""" Test the pipeline module. """ from tempfile import mkdtemp import shutil import time import re import itertools import pytest import numpy as np from scipy import sparse import joblib from sklearn.utils.fixes import parse_version from sklearn.utils._testing import ( assert_raises, assert_raises_regex, assert_raise_message, assert_allclose, assert_array_equal, assert_array_almost_equal, assert_no_warnings, MinimalClassifier, MinimalRegressor, MinimalTransformer, ) from sklearn.base import clone, is_classifier, BaseEstimator, TransformerMixin from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline, make_union from sklearn.svm import SVC from sklearn.neighbors import LocalOutlierFactor from sklearn.linear_model import LogisticRegression, Lasso from sklearn.linear_model import LinearRegression from sklearn.metrics import accuracy_score, r2_score from sklearn.cluster import KMeans from sklearn.feature_selection import SelectKBest, f_classif from sklearn.dummy import DummyRegressor from sklearn.decomposition import PCA, TruncatedSVD from sklearn.datasets import load_iris from sklearn.preprocessing import StandardScaler from sklearn.feature_extraction.text import CountVectorizer from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.impute import SimpleImputer iris = load_iris() JUNK_FOOD_DOCS = ( "the pizza pizza beer copyright", "the pizza burger beer copyright", "the the pizza beer beer copyright", "the burger beer beer copyright", "the coke burger coke copyright", "the coke burger burger", ) class NoFit: """Small class to test parameter dispatching. """ def __init__(self, a=None, b=None): self.a = a self.b = b class NoTrans(NoFit): def fit(self, X, y): return self def get_params(self, deep=False): return {'a': self.a, 'b': self.b} def set_params(self, **params): self.a = params['a'] return self class NoInvTransf(NoTrans): def transform(self, X): return X class Transf(NoInvTransf): def transform(self, X): return X def inverse_transform(self, X): return X class TransfFitParams(Transf): def fit(self, X, y, **fit_params): self.fit_params = fit_params return self class Mult(BaseEstimator): def __init__(self, mult=1): self.mult = mult def fit(self, X, y): return self def transform(self, X): return np.asarray(X) * self.mult def inverse_transform(self, X): return np.asarray(X) / self.mult def predict(self, X): return (np.asarray(X) * self.mult).sum(axis=1) predict_proba = predict_log_proba = decision_function = predict def score(self, X, y=None): return np.sum(X) class FitParamT(BaseEstimator): """Mock classifier """ def __init__(self): self.successful = False def fit(self, X, y, should_succeed=False): self.successful = should_succeed def predict(self, X): return self.successful def fit_predict(self, X, y, should_succeed=False): self.fit(X, y, should_succeed=should_succeed) return self.predict(X) def score(self, X, y=None, sample_weight=None): if sample_weight is not None: X = X * sample_weight return np.sum(X) class DummyTransf(Transf): """Transformer which store the column means""" def fit(self, X, y): self.means_ = np.mean(X, axis=0) # store timestamp to figure out whether the result of 'fit' has been # cached or not self.timestamp_ = time.time() return self class DummyEstimatorParams(BaseEstimator): """Mock classifier that takes params on predict""" def fit(self, X, y): return self def predict(self, X, got_attribute=False): self.got_attribute = got_attribute return self def test_pipeline_init(): # Test the various init parameters of the pipeline. assert_raises(TypeError, Pipeline) # Check that we can't instantiate pipelines with objects without fit # method assert_raises_regex(TypeError, 'Last step of Pipeline should implement fit ' 'or be the string \'passthrough\'' '.*NoFit.*', Pipeline, [('clf', NoFit())]) # Smoke test with only an estimator clf = NoTrans() pipe = Pipeline([('svc', clf)]) assert (pipe.get_params(deep=True) == dict(svc__a=None, svc__b=None, svc=clf, **pipe.get_params(deep=False))) # Check that params are set pipe.set_params(svc__a=0.1) assert clf.a == 0.1 assert clf.b is None # Smoke test the repr: repr(pipe) # Test with two objects clf = SVC() filter1 = SelectKBest(f_classif) pipe = Pipeline([('anova', filter1), ('svc', clf)]) # Check that estimators are not cloned on pipeline construction assert pipe.named_steps['anova'] is filter1 assert pipe.named_steps['svc'] is clf # Check that we can't instantiate with non-transformers on the way # Note that NoTrans implements fit, but not transform assert_raises_regex(TypeError, 'All intermediate steps should be transformers' '.*\\bNoTrans\\b.