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sklearn/utils/tests/test_pprint.py
699 строк
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Jérémie du Boisberranger
FIX Set default transform_input of Pipeline to ["X_val"] (#34263)
12 июн 2026, 17:14
Не верифицирован
12 июн 2026, 17:14
70241a5
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import re from pprint import PrettyPrinter import numpy as np import pytest from sklearn import config_context from sklearn.base import BaseEstimator, TransformerMixin from sklearn.feature_selection import SelectKBest, chi2 from sklearn.linear_model import LogisticRegressionCV from sklearn.pipeline import make_pipeline from sklearn.utils._pprint import _EstimatorPrettyPrinter # Constructors excerpted to test pprinting class LogisticRegression(BaseEstimator): def __init__( self, C=1.0, l1_ratio=0, dual=False, tol=1e-4, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver="warn", max_iter=100, multi_class="warn", verbose=0, warm_start=False, n_jobs=None, ): self.C = C self.l1_ratio = l1_ratio self.dual = dual self.tol = tol self.fit_intercept = fit_intercept self.intercept_scaling = intercept_scaling self.class_weight = class_weight self.random_state = random_state self.solver = solver self.max_iter = max_iter self.multi_class = multi_class self.verbose = verbose self.warm_start = warm_start self.n_jobs = n_jobs def fit(self, X, y): return self class StandardScaler(TransformerMixin, BaseEstimator): def __init__(self, copy=True, with_mean=True, with_std=True): self.with_mean = with_mean self.with_std = with_std self.copy = copy def transform(self, X, copy=None): return self class RFE(BaseEstimator): def __init__(self, estimator, n_features_to_select=None, step=1, verbose=0): self.estimator = estimator self.n_features_to_select = n_features_to_select self.step = step self.verbose = verbose class GridSearchCV(BaseEstimator): def __init__( self, estimator, param_grid, scoring=None, n_jobs=None, iid="warn", refit=True, cv="warn", verbose=0, pre_dispatch="2*n_jobs", error_score="raise-deprecating", return_train_score=False, ): self.estimator = estimator self.param_grid = param_grid self.scoring = scoring self.n_jobs = n_jobs self.iid = iid self.refit = refit self.cv = cv self.verbose = verbose self.pre_dispatch = pre_dispatch self.error_score = error_score self.return_train_score = return_train_score class CountVectorizer(BaseEstimator): def __init__( self, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer="word", max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=np.int64, ): self.input = input self.encoding = encoding self.decode_error = decode_error self.strip_accents = strip_accents self.preprocessor = preprocessor self.tokenizer = tokenizer self.analyzer = analyzer self.lowercase = lowercase self.token_pattern = token_pattern self.stop_words = stop_words self.max_df = max_df self.min_df = min_df self.max_features = max_features self.ngram_range = ngram_range self.vocabulary = vocabulary self.binary = binary self.dtype = dtype class Pipeline(BaseEstimator): def __init__(self, steps, memory=None): self.steps = steps self.memory = memory class SVC(BaseEstimator): def __init__( self, C=1.0, kernel="rbf", degree=3, gamma="auto_deprecated", coef0=0.0, shrinking=True, probability=False, tol=1e-3, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape="ovr", random_state=None, ): self.kernel = kernel self.degree = degree self.gamma = gamma self.coef0 = coef0 self.tol = tol self.C = C self.shrinking = shrinking self.probability = probability self.cache_size = cache_size self.class_weight = class_weight self.verbose = verbose self.max_iter = max_iter self.decision_function_shape = decision_function_shape self.random_state = random_state class PCA(BaseEstimator): def __init__( self, n_components=None, copy=True, whiten=False, svd_solver="auto", tol=0.0, iterated_power="auto", random_state=None, ): self.n_components = n_components self.copy = copy self.whiten = whiten self.svd_solver = svd_solver self.tol = tol self.iterated_power = iterated_power self.random_state = random_state class NMF(BaseEstimator): def __init__( self, n_components=None, init=None, solver="cd", beta_loss="frobenius", tol=1e-4, max_iter=200, random_state=None, alpha=0.0, l1_ratio=0.0, verbose=0, shuffle=False, ): self.n_components = n_components self.init = init self.solver = solver self.beta_loss = beta_loss self.tol = tol self.max_iter = max_iter self.random_state = random_state self.alpha = alpha self.l1_ratio = l1_ratio self.verbose = verbose self.shuffle = shuffle class SimpleImputer(BaseEstimator): def __init__( self, missing_values=np.nan, strategy="mean", fill_value=None, verbose=0, copy=True, ): self.missing_values = missing_values self.strategy = strategy self.fill_value = fill_value self.verbose = verbose self.copy = copy @config_context(print_changed_only=False) def test_basic(): # Basic pprint test lr = LogisticRegression() expected = """ LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=0, max_iter=100, multi_class='warn', n_jobs=None, random_state=None, solver='warn', tol=0.0001, verbose=0, warm_start=False)""" expected = expected[1:] # remove first \n assert lr.