if(output):
print(' %s' % prectype[prechead[0]])
print(' Resume Count : %u' % \
record[1])
print(' FullResume : %u ns' % \
record[2])
print(' AverageResume : %u ns' % \
record[3])
elif(prechead[0] == 1):
record = struct.unpack('QQ', recdata[j+4:j+prechead[1]])
fwData[0] = record[1] - record[0]
if(output):
print(' %s' % prectype[prechead[0]])
print(' SuspendStart : %u ns' % \
record[0])
print(' SuspendEnd : %u ns' % \
record[1])
print(' SuspendTime : %u ns' % \
fwData[0])
j += prechead[1]
if(output):
print('')
i += header[1]
fp.close()
return fwData
# Function: statusCheck
# Description:
# Verify that the requested command and options will work, and
# print the results to the terminal
# Output:
# True if the test will work, False if not
def statusCheck(probecheck=False):
global sysvals
status = True
print('Checking this system (%s)...' % platform.node())
# check we have root access
res = sysvals.colorText('NO (No features of this tool will work!)')
if(rootCheck(False)):
res = 'YES'
print(' have root access: %s' % res)
if(res != 'YES'):
print(' Try running this script with sudo')
return False
# check sysfs is mounted
res = sysvals.colorText('NO (No features of this tool will work!)')
if(os.path.exists(sysvals.powerfile)):
res = 'YES'
print(' is sysfs mounted: %s' % res)
if(res != 'YES'):
return False
# check target mode is a valid mode
if sysvals.suspendmode != 'command':
res = sysvals.colorText('NO')
modes = getModes()
if(sysvals.suspendmode in modes):
res = 'YES'
else:
status = False
print(' is "%s" a valid power mode: %s' % (sysvals.suspendmode, res))
if(res == 'NO'):
print(' valid power modes are: %s' % modes)
print(' please choose one with -m')
# check if ftrace is available
res = sysvals.colorText('NO')
ftgood = sysvals.verifyFtrace()
if(ftgood):
res = 'YES'
elif(sysvals.usecallgraph):
status = False
print(' is ftrace supported: %s' % res)
# check if kprobes are available
res = sysvals.colorText('NO')
sysvals.usekprobes = sysvals.verifyKprobes()
if(sysvals.usekprobes):
res = 'YES'
else:
sysvals.usedevsrc = False
print(' are kprobes supported: %s' % res)
# what data source are we using
res = 'DMESG'
if(ftgood):
sysvals.usetraceeventsonly = True
sysvals.usetraceevents = False
for e in sysvals.traceevents:
check = False
if(os.path.exists(sysvals.epath+e)):
check = True
if(not check):
sysvals.usetraceeventsonly = False
if(e == 'suspend_resume' and check):
sysvals.usetraceevents = True
if(sysvals.usetraceevents and sysvals.usetraceeventsonly):
res = 'FTRACE (all trace events found)'
elif(sysvals.usetraceevents):
res = 'DMESG and FTRACE (suspend_resume trace event found)'
print(' timeline data source: %s' % res)
# check if rtcwake
res = sysvals.colorText('NO')
if(sysvals.rtcpath != ''):
res = 'YES'
elif(sysvals.rtcwake):
status = False
print(' is rtcwake supported: %s' % res)
if not probecheck:
return status
if (sysvals.usecallgraph and len(sysvals.debugfuncs) > 0) or len(sysvals.kprobes) > 0:
sysvals.initFtrace(True)
# verify callgraph debugfuncs
if sysvals.usecallgraph and len(sysvals.debugfuncs) > 0:
print(' verifying these ftrace callgraph functions work:')
sysvals.setFtraceFilterFunctions(sysvals.debugfuncs)
fp = open(sysvals.tpath+'set_graph_function', 'r')
flist = fp.read().split('\n')
fp.close()
for func in sysvals.debugfuncs:
res = sysvals.colorText('NO')
if func in flist:
res = 'YES'
else:
for i in flist:
if ' [' in i and func == i.split(' ')[0]:
res = 'YES'
break
print(' %s: %s' % (func, res))
# verify kprobes
if len(sysvals.kprobes) > 0:
print(' verifying these kprobes work:')
for name in sorted(sysvals.kprobes):
if name in sysvals.tracefuncs:
continue
res = sysvals.colorText('NO')
if sysvals.testKprobe(sysvals.kprobes[name]):
res = 'YES'
print(' %s: %s' % (name, res))
return status
# Function: doError
# Description:
# generic error function for catastrphic failures
# Arguments:
# msg: the error message to print
# help: True if printHelp should be called after, False otherwise
def doError(msg, help):
if(help == True):
printHelp()
print('ERROR: %s\n') % msg
sys.exit()
# Function: doWarning
# Description:
# generic warning function for non-catastrophic anomalies
# Arguments:
# msg: the warning message to print
# file: If not empty, a filename to request be sent to the owner for debug
def doWarning(msg, file=''):
print('/* %s */') % msg
if(file):
print('/* For a fix, please send this'+\
' %s file to <todd.e.brandt@intel.com> */' % file)
# Function: rootCheck
# Description:
# quick check to see if we have root access
def rootCheck(fatal):
global sysvals
if(os.access(sysvals.powerfile, os.W_OK)):
return True
if fatal:
doError('This command must be run as root', False)
return False
# Function: getArgInt
# Description:
# pull out an integer argument from the command line with checks
def getArgInt(name, args, min, max, main=True):
if main:
try:
arg = args.next()
except:
doError(name+': no argument supplied', True)
else:
arg = args
try:
val = int(arg)
except:
doError(name+': non-integer value given', True)
if(val < min or val > max):
doError(name+': value should be between %d and %d' % (min, max), True)
return val
# Function: getArgFloat
# Description:
# pull out a float argument from the command line with checks
def getArgFloat(name, args, min, max, main=True):
if main:
try:
arg = args.next()
except:
doError(name+': no argument supplied', True)
else:
arg = args
try:
val = float(arg)
except:
doError(name+': non-numerical value given', True)
if(val < min or val > max):
doError(name+': value should be between %f and %f' % (min, max), True)
return val
# Function: rerunTest
# Description:
# generate an output from an existing set of ftrace/dmesg logs
def rerunTest():
global sysvals
if(sysvals.ftracefile != ''):
doesTraceLogHaveTraceEvents()
if(sysvals.dmesgfile == '' and not sysvals.usetraceeventsonly):
doError('recreating this html output '+\
'requires a dmesg file', False)
sysvals.setOutputFile()
vprint('Output file: %s' % sysvals.htmlfile)
print('PROCESSING DATA')
if(sysvals.usetraceeventsonly):
testruns = parseTraceLog()
else:
testruns = loadKernelLog()
for data in testruns:
parseKernelLog(data)
if(sysvals.ftracefile != ''):
appendIncompleteTraceLog(testruns)
createHTML(testruns)
# Function: runTest
# Description:
# execute a suspend/resume, gather the logs, and generate the output
def runTest(subdir, testpath=''):
global sysvals
# prepare for the test
sysvals.initFtrace()
sysvals.initTestOutput(subdir, testpath)
vprint('Output files:\n %s' % sysvals.dmesgfile)
if(sysvals.usecallgraph or
sysvals.usetraceevents or
sysvals.usetraceeventsonly):
vprint(' %s' % sysvals.ftracefile)
vprint(' %s' % sysvals.htmlfile)
# execute the test
executeSuspend()
sysvals.cleanupFtrace()
# analyze the data and create the html output
print('PROCESSING DATA')
if(sysvals.usetraceeventsonly):
# data for kernels 3.15 or newer is entirely in ftrace
testruns = parseTraceLog()
else:
# data for kernels older than 3.15 is primarily in dmesg
testruns = loadKernelLog()
for data in testruns:
parseKernelLog(data)
if(sysvals.usecallgraph or sysvals.usetraceevents):
appendIncompleteTraceLog(testruns)
createHTML(testruns)
# Function: runSummary
# Description:
# create a summary of tests in a sub-directory
def runSummary(subdir, output):
global sysvals
# get a list of ftrace output files
files = []
for dirname, dirnames, filenames in os.walk(subdir):
for filename in filenames:
if(re.match('.*_ftrace.txt', filename)):
files.append("%s/%s" % (dirname, filename))
# process the files in order and get an array of data objects
testruns = []
for file in sorted(files):
if output:
print("Test found in %s" % os.path.dirname(file))
sysvals.ftracefile = file
sysvals.dmesgfile = file.replace('_ftrace.txt', '_dmesg.txt')
doesTraceLogHaveTraceEvents()
sysvals.usecallgraph = False
if not sysvals.usetraceeventsonly:
if(not os.path.exists(sysvals.dmesgfile)):
print("Skipping %s: not a valid test input" % file)
continue
else:
if output:
f = os.path.basename(sysvals.ftracefile)
d = os.path.basename(sysvals.dmesgfile)
print("\tInput files: %s and %s" % (f, d))
testdata = loadKernelLog()
data = testdata[0]
parseKernelLog(data)
testdata = [data]
appendIncompleteTraceLog(testdata)
else:
if output:
print("\tInput file: %s" % os.path.basename(sysvals.ftracefile))
testdata = parseTraceLog()
data = testdata[0]
data.normalizeTime(data.tSuspended)
link = file.replace(subdir+'/', '').replace('_ftrace.txt', '.html')
data.outfile = link
testruns.append(data)
createHTMLSummarySimple(testruns, subdir+'/summary.html')
# Function: checkArgBool
# Description:
# check if a boolean string value is true or false
def checkArgBool(value):
yes = ['1', 'true', 'yes', 'on']
if value.lower() in yes:
return True
return False
# Function: configFromFile
# Description:
# Configure the script via the info in a config file
def configFromFile(file):
global sysvals
Config = ConfigParser.ConfigParser()
ignorekprobes = False
Config.read(file)
sections = Config.sections()
if 'Settings' in sections:
for opt in Config.options('Settings'):
value = Config.get('Settings', opt).lower()
if(opt.lower() == 'verbose'):
sysvals.verbose = checkArgBool(value)
elif(opt.lower() == 'addlogs'):
sysvals.addlogs = checkArgBool(value)
elif(opt.lower() == 'dev'):
sysvals.usedevsrc = checkArgBool(value)
elif(opt.lower() == 'ignorekprobes'):
ignorekprobes = checkArgBool(value)
elif(opt.lower() == 'x2'):
if checkArgBool(value):
sysvals.execcount = 2
elif(opt.lower() == 'callgraph'):
sysvals.usecallgraph = checkArgBool(value)
elif(opt.lower() == 'callgraphfunc'):
sysvals.debugfuncs = []
if value:
value = value.split(',')
for i in value:
sysvals.debugfuncs.append(i.strip())
elif(opt.lower() == 'expandcg'):
sysvals.cgexp = checkArgBool(value)
elif(opt.lower() == 'srgap'):
if checkArgBool(value):
sysvals.srgap = 5
elif(opt.lower() == 'mode'):
sysvals.suspendmode = value
elif(opt.lower() == 'command'):
sysvals.testcommand = value
elif(opt.lower() == 'x2delay'):
sysvals.x2delay = getArgInt('-x2delay', value, 0, 60000, False)
elif(opt.lower() == 'postres'):
sysvals.postresumetime = getArgInt('-postres', value, 0, 3600, False)
elif(opt.lower() == 'rtcwake'):
sysvals.rtcwake = True
sysvals.rtcwaketime = getArgInt('-rtcwake', value, 0, 3600, False)
elif(opt.lower() == 'timeprec'):
sysvals.setPrecision(getArgInt('-timeprec', value, 0, 6, False))
elif(opt.lower() == 'mindev'):
sysvals.mindevlen = getArgFloat('-mindev', value, 0.0, 10000.0, False)
elif(opt.lower() == 'mincg'):
sysvals.mincglen = getArgFloat('-mincg', value, 0.0, 10000.0, False)
elif(opt.lower() == 'kprobecolor'):
try:
val = int(value, 16)
sysvals.kprobecolor = '#'+value
except:
sysvals.kprobecolor = value
elif(opt.lower() == 'synccolor'):
try:
val = int(value, 16)
sysvals.synccolor = '#'+value
except:
sysvals.synccolor = value
elif(opt.lower() == 'output-dir'):
args = dict()
n = datetime.now()
args['date'] = n.strftime('%y%m%d')
args['time'] = n.strftime('%H%M%S')
args['hostname'] = sysvals.hostname
sysvals.outdir = value.format(**args)
if sysvals.suspendmode == 'command' and not sysvals.testcommand:
doError('No command supplied for mode "command"', False)
if sysvals.usedevsrc and sysvals.usecallgraph:
doError('dev and callgraph cannot both be true', False)
if sysvals.usecallgraph and sysvals.execcount > 1:
doError('-x2 is not compatible with -f', False)
if ignorekprobes:
return
kprobes = dict()
archkprobe = 'Kprobe_'+platform.machine()
if archkprobe in sections:
for name in Config.options(archkprobe):
kprobes[name] = Config.get(archkprobe, name)
if 'Kprobe' in sections:
for name in Config.options('Kprobe'):
kprobes[name] = Config.get('Kprobe', name)
for name in kprobes:
function = name
format = name
color = ''
args = dict()
data = kprobes[name].split()
i = 0
for val in data:
# bracketted strings are special formatting, read them separately
if val[0] == '[' and val[-1] == ']':
for prop in val[1:-1].split(','):
p = prop.split('=')
if p[0] == 'color':
try:
color = int(p[1], 16)
color = '#'+p[1]
except:
color = p[1]
continue
# first real arg should be the format string
if i == 0:
format = val
# all other args are actual function args
else:
d = val.split('=')
args[d[0]] = d[1]
i += 1
if not function or not format:
doError('Invalid kprobe: %s' % name, False)
for arg in re.findall('{(?P<n>[a-z,A-Z,0-9]*)}', format):
if arg not in args:
doError('Kprobe "%s" is missing argument "%s"' % (name, arg), False)
if name in sysvals.kprobes:
doError('Duplicate kprobe found "%s"' % (name), False)
vprint('Adding KPROBE: %s %s %s %s' % (name, function, format, args))
sysvals.kprobes[name] = {
'name': name,
'func': function,
'format': format,
'args': args,
'mask': re.sub('{(?P<n>[a-z,A-Z,0-9]*)}', '.*', format)
}
if color:
sysvals.kprobes[name]['color'] = color
# Function: printHelp
# Description:
# print out the help text
def printHelp():
global sysvals
modes = getModes()
print('')
print('AnalyzeSuspend v%s' % sysvals.version)
print('Usage: sudo analyze_suspend.py <options>')
print('')
print('Description:')
print(' This tool is designed to assist kernel and OS developers in optimizing')
print(' their linux stack\'s suspend/resume time. Using a kernel image built')
print(' with a few extra options enabled, the tool will execute a suspend and')
print(' capture dmesg and ftrace data until resume is complete. This data is')
print(' transformed into a device timeline and an optional callgraph to give')
print(' a detailed view of which devices/subsystems are taking the most')
print(' time in suspend/resume.')
print('')
print(' Generates output files in subdirectory: suspend-mmddyy-HHMMSS')
print(' HTML output: <hostname>_<mode>.html')
print(' raw dmesg output: <hostname>_<mode>_dmesg.txt')
print(' raw ftrace output: <hostname>_<mode>_ftrace.txt')
print('')
print('Options:')
print(' [general]')
print(' -h Print this help text')
print(' -v Print the current tool version')
print(' -config file Pull arguments and config options from a file')
print(' -verbose Print extra information during execution and analysis')
print(' -status Test to see if the system is enabled to run this tool')
print(' -modes List available suspend modes')
print(' -m mode Mode to initiate for suspend %s (default: %s)') % (modes, sysvals.suspendmode)
print(' -o subdir Override the output subdirectory')
print(' [advanced]')
print(' -rtcwake t Use rtcwake to autoresume after <t> seconds (default: disabled)')
print(' -addlogs Add the dmesg and ftrace logs to the html output')
print(' -multi n d Execute <n> consecutive tests at <d> seconds intervals. The outputs will')
print(' be created in a new subdirectory with a summary page.')
