1424 lines
56 KiB
Python
1424 lines
56 KiB
Python
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"""Test the validation module"""
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from __future__ import division
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import sys
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import warnings
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import tempfile
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import os
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from time import sleep
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import numpy as np
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from scipy.sparse import coo_matrix, csr_matrix
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_false
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_raise_message
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from sklearn.utils.testing import assert_warns
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from sklearn.utils.testing import assert_warns_message
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from sklearn.utils.testing import assert_no_warnings
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from sklearn.utils.testing import assert_raises_regex
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_less
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.mocking import CheckingClassifier, MockDataFrame
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from sklearn.model_selection import cross_val_score
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from sklearn.model_selection import cross_val_predict
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from sklearn.model_selection import cross_validate
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from sklearn.model_selection import permutation_test_score
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from sklearn.model_selection import KFold
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from sklearn.model_selection import StratifiedKFold
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from sklearn.model_selection import LeaveOneOut
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from sklearn.model_selection import LeaveOneGroupOut
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from sklearn.model_selection import LeavePGroupsOut
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from sklearn.model_selection import GroupKFold
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from sklearn.model_selection import GroupShuffleSplit
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from sklearn.model_selection import learning_curve
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from sklearn.model_selection import validation_curve
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from sklearn.model_selection._validation import _check_is_permutation
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from sklearn.datasets import make_regression
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from sklearn.datasets import load_boston
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from sklearn.datasets import load_iris
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from sklearn.datasets import load_digits
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from sklearn.metrics import explained_variance_score
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from sklearn.metrics import make_scorer
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import precision_recall_fscore_support
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from sklearn.metrics import precision_score
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from sklearn.metrics import r2_score
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from sklearn.metrics.scorer import check_scoring
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from sklearn.linear_model import Ridge, LogisticRegression, SGDClassifier
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from sklearn.linear_model import PassiveAggressiveClassifier, RidgeClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.svm import SVC
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from sklearn.cluster import KMeans
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from sklearn.preprocessing import Imputer
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from sklearn.preprocessing import LabelEncoder
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from sklearn.pipeline import Pipeline
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from sklearn.externals.six.moves import cStringIO as StringIO
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from sklearn.base import BaseEstimator
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from sklearn.base import clone
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.utils import shuffle
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from sklearn.datasets import make_classification
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from sklearn.datasets import make_multilabel_classification
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from sklearn.model_selection.tests.common import OneTimeSplitter
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from sklearn.model_selection import GridSearchCV
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try:
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WindowsError
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except NameError:
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WindowsError = None
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class MockImprovingEstimator(BaseEstimator):
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"""Dummy classifier to test the learning curve"""
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def __init__(self, n_max_train_sizes):
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self.n_max_train_sizes = n_max_train_sizes
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self.train_sizes = 0
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self.X_subset = None
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def fit(self, X_subset, y_subset=None):
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self.X_subset = X_subset
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self.train_sizes = X_subset.shape[0]
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return self
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def predict(self, X):
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raise NotImplementedError
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def score(self, X=None, Y=None):
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# training score becomes worse (2 -> 1), test error better (0 -> 1)
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if self._is_training_data(X):
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return 2. - float(self.train_sizes) / self.n_max_train_sizes
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else:
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return float(self.train_sizes) / self.n_max_train_sizes
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def _is_training_data(self, X):
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return X is self.X_subset
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class MockIncrementalImprovingEstimator(MockImprovingEstimator):
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"""Dummy classifier that provides partial_fit"""
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def __init__(self, n_max_train_sizes):
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super(MockIncrementalImprovingEstimator,
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self).__init__(n_max_train_sizes)
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self.x = None
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def _is_training_data(self, X):
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return self.x in X
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def partial_fit(self, X, y=None, **params):
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self.train_sizes += X.shape[0]
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self.x = X[0]
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class MockEstimatorWithParameter(BaseEstimator):
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"""Dummy classifier to test the validation curve"""
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def __init__(self, param=0.5):
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self.X_subset = None
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self.param = param
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def fit(self, X_subset, y_subset):
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self.X_subset = X_subset
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self.train_sizes = X_subset.shape[0]
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return self
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def predict(self, X):
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raise NotImplementedError
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def score(self, X=None, y=None):
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return self.param if self._is_training_data(X) else 1 - self.param
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def _is_training_data(self, X):