*', Pipeline, [('t', NoTrans()), ('svc', clf)]) # Check that params are set pipe.set_params(svc__C=0.1) assert clf.C == 0.1 # Smoke test the repr: repr(pipe) # Check that params are not set when naming them wrong assert_raises(ValueError, pipe.set_params, anova__C=0.1) # Test clone pipe2 = assert_no_warnings(clone, pipe) assert not pipe.named_steps['svc'] is pipe2.named_steps['svc'] # Check that apart from estimators, the parameters are the same params = pipe.get_params(deep=True) params2 = pipe2.get_params(deep=True) for x in pipe.get_params(deep=False): params.pop(x) for x in pipe2.get_params(deep=False): params2.pop(x) # Remove estimators that where copied params.pop('svc') params.pop('anova') params2.pop('svc') params2.pop('anova') assert params == params2 def test_pipeline_init_tuple(): # Pipeline accepts steps as tuple X = np.array([[1, 2]]) pipe = Pipeline((('transf', Transf()), ('clf', FitParamT()))) pipe.fit(X, y=None) pipe.score(X) pipe.set_params(transf='passthrough') pipe.fit(X, y=None) pipe.score(X) def test_pipeline_methods_anova(): # Test the various methods of the pipeline (anova). X = iris.data y = iris.target # Test with Anova + LogisticRegression clf = LogisticRegression() filter1 = SelectKBest(f_classif, k=2) pipe = Pipeline([('anova', filter1), ('logistic', clf)]) pipe.fit(X, y) pipe.predict(X) pipe.predict_proba(X) pipe.predict_log_proba(X) pipe.score(X, y) def test_pipeline_fit_params(): # Test that the pipeline can take fit parameters pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())]) pipe.fit(X=None, y=None, clf__should_succeed=True) # classifier should return True assert pipe.predict(None) # and transformer params should not be changed assert pipe.named_steps['transf'].a is None assert pipe.named_steps['transf'].b is None # invalid parameters should raise an error message assert_raise_message( TypeError, "fit() got an unexpected keyword argument 'bad'", pipe.fit, None, None, clf__bad=True ) def test_pipeline_sample_weight_supported(): # Pipeline should pass sample_weight X = np.array([[1, 2]]) pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())]) pipe.fit(X, y=None) assert pipe.score(X) == 3 assert pipe.score(X, y=None) == 3 assert pipe.score(X, y=None, sample_weight=None) == 3 assert pipe.score(X, sample_weight=np.array([2, 3])) == 8 def test_pipeline_sample_weight_unsupported(): # When sample_weight is None it shouldn't be passed X = np.array([[1, 2]]) pipe = Pipeline([('transf', Transf()), ('clf', Mult())]) pipe.fit(X, y=None) assert pipe.score(X) == 3 assert pipe.score(X, sample_weight=None) == 3 assert_raise_message( TypeError, "score() got an unexpected keyword argument 'sample_weight'", pipe.score, X, sample_weight=np.array([2, 3]) ) def test_pipeline_raise_set_params_error(): # Test pipeline raises set params error message for nested models. pipe = Pipeline([('cls', LinearRegression())]) # expected error message error_msg = ('Invalid parameter %s for estimator %s. ' 'Check the list of available parameters ' 'with `estimator.get_params().keys()`.') assert_raise_message(ValueError, error_msg % ('fake', pipe), pipe.set_params, fake='nope') # nested model check assert_raise_message(ValueError, error_msg % ("fake", pipe), pipe.set_params, fake__estimator='nope') def test_pipeline_methods_pca_svm(): # Test the various methods of the pipeline (pca + svm). X = iris.data y = iris.target # Test with PCA + SVC clf = SVC(probability=True, random_state=0) pca = PCA(svd_solver='full', n_components='mle', whiten=True) pipe = Pipeline([('pca', pca), ('svc', clf)]) pipe.fit(X, y) pipe.predict(X) pipe.predict_proba(X) pipe.predict_log_proba(X) pipe.score(X, y) def test_pipeline_score_samples_pca_lof(): X = iris.data # Test that the score_samples method is implemented on a pipeline. # Test that the score_samples method on pipeline yields same results as # applying transform and score_samples steps separately. pca = PCA(svd_solver='full', n_components='mle', whiten=True) lof = LocalOutlierFactor(novelty=True) pipe = Pipeline([('pca', pca), ('lof', lof)]) pipe.fit(X) # Check the shapes assert pipe.score_samples(X).shape == (X.shape[0],) # Check the values lof.fit(pca.fit_transform(X)) assert_allclose(pipe.score_samples(X), lof.score_samples(pca.transform(X))) def test_score_samples_on_pipeline_without_score_samples(): X = np.array([[1], [2]]) y = np.array([1, 2]) # Test that a pipeline does not have score_samples method when the final # step of the pipeline does not have score_samples defined. pipe = make_pipeline(LogisticRegression()) pipe.fit(X, y) with pytest.raises(AttributeError, match="'LogisticRegression' object has no attribute " "'score_samples'"): pipe.score_samples(X) def test_pipeline_methods_preprocessing_svm(): # Test the various methods of the pipeline (preprocessing + svm). X = iris.data y = iris.target n_samples = X.shape[0] n_classes = len(np.unique(y)) scaler = StandardScaler() pca = PCA(n_components=2, svd_solver='randomized', whiten=True) clf = SVC(probability=True, random_state=0, decision_function_shape='ovr') for preprocessing in [scaler, pca]: pipe = Pipeline([('preprocess', preprocessing), ('svc', clf)]) pipe.fit(X, y) # check shapes of various prediction functions predict = pipe.predict(X) assert predict.shape == (n_samples,) proba = pipe.predict_proba(X) assert proba.shape == (n_samples, n_classes) log_proba = pipe.predict_log_proba(X) assert log_proba.shape == (n_samples, n_classes) decision_function = pipe.decision_function(X) assert decision_function.shape == (n_samples, n_classes) pipe.score(X, y) def test_fit_predict_on_pipeline(): # test that the fit_predict method is implemented on a pipeline # test that the fit_predict on pipeline yields same results as applying # transform and clustering steps separately scaler = StandardScaler() km = KMeans(random_state=0) # As pipeline doesn't clone estimators on construction, # it must have its own estimators scaler_for_pipeline = StandardScaler() km_for_pipeline = KMeans(random_state=0) # first compute the transform and clustering step separately scaled = scaler.fit_transform(iris.data) separate_pred = km.fit_predict(scaled) # use a pipeline to do the transform and clustering in one step pipe = Pipeline([ ('scaler', scaler_for_pipeline), ('Kmeans', km_for_pipeline) ]) pipeline_pred = pipe.fit_predict(iris.data) assert_array_almost_equal(pipeline_pred, separate_pred) def test_fit_predict_on_pipeline_without_fit_predict(): # tests that a pipeline does not have fit_predict method when final # step of pipeline does not have fit_predict defined scaler = StandardScaler() pca = PCA(svd_solver='full') pipe = Pipeline([('scaler', scaler), ('pca', pca)]) assert_raises_regex(AttributeError, "'PCA' object has no attribute 'fit_predict'", getattr, pipe, 'fit_predict') def test_fit_predict_with_intermediate_fit_params(): # tests that Pipeline passes fit_params to intermediate steps # when fit_predict is invoked pipe = Pipeline([('transf', TransfFitParams()), ('clf', FitParamT())]) pipe.fit_predict(X=None, y=None, transf__should_get_this=True, clf__should_succeed=True) assert pipe.named_steps['transf'].fit_params['should_get_this'] assert pipe.named_steps['clf'].successful assert 'should_succeed' not in pipe.named_steps['transf'].fit_params def test_predict_with_predict_params(): # tests that Pipeline passes predict_params to the final estimator # when predict is invoked pipe = Pipeline([('transf', Transf()), ('clf', DummyEstimatorParams())]) pipe.fit(None, None) pipe.predict(X=None, got_attribute=True) assert pipe.named_steps['clf'].got_attribute def test_feature_union(): # basic sanity check for feature union X = iris.data X -= X.mean(axis=0) y = iris.target svd = TruncatedSVD(n_components=2, random_state=0) select = SelectKBest(k=1) fs = FeatureUnion([("svd", svd), ("select", select)]) fs.fit(X, y) X_transformed = fs.transform(X) assert X_transformed.shape == (X.shape[0], 3) # check if it does the expected thing assert_array_almost_equal(X_transformed[:, :-1], svd.fit_transform(X)) assert_array_equal(X_transformed[:, -1], select.fit_transform(X, y).ravel()) # test if it also works for sparse input # We use a different svd object to control the random_state stream fs = FeatureUnion([("svd", svd), ("select", select)]) X_sp = sparse.csr_matrix(X) X_sp_transformed = fs.fit_transform(X_sp, y) assert_array_almost_equal(X_transformed, X_sp_transformed.toarray()) # Test clone fs2 = assert_no_warnings(clone, fs) assert fs.transformer_list[0][1] is not fs2.transformer_list[0][1] # test setting parameters fs.set_params(select__k=2) assert fs.fit_transform(X, y).shape == (X.shape[0], 4) # test it works with transformers missing fit_transform fs = FeatureUnion([("mock", Transf()), ("svd", svd), ("select", select)]) X_transformed = fs.fit_transform(X, y) assert X_transformed.shape == (X.shape[0], 8) # test error if some elements do not support transform assert_raises_regex(TypeError, 'All estimators should implement fit and ' 'transform.*\\bNoTrans\\b', FeatureUnion, [("transform", Transf()), ("no_transform", NoTrans())]) # test that init accepts tuples fs = FeatureUnion((("svd", svd), ("select", select))) fs.fit(X, y) def test_make_union(): pca = PCA(svd_solver='full') mock = Transf() fu = make_union(pca, mock) names, transformers = zip(*fu.transformer_list) assert names == ("pca", "transf") assert transformers == (pca, mock) def test_make_union_kwargs(): pca = PCA(svd_solver='full') mock = Transf() fu = make_union(pca, mock, n_jobs=3) assert fu.transformer_list == make_union(pca, mock).transformer_list assert 3 == fu.n_jobs # invalid keyword parameters should raise an error message assert_raise_message( TypeError, "make_union() got an unexpected " "keyword argument 'transformer_weights'", make_union, pca, mock, transformer_weights={'pca': 10, 'Transf': 1} ) def test_pipeline_transform(): # Test whether pipeline works with a transformer at the end. # Also test pipeline.transform