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/ model_selection / tests / test_validation.py
"""Test the validation module"""
import sys
import warnings
import tempfile
import os
from time import sleep
import pytest
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
from sklearn.exceptions import FitFailedWarning
from sklearn.model_selection.tests.test_search import FailingClassifier
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_raises
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 assert_raises_regex
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_allclose
from sklearn.utils._mocking import CheckingClassifier, MockDataFrame
from sklearn.model_selection import cross_val_score, ShuffleSplit
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_validate
from sklearn.model_selection import permutation_test_score
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import LeaveOneOut
from sklearn.model_selection import LeaveOneGroupOut
from sklearn.model_selection import LeavePGroupsOut
from sklearn.model_selection import GroupKFold
from sklearn.model_selection import GroupShuffleSplit
from sklearn.model_selection import learning_curve
from sklearn.model_selection import validation_curve
from sklearn.model_selection._validation import _check_is_permutation
from sklearn.model_selection._validation import _fit_and_score
from sklearn.model_selection._validation import _score
from sklearn.datasets import make_regression
from sklearn.datasets import load_boston
from sklearn.datasets import load_iris
from sklearn.datasets import load_digits
from sklearn.metrics import explained_variance_score
from sklearn.metrics import make_scorer
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import precision_score
from sklearn.metrics import r2_score
from sklearn.metrics import check_scoring
from sklearn.linear_model import Ridge, LogisticRegression, SGDClassifier
from sklearn.linear_model import PassiveAggressiveClassifier, RidgeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.cluster import KMeans
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline
from io import StringIO
from sklearn.base import BaseEstimator
from sklearn.base import clone
from sklearn.multiclass import OneVsRestClassifier
from sklearn.utils import shuffle
from sklearn.datasets import make_classification
from sklearn.datasets import make_multilabel_classification
from sklearn.model_selection.tests.common import OneTimeSplitter
from sklearn.model_selection import GridSearchCV
try:
WindowsError
except NameError:
WindowsError = None
class MockImprovingEstimator(BaseEstimator):
"""Dummy classifier to test the learning curve"""
def __init__(self, n_max_train_sizes):
self.n_max_train_sizes = n_max_train_sizes
self.train_sizes = 0
self.X_subset = None
def fit(self, X_subset, y_subset=None):
self.X_subset = X_subset
self.train_sizes = X_subset.shape[0]
return self
def predict(self, X):
raise NotImplementedError
def score(self, X=None, Y=None):
# training score becomes worse (2 -> 1), test error better (0 -> 1)
if self._is_training_data(X):
return 2. - float(self.train_sizes) / self.n_max_train_sizes
else:
return float(self.train_sizes) / self.n_max_train_sizes
def _is_training_data(self, X):
return X is self.X_subset
class MockIncrementalImprovingEstimator(MockImprovingEstimator):
"""Dummy classifier that provides partial_fit"""
def __init__(self, n_max_train_sizes):
super().__init__(n_max_train_sizes)
self.x = None
def _is_training_data(self, X):
return self.x in X
def partial_fit(self, X, y=None, **params):
self.train_sizes += X.shape[0]
self.x = X[0]
class MockEstimatorWithParameter(BaseEstimator):
"""Dummy classifier to test the validation curve"""
def __init__(self, param=0.5):
self.X_subset = None
self.param = param
def fit(self, X_subset, y_subset):
self.X_subset = X_subset
self.train_sizes = X_subset.shape[0]
return self
def predict(self, X):
raise NotImplementedError
def score(self, X=None, y=None):