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return X is self.X_subset
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class MockEstimatorWithSingleFitCallAllowed(MockEstimatorWithParameter):
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"""Dummy classifier that disallows repeated calls of fit method"""
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def fit(self, X_subset, y_subset):
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assert_false(
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hasattr(self, 'fit_called_'),
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'fit is called the second time'
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)
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self.fit_called_ = True
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return super(type(self), self).fit(X_subset, y_subset)
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def predict(self, X):
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raise NotImplementedError
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class MockClassifier(object):
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"""Dummy classifier to test the cross-validation"""
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def __init__(self, a=0, allow_nd=False):
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self.a = a
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self.allow_nd = allow_nd
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def fit(self, X, Y=None, sample_weight=None, class_prior=None,
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sparse_sample_weight=None, sparse_param=None, dummy_int=None,
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dummy_str=None, dummy_obj=None, callback=None):
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"""The dummy arguments are to test that this fit function can
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accept non-array arguments through cross-validation, such as:
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- int
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- str (this is actually array-like)
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- object
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- function
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"""
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self.dummy_int = dummy_int
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self.dummy_str = dummy_str
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self.dummy_obj = dummy_obj
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if callback is not None:
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callback(self)
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if self.allow_nd:
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X = X.reshape(len(X), -1)
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if X.ndim >= 3 and not self.allow_nd:
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raise ValueError('X cannot be d')
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if sample_weight is not None:
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assert_true(sample_weight.shape[0] == X.shape[0],
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'MockClassifier extra fit_param sample_weight.shape[0]'
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' is {0}, should be {1}'.format(sample_weight.shape[0],
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X.shape[0]))
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if class_prior is not None:
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assert_true(class_prior.shape[0] == len(np.unique(y)),
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'MockClassifier extra fit_param class_prior.shape[0]'
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' is {0}, should be {1}'.format(class_prior.shape[0],
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len(np.unique(y))))
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if sparse_sample_weight is not None:
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fmt = ('MockClassifier extra fit_param sparse_sample_weight'
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'.shape[0] is {0}, should be {1}')
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assert_true(sparse_sample_weight.shape[0] == X.shape[0],
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fmt.format(sparse_sample_weight.shape[0], X.shape[0]))
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if sparse_param is not None:
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fmt = ('MockClassifier extra fit_param sparse_param.shape '
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'is ({0}, {1}), should be ({2}, {3})')
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assert_true(sparse_param.shape == P_sparse.shape,
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fmt.format(sparse_param.shape[0],
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sparse_param.shape[1],
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P_sparse.shape[0], P_sparse.shape[1]))
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return self
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def predict(self, T):
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if self.allow_nd:
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T = T.reshape(len(T), -1)
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return T[:, 0]
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def score(self, X=None, Y=None):
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return 1. / (1 + np.abs(self.a))
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def get_params(self, deep=False):
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return {'a': self.a, 'allow_nd': self.allow_nd}
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# XXX: use 2D array, since 1D X is being detected as a single sample in
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# check_consistent_length
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X = np.ones((10, 2))
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X_sparse = coo_matrix(X)
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y = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
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# The number of samples per class needs to be > n_splits,
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# for StratifiedKFold(n_splits=3)
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y2 = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 3])
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P_sparse = coo_matrix(np.eye(5))
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def test_cross_val_score():
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clf = MockClassifier()
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for a in range(-10, 10):
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clf.a = a
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# Smoke test
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scores = cross_val_score(clf, X, y2)
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assert_array_equal(scores, clf.score(X, y2))
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# test with multioutput y
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multioutput_y = np.column_stack([y2, y2[::-1]])
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scores = cross_val_score(clf, X_sparse, multioutput_y)
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assert_array_equal(scores, clf.score(X_sparse, multioutput_y))
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scores = cross_val_score(clf, X_sparse, y2)
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assert_array_equal(scores, clf.score(X_sparse, y2))
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# test with multioutput y
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scores = cross_val_score(clf, X_sparse, multioutput_y)
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assert_array_equal(scores, clf.score(X_sparse, multioutput_y))
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# test with X and y as list
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list_check = lambda x: isinstance(x, list)
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clf = CheckingClassifier(check_X=list_check)
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scores = cross_val_score(clf, X.tolist(), y2.tolist())
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clf = CheckingClassifier(check_y=list_check)
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scores = cross_val_score(clf, X, y2.tolist())
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assert_raises(ValueError, cross_val_score, clf, X, y2, scoring="sklearn")
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# test with 3d X and
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X_3d = X[:, :, np.newaxis]
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clf = MockClassifier(allow_nd=True)
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scores = cross_val_score(clf, X_3d, y2)
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clf = MockClassifier(allow_nd=False)
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assert_raises(ValueError, cross_val_score, clf, X_3d, y2)
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def test_cross_validate_invalid_scoring_param():
