141 lines
4.9 KiB
Python
141 lines
4.9 KiB
Python
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"""Common tests for metaestimators"""
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import functools
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import numpy as np
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from sklearn.base import BaseEstimator
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from sklearn.externals.six import iterkeys
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from sklearn.datasets import make_classification
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from sklearn.utils.testing import assert_true, assert_false, assert_raises
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from sklearn.utils.validation import check_is_fitted
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
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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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class DelegatorData(object):
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def __init__(self, name, construct, skip_methods=(),
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fit_args=make_classification()):
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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('GridSearchCV',
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lambda est: GridSearchCV(
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est, param_grid={'param': [5]}, cv=2),
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skip_methods=['score']),
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DelegatorData('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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skip_methods=['score']),
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DelegatorData('RFE', RFE,
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skip_methods=['transform', 'inverse_transform', 'score']),
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DelegatorData('RFECV', RFECV,
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skip_methods=['transform', 'inverse_transform', 'score']),
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DelegatorData('BaggingClassifier', BaggingClassifier,
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skip_methods=['transform', 'inverse_transform', 'score',
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'predict_proba', 'predict_log_proba',
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'predict'])
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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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return True
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def _check_fit(self):
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check_is_fitted(self, 'coef_')
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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, *args, **kwargs):
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self._check_fit()
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return 1.0
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methods = [k for k in iterkeys(SubEstimator.__dict__)
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if not k.startswith('_') and not k.startswith('fit')]
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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_true(hasattr(delegate, method))
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assert_true(hasattr(delegator, method),
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msg="%s does not have method %r when its delegate does"
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% (delegator_data.name, method))
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# delegation before fit raises a NotFittedError
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assert_raises(NotFittedError, getattr(delegator, method),
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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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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_false(hasattr(delegate, method))
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assert_false(hasattr(delegator, method),
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msg="%s has method %r when its delegate does not"
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% (delegator_data.name, method))
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