return self.param if self._is_training_data(X) else 1 - self.param
def _is_training_data(self, X):
return X is self.X_subset
class MockEstimatorWithSingleFitCallAllowed(MockEstimatorWithParameter):
"""Dummy classifier that disallows repeated calls of fit method"""
def fit(self, X_subset, y_subset):
assert not hasattr(self, 'fit_called_'), \
'fit is called the second time'
self.fit_called_ = True
return super().fit(X_subset, y_subset)
def predict(self, X):
raise NotImplementedError
class MockClassifier:
"""Dummy classifier to test the cross-validation"""
def __init__(self, a=0, allow_nd=False):
self.a = a
self.allow_nd = allow_nd
def fit(self, X, Y=None, sample_weight=None, class_prior=None,
sparse_sample_weight=None, sparse_param=None, dummy_int=None,
dummy_str=None, dummy_obj=None, callback=None):
"""The dummy arguments are to test that this fit function can
accept non-array arguments through cross-validation, such as:
- int
- str (this is actually array-like)
- object
- function
"""
self.dummy_int = dummy_int
self.dummy_str = dummy_str
self.dummy_obj = dummy_obj
if callback is not None:
callback(self)
if self.allow_nd:
X = X.reshape(len(X), -1)
if X.ndim >= 3 and not self.allow_nd:
raise ValueError('X cannot be d')
if sample_weight is not None:
assert sample_weight.shape[0] == X.shape[0], (
'MockClassifier extra fit_param '
'sample_weight.shape[0] is {0}, should be {1}'
.format(sample_weight.shape[0], X.shape[0]))
if class_prior is not None:
assert class_prior.shape[0] == len(np.unique(y)), (
'MockClassifier extra fit_param class_prior.shape[0]'
' is {0}, should be {1}'.format(class_prior.shape[0],
len(np.unique(y))))
if sparse_sample_weight is not None:
fmt = ('MockClassifier extra fit_param sparse_sample_weight'
'.shape[0] is {0}, should be {1}')
assert sparse_sample_weight.shape[0] == X.shape[0], \
fmt.format(sparse_sample_weight.shape[0], X.shape[0])
if sparse_param is not None:
fmt = ('MockClassifier extra fit_param sparse_param.shape '
'is ({0}, {1}), should be ({2}, {3})')
assert sparse_param.shape == P_sparse.shape, (
fmt.format(sparse_param.shape[0],
sparse_param.shape[1],
P_sparse.shape[0], P_sparse.shape[1]))
return self
def predict(self, T):
if self.allow_nd:
T = T.reshape(len(T), -1)
return T[:, 0]
def score(self, X=None, Y=None):
return 1. / (1 + np.abs(self.a))
def get_params(self, deep=False):
return {'a': self.a, 'allow_nd': self.allow_nd}
# XXX: use 2D array, since 1D X is being detected as a single sample in
# check_consistent_length
X = np.ones((10, 2))
X_sparse = coo_matrix(X)
y = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
# The number of samples per class needs to be > n_splits,
# for StratifiedKFold(n_splits=3)
y2 = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 3])
P_sparse = coo_matrix(np.eye(5))
def test_cross_val_score():
clf = MockClassifier()
for a in range(-10, 10):
clf.a = a
# Smoke test
scores = cross_val_score(clf, X, y2)
assert_array_equal(scores, clf.score(X, y2))
# test with multioutput y
multioutput_y = np.column_stack([y2, y2[::-1]])
scores = cross_val_score(clf, X_sparse, multioutput_y)
assert_array_equal(scores, clf.score(X_sparse, multioutput_y))
scores = cross_val_score(clf, X_sparse, y2)
assert_array_equal(scores, clf.score(X_sparse, y2))
# test with multioutput y
scores = cross_val_score(clf, X_sparse, multioutput_y)
assert_array_equal(scores, clf.score(X_sparse, multioutput_y))
# test with X and y as list
list_check = lambda x: isinstance(x, list)
clf = CheckingClassifier(check_X=list_check)
scores = cross_val_score(clf, X.tolist(), y2.tolist(), cv=3)
clf = CheckingClassifier(check_y=list_check)
scores = cross_val_score(clf, X, y2.tolist(), cv=3)
assert_raises(ValueError, cross_val_score, clf, X, y2, scoring="sklearn")
# test with 3d X and
X_3d = X[:, :, np.newaxis]
clf = MockClassifier(allow_nd=True)
scores = cross_val_score(clf, X_3d, y2)
clf = MockClassifier(allow_nd=False)
assert_raises(ValueError, cross_val_score, clf, X_3d, y2,
error_score='raise')
def test_cross_validate_many_jobs():