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X, y = make_classification(random_state=0)
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estimator = MockClassifier()
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# Test the errors
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error_message_regexp = ".*must be unique strings.*"
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# List/tuple of callables should raise a message advising users to use
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# dict of names to callables mapping
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assert_raises_regex(ValueError, error_message_regexp,
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cross_validate, estimator, X, y,
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scoring=(make_scorer(precision_score),
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make_scorer(accuracy_score)))
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assert_raises_regex(ValueError, error_message_regexp,
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cross_validate, estimator, X, y,
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scoring=(make_scorer(precision_score),))
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# So should empty lists/tuples
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assert_raises_regex(ValueError, error_message_regexp + "Empty list.*",
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cross_validate, estimator, X, y, scoring=())
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# So should duplicated entries
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assert_raises_regex(ValueError, error_message_regexp + "Duplicate.*",
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cross_validate, estimator, X, y,
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scoring=('f1_micro', 'f1_micro'))
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# Nested Lists should raise a generic error message
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assert_raises_regex(ValueError, error_message_regexp,
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cross_validate, estimator, X, y,
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scoring=[[make_scorer(precision_score)]])
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error_message_regexp = (".*should either be.*string or callable.*for "
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"single.*.*dict.*for multi.*")
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# Empty dict should raise invalid scoring error
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assert_raises_regex(ValueError, "An empty dict",
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cross_validate, estimator, X, y, scoring=(dict()))
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# And so should any other invalid entry
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assert_raises_regex(ValueError, error_message_regexp,
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cross_validate, estimator, X, y, scoring=5)
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multiclass_scorer = make_scorer(precision_recall_fscore_support)
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# Multiclass Scorers that return multiple values are not supported yet
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assert_raises_regex(ValueError,
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"Classification metrics can't handle a mix of "
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"binary and continuous targets",
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cross_validate, estimator, X, y,
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scoring=multiclass_scorer)
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assert_raises_regex(ValueError,
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"Classification metrics can't handle a mix of "
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"binary and continuous targets",
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cross_validate, estimator, X, y,
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scoring={"foo": multiclass_scorer})
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multivalued_scorer = make_scorer(confusion_matrix)
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# Multiclass Scorers that return multiple values are not supported yet
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assert_raises_regex(ValueError, "scoring must return a number, got",
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cross_validate, SVC(), X, y,
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scoring=multivalued_scorer)
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assert_raises_regex(ValueError, "scoring must return a number, got",
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cross_validate, SVC(), X, y,
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scoring={"foo": multivalued_scorer})
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assert_raises_regex(ValueError, "'mse' is not a valid scoring value.",
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cross_validate, SVC(), X, y, scoring="mse")
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def test_cross_validate():
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# Compute train and test mse/r2 scores
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cv = KFold(n_splits=5)
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# Regression
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X_reg, y_reg = make_regression(n_samples=30, random_state=0)
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|
|
reg = Ridge(random_state=0)
|
||
|
|
|
||
|
|
# Classification
|
||
|
|
X_clf, y_clf = make_classification(n_samples=30, random_state=0)
|
||
|
|
clf = SVC(kernel="linear", random_state=0)
|
||
|
|
|
||
|
|
for X, y, est in ((X_reg, y_reg, reg), (X_clf, y_clf, clf)):
|
||
|
|
# It's okay to evaluate regression metrics on classification too
|
||
|
|
mse_scorer = check_scoring(est, 'neg_mean_squared_error')
|
||
|
|
r2_scorer = check_scoring(est, 'r2')
|
||
|
|
train_mse_scores = []
|
||
|
|
test_mse_scores = []
|
||
|
|
train_r2_scores = []
|
||
|
|
test_r2_scores = []
|
||
|
|
for train, test in cv.split(X, y):
|
||
|
|
est = clone(reg).fit(X[train], y[train])
|
||
|
|
train_mse_scores.append(mse_scorer(est, X[train], y[train]))
|
||
|
|
train_r2_scores.append(r2_scorer(est, X[train], y[train]))
|
||
|
|
test_mse_scores.append(mse_scorer(est, X[test], y[test]))
|
||
|
|
test_r2_scores.append(r2_scorer(est, X[test], y[test]))
|
||
|
|
|
||
|
|
train_mse_scores = np.array(train_mse_scores)
|
||
|
|
test_mse_scores = np.array(test_mse_scores)
|
||
|
|
train_r2_scores = np.array(train_r2_scores)
|
||
|
|
test_r2_scores = np.array(test_r2_scores)
|
||
|
|
|
||
|
|
scores = (train_mse_scores, test_mse_scores, train_r2_scores,
|
||
|
|
test_r2_scores)
|
||
|
|
|
||
|
|
yield check_cross_validate_single_metric, est, X, y, scores
|
||
|
|
yield check_cross_validate_multi_metric, est, X, y, scores
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_validate_return_train_score_warn():
|
||
|
|
# Test that warnings are raised. Will be removed in 0.21
|
||
|
|
|
||
|
|
X, y = make_classification(random_state=0)
|
||
|
|
estimator = MockClassifier()
|
||
|
|
|
||
|
|
result = {}
|
||
|
|
for val in [False, True, 'warn']:
|
||
|
|
result[val] = assert_no_warnings(cross_validate, estimator, X, y,
|
||
|
|
return_train_score=val)
|
||
|
|
|
||
|
|
msg = (
|
||
|
|
'You are accessing a training score ({!r}), '
|
||
|
|
'which will not be available by default '
|
||
|
|
'any more in 0.21. If you need training scores, '
|
||
|
|
'please set return_train_score=True').format('train_score')
|
||
|
|
train_score = assert_warns_message(FutureWarning, msg,
|
||
|
|
result['warn'].get, 'train_score')
|
||
|
|
assert np.allclose(train_score, result[True]['train_score'])
|
||
|
|
assert 'train_score' not in result[False]
|
||
|
|
|
||
|
|
|
||
|
|
def check_cross_validate_single_metric(clf, X, y, scores):
|
||
|
|
(train_mse_scores, test_mse_scores, train_r2_scores,
|
||
|
|
test_r2_scores) = scores
|
||
|
|
# Test single metric evaluation when scoring is string or singleton list
|
||
|
|
for (return_train_score, dict_len) in ((True, 4), (False, 3)):
|
||
|
|
# Single metric passed as a string
|
||
|
|
if return_train_score:
|
||
|
|
# It must be True by default
|
||
|
|
mse_scores_dict = cross_validate(clf, X, y, cv=5,
|
||
|
|
scoring='neg_mean_squared_error')
|
||
|
|
assert_array_almost_equal(mse_scores_dict['train_score'],
|
||
|
|
train_mse_scores)
|
||
|
|
else:
|
||
|
|
mse_scores_dict = cross_validate(clf, X, y, cv=5,
|
||
|
|
scoring='neg_mean_squared_error',
|
||
|
|
return_train_score=False)
|
||
|
|
assert_true(isinstance(mse_scores_dict, dict))
|
||
|
|
assert_equal(len(mse_scores_dict), dict_len)
|
||
|
|
assert_array_almost_equal(mse_scores_dict['test_score'],
|
||
|
|
test_mse_scores)
|
||
|
|
|
||
|
|
# Single metric passed as a list
|
||
|
|
if return_train_score:
|
||
|
|
# It must be True by default
|
||
|
|
r2_scores_dict = cross_validate(clf, X, y, cv=5, scoring=['r2'])
|
||
|
|
assert_array_almost_equal(r2_scores_dict['train_r2'],
|
||
|
|
train_r2_scores)
|
||
|
|
else:
|
||
|
|
r2_scores_dict = cross_validate(clf, X, y, cv=5, scoring=['r2'],
|
||
|
|
return_train_score=False)