print(' -srgap Add a visible gap in the timeline between sus/res (default: disabled)')
print(' -cmd {s} Instead of suspend/resume, run a command, e.g. "sync -d"')
print(' -mindev ms Discard all device blocks shorter than ms milliseconds (e.g. 0.001 for us)')
print(' -mincg ms Discard all callgraphs shorter than ms milliseconds (e.g. 0.001 for us)')
print(' -timeprec N Number of significant digits in timestamps (0:S, [3:ms], 6:us)')
print(' [debug]')
print(' -f Use ftrace to create device callgraphs (default: disabled)')
print(' -expandcg pre-expand the callgraph data in the html output (default: disabled)')
print(' -flist Print the list of functions currently being captured in ftrace')
print(' -flistall Print all functions capable of being captured in ftrace')
print(' -fadd file Add functions to be graphed in the timeline from a list in a text file')
print(' -filter "d1 d2 ..." Filter out all but this list of device names')
print(' -dev Display common low level functions in the timeline')
print(' [post-resume task analysis]')
print(' -x2 Run two suspend/resumes back to back (default: disabled)')
print(' -x2delay t Minimum millisecond delay <t> between the two test runs (default: 0 ms)')
print(' -postres t Time after resume completion to wait for post-resume events (default: 0 S)')
print(' [utilities]')
print(' -fpdt Print out the contents of the ACPI Firmware Performance Data Table')
print(' -usbtopo Print out the current USB topology with power info')
print(' -usbauto Enable autosuspend for all connected USB devices')
print(' [re-analyze data from previous runs]')
print(' -ftrace ftracefile Create HTML output using ftrace input')
print(' -dmesg dmesgfile Create HTML output using dmesg (not needed for kernel >= 3.15)')
print(' -summary directory Create a summary of all test in this dir')
print('')
return True
# ----------------- MAIN --------------------
# exec start (skipped if script is loaded as library)
if __name__ == '__main__':
cmd = ''
cmdarg = ''
multitest = {'run': False, 'count': 0, 'delay': 0}
simplecmds = ['-modes', '-fpdt', '-flist', '-flistall', '-usbtopo', '-usbauto', '-status']
# loop through the command line arguments
args = iter(sys.argv[1:])
for arg in args:
if(arg == '-m'):
try:
val = args.next()
except:
doError('No mode supplied', True)
if val == 'command' and not sysvals.testcommand:
doError('No command supplied for mode "command"', True)
sysvals.suspendmode = val
elif(arg in simplecmds):
cmd = arg[1:]
elif(arg == '-h'):
printHelp()
sys.exit()
elif(arg == '-v'):
print("Version %s" % sysvals.version)
sys.exit()
elif(arg == '-x2'):
sysvals.execcount = 2
if(sysvals.usecallgraph):
doError('-x2 is not compatible with -f', False)
elif(arg == '-x2delay'):
sysvals.x2delay = getArgInt('-x2delay', args, 0, 60000)
elif(arg == '-postres'):
sysvals.postresumetime = getArgInt('-postres', args, 0, 3600)
elif(arg == '-f'):
sysvals.usecallgraph = True
if(sysvals.execcount > 1):
doError('-x2 is not compatible with -f', False)
if(sysvals.usedevsrc):
doError('-dev is not compatible with -f', False)
elif(arg == '-addlogs'):
sysvals.addlogs = True
elif(arg == '-verbose'):
sysvals.verbose = True
elif(arg == '-dev'):
sysvals.usedevsrc = True
if(sysvals.usecallgraph):
doError('-dev is not compatible with -f', False)
elif(arg == '-rtcwake'):
sysvals.rtcwake = True
sysvals.rtcwaketime = getArgInt('-rtcwake', args, 0, 3600)
elif(arg == '-timeprec'):
sysvals.setPrecision(getArgInt('-timeprec', args, 0, 6))
elif(arg == '-mindev'):
sysvals.mindevlen = getArgFloat('-mindev', args, 0.0, 10000.0)
elif(arg == '-mincg'):
sysvals.mincglen = getArgFloat('-mincg', args, 0.0, 10000.0)
elif(arg == '-cmd'):
try:
val = args.next()
except:
doError('No command string supplied', True)
sysvals.testcommand = val
sysvals.suspendmode = 'command'
elif(arg == '-expandcg'):
sysvals.cgexp = True
elif(arg == '-srgap'):
sysvals.srgap = 5
elif(arg == '-multi'):
multitest['run'] = True
multitest['count'] = getArgInt('-multi n (exec count)', args, 2, 1000000)
multitest['delay'] = getArgInt('-multi d (delay between tests)', args, 0, 3600)
elif(arg == '-o'):
try:
val = args.next()
except:
doError('No subdirectory name supplied', True)
sysvals.outdir = val
elif(arg == '-config'):
try:
val = args.next()
except:
doError('No text file supplied', True)
if(os.path.exists(val) == False):
doError('%s does not exist' % val, False)
configFromFile(val)
elif(arg == '-fadd'):
try:
val = args.next()
except:
doError('No text file supplied', True)
if(os.path.exists(val) == False):
doError('%s does not exist' % val, False)
sysvals.addFtraceFilterFunctions(val)
elif(arg == '-dmesg'):
try:
val = args.next()
except:
doError('No dmesg file supplied', True)
sysvals.notestrun = True
sysvals.dmesgfile = val
if(os.path.exists(sysvals.dmesgfile) == False):
doError('%s does not exist' % sysvals.dmesgfile, False)
elif(arg == '-ftrace'):
try:
val = args.next()
except:
doError('No ftrace file supplied', True)
sysvals.notestrun = True
sysvals.ftracefile = val
if(os.path.exists(sysvals.ftracefile) == False):
doError('%s does not exist' % sysvals.ftracefile, False)
elif(arg == '-summary'):
try:
val = args.next()
except:
doError('No directory supplied', True)
cmd = 'summary'
cmdarg = val
sysvals.notestrun = True
if(os.path.isdir(val) == False):
doError('%s is not accesible' % val, False)
elif(arg == '-filter'):
try:
val = args.next()
except:
doError('No devnames supplied', True)
sysvals.setDeviceFilter(val)
else:
doError('Invalid argument: '+arg, True)
# callgraph size cannot exceed device size
if sysvals.mincglen < sysvals.mindevlen:
sysvals.mincglen = sysvals.mindevlen
# just run a utility command and exit
if(cmd != ''):
if(cmd == 'status'):
statusCheck(True)
elif(cmd == 'fpdt'):
getFPDT(True)
elif(cmd == 'usbtopo'):
detectUSB()
elif(cmd == 'modes'):
modes = getModes()
print modes
elif(cmd == 'flist'):
sysvals.getFtraceFilterFunctions(True)
elif(cmd == 'flistall'):
sysvals.getFtraceFilterFunctions(False)
elif(cmd == 'usbauto'):
setUSBDevicesAuto()
elif(cmd == 'summary'):
print("Generating a summary of folder \"%s\"" % cmdarg)
runSummary(cmdarg, True)
sys.exit()
# if instructed, re-analyze existing data files
if(sysvals.notestrun):
rerunTest()
sys.exit()
# verify that we can run a test
if(not statusCheck()):
print('Check FAILED, aborting the test run!')
sys.exit()
if multitest['run']:
# run multiple tests in a separate subdirectory
s = 'x%d' % multitest['count']
if not sysvals.outdir:
sysvals.outdir = datetime.now().strftime('suspend-'+s+'-%m%d%y-%H%M%S')
if not os.path.isdir(sysvals.outdir):
os.mkdir(sysvals.outdir)
for i in range(multitest['count']):
if(i != 0):
print('Waiting %d seconds...' % (multitest['delay']))
time.sleep(multitest['delay'])
print('TEST (%d/%d) START' % (i+1, multitest['count']))
runTest(sysvals.outdir)
print('TEST (%d/%d) COMPLETE' % (i+1, multitest['count']))
runSummary(sysvals.outdir, False)
else:
# run the test in the current directory
runTest('.', sysvals.outdir)
| gpl-2.0 |
Mazecreator/tensorflow | tensorflow/examples/learn/hdf5_classification.py | 74 | 2899 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Example of DNNClassifier for Iris plant dataset, hdf5 format."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
import h5py # pylint: disable=g-bad-import-order
X_FEATURE = 'x' # Name of the input feature.
def main(unused_argv):
# Load dataset.
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = model_selection.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
# Note that we are saving and load iris data as h5 format as a simple
# demonstration here.
h5f = h5py.File('/tmp/test_hdf5.h5', 'w')
h5f.create_dataset('X_train', data=x_train)
h5f.create_dataset('X_test', data=x_test)
h5f.create_dataset('y_train', data=y_train)
h5f.create_dataset('y_test', data=y_test)
h5f.close()
h5f = h5py.File('/tmp/test_hdf5.h5', 'r')
x_train = np.array(h5f['X_train'])
x_test = np.array(h5f['X_test'])
y_train = np.array(h5f['y_train'])
y_test = np.array(h5f['y_test'])
# Build 3 layer DNN with 10, 20, 10 units respectively.
feature_columns = [
tf.feature_column.numeric_column(
X_FEATURE, shape=np.array(x_train).shape[1:])]
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
# Train.
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=200)
# Predict.
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=test_input_fn)
y_predicted = np.array(list(p['class_ids'] for p in predictions))
y_predicted = y_predicted.reshape(np.array(y_test).shape)
# Score with sklearn.
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy (sklearn): {0:f}'.format(score))
# Score with tensorflow.
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()
| apache-2.0 |
shyamalschandra/scikit-learn | sklearn/utils/tests/test_estimator_html_repr.py | 3 | 9774 | from contextlib import closing
from io import StringIO
import pytest
from sklearn import config_context
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
from sklearn.impute import SimpleImputer
from sklearn.decomposition import PCA
from sklearn.decomposition import TruncatedSVD
from sklearn.pipeline import Pipeline
from sklearn.pipeline import FeatureUnion
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import VotingClassifier
from sklearn.feature_selection import SelectPercentile
from sklearn.cluster import Birch
from sklearn.cluster import AgglomerativeClustering
from sklearn.preprocessing import OneHotEncoder
from sklearn.svm import LinearSVC
from sklearn.svm import LinearSVR
from sklearn.tree import DecisionTreeClassifier
from sklearn.multiclass import OneVsOneClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.ensemble import StackingRegressor
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RationalQuadratic
from sklearn.utils._estimator_html_repr import _write_label_html
from sklearn.utils._estimator_html_repr import _get_visual_block
from sklearn.utils._estimator_html_repr import estimator_html_repr
@pytest.mark.parametrize("checked", [True, False])
def test_write_label_html(checked):
# Test checking logic and labeling
name = "LogisticRegression"
tool_tip = "hello-world"
with closing(StringIO()) as out:
_write_label_html(out, name, tool_tip, checked=checked)
html_label = out.getvalue()
assert "LogisticRegression</label>" in html_label
assert html_label.startswith('<div class="sk-label-container">')
assert "<pre>hello-world</pre>" in html_label
if checked:
assert "checked>" in html_label
@pytest.mark.parametrize("est", ["passthrough", "drop", None])
def test_get_visual_block_single_str_none(est):
# Test estimators that are represnted by strings
est_html_info = _get_visual_block(est)
assert est_html_info.kind == "single"
assert est_html_info.estimators == est
assert est_html_info.names == str(est)
assert est_html_info.name_details == str(est)
def test_get_visual_block_single_estimator():
est = LogisticRegression(C=10.0)
est_html_info = _get_visual_block(est)
assert est_html_info.kind == "single"
assert est_html_info.estimators == est
assert est_html_info.names == est.__class__.__name__
assert est_html_info.name_details == str(est)
def test_get_visual_block_pipeline():
pipe = Pipeline(
[
("imputer", SimpleImputer()),
("do_nothing", "passthrough"),
("do_nothing_more", None),
("classifier", LogisticRegression()),
]
)
est_html_info = _get_visual_block(pipe)
assert est_html_info.kind == "serial"
assert est_html_info.estimators == tuple(step[1] for step in pipe.steps)
assert est_html_info.names == [
"imputer: SimpleImputer",
"do_nothing: passthrough",
"do_nothing_more: passthrough",
"classifier: LogisticRegression",
]
assert est_html_info.name_details == [str(est) for _, est in pipe.steps]
def test_get_visual_block_feature_union():
f_union = FeatureUnion([("pca", PCA()), ("svd", TruncatedSVD())])
est_html_info = _get_visual_block(f_union)
assert est_html_info.kind == "parallel"
assert est_html_info.names == ("pca", "svd")
assert est_html_info.estimators == tuple(
trans[1] for trans in f_union.transformer_list
)
assert est_html_info.name_details == (None, None)
def test_get_visual_block_voting():
clf = VotingClassifier(
[("log_reg", LogisticRegression()), ("mlp", MLPClassifier())]
)
est_html_info = _get_visual_block(clf)
assert est_html_info.kind == "parallel"
assert est_html_info.estimators == tuple(trans[1] for trans in clf.estimators)
assert est_html_info.names == ("log_reg", "mlp")
assert est_html_info.name_details == (None, None)
def test_get_visual_block_column_transformer():
ct = ColumnTransformer(
[("pca", PCA(), ["num1", "num2"]), ("svd", TruncatedSVD, [0, 3])]
)
est_html_info = _get_visual_block(ct)
assert est_html_info.kind == "parallel"
assert est_html_info.estimators == tuple(trans[1] for trans in ct.transformers)
assert est_html_info.names == ("pca", "svd")
assert est_html_info.name_details == (["num1", "num2"], [0, 3])
def test_estimator_html_repr_pipeline():
num_trans = Pipeline(
steps=[("pass", "passthrough"), ("imputer", SimpleImputer(strategy="median"))]
)
cat_trans = Pipeline(
steps=[
("imputer", SimpleImputer(strategy="constant", missing_values="empty")),
("one-hot", OneHotEncoder(drop="first")),
]
)
preprocess = ColumnTransformer(
[
("num", num_trans, ["a", "b", "c", "d", "e"]),
("cat", cat_trans, [0, 1, 2, 3]),
]
)
feat_u = FeatureUnion(
[
("pca", PCA(n_components=1)),
(
"tsvd",
Pipeline(
[
("first", TruncatedSVD(n_components=3)),
("select", SelectPercentile()),
]
),
),
]
)
clf = VotingClassifier(
[
("lr", LogisticRegression(solver="lbfgs", random_state=1)),
("mlp", MLPClassifier(alpha=0.001)),
]
)
pipe = Pipeline(
[("preprocessor", preprocess), ("feat_u", feat_u), ("classifier", clf)]
)
html_output = estimator_html_repr(pipe)
# top level estimators show estimator with changes
assert str(pipe) in html_output
for _, est in pipe.steps:
assert f'<div class="sk-toggleable__content"><pre>{str(est)}' in html_output
# low level estimators do not show changes
with config_context(print_changed_only=True):
assert str(num_trans["pass"]) in html_output
assert "passthrough</label>" in html_output
assert str(num_trans["imputer"]) in html_output
for _, _, cols in preprocess.transformers:
assert f"<pre>{cols}</pre>" in html_output
# feature union
for name, _ in feat_u.transformer_list:
assert f"<label>{name}</label>" in html_output
pca = feat_u.transformer_list[0][1]
assert f"<pre>{str(pca)}</pre>" in html_output
tsvd = feat_u.transformer_list[1][1]
first = tsvd["first"]
select = tsvd["select"]
assert f"<pre>{str(first)}</pre>" in html_output
assert f"<pre>{str(select)}</pre>" in html_output
# voting classifer
for name, est in clf.estimators:
assert f"<label>{name}</label>" in html_output
assert f"<pre>{str(est)}</pre>" in html_output
@pytest.mark.parametrize("final_estimator", [None, LinearSVC()])
def test_stacking_classsifer(final_estimator):
estimators = [
("mlp", MLPClassifier(alpha=0.001)),
("tree", DecisionTreeClassifier()),
]
clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)
html_output = estimator_html_repr(clf)
assert str(clf) in html_output
# If final_estimator's default changes from LogisticRegression
# this should be updated
if final_estimator is None:
assert "LogisticRegression(" in html_output
else:
assert final_estimator.__class__.__name__ in html_output
@pytest.mark.parametrize("final_estimator", [None, LinearSVR()])
def test_stacking_regressor(final_estimator):
reg = StackingRegressor(
estimators=[("svr", LinearSVR())], final_estimator=final_estimator
)
html_output = estimator_html_repr(reg)
assert str(reg.estimators[0][0]) in html_output
assert "LinearSVR</label>" in html_output
if final_estimator is None:
assert "RidgeCV</label>" in html_output
else:
assert final_estimator.__class__.__name__ in html_output
def test_birch_duck_typing_meta():
# Test duck typing meta estimators with Birch
birch = Birch(n_clusters=AgglomerativeClustering(n_clusters=3))
html_output = estimator_html_repr(birch)
# inner estimators do not show changes
with config_context(print_changed_only=True):
assert f"<pre>{str(birch.n_clusters)}" in html_output
assert "AgglomerativeClustering</label>" in html_output
# outer estimator contains all changes
assert f"<pre>{str(birch)}" in html_output
def test_ovo_classifier_duck_typing_meta():
# Test duck typing metaestimators with OVO
ovo = OneVsOneClassifier(LinearSVC(penalty="l1"))
html_output = estimator_html_repr(ovo)
# inner estimators do not show changes
with config_context(print_changed_only=True):
assert f"<pre>{str(ovo.estimator)}" in html_output
assert "LinearSVC</label>" in html_output
# outter estimator
assert f"<pre>{str(ovo)}" in html_output
def test_duck_typing_nested_estimator():
# Test duck typing metaestimators with GP
kernel = RationalQuadratic(length_scale=1.0, alpha=0.1)
gp = GaussianProcessRegressor(kernel=kernel)
html_output = estimator_html_repr(gp)
assert f"<pre>{str(kernel)}" in html_output
assert f"<pre>{str(gp)}" in html_output
@pytest.mark.parametrize("print_changed_only", [True, False])
def test_one_estimator_print_change_only(print_changed_only):
pca = PCA(n_components=10)
with config_context(print_changed_only=print_changed_only):
pca_repr = str(pca)
html_output = estimator_html_repr(pca)
assert pca_repr in html_output
| bsd-3-clause |
anilmuthineni/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_test.py | 1 | 42890 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for Estimator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import itertools