and pipeline.inverse_transform X = iris.data pca = PCA(n_components=2, svd_solver='full') pipeline = Pipeline([('pca', pca)]) # test transform and fit_transform: X_trans = pipeline.fit(X).transform(X) X_trans2 = pipeline.fit_transform(X) X_trans3 = pca.fit_transform(X) assert_array_almost_equal(X_trans, X_trans2) assert_array_almost_equal(X_trans, X_trans3) X_back = pipeline.inverse_transform(X_trans) X_back2 = pca.inverse_transform(X_trans) assert_array_almost_equal(X_back, X_back2) def test_pipeline_fit_transform(): # Test whether pipeline works with a transformer missing fit_transform X = iris.data y = iris.target transf = Transf() pipeline = Pipeline([('mock', transf)]) # test fit_transform: X_trans = pipeline.fit_transform(X, y) X_trans2 = transf.fit(X, y).transform(X) assert_array_almost_equal(X_trans, X_trans2) @pytest.mark.parametrize("start, end", [(0, 1), (0, 2), (1, 2), (1, 3), (None, 1), (1, None), (None, None)]) def test_pipeline_slice(start, end): pipe = Pipeline( [("transf1", Transf()), ("transf2", Transf()), ("clf", FitParamT())], memory="123", verbose=True, ) pipe_slice = pipe[start:end] # Test class assert isinstance(pipe_slice, Pipeline) # Test steps assert pipe_slice.steps == pipe.steps[start:end] # Test named_steps attribute assert list(pipe_slice.named_steps.items()) == list( pipe.named_steps.items())[start:end] # Test the rest of the parameters pipe_params = pipe.get_params(deep=False) pipe_slice_params = pipe_slice.get_params(deep=False) del pipe_params["steps"] del pipe_slice_params["steps"] assert pipe_params == pipe_slice_params # Test exception msg = "Pipeline slicing only supports a step of 1" with pytest.raises(ValueError, match=msg): pipe[start:end:-1] def test_pipeline_index(): transf = Transf() clf = FitParamT() pipe = Pipeline([('transf', transf), ('clf', clf)]) assert pipe[0] == transf assert pipe['transf'] == transf assert pipe[-1] == clf assert pipe['clf'] == clf assert_raises(IndexError, lambda: pipe[3]) assert_raises(KeyError, lambda: pipe['foobar']) def test_set_pipeline_steps(): transf1 = Transf() transf2 = Transf() pipeline = Pipeline([('mock', transf1)]) assert pipeline.named_steps['mock'] is transf1 # Directly setting attr pipeline.steps = [('mock2', transf2)] assert 'mock' not in pipeline.named_steps assert pipeline.named_steps['mock2'] is transf2 assert [('mock2', transf2)] == pipeline.steps # Using set_params pipeline.set_params(steps=[('mock', transf1)]) assert [('mock', transf1)] == pipeline.steps # Using set_params to replace single step pipeline.set_params(mock=transf2) assert [('mock', transf2)] == pipeline.steps # With invalid data pipeline.set_params(steps=[('junk', ())]) assert_raises(TypeError, pipeline.fit, [[1]], [1]) assert_raises(TypeError, pipeline.fit_transform, [[1]], [1]) def test_pipeline_named_steps(): transf = Transf() mult2 = Mult(mult=2) pipeline = Pipeline([('mock', transf), ("mult", mult2)]) # Test access via named_steps bunch object assert 'mock' in pipeline.named_steps assert 'mock2' not in pipeline.named_steps assert pipeline.named_steps.mock is transf assert pipeline.named_steps.mult is mult2 # Test bunch with conflict attribute of dict pipeline = Pipeline([('values', transf), ("mult", mult2)]) assert pipeline.named_steps.values is not transf assert pipeline.named_steps.mult is mult2 @pytest.mark.parametrize('passthrough', [None, 'passthrough']) def test_pipeline_correctly_adjusts_steps(passthrough): X = np.array([[1]]) y = np.array([1]) mult2 = Mult(mult=2) mult3 = Mult(mult=3) mult5 = Mult(mult=5) pipeline = Pipeline([ ('m2', mult2), ('bad', passthrough), ('m3', mult3), ('m5', mult5) ]) pipeline.fit(X, y) expected_names = ['m2', 'bad', 'm3', 'm5'] actual_names = [name for name, _ in pipeline.steps] assert expected_names == actual_names @pytest.mark.parametrize('passthrough', [None, 'passthrough']) def test_set_pipeline_step_passthrough(passthrough): X = np.array([[1]]) y = np.array([1]) mult2 = Mult(mult=2) mult3 = Mult(mult=3) mult5 = Mult(mult=5) def make(): return Pipeline([('m2', mult2), ('m3', mult3), ('last', mult5)]) pipeline = make() exp = 2 * 3 * 5 assert_array_equal([[exp]], pipeline.fit_transform(X, y)) assert_array_equal([exp], pipeline.fit(X).predict(X)) assert_array_equal(X, pipeline.inverse_transform([[exp]])) pipeline.set_params(m3=passthrough) exp = 2 * 5 assert_array_equal([[exp]], pipeline.fit_transform(X, y)) assert_array_equal([exp], pipeline.fit(X).predict(X)) assert_array_equal(X, pipeline.inverse_transform([[exp]])) assert (pipeline.get_params(deep=True) == {'steps': pipeline.steps, 'm2': mult2, 'm3': passthrough, 'last': mult5, 'memory': None, 'm2__mult': 2, 'last__mult': 5, 'verbose': False }) pipeline.set_params(m2=passthrough) exp = 5 assert_array_equal([[exp]], pipeline.fit_transform(X, y)) assert_array_equal([exp], pipeline.fit(X).predict(X)) assert_array_equal(X, pipeline.inverse_transform([[exp]])) # for