|
||
|
|
assert_true(isinstance(r2_scores_dict, dict))
|
||
|
|
assert_equal(len(r2_scores_dict), dict_len)
|
||
|
|
assert_array_almost_equal(r2_scores_dict['test_r2'], test_r2_scores)
|
||
|
|
|
||
|
|
|
||
|
|
def check_cross_validate_multi_metric(clf, X, y, scores):
|
||
|
|
# Test multimetric evaluation when scoring is a list / dict
|
||
|
|
(train_mse_scores, test_mse_scores, train_r2_scores,
|
||
|
|
test_r2_scores) = scores
|
||
|
|
all_scoring = (('r2', 'neg_mean_squared_error'),
|
||
|
|
{'r2': make_scorer(r2_score),
|
||
|
|
'neg_mean_squared_error': 'neg_mean_squared_error'})
|
||
|
|
|
||
|
|
keys_sans_train = set(('test_r2', 'test_neg_mean_squared_error',
|
||
|
|
'fit_time', 'score_time'))
|
||
|
|
keys_with_train = keys_sans_train.union(
|
||
|
|
set(('train_r2', 'train_neg_mean_squared_error')))
|
||
|
|
|
||
|
|
for return_train_score in (True, False):
|
||
|
|
for scoring in all_scoring:
|
||
|
|
if return_train_score:
|
||
|
|
# return_train_score must be True by default
|
||
|
|
cv_results = cross_validate(clf, X, y, cv=5, scoring=scoring)
|
||
|
|
assert_array_almost_equal(cv_results['train_r2'],
|
||
|
|
train_r2_scores)
|
||
|
|
assert_array_almost_equal(
|
||
|
|
cv_results['train_neg_mean_squared_error'],
|
||
|
|
train_mse_scores)
|
||
|
|
else:
|
||
|
|
cv_results = cross_validate(clf, X, y, cv=5, scoring=scoring,
|
||
|
|
return_train_score=False)
|
||
|
|
assert_true(isinstance(cv_results, dict))
|
||
|
|
assert_equal(set(cv_results.keys()),
|
||
|
|
keys_with_train if return_train_score
|
||
|
|
else keys_sans_train)
|
||
|
|
assert_array_almost_equal(cv_results['test_r2'], test_r2_scores)
|
||
|
|
assert_array_almost_equal(
|
||
|
|
cv_results['test_neg_mean_squared_error'], test_mse_scores)
|
||
|
|
|
||
|
|
# Make sure all the arrays are of np.ndarray type
|
||
|
|
assert type(cv_results['test_r2']) == np.ndarray
|
||
|
|
assert (type(cv_results['test_neg_mean_squared_error']) ==
|
||
|
|
np.ndarray)
|
||
|
|
assert type(cv_results['fit_time']) == np.ndarray
|
||
|
|
assert type(cv_results['score_time']) == np.ndarray
|
||
|
|
|
||
|
|
# Ensure all the times are within sane limits
|
||
|
|
assert np.all(cv_results['fit_time'] >= 0)
|
||
|
|
assert np.all(cv_results['fit_time'] < 10)
|
||
|
|
assert np.all(cv_results['score_time'] >= 0)
|
||
|
|
assert np.all(cv_results['score_time'] < 10)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_predict_groups():
|
||
|
|
# Check if ValueError (when groups is None) propagates to cross_val_score
|
||
|
|
# and cross_val_predict
|
||
|
|
# And also check if groups is correctly passed to the cv object
|
||
|
|
X, y = make_classification(n_samples=20, n_classes=2, random_state=0)
|
||
|
|
|
||
|
|
clf = SVC(kernel="linear")
|
||
|
|
|
||
|
|
group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(),
|
||
|
|
GroupShuffleSplit()]
|
||
|
|
for cv in group_cvs:
|
||
|
|
assert_raise_message(ValueError,
|
||
|
|
"The 'groups' parameter should not be None.",
|
||
|
|
cross_val_score, estimator=clf, X=X, y=y, cv=cv)
|
||
|
|
assert_raise_message(ValueError,
|
||
|
|
"The 'groups' parameter should not be None.",
|
||
|
|
cross_val_predict, estimator=clf, X=X, y=y, cv=cv)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_pandas():
|
||
|
|
# check cross_val_score doesn't destroy pandas dataframe
|
||
|
|
types = [(MockDataFrame, MockDataFrame)]
|
||
|
|
try:
|
||
|
|
from pandas import Series, DataFrame
|
||
|
|
types.append((Series, DataFrame))
|
||
|
|
except ImportError:
|
||
|
|
pass
|
||
|
|
for TargetType, InputFeatureType in types:
|
||
|
|
# X dataframe, y series
|
||
|
|
# 3 fold cross val is used so we need atleast 3 samples per class
|
||
|
|
X_df, y_ser = InputFeatureType(X), TargetType(y2)
|
||
|
|
check_df = lambda x: isinstance(x, InputFeatureType)
|
||
|
|
check_series = lambda x: isinstance(x, TargetType)
|
||
|
|
clf = CheckingClassifier(check_X=check_df, check_y=check_series)
|
||
|
|
cross_val_score(clf, X_df, y_ser)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_mask():
|
||
|
|
# test that cross_val_score works with boolean masks
|
||
|
|
svm = SVC(kernel="linear")
|
||
|
|
iris = load_iris()
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
kfold = KFold(5)
|
||
|
|
scores_indices = cross_val_score(svm, X, y, cv=kfold)
|
||
|
|
kfold = KFold(5)
|
||
|
|
cv_masks = []
|
||
|
|
for train, test in kfold.split(X, y):
|
||
|
|
mask_train = np.zeros(len(y), dtype=np.bool)
|
||
|
|
mask_test = np.zeros(len(y), dtype=np.bool)
|
||
|
|
mask_train[train] = 1
|
||
|
|
mask_test[test] = 1
|
||
|
|
cv_masks.append((train, test))
|
||
|
|
scores_masks = cross_val_score(svm, X, y, cv=cv_masks)
|
||
|
|
assert_array_equal(scores_indices, scores_masks)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_precomputed():
|
||
|
|
# test for svm with precomputed kernel
|
||
|
|
svm = SVC(kernel="precomputed")
|
||
|
|
iris = load_iris()
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
linear_kernel = np.dot(X, X.T)
|
||
|
|
score_precomputed = cross_val_score(svm, linear_kernel, y)
|
||
|
|
svm = SVC(kernel="linear")
|
||
|
|
score_linear = cross_val_score(svm, X, y)
|
||
|
|
assert_array_almost_equal(score_precomputed, score_linear)
|
||
|
|
|
||
|
|
# test with callable
|
||
|
|
svm = SVC(kernel=lambda x, y: np.dot(x, y.T))
|
||
|
|
score_callable = cross_val_score(svm, X, y)
|
||
|
|
assert_array_almost_equal(score_precomputed, score_callable)
|
||
|
|
|
||
|
|
# Error raised for non-square X
|
||
|
|
svm = SVC(kernel="precomputed")
|
||
|
|
assert_raises(ValueError, cross_val_score, svm, X, y)
|
||
|
|
|
||
|
|
# test error is raised when the precomputed kernel is not array-like
|
||
|
|
# or sparse
|
||
|
|
assert_raises(ValueError, cross_val_score, svm,
|
||
|
|
linear_kernel.tolist(), y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_fit_params():
|
||
|
|
clf = MockClassifier()
|
||
|
|
n_samples = X.shape[0]
|
||
|
|
n_classes = len(np.unique(y))
|
||
|
|
|
||
|
|
W_sparse = coo_matrix((np.array([1]), (np.array([1]), np.array([0]))),
|
||
|
|
shape=(10, 1))
|
||
|
|
P_sparse = coo_matrix(np.eye(5))
|
||
|
|
|
||
|
|
DUMMY_INT = 42
|
||
|
|
DUMMY_STR = '42'
|
||
|
|
DUMMY_OBJ = object()
|
||
|
|
|
||
|
|
def assert_fit_params(clf):
|
||
|
|
# Function to test that the values are passed correctly to the
|
||
|
|
# classifier arguments for non-array type
|
||
|
|
|
||
|
|
assert_equal(clf.dummy_int, DUMMY_INT)
|
||
|
|
assert_equal(clf.dummy_str, DUMMY_STR)
|
||
|
|
assert_equal(clf.dummy_obj, DUMMY_OBJ)
|
||
|
|
|
||
|
|
fit_params = {'sample_weight': np.ones(n_samples),
|
||
|
|
'class_prior': np.ones(n_classes) / n_classes,
|
||
|
|
'sparse_sample_weight': W_sparse,
|
||
|
|
'sparse_param': P_sparse,
|
||
|
|
'dummy_int': DUMMY_INT,
|
||
|
|
'dummy_str': DUMMY_STR,
|
||
|
|
'dummy_obj': DUMMY_OBJ,
|
||
|
|
'callback': assert_fit_params}
|
||
|
|
cross_val_score(clf, X, y, fit_params=fit_params)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_score_func():
|
||
|
|
clf = MockClassifier()
|
||
|
|
_score_func_args = []
|
||
|
|
|
||
|
|
def score_func(y_test, y_predict):
|
||
|
|
_score_func_args.append((y_test, y_predict))
|
||
|
|
return 1.0
|
||
|
|
|
||
|
|
with warnings.catch_warnings(record=True):
|
||
|
|
scoring = make_scorer(score_func)
|
||
|
|
score = cross_val_score(clf, X, y, scoring=scoring, cv=3)
|
||
|
|
assert_array_equal(score, [1.0, 1.0, 1.0])
|
||
|
|
# Test that score function is called only 3 times (for cv=3)
|
||
|
|
assert len(_score_func_args) == 3
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_errors():
|
||
|
|
class BrokenEstimator:
|
||
|
|
pass
|
||
|
|
|
||
|
|
assert_raises(TypeError, cross_val_score, BrokenEstimator(), X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_with_score_func_classification():
|
||
|
|
iris = load_iris()
|
||
|
|
clf = SVC(kernel='linear')
|
||
|
|
|
||
|
|
# Default score (should be the accuracy score)
|
||
|
|
scores = cross_val_score(clf, iris.data, iris.target, cv=5)
|
||
|
|
assert_array_almost_equal(scores, [0.97, 1., 0.97, 0.97, 1.], 2)
|
||
|
|
|
||
|
|
# Correct classification score (aka. zero / one score) - should be the
|
||
|
|
# same as the default estimator score
|
||
|
|
zo_scores = cross_val_score(clf, iris.data, iris.target,
|
||
|
|
scoring="accuracy", cv=5)
|
||
|
|
assert_array_almost_equal(zo_scores, [0.97, 1., 0.97, 0.97, 1.], 2)
|
||
|
|
|
||
|
|
# F1 score (class are balanced so f1_score should be equal to zero/one
|
||
|
|
# score
|
||
|
|
f1_scores = cross_val_score(clf, iris.data, iris.target,
|
||
|
|
scoring="f1_weighted", cv=5)
|
||
|
|
assert_array_almost_equal(f1_scores, [0.97, 1., 0.97, 0.97, 1.], 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_with_score_func_regression():
|
||
|
|
X, y = make_regression(n_samples=30, n_features=20, n_informative=5,
|
||
|
|
random_state=0)
|
||
|
|
reg = Ridge()
|
||
|
|
|
||
|
|
# Default score of the Ridge regression estimator
|
||
|
|
scores = cross_val_score(reg, X, y, cv=5)
|
||
|
|
assert_array_almost_equal(scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
|
||
|
|
|
||
|
|
# R2 score (aka. determination coefficient) - should be the
|
||
|
|
# same as the default estimator score
|
||
|
|
r2_scores = cross_val_score(reg, X, y, scoring="r2", cv=5)
|
||
|
|
assert_array_almost_equal(r2_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
|
||
|
|
|
||
|
|
# Mean squared error; this is a loss function, so "scores" are negative
|
||
|
|
neg_mse_scores = cross_val_score(reg, X, y, cv=5,
|
||
|
|
