306 lines
10 KiB
Python
306 lines
10 KiB
Python
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"""Common tests for metaestimators"""
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import functools
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from inspect import signature
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import numpy as np
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import pytest
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from sklearn.base import BaseEstimator
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from sklearn.base import is_regressor
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from sklearn.datasets import make_classification
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from sklearn.utils import all_estimators
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from sklearn.utils.estimator_checks import _enforce_estimator_tags_X
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from sklearn.utils.estimator_checks import _enforce_estimator_tags_y
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from sklearn.utils.validation import check_is_fitted
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from sklearn.utils._testing import set_random_state
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.feature_selection import RFE, RFECV
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from sklearn.ensemble import BaggingClassifier
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from sklearn.exceptions import NotFittedError
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from sklearn.semi_supervised import SelfTrainingClassifier
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from sklearn.linear_model import Ridge, LogisticRegression
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from sklearn.preprocessing import StandardScaler, MaxAbsScaler
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class DelegatorData:
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def __init__(
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self,
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name,
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construct,
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skip_methods=(),
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fit_args=make_classification(random_state=0),
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):
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self.name = name
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self.construct = construct
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self.fit_args = fit_args
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self.skip_methods = skip_methods
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DELEGATING_METAESTIMATORS = [
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DelegatorData("Pipeline", lambda est: Pipeline([("est", est)])),
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DelegatorData(
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"GridSearchCV",
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lambda est: GridSearchCV(est, param_grid={"param": [5]}, cv=2),
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skip_methods=["score"],
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),
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DelegatorData(
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"RandomizedSearchCV",
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lambda est: RandomizedSearchCV(
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est, param_distributions={"param": [5]}, cv=2, n_iter=1
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),
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skip_methods=["score"],
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),
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DelegatorData("RFE", RFE, skip_methods=["transform", "inverse_transform"]),
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DelegatorData("RFECV", RFECV, skip_methods=["transform", "inverse_transform"]),
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DelegatorData(
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"BaggingClassifier",
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BaggingClassifier,
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skip_methods=[
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"transform",
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"inverse_transform",
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"score",
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"predict_proba",
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"predict_log_proba",
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"predict",
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],
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),
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DelegatorData(
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"SelfTrainingClassifier",
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lambda est: SelfTrainingClassifier(est),
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skip_methods=["transform", "inverse_transform", "predict_proba"],
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),
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]
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def test_metaestimator_delegation():
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# Ensures specified metaestimators have methods iff subestimator does
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def hides(method):
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@property
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def wrapper(obj):
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if obj.hidden_method == method.__name__:
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raise AttributeError("%r is hidden" % obj.hidden_method)
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return functools.partial(method, obj)
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return wrapper
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class SubEstimator(BaseEstimator):
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def __init__(self, param=1, hidden_method=None):
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self.param = param
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self.hidden_method = hidden_method
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def fit(self, X, y=None, *args, **kwargs):
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self.coef_ = np.arange(X.shape[1])
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self.classes_ = []
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return True
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def _check_fit(self):
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check_is_fitted(self)
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@hides
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def inverse_transform(self, X, *args, **kwargs):
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self._check_fit()
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return X
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@hides
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def transform(self, X, *args, **kwargs):
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self._check_fit()
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return X
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@hides
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def predict(self, X, *args, **kwargs):
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self._check_fit()
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return np.ones(X.shape[0])
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@hides
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def predict_proba(self, X, *args, **kwargs):
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self._check_fit()
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return np.ones(X.shape[0])
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@hides
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def predict_log_proba(self, X, *args, **kwargs):
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self._check_fit()
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return np.ones(X.shape[0])
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@hides
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def decision_function(self, X, *args, **kwargs):
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self._check_fit()
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return np.ones(X.shape[0])
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@hides
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def score(self, X, y, *args, **kwargs):
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self._check_fit()
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return 1.0
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methods = [
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k
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for k in SubEstimator.__dict__.keys()
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if not k.startswith("_") and not k.startswith("fit")
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]
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methods.sort()
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for delegator_data in DELEGATING_METAESTIMATORS:
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delegate = SubEstimator()
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delegator = delegator_data.construct(delegate)
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for method in methods:
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if method in delegator_data.skip_methods:
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continue
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assert hasattr(delegate, method)
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assert hasattr(
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delegator, method
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), "%s does not have method %r when its delegate does" % (
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delegator_data.name,
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method,
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)
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# delegation before fit raises a NotFittedError
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if method == "score":
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with pytest.raises(NotFittedError):
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getattr(delegator, method)(
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delegator_data.fit_args[0], delegator_data.fit_args[1]
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)
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else:
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with pytest.raises(NotFittedError):
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getattr(delegator, method)(delegator_data.fit_args[0])
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delegator.fit(*delegator_data.fit_args)
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for method in methods:
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if method in delegator_data.skip_methods:
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continue
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# smoke test delegation
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if method == "score":
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getattr(delegator, method)(
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delegator_data.fit_args[0], delegator_data.fit_args[1]
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)
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else:
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getattr(delegator, method)(delegator_data.fit_args[0])
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for method in methods:
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if method in delegator_data.skip_methods:
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continue
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delegate = SubEstimator(hidden_method=method)
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delegator = delegator_data.construct(delegate)
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assert not hasattr(delegate, method)
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assert not hasattr(
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delegator, method
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), "%s has method %r when its delegate does not" % (
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delegator_data.name,
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method,
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)
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def _generate_meta_estimator_instances_with_pipeline():
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"""Generate instances of meta-estimators fed with a pipeline
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Are considered meta-estimators all estimators accepting one of "estimator",
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"base_estimator" or "estimators".