# regression test for #12154: cv='warn' with n_jobs>1 trigger a copy of
# the parameters leading to a failure in check_cv due to cv is 'warn'
# instead of cv == 'warn'.
X, y = load_iris(return_X_y=True)
clf = SVC(gamma='auto')
grid = GridSearchCV(clf, param_grid={'C': [1, 10]})
cross_validate(grid, X, y, n_jobs=2)
def test_cross_validate_invalid_scoring_param():
X, y = make_classification(random_state=0)
estimator = MockClassifier()
# Test the errors
error_message_regexp = ".*must be unique strings.*"
# List/tuple of callables should raise a message advising users to use
# dict of names to callables mapping
assert_raises_regex(ValueError, error_message_regexp,
cross_validate, estimator, X, y,
scoring=(make_scorer(precision_score),
make_scorer(accuracy_score)))
assert_raises_regex(ValueError, error_message_regexp,
cross_validate, estimator, X, y,
scoring=(make_scorer(precision_score),))
# So should empty lists/tuples
assert_raises_regex(ValueError, error_message_regexp + "Empty list.*",
cross_validate, estimator, X, y, scoring=())
# So should duplicated entries
assert_raises_regex(ValueError, error_message_regexp + "Duplicate.*",
cross_validate, estimator, X, y,
scoring=('f1_micro', 'f1_micro'))
# Nested Lists should raise a generic error message
assert_raises_regex(ValueError, error_message_regexp,
cross_validate, estimator, X, y,
scoring=[[make_scorer(precision_score)]])
error_message_regexp = (".*should either be.*string or callable.*for "
"single.*.*dict.*for multi.*")
# Empty dict should raise invalid scoring error
assert_raises_regex(ValueError, "An empty dict",
cross_validate, estimator, X, y, scoring=(dict()))
# And so should any other invalid entry
assert_raises_regex(ValueError, error_message_regexp,
cross_validate, estimator, X, y, scoring=5)
multiclass_scorer = make_scorer(precision_recall_fscore_support)
# Multiclass Scorers that return multiple values are not supported yet
assert_raises_regex(ValueError,
"Classification metrics can't handle a mix of "
"binary and continuous targets",
cross_validate, estimator, X, y,
scoring=multiclass_scorer)
assert_raises_regex(ValueError,
"Classification metrics can't handle a mix of "
"binary and continuous targets",
cross_validate, estimator, X, y,
scoring={"foo": multiclass_scorer})
multivalued_scorer = make_scorer(confusion_matrix)
# Multiclass Scorers that return multiple values are not supported yet
assert_raises_regex(ValueError, "scoring must return a number, got",
cross_validate, SVC(), X, y,
scoring=multivalued_scorer)
assert_raises_regex(ValueError, "scoring must return a number, got",
cross_validate, SVC(), X, y,
scoring={"foo": multivalued_scorer})
assert_raises_regex(ValueError, "'mse' is not a valid scoring value.",
cross_validate, SVC(), X, y, scoring="mse")
def test_cross_validate():
# Compute train and test mse/r2 scores
cv = KFold()
# Regression
X_reg, y_reg = make_regression(n_samples=30, random_state=0)
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 = []
fitted_estimators = []
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]))
fitted_estimators.append(est)
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)
fitted_estimators = np.array(fitted_estimators)
scores = (train_mse_scores, test_mse_scores, train_r2_scores,
test_r2_scores, fitted_estimators)
check_cross_validate_single_metric(est, X, y, scores)
check_cross_validate_multi_metric(est, X, y, scores)
def check_cross_validate_single_metric(clf, X, y, scores):
(train_mse_scores, test_mse_scores, train_r2_scores,
test_r2_scores, fitted_estimators) = 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:
mse_scores_dict = cross_validate(clf, X, y,
scoring='neg_mean_squared_error',
return_train_score=True)
assert_array_almost_equal(mse_scores_dict['train_score'],
train_mse_scores)
else:
mse_scores_dict = cross_validate(clf, X, y,
scoring='neg_mean_squared_error',
return_train_score=False)
assert isinstance(mse_scores_dict, dict)
assert 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 - deprecated
r2_scores_dict = cross_validate(clf, X, y, scoring=['r2'],
return_train_score=True)
assert_array_almost_equal(r2_scores_dict['train_r2'],
train_r2_scores, True)
else:
r2_scores_dict = cross_validate(clf, X, y, scoring=['r2'],
return_train_score=False)
assert isinstance(r2_scores_dict, dict)
assert len(r2_scores_dict) == dict_len