scoring="neg_mean_squared_error")
|
||
|
|
expected_neg_mse = np.array([-763.07, -553.16, -274.38, -273.26, -1681.99])
|
||
|
|
assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)
|
||
|
|
|
||
|
|
# Explained variance
|
||
|
|
scoring = make_scorer(explained_variance_score)
|
||
|
|
ev_scores = cross_val_score(reg, X, y, cv=5, scoring=scoring)
|
||
|
|
assert_array_almost_equal(ev_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_permutation_score():
|
||
|
|
iris = load_iris()
|
||
|
|
X = iris.data
|
||
|
|
X_sparse = coo_matrix(X)
|
||
|
|
y = iris.target
|
||
|
|
svm = SVC(kernel='linear')
|
||
|
|
cv = StratifiedKFold(2)
|
||
|
|
|
||
|
|
score, scores, pvalue = permutation_test_score(
|
||
|
|
svm, X, y, n_permutations=30, cv=cv, scoring="accuracy")
|
||
|
|
assert_greater(score, 0.9)
|
||
|
|
assert_almost_equal(pvalue, 0.0, 1)
|
||
|
|
|
||
|
|
score_group, _, pvalue_group = permutation_test_score(
|
||
|
|
svm, X, y, n_permutations=30, cv=cv, scoring="accuracy",
|
||
|
|
groups=np.ones(y.size), random_state=0)
|
||
|
|
assert_true(score_group == score)
|
||
|
|
assert_true(pvalue_group == pvalue)
|
||
|
|
|
||
|
|
# check that we obtain the same results with a sparse representation
|
||
|
|
svm_sparse = SVC(kernel='linear')
|
||
|
|
cv_sparse = StratifiedKFold(2)
|
||
|
|
score_group, _, pvalue_group = permutation_test_score(
|
||
|
|
svm_sparse, X_sparse, y, n_permutations=30, cv=cv_sparse,
|
||
|
|
scoring="accuracy", groups=np.ones(y.size), random_state=0)
|
||
|
|
|
||
|
|
assert_true(score_group == score)
|
||
|
|
assert_true(pvalue_group == pvalue)
|
||
|
|
|
||
|
|
# test with custom scoring object
|
||
|
|
def custom_score(y_true, y_pred):
|
||
|
|
return (((y_true == y_pred).sum() - (y_true != y_pred).sum()) /
|
||
|
|
y_true.shape[0])
|
||
|
|
|
||
|
|
scorer = make_scorer(custom_score)
|
||
|
|
score, _, pvalue = permutation_test_score(
|
||
|
|
svm, X, y, n_permutations=100, scoring=scorer, cv=cv, random_state=0)
|
||
|
|
assert_almost_equal(score, .93, 2)
|
||
|
|
assert_almost_equal(pvalue, 0.01, 3)
|
||
|
|
|
||
|
|
# set random y
|
||
|
|
y = np.mod(np.arange(len(y)), 3)
|
||
|
|
|
||
|
|
score, scores, pvalue = permutation_test_score(
|
||
|
|
svm, X, y, n_permutations=30, cv=cv, scoring="accuracy")
|
||
|
|
|
||
|
|
assert_less(score, 0.5)
|
||
|
|
assert_greater(pvalue, 0.2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_permutation_test_score_allow_nans():
|
||
|
|
# Check that permutation_test_score allows input data with NaNs
|
||
|
|
X = np.arange(200, dtype=np.float64).reshape(10, -1)
|
||
|
|
X[2, :] = np.nan
|
||
|
|
y = np.repeat([0, 1], X.shape[0] / 2)
|
||
|
|
p = Pipeline([
|
||
|
|
('imputer', Imputer(strategy='mean', missing_values='NaN')),
|
||
|
|
('classifier', MockClassifier()),
|
||
|
|
])
|
||
|
|
permutation_test_score(p, X, y, cv=5)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_allow_nans():
|
||
|
|
# Check that cross_val_score allows input data with NaNs
|
||
|
|
X = np.arange(200, dtype=np.float64).reshape(10, -1)
|
||
|
|
X[2, :] = np.nan
|
||
|
|
y = np.repeat([0, 1], X.shape[0] / 2)
|
||
|
|
p = Pipeline([
|
||
|
|
('imputer', Imputer(strategy='mean', missing_values='NaN')),
|
||
|
|
('classifier', MockClassifier()),
|
||
|
|
])
|
||
|
|
cross_val_score(p, X, y, cv=5)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_multilabel():
|
||
|
|
X = np.array([[-3, 4], [2, 4], [3, 3], [0, 2], [-3, 1],
|
||
|
|
[-2, 1], [0, 0], [-2, -1], [-1, -2], [1, -2]])
|
||
|
|
y = np.array([[1, 1], [0, 1], [0, 1], [0, 1], [1, 1],
|
||
|
|
[0, 1], [1, 0], [1, 1], [1, 0], [0, 0]])
|
||
|
|
clf = KNeighborsClassifier(n_neighbors=1)
|
||
|
|
scoring_micro = make_scorer(precision_score, average='micro')
|
||
|
|
scoring_macro = make_scorer(precision_score, average='macro')
|
||
|
|
scoring_samples = make_scorer(precision_score, average='samples')
|
||
|
|
score_micro = cross_val_score(clf, X, y, scoring=scoring_micro, cv=5)
|
||
|
|
score_macro = cross_val_score(clf, X, y, scoring=scoring_macro, cv=5)
|
||
|
|
score_samples = cross_val_score(clf, X, y, scoring=scoring_samples, cv=5)
|
||
|
|
assert_almost_equal(score_micro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 3])
|
||
|
|
assert_almost_equal(score_macro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])
|
||
|
|
assert_almost_equal(score_samples, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict():
|
||
|
|
boston = load_boston()
|
||
|
|
X, y = boston.data, boston.target
|
||
|
|
cv = KFold()
|
||
|
|
|
||
|
|
est = Ridge()
|
||
|
|
|
||
|
|
# Naive loop (should be same as cross_val_predict):
|
||
|
|
preds2 = np.zeros_like(y)
|
||
|
|
for train, test in cv.split(X, y):
|
||
|
|
est.fit(X[train], y[train])
|
||
|
|
preds2[test] = est.predict(X[test])
|
||
|
|
|
||
|
|
preds = cross_val_predict(est, X, y, cv=cv)
|
||
|
|
assert_array_almost_equal(preds, preds2)
|
||
|
|
|
||
|
|
preds = cross_val_predict(est, X, y)
|
||
|
|
assert_equal(len(preds), len(y))
|
||
|
|
|
||
|
|
cv = LeaveOneOut()
|
||
|
|
preds = cross_val_predict(est, X, y, cv=cv)
|
||
|
|
assert_equal(len(preds), len(y))
|
||
|
|
|
||
|
|
Xsp = X.copy()
|
||
|
|
Xsp *= (Xsp > np.median(Xsp))
|
||
|
|
Xsp = coo_matrix(Xsp)
|
||
|
|
preds = cross_val_predict(est, Xsp, y)
|
||
|
|
assert_array_almost_equal(len(preds), len(y))
|
||
|
|
|
||
|
|
preds = cross_val_predict(KMeans(), X)
|
||
|
|
assert_equal(len(preds), len(y))
|
||
|
|
|
||
|
|
class BadCV():
|
||
|
|
def split(self, X, y=None, groups=None):
|
||
|
|
for i in range(4):
|
||
|
|
yield np.array([0, 1, 2, 3]), np.array([4, 5, 6, 7, 8])
|
||
|
|
|
||
|
|
assert_raises(ValueError, cross_val_predict, est, X, y, cv=BadCV())
|
||
|
|
|
||
|
|
X, y = load_iris(return_X_y=True)
|
||
|
|
|
||
|
|
warning_message = ('Number of classes in training fold (2) does '
|
||
|
|
'not match total number of classes (3). '
|
||
|
|
'Results may not be appropriate for your use case.')
|
||
|
|
assert_warns_message(RuntimeWarning, warning_message,
|
||
|
|
cross_val_predict, LogisticRegression(),
|
||
|
|
X, y, method='predict_proba', cv=KFold(2))
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict_decision_function_shape():
|
||
|
|
X, y = make_classification(n_classes=2, n_samples=50, random_state=0)
|
||
|
|
|
||
|
|
preds = cross_val_predict(LogisticRegression(), X, y,
|
||
|
|
method='decision_function')
|
||
|
|
assert_equal(preds.shape, (50,))
|
||
|
|
|
||
|
|
X, y = load_iris(return_X_y=True)
|
||
|
|
|
||
|
|
preds = cross_val_predict(LogisticRegression(), X, y,
|
||
|
|
method='decision_function')
|
||
|
|
assert_equal(preds.shape, (150, 3))
|
||
|
|
|
||
|
|
# This specifically tests imbalanced splits for binary
|
||
|
|
# classification with decision_function. This is only
|
||
|
|
# applicable to classifiers that can be fit on a single
|
||
|
|
# class.
|
||
|
|
X = X[:100]
|
||
|
|
y = y[:100]
|
||
|
|
assert_raise_message(ValueError,
|
||
|
|
'Only 1 class/es in training fold, this'
|
||
|
|
' is not supported for decision_function'
|
||
|
|
' with imbalanced folds. To fix '
|
||
|
|
'this, use a cross-validation technique '
|
||
|
|
'resulting in properly stratified folds',
|
||
|
|
cross_val_predict, RidgeClassifier(), X, y,
|
||
|
|
method='decision_function', cv=KFold(2))
|
||
|
|
|
||
|
|
X, y = load_digits(return_X_y=True)
|
||
|
|
est = SVC(kernel='linear', decision_function_shape='ovo')
|
||
|
|
|
||
|
|
preds = cross_val_predict(est,
|
||
|
|
X, y,
|
||
|
|
method='decision_function')
|
||
|
|
assert_equal(preds.shape, (1797, 45))
|
||
|
|
|
||
|
|
ind = np.argsort(y)
|
||
|
|
X, y = X[ind], y[ind]
|
||
|
|
assert_raises_regex(ValueError,
|
||
|
|
'Output shape \(599L?, 21L?\) of decision_function '
|
||
|
|
'does not match number of classes \(7\) in fold. '
|
||
|
|
'Irregular decision_function .*',
|
||
|
|
cross_val_predict, est, X, y,
|
||
|
|
cv=KFold(n_splits=3), method='decision_function')
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict_predict_proba_shape():
|
||
|
|
X, y = make_classification(n_classes=2, n_samples=50, random_state=0)
|
||
|
|
|
||
|
|
preds = cross_val_predict(LogisticRegression(), X, y,
|
||
|
|
method='predict_proba')
|
||
|
|
assert_equal(preds.shape, (50, 2))
|
||
|
|
|
||
|
|
X, y = load_iris(return_X_y=True)
|
||
|
|
|
||
|
|
preds = cross_val_predict(LogisticRegression(), X, y,
|
||
|
|
method='predict_proba')
|
||
|
|
assert_equal(preds.shape, (150, 3))
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict_predict_log_proba_shape():
|
||
|
|
X, y = make_classification(n_classes=2, n_samples=50, random_state=0)
|
||
|
|
|
||
|
|
preds = cross_val_predict(LogisticRegression(), X, y,
|
||
|
|
method='predict_log_proba')
|
||
|
|
assert_equal(preds.shape, (50, 2))
|
||
|
|
|
||
|
|
X, y = load_iris(return_X_y=True)
|
||
|
|
|
||
|
|
preds = cross_val_predict(LogisticRegression(), X, y,
|
||
|
|
method='predict_log_proba')
|
||
|
|
assert_equal(preds.shape, (150, 3))
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict_input_types():
|
||
|
|
iris = load_iris()
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
X_sparse = coo_matrix(X)
|
||
|
|
multioutput_y = np.column_stack([y, y[::-1]])
|
||
|
|
|
||
|
|
clf = Ridge(fit_intercept=False, random_state=0)
|
||
|
|
# 3 fold cv is used --> atleast 3 samples per class
|
||
|
|
# Smoke test
|
||
|
|
predictions = cross_val_predict(clf, X, y)
|
||
|
|
assert_equal(predictions.shape, (150,))
|
||
|
|
|
||
|
|
# test with multioutput y
|
||
|
|
predictions = cross_val_predict(clf, X_sparse, multioutput_y)
|
||
|
|