import json
import os
import sys
import tempfile
# TODO: #6568 Remove this hack that makes dlopen() not crash.
if hasattr(sys, 'getdlopenflags') and hasattr(sys, 'setdlopenflags'):
import ctypes
sys.setdlopenflags(sys.getdlopenflags() | ctypes.RTLD_GLOBAL)
import numpy as np
import six
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.contrib import learn
from tensorflow.contrib.framework.python.ops import variables
from tensorflow.contrib.layers.python.layers import feature_column as feature_column_lib
from tensorflow.contrib.layers.python.layers import optimizers
from tensorflow.contrib.learn.python.learn import experiment
from tensorflow.contrib.learn.python.learn import metric_spec
from tensorflow.contrib.learn.python.learn import models
from tensorflow.contrib.learn.python.learn import monitors as monitors_lib
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.contrib.learn.python.learn.estimators import _sklearn
from tensorflow.contrib.learn.python.learn.estimators import estimator
from tensorflow.contrib.learn.python.learn.estimators import linear
from tensorflow.contrib.learn.python.learn.estimators import model_fn
from tensorflow.contrib.learn.python.learn.estimators import run_config
from tensorflow.contrib.learn.python.learn.utils import input_fn_utils
from tensorflow.contrib.lookup import lookup_ops
from tensorflow.contrib.metrics.python.ops import metric_ops
from tensorflow.contrib.testing.python.framework import util_test
from tensorflow.python.client import session as session_lib
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import parsing_ops
from tensorflow.python.ops import variables as variables_lib
from tensorflow.python.platform import gfile
from tensorflow.python.platform import test
from tensorflow.python.saved_model import loader
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.training import basic_session_run_hooks
from tensorflow.python.training import input as input_lib
from tensorflow.python.training import monitored_session
from tensorflow.python.training import queue_runner_impl
from tensorflow.python.training import session_run_hook
from tensorflow.python.util import compat
_BOSTON_INPUT_DIM = 13
_IRIS_INPUT_DIM = 4
def boston_input_fn(num_epochs=None):
boston = base.load_boston()
features = input_lib.limit_epochs(
array_ops.reshape(
constant_op.constant(boston.data), [-1, _BOSTON_INPUT_DIM]),
num_epochs=num_epochs)
labels = array_ops.reshape(constant_op.constant(boston.target), [-1, 1])
return features, labels
def boston_input_fn_with_queue(num_epochs=None):
features, labels = boston_input_fn(num_epochs=num_epochs)
# Create a minimal queue runner.
fake_queue = data_flow_ops.FIFOQueue(30, dtypes.int32)
queue_runner = queue_runner_impl.QueueRunner(fake_queue,
[constant_op.constant(0)])
queue_runner_impl.add_queue_runner(queue_runner)
return features, labels
def iris_input_fn():
iris = base.load_iris()
features = array_ops.reshape(
constant_op.constant(iris.data), [-1, _IRIS_INPUT_DIM])
labels = array_ops.reshape(constant_op.constant(iris.target), [-1])
return features, labels
def iris_input_fn_labels_dict():
iris = base.load_iris()
features = array_ops.reshape(
constant_op.constant(iris.data), [-1, _IRIS_INPUT_DIM])
labels = {
'labels': array_ops.reshape(constant_op.constant(iris.target), [-1])
}
return features, labels
def boston_eval_fn():
boston = base.load_boston()
n_examples = len(boston.target)
features = array_ops.reshape(
constant_op.constant(boston.data), [n_examples, _BOSTON_INPUT_DIM])
labels = array_ops.reshape(
constant_op.constant(boston.target), [n_examples, 1])
return array_ops.concat([features, features], 0), array_ops.concat(
[labels, labels], 0)
def extract(data, key):
if isinstance(data, dict):
assert key in data
return data[key]
else:
return data
def linear_model_params_fn(features, labels, mode, params):
features = extract(features, 'input')
labels = extract(labels, 'labels')
assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
model_fn.ModeKeys.INFER)
prediction, loss = (models.linear_regression_zero_init(features, labels))
train_op = optimizers.optimize_loss(
loss,
variables.get_global_step(),
optimizer='Adagrad',
learning_rate=params['learning_rate'])
return prediction, loss, train_op
def linear_model_fn(features, labels, mode):
features = extract(features, 'input')
labels = extract(labels, 'labels')
assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
model_fn.ModeKeys.INFER)
if isinstance(features, dict):
(_, features), = features.items()
prediction, loss = (models.linear_regression_zero_init(features, labels))
train_op = optimizers.optimize_loss(
loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
return prediction, loss, train_op
def linear_model_fn_with_model_fn_ops(features, labels, mode):
"""Same as linear_model_fn, but returns `ModelFnOps`."""
assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
model_fn.ModeKeys.INFER)
prediction, loss = (models.linear_regression_zero_init(features, labels))
train_op = optimizers.optimize_loss(
loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
return model_fn.ModelFnOps(
mode=mode, predictions=prediction, loss=loss, train_op=train_op)
def logistic_model_no_mode_fn(features, labels):
features = extract(features, 'input')
labels = extract(labels, 'labels')
labels = array_ops.one_hot(labels, 3, 1, 0)
prediction, loss = (models.logistic_regression_zero_init(features, labels))
train_op = optimizers.optimize_loss(
loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
return {
'class': math_ops.argmax(prediction, 1),
'prob': prediction
}, loss, train_op
VOCAB_FILE_CONTENT = 'emerson\nlake\npalmer\n'
EXTRA_FILE_CONTENT = 'kermit\npiggy\nralph\n'
def _build_estimator_for_export_tests(tmpdir):
def _input_fn():
iris = base.load_iris()
return {
'feature': constant_op.constant(
iris.data, dtype=dtypes.float32)
}, constant_op.constant(
iris.target, shape=[150], dtype=dtypes.int32)
feature_columns = [
feature_column_lib.real_valued_column(
'feature', dimension=4)
]
est = linear.LinearRegressor(feature_columns)
est.fit(input_fn=_input_fn, steps=20)
feature_spec = feature_column_lib.create_feature_spec_for_parsing(
feature_columns)
serving_input_fn = input_fn_utils.build_parsing_serving_input_fn(feature_spec)
# hack in an op that uses an asset, in order to test asset export.
# this is not actually valid, of course.
def serving_input_fn_with_asset():
features, labels, inputs = serving_input_fn()
vocab_file_name = os.path.join(tmpdir, 'my_vocab_file')
vocab_file = gfile.GFile(vocab_file_name, mode='w')
vocab_file.write(VOCAB_FILE_CONTENT)
vocab_file.close()
hashtable = lookup_ops.HashTable(
lookup_ops.TextFileStringTableInitializer(vocab_file_name), 'x')
features['bogus_lookup'] = hashtable.lookup(
math_ops.to_int64(features['feature']))
return input_fn_utils.InputFnOps(features, labels, inputs)
return est, serving_input_fn_with_asset
class CheckCallsMonitor(monitors_lib.BaseMonitor):
def __init__(self, expect_calls):
super(CheckCallsMonitor, self).__init__()
self.begin_calls = None
self.end_calls = None
self.expect_calls = expect_calls
def begin(self, max_steps):
self.begin_calls = 0
self.end_calls = 0
def step_begin(self, step):
self.begin_calls += 1
return {}
def step_end(self, step, outputs):
self.end_calls += 1
return False
def end(self):
assert (self.end_calls == self.expect_calls and
self.begin_calls == self.expect_calls)
class EstimatorTest(test.TestCase):
def testExperimentIntegration(self):
exp = experiment.Experiment(
estimator=estimator.Estimator(model_fn=linear_model_fn),
train_input_fn=boston_input_fn,
eval_input_fn=boston_input_fn)
exp.test()
def testModelFnArgs(self):
expected_param = {'some_param': 'some_value'}
expected_config = run_config.RunConfig()
expected_config.i_am_test = True
def _argument_checker(features, labels, mode, params, config):
_, _ = features, labels
self.assertEqual(model_fn.ModeKeys.TRAIN, mode)
self.assertEqual(expected_param, params)
self.assertTrue(config.i_am_test)
return constant_op.constant(0.), constant_op.constant(
0.), constant_op.constant(0.)
est = estimator.Estimator(
model_fn=_argument_checker,
params=expected_param,
config=expected_config)
est.fit(input_fn=boston_input_fn, steps=1)
def testModelFnWithModelDir(self):
expected_param = {'some_param': 'some_value'}
expected_model_dir = tempfile.mkdtemp()
def _argument_checker(features, labels, mode, params, config=None,
model_dir=None):
_, _, _ = features, labels, config
self.assertEqual(model_fn.ModeKeys.TRAIN, mode)
self.assertEqual(expected_param, params)
self.assertEqual(model_dir, expected_model_dir)
return constant_op.constant(0.), constant_op.constant(
0.), constant_op.constant(0.)
est = estimator.Estimator(model_fn=_argument_checker,
params=expected_param,
model_dir=expected_model_dir)
est.fit(input_fn=boston_input_fn, steps=1)
def testInvalidModelFn_no_train_op(self):
def _invalid_model_fn(features, labels):
# pylint: disable=unused-argument
w = variables_lib.Variable(42.0, 'weight')
loss = 100.0 - w
return None, loss, None
est = estimator.Estimator(model_fn=_invalid_model_fn)
with self.assertRaisesRegexp(ValueError, 'Missing training_op'):
est.fit(input_fn=boston_input_fn, steps=1)
def testInvalidModelFn_no_loss(self):
def _invalid_model_fn(features, labels, mode):
# pylint: disable=unused-argument
w = variables_lib.Variable(42.0, 'weight')
loss = 100.0 - w
train_op = w.assign_add(loss / 100.0)
predictions = loss
if mode == model_fn.ModeKeys.EVAL:
loss = None
return predictions, loss, train_op
est = estimator.Estimator(model_fn=_invalid_model_fn)
est.fit(input_fn=boston_input_fn, steps=1)
with self.assertRaisesRegexp(ValueError, 'Missing loss'):
est.evaluate(input_fn=boston_eval_fn, steps=1)
def testInvalidModelFn_no_prediction(self):
def _invalid_model_fn(features, labels):
# pylint: disable=unused-argument
w = variables_lib.Variable(42.0, 'weight')
loss = 100.0 - w
train_op = w.assign_add(loss / 100.0)
return None, loss, train_op
est = estimator.Estimator(model_fn=_invalid_model_fn)
est.fit(input_fn=boston_input_fn, steps=1)
with self.assertRaisesRegexp(ValueError, 'Missing prediction'):
est.evaluate(input_fn=boston_eval_fn, steps=1)
with self.assertRaisesRegexp(ValueError, 'Missing prediction'):
est.predict(input_fn=boston_input_fn)
with self.assertRaisesRegexp(ValueError, 'Missing prediction'):
est.predict(
input_fn=functools.partial(
boston_input_fn, num_epochs=1),
as_iterable=True)
def testModelFnScaffold(self):
self.is_init_fn_called = False
def _init_fn(scaffold, session):
_, _ = scaffold, session
self.is_init_fn_called = True
def _model_fn_scaffold(features, labels, mode):
_, _ = features, labels
return model_fn.ModelFnOps(
mode=mode,
predictions=constant_op.constant(0.),
loss=constant_op.constant(0.),
train_op=constant_op.constant(0.),
training_scaffold=monitored_session.Scaffold(init_fn=_init_fn))
est = estimator.Estimator(model_fn=_model_fn_scaffold)
est.fit(input_fn=boston_input_fn, steps=1)
self.assertTrue(self.is_init_fn_called)
def testCheckpointSaverHookSuppressesTheDefaultOne(self):
saver_hook = test.mock.Mock(
spec=basic_session_run_hooks.CheckpointSaverHook)
saver_hook.before_run.return_value = None
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, steps=1, monitors=[saver_hook])
# test nothing is saved, due to suppressing default saver
with self.assertRaises(learn.NotFittedError):
est.evaluate(input_fn=boston_input_fn, steps=1)
def testCustomConfig(self):
test_random_seed = 5783452
class TestInput(object):
def __init__(self):
self.random_seed = 0
def config_test_input_fn(self):
self.random_seed = ops.get_default_graph().seed
return constant_op.constant([[1.]]), constant_op.constant([1.])