assert_array_almost_equal(r2_scores_dict['test_r2'], test_r2_scores)
# Test return_estimator option
mse_scores_dict = cross_validate(clf, X, y,
scoring='neg_mean_squared_error',
return_estimator=True)
for k, est in enumerate(mse_scores_dict['estimator']):
assert_almost_equal(est.coef_, fitted_estimators[k].coef_)
assert_almost_equal(est.intercept_, fitted_estimators[k].intercept_)
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, fitted_estimators) = scores
all_scoring = (('r2', 'neg_mean_squared_error'),
{'r2': make_scorer(r2_score),
'neg_mean_squared_error': 'neg_mean_squared_error'})
keys_sans_train = {'test_r2', 'test_neg_mean_squared_error',
'fit_time', 'score_time'}
keys_with_train = keys_sans_train.union(
{'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 - deprecated
cv_results = cross_validate(clf, X, y, scoring=scoring,
return_train_score=True)
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, scoring=scoring,
return_train_score=False)
assert isinstance(cv_results, dict)
assert (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)
@pytest.mark.filterwarnings('ignore: Using or importing the ABCs from')
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, cv=3)
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 clf.dummy_int == DUMMY_INT
assert clf.dummy_str == DUMMY_STR
assert clf.dummy_obj == DUMMY_OBJ
fit_params = {'sample_weight': np.ones(n_samples),
'class_prior': np.full(n_classes, 1. / 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)
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")
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")
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)
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")
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,
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, 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 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 score_group == score
assert 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 score_group == score
assert 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 score < 0.5
assert 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', SimpleImputer(strategy='mean', missing_values=np.nan)),
('classifier', MockClassifier()),
])
permutation_test_score(p, X, y)
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', SimpleImputer(strategy='mean', missing_values=np.nan)),
('classifier', MockClassifier()),
])
cross_val_score(p, X, y)
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)
score_macro = cross_val_score(clf, X, y, scoring=scoring_macro)
score_samples = cross_val_score(clf, X, y, scoring=scoring_samples)
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():
X, y = load_boston(return_X_y=True)
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 len(preds) == len(y)
cv = LeaveOneOut()
preds = cross_val_predict(est, X, y, cv=cv)
assert 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 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(solver="liblinear"),
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(solver="liblinear"), X, y,
method='decision_function')
assert preds.shape == (50,)
X, y = load_iris(return_X_y=True)
preds = cross_val_predict(LogisticRegression(solver="liblinear"), X, y,
method='decision_function')
assert 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,'
' but 2 in overall dataset. 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 preds.shape == (1797, 45)
ind = np.argsort(y)
X, y = X[ind], y[ind]
assert_raises_regex(ValueError,
r'Output shape \(599L?, 21L?\) of decision_function '
r'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(solver="liblinear"), X, y,
method='predict_proba')
assert preds.shape == (50, 2)
X, y = load_iris(return_X_y=True)
preds = cross_val_predict(LogisticRegression(solver="liblinear"), X, y,
method='predict_proba')
assert 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(solver="liblinear"), X, y,
method='predict_log_proba')
assert preds.shape == (50, 2)
X, y = load_iris(return_X_y=True)
preds = cross_val_predict(LogisticRegression(solver="liblinear"), X, y,
method='predict_log_proba')
assert 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 predictions.shape == (150,)
# test with multioutput y
predictions = cross_val_predict(clf, X_sparse, multioutput_y)
assert 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(solver="liblinear"),