assert_equal(predictions.shape, (150, 2))
|
||
|
|
|
||
|
|
predictions = cross_val_predict(clf, X_sparse, y)
|
||
|
|
assert_array_equal(predictions.shape, (150,))
|
||
|
|
|
||
|
|
# test with multioutput y
|
||
|
|
predictions = cross_val_predict(clf, X_sparse, multioutput_y)
|
||
|
|
assert_array_equal(predictions.shape, (150, 2))
|
||
|
|
|
||
|
|
# test with X and y as list
|
||
|
|
list_check = lambda x: isinstance(x, list)
|
||
|
|
clf = CheckingClassifier(check_X=list_check)
|
||
|
|
predictions = cross_val_predict(clf, X.tolist(), y.tolist())
|
||
|
|
|
||
|
|
clf = CheckingClassifier(check_y=list_check)
|
||
|
|
predictions = cross_val_predict(clf, X, y.tolist())
|
||
|
|
|
||
|
|
# test with X and y as list and non empty method
|
||
|
|
predictions = cross_val_predict(LogisticRegression(), X.tolist(),
|
||
|
|
y.tolist(), method='decision_function')
|
||
|
|
predictions = cross_val_predict(LogisticRegression(), X,
|
||
|
|
y.tolist(), method='decision_function')
|
||
|
|
|
||
|
|
# test with 3d X and
|
||
|
|
X_3d = X[:, :, np.newaxis]
|
||
|
|
check_3d = lambda x: x.ndim == 3
|
||
|
|
clf = CheckingClassifier(check_X=check_3d)
|
||
|
|
predictions = cross_val_predict(clf, X_3d, y)
|
||
|
|
assert_array_equal(predictions.shape, (150,))
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict_pandas():
|
||
|
|
# check cross_val_score doesn't destroy pandas dataframe
|
||
|
|
types = [(MockDataFrame, MockDataFrame)]
|
||
|
|
try:
|
||
|
|
from pandas import Series, DataFrame
|
||
|
|
types.append((Series, DataFrame))
|
||
|
|
except ImportError:
|
||
|
|
pass
|
||
|
|
for TargetType, InputFeatureType in types:
|
||
|
|
# X dataframe, y series
|
||
|
|
X_df, y_ser = InputFeatureType(X), TargetType(y2)
|
||
|
|
check_df = lambda x: isinstance(x, InputFeatureType)
|
||
|
|
check_series = lambda x: isinstance(x, TargetType)
|
||
|
|
clf = CheckingClassifier(check_X=check_df, check_y=check_series)
|
||
|
|
cross_val_predict(clf, X_df, y_ser)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_score_sparse_fit_params():
|
||
|
|
iris = load_iris()
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
clf = MockClassifier()
|
||
|
|
fit_params = {'sparse_sample_weight': coo_matrix(np.eye(X.shape[0]))}
|
||
|
|
a = cross_val_score(clf, X, y, fit_params=fit_params)
|
||
|
|
assert_array_equal(a, np.ones(3))
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve():
|
||
|
|
n_samples = 30
|
||
|
|
n_splits = 3
|
||
|
|
X, y = make_classification(n_samples=n_samples, n_features=1,
|
||
|
|
n_informative=1, n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
estimator = MockImprovingEstimator(n_samples * ((n_splits - 1) / n_splits))
|
||
|
|
for shuffle_train in [False, True]:
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
train_sizes, train_scores, test_scores = learning_curve(
|
||
|
|
estimator, X, y, cv=KFold(n_splits=n_splits),
|
||
|
|
train_sizes=np.linspace(0.1, 1.0, 10),
|
||
|
|
shuffle=shuffle_train)
|
||
|
|
if len(w) > 0:
|
||
|
|
raise RuntimeError("Unexpected warning: %r" % w[0].message)
|
||
|
|
assert_equal(train_scores.shape, (10, 3))
|
||
|
|
assert_equal(test_scores.shape, (10, 3))
|
||
|
|
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
|
||
|
|
assert_array_almost_equal(train_scores.mean(axis=1),
|
||
|
|
np.linspace(1.9, 1.0, 10))
|
||
|
|
assert_array_almost_equal(test_scores.mean(axis=1),
|
||
|
|
np.linspace(0.1, 1.0, 10))
|
||
|
|
|
||
|
|
# Test a custom cv splitter that can iterate only once
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
train_sizes2, train_scores2, test_scores2 = learning_curve(
|
||
|
|
estimator, X, y,
|
||
|
|
cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples),
|
||
|
|
train_sizes=np.linspace(0.1, 1.0, 10),
|
||
|
|
shuffle=shuffle_train)
|
||
|
|
if len(w) > 0:
|
||
|
|
raise RuntimeError("Unexpected warning: %r" % w[0].message)
|
||
|
|
assert_array_almost_equal(train_scores2, train_scores)
|
||
|
|
assert_array_almost_equal(test_scores2, test_scores)
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_unsupervised():
|
||
|
|
X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
estimator = MockImprovingEstimator(20)
|
||
|
|
train_sizes, train_scores, test_scores = learning_curve(
|
||
|
|
estimator, X, y=None, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
|
||
|
|
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
|
||
|
|
assert_array_almost_equal(train_scores.mean(axis=1),
|
||
|
|
np.linspace(1.9, 1.0, 10))
|
||
|
|
assert_array_almost_equal(test_scores.mean(axis=1),
|
||
|
|
np.linspace(0.1, 1.0, 10))
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_verbose():
|
||
|
|
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
estimator = MockImprovingEstimator(20)
|
||
|
|
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
train_sizes, train_scores, test_scores = \
|
||
|
|
learning_curve(estimator, X, y, cv=3, verbose=1)
|
||
|
|
finally:
|
||
|
|
out = sys.stdout.getvalue()
|
||
|
|
sys.stdout.close()
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
|
||
|
|
assert("[learning_curve]" in out)
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_incremental_learning_not_possible():
|
||
|
|
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
# The mockup does not have partial_fit()
|
||
|
|
estimator = MockImprovingEstimator(1)
|
||
|
|
assert_raises(ValueError, learning_curve, estimator, X, y,
|
||
|
|
exploit_incremental_learning=True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_incremental_learning():
|
||
|
|
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
estimator = MockIncrementalImprovingEstimator(20)
|
||
|
|
for shuffle_train in [False, True]:
|
||
|
|
train_sizes, train_scores, test_scores = learning_curve(
|
||
|
|
estimator, X, y, cv=3, exploit_incremental_learning=True,
|
||
|
|
train_sizes=np.linspace(0.1, 1.0, 10), shuffle=shuffle_train)
|
||
|
|
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
|
||
|
|
assert_array_almost_equal(train_scores.mean(axis=1),
|
||
|
|
np.linspace(1.9, 1.0, 10))
|
||
|
|
assert_array_almost_equal(test_scores.mean(axis=1),
|
||
|
|
np.linspace(0.1, 1.0, 10))
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_incremental_learning_unsupervised():
|
||
|
|
X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
estimator = MockIncrementalImprovingEstimator(20)
|
||
|
|
train_sizes, train_scores, test_scores = learning_curve(
|
||
|
|
estimator, X, y=None, cv=3, exploit_incremental_learning=True,
|
||
|
|
train_sizes=np.linspace(0.1, 1.0, 10))
|
||
|
|
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
|
||
|
|
assert_array_almost_equal(train_scores.mean(axis=1),
|
||
|
|
np.linspace(1.9, 1.0, 10))
|
||
|
|
assert_array_almost_equal(test_scores.mean(axis=1),
|
||
|
|
np.linspace(0.1, 1.0, 10))
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_batch_and_incremental_learning_are_equal():
|
||
|
|
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
train_sizes = np.linspace(0.2, 1.0, 5)
|
||
|
|
estimator = PassiveAggressiveClassifier(max_iter=1, tol=None,
|
||
|
|
shuffle=False)
|
||
|
|
|
||
|
|
train_sizes_inc, train_scores_inc, test_scores_inc = \
|
||
|
|
learning_curve(
|
||
|
|
estimator, X, y, train_sizes=train_sizes,
|
||
|
|
cv=3, exploit_incremental_learning=True)
|
||
|
|
train_sizes_batch, train_scores_batch, test_scores_batch = \
|
||
|
|
learning_curve(
|
||
|
|
estimator, X, y, cv=3, train_sizes=train_sizes,
|
||
|
|
exploit_incremental_learning=False)
|
||
|
|
|
||
|
|
assert_array_equal(train_sizes_inc, train_sizes_batch)
|
||
|
|
assert_array_almost_equal(train_scores_inc.mean(axis=1),
|
||
|
|
train_scores_batch.mean(axis=1))
|
||
|
|
assert_array_almost_equal(test_scores_inc.mean(axis=1),
|
||
|
|
test_scores_batch.mean(axis=1))
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_n_sample_range_out_of_bounds():
|
||
|
|
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
estimator = MockImprovingEstimator(20)
|
||
|
|
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
|
||
|
|
train_sizes=[0, 1])
|
||
|
|
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
|
||
|
|
train_sizes=[0.0, 1.0])
|
||
|
|
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
|
||
|
|
train_sizes=[0.1, 1.1])
|
||
|
|
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
|
||
|
|
train_sizes=[0, 20])
|
||
|
|
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
|
||
|
|
train_sizes=[1, 21])
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_remove_duplicate_sample_sizes():
|
||
|
|
X, y = make_classification(n_samples=3, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
estimator = MockImprovingEstimator(2)
|
||
|
|
train_sizes, _, _ = assert_warns(
|
||
|
|
RuntimeWarning, learning_curve, estimator, X, y, cv=3,
|
||
|
|
train_sizes=np.linspace(0.33, 1.0, 3))
|
||
|
|
assert_array_equal(train_sizes, [1, 2])
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_with_boolean_indices():
|
||
|
|
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
estimator = MockImprovingEstimator(20)
|
||
|
|
cv = KFold(n_splits=3)
|
||
|
|
train_sizes, train_scores, test_scores = learning_curve(
|
||
|
|
estimator, X, y, cv=cv, train_sizes=np.linspace(0.1, 1.0, 10))
|
||
|
|
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
|
||
|
|
assert_array_almost_equal(train_scores.mean(axis=1),
|
||
|
|
np.linspace(1.9, 1.0, 10))
|
||
|
|
assert_array_almost_equal(test_scores.mean(axis=1),
|
||
|
|
np.linspace(0.1, 1.0, 10))
|
||
|
|
|
||
|
|
|
||
|
|
def test_learning_curve_with_shuffle():
