"""Test the search module""" from collections.abc import Iterable, Sized from io import StringIO from itertools import chain, product from functools import partial import pickle import sys from types import GeneratorType import re import numpy as np import scipy.sparse as sp import pytest from sklearn.utils.fixes import sp_version, parse_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import clone from sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification from sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection import cross_val_score from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit 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 GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics import recall_score from sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the following two estimators inherit from BaseEstimator, # to test hyperparameter search on user-defined classifiers. class MockClassifier: """Dummy classifier to test the parameter search algorithms""" def __init__(self, foo_param=0): self.foo_param = foo_param def fit(self, X, Y): assert len(X) == len(Y) self.classes_ = np.unique(Y) return self def predict(self, T): return T.shape[0] def transform(self, X): return X + self.foo_param def inverse_transform(self, X): return X - self.foo_param predict_proba = predict predict_log_proba = predict decision_function = predict def score(self, X=None, Y=None): if self.foo_param > 1: score = 1. else: score = 0. return score def get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC): """An LinearSVC classifier that has no score method.""" @property def score(self): raise AttributeError X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y = np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i in range(len(grid))] @pytest.mark.parametrize("klass", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( "input, error_type, error_message", [(0, TypeError, r'Parameter .* is not a dict or a list \(0\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .* is not a dict \(0\)'), ({'foo': 0}, TypeError, "Parameter.* value is not iterable .*" r"\(key='foo', value=0\)")] ) def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test basic properties of ParameterGrid. params1 = {"foo": [1, 2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {"foo": [4, 2], "bar": ["ham", "spam", "eggs"]} grid2 = ParameterGrid(params2) assert len(grid2) == 6 # loop to assert we can iterate over the grid multiple times for i in range(2): # tuple + chain transforms {"a": 1, "b": 2} to ("a", 1, "b", 2) points = set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points == set(("bar", x, "foo", y) for x, y in product(params2["bar"], params2["foo"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful to get default estimator settings) empty = ParameterGrid({}) assert len(empty) == 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert len(has_empty) == 4 assert list(has_empty) == [{'C': 1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the best estimator contains the right value for foo_param clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) # make sure it selects the smallest parameter in case of ties old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_["param_foo_param"].data, [1, 2, 3]) # Smoke test the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check that parameters that are estimators are cloned before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ # check that we didn't modify the parameter grid that was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize("SearchCV", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2, error_score="raise" ) # The CheckingClassifier generates an assertion error if # a parameter is missing or has length != len(X). err_msg = r"Expected fit parameter\(s\) \['eggs'\] not seen." with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = "Fit parameter spam has length 1; expected" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search on classifier that has no score function. clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y) # check that best params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that we can call score and that it gives the correct result assert grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving no scoring function raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, "no scoring", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning only occurs in situation where behavior changed: # estimator requires score method to compete with scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) # ensure the test is sane assert score_auc < 1.0 assert score_accuracy < 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if ValueError (when groups is None) propagates to GridSearchCV # And also check if groups is correctly passed to the cv object rng = np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, "The 'groups' parameter should not be None.", gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) # Should not raise an error gs.fit(X, y) def test_classes__property(): # Test that classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1] * 5) Cs = [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do not have a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') # Test that the grid searcher has no classes_ attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_') # Test that the grid searcher has no classes_ attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search over a "grid" with only one point. clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, "cv_results_") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, "cv_results_") def test_no_refit(): # Test that GSCV can be used for model selection alone without refitting clf = MockClassifier() for scoring in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search, "best_estimator_") and \ hasattr(grid_search, "best_index_") and \ hasattr(grid_search, "best_params_") # Make sure the functions predict/transform etc raise meaningful # error messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only after ' 'refitting on the best parameters' % fn_name), getattr(grid_search, fn_name), X) # Test that an invalid refit param raises appropriate error messages for refit in ["", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, "For multi-metric scoring, the " "parameter refit must be set to a scorer key", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y) def test_grid_search_error(): # Test that