X.tolist(),
y.tolist(), method='decision_function')
predictions = cross_val_predict(LogisticRegression(solver="liblinear"),
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,))
@pytest.mark.filterwarnings('ignore: Using or importing the ABCs from')
# python3.7 deprecation warnings in pandas via matplotlib :-/
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, cv=3)
def test_cross_val_predict_unbalanced():
X, y = make_classification(n_samples=100, n_features=2, n_redundant=0,
n_informative=2, n_clusters_per_class=1,
random_state=1)
# Change the first sample to a new class
y[0] = 2
clf = LogisticRegression(random_state=1, solver="liblinear")
cv = StratifiedKFold(n_splits=2)
train, test = list(cv.split(X, y))
yhat_proba = cross_val_predict(clf, X, y, cv=cv, method="predict_proba")
assert y[test[0]][0] == 2 # sanity check for further assertions
assert np.all(yhat_proba[test[0]][:, 2] == 0)
assert np.all(yhat_proba[test[0]][:, 0:1] > 0)
assert np.all(yhat_proba[test[1]] > 0)
assert_array_almost_equal(yhat_proba.sum(axis=1), np.ones(y.shape),
decimal=12)
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, cv=3)
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, fit_times, score_times = \
learning_curve(estimator, X, y, cv=KFold(n_splits=n_splits),
train_sizes=np.linspace(0.1, 1.0, 10),
shuffle=shuffle_train, return_times=True)
if len(w) > 0:
raise RuntimeError("Unexpected warning: %r" % w[0].message)
assert train_scores.shape == (10, 3)
assert test_scores.shape == (10, 3)
assert fit_times.shape == (10, 3)
assert score_times.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))
# Cannot use assert_array_almost_equal for fit and score times because
# the values are hardware-dependant
assert fit_times.dtype == "float64"
assert score_times.dtype == "float64"
# 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))
# 0.23. warning about tol not having its correct default value.
@pytest.mark.filterwarnings('ignore:max_iter and tol parameters have been')
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))
# 0.23. warning about tol not having its correct default value.
@pytest.mark.filterwarnings('ignore:max_iter and tol parameters have been')
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,
error_score='raise')
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 _check_is_permutation(p, 100)
assert not _check_is_permutation(np.delete(p, 23), 100)
p[0] = 23
assert not _check_is_permutation(p, 100)
# Check if the additional duplicate indices are caught
assert not _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_binary(est, X, y, method):
"""Helper for tests of cross_val_predict with binary classification"""
cv = KFold(n_splits=3, shuffle=False)
# Generate expected outputs
if y.ndim == 1:
exp_shape = (len(X),) if method == 'decision_function' else (len(X), 2)
else:
exp_shape = y.shape
expected_predictions = np.zeros(exp_shape)
for train, test in cv.split(X, y):
est = clone(est).fit(X[train], y[train])
expected_predictions[test] = getattr(est, method)(X[test])
# Check actual outputs for several representations of y
for tg in [y, y + 1, y - 2, y.astype('str')]:
assert_allclose(cross_val_predict(est, X, tg, method=method, cv=cv),
expected_predictions)
def check_cross_val_predict_multiclass(est, X, y, method):
"""Helper for tests of cross_val_predict with multiclass classification"""
cv = KFold(n_splits=3, shuffle=False)
# Generate expected outputs
float_min = np.finfo(np.float64).min
default_values = {'decision_function': float_min,
'predict_log_proba': float_min,
'predict_proba': 0}
expected_predictions = np.full((len(X), len(set(y))),
default_values[method],
dtype=np.float64)
_, y_enc = np.unique(y, return_inverse=True)
for train, test in cv.split(X, y_enc):
est = clone(est).fit(X[train], y_enc[train])
fold_preds = getattr(est, method)(X[test])
i_cols_fit = np.unique(y_enc[train])
expected_predictions[np.ix_(test, i_cols_fit)] = fold_preds
# Check actual outputs for several representations of y
for tg in [y, y + 1, y - 2, y.astype('str')]:
assert_allclose(cross_val_predict(est, X, tg, method=method, cv=cv),
expected_predictions)
def check_cross_val_predict_multilabel(est, X, y, method):
"""Check the output of cross_val_predict for 2D targets using
Estimators which provide a predictions as a list with one
element per class.