|
||
|
|
# Following test case was designed this way to verify the code
|
||
|
|
# changes made in pull request: #7506.
|
||
|
|
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [11, 12], [13, 14], [15, 16],
|
||
|
|
[17, 18], [19, 20], [7, 8], [9, 10], [11, 12], [13, 14],
|
||
|
|
[15, 16], [17, 18]])
|
||
|
|
y = np.array([1, 1, 1, 2, 3, 4, 1, 1, 2, 3, 4, 1, 2, 3, 4])
|
||
|
|
groups = np.array([1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 4, 4, 4])
|
||
|
|
# Splits on these groups fail without shuffle as the first iteration
|
||
|
|
# of the learning curve doesn't contain label 4 in the training set.
|
||
|
|
estimator = PassiveAggressiveClassifier(max_iter=5, tol=None,
|
||
|
|
shuffle=False)
|
||
|
|
|
||
|
|
cv = GroupKFold(n_splits=2)
|
||
|
|
train_sizes_batch, train_scores_batch, test_scores_batch = learning_curve(
|
||
|
|
estimator, X, y, cv=cv, n_jobs=1, train_sizes=np.linspace(0.3, 1.0, 3),
|
||
|
|
groups=groups, shuffle=True, random_state=2)
|
||
|
|
assert_array_almost_equal(train_scores_batch.mean(axis=1),
|
||
|
|
np.array([0.75, 0.3, 0.36111111]))
|
||
|
|
assert_array_almost_equal(test_scores_batch.mean(axis=1),
|
||
|
|
np.array([0.36111111, 0.25, 0.25]))
|
||
|
|
assert_raises(ValueError, learning_curve, estimator, X, y, cv=cv, n_jobs=1,
|
||
|
|
train_sizes=np.linspace(0.3, 1.0, 3), groups=groups)
|
||
|
|
|
||
|
|
train_sizes_inc, train_scores_inc, test_scores_inc = learning_curve(
|
||
|
|
estimator, X, y, cv=cv, n_jobs=1, train_sizes=np.linspace(0.3, 1.0, 3),
|
||
|
|
groups=groups, shuffle=True, random_state=2,
|
||
|
|
exploit_incremental_learning=True)
|
||
|
|
assert_array_almost_equal(train_scores_inc.mean(axis=1),
|
||
|
|
train_scores_batch.mean(axis=1))
|
||
|
|
assert_array_almost_equal(test_scores_inc.mean(axis=1),
|
||
|
|
test_scores_batch.mean(axis=1))
|
||
|
|
|
||
|
|
|
||
|
|
def test_validation_curve():
|
||
|
|
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
param_range = np.linspace(0, 1, 10)
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
train_scores, test_scores = validation_curve(
|
||
|
|
MockEstimatorWithParameter(), X, y, param_name="param",
|
||
|
|
param_range=param_range, cv=2
|
||
|
|
)
|
||
|
|
if len(w) > 0:
|
||
|
|
raise RuntimeError("Unexpected warning: %r" % w[0].message)
|
||
|
|
|
||
|
|
assert_array_almost_equal(train_scores.mean(axis=1), param_range)
|
||
|
|
assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)
|
||
|
|
|
||
|
|
|
||
|
|
def test_validation_curve_clone_estimator():
|
||
|
|
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
|
||
|
|
n_redundant=0, n_classes=2,
|
||
|
|
n_clusters_per_class=1, random_state=0)
|
||
|
|
|
||
|
|
param_range = np.linspace(1, 0, 10)
|
||
|
|
_, _ = validation_curve(
|
||
|
|
MockEstimatorWithSingleFitCallAllowed(), X, y,
|
||
|
|
param_name="param", param_range=param_range, cv=2
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def test_validation_curve_cv_splits_consistency():
|
||
|
|
n_samples = 100
|
||
|
|
n_splits = 5
|
||
|
|
X, y = make_classification(n_samples=100, random_state=0)
|
||
|
|
|
||
|
|
scores1 = validation_curve(SVC(kernel='linear', random_state=0), X, y,
|
||
|
|
'C', [0.1, 0.1, 0.2, 0.2],
|
||
|
|
cv=OneTimeSplitter(n_splits=n_splits,
|
||
|
|
n_samples=n_samples))
|
||
|
|
# The OneTimeSplitter is a non-re-entrant cv splitter. Unless, the
|
||
|
|
# `split` is called for each parameter, the following should produce
|
||
|
|
# identical results for param setting 1 and param setting 2 as both have
|
||
|
|
# the same C value.
|
||
|
|
assert_array_almost_equal(*np.vsplit(np.hstack(scores1)[(0, 2, 1, 3), :],
|
||
|
|
2))
|
||
|
|
|
||
|
|
scores2 = validation_curve(SVC(kernel='linear', random_state=0), X, y,
|
||
|
|
'C', [0.1, 0.1, 0.2, 0.2],
|
||
|
|
cv=KFold(n_splits=n_splits, shuffle=True))
|
||
|
|
|
||
|
|
# For scores2, compare the 1st and 2nd parameter's scores
|
||
|
|
# (Since the C value for 1st two param setting is 0.1, they must be
|
||
|
|
# consistent unless the train test folds differ between the param settings)
|
||
|
|
assert_array_almost_equal(*np.vsplit(np.hstack(scores2)[(0, 2, 1, 3), :],
|
||
|
|
2))
|
||
|
|
|
||
|
|
scores3 = validation_curve(SVC(kernel='linear', random_state=0), X, y,
|
||
|
|
'C', [0.1, 0.1, 0.2, 0.2],
|
||
|
|
cv=KFold(n_splits=n_splits))