other methods, ensure no AttributeErrors on None: other_methods = ['predict_proba', 'predict_log_proba', 'decision_function', 'transform', 'score'] for method in other_methods: getattr(pipeline, method)(X) pipeline.set_params(m2=mult2) exp = 2 * 5 assert_array_equal([[exp]], pipeline.fit_transform(X, y)) assert_array_equal([exp], pipeline.fit(X).predict(X)) assert_array_equal(X, pipeline.inverse_transform([[exp]])) pipeline = make() pipeline.set_params(last=passthrough) # mult2 and mult3 are active exp = 6 assert_array_equal([[exp]], pipeline.fit(X, y).transform(X)) assert_array_equal([[exp]], pipeline.fit_transform(X, y)) assert_array_equal(X, pipeline.inverse_transform([[exp]])) assert_raise_message(AttributeError, "'str' object has no attribute 'predict'", getattr, pipeline, 'predict') # Check 'passthrough' step at construction time exp = 2 * 5 pipeline = Pipeline( [('m2', mult2), ('m3', passthrough), ('last', mult5)]) assert_array_equal([[exp]], pipeline.fit_transform(X, y)) assert_array_equal([exp], pipeline.fit(X).predict(X)) assert_array_equal(X, pipeline.inverse_transform([[exp]])) def test_pipeline_ducktyping(): pipeline = make_pipeline(Mult(5)) pipeline.predict pipeline.transform pipeline.inverse_transform pipeline = make_pipeline(Transf()) assert not hasattr(pipeline, 'predict') pipeline.transform pipeline.inverse_transform pipeline = make_pipeline('passthrough') assert pipeline.steps[0] == ('passthrough', 'passthrough') assert not hasattr(pipeline, 'predict') pipeline.transform pipeline.inverse_transform pipeline = make_pipeline(Transf(), NoInvTransf()) assert not hasattr(pipeline, 'predict') pipeline.transform assert not hasattr(pipeline, 'inverse_transform') pipeline = make_pipeline(NoInvTransf(), Transf()) assert not hasattr(pipeline, 'predict') pipeline.transform assert not hasattr(pipeline, 'inverse_transform') def test_make_pipeline(): t1 = Transf() t2 = Transf() pipe = make_pipeline(t1, t2) assert isinstance(pipe, Pipeline) assert pipe.steps[0][0] == "transf-1" assert pipe.steps[1][0] == "transf-2" pipe = make_pipeline(t1, t2, FitParamT()) assert isinstance(pipe, Pipeline) assert pipe.steps[0][0] == "transf-1" assert pipe.steps[1][0] == "transf-2" assert pipe.steps[2][0] == "fitparamt" def test_feature_union_weights(): # test feature union with transformer weights X = iris.data y = iris.target pca = PCA(n_components=2, svd_solver='randomized', random_state=0) select = SelectKBest(k=1) # test using fit followed by transform fs = FeatureUnion([("pca", pca), ("select", select)], transformer_weights={"pca": 10}) fs.fit(X, y) X_transformed = fs.transform(X) # test using fit_transform fs = FeatureUnion([("pca", pca), ("select", select)], transformer_weights={"pca": 10}) X_fit_transformed = fs.fit_transform(X, y) # test it works with transformers missing fit_transform fs = FeatureUnion([("mock", Transf()), ("pca", pca), ("select", select)], transformer_weights={"mock": 10}) X_fit_transformed_wo_method = fs.fit_transform(X, y) # check against expected result # We use a different pca object to control the random_state stream assert_array_almost_equal(X_transformed[:, :-1], 10 * pca.fit_transform(X)) assert_array_equal(X_transformed[:, -1], select.fit_transform(X, y).ravel()) assert_array_almost_equal(X_fit_transformed[:, :-1], 10 * pca.fit_transform(X)) assert_array_equal(X_fit_transformed[:, -1], select.fit_transform(X, y).ravel()) assert X_fit_transformed_wo_method.shape == (X.shape[0], 7) def test_feature_union_parallel(): # test that n_jobs work for FeatureUnion X = JUNK_FOOD_DOCS fs = FeatureUnion([ ("words", CountVectorizer(analyzer='word')), ("chars", CountVectorizer(analyzer='char')), ]) fs_parallel = FeatureUnion([ ("words", CountVectorizer(analyzer='word')), ("chars", CountVectorizer(analyzer='char')), ], n_jobs=2) fs_parallel2 = FeatureUnion([ ("words", CountVectorizer(analyzer='word')), ("chars", CountVectorizer(analyzer='char')), ], n_jobs=2) fs.fit(X) X_transformed = fs.transform(X) assert X_transformed.shape[0] == len(X) fs_parallel.fit(X) X_transformed_parallel = fs_parallel.transform(X) assert X_transformed.shape == X_transformed_parallel.shape assert_array_equal( X_transformed.toarray(), X_transformed_parallel.toarray() ) # fit_transform should behave the same X_transformed_parallel2 = fs_parallel2.fit_transform(X) assert_array_equal( X_transformed.toarray(), X_transformed_parallel2.toarray() ) # transformers should stay fit after fit_transform X_transformed_parallel2 = fs_parallel2.transform(X) assert_array_equal( X_transformed.toarray(), X_transformed_parallel2.toarray() ) def test_feature_union_feature_names(): word_vect = CountVectorizer(analyzer="word") char_vect = CountVectorizer(analyzer="char_wb", ngram_range=(3, 3)) ft = FeatureUnion([("chars", char_vect), ("words", word_vect)]) ft.fit(JUNK_FOOD_DOCS) feature_names = ft.get_feature_names() for feat in feature_names: assert "chars__" in feat or "words__" in feat assert len(feature_names) == 35 ft = FeatureUnion([("tr1", Transf())]).fit([[1]]) assert_raise_message(AttributeError, 'Transformer tr1 (type Transf) does not provide ' 'get_feature_names', ft.get_feature_names) def test_classes_property(): X = iris.data y = iris.target reg = make_pipeline(SelectKBest(k=1), LinearRegression()) reg.fit(X, y) assert_raises(AttributeError, getattr, reg, "classes_") clf = make_pipeline(SelectKBest(k=1), LogisticRegression(random_state=0)) assert_raises(AttributeError, getattr, clf, "classes_") clf.fit(X, y) assert_array_equal(clf.classes_, np.unique(y)) def test_set_feature_union_steps(): mult2 = Mult(2) mult2.get_feature_names = lambda: ['x2'] mult3 = Mult(3) mult3.get_feature_names = lambda: ['x3'] mult5 = Mult(5) mult5.get_feature_names = lambda: ['x5'] ft = FeatureUnion([('m2', mult2), ('m3', mult3)]) assert_array_equal([[2, 3]], ft.transform(np.asarray([[1]]))) assert ['m2__x2', 'm3__x3'] == ft.get_feature_names() # Directly setting attr ft.transformer_list = [('m5', mult5)] assert_array_equal([[5]], ft.transform(np.asarray([[1]]))) assert ['m5__x5'] == ft.get_feature_names() # Using set_params ft.set_params(transformer_list=[('mock', mult3)]) assert_array_equal([[3]], ft.transform(np.asarray([[1]]))) assert ['mock__x3'] == ft.get_feature_names() # Using set_params to replace single step ft.set_params(mock=mult5) assert_array_equal([[5]], ft.transform(np.asarray([[1]]))) assert ['mock__x5'] == ft.get_feature_names() def test_set_feature_union_step_drop(): mult2 = Mult(2) mult2.get_feature_names = lambda: ['x2'] mult3 = Mult(3) mult3.get_feature_names = lambda: ['x3'] X = np.asarray([[1]]) ft = FeatureUnion([('m2', mult2), ('m3', mult3)]) assert_array_equal([[2, 3]], ft.fit(X).transform(X)) assert_array_equal([[2, 3]], ft.fit_transform(X)) assert ['m2__x2', 'm3__x3'] == ft.get_feature_names() with pytest.warns(None) as record: ft.set_params(m2='drop') assert_array_equal([[3]], ft.fit(X).transform(X)) assert_array_equal([[3]], ft.fit_transform(X)) assert ['m3__x3'] == ft.get_feature_names() assert not record with pytest.warns(None) as record: ft.set_params(m3='drop') assert_array_equal([[]], ft.fit(X).transform(X)) assert_array_equal([[]], ft.fit_transform(X)) assert [] == ft.get_feature_names() assert not record with pytest.warns(None) as record: # check we can change back ft.set_params(m3=mult3) assert_array_equal([[3]], ft.fit(X).transform(X)) assert not record with pytest.warns(None) as record: # Check 'drop' step at construction time ft = FeatureUnion([('m2', 'drop'), ('m3', mult3)]) assert_array_equal([[3]], ft.fit(X).transform(X)) assert_array_equal([[3]], ft.fit_transform(X)) assert ['m3__x3'] == ft.get_feature_names() assert not record def test_step_name_validation(): bad_steps1 = [('a__q', Mult(2)), ('b', Mult(3))] bad_steps2 = [('a', Mult(2)), ('a', Mult(3))] for cls, param in [(Pipeline, 'steps'), (FeatureUnion, 'transformer_list')]: # we validate in construction (despite scikit-learn convention) bad_steps3 = [('a', Mult(2)), (param, Mult(3))] for bad_steps, message in [ (bad_steps1, "Estimator names must not contain __: got ['a__q']"), (bad_steps2, "Names provided are not unique: ['a', 'a']"), (bad_steps3, "Estimator names conflict with constructor " "arguments: ['%s']" % param), ]: # three ways to make invalid: # - construction assert_raise_message(ValueError, message, cls, **{param: bad_steps}) # - setattr est = cls(**{param: [('a', Mult(1))]}) setattr(est, param, bad_steps) assert_raise_message(ValueError, message, est.fit, [[1]], [1]) assert_raise_message(ValueError, message, est.fit_transform, [[1]], [1]) # - set_params est = cls(**{param: [('a', Mult(1))]}) est.set_params(**{param: bad_steps}) assert_raise_message(ValueError, message, est.fit, [[1]], [1]) assert_raise_message(ValueError, message, est.fit_transform, [[1]], [1]) def test_set_params_nested_pipeline(): estimator = Pipeline([ ('a', Pipeline([ ('b', DummyRegressor()) ])) ]) estimator.set_params(a__b__alpha=0.001, a__b=Lasso()) estimator.set_params(a__steps=[('b', LogisticRegression())], a__b__C=5) def test_pipeline_wrong_memory(): # Test that an error is raised when memory is not a string or a Memory # instance X = iris.data y = iris.target # Define memory as an integer memory = 1 cached_pipe = Pipeline([('transf', DummyTransf()), ('svc', SVC())], memory=memory) assert_raises_regex(ValueError, "'memory' should be None, a string or" " have the same interface as joblib.Memory." " Got memory='1' instead.", cached_pipe.fit, X, y) class DummyMemory: def cache(self, func): return func class WrongDummyMemory: pass def test_pipeline_with_cache_attribute(): X = np.array([[1, 2]]) pipe = Pipeline([('transf', Transf()), ('clf', Mult())], memory=DummyMemory()) pipe.fit(X, y=None) dummy = WrongDummyMemory() pipe = Pipeline([('transf', Transf()), ('clf', Mult())], memory=dummy) assert_raises_regex(ValueError, "'memory' should be None, a string or" " have the same interface as joblib.Memory." " Got memory='{}' instead.".format(dummy), pipe.fit, X) def test_pipeline_memory(): X = iris.data y = iris.target cachedir = mkdtemp() try: if parse_version(joblib.