config = run_config.RunConfig(tf_random_seed=test_random_seed)
test_input = TestInput()
est = estimator.Estimator(model_fn=linear_model_fn, config=config)
est.fit(input_fn=test_input.config_test_input_fn, steps=1)
# If input_fn ran, it will have given us the random seed set on the graph.
self.assertEquals(test_random_seed, test_input.random_seed)
def testCheckInputs(self):
est = estimator.SKCompat(estimator.Estimator(model_fn=linear_model_fn))
# Lambdas so we have to different objects to compare
right_features = lambda: np.ones(shape=[7, 8], dtype=np.float32)
right_labels = lambda: np.ones(shape=[7, 10], dtype=np.int32)
est.fit(right_features(), right_labels(), steps=1)
# TODO(wicke): This does not fail for np.int32 because of data_feeder magic.
wrong_type_features = np.ones(shape=[7., 8.], dtype=np.int64)
wrong_size_features = np.ones(shape=[7, 10])
wrong_type_labels = np.ones(shape=[7., 10.], dtype=np.float32)
wrong_size_labels = np.ones(shape=[7, 11])
est.fit(x=right_features(), y=right_labels(), steps=1)
with self.assertRaises(ValueError):
est.fit(x=wrong_type_features, y=right_labels(), steps=1)
with self.assertRaises(ValueError):
est.fit(x=wrong_size_features, y=right_labels(), steps=1)
with self.assertRaises(ValueError):
est.fit(x=right_features(), y=wrong_type_labels, steps=1)
with self.assertRaises(ValueError):
est.fit(x=right_features(), y=wrong_size_labels, steps=1)
def testBadInput(self):
est = estimator.Estimator(model_fn=linear_model_fn)
self.assertRaisesRegexp(
ValueError,
'Either x or input_fn must be provided.',
est.fit,
x=None,
input_fn=None,
steps=1)
self.assertRaisesRegexp(
ValueError,
'Can not provide both input_fn and x or y',
est.fit,
x='X',
input_fn=iris_input_fn,
steps=1)
self.assertRaisesRegexp(
ValueError,
'Can not provide both input_fn and x or y',
est.fit,
y='Y',
input_fn=iris_input_fn,
steps=1)
self.assertRaisesRegexp(
ValueError,
'Can not provide both input_fn and batch_size',
est.fit,
input_fn=iris_input_fn,
batch_size=100,
steps=1)
self.assertRaisesRegexp(
ValueError,
'Inputs cannot be tensors. Please provide input_fn.',
est.fit,
x=constant_op.constant(1.),
steps=1)
def testUntrained(self):
boston = base.load_boston()
est = estimator.SKCompat(estimator.Estimator(model_fn=linear_model_fn))
with self.assertRaises(learn.NotFittedError):
_ = est.score(x=boston.data, y=boston.target.astype(np.float64))
with self.assertRaises(learn.NotFittedError):
est.predict(x=boston.data)
def testContinueTrainingDictionaryInput(self):
boston = base.load_boston()
output_dir = tempfile.mkdtemp()
est = estimator.Estimator(model_fn=linear_model_fn, model_dir=output_dir)
boston_input = {'input': boston.data}
float64_target = {'labels': boston.target.astype(np.float64)}
est.fit(x=boston_input, y=float64_target, steps=50)
scores = est.evaluate(
x=boston_input,
y=float64_target,
metrics={'MSE': metric_ops.streaming_mean_squared_error})
del est
# Create another estimator object with the same output dir.
est2 = estimator.Estimator(model_fn=linear_model_fn, model_dir=output_dir)
# Check we can evaluate and predict.
scores2 = est2.evaluate(
x=boston_input,
y=float64_target,
metrics={'MSE': metric_ops.streaming_mean_squared_error})
self.assertAllClose(scores2['MSE'], scores['MSE'])
predictions = np.array(list(est2.predict(x=boston_input)))
other_score = _sklearn.mean_squared_error(predictions,
float64_target['labels'])
self.assertAllClose(other_score, scores['MSE'])
def testContinueTraining(self):
boston = base.load_boston()
output_dir = tempfile.mkdtemp()
est = estimator.SKCompat(
estimator.Estimator(
model_fn=linear_model_fn, model_dir=output_dir))
float64_labels = boston.target.astype(np.float64)
est.fit(x=boston.data, y=float64_labels, steps=50)
scores = est.score(
x=boston.data,
y=float64_labels,
metrics={'MSE': metric_ops.streaming_mean_squared_error})
del est
# Create another estimator object with the same output dir.
est2 = estimator.SKCompat(
estimator.Estimator(
model_fn=linear_model_fn, model_dir=output_dir))
# Check we can evaluate and predict.
scores2 = est2.score(
x=boston.data,
y=float64_labels,
metrics={'MSE': metric_ops.streaming_mean_squared_error})
self.assertAllClose(scores['MSE'], scores2['MSE'])
predictions = np.array(list(est2.predict(x=boston.data)))
other_score = _sklearn.mean_squared_error(predictions, float64_labels)
self.assertAllClose(scores['MSE'], other_score)
# Check we can keep training.
est2.fit(x=boston.data, y=float64_labels, steps=100)
scores3 = est2.score(
x=boston.data,
y=float64_labels,
metrics={'MSE': metric_ops.streaming_mean_squared_error})
self.assertLess(scores3['MSE'], scores['MSE'])
def testEstimatorParams(self):
boston = base.load_boston()
est = estimator.SKCompat(
estimator.Estimator(
model_fn=linear_model_params_fn, params={'learning_rate': 0.01}))
est.fit(x=boston.data, y=boston.target, steps=100)
def testBostonAll(self):
boston = base.load_boston()
est = estimator.SKCompat(estimator.Estimator(model_fn=linear_model_fn))
float64_labels = boston.target.astype(np.float64)
est.fit(x=boston.data, y=float64_labels, steps=100)
scores = est.score(
x=boston.data,
y=float64_labels,
metrics={'MSE': metric_ops.streaming_mean_squared_error})
predictions = np.array(list(est.predict(x=boston.data)))
other_score = _sklearn.mean_squared_error(predictions, boston.target)
self.assertAllClose(scores['MSE'], other_score)
self.assertTrue('global_step' in scores)
self.assertEqual(100, scores['global_step'])
def testBostonAllDictionaryInput(self):
boston = base.load_boston()
est = estimator.Estimator(model_fn=linear_model_fn)
boston_input = {'input': boston.data}
float64_target = {'labels': boston.target.astype(np.float64)}
est.fit(x=boston_input, y=float64_target, steps=100)
scores = est.evaluate(
x=boston_input,
y=float64_target,
metrics={'MSE': metric_ops.streaming_mean_squared_error})
predictions = np.array(list(est.predict(x=boston_input)))
other_score = _sklearn.mean_squared_error(predictions, boston.target)
self.assertAllClose(other_score, scores['MSE'])
self.assertTrue('global_step' in scores)
self.assertEqual(scores['global_step'], 100)
def testIrisAll(self):
iris = base.load_iris()
est = estimator.SKCompat(
estimator.Estimator(model_fn=logistic_model_no_mode_fn))
est.fit(iris.data, iris.target, steps=100)
scores = est.score(
x=iris.data,
y=iris.target,
metrics={('accuracy', 'class'): metric_ops.streaming_accuracy})
predictions = est.predict(x=iris.data)
predictions_class = est.predict(x=iris.data, outputs=['class'])['class']
self.assertEqual(predictions['prob'].shape[0], iris.target.shape[0])
self.assertAllClose(predictions['class'], predictions_class)
self.assertAllClose(
predictions['class'], np.argmax(
predictions['prob'], axis=1))
other_score = _sklearn.accuracy_score(iris.target, predictions['class'])
self.assertAllClose(scores['accuracy'], other_score)
self.assertTrue('global_step' in scores)
self.assertEqual(100, scores['global_step'])
def testIrisAllDictionaryInput(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
iris_data = {'input': iris.data}
iris_target = {'labels': iris.target}
est.fit(iris_data, iris_target, steps=100)
scores = est.evaluate(
x=iris_data,
y=iris_target,
metrics={('accuracy', 'class'): metric_ops.streaming_accuracy})
predictions = list(est.predict(x=iris_data))
predictions_class = list(est.predict(x=iris_data, outputs=['class']))
self.assertEqual(len(predictions), iris.target.shape[0])
classes_batch = np.array([p['class'] for p in predictions])
self.assertAllClose(classes_batch,
np.array([p['class'] for p in predictions_class]))
self.assertAllClose(
classes_batch,
np.argmax(
np.array([p['prob'] for p in predictions]), axis=1))
other_score = _sklearn.accuracy_score(iris.target, classes_batch)
self.assertAllClose(other_score, scores['accuracy'])
self.assertTrue('global_step' in scores)
self.assertEqual(scores['global_step'], 100)
def testIrisInputFn(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
est.fit(input_fn=iris_input_fn, steps=100)
_ = est.evaluate(input_fn=iris_input_fn, steps=1)
predictions = list(est.predict(x=iris.data))
self.assertEqual(len(predictions), iris.target.shape[0])
def testHooksNotChanged(self):
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
# We pass empty array and expect it to remain empty after calling
# fit and evaluate. Requires inside to copy this array if any hooks were
# added.
my_array = []
est.fit(input_fn=iris_input_fn, steps=100, monitors=my_array)
_ = est.evaluate(input_fn=iris_input_fn, steps=1, hooks=my_array)
self.assertEqual(my_array, [])
def testIrisInputFnLabelsDict(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
est.fit(input_fn=iris_input_fn_labels_dict, steps=100)
_ = est.evaluate(
input_fn=iris_input_fn_labels_dict,
steps=1,
metrics={
'accuracy':
metric_spec.MetricSpec(
metric_fn=metric_ops.streaming_accuracy,
prediction_key='class',
label_key='labels')
})
predictions = list(est.predict(x=iris.data))
self.assertEqual(len(predictions), iris.target.shape[0])
def testIrisIterator(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
x_iter = itertools.islice(iris.data, 100)
y_iter = itertools.islice(iris.target, 100)
estimator.SKCompat(est).fit(x_iter, y_iter, steps=20)
eval_result = est.evaluate(input_fn=iris_input_fn, steps=1)
x_iter_eval = itertools.islice(iris.data, 100)
y_iter_eval = itertools.islice(iris.target, 100)
score_result = estimator.SKCompat(est).score(x_iter_eval, y_iter_eval)
print(score_result)
self.assertItemsEqual(eval_result.keys(), score_result.keys())
self.assertItemsEqual(['global_step', 'loss'], score_result.keys())
predictions = estimator.SKCompat(est).predict(x=iris.data)['class']
self.assertEqual(len(predictions), iris.target.shape[0])
def testIrisIteratorArray(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
x_iter = itertools.islice(iris.data, 100)
y_iter = (np.array(x) for x in iris.target)
est.fit(x_iter, y_iter, steps=100)
_ = est.evaluate(input_fn=iris_input_fn, steps=1)
_ = six.next(est.predict(x=iris.data))['class']
def testIrisIteratorPlainInt(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
x_iter = itertools.islice(iris.data, 100)
y_iter = (v for v in iris.target)
est.fit(x_iter, y_iter, steps=100)
_ = est.evaluate(input_fn=iris_input_fn, steps=1)
_ = six.next(est.predict(x=iris.data))['class']
def testIrisTruncatedIterator(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
x_iter = itertools.islice(iris.data, 50)
y_iter = ([np.int32(v)] for v in iris.target)
est.fit(x_iter, y_iter, steps=100)
def testTrainInputFn(self):
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, steps=1)
_ = est.evaluate(input_fn=boston_eval_fn, steps=1)
def testTrainStepsIsIncremental(self):
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, steps=10)
self.assertEqual(10, est.get_variable_value('global_step'))
est.fit(input_fn=boston_input_fn, steps=15)
self.assertEqual(25, est.get_variable_value('global_step'))
def testTrainMaxStepsIsNotIncremental(self):
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, max_steps=10)
self.assertEqual(10, est.get_variable_value('global_step'))
est.fit(input_fn=boston_input_fn, max_steps=15)
self.assertEqual(15, est.get_variable_value('global_step'))
def testPredict(self):
est = estimator.Estimator(model_fn=linear_model_fn)
boston = base.load_boston()
est.fit(input_fn=boston_input_fn, steps=1)
output = list(est.predict(x=boston.data, batch_size=10))
self.assertEqual(len(output), boston.target.shape[0])
def testPredictInputFn(self):
est = estimator.Estimator(model_fn=linear_model_fn)
boston = base.load_boston()
est.fit(input_fn=boston_input_fn, steps=1)
input_fn = functools.partial(boston_input_fn, num_epochs=1)
output = list(est.predict(input_fn=input_fn))
self.assertEqual(len(output), boston.target.shape[0])
def testPredictInputFnWithQueue(self):
est = estimator.Estimator(model_fn=linear_model_fn)
boston = base.load_boston()
est.fit(input_fn=boston_input_fn, steps=1)
input_fn = functools.partial(boston_input_fn_with_queue, num_epochs=2)
output = list(est.predict(input_fn=input_fn))
self.assertEqual(len(output), boston.target.shape[0] * 2)
def testPredictConstInputFn(self):
est = estimator.Estimator(model_fn=linear_model_fn)
boston = base.load_boston()
est.fit(input_fn=boston_input_fn, steps=1)
def input_fn():
features = array_ops.reshape(
constant_op.constant(boston.data), [-1, _BOSTON_INPUT_DIM])
labels = array_ops.reshape(constant_op.constant(boston.target), [-1, 1])
return features, labels
output = list(est.predict(input_fn=input_fn))
self.assertEqual(len(output), boston.target.shape[0])
def testWithModelFnOps(self):
"""Test for model_fn that returns `ModelFnOps`."""
est = estimator.Estimator(model_fn=linear_model_fn_with_model_fn_ops)
boston = base.load_boston()
est.fit(input_fn=boston_input_fn, steps=1)
input_fn = functools.partial(boston_input_fn, num_epochs=1)
scores = est.evaluate(input_fn=input_fn, steps=1)
self.assertIn('loss', scores.keys())
output = list(est.predict(input_fn=input_fn))
self.assertEqual(len(output), boston.target.shape[0])
def testWrongInput(self):
def other_input_fn():
return {
'other': constant_op.constant([0, 0, 0])
}, constant_op.constant([0, 0, 0])
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, steps=1)
with self.assertRaises(ValueError):
est.fit(input_fn=other_input_fn, steps=1)
def testMonitorsForFit(self):
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn,
steps=21,
monitors=[CheckCallsMonitor(expect_calls=21)])
def testHooksForEvaluate(self):
class CheckCallHook(session_run_hook.SessionRunHook):
def __init__(self):
self.run_count = 0
def after_run(self, run_context, run_values):
self.run_count += 1
est = learn.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, steps=1)
hook = CheckCallHook()
est.evaluate(input_fn=boston_eval_fn, steps=3, hooks=[hook])
self.assertEqual(3, hook.run_count)
def testSummaryWriting(self):
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, steps=200)
est.evaluate(input_fn=boston_input_fn, steps=200)
loss_summary = util_test.simple_values_from_events(
util_test.latest_events(est.model_dir), ['OptimizeLoss/loss'])
self.assertEqual(1, len(loss_summary))
def testLossInGraphCollection(self):
class _LossCheckerHook(session_run_hook.SessionRunHook):
def begin(self):
self.loss_collection = ops.get_collection(ops.GraphKeys.LOSSES)
hook = _LossCheckerHook()
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, steps=200, monitors=[hook])
self.assertTrue(hook.loss_collection)
def test_export_returns_exported_dirname(self):
expected = '/path/to/some_dir'
with test.mock.patch.object(estimator, 'export') as mock_export_module:
mock_export_module._export_estimator.return_value = expected
est = estimator.Estimator(model_fn=linear_model_fn)
actual = est.export('/path/to')
self.assertEquals(expected, actual)
def test_export_savedmodel(self):
tmpdir = tempfile.mkdtemp()
est, serving_input_fn = _build_estimator_for_export_tests(tmpdir)
extra_file_name = os.path.join(
compat.as_bytes(tmpdir), compat.as_bytes('my_extra_file'))
extra_file = gfile.GFile(extra_file_name, mode='w')
extra_file.write(EXTRA_FILE_CONTENT)
extra_file.close()
assets_extra = {'some/sub/directory/my_extra_file': extra_file_name}
export_dir_base = os.path.join(
compat.as_bytes(tmpdir), compat.as_bytes('export'))
export_dir = est.export_savedmodel(
export_dir_base, serving_input_fn, assets_extra=assets_extra)
self.assertTrue(gfile.Exists(export_dir_base))
self.assertTrue(gfile.Exists(export_dir))
self.assertTrue(
gfile.Exists(
os.path.join(
compat.as_bytes(export_dir), compat.as_bytes(
'saved_model.pb'))))
self.assertTrue(
gfile.Exists(
os.path.join(
compat.as_bytes(export_dir), compat.as_bytes('variables'))))
self.assertTrue(
gfile.Exists(
os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes('variables/variables.index'))))
self.assertTrue(
gfile.Exists(
os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes('variables/variables.data-00000-of-00001'))))
self.assertTrue(
gfile.Exists(
os.path.join(
compat.as_bytes(export_dir), compat.as_bytes('assets'))))
self.assertTrue(
gfile.Exists(
os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes('assets/my_vocab_file'))))
self.assertEqual(
compat.as_bytes(VOCAB_FILE_CONTENT),
compat.as_bytes(
gfile.GFile(
os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes('assets/my_vocab_file'))).read()))
expected_extra_path = os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes('assets.extra/some/sub/directory/my_extra_file'))
self.assertTrue(
gfile.Exists(
os.path.join(
compat.as_bytes(export_dir), compat.as_bytes('assets.extra'))))
self.assertTrue(gfile.Exists(expected_extra_path))
self.assertEqual(
compat.as_bytes(EXTRA_FILE_CONTENT),
compat.as_bytes(gfile.GFile(expected_extra_path).read()))
expected_vocab_file = os.path.join(
compat.as_bytes(tmpdir), compat.as_bytes('my_vocab_file'))