|
||
|
|
|
||
|
|
# OneTimeSplitter is basically unshuffled KFold(n_splits=5). Sanity check.
|
||
|
|
assert_array_almost_equal(np.array(scores3), np.array(scores1))
|
||
|
|
|
||
|
|
|
||
|
|
def test_check_is_permutation():
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
p = np.arange(100)
|
||
|
|
rng.shuffle(p)
|
||
|
|
assert_true(_check_is_permutation(p, 100))
|
||
|
|
assert_false(_check_is_permutation(np.delete(p, 23), 100))
|
||
|
|
|
||
|
|
p[0] = 23
|
||
|
|
assert_false(_check_is_permutation(p, 100))
|
||
|
|
|
||
|
|
# Check if the additional duplicate indices are caught
|
||
|
|
assert_false(_check_is_permutation(np.hstack((p, 0)), 100))
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict_sparse_prediction():
|
||
|
|
# check that cross_val_predict gives same result for sparse and dense input
|
||
|
|
X, y = make_multilabel_classification(n_classes=2, n_labels=1,
|
||
|
|
allow_unlabeled=False,
|
||
|
|
return_indicator=True,
|
||
|
|
random_state=1)
|
||
|
|
X_sparse = csr_matrix(X)
|
||
|
|
y_sparse = csr_matrix(y)
|
||
|
|
classif = OneVsRestClassifier(SVC(kernel='linear'))
|
||
|
|
preds = cross_val_predict(classif, X, y, cv=10)
|
||
|
|
preds_sparse = cross_val_predict(classif, X_sparse, y_sparse, cv=10)
|
||
|
|
preds_sparse = preds_sparse.toarray()
|
||
|
|
assert_array_almost_equal(preds_sparse, preds)
|
||
|
|
|
||
|
|
|
||
|
|
def check_cross_val_predict_with_method(est):
|
||
|
|
iris = load_iris()
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
X, y = shuffle(X, y, random_state=0)
|
||
|
|
classes = len(set(y))
|
||
|
|
|
||
|
|
kfold = KFold()
|
||
|
|
|
||
|
|
methods = ['decision_function', 'predict_proba', 'predict_log_proba']
|
||
|
|
for method in methods:
|
||
|
|
predictions = cross_val_predict(est, X, y, method=method)
|
||
|
|
assert_equal(len(predictions), len(y))
|
||
|
|
|
||
|
|
expected_predictions = np.zeros([len(y), classes])
|
||
|
|
func = getattr(est, method)
|
||
|
|
|
||
|
|
# Naive loop (should be same as cross_val_predict):
|
||
|
|
for train, test in kfold.split(X, y):
|
||
|
|
est.fit(X[train], y[train])
|
||
|
|
expected_predictions[test] = func(X[test])
|
||
|
|
|
||
|
|
predictions = cross_val_predict(est, X, y, method=method,
|
||
|
|
cv=kfold)
|
||
|
|
assert_array_almost_equal(expected_predictions, predictions)
|
||
|
|
|
||
|
|
# Test alternative representations of y
|
||
|
|
predictions_y1 = cross_val_predict(est, X, y + 1, method=method,
|
||
|
|
cv=kfold)
|
||
|
|
assert_array_equal(predictions, predictions_y1)
|
||
|
|
|
||
|
|
predictions_y2 = cross_val_predict(est, X, y - 2, method=method,
|
||
|
|
cv=kfold)
|
||
|
|
assert_array_equal(predictions, predictions_y2)
|
||
|
|
|
||
|
|
predictions_ystr = cross_val_predict(est, X, y.astype('str'),
|
||
|
|
method=method, cv=kfold)
|
||
|
|
assert_array_equal(predictions, predictions_ystr)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict_with_method():
|
||
|
|
check_cross_val_predict_with_method(LogisticRegression())
|
||
|
|
|
||
|
|
|
||
|
|
def test_cross_val_predict_method_checking():
|
||
|
|
# Regression test for issue #9639. Tests that cross_val_predict does not
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# check estimator methods (e.g. predict_proba) before fitting
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est = SGDClassifier(loss='log', random_state=2)
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check_cross_val_predict_with_method(est)
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def test_gridsearchcv_cross_val_predict_with_method():
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est = GridSearchCV(LogisticRegression(random_state=42),
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{'C': [0.1, 1]},
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cv=2)
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check_cross_val_predict_with_method(est)
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def get_expected_predictions(X, y, cv, classes, est, method):
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expected_predictions = np.zeros([len(y), classes])
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func = getattr(est, method)
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for train, test in cv.split(X, y):
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est.fit(X[train], y[train])
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expected_predictions_ = func(X[test])
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# To avoid 2 dimensional indexing
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if method is 'predict_proba':
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exp_pred_test = np.zeros((len(test), classes))
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else:
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exp_pred_test = np.full((len(test), classes),
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np.finfo(expected_predictions.dtype).min)
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exp_pred_test[:, est.classes_] = expected_predictions_
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expected_predictions[test] = exp_pred_test
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return expected_predictions
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def test_cross_val_predict_class_subset():
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X = np.arange(200).reshape(100, 2)
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y = np.array([x//10 for x in range(100)])
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classes = 10
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kfold3 = KFold(n_splits=3)
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kfold4 = KFold(n_splits=4)
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le = LabelEncoder()
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|
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methods = ['decision_function', 'predict_proba', 'predict_log_proba']
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for method in methods:
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est = LogisticRegression()
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|
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||
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|
# Test with n_splits=3
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predictions = cross_val_predict(est, X, y, method=method,
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cv=kfold3)
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||
|
|
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# Runs a naive loop (should be same as cross_val_predict):
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||
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|
expected_predictions = get_expected_predictions(X, y, kfold3, classes,
|
||
|
|
est, method)
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||
|
|
assert_array_almost_equal(expected_predictions, predictions)
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||
|
|
|
||
|
|
# Test with n_splits=4
|
||
|
|
predictions = cross_val_predict(est, X, y, method=method,
|
||
|
|
cv=kfold4)
|
||
|
|
expected_predictions = get_expected_predictions(X, y, kfold4, classes,
|
||
|
|
est, method)
|
||
|
|
assert_array_almost_equal(expected_predictions, predictions)
|
||
|
|
|
||
|
|
# Testing unordered labels
|
||
|
|
y = shuffle(np.repeat(range(10), 10), random_state=0)
|
||
|
|
predictions = cross_val_predict(est, X, y, method=method,
|
||
|
|
cv=kfold3)
|
||
|
|
y = le.fit_transform(y)
|
||
|
|
expected_predictions = get_expected_predictions(X, y, kfold3, classes,
|
||
|
|
est, method)
|
||
|
|
assert_array_almost_equal(expected_predictions, predictions)
|
||
|
|
|
||
|
|
|
||
|
|
def test_score_memmap():
|
||
|
|
# Ensure a scalar score of memmap type is accepted
|
||
|
|
iris = load_iris()
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
clf = MockClassifier()
|
||
|
|
tf = tempfile.NamedTemporaryFile(mode='wb', delete=False)
|
||
|
|
tf.write(b'Hello world!!!!!')
|
||
|
|
tf.close()
|
||
|
|
scores = np.memmap(tf.name, dtype=np.float64)
|
||
|
|
score = np.memmap(tf.name, shape=(), mode='r', dtype=np.float64)
|
||
|
|
try:
|
||
|
|
cross_val_score(clf, X, y, scoring=lambda est, X, y: score)
|
||
|
|
# non-scalar should still fail
|
||
|
|
assert_raises(ValueError, cross_val_score, clf, X, y,
|
||
|
|
scoring=lambda est, X, y: scores)
|
||
|
|
finally:
|
||
|
|
# Best effort to release the mmap file handles before deleting the
|
||
|
|
# backing file under Windows
|
||
|
|
scores, score = None, None
|
||
|
|
for _ in range(3):
|
||
|
|
try:
|
||
|
|
os.unlink(tf.name)
|
||
|
|
break
|
||
|
|
except WindowsError:
|
||
|
|
sleep(1.)
|
||
|
|
|
||
|
|
|
||
|
|
def test_permutation_test_score_pandas():
|
||
|
|
# check permutation_test_score doesn't destroy pandas dataframe
|
||
|
|
types = [(MockDataFrame, MockDataFrame)]
|
||
|
|
try:
|
||
|
|
from pandas import Series, DataFrame
|
||
|
|
types.append((Series, DataFrame))
|
||
|
|
except ImportError:
|
||
|
|
pass
|
||
|
|
for TargetType, InputFeatureType in types:
|
||
|
|
# X dataframe, y series
|
||
|
|
iris = load_iris()
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
X_df, y_ser = InputFeatureType(X), TargetType(y)
|
||
|
|
check_df = lambda x: isinstance(x, InputFeatureType)
|
||
|
|
check_series = lambda x: isinstance(x, TargetType)
|
||
|
|
clf = CheckingClassifier(check_X=check_df, check_y=check_series)
|
||
|
|
permutation_test_score(clf, X_df, y_ser)
|