__version__) < parse_version('0.12'): # Deal with change of API in joblib memory = joblib.Memory(cachedir=cachedir, verbose=10) else: memory = joblib.Memory(location=cachedir, verbose=10) # Test with Transformer + SVC clf = SVC(probability=True, random_state=0) transf = DummyTransf() pipe = Pipeline([('transf', clone(transf)), ('svc', clf)]) cached_pipe = Pipeline([('transf', transf), ('svc', clf)], memory=memory) # Memoize the transformer at the first fit cached_pipe.fit(X, y) pipe.fit(X, y) # Get the time stamp of the transformer in the cached pipeline ts = cached_pipe.named_steps['transf'].timestamp_ # Check that cached_pipe and pipe yield identical results assert_array_equal(pipe.predict(X), cached_pipe.predict(X)) assert_array_equal(pipe.predict_proba(X), cached_pipe.predict_proba(X)) assert_array_equal(pipe.predict_log_proba(X), cached_pipe.predict_log_proba(X)) assert_array_equal(pipe.score(X, y), cached_pipe.score(X, y)) assert_array_equal(pipe.named_steps['transf'].means_, cached_pipe.named_steps['transf'].means_) assert not hasattr(transf, 'means_') # Check that we are reading the cache while fitting # a second time cached_pipe.fit(X, y) # Check that cached_pipe and pipe yield identical results assert_array_equal(pipe.predict(X), cached_pipe.predict(X)) assert_array_equal(pipe.predict_proba(X), cached_pipe.predict_proba(X)) assert_array_equal(pipe.predict_log_proba(X), cached_pipe.predict_log_proba(X)) assert_array_equal(pipe.score(X, y), cached_pipe.score(X, y)) assert_array_equal(pipe.named_steps['transf'].means_, cached_pipe.named_steps['transf'].means_) assert ts == cached_pipe.named_steps['transf'].timestamp_ # Create a new pipeline with cloned estimators # Check that even changing the name step does not affect the cache hit clf_2 = SVC(probability=True, random_state=0) transf_2 = DummyTransf() cached_pipe_2 = Pipeline([('transf_2', transf_2), ('svc', clf_2)], memory=memory) cached_pipe_2.fit(X, y) # Check that cached_pipe and pipe yield identical results assert_array_equal(pipe.predict(X), cached_pipe_2.predict(X)) assert_array_equal(pipe.predict_proba(X), cached_pipe_2.predict_proba(X)) assert_array_equal(pipe.predict_log_proba(X), cached_pipe_2.predict_log_proba(X)) assert_array_equal(pipe.score(X, y), cached_pipe_2.score(X, y)) assert_array_equal(pipe.named_steps['transf'].means_, cached_pipe_2.named_steps['transf_2'].means_) assert ts == cached_pipe_2.named_steps['transf_2'].timestamp_ finally: shutil.rmtree(cachedir) def test_make_pipeline_memory(): cachedir = mkdtemp() if parse_version(joblib.__version__) < parse_version('0.12'): # Deal with change of API in joblib memory = joblib.Memory(cachedir=cachedir, verbose=10) else: memory = joblib.Memory(location=cachedir, verbose=10) pipeline = make_pipeline(DummyTransf(), SVC(), memory=memory) assert pipeline.memory is memory pipeline = make_pipeline(DummyTransf(), SVC()) assert pipeline.memory is None assert len(pipeline) == 2 shutil.rmtree(cachedir) def test_pipeline_param_error(): clf = make_pipeline(LogisticRegression()) with pytest.raises(ValueError, match="Pipeline.fit does not accept " "the sample_weight parameter"): clf.fit([[0], [0]], [0, 1], sample_weight=[1, 1]) parameter_grid_test_verbose = ((est, pattern, method) for (est, pattern), method in itertools.product( [ (Pipeline([('transf', Transf()), ('clf', FitParamT())]), r'\[Pipeline\].*\(step 1 of 2\) Processing transf.* total=.*\n' r'\[Pipeline\].*\(step 2 of 2\) Processing clf.* total=.*\n$'), (Pipeline([('transf', Transf()), ('noop', None), ('clf', FitParamT())]), r'\[Pipeline\].*\(step 1 of 3\) Processing transf.* total=.*\n' r'\[Pipeline\].*\(step 2 of 3\) Processing noop.* total=.*\n' r'\[Pipeline\].*\(step 3 of 3\) Processing clf.* total=.*\n$'), (Pipeline([('transf', Transf()), ('noop', 'passthrough'), ('clf', FitParamT())]), r'\[Pipeline\].*\(step 1 of 3\) Processing transf.* total=.*\n' r'\[Pipeline\].*\(step 2 of 3\) Processing noop.* total=.*\n' r'\[Pipeline\].*\(step 3 of 3\) Processing clf.* total=.*\n$'), (Pipeline([('transf', Transf()), ('clf', None)]), r'\[Pipeline\].*\(step 1 of 2\) Processing transf.* total=.*\n' r'\[Pipeline\].*\(step 2 of 2\) Processing clf.* total=.*\n$'), (Pipeline([('transf', None), ('mult', Mult())]), r'\[Pipeline\].*\(step 1 of 2\) Processing transf.* total=.*\n' r'\[Pipeline\].*\(step 2 of 2\) Processing mult.* total=.