grid search will capture errors on data with different length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {"C": [1.0], "kernel": ["rbf"], "gamma": [0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel="rbf", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the best estimator contains the right value for foo_param clf = MockClassifier() grid_search = None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict = {"C": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError, "Parameter grid for parameter (C) needs to" " be a list or numpy array, but got ()." " Single values need to be wrapped in a list" " with one element.", GridSearchCV, clf, param_dict) param_dict = {"C": []} clf = SVC() assert_raise_message( ValueError, "Parameter values for parameter (C) need to be a non-empty sequence.", GridSearchCV, clf, param_dict) param_dict = {"C": "1,2,3"} clf = SVC(gamma='auto') assert_raise_message( ValueError, "Parameter grid for parameter (C) needs to" " be a list or numpy array, but got ()." " Single values need to be wrapped in a list" " with one element.", GridSearchCV, clf, param_dict) param_dict = {"C": np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test that grid search works with both dense and sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9 assert C == C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring="f1") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring="f1") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2 # Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test loss where greater is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid search works when the input features are given in the # form of a precomputed kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute the training kernel matrix corresponding to the linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0 # compute the test kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0 # test error is raised when the precomputed kernel is not array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns an error with a non-square precomputed # training kernel matrix K_train = np.zeros((10, 20)) y_train = np.ones((10, )) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): """Broken classifier that cannot be fit twice""" def __init__(self, parameter=None): self.parameter = parameter def fit(self, X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test for bug in refitting # Simulates re-fitting a broken estimator; this used to break with # sparse SVMs. X = np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring="precision", refit=True) clf.fit(X, y) def test_refit_callable(): """ Test refit=callable, which adds flexibility in identifying the "best" estimator. """ def refit_callable(cv_results): """ A dummy function tests `refit=callable` interface. Return the index of a model that has the least `mean_test_score`. """ # Fit a dummy clf with `refit=True` to get a list of keys in # clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure that `best_index_ != 0` for this dummy clf assert clf.best_index_ != 0 # Assert every key matches those in `cv_results` for key in clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 # Ensure `best_score_` is disabled when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): """ Test implementation catches the errors when 'best_index_' returns an invalid result. """ def refit_callable_invalid_type(cv_results): """ A dummy function tests when returned 'best_index_' is not integer. """ return None X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): """ Test implementation catches the errors when 'best_index_' returns an out of bound result. """ def refit_callable_out_bound(cv_results): """ A dummy function tests when returned 'best_index_' is out of bounds. """ return out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): """ Test refit=callable in multiple metric evaluation setting """ def refit_callable(cv_results): """ A dummy function tests `refit=callable` interface. Return the index of a model that has the least `mean_test_prec`. """ assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 # Ensure `best_score_` is disabled when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X as list in GridSearchCV X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2) y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11) check_X = lambda x: x.shape[1:] == (5, 3, 2) check_y = lambda x: x.shape[1:] == (7, 11) clf = CheckingClassifier(check_X=check_X, check_y=check_y) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, "cv_results_") def test_X_as_list(): # Pass X as list in GridSearchCV X = np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1] * 5) clf = CheckingClassifier(check_X=lambda x: isinstance(x, list)) cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, "cv_results_") def test_y_as_list(): # Pass y as list in GridSearchCV X = np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1] * 5) clf = CheckingClassifier(check_y=lambda x: isinstance(x, list)) cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, "cv_results_") @ignore_warnings def test_pandas_input(): # check cross_val_score doesn't destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass X = np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1] * 5) for InputFeatureType, TargetType in types: # X dataframe, y series X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, "cv_results_") def test_unsupervised_grid_search(): # test grid-search with unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init="random", n_init=1) # Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI and FMS can find the right number :) assert grid_search.best_params_["n_clusters"] == 3 # Single metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_["n_clusters"] == 3 # Now without a score, and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_["n_clusters"] == 4 def test_gridsearch_no_predict(): # test grid-search with an estimator without predict. # slight duplication of a test from KDE def custom_scoring(estimator, X): return 42 if estimator.bandwidth == .1 else 0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42 def test_param_sampler(): # test basic properties of param sampler param_distributions = {"kernel": ["rbf", "linear"], "C": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for x in sampler] assert len(samples) == 10 for sample in samples: assert sample["kernel"] in ["rbf", "linear"] assert 0 <= sample["C"] <= 1 # test that repeated calls yield