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"""
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for _, Estimator in sorted(all_estimators()):
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sig = set(signature(Estimator).parameters)
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if "estimator" in sig or "base_estimator" in sig or "regressor" in sig:
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if is_regressor(Estimator):
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estimator = make_pipeline(TfidfVectorizer(), Ridge())
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param_grid = {"ridge__alpha": [0.1, 1.0]}
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else:
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estimator = make_pipeline(TfidfVectorizer(), LogisticRegression())
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param_grid = {"logisticregression__C": [0.1, 1.0]}
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if "param_grid" in sig or "param_distributions" in sig:
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# SearchCV estimators
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extra_params = {"n_iter": 2} if "n_iter" in sig else {}
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yield Estimator(estimator, param_grid, **extra_params)
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else:
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yield Estimator(estimator)
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elif "transformer_list" in sig:
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# FeatureUnion
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transformer_list = [
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("trans1", make_pipeline(TfidfVectorizer(), MaxAbsScaler())),
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(
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"trans2",
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make_pipeline(TfidfVectorizer(), StandardScaler(with_mean=False)),
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),
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]
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yield Estimator(transformer_list)
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elif "estimators" in sig:
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# stacking, voting
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if is_regressor(Estimator):
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estimator = [
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("est1", make_pipeline(TfidfVectorizer(), Ridge(alpha=0.1))),
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("est2", make_pipeline(TfidfVectorizer(), Ridge(alpha=1))),
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]
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else:
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estimator = [
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(
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"est1",
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make_pipeline(TfidfVectorizer(), LogisticRegression(C=0.1)),
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),
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("est2", make_pipeline(TfidfVectorizer(), LogisticRegression(C=1))),
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]
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yield Estimator(estimator)
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else:
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continue
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# TODO: remove data validation for the following estimators
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# They should be able to work on any data and delegate data validation to
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# their inner estimator(s).
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DATA_VALIDATION_META_ESTIMATORS_TO_IGNORE = [
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"AdaBoostClassifier",
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"AdaBoostRegressor",
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"BaggingClassifier",
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"BaggingRegressor",
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"ClassifierChain", # data validation is necessary
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"IterativeImputer",
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"OneVsOneClassifier", # input validation can't be avoided
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"RANSACRegressor",
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"RFE",
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"RFECV",
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"RegressorChain", # data validation is necessary
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"SelfTrainingClassifier",
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"SequentialFeatureSelector", # not applicable (2D data mandatory)
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]
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DATA_VALIDATION_META_ESTIMATORS = [
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est
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for est in _generate_meta_estimator_instances_with_pipeline()
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if est.__class__.__name__ not in DATA_VALIDATION_META_ESTIMATORS_TO_IGNORE
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]
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def _get_meta_estimator_id(estimator):
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return estimator.__class__.__name__
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@pytest.mark.parametrize(
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"estimator", DATA_VALIDATION_META_ESTIMATORS, ids=_get_meta_estimator_id
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)
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def test_meta_estimators_delegate_data_validation(estimator):
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# Check that meta-estimators delegate data validation to the inner
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# estimator(s).
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rng = np.random.RandomState(0)
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set_random_state(estimator)
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n_samples = 30
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X = rng.choice(np.array(["aa", "bb", "cc"], dtype=object), size=n_samples)
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if is_regressor(estimator):
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y = rng.normal(size=n_samples)
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else:
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y = rng.randint(3, size=n_samples)
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# We convert to lists to make sure it works on array-like
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X = _enforce_estimator_tags_X(estimator, X).tolist()
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y = _enforce_estimator_tags_y(estimator, y).tolist()
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# Calling fit should not raise any data validation exception since X is a
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# valid input datastructure for the first step of the pipeline passed as
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# base estimator to the meta estimator.
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estimator.fit(X, y)
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# n_features_in_ should not be defined since data is not tabular data.
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assert not hasattr(estimator, "n_features_in_")
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