# Restore, to validate that the export was well-formed.
with ops.Graph().as_default() as graph:
with session_lib.Session(graph=graph) as sess:
loader.load(sess, [tag_constants.SERVING], export_dir)
assets = [
x.eval()
for x in graph.get_collection(ops.GraphKeys.ASSET_FILEPATHS)
]
self.assertItemsEqual([expected_vocab_file], assets)
graph_ops = [x.name for x in graph.get_operations()]
self.assertTrue('input_example_tensor' in graph_ops)
self.assertTrue('ParseExample/ParseExample' in graph_ops)
self.assertTrue('linear/linear/feature/matmul' in graph_ops)
# cleanup
gfile.DeleteRecursively(tmpdir)
class InferRealValuedColumnsTest(test.TestCase):
def testInvalidArgs(self):
with self.assertRaisesRegexp(ValueError, 'x or input_fn must be provided'):
estimator.infer_real_valued_columns_from_input(None)
with self.assertRaisesRegexp(ValueError, 'cannot be tensors'):
estimator.infer_real_valued_columns_from_input(constant_op.constant(1.0))
def _assert_single_feature_column(self, expected_shape, expected_dtype,
feature_columns):
self.assertEqual(1, len(feature_columns))
feature_column = feature_columns[0]
self.assertEqual('', feature_column.name)
self.assertEqual(
{
'':
parsing_ops.FixedLenFeature(
shape=expected_shape, dtype=expected_dtype)
},
feature_column.config)
def testInt32Input(self):
feature_columns = estimator.infer_real_valued_columns_from_input(
np.ones(
shape=[7, 8], dtype=np.int32))
self._assert_single_feature_column([8], dtypes.int32, feature_columns)
def testInt32InputFn(self):
feature_columns = estimator.infer_real_valued_columns_from_input_fn(
lambda: (array_ops.ones(shape=[7, 8], dtype=dtypes.int32), None))
self._assert_single_feature_column([8], dtypes.int32, feature_columns)
def testInt64Input(self):
feature_columns = estimator.infer_real_valued_columns_from_input(
np.ones(
shape=[7, 8], dtype=np.int64))
self._assert_single_feature_column([8], dtypes.int64, feature_columns)
def testInt64InputFn(self):
feature_columns = estimator.infer_real_valued_columns_from_input_fn(
lambda: (array_ops.ones(shape=[7, 8], dtype=dtypes.int64), None))
self._assert_single_feature_column([8], dtypes.int64, feature_columns)
def testFloat32Input(self):
feature_columns = estimator.infer_real_valued_columns_from_input(
np.ones(
shape=[7, 8], dtype=np.float32))
self._assert_single_feature_column([8], dtypes.float32, feature_columns)
def testFloat32InputFn(self):
feature_columns = estimator.infer_real_valued_columns_from_input_fn(
lambda: (array_ops.ones(shape=[7, 8], dtype=dtypes.float32), None))
self._assert_single_feature_column([8], dtypes.float32, feature_columns)
def testFloat64Input(self):
feature_columns = estimator.infer_real_valued_columns_from_input(
np.ones(
shape=[7, 8], dtype=np.float64))
self._assert_single_feature_column([8], dtypes.float64, feature_columns)
def testFloat64InputFn(self):
feature_columns = estimator.infer_real_valued_columns_from_input_fn(
lambda: (array_ops.ones(shape=[7, 8], dtype=dtypes.float64), None))
self._assert_single_feature_column([8], dtypes.float64, feature_columns)
def testBoolInput(self):
with self.assertRaisesRegexp(
ValueError, 'on integer or non floating types are not supported'):
estimator.infer_real_valued_columns_from_input(
np.array([[False for _ in xrange(8)] for _ in xrange(7)]))
def testBoolInputFn(self):
with self.assertRaisesRegexp(
ValueError, 'on integer or non floating types are not supported'):
# pylint: disable=g-long-lambda
estimator.infer_real_valued_columns_from_input_fn(
lambda: (constant_op.constant(False, shape=[7, 8], dtype=dtypes.bool), None))
def testStringInput(self):
with self.assertRaisesRegexp(
ValueError, 'on integer or non floating types are not supported'):
# pylint: disable=g-long-lambda
estimator.infer_real_valued_columns_from_input(
np.array([['%d.0' % i for i in xrange(8)] for _ in xrange(7)]))
def testStringInputFn(self):
with self.assertRaisesRegexp(
ValueError, 'on integer or non floating types are not supported'):
# pylint: disable=g-long-lambda
estimator.infer_real_valued_columns_from_input_fn(
lambda: (
constant_op.constant([['%d.0' % i for i in xrange(8)] for _ in xrange(7)]),
None))
def testBostonInputFn(self):
feature_columns = estimator.infer_real_valued_columns_from_input_fn(
boston_input_fn)
self._assert_single_feature_column([_BOSTON_INPUT_DIM], dtypes.float64,
feature_columns)
def testIrisInputFn(self):
feature_columns = estimator.infer_real_valued_columns_from_input_fn(
iris_input_fn)
self._assert_single_feature_column([_IRIS_INPUT_DIM], dtypes.float64,
feature_columns)
class ReplicaDeviceSetterTest(test.TestCase):
def testVariablesAreOnPs(self):
tf_config = {'cluster': {run_config.TaskType.PS: ['fake_ps_0']}}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = run_config.RunConfig()
with ops.device(estimator._get_replica_device_setter(config)):
v = variables_lib.Variable([1, 2])
w = variables_lib.Variable([2, 1])
a = v + w
self.assertDeviceEqual('/job:ps/task:0', v.device)
self.assertDeviceEqual('/job:ps/task:0', v.initializer.device)
self.assertDeviceEqual('/job:ps/task:0', w.device)
self.assertDeviceEqual('/job:ps/task:0', w.initializer.device)
self.assertDeviceEqual('/job:worker', a.device)
def testVariablesAreLocal(self):
with ops.device(
estimator._get_replica_device_setter(run_config.RunConfig())):
v = variables_lib.Variable([1, 2])
w = variables_lib.Variable([2, 1])
a = v + w
self.assertDeviceEqual('', v.device)
self.assertDeviceEqual('', v.initializer.device)
self.assertDeviceEqual('', w.device)
self.assertDeviceEqual('', w.initializer.device)
self.assertDeviceEqual('', a.device)
def testMutableHashTableIsOnPs(self):
tf_config = {'cluster': {run_config.TaskType.PS: ['fake_ps_0']}}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = run_config.RunConfig()
with ops.device(estimator._get_replica_device_setter(config)):
default_val = constant_op.constant([-1, -1], dtypes.int64)
table = lookup_ops.MutableHashTable(dtypes.string, dtypes.int64,
default_val)
input_string = constant_op.constant(['brain', 'salad', 'tank'])
output = table.lookup(input_string)
self.assertDeviceEqual('/job:ps/task:0', table._table_ref.device)
self.assertDeviceEqual('/job:ps/task:0', output.device)
def testMutableHashTableIsLocal(self):
with ops.device(
estimator._get_replica_device_setter(run_config.RunConfig())):
default_val = constant_op.constant([-1, -1], dtypes.int64)
table = lookup_ops.MutableHashTable(dtypes.string, dtypes.int64,
default_val)
input_string = constant_op.constant(['brain', 'salad', 'tank'])
output = table.lookup(input_string)
self.assertDeviceEqual('', table._table_ref.device)
self.assertDeviceEqual('', output.device)
def testTaskIsSetOnWorkerWhenJobNameIsSet(self):
tf_config = {
'cluster': {
run_config.TaskType.PS: ['fake_ps_0']
},
'task': {
'type': run_config.TaskType.WORKER,
'index': 3
}
}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = run_config.RunConfig()
with ops.device(estimator._get_replica_device_setter(config)):
v = variables_lib.Variable([1, 2])
w = variables_lib.Variable([2, 1])
a = v + w
self.assertDeviceEqual('/job:ps/task:0', v.device)
self.assertDeviceEqual('/job:ps/task:0', v.initializer.device)
self.assertDeviceEqual('/job:ps/task:0', w.device)
self.assertDeviceEqual('/job:ps/task:0', w.initializer.device)
self.assertDeviceEqual('/job:worker/task:3', a.device)
if __name__ == '__main__':
test.main()
| apache-2.0 |
festeh/BuildingMachineLearningSystemsWithPython | ch08/chapter.py | 21 | 6372 | import numpy as np # NOT IN BOOK
from matplotlib import pyplot as plt # NOT IN BOOK
def load():
import numpy as np
from scipy import sparse
data = np.loadtxt('data/ml-100k/u.data')
ij = data[:, :2]
ij -= 1 # original data is in 1-based system
values = data[:, 2]
reviews = sparse.csc_matrix((values, ij.T)).astype(float)
return reviews.toarray()
reviews = load()
U,M = np.where(reviews)
import random
test_idxs = np.array(random.sample(range(len(U)), len(U)//10))
train = reviews.copy()
train[U[test_idxs], M[test_idxs]] = 0
test = np.zeros_like(reviews)
test[U[test_idxs], M[test_idxs]] = reviews[U[test_idxs], M[test_idxs]]
class NormalizePositive(object):
def __init__(self, axis=0):
self.axis = axis
def fit(self, features, y=None):
if self.axis == 1:
features = features.T
# count features that are greater than zero in axis 0:
binary = (features > 0)
count0 = binary.sum(axis=0)
# to avoid division by zero, set zero counts to one:
count0[count0 == 0] = 1.
# computing the mean is easy:
self.mean = features.sum(axis=0)/count0
# only consider differences where binary is True:
diff = (features - self.mean) * binary
diff **= 2
# regularize the estimate of std by adding 0.1
self.std = np.sqrt(0.1 + diff.sum(axis=0)/count0)
return self
def transform(self, features):
if self.axis == 1:
features = features.T
binary = (features > 0)
features = features - self.mean
features /= self.std
features *= binary
if self.axis == 1:
features = features.T
return features
def inverse_transform(self, features, copy=True):
if copy:
features = features.copy()
if self.axis == 1:
features = features.T
features *= self.std
features += self.mean
if self.axis == 1:
features = features.T
return features
def fit_transform(self, features):
return self.fit(features).transform(features)
norm = NormalizePositive(axis=1)
binary = (train > 0)
train = norm.fit_transform(train)
# plot just 200x200 area for space reasons
plt.imshow(binary[:200, :200], interpolation='nearest')
from scipy.spatial import distance
# compute all pair-wise distances:
dists = distance.pdist(binary, 'correlation')
# Convert to square form, so that dists[i,j]
# is distance between binary[i] and binary[j]:
dists = distance.squareform(dists)
neighbors = dists.argsort(axis=1)
# We are going to fill this matrix with results
filled = train.copy()
for u in range(filled.shape[0]):
# n_u is neighbors of user
n_u = neighbors[u, 1:]
for m in range(filled.shape[1]):
# get relevant reviews in order!
revs = [train[neigh, m]
for neigh in n_u
if binary [neigh, m]]
if len(revs):
# n is the number of reviews for this movie
n = len(revs)
# take half of the reviews plus one into consideration:
n //= 2
n += 1
revs = revs[:n]
filled[u,m] = np.mean(revs)
predicted = norm.inverse_transform(filled)
from sklearn import metrics
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print('R2 score (binary neighbors): {:.1%}'.format(r2))
reviews = reviews.T
# use same code as before
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print('R2 score (binary movie neighbors): {:.1%}'.format(r2))
from sklearn.linear_model import ElasticNetCV # NOT IN BOOK
reg = ElasticNetCV(alphas=[
0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.])
filled = train.copy()
# iterate over all users:
for u in range(train.shape[0]):
curtrain = np.delete(train, u, axis=0)
bu = binary[u]
reg.fit(curtrain[:,bu].T, train[u, bu])
filled[u, ~bu] = reg.predict(curtrain[:,~bu].T)
predicted = norm.inverse_transform(filled)
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print('R2 score (user regression): {:.1%}'.format(r2))
# SHOPPING BASKET ANALYSIS
# This is the slow version of the code, which will take a long time to
# complete.
from collections import defaultdict
from itertools import chain
# File is downloaded as a compressed file
import gzip
# file format is a line per transaction
# of the form '12 34 342 5...'
dataset = [[int(tok) for tok in line.strip().split()]
for line in gzip.open('data/retail.dat.gz')]
dataset = [set(d) for d in dataset]
# count how often each product was purchased:
counts = defaultdict(int)
for elem in chain(*dataset):
counts[elem] += 1
minsupport = 80
valid = set(k for k,v in counts.items() if (v >= minsupport))
itemsets = [frozenset([v]) for v in valid]
freqsets = []
for i in range(16):
nextsets = []
tested = set()
for it in itemsets:
for v in valid:
if v not in it:
# Create a new candidate set by adding v to it
c = (it | frozenset([v]))
# check If we have tested it already
if c in tested:
continue
tested.add(c)
# Count support by looping over dataset
# This step is slow.
# Check `apriori.py` for a better implementation.
support_c = sum(1 for d in dataset if d.issuperset(c))
if support_c > minsupport:
nextsets.append(c)
freqsets.extend(nextsets)
itemsets = nextsets
if not len(itemsets):
break
print("Finished!")
minlift = 5.0
nr_transactions = float(len(dataset))
for itemset in freqsets:
for item in itemset:
consequent = frozenset([item])
antecedent = itemset-consequent
base = 0.0
# acount: antecedent count
acount = 0.0
# ccount : consequent count
ccount = 0.0
for d in dataset:
if item in d: base += 1
if d.issuperset(itemset): ccount += 1
if d.issuperset(antecedent): acount += 1
base /= nr_transactions
p_y_given_x = ccount/acount
lift = p_y_given_x / base
if lift > minlift:
print('Rule {0} -> {1} has lift {2}'
.format(antecedent, consequent,lift))
| mit |
wesm/statsmodels | scikits/statsmodels/tsa/filters/cf_filter.py | 1 | 2605 | import numpy as np
# the data is sampled quarterly, so cut-off frequency of 18
# Wn is normalized cut-off freq
#Cutoff frequency is that frequency where the magnitude response of the filter
# is sqrt(1/2.). For butter, the normalized cutoff frequency Wn must be a
# number between 0 and 1, where 1 corresponds to the Nyquist frequency, p
# radians per sample.
#NOTE: uses a loop, could probably be sped-up for very large datasets
def cffilter(X, low=6, high=32, drift=True):
"""
Christiano Fitzgerald asymmetric, random walk filter
Parameters
----------
X : array-like
1 or 2d array to filter. If 2d, variables are assumed to be in columns.
low : float
Minimum period of oscillations. Features below low periodicity are
filtered out. Default is 6 for quarterly data, giving a 1.5 year
periodicity.
high : float
Maximum period of oscillations. Features above high periodicity are
filtered out. Default is 32 for quarterly data, giving an 8 year
periodicity.
drift : bool
Whether or not to remove a trend from the data. The trend is estimated
as np.arange(nobs)*(X[-1] - X[0])/(len(X)-1)
Returns
-------
cycle : array
The features of `X` between periodicities given by low and high
trend : array
The trend in the data with the cycles removed.