identical parameters param_distributions = {"C": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x in sampler] == [x for x in sampler] if sp_version >= parse_version("0.16"): param_distributions = {"C": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x in sampler] == [x for x in sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check if the search `cv_results`'s array are of correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype == object for key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype == np.float64 for key in score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score'] for key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,) for key in param_keys + score_keys) @pytest.mark.filterwarnings("ignore:The parameter 'iid' is deprecated") # 0.24 def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points = 6 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points for iid in (False, True): search = GridSearchCV(SVC(), cv=n_splits, iid=iid, param_grid=params, return_train_score=True) search.fit(X, y) assert iid == search.iid cv_results = search.cv_results_ # Check if score and timing are reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0) for k in score_keys if k != 'rank_test_score') assert (all(cv_results[k] <= 1) for k in score_keys if 'time' not in k and k != 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.filterwarnings("ignore:The parameter 'iid' is deprecated") # 0.24 def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter for iid in (False, True): search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, iid=iid, param_distributions=params, return_train_score=True) search.fit(X, y) assert iid == search.iid cv_results = search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( "SearchCV, specialized_params", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter # noise-free simple 2d-data X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into two folds that are not iid # first one contains data of all 4 blobs, second only from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] = 0 # this leads to perfect classification on one fold and a score of 1/3 on # the other # create "cv" for splits cv = [[mask, ~mask], [~mask, mask]] common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores are the same as above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For the train scores, we do not take a weighted mean irrespective of # i.i.d. or not assert train_mean == pytest.approx(1) assert train_std == pytest.approx(0) @pytest.mark.filterwarnings("ignore:The parameter 'iid' is deprecated") # 0.24 def test_search_iid_param(): # Test the IID parameter # noise-free simple 2d-data X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into two folds that are not iid # first one contains data of all 4 blobs, second only from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] = 0 # this leads to perfect classification on one fold and a score of 1/3 on # the other # create "cv" for splits cv = [[mask, ~mask], [~mask, mask]] # once with iid=True (default) grid_search = GridSearchCV(SVC(gamma='auto'), param_grid={'C': [1, 10]}, cv=cv, return_train_score=True, iid=True) random_search = RandomizedSearchCV(SVC(gamma='auto'), n_iter=2, param_distributions={'C': [1, 10]}, cv=cv, iid=True, return_train_score=True) for search in (grid_search, random_search): search.fit(X, y) assert search.iid or search.iid is None test_cv_scores = np.array(list(search.cv_results_['split%d_test_score' % s_i][0] for s_i in range(search.n_splits_))) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array(list(search.cv_results_['split%d_train_' 'score' % s_i][0] for s_i in range(search.n_splits_))) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] # Test the first candidate assert search.cv_results_['param_C'][0] == 1 assert_array_almost_equal(test_cv_scores, [1, 1. / 3.]) assert_array_almost_equal(train_cv_scores, [1, 1]) # for first split, 1/4 of dataset is in test, for second 3/4. # take weighted average and weighted std expected_test_mean = 1 * 1. / 4. + 1. / 3. * 3. / 4. expected_test_std = np.sqrt(1. / 4 * (expected_test_mean - 1) ** 2 + 3. / 4 * (expected_test_mean - 1. / 3.) ** 2) assert_almost_equal(test_mean, expected_test_mean) assert_almost_equal(test_std, expected_test_std) assert_array_almost_equal(test_cv_scores, cross_val_score(SVC(C=1, gamma='auto'), X, y, cv=cv)) # For the train scores, we do not take a weighted mean irrespective of # i.i.d. or not assert_almost_equal(train_mean, 1) assert_almost_equal(train_std, 0) # once with iid=False grid_search = GridSearchCV(SVC(gamma='auto'), param_grid={'C': [1, 10]}, cv=cv, iid=False, return_train_score=True) random_search = RandomizedSearchCV(SVC(gamma='auto'), n_iter=2, param_distributions={'C': [1, 10]}, cv=cv, iid=False, return_train_score=True) for search in (grid_search, random_search): search.fit(X, y) assert not search.iid test_cv_scores = np.array(list(search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_))) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array(list(search.cv_results_['split%d_train_' 'score' % s][0] for s in range(search.n_splits_))) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores are the same as above assert_array_almost_equal(test_cv_scores, [1, 1. / 3.]) # Unweighted mean/std is used assert_almost_equal(test_mean, np.mean(test_cv_scores)) assert_almost_equal(test_std, np.std(test_cv_scores)) # For the train scores, we do not take a weighted mean irrespective of # i.i.d. or not assert_almost_equal(train_mean, 1) assert_almost_equal(train_std, 0) @pytest.mark.filterwarnings("ignore:The parameter 'iid' is deprecated") # 0.24 def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] for iid in (False, True): grid_searches = [] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, iid=iid, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) assert grid_search.iid == iid grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches, iid=iid) @pytest.mark.filterwarnings("ignore:The parameter 'iid' is deprecated") # 0.24 def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 # Scipy 0.12's stats dists do not accept seed, hence we use param grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for iid in (True, False): for refit in (True, False): random_searches = [] for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): # If True, for multi-metric pass refit='accuracy' if refit: probability = True refit = 'accuracy' if isinstance(scoring, tuple) else refit else: probability = False clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, iid=iid, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches, iid=iid) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) @pytest.mark.filterwarnings("ignore:The parameter 'iid' is deprecated") # 0.24 def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec, iid): """Compare multi-metric cv_results with the ensemble of multiple single metric cv_results from single metric grid/random search""" assert search_multi.iid == iid assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v in search_rec.cv_results_.items()}) # Check if score and timing are reasonable, also checks if the keys # are present assert all((np.all(cv_results_multi[k] <= 1) for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys, other than time keys, among multi-metric and # single metric grid search results. np.testing.assert_equal performs a # deep nested comparison of the two cv_results dicts np.testing.assert_equal({k: v for k, v in cv_results_multi.items() if not k.endswith('_time')}, {k: v for k, v in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): """Compare refit multi-metric search methods with single metric methods""" assert search_acc.refit == refit if refit: assert search_multi.refit == 'accuracy' else: assert not search_multi.refit return # search cannot predict/score without refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) # The two C values are close enough to give similar models # which would result in a tie of their mean cv-scores param_grid = {'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_ # Check tie breaking strategy - # Check that there is a tie in the mean scores between # candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X, y = [[1], [2], [3], [4], [5]], [0, 0, 0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {"random_state": [0, None]} cv = KFold() for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1, ], [2, ], [3, ], [4, ]] y = [0, 1, 1, 0] gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for search in (gs, rs): search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']: # NOTE The precision of time.time in windows is not high # enough for the fit/score times to be non-zero for trivial X and y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1) for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search, "refit_time_") assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test that correct scores are used n_splits = 3 clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] for score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test scorer names result_keys = list(cv_results.keys()) expected_keys = (("mean_test_score", "rank_test_score") + tuple("split%d_test_score" % cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits))) for i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score == "f1": correct_score = f1_score(y[test], clf.predict(X[test])) elif score == "roc_auc": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as the function will be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for train, test in cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point( X, y, clone(svc), params, train, test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test]) # Test the return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params assert n_test_samples == test.size # Should raise an error upon multimetric scorer assert_raise_message(ValueError, "For evaluating multiple scores, use " "sklearn.model_selection.cross_validate instead.", fit_grid_point, X, y, svc, params, train, test, {'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will be removed on 0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = ("fit_grid_point is deprecated in version 0.23 " "and will be removed in version 0.25") params = {'C': 0.1} train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train, test, scorer, verbose=False) def test_pickle(): # Test that a fit search can be pickled clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {"max_depth": [1, 2, 3, 4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search cv for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a randomized search for est in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when disabled on estimator. X = np.arange(20).reshape(5, -1) y = [0, 0, 1, 1, 1] clf = SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs, "predict_proba") def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y = [0, 0, 1, 1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): """Classifier that raises a ValueError on fit()""" FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter = parameter def fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError("Failing classifier failed as required") def predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise' # Ensures that a warning is raised and score reset where appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() # refit=False because we only want to check that errors caused by fits # to individual folds will be caught and warnings raised instead. If # refit was done, then an exception would be raised on refit and not # caught by grid_search (expected behavior), and this would cause an # error in this test. gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) # Ensure that grid scores were set to zero as required for those fits # that are expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that succeeded estimators have lower ranks assert ranks[0] <= 2 and ranks[1] <= 2 # Check that failed estimator has the highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise' raises the error X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() # refit=False because we want to test the behaviour of the grid search part gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a ValueError so this is what we look for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): # raise warning if n_iter is bigger than total parameter space params = [{'first': [0, 1], 'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter = 9 grid_size = 8 expected_warning = ('The total space of parameters %d is smaller ' 'than n_iter=%d. Running %d iterations. For ' 'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to GridSearchCV if n_iter the same as grid_size sampler = ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples) == 8 for values in ParameterGrid(params): assert values in samples # test sampling without replacement in a large grid params = {'a': range(10), 'b': range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert len(samples) == 99 hashable_samples = ["a%db%dc%d" % (p['a'], p['b'], p['c']) for p in samples] assert len(set(hashable_samples)) == 99 # doesn't go into infinite loops params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make sure the predict_proba works when loss is specified # as one of the parameters in the param_grid. param_grid = { 'loss': ['log'], } X = np.arange(24).reshape(6, -1) y = [0, 0, 0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator is not fitted, `predict_proba` is not available as the # loss is 'hinge'. assert not hasattr(clf, "predict_proba") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is not available when setting loss=['hinge'] # in param_grid param_grid = { 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, "predict_proba") clf.fit(X, y) assert not hasattr(clf, "predict_proba") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y = [0, 0, 0, 1, 1, 1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if a one time iterable is accepted as a cv parameter. n_samples = 100 n_splits = 5 X, y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check if generators are supported as cv and # that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv where split can be called only # once if ``cv.split`` is called once per param setting in GridSearchCV.fit # the 2nd and 3rd parameter will not be evaluated as no train/test indices # will be generated for the 2nd and subsequent cv.split calls. # This is a check to make sure cv.split is not called once per param # setting. np.testing.assert_equal({k: v for k, v in gs.cv_results_.items() if not k.endswith('_time')}, {k: v for k, v in gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency of folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As the first two param settings (C=0.1) and the next two param # settings (C=0.2) are same, the test and train scores must also be # same as long as the same train/test indices are generated for all # the cv splits, for both param setting for score_type in ('train', 'test'): per_param_scores = {} for param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k in results: if not k.endswith('_time'): # XXX: results['params'] is a list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) # Using regressor to make sure each score differs clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results, gscv) # TODO: remove in v0.24, the deprecation goes away then. with pytest.warns(FutureWarning, match="attribute is to be deprecated from version 0.22"): for attr in dir(gscv): if (attr[0].islower() and attr[-1:] == '_' and attr not in {'cv_results_', 'best_estimator_', 'refit_time_', }): assert getattr(gscv, attr) == getattr(mycv, attr), \ "Attribute %s not equal" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None, **fit_params): return self # this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match="_run_search not implemented."): # this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) @pytest.mark.parametrize("iid", [False, True]) def test_deprecated_grid_search_iid(iid): # FIXME: remove in 0.24 depr_msg = "The parameter 'iid' is deprecated in 0.22 and will be removed" X, y = make_blobs(n_samples=54, random_state=0, centers=2) grid = GridSearchCV( SVC(random_state=0), param_grid={'C': [10]}, cv=3, iid=iid ) with pytest.warns(FutureWarning, match=depr_msg): grid.fit(X, y) def test_empty_cv_iterator_error(): # Use global X, y # create cv cv = KFold(n_splits=3).split(X) # pop all of it, this should cause the expected ValueError [u for u in cv] # cv is empty now train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that this raises an error with pytest.raises(ValueError, match='No fits were performed. ' 'Was the CV iterator empty\\? ' 'Were there no candidates\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global X, y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1 # create bad cv cv = BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that this raises an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent results. Expected \\d+ ' 'splits, got \\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure grid search and random search delegate n_features_in to the # best estimator n_features = 4 X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): """ Test implementation of BaseSearchCV has the _pairwise property which matches the _pairwise property of its estimator. This test make sure _pairwise is delegated to the base estimator. Non-regression test for issue #13920. """ est = BaseEstimator() attr_message = "BaseSearchCV _pairwise property must match estimator" for _pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): """ Test implementation of BaseSearchCV has the _pairwise property which matches the _pairwise property of its estimator. This test ensures the equivalence of 'precomputed'. Non-regression test for issue #13920. """ n_samples = 50 n_splits = 2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} # defaults to euclidean metric (minkowski p = 2) clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) # precompute euclidean metric to validate _pairwise is working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message = "GridSearchCV not identical with precomputed metric" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( "SearchCV, param_search", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for scalar values in fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a = a def fit(self, X, y, r=None): self.r_ = r def predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( "SearchCV, param_search", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar values in fit_params, for instance in LightGBM # that do not exactly respect the scikit-learn API contract but that we do # not want to break without an explicit deprecation cycle and API # recommendations for implementing early stopping with a user provided # validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param) # The tuple of arrays should be preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params` should be data dependent (e.g. `sample_weight`) which # is not the case for the following parameters. But this abuse is common in # popular third-party libraries and we should tolerate this behavior for # now and be careful not to break support for those without following # proper deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, } model.fit(X_train, y_train, **fit_params)