*\n$'), (Pipeline([('transf', 'passthrough'), ('mult', Mult())]), r'\[Pipeline\].*\(step 1 of 2\) Processing transf.* total=.*\n' r'\[Pipeline\].*\(step 2 of 2\) Processing mult.* total=.*\n$'), (FeatureUnion([('mult1', Mult()), ('mult2', Mult())]), r'\[FeatureUnion\].*\(step 1 of 2\) Processing mult1.* total=.*\n' r'\[FeatureUnion\].*\(step 2 of 2\) Processing mult2.* total=.*\n$'), (FeatureUnion([('mult1', 'drop'), ('mult2', Mult()), ('mult3', 'drop')]), r'\[FeatureUnion\].*\(step 1 of 1\) Processing mult2.* total=.*\n$') ], ['fit', 'fit_transform', 'fit_predict']) if hasattr(est, method) and not ( method == 'fit_transform' and hasattr(est, 'steps') and isinstance(est.steps[-1][1], FitParamT)) ) @pytest.mark.parametrize('est, pattern, method', parameter_grid_test_verbose) def test_verbose(est, method, pattern, capsys): func = getattr(est, method) X = [[1, 2, 3], [4, 5, 6]] y = [[7], [8]] est.set_params(verbose=False) func(X, y) assert not capsys.readouterr().out, 'Got output for verbose=False' est.set_params(verbose=True) func(X, y) assert re.match(pattern, capsys.readouterr().out) def test_n_features_in_pipeline(): # make sure pipelines delegate n_features_in to the first step X = [[1, 2], [3, 4], [5, 6]] y = [0, 1, 2] ss = StandardScaler() gbdt = HistGradientBoostingClassifier() pipe = make_pipeline(ss, gbdt) assert not hasattr(pipe, 'n_features_in_') pipe.fit(X, y) assert pipe.n_features_in_ == ss.n_features_in_ == 2 # if the first step has the n_features_in attribute then the pipeline also # has it, even though it isn't fitted. ss = StandardScaler() gbdt = HistGradientBoostingClassifier() pipe = make_pipeline(ss, gbdt) ss.fit(X, y) assert pipe.n_features_in_ == ss.n_features_in_ == 2 assert not hasattr(gbdt, 'n_features_in_') def test_n_features_in_feature_union(): # make sure FeatureUnion delegates n_features_in to the first transformer X = [[1, 2], [3, 4], [5, 6]] y = [0, 1, 2] ss = StandardScaler() fu = make_union(ss) assert not hasattr(fu, 'n_features_in_') fu.fit(X, y) assert fu.n_features_in_ == ss.n_features_in_ == 2 # if the first step has the n_features_in attribute then the feature_union # also has it, even though it isn't fitted. ss = StandardScaler() fu = make_union(ss) ss.fit(X, y) assert fu.n_features_in_ == ss.n_features_in_ == 2 def test_feature_union_fit_params(): # Regression test for issue: #15117 class Dummy(TransformerMixin, BaseEstimator): def fit(self, X, y=None, **fit_params): if fit_params != {'a': 0}: raise ValueError return self def transform(self, X, y=None): return X X, y = iris.data, iris.target t = FeatureUnion([('dummy0', Dummy()), ('dummy1', Dummy())]) with pytest.raises(ValueError): t.fit(X, y) with pytest.raises(ValueError): t.fit_transform(X, y) t.fit(X, y, a=0) t.fit_transform(X, y, a=0) def test_pipeline_missing_values_leniency(): # check that pipeline let the missing values validation to # the underlying transformers and predictors. X, y = iris.data, iris.target mask = np.random.choice([1, 0], X.shape, p=[.1, .9]).astype(bool) X[mask] = np.nan pipe = make_pipeline(SimpleImputer(), LogisticRegression()) assert pipe.fit(X, y).score(X, y) > 0.4 def test_feature_union_warns_unknown_transformer_weight(): # Warn user when transformer_weights containers a key not present in # transformer_list X = [[1, 2], [3, 4], [5, 6]] y = [0, 1, 2] transformer_list = [('transf', Transf())] # Transformer weights dictionary with incorrect name weights = {'transformer': 1} expected_msg = ('Attempting to weight transformer "transformer", ' 'but it is not present in transformer_list.') union = FeatureUnion(transformer_list, transformer_weights=weights) with pytest.raises(ValueError, match=expected_msg): union.fit(X, y) @pytest.mark.parametrize('passthrough', [None, 'passthrough']) def test_pipeline_get_tags_none(passthrough): # Checks that tags are set correctly when the first transformer is None or # 'passthrough' # Non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/18815 pipe = make_pipeline(passthrough, SVC()) assert not pipe._get_tags()['pairwise'] # FIXME: Replace this test with a full `check_estimator` once we have API only # checks. @pytest.mark.parametrize("Predictor", [MinimalRegressor, MinimalClassifier]) def test_search_cv_using_minimal_compatible_estimator(Predictor): # Check that third-party library estimators can be part of a pipeline # and tuned by grid-search without inheriting from BaseEstimator. rng = np.random.RandomState(0) X, y = rng.randn(25, 2), np.array([0] * 5 + [1] * 20) model = Pipeline([ ("transformer", MinimalTransformer()), ("predictor", Predictor()) ]) model.fit(X, y) y_pred = model.predict(X) if is_classifier(model): assert_array_equal(y_pred, 1) assert model.score(X, y) == pytest.approx(accuracy_score(y, y_pred)) else: assert_allclose(y_pred, y.mean()) assert model.score(X, y) == pytest.approx(r2_score(y, y_pred))