"""
cv = KFold(n_splits=3, shuffle=False)
# Create empty arrays of the correct size to hold outputs
float_min = np.finfo(np.float64).min
default_values = {'decision_function': float_min,
'predict_log_proba': float_min,
'predict_proba': 0}
n_targets = y.shape[1]
expected_preds = []
for i_col in range(n_targets):
n_classes_in_label = len(set(y[:, i_col]))
if n_classes_in_label == 2 and method == 'decision_function':
exp_shape = (len(X),)
else:
exp_shape = (len(X), n_classes_in_label)
expected_preds.append(np.full(exp_shape, default_values[method],
dtype=np.float64))
# Generate expected outputs
y_enc_cols = [np.unique(y[:, i], return_inverse=True)[1][:, np.newaxis]
for i in range(y.shape[1])]
y_enc = np.concatenate(y_enc_cols, axis=1)
for train, test in cv.split(X, y_enc):
est = clone(est).fit(X[train], y_enc[train])
fold_preds = getattr(est, method)(X[test])
for i_col in range(n_targets):
fold_cols = np.unique(y_enc[train][:, i_col])
if expected_preds[i_col].ndim == 1:
# Decision function with <=2 classes
expected_preds[i_col][test] = fold_preds[i_col]
else:
idx = np.ix_(test, fold_cols)
expected_preds[i_col][idx] = fold_preds[i_col]
# Check actual outputs for several representations of y
for tg in [y, y + 1, y - 2, y.astype('str')]:
cv_predict_output = cross_val_predict(est, X, tg, method=method, cv=cv)
assert len(cv_predict_output) == len(expected_preds)
for i in range(len(cv_predict_output)):
assert_allclose(cv_predict_output[i], expected_preds[i])
def check_cross_val_predict_with_method_binary(est):
# This test includes the decision_function with two classes.
# This is a special case: it has only one column of output.
X, y = make_classification(n_classes=2, random_state=0)
for method in ['decision_function', 'predict_proba', 'predict_log_proba']:
check_cross_val_predict_binary(est, X, y, method)
def check_cross_val_predict_with_method_multiclass(est):
iris = load_iris()
X, y = iris.data, iris.target
X, y = shuffle(X, y, random_state=0)
for method in ['decision_function', 'predict_proba', 'predict_log_proba']:
check_cross_val_predict_multiclass(est, X, y, method)
def test_cross_val_predict_with_method():
check_cross_val_predict_with_method_binary(
LogisticRegression(solver="liblinear"))
check_cross_val_predict_with_method_multiclass(
LogisticRegression(solver="liblinear"))
@pytest.mark.filterwarnings('ignore: max_iter and tol parameters')
def test_cross_val_predict_method_checking():
# Regression test for issue #9639. Tests that cross_val_predict does not
# check estimator methods (e.g. predict_proba) before fitting
iris = load_iris()
X, y = iris.data, iris.target
X, y = shuffle(X, y, random_state=0)
for method in ['decision_function', 'predict_proba', 'predict_log_proba']:
est = SGDClassifier(loss='log', random_state=2)
check_cross_val_predict_multiclass(est, X, y, method)
def test_gridsearchcv_cross_val_predict_with_method():
iris = load_iris()
X, y = iris.data, iris.target
X, y = shuffle(X, y, random_state=0)
est = GridSearchCV(LogisticRegression(random_state=42, solver="liblinear"),
{'C': [0.1, 1]},
cv=2)
for method in ['decision_function', 'predict_proba', 'predict_log_proba']:
check_cross_val_predict_multiclass(est, X, y, method)
def test_cross_val_predict_with_method_multilabel_ovr():
# OVR does multilabel predictions, but only arrays of
# binary indicator columns. The output of predict_proba
# is a 2D array with shape (n_samples, n_classes).
n_samp = 100
n_classes = 4
X, y = make_multilabel_classification(n_samples=n_samp, n_labels=3,
n_classes=n_classes, n_features=5,
random_state=42)
est = OneVsRestClassifier(LogisticRegression(solver="liblinear",
random_state=0))
for method in ['predict_proba', 'decision_function']:
check_cross_val_predict_binary(est, X, y, method=method)
class RFWithDecisionFunction(RandomForestClassifier):
# None of the current multioutput-multiclass estimators have
# decision function methods. Create a mock decision function
# to test the cross_val_predict function's handling of this case.
def decision_function(self, X):
probs = self.predict_proba(X)
msg = "This helper should only be used on multioutput-multiclass tasks"
assert isinstance(probs, list), msg
probs = [p[:, -1] if p.shape[1] == 2 else p for p in probs]
return probs
def test_cross_val_predict_with_method_multilabel_rf():