__repr__() == expected def test_changed_only(): # Make sure the changed_only param is correctly used when True (default) lr = LogisticRegression(C=99) expected = """LogisticRegression(C=99)""" assert lr.__repr__() == expected # Check with a repr that doesn't fit on a single line lr = LogisticRegression( C=99, class_weight=0.4, fit_intercept=False, tol=1234, verbose=True ) expected = """ LogisticRegression(C=99, class_weight=0.4, fit_intercept=False, tol=1234, verbose=True)""" expected = expected[1:] # remove first \n assert lr.__repr__() == expected imputer = SimpleImputer(missing_values=0) expected = """SimpleImputer(missing_values=0)""" assert imputer.__repr__() == expected # Defaults to np.nan, trying with float('NaN') imputer = SimpleImputer(missing_values=float("NaN")) expected = """SimpleImputer()""" assert imputer.__repr__() == expected # make sure array parameters don't throw error (see #13583) repr( LogisticRegressionCV( Cs=np.array([0.1, 1]), use_legacy_attributes=False, scoring="neg_log_loss", # TODO(1.11): remove because it is default now ) ) @config_context(print_changed_only=False) def test_pipeline(): # Render a pipeline object pipeline = make_pipeline(StandardScaler(), LogisticRegression(C=999)) expected = """ Pipeline(memory=None, steps=[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('logisticregression', LogisticRegression(C=999, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=0, max_iter=100, multi_class='warn', n_jobs=None, random_state=None, solver='warn', tol=0.0001, verbose=0, warm_start=False))], transform_input=('X_val',), verbose=False)""" expected = expected[1:] # remove first \n assert pipeline.__repr__() == expected @config_context(print_changed_only=False) def test_deeply_nested(): # Render a deeply nested estimator rfe = RFE(RFE(RFE(RFE(RFE(RFE(RFE(LogisticRegression()))))))) expected = """ RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=0, max_iter=100, multi_class='warn', n_jobs=None, random_state=None, solver='warn', tol=0.0001, verbose=0, warm_start=False), n_features_to_select=None, step=1, verbose=0), n_features_to_select=None, step=1, verbose=0), n_features_to_select=None, step=1, verbose=0), n_features_to_select=None, step=1, verbose=0), n_features_to_select=None, step=1, verbose=0), n_features_to_select=None, step=1, verbose=0), n_features_to_select=None, step=1, verbose=0)""" expected = expected[1:] # remove first \n assert rfe.__repr__() == expected @pytest.mark.parametrize( ("print_changed_only", "expected"), [ (True, "RFE(estimator=RFE(...))"), ( False, "RFE(estimator=RFE(...), n_features_to_select=None, step=1, verbose=0)", ), ], ) def test_print_estimator_max_depth(print_changed_only, expected): with config_context(print_changed_only=print_changed_only): pp = _EstimatorPrettyPrinter(depth=1) rfe = RFE(RFE(RFE(RFE(RFE(LogisticRegression()))))) assert pp.pformat(rfe) == expected @config_context(print_changed_only=False) def test_gridsearch(): # render a gridsearch param_grid = [ {"kernel": ["rbf"], "gamma": [1e-3, 1e-4], "C": [1, 10, 100, 1000]}, {"kernel": ["linear"], "C": [1, 10, 100, 1000]}, ] gs = GridSearchCV(SVC(), param_grid, cv=5) expected = """ GridSearchCV(cv=5, error_score='raise-deprecating', estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto_deprecated', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False), iid='warn', n_jobs=None, param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']}, {'C': [1, 10, 100, 1000], 'kernel': ['linear']}], pre_dispatch='2*n_jobs', refit=True, return_train_score=False, scoring=None, verbose=0)""" expected = expected[1:] # remove first \n assert gs.