"""
#TODO: cythonize/vectorize loop?, add ability for symmetric filter,
# and estimates of theta other than random walk.
if low < 2:
raise ValueError("low must be >= 2")
X = np.asanyarray(X)
if X.ndim == 1:
X = X[:,None]
nobs, nseries = X.shape
a = 2*np.pi/high
b = 2*np.pi/low
if drift: # get drift adjusted series
X = X - np.arange(nobs)[:,None]*(X[-1] - X[0])/(nobs-1)
J = np.arange(1,nobs+1)
Bj = (np.sin(b*J)-np.sin(a*J))/(np.pi*J)
B0 = (b-a)/np.pi
Bj = np.r_[B0,Bj][:,None]
y = np.zeros((nobs,nseries))
for i in xrange(nobs):
B = -.5*Bj[0] -np.sum(Bj[1:-i-2])
A = -Bj[0] - np.sum(Bj[1:-i-2]) - np.sum(Bj[1:i]) - B
y[i] = Bj[0] * X[i] + np.dot(Bj[1:-i-2].T,X[i+1:-1]) + B*X[-1] + \
np.dot(Bj[1:i].T, X[1:i][::-1]) + A*X[0]
y = y.squeeze()
return y, X.squeeze()-y
if __name__ == "__main__":
import scikits.statsmodels as sm
dta = sm.datasets.macrodata.load().data[['infl','tbilrate']].view((float,2))[1:]
cycle, trend = cffilter(dta, 6, 32, drift=True)
dta = sm.datasets.macrodata.load().data['tbilrate'][1:]
cycle2, trend2 = cffilter(dta, 6, 32, drift=True)
| bsd-3-clause |
zorroblue/scikit-learn | sklearn/tests/test_pipeline.py | 14 | 33955 | """
Test the pipeline module.
"""
from tempfile import mkdtemp
import shutil
import time
import numpy as np
from scipy import sparse
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regex
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_dict_equal
from sklearn.utils.testing import assert_no_warnings
from sklearn.base import clone, BaseEstimator
from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline, make_union
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression, Lasso
from sklearn.linear_model import LinearRegression
from sklearn.cluster import KMeans
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.dummy import DummyRegressor
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.externals.joblib import Memory
JUNK_FOOD_DOCS = (
"the pizza pizza beer copyright",
"the pizza burger beer copyright",
"the the pizza beer beer copyright",
"the burger beer beer copyright",
"the coke burger coke copyright",
"the coke burger burger",
)
class NoFit(object):
"""Small class to test parameter dispatching.
"""
def __init__(self, a=None, b=None):
self.a = a
self.b = b
class NoTrans(NoFit):
def fit(self, X, y):
return self
def get_params(self, deep=False):
return {'a': self.a, 'b': self.b}
def set_params(self, **params):
self.a = params['a']
return self
class NoInvTransf(NoTrans):
def transform(self, X):
return X
class Transf(NoInvTransf):
def transform(self, X):
return X
def inverse_transform(self, X):
return X
class TransfFitParams(Transf):
def fit(self, X, y, **fit_params):
self.fit_params = fit_params
return self
class Mult(BaseEstimator):
def __init__(self, mult=1):
self.mult = mult
def fit(self, X, y):
return self
def transform(self, X):
return np.asarray(X) * self.mult
def inverse_transform(self, X):
return np.asarray(X) / self.mult
def predict(self, X):
return (np.asarray(X) * self.mult).sum(axis=1)
predict_proba = predict_log_proba = decision_function = predict
def score(self, X, y=None):
return np.sum(X)
class FitParamT(BaseEstimator):
"""Mock classifier
"""
def __init__(self):
self.successful = False
def fit(self, X, y, should_succeed=False):
self.successful = should_succeed
def predict(self, X):
return self.successful
def fit_predict(self, X, y, should_succeed=False):
self.fit(X, y, should_succeed=should_succeed)
return self.predict(X)
def score(self, X, y=None, sample_weight=None):
if sample_weight is not None:
X = X * sample_weight
return np.sum(X)
class DummyTransf(Transf):
"""Transformer which store the column means"""
def fit(self, X, y):
self.means_ = np.mean(X, axis=0)
# store timestamp to figure out whether the result of 'fit' has been
# cached or not
self.timestamp_ = time.time()
return self
def test_pipeline_init():
# Test the various init parameters of the pipeline.
assert_raises(TypeError, Pipeline)
# Check that we can't instantiate pipelines with objects without fit
# method
assert_raises_regex(TypeError,
'Last step of Pipeline should implement fit. '
'.*NoFit.*',
Pipeline, [('clf', NoFit())])
# Smoke test with only an estimator
clf = NoTrans()
pipe = Pipeline([('svc', clf)])
assert_equal(pipe.get_params(deep=True),
dict(svc__a=None, svc__b=None, svc=clf,
**pipe.get_params(deep=False)))
# Check that params are set
pipe.set_params(svc__a=0.1)
assert_equal(clf.a, 0.1)
assert_equal(clf.b, None)
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC()
filter1 = SelectKBest(f_classif)
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that we can't instantiate with non-transformers on the way
# Note that NoTrans implements fit, but not transform
assert_raises_regex(TypeError,
'All intermediate steps should be transformers'
'.*\\bNoTrans\\b.*',
Pipeline, [('t', NoTrans()), ('svc', clf)])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert_equal(clf.C, 0.1)
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
assert_raises(ValueError, pipe.set_params, anova__C=0.1)
# Test clone
pipe2 = assert_no_warnings(clone, pipe)
assert_false(pipe.named_steps['svc'] is pipe2.named_steps['svc'])
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop('svc')
params.pop('anova')
params2.pop('svc')
params2.pop('anova')
assert_equal(params, params2)
def test_pipeline_init_tuple():
# Pipeline accepts steps as tuple
X = np.array([[1, 2]])
pipe = Pipeline((('transf', Transf()), ('clf', FitParamT())))
pipe.fit(X, y=None)
pipe.score(X)
pipe.set_params(transf=None)
pipe.fit(X, y=None)
pipe.score(X)
def test_pipeline_methods_anova():
# Test the various methods of the pipeline (anova).
iris = load_iris()
X = iris.data
y = iris.target
# Test with Anova + LogisticRegression
clf = LogisticRegression()
filter1 = SelectKBest(f_classif, k=2)
pipe = Pipeline([('anova', filter1), ('logistic', clf)])
pipe.fit(X, y)
pipe.predict(X)
pipe.predict_proba(X)
pipe.predict_log_proba(X)
pipe.score(X, y)
def test_pipeline_fit_params():
# Test that the pipeline can take fit parameters
pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())])
pipe.fit(X=None, y=None, clf__should_succeed=True)
# classifier should return True
assert_true(pipe.predict(None))
# and transformer params should not be changed
assert_true(pipe.named_steps['transf'].a is None)
assert_true(pipe.named_steps['transf'].b is None)
# invalid parameters should raise an error message
assert_raise_message(
TypeError,
"fit() got an unexpected keyword argument 'bad'",
pipe.fit, None, None, clf__bad=True
)
def test_pipeline_sample_weight_supported():
# Pipeline should pass sample_weight
X = np.array([[1, 2]])
pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())])
pipe.fit(X, y=None)
assert_equal(pipe.score(X), 3)
assert_equal(pipe.score(X, y=None), 3)
assert_equal(pipe.score(X, y=None, sample_weight=None), 3)
assert_equal(pipe.score(X, sample_weight=np.array([2, 3])), 8)
def test_pipeline_sample_weight_unsupported():
# When sample_weight is None it shouldn't be passed
X = np.array([[1, 2]])
pipe = Pipeline([('transf', Transf()), ('clf', Mult())])
pipe.fit(X, y=None)
assert_equal(pipe.score(X), 3)
assert_equal(pipe.score(X, sample_weight=None), 3)
assert_raise_message(
TypeError,
"score() got an unexpected keyword argument 'sample_weight'",
pipe.score, X, sample_weight=np.array([2, 3])
)
def test_pipeline_raise_set_params_error():
# Test pipeline raises set params error message for nested models.
pipe = Pipeline([('cls', LinearRegression())])
# expected error message
error_msg = ('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.')
assert_raise_message(ValueError,
error_msg % ('fake', pipe),
pipe.set_params,
fake='nope')
# nested model check
assert_raise_message(ValueError,
error_msg % ("fake", pipe),
pipe.set_params,
fake__estimator='nope')
def test_pipeline_methods_pca_svm():
# Test the various methods of the pipeline (pca + svm).
iris = load_iris()
X = iris.data
y = iris.target
# Test with PCA + SVC
clf = SVC(probability=True, random_state=0)
pca = PCA(svd_solver='full', n_components='mle', whiten=True)
pipe = Pipeline([('pca', pca), ('svc', clf)])
pipe.fit(X, y)
pipe.predict(X)
pipe.predict_proba(X)
pipe.predict_log_proba(X)
pipe.score(X, y)
def test_pipeline_methods_preprocessing_svm():
# Test the various methods of the pipeline (preprocessing + svm).
iris = load_iris()
X = iris.data
y = iris.target
n_samples = X.shape[0]
n_classes = len(np.unique(y))
scaler = StandardScaler()
pca = PCA(n_components=2, svd_solver='randomized', whiten=True)
clf = SVC(probability=True, random_state=0, decision_function_shape='ovr')
for preprocessing in [scaler, pca]:
pipe = Pipeline([('preprocess', preprocessing), ('svc', clf)])
pipe.fit(X, y)
# check shapes of various prediction functions
predict = pipe.predict(X)
assert_equal(predict.shape, (n_samples,))
proba = pipe.predict_proba(X)
assert_equal(proba.shape, (n_samples, n_classes))
log_proba = pipe.predict_log_proba(X)
assert_equal(log_proba.shape, (n_samples, n_classes))
decision_function = pipe.decision_function(X)
assert_equal(decision_function.shape, (n_samples, n_classes))
pipe.score(X, y)
def test_fit_predict_on_pipeline():
# test that the fit_predict method is implemented on a pipeline
# test that the fit_predict on pipeline yields same results as applying
# transform and clustering steps separately
iris = load_iris()
scaler = StandardScaler()
km = KMeans(random_state=0)
# As pipeline doesn't clone estimators on construction,
# it must have its own estimators
scaler_for_pipeline = StandardScaler()
km_for_pipeline = KMeans(random_state=0)
# first compute the transform and clustering step separately
scaled = scaler.fit_transform(iris.data)
separate_pred = km.fit_predict(scaled)
# use a pipeline to do the transform and clustering in one step
pipe = Pipeline([
('scaler', scaler_for_pipeline),
('Kmeans', km_for_pipeline)
])
pipeline_pred = pipe.fit_predict(iris.data)
assert_array_almost_equal(pipeline_pred, separate_pred)
def test_fit_predict_on_pipeline_without_fit_predict():
# tests that a pipeline does not have fit_predict method when final
# step of pipeline does not have fit_predict defined
scaler = StandardScaler()
pca = PCA(svd_solver='full')
pipe = Pipeline([('scaler', scaler), ('pca', pca)])
assert_raises_regex(AttributeError,
"'PCA' object has no attribute 'fit_predict'",
getattr, pipe, 'fit_predict')
def test_fit_predict_with_intermediate_fit_params():
# tests that Pipeline passes fit_params to intermediate steps
# when fit_predict is invoked
pipe = Pipeline([('transf', TransfFitParams()), ('clf', FitParamT())])
pipe.fit_predict(X=None,
y=None,
transf__should_get_this=True,
clf__should_succeed=True)
assert_true(pipe.named_steps['transf'].fit_params['should_get_this'])
assert_true(pipe.named_steps['clf'].successful)
assert_false('should_succeed' in pipe.named_steps['transf'].fit_params)
def test_feature_union():
# basic sanity check for feature union
iris = load_iris()
X = iris.data
X -= X.mean(axis=0)
y = iris.target
svd = TruncatedSVD(n_components=2, random_state=0)
select = SelectKBest(k=1)
fs = FeatureUnion([("svd", svd), ("select", select)])
fs.fit(X, y)
X_transformed = fs.transform(X)
assert_equal(X_transformed.shape, (X.shape[0], 3))
# check if it does the expected thing
assert_array_almost_equal(X_transformed[:, :-1], svd.fit_transform(X))
assert_array_equal(X_transformed[:, -1],
select.fit_transform(X, y).ravel())
# test if it also works for sparse input
# We use a different svd object to control the random_state stream
fs = FeatureUnion([("svd", svd), ("select", select)])
X_sp = sparse.csr_matrix(X)
X_sp_transformed = fs.fit_transform(X_sp, y)
assert_array_almost_equal(X_transformed, X_sp_transformed.toarray())
# Test clone
fs2 = assert_no_warnings(clone, fs)
assert_false(fs.transformer_list[0][1] is fs2.transformer_list[0][1])
# test setting parameters
fs.set_params(select__k=2)
assert_equal(fs.fit_transform(X, y).shape, (X.shape[0], 4))
# test it works with transformers missing fit_transform
fs = FeatureUnion([("mock", Transf()), ("svd", svd), ("select", select)])
X_transformed = fs.fit_transform(X, y)
assert_equal(X_transformed.shape, (X.shape[0], 8))
# test error if some elements do not support transform
assert_raises_regex(TypeError,
'All estimators should implement fit and '
'transform.*\\bNoTrans\\b',
FeatureUnion,
[("transform", Transf()), ("no_transform", NoTrans())])
# test that init accepts tuples
fs = FeatureUnion((("svd", svd), ("select", select)))
fs.fit(X, y)
def test_make_union():
pca = PCA(svd_solver='full')
mock = Transf()
fu = make_union(pca, mock)
names, transformers = zip(*fu.transformer_list)
assert_equal(names, ("pca", "transf"))
assert_equal(transformers, (pca, mock))
def test_make_union_kwargs():
pca = PCA(svd_solver='full')
mock = Transf()
fu = make_union(pca, mock, n_jobs=3)
assert_equal(fu.transformer_list, make_union(pca, mock).transformer_list)
assert_equal(3, fu.n_jobs)
# invalid keyword parameters should raise an error message
assert_raise_message(
TypeError,
'Unknown keyword arguments: "transformer_weights"',
make_union, pca, mock, transformer_weights={'pca': 10, 'Transf': 1}
)
def test_pipeline_transform():