# The RandomForest allows multiple classes in each label.
# Output of predict_proba is a list of outputs of predict_proba
# for each individual label.
n_classes = 4
X, y = make_multilabel_classification(n_samples=100, n_labels=3,
n_classes=n_classes, n_features=5,
random_state=42)
y[:, 0] += y[:, 1] # Put three classes in the first column
for method in ['predict_proba', 'predict_log_proba', 'decision_function']:
est = RFWithDecisionFunction(n_estimators=5, random_state=0)
with warnings.catch_warnings():
# Suppress "RuntimeWarning: divide by zero encountered in log"
warnings.simplefilter('ignore')
check_cross_val_predict_multilabel(est, X, y, method=method)
def test_cross_val_predict_with_method_rare_class():
# Test a multiclass problem where one class will be missing from
# one of the CV training sets.
rng = np.random.RandomState(0)
X = rng.normal(0, 1, size=(14, 10))
y = np.array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 3])
est = LogisticRegression(solver="liblinear")
for method in ['predict_proba', 'predict_log_proba', 'decision_function']:
with warnings.catch_warnings():
# Suppress warning about too few examples of a class
warnings.simplefilter('ignore')
check_cross_val_predict_multiclass(est, X, y, method)
def test_cross_val_predict_with_method_multilabel_rf_rare_class():
# The RandomForest allows anything for the contents of the labels.
# Output of predict_proba is a list of outputs of predict_proba
# for each individual label.
# In this test, the first label has a class with a single example.
# We'll have one CV fold where the training data don't include it.
rng = np.random.RandomState(0)
X = rng.normal(0, 1, size=(5, 10))
y = np.array([[0, 0], [1, 1], [2, 1], [0, 1], [1, 0]])
for method in ['predict_proba', 'predict_log_proba']:
est = RFWithDecisionFunction(n_estimators=5, random_state=0)
with warnings.catch_warnings():
# Suppress "RuntimeWarning: divide by zero encountered in log"
warnings.simplefilter('ignore')
check_cross_val_predict_multilabel(est, X, y, method=method)
def get_expected_predictions(X, y, cv, classes, est, method):
expected_predictions = np.zeros([len(y), classes])
func = getattr(est, method)
for train, test in cv.split(X, y):
est.fit(X[train], y[train])
expected_predictions_ = func(X[test])
# To avoid 2 dimensional indexing
if method == 'predict_proba':
exp_pred_test = np.zeros((len(test), classes))
else:
exp_pred_test = np.full((len(test), classes),
np.finfo(expected_predictions.dtype).min)
exp_pred_test[:, est.classes_] = expected_predictions_
expected_predictions[test] = exp_pred_test
return expected_predictions
def test_cross_val_predict_class_subset():
X = np.arange(200).reshape(100, 2)
y = np.array([x // 10 for x in range(100)])
classes = 10
kfold3 = KFold(n_splits=3)
kfold4 = KFold(n_splits=4)
le = LabelEncoder()
methods = ['decision_function', 'predict_proba', 'predict_log_proba']
for method in methods:
est = LogisticRegression(solver="liblinear")
# Test with n_splits=3
predictions = cross_val_predict(est, X, y, method=method,
cv=kfold3)
# Runs a naive loop (should be same as cross_val_predict):
expected_predictions = get_expected_predictions(X, y, kfold3, classes,
est, method)
assert_array_almost_equal(expected_predictions, predictions)