__repr__() == expected @config_context(print_changed_only=False) def test_gridsearch_pipeline(): # render a pipeline inside a gridsearch pp = _EstimatorPrettyPrinter(compact=True, indent=1, indent_at_name=True) pipeline = Pipeline([("reduce_dim", PCA()), ("classify", SVC())]) N_FEATURES_OPTIONS = [2, 4, 8] C_OPTIONS = [1, 10, 100, 1000] param_grid = [ { "reduce_dim": [PCA(iterated_power=7), NMF()], "reduce_dim__n_components": N_FEATURES_OPTIONS, "classify__C": C_OPTIONS, }, { "reduce_dim": [SelectKBest(chi2)], "reduce_dim__k": N_FEATURES_OPTIONS, "classify__C": C_OPTIONS, }, ] gspipeline = GridSearchCV(pipeline, cv=3, n_jobs=1, param_grid=param_grid) expected = """ GridSearchCV(cv=3, error_score='raise-deprecating', estimator=Pipeline(memory=None, steps=[('reduce_dim', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None, svd_solver='auto', tol=0.0, whiten=False)), ('classify', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto_deprecated', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False))]), iid='warn', n_jobs=1, param_grid=[{'classify__C': [1, 10, 100, 1000], 'reduce_dim': [PCA(copy=True, iterated_power=7, n_components=None, random_state=None, svd_solver='auto', tol=0.0, whiten=False), NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200, n_components=None, random_state=None, shuffle=False, solver='cd', tol=0.0001, verbose=0)], 'reduce_dim__n_components': [2, 4, 8]}, {'classify__C': [1, 10, 100, 1000], 'reduce_dim': [SelectKBest(k=10, score_func=<function chi2 at some_address>)], 'reduce_dim__k': [2, 4, 8]}], pre_dispatch='2*n_jobs', refit=True, return_train_score=False, scoring=None, verbose=0)""" # noqa: E501 expected = expected[1:] # remove first \n repr_ = pp.pformat(gspipeline) # Remove address of '<function chi2 at 0x.....>' for reproducibility repr_ = re.sub("function chi2 at 0x.*>", "function chi2 at some_address>", repr_) assert repr_ == expected @config_context(print_changed_only=False) def test_n_max_elements_to_show(): n_max_elements_to_show = 30 pp = _EstimatorPrettyPrinter( compact=True, indent=1, indent_at_name=True, n_max_elements_to_show=n_max_elements_to_show, ) # No ellipsis vocabulary = {i: i for i in range(n_max_elements_to_show)} vectorizer = CountVectorizer(vocabulary=vocabulary) expected = r""" CountVectorizer(analyzer='word', binary=False, decode_error='strict', dtype=<class 'numpy.int64'>, encoding='utf-8', input='content', lowercase=True, max_df=1.0, max_features=None, min_df=1, ngram_range=(1, 1), preprocessor=None, stop_words=None, strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29})""" expected = expected[1:] # remove first \n assert pp.pformat(vectorizer) == expected # Now with ellipsis vocabulary = {i: i for i in range(n_max_elements_to_show + 1)} vectorizer = CountVectorizer(vocabulary=vocabulary) expected = r""" CountVectorizer(analyzer='word', binary=False, decode_error='strict', dtype=<class 'numpy.int64'>, encoding='utf-8', input='content', lowercase=True, max_df=1.0, max_features=None, min_df=1, ngram_range=(1, 1), preprocessor=None, stop_words=None, strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, ...})""" expected = expected[1:] # remove first \n assert pp.pformat(vectorizer) == expected # Also test with lists param_grid = {"C": list(range(n_max_elements_to_show))} gs = GridSearchCV(SVC(), param_grid) expected = """ GridSearchCV(cv='warn', error_score='raise-deprecating', estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto_deprecated', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False), iid='warn', n_jobs=None, param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]}, pre_dispatch='2*n_jobs', refit=True, return_train_score=False, scoring=None, verbose=0)""" expected = expected[1:] # remove first \n assert pp.pformat(gs) == expected # Now with ellipsis param_grid = {"C": list(range(n_max_elements_to_show + 1))} gs = GridSearchCV(SVC(), param_grid) expected = """ GridSearchCV(cv='warn', error_score='raise-deprecating', estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto_deprecated', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False), iid='warn', n_jobs=None, param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, ...]}, pre_dispatch='2*n_jobs', refit=True, return_train_score=False, scoring=None, verbose=0)""" expected = expected[1:] # remove first \n assert pp.pformat(gs) == expected @config_context(print_changed_only=False) def test_bruteforce_ellipsis(): # Check that the bruteforce ellipsis (used when the number of non-blank # characters exceeds N_CHAR_MAX) renders correctly. lr = LogisticRegression() # test when the left and right side of the ellipsis aren't on the same # line. expected = """ LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, in... multi_class='warn', n_jobs=None, random_state=None, solver='warn', tol=0.0001, verbose=0, warm_start=False)""" expected = expected[1:] # remove first \n assert lr.