# Test whether pipeline works with a transformer at the end.
# Also test pipeline.transform and pipeline.inverse_transform
iris = load_iris()
X = iris.data
pca = PCA(n_components=2, svd_solver='full')
pipeline = Pipeline([('pca', pca)])
# test transform and fit_transform:
X_trans = pipeline.fit(X).transform(X)
X_trans2 = pipeline.fit_transform(X)
X_trans3 = pca.fit_transform(X)
assert_array_almost_equal(X_trans, X_trans2)
assert_array_almost_equal(X_trans, X_trans3)
X_back = pipeline.inverse_transform(X_trans)
X_back2 = pca.inverse_transform(X_trans)
assert_array_almost_equal(X_back, X_back2)
def test_pipeline_fit_transform():
# Test whether pipeline works with a transformer missing fit_transform
iris = load_iris()
X = iris.data
y = iris.target
transf = Transf()
pipeline = Pipeline([('mock', transf)])
# test fit_transform:
X_trans = pipeline.fit_transform(X, y)
X_trans2 = transf.fit(X, y).transform(X)
assert_array_almost_equal(X_trans, X_trans2)
def test_set_pipeline_steps():
transf1 = Transf()
transf2 = Transf()
pipeline = Pipeline([('mock', transf1)])
assert_true(pipeline.named_steps['mock'] is transf1)
# Directly setting attr
pipeline.steps = [('mock2', transf2)]
assert_true('mock' not in pipeline.named_steps)
assert_true(pipeline.named_steps['mock2'] is transf2)
assert_equal([('mock2', transf2)], pipeline.steps)
# Using set_params
pipeline.set_params(steps=[('mock', transf1)])
assert_equal([('mock', transf1)], pipeline.steps)
# Using set_params to replace single step
pipeline.set_params(mock=transf2)
assert_equal([('mock', transf2)], pipeline.steps)
# With invalid data
pipeline.set_params(steps=[('junk', ())])
assert_raises(TypeError, pipeline.fit, [[1]], [1])
assert_raises(TypeError, pipeline.fit_transform, [[1]], [1])
def test_pipeline_named_steps():
transf = Transf()
mult2 = Mult(mult=2)
pipeline = Pipeline([('mock', transf), ("mult", mult2)])
# Test access via named_steps bunch object
assert_true('mock' in pipeline.named_steps)
assert_true('mock2' not in pipeline.named_steps)
assert_true(pipeline.named_steps.mock is transf)
assert_true(pipeline.named_steps.mult is mult2)
# Test bunch with conflict attribute of dict
pipeline = Pipeline([('values', transf), ("mult", mult2)])
assert_true(pipeline.named_steps.values is not transf)
assert_true(pipeline.named_steps.mult is mult2)
def test_set_pipeline_step_none():
# Test setting Pipeline steps to None
X = np.array([[1]])
y = np.array([1])
mult2 = Mult(mult=2)
mult3 = Mult(mult=3)
mult5 = Mult(mult=5)
def make():
return Pipeline([('m2', mult2), ('m3', mult3), ('last', mult5)])
pipeline = make()
exp = 2 * 3 * 5
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
pipeline.set_params(m3=None)
exp = 2 * 5
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
assert_dict_equal(pipeline.get_params(deep=True),
{'steps': pipeline.steps,
'm2': mult2,
'm3': None,
'last': mult5,
'memory': None,
'm2__mult': 2,
'last__mult': 5,
})
pipeline.set_params(m2=None)
exp = 5
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
# for other methods, ensure no AttributeErrors on None:
other_methods = ['predict_proba', 'predict_log_proba',
'decision_function', 'transform', 'score']
for method in other_methods:
getattr(pipeline, method)(X)
pipeline.set_params(m2=mult2)
exp = 2 * 5
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
pipeline = make()
pipeline.set_params(last=None)
# mult2 and mult3 are active
exp = 6
assert_array_equal([[exp]], pipeline.fit(X, y).transform(X))
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
assert_raise_message(AttributeError,
"'NoneType' object has no attribute 'predict'",
getattr, pipeline, 'predict')
# Check None step at construction time
exp = 2 * 5
pipeline = Pipeline([('m2', mult2), ('m3', None), ('last', mult5)])
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
def test_pipeline_ducktyping():
pipeline = make_pipeline(Mult(5))
pipeline.predict
pipeline.transform
pipeline.inverse_transform
pipeline = make_pipeline(Transf())
assert_false(hasattr(pipeline, 'predict'))
pipeline.transform
pipeline.inverse_transform
pipeline = make_pipeline(None)
assert_false(hasattr(pipeline, 'predict'))
pipeline.transform
pipeline.inverse_transform
pipeline = make_pipeline(Transf(), NoInvTransf())
assert_false(hasattr(pipeline, 'predict'))
pipeline.transform
assert_false(hasattr(pipeline, 'inverse_transform'))
pipeline = make_pipeline(NoInvTransf(), Transf())
assert_false(hasattr(pipeline, 'predict'))
pipeline.transform
assert_false(hasattr(pipeline, 'inverse_transform'))
def test_make_pipeline():
t1 = Transf()
t2 = Transf()
pipe = make_pipeline(t1, t2)
assert_true(isinstance(pipe, Pipeline))
assert_equal(pipe.steps[0][0], "transf-1")
assert_equal(pipe.steps[1][0], "transf-2")
pipe = make_pipeline(t1, t2, FitParamT())
assert_true(isinstance(pipe, Pipeline))
assert_equal(pipe.steps[0][0], "transf-1")
assert_equal(pipe.steps[1][0], "transf-2")
assert_equal(pipe.steps[2][0], "fitparamt")
assert_raise_message(
TypeError,
'Unknown keyword arguments: "random_parameter"',
make_pipeline, t1, t2, random_parameter='rnd'
)
def test_feature_union_weights():
# test feature union with transformer weights
iris = load_iris()
X = iris.data
y = iris.target
pca = PCA(n_components=2, svd_solver='randomized', random_state=0)
select = SelectKBest(k=1)
# test using fit followed by transform
fs = FeatureUnion([("pca", pca), ("select", select)],
transformer_weights={"pca": 10})
fs.fit(X, y)
X_transformed = fs.transform(X)
# test using fit_transform
fs = FeatureUnion([("pca", pca), ("select", select)],
transformer_weights={"pca": 10})
X_fit_transformed = fs.fit_transform(X, y)
# test it works with transformers missing fit_transform
fs = FeatureUnion([("mock", Transf()), ("pca", pca), ("select", select)],
transformer_weights={"mock": 10})
X_fit_transformed_wo_method = fs.fit_transform(X, y)
# check against expected result
# We use a different pca object to control the random_state stream
assert_array_almost_equal(X_transformed[:, :-1], 10 * pca.fit_transform(X))
assert_array_equal(X_transformed[:, -1],
select.fit_transform(X, y).ravel())
assert_array_almost_equal(X_fit_transformed[:, :-1],
10 * pca.fit_transform(X))
assert_array_equal(X_fit_transformed[:, -1],
select.fit_transform(X, y).ravel())
assert_equal(X_fit_transformed_wo_method.shape, (X.shape[0], 7))
def test_feature_union_parallel():
# test that n_jobs work for FeatureUnion
X = JUNK_FOOD_DOCS
fs = FeatureUnion([
("words", CountVectorizer(analyzer='word')),
("chars", CountVectorizer(analyzer='char')),
])
fs_parallel = FeatureUnion([
("words", CountVectorizer(analyzer='word')),
("chars", CountVectorizer(analyzer='char')),
], n_jobs=2)
fs_parallel2 = FeatureUnion([
("words", CountVectorizer(analyzer='word')),
("chars", CountVectorizer(analyzer='char')),
], n_jobs=2)
fs.fit(X)
X_transformed = fs.transform(X)
assert_equal(X_transformed.shape[0], len(X))
fs_parallel.fit(X)
X_transformed_parallel = fs_parallel.transform(X)
assert_equal(X_transformed.shape, X_transformed_parallel.shape)
assert_array_equal(
X_transformed.toarray(),
X_transformed_parallel.toarray()
)
# fit_transform should behave the same
X_transformed_parallel2 = fs_parallel2.fit_transform(X)
assert_array_equal(
X_transformed.toarray(),
X_transformed_parallel2.toarray()
)
# transformers should stay fit after fit_transform
X_transformed_parallel2 = fs_parallel2.transform(X)
assert_array_equal(
X_transformed.toarray(),
X_transformed_parallel2.toarray()
)
def test_feature_union_feature_names():
word_vect = CountVectorizer(analyzer="word")
char_vect = CountVectorizer(analyzer="char_wb", ngram_range=(3, 3))
ft = FeatureUnion([("chars", char_vect), ("words", word_vect)])
ft.fit(JUNK_FOOD_DOCS)
feature_names = ft.get_feature_names()
for feat in feature_names:
assert_true("chars__" in feat or "words__" in feat)
assert_equal(len(feature_names), 35)
ft = FeatureUnion([("tr1", Transf())]).fit([[1]])
assert_raise_message(AttributeError,
'Transformer tr1 (type Transf) does not provide '
'get_feature_names', ft.get_feature_names)
def test_classes_property():
iris = load_iris()
X = iris.data
y = iris.target
reg = make_pipeline(SelectKBest(k=1), LinearRegression())
reg.fit(X, y)
assert_raises(AttributeError, getattr, reg, "classes_")
clf = make_pipeline(SelectKBest(k=1), LogisticRegression(random_state=0))
assert_raises(AttributeError, getattr, clf, "classes_")
clf.fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
def test_set_feature_union_steps():
mult2 = Mult(2)
mult2.get_feature_names = lambda: ['x2']
mult3 = Mult(3)
mult3.get_feature_names = lambda: ['x3']
mult5 = Mult(5)
mult5.get_feature_names = lambda: ['x5']
ft = FeatureUnion([('m2', mult2), ('m3', mult3)])
assert_array_equal([[2, 3]], ft.transform(np.asarray([[1]])))
assert_equal(['m2__x2', 'm3__x3'], ft.get_feature_names())
# Directly setting attr
ft.transformer_list = [('m5', mult5)]
assert_array_equal([[5]], ft.transform(np.asarray([[1]])))
assert_equal(['m5__x5'], ft.get_feature_names())
# Using set_params
ft.set_params(transformer_list=[('mock', mult3)])
assert_array_equal([[3]], ft.transform(np.asarray([[1]])))
assert_equal(['mock__x3'], ft.get_feature_names())
# Using set_params to replace single step
ft.set_params(mock=mult5)
assert_array_equal([[5]], ft.transform(np.asarray([[1]])))
assert_equal(['mock__x5'], ft.get_feature_names())
def test_set_feature_union_step_none():
mult2 = Mult(2)
mult2.get_feature_names = lambda: ['x2']
mult3 = Mult(3)
mult3.get_feature_names = lambda: ['x3']
X = np.asarray([[1]])
ft = FeatureUnion([('m2', mult2), ('m3', mult3)])
assert_array_equal([[2, 3]], ft.fit(X).transform(X))
assert_array_equal([[2, 3]], ft.fit_transform(X))
assert_equal(['m2__x2', 'm3__x3'], ft.get_feature_names())
ft.set_params(m2=None)
assert_array_equal([[3]], ft.fit(X).transform(X))
assert_array_equal([[3]], ft.fit_transform(X))
assert_equal(['m3__x3'], ft.get_feature_names())
ft.set_params(m3=None)
assert_array_equal([[]], ft.fit(X).transform(X))
assert_array_equal([[]], ft.fit_transform(X))
assert_equal([], ft.get_feature_names())
# check we can change back
ft.set_params(m3=mult3)
assert_array_equal([[3]], ft.fit(X).transform(X))
def test_step_name_validation():
bad_steps1 = [('a__q', Mult(2)), ('b', Mult(3))]
bad_steps2 = [('a', Mult(2)), ('a', Mult(3))]
for cls, param in [(Pipeline, 'steps'),
(FeatureUnion, 'transformer_list')]:
# we validate in construction (despite scikit-learn convention)
bad_steps3 = [('a', Mult(2)), (param, Mult(3))]
for bad_steps, message in [
(bad_steps1, "Estimator names must not contain __: got ['a__q']"),
(bad_steps2, "Names provided are not unique: ['a', 'a']"),
(bad_steps3, "Estimator names conflict with constructor "
"arguments: ['%s']" % param),
]:
# three ways to make invalid:
# - construction
assert_raise_message(ValueError, message, cls,
**{param: bad_steps})
# - setattr
est = cls(**{param: [('a', Mult(1))]})
setattr(est, param, bad_steps)
assert_raise_message(ValueError, message, est.fit, [[1]], [1])
assert_raise_message(ValueError, message, est.fit_transform,
[[1]], [1])
# - set_params
est = cls(**{param: [('a', Mult(1))]})
est.set_params(**{param: bad_steps})
assert_raise_message(ValueError, message, est.fit, [[1]], [1])
assert_raise_message(ValueError, message, est.fit_transform,
[[1]], [1])
def test_set_params_nested_pipeline():
estimator = Pipeline([
('a', Pipeline([
('b', DummyRegressor())
]))
])
estimator.set_params(a__b__alpha=0.001, a__b=Lasso())
estimator.set_params(a__steps=[('b', LogisticRegression())], a__b__C=5)
def test_pipeline_wrong_memory():
# Test that an error is raised when memory is not a string or a Memory
# instance
iris = load_iris()
X = iris.data
y = iris.target
# Define memory as an integer
memory = 1
cached_pipe = Pipeline([('transf', DummyTransf()), ('svc', SVC())],
memory=memory)
assert_raises_regex(ValueError, "'memory' should be None, a string or"
" have the same interface as "
"sklearn.externals.joblib.Memory."
" Got memory='1' instead.", cached_pipe.fit, X, y)
class DummyMemory(object):
def cache(self, func):
return func
class WrongDummyMemory(object):
pass
def test_pipeline_with_cache_attribute():
X = np.array([[1, 2]])
pipe = Pipeline([('transf', Transf()), ('clf', Mult())],
memory=DummyMemory())
pipe.fit(X, y=None)
dummy = WrongDummyMemory()
pipe = Pipeline([('transf', Transf()), ('clf', Mult())],
memory=dummy)
assert_raises_regex(ValueError, "'memory' should be None, a string or"
" have the same interface as "
"sklearn.externals.joblib.Memory."