# 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.)
@pytest.mark.filterwarnings('ignore: Using or importing the ABCs from')
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)
def test_fit_and_score_failing():
# Create a failing classifier to deliberately fail
failing_clf = FailingClassifier(FailingClassifier.FAILING_PARAMETER)
# dummy X data
X = np.arange(1, 10)
y = np.ones(9)
fit_and_score_args = [failing_clf, X, None, dict(), None, None, 0,
None, None]
# passing error score to trigger the warning message
fit_and_score_kwargs = {'error_score': 0}
# check if the warning message type is as expected
assert_warns(FitFailedWarning, _fit_and_score, *fit_and_score_args,
**fit_and_score_kwargs)
# since we're using FailingClassfier, our error will be the following
error_message = "ValueError: Failing classifier failed as required"
# the warning message we're expecting to see
warning_message = ("Estimator fit failed. The score on this train-test "
"partition for these parameters will be set to %f. "
"Details: \n%s" % (fit_and_score_kwargs['error_score'],
error_message))
# check if the same warning is triggered
assert_warns_message(FitFailedWarning, warning_message, _fit_and_score,
*fit_and_score_args, **fit_and_score_kwargs)
fit_and_score_kwargs = {'error_score': 'raise'}
# check if exception was raised, with default error_score='raise'
assert_raise_message(ValueError, "Failing classifier failed as required",
_fit_and_score, *fit_and_score_args,
**fit_and_score_kwargs)
# check that functions upstream pass error_score param to _fit_and_score
error_message = ("error_score must be the string 'raise' or a"
" numeric value. (Hint: if using 'raise', please"
" make sure that it has been spelled correctly.)")
assert_raise_message(ValueError, error_message, cross_validate,
failing_clf, X, cv=3, error_score='unvalid-string')
assert_raise_message(ValueError, error_message, cross_val_score,
failing_clf, X, cv=3, error_score='unvalid-string')
assert_raise_message(ValueError, error_message, learning_curve,
failing_clf, X, y, cv=3, error_score='unvalid-string')
assert_raise_message(ValueError, error_message, validation_curve,
failing_clf, X, y, 'parameter',
[FailingClassifier.FAILING_PARAMETER], cv=3,
error_score='unvalid-string')
assert failing_clf.score() == 0. # FailingClassifier coverage
def test_fit_and_score_working():
X, y = make_classification(n_samples=30, random_state=0)
clf = SVC(kernel="linear", random_state=0)
train, test = next(ShuffleSplit().split(X))
# Test return_parameters option
fit_and_score_args = [clf, X, y, dict(), train, test, 0]
fit_and_score_kwargs = {'parameters': {'max_iter': 100, 'tol': 0.1},
'fit_params': None,
'return_parameters': True}
result = _fit_and_score(*fit_and_score_args,
**fit_and_score_kwargs)
assert result[-1] == fit_and_score_kwargs['parameters']
def three_params_scorer(i, j, k):
return 3.4213
@pytest.mark.parametrize("return_train_score, scorer, expected", [
(False, three_params_scorer,
"[CV] .................................... , score=3.421, total= 0.0s"),
(True, three_params_scorer,
"[CV] ................ , score=(train=3.421, test=3.421), total= 0.0s"),
(True, {'sc1': three_params_scorer, 'sc2': three_params_scorer},
"[CV] , sc1=(train=3.421, test=3.421)"
", sc2=(train=3.421, test=3.421), total= 0.0s")
])
def test_fit_and_score_verbosity(capsys, return_train_score, scorer, expected):
X, y = make_classification(n_samples=30, random_state=0)
clf = SVC(kernel="linear", random_state=0)
train, test = next(ShuffleSplit().split(X))
# test print without train score
fit_and_score_args = [clf, X, y, scorer, train, test, 10, None, None]
fit_and_score_kwargs = {'return_train_score': return_train_score}
_fit_and_score(*fit_and_score_args, **fit_and_score_kwargs)
out, _ = capsys.readouterr()
assert out.split('\n')[1] == expected
def test_score():
error_message = "scoring must return a number, got None"
def two_params_scorer(estimator, X_test):
return None
fit_and_score_args = [None, None, None, two_params_scorer]
assert_raise_message(ValueError, error_message,
_score, *fit_and_score_args)