__repr__(N_CHAR_MAX=150) == expected # test with very small N_CHAR_MAX # Note that N_CHAR_MAX is not strictly enforced, but it's normal: to avoid # weird reprs we still keep the whole line of the right part (after the # ellipsis). expected = """ Lo... solver='warn', tol=0.0001, verbose=0, warm_start=False)""" expected = expected[1:] # remove first \n assert lr.__repr__(N_CHAR_MAX=4) == expected # test with N_CHAR_MAX == number of non-blank characters: In this case we # don't want ellipsis full_repr = lr.__repr__(N_CHAR_MAX=float("inf")) n_nonblank = len("".join(full_repr.split())) assert lr.__repr__(N_CHAR_MAX=n_nonblank) == full_repr assert "..." not in full_repr # test with N_CHAR_MAX == number of non-blank characters - 10: the left and # right side of the ellispsis are on different lines. In this case we # want to expend the whole line of the right side expected = """ LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=0,...00, multi_class='warn', n_jobs=None, random_state=None, solver='warn', tol=0.0001, verbose=0, warm_start=False)""" expected = expected[1:] # remove first \n assert lr.__repr__(N_CHAR_MAX=n_nonblank - 10) == expected # test with N_CHAR_MAX == number of non-blank characters - 10: the left and # right side of the ellispsis are on the same line. In this case we don't # want to expend the whole line of the right side, just add the ellispsis # between the 2 sides. expected = """ LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=0, max...r=100, multi_class='warn', n_jobs=None, random_state=None, solver='warn', tol=0.0001, verbose=0, warm_start=False)""" expected = expected[1:] # remove first \n assert lr.__repr__(N_CHAR_MAX=n_nonblank - 4) == expected # test with N_CHAR_MAX == number of non-blank characters - 2: the left and # right side of the ellispsis are on the same line, but adding the ellipsis # would actually make the repr longer. So we don't add the ellipsis. expected = """ LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=0, max_iter=100, multi_class='warn', n_jobs=None, random_state=None, solver='warn', tol=0.0001, verbose=0, warm_start=False)""" expected = expected[1:] # remove first \n assert lr.__repr__(N_CHAR_MAX=n_nonblank - 2) == expected def test_builtin_prettyprinter(): # non regression test than ensures we can still use the builtin # PrettyPrinter class for estimators (as done e.g. by joblib). # Used to be a bug PrettyPrinter().pprint(LogisticRegression()) def test_kwargs_in_init(): # Make sure the changed_only=True mode is OK when an argument is passed as # kwargs. # Non-regression test for # https://github.com/scikit-learn/scikit-learn/issues/17206 class WithKWargs(BaseEstimator): # Estimator with a kwargs argument. These need to hack around # set_params and get_params. Here we mimic what LightGBM does. def __init__(self, a="willchange", b="unchanged", **kwargs): self.a = a self.b = b self._other_params = {} self.set_params(**kwargs) def get_params(self, deep=True): params = super().get_params(deep=deep) params.update(self._other_params) return params def set_params(self, **params): for key, value in params.items(): setattr(self, key, value) self._other_params[key] = value return self est = WithKWargs(a="something", c="abcd", d=None) expected = "WithKWargs(a='something', c='abcd', d=None)" assert est.__repr__() == expected with config_context(print_changed_only=False): expected = "WithKWargs(a='something', b='unchanged', c='abcd', d=None)" assert est.__repr__() == expected def test_complexity_print_changed_only(): # Make sure `__repr__` is called the same amount of times # whether `print_changed_only` is True or False # Non-regression test for # https://github.com/scikit-learn/scikit-learn/issues/18490 class DummyEstimator(TransformerMixin, BaseEstimator): nb_times_repr_called = 0 def __init__(self, estimator=None): self.estimator = estimator def __repr__(self): DummyEstimator.nb_times_repr_called += 1 return super().__repr__() def transform(self, X, copy=None): # pragma: no cover return X estimator = DummyEstimator( make_pipeline(DummyEstimator(DummyEstimator()), DummyEstimator(), "passthrough") ) with config_context(print_changed_only=False): repr(estimator) nb_repr_print_changed_only_false = DummyEstimator.nb_times_repr_called DummyEstimator.nb_times_repr_called = 0 with config_context(print_changed_only=True): repr(estimator) nb_repr_print_changed_only_true = DummyEstimator.nb_times_repr_called assert nb_repr_print_changed_only_false == nb_repr_print_changed_only_true