" Got memory='{}' instead.".format(dummy), pipe.fit, X)
def test_pipeline_memory():
iris = load_iris()
X = iris.data
y = iris.target
cachedir = mkdtemp()
try:
memory = Memory(cachedir=cachedir, verbose=10)
# Test with Transformer + SVC
clf = SVC(probability=True, random_state=0)
transf = DummyTransf()
pipe = Pipeline([('transf', clone(transf)), ('svc', clf)])
cached_pipe = Pipeline([('transf', transf), ('svc', clf)],
memory=memory)
# Memoize the transformer at the first fit
cached_pipe.fit(X, y)
pipe.fit(X, y)
# Get the time stamp of the transformer in the cached pipeline
ts = cached_pipe.named_steps['transf'].timestamp_
# Check that cached_pipe and pipe yield identical results
assert_array_equal(pipe.predict(X), cached_pipe.predict(X))
assert_array_equal(pipe.predict_proba(X), cached_pipe.predict_proba(X))
assert_array_equal(pipe.predict_log_proba(X),
cached_pipe.predict_log_proba(X))
assert_array_equal(pipe.score(X, y), cached_pipe.score(X, y))
assert_array_equal(pipe.named_steps['transf'].means_,
cached_pipe.named_steps['transf'].means_)
assert_false(hasattr(transf, 'means_'))
# Check that we are reading the cache while fitting
# a second time
cached_pipe.fit(X, y)
# Check that cached_pipe and pipe yield identical results
assert_array_equal(pipe.predict(X), cached_pipe.predict(X))
assert_array_equal(pipe.predict_proba(X), cached_pipe.predict_proba(X))
assert_array_equal(pipe.predict_log_proba(X),
cached_pipe.predict_log_proba(X))
assert_array_equal(pipe.score(X, y), cached_pipe.score(X, y))
assert_array_equal(pipe.named_steps['transf'].means_,
cached_pipe.named_steps['transf'].means_)
assert_equal(ts, cached_pipe.named_steps['transf'].timestamp_)
# Create a new pipeline with cloned estimators
# Check that even changing the name step does not affect the cache hit
clf_2 = SVC(probability=True, random_state=0)
transf_2 = DummyTransf()
cached_pipe_2 = Pipeline([('transf_2', transf_2), ('svc', clf_2)],
memory=memory)
cached_pipe_2.fit(X, y)
# Check that cached_pipe and pipe yield identical results
assert_array_equal(pipe.predict(X), cached_pipe_2.predict(X))
assert_array_equal(pipe.predict_proba(X),
cached_pipe_2.predict_proba(X))
assert_array_equal(pipe.predict_log_proba(X),
cached_pipe_2.predict_log_proba(X))
assert_array_equal(pipe.score(X, y), cached_pipe_2.score(X, y))
assert_array_equal(pipe.named_steps['transf'].means_,
cached_pipe_2.named_steps['transf_2'].means_)
assert_equal(ts, cached_pipe_2.named_steps['transf_2'].timestamp_)
finally:
shutil.rmtree(cachedir)
def test_make_pipeline_memory():
cachedir = mkdtemp()
memory = Memory(cachedir=cachedir)
pipeline = make_pipeline(DummyTransf(), SVC(), memory=memory)
assert_true(pipeline.memory is memory)
pipeline = make_pipeline(DummyTransf(), SVC())
assert_true(pipeline.memory is None)
shutil.rmtree(cachedir)
| bsd-3-clause |
MadsJensen/RP_scripts | perm_test_classifier_eigen_pre.py | 1 | 1889 | import numpy as np
import bct
from sklearn.externals import joblib
from my_settings import (bands, source_folder)
from sklearn.ensemble import AdaBoostClassifier
from sklearn.cross_validation import (StratifiedKFold, cross_val_score,
permutation_test_score)
from sklearn.grid_search import GridSearchCV
subjects = [
"0008", "0009", "0010", "0012", "0014", "0015", "0016", "0017", "0018",
"0019", "0020", "0021", "0022"
]
cls_all = []
pln_all = []
scores_all = np.empty([4, 10])
results_all = {}
for subject in subjects:
cls = np.load(source_folder + "graph_data/%s_classic_pow_pre.npy" %
subject).item()
pln = np.load(source_folder + "graph_data/%s_plan_pow_pre.npy" %
subject).item()
cls_all.append(cls)
pln_all.append(pln)
for k, band in enumerate(bands.keys()):
data_cls = []
for j in range(len(cls_all)):
tmp = cls_all[j][band]
data_cls.append(
np.asarray([bct.strengths_und(g) for g in tmp]).mean(axis=0))
data_pln = []
for j in range(len(pln_all)):
tmp = pln_all[j][band]
data_pln.append(
np.asarray([bct.strengths_und(g) for g in tmp]).mean(axis=0))
data_cls = np.asarray(data_cls)
data_pln = np.asarray(data_pln)
X = np.vstack([data_cls, data_pln])
y = np.concatenate([np.zeros(len(data_cls)), np.ones(len(data_pln))])
cv = StratifiedKFold(y, n_folds=6, shuffle=True)
model = joblib.load(source_folder +
"graph_data/sk_models/eigen_ada_pre_%s.plk" % band)
score, perm_scores, pval = permutation_test_score(
model, X, y, cv=cv, n_permutations=5000, n_jobs=1)
result = {"score": score, "perm_scores": perm_scores, "pval": pval}
results_all[band] = result
np.save(source_folder + "graph_data/perm_test_eigen_pre.npy", results_all)
| bsd-3-clause |
shyamalschandra/scikit-learn | examples/linear_model/plot_lasso_lars.py | 18 | 1048 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regularization parameter.
"""
print(__doc__)
# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn import datasets
X, y = datasets.load_diabetes(return_X_y=True)
print("Computing regularization path using the LARS ...")
_, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)
xx = np.sum(np.abs(coefs.T), axis=1)
xx /= xx[-1]
plt.plot(xx, coefs.T)
ymin, ymax = plt.ylim()
plt.vlines(xx, ymin, ymax, linestyle='dashed')
plt.xlabel('|coef| / max|coef|')
plt.ylabel('Coefficients')
plt.title('LASSO Path')
plt.axis('tight')
plt.show()
| bsd-3-clause |
waytai/django | tests/gis_tests/test_geoip2.py | 21 | 5666 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os
import unittest
from unittest import skipUnless
from django.conf import settings
from django.contrib.gis.geoip2 import HAS_GEOIP2
from django.contrib.gis.geos import HAS_GEOS, GEOSGeometry
from django.utils import six
if HAS_GEOIP2:
from django.contrib.gis.geoip2 import GeoIP2, GeoIP2Exception
# Note: Requires both the GeoIP country and city datasets.
# The GEOIP_DATA path should be the only setting set (the directory
# should contain links or the actual database files 'GeoLite2-City.mmdb' and
# 'GeoLite2-City.mmdb'.
@skipUnless(HAS_GEOIP2 and getattr(settings, "GEOIP_PATH", None),
"GeoIP is required along with the GEOIP_PATH setting.")
class GeoIPTest(unittest.TestCase):
addr = '128.249.1.1'
fqdn = 'tmc.edu'
def test01_init(self):
"GeoIP initialization."
g1 = GeoIP2() # Everything inferred from GeoIP path
path = settings.GEOIP_PATH
g2 = GeoIP2(path, 0) # Passing in data path explicitly.
g3 = GeoIP2.open(path, 0) # MaxMind Python API syntax.
for g in (g1, g2, g3):
self.assertTrue(g._country)
self.assertTrue(g._city)
# Only passing in the location of one database.
city = os.path.join(path, 'GeoLite2-City.mmdb')
cntry = os.path.join(path, 'GeoLite2-Country.mmdb')
g4 = GeoIP2(city, country='')
self.assertIsNone(g4._country)
g5 = GeoIP2(cntry, city='')
self.assertIsNone(g5._city)
# Improper parameters.
bad_params = (23, 'foo', 15.23)
for bad in bad_params:
self.assertRaises(GeoIP2Exception, GeoIP2, cache=bad)
if isinstance(bad, six.string_types):
e = GeoIP2Exception
else:
e = TypeError
self.assertRaises(e, GeoIP2, bad, 0)
def test02_bad_query(self):
"GeoIP query parameter checking."
cntry_g = GeoIP2(city='<foo>')
# No city database available, these calls should fail.
self.assertRaises(GeoIP2Exception, cntry_g.city, 'tmc.edu')
self.assertRaises(GeoIP2Exception, cntry_g.coords, 'tmc.edu')
# Non-string query should raise TypeError
self.assertRaises(TypeError, cntry_g.country_code, 17)
self.assertRaises(TypeError, cntry_g.country_name, GeoIP2)
def test03_country(self):
"GeoIP country querying methods."
g = GeoIP2(city='<foo>')
for query in (self.fqdn, self.addr):
self.assertEqual(
'US',
g.country_code(query),
'Failed for func country_code and query %s' % query
)
self.assertEqual(
'United States',
g.country_name(query),
'Failed for func country_name and query %s' % query
)
self.assertEqual(
{'country_code': 'US', 'country_name': 'United States'},
g.country(query)
)
@skipUnless(HAS_GEOS, "Geos is required")
def test04_city(self):
"GeoIP city querying methods."
g = GeoIP2(country='<foo>')
for query in (self.fqdn, self.addr):
# Country queries should still work.
self.assertEqual(
'US',
g.country_code(query),
'Failed for func country_code and query %s' % query
)
self.assertEqual(
'United States',
g.country_name(query),
'Failed for func country_name and query %s' % query
)
self.assertEqual(
{'country_code': 'US', 'country_name': 'United States'},
g.country(query)
)
# City information dictionary.
d = g.city(query)
self.assertEqual('US', d['country_code'])
self.assertEqual('Houston', d['city'])
self.assertEqual('TX', d['region'])
geom = g.geos(query)
self.assertIsInstance(geom, GEOSGeometry)
lon, lat = (-95.4010, 29.7079)
lat_lon = g.lat_lon(query)
lat_lon = (lat_lon[1], lat_lon[0])
for tup in (geom.tuple, g.coords(query), g.lon_lat(query), lat_lon):
self.assertAlmostEqual(lon, tup[0], 4)
self.assertAlmostEqual(lat, tup[1], 4)
def test05_unicode_response(self):
"GeoIP strings should be properly encoded (#16553)."
g = GeoIP2()
d = g.city("duesseldorf.de")
self.assertEqual('Düsseldorf', d['city'])
d = g.country('200.26.205.1')
# Some databases have only unaccented countries
self.assertIn(d['country_name'], ('Curaçao', 'Curacao'))
def test06_ipv6_query(self):
"GeoIP can lookup IPv6 addresses."
g = GeoIP2()
d = g.city('2002:81ed:c9a5::81ed:c9a5') # IPv6 address for www.nhm.ku.edu
self.assertEqual('US', d['country_code'])
self.assertEqual('Lawrence', d['city'])
self.assertEqual('KS', d['region'])
def test_repr(self):
path = settings.GEOIP_PATH
g = GeoIP2(path=path)
meta = g._reader.metadata()
version = '%s.%s' % (meta.binary_format_major_version, meta.binary_format_minor_version)
country_path = g._country_file
city_path = g._city_file
expected = '<GeoIP2 [v%(version)s] _country_file="%(country)s", _city_file="%(city)s">' % {
'version': version,
'country': country_path,
'city': city_path,
}
self.assertEqual(repr(g), expected)
| bsd-3-clause |
shikhardb/scikit-learn | examples/datasets/plot_random_dataset.py | 345 | 2254 | """
==============================================
Plot randomly generated classification dataset
==============================================
Plot several randomly generated 2D classification datasets.
This example illustrates the :func:`datasets.make_classification`
:func:`datasets.make_blobs` and :func:`datasets.make_gaussian_quantiles`
functions.
For ``make_classification``, three binary and two multi-class classification
datasets are generated, with different numbers of informative features and
clusters per class. """
print(__doc__)
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.datasets import make_blobs
from sklearn.datasets import make_gaussian_quantiles
plt.figure(figsize=(8, 8))
plt.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)
plt.subplot(321)
plt.title("One informative feature, one cluster per class", fontsize='small')
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=1,
n_clusters_per_class=1)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
plt.subplot(322)
plt.title("Two informative features, one cluster per class", fontsize='small')
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
n_clusters_per_class=1)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
plt.subplot(323)
plt.title("Two informative features, two clusters per class", fontsize='small')
X2, Y2 = make_classification(n_features=2, n_redundant=0, n_informative=2)
plt.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2)
plt.subplot(324)
plt.title("Multi-class, two informative features, one cluster",
fontsize='small')
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
n_clusters_per_class=1, n_classes=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
plt.subplot(325)
plt.title("Three blobs", fontsize='small')
X1, Y1 = make_blobs(n_features=2, centers=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
plt.subplot(326)
plt.title("Gaussian divided into three quantiles", fontsize='small')
X1, Y1 = make_gaussian_quantiles(n_features=2, n_classes=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
plt.show()
| bsd-3-clause |
anilmuthineni/tensorflow | tensorflow/examples/learn/iris_custom_model.py | 11 | 2621 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Example of Estimator for Iris plant dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import cross_validation
from sklearn import datasets
from sklearn import metrics
import tensorflow as tf
layers = tf.contrib.layers
learn = tf.contrib.learn
def my_model(features, target):
"""DNN with three hidden layers, and dropout of 0.1 probability."""
# Convert the target to a one-hot tensor of shape (length of features, 3) and
# with a on-value of 1 for each one-hot vector of length 3.
target = tf.one_hot(target, 3, 1, 0)
# Create three fully connected layers respectively of size 10, 20, and 10 with
# each layer having a dropout probability of 0.1.
normalizer_fn = layers.dropout
normalizer_params = {'keep_prob': 0.9}
features = layers.stack(
features,
layers.fully_connected, [10, 20, 10],
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params)
# Compute logits (1 per class) and compute loss.
logits = layers.fully_connected(features, 3, activation_fn=None)
loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
# Create a tensor for training op.
train_op = tf.contrib.layers.optimize_loss(
loss,
tf.contrib.framework.get_global_step(),
optimizer='Adagrad',
learning_rate=0.1)
return ({
'class': tf.argmax(logits, 1),
'prob': tf.nn.softmax(logits)
}, loss, train_op)
def main(unused_argv):
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
classifier = learn.Estimator(model_fn=my_model)
classifier.fit(x_train, y_train, steps=1000)
y_predicted = [
p['class'] for p in classifier.predict(
x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
if __name__ == '__main__':
tf.app.run()
| apache-2.0 |
wdurhamh/statsmodels | statsmodels/datasets/modechoice/data.py | 25 | 3031 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
"""Travel Mode Choice"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """This is public domain."""
TITLE = __doc__
SOURCE = """
Greene, W.H. and D. Hensher (1997) Multinomial logit and discrete choice models
in Greene, W. H. (1997) LIMDEP version 7.0 user's manual revised, Plainview,
New York econometric software, Inc.
Download from on-line complements to Greene, W.H. (2011) Econometric Analysis,
Prentice Hall, 7th Edition (data table F18-2)
http://people.stern.nyu.edu/wgreene/Text/Edition7/TableF18-2.csv
"""
DESCRSHORT = """Data used to study travel mode choice between Australian cities
"""
DESCRLONG = """The data, collected as part of a 1987 intercity mode choice
study, are a sub-sample of 210 non-business trips between Sydney, Canberra and
Melbourne in which the traveler chooses a mode from four alternatives (plane,
car, bus and train). The sample, 840 observations, is choice based with
over-sampling of the less popular modes (plane, train and bus) and under-sampling
of the more popular mode, car. The level of service data was derived from highway
and transport networks in Sydney, Melbourne, non-metropolitan N.S.W. and Victoria,
including the Australian Capital Territory."""
NOTE = """::
Number of observations: 840 Observations On 4 Modes for 210 Individuals.
Number of variables: 8
Variable name definitions::
individual = 1 to 210
mode =
1 - air
2 - train
3 - bus
4 - car
choice =
0 - no
1 - yes
ttme = terminal waiting time for plane, train and bus (minutes); 0
for car.
invc = in vehicle cost for all stages (dollars).
invt = travel time (in-vehicle time) for all stages (minutes).
gc = generalized cost measure:invc+(invt*value of travel time savings)
(dollars).
hinc = household income ($1000s).
psize = traveling group size in mode chosen (number)."""
import numpy as np
from statsmodels.datasets import utils as du
from os.path import dirname, abspath
def load():
"""
Load the data modechoice data and return a Dataset class instance.
Returns
-------
Dataset instance:
See DATASET_PROPOSAL.txt for more information.
"""
data = _get_data()
return du.process_recarray(data, endog_idx=2, exog_idx=[3,4,5,6,7,8],
dtype=float)
def load_pandas():
"""
Load the data modechoice data and return a Dataset class instance.
Returns
-------
Dataset instance:
See DATASET_PROPOSAL.txt for more information.
"""
data = _get_data()
return du.process_recarray_pandas(data, endog_idx = 2, exog_idx=[3,4,5,6,7,8],
dtype=float)
def _get_data():
filepath = dirname(abspath(__file__))
data = np.recfromtxt(open(filepath + '/modechoice.csv', 'rb'),
delimiter=";", names = True, dtype=float)
return data
| bsd-3-clause |
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