forked from 170010011/fr
651 lines
24 KiB
Python
651 lines
24 KiB
Python
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import pytest
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import numpy as np
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import scipy.sparse as sp
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from joblib import cpu_count
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from sklearn.utils._testing import assert_almost_equal
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from sklearn.utils._testing import assert_raises
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from sklearn.utils._testing import assert_raises_regex
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from sklearn.utils._testing import assert_raise_message
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn import datasets
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from sklearn.base import clone
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from sklearn.datasets import make_classification
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from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier
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from sklearn.exceptions import NotFittedError
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from sklearn.linear_model import Lasso
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from sklearn.linear_model import LogisticRegression
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from sklearn.linear_model import OrthogonalMatchingPursuit
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from sklearn.linear_model import Ridge
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from sklearn.linear_model import SGDClassifier
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from sklearn.linear_model import SGDRegressor
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from sklearn.metrics import jaccard_score, mean_squared_error
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.multioutput import ClassifierChain, RegressorChain
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from sklearn.multioutput import MultiOutputClassifier
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from sklearn.multioutput import MultiOutputRegressor
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from sklearn.svm import LinearSVC
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from sklearn.base import ClassifierMixin
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from sklearn.utils import shuffle
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from sklearn.model_selection import GridSearchCV
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from sklearn.dummy import DummyRegressor, DummyClassifier
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from sklearn.pipeline import make_pipeline
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from sklearn.impute import SimpleImputer
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def test_multi_target_regression():
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X, y = datasets.make_regression(n_targets=3)
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X_train, y_train = X[:50], y[:50]
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X_test, y_test = X[50:], y[50:]
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references = np.zeros_like(y_test)
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for n in range(3):
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rgr = GradientBoostingRegressor(random_state=0)
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rgr.fit(X_train, y_train[:, n])
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references[:, n] = rgr.predict(X_test)
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rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
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rgr.fit(X_train, y_train)
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y_pred = rgr.predict(X_test)
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assert_almost_equal(references, y_pred)
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def test_multi_target_regression_partial_fit():
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X, y = datasets.make_regression(n_targets=3)
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X_train, y_train = X[:50], y[:50]
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X_test, y_test = X[50:], y[50:]
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references = np.zeros_like(y_test)
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half_index = 25
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for n in range(3):
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sgr = SGDRegressor(random_state=0, max_iter=5)
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sgr.partial_fit(X_train[:half_index], y_train[:half_index, n])
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sgr.partial_fit(X_train[half_index:], y_train[half_index:, n])
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references[:, n] = sgr.predict(X_test)
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sgr = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
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sgr.partial_fit(X_train[:half_index], y_train[:half_index])
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sgr.partial_fit(X_train[half_index:], y_train[half_index:])
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y_pred = sgr.predict(X_test)
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assert_almost_equal(references, y_pred)
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assert not hasattr(MultiOutputRegressor(Lasso), 'partial_fit')
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def test_multi_target_regression_one_target():
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# Test multi target regression raises
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X, y = datasets.make_regression(n_targets=1)
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rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
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assert_raises(ValueError, rgr.fit, X, y)
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def test_multi_target_sparse_regression():
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X, y = datasets.make_regression(n_targets=3)
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X_train, y_train = X[:50], y[:50]
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X_test = X[50:]
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for sparse in [sp.csr_matrix, sp.csc_matrix, sp.coo_matrix, sp.dok_matrix,
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sp.lil_matrix]:
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rgr = MultiOutputRegressor(Lasso(random_state=0))
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rgr_sparse = MultiOutputRegressor(Lasso(random_state=0))
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rgr.fit(X_train, y_train)
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rgr_sparse.fit(sparse(X_train), y_train)
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assert_almost_equal(rgr.predict(X_test),
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rgr_sparse.predict(sparse(X_test)))
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def test_multi_target_sample_weights_api():
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X = [[1, 2, 3], [4, 5, 6]]
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y = [[3.141, 2.718], [2.718, 3.141]]
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w = [0.8, 0.6]
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rgr = MultiOutputRegressor(OrthogonalMatchingPursuit())
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assert_raises_regex(ValueError, "does not support sample weights",
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rgr.fit, X, y, w)
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# no exception should be raised if the base estimator supports weights
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rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
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rgr.fit(X, y, w)
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def test_multi_target_sample_weight_partial_fit():
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# weighted regressor
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X = [[1, 2, 3], [4, 5, 6]]
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y = [[3.141, 2.718], [2.718, 3.141]]
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w = [2., 1.]
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rgr_w = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
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rgr_w.partial_fit(X, y, w)
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# weighted with different weights
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w = [2., 2.]
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rgr = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
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rgr.partial_fit(X, y, w)
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assert rgr.predict(X)[0][0] != rgr_w.predict(X)[0][0]
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def test_multi_target_sample_weights():
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# weighted regressor
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Xw = [[1, 2, 3], [4, 5, 6]]
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yw = [[3.141, 2.718], [2.718, 3.141]]
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w = [2., 1.]
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rgr_w = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
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rgr_w.fit(Xw, yw, w)
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# unweighted, but with repeated samples
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X = [[1, 2, 3], [1, 2, 3], [4, 5, 6]]
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y = [[3.141, 2.718], [3.141, 2.718], [2.718, 3.141]]
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rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
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rgr.fit(X, y)
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X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]
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assert_almost_equal(rgr.predict(X_test), rgr_w.predict(X_test))
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# Import the data
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iris = datasets.load_iris()
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# create a multiple targets by randomized shuffling and concatenating y.
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X = iris.data
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y1 = iris.target
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y2 = shuffle(y1, random_state=1)
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y3 = shuffle(y1, random_state=2)
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y = np.column_stack((y1, y2, y3))
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n_samples, n_features = X.shape
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n_outputs = y.shape[1]
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n_classes = len(np.unique(y1))
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classes = list(map(np.unique, (y1, y2, y3)))
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def test_multi_output_classification_partial_fit_parallelism():
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sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
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mor = MultiOutputClassifier(sgd_linear_clf, n_jobs=4)
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mor.partial_fit(X, y, classes)
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est1 = mor.estimators_[0]
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mor.partial_fit(X, y)
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est2 = mor.estimators_[0]
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if cpu_count() > 1:
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# parallelism requires this to be the case for a sane implementation
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assert est1 is not est2
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# check multioutput has predict_proba
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def test_hasattr_multi_output_predict_proba():
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# default SGDClassifier has loss='hinge'
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# which does not expose a predict_proba method
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sgd_linear_clf = SGDClassifier(random_state=1, max_iter=5)
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multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
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multi_target_linear.fit(X, y)
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assert not hasattr(multi_target_linear, "predict_proba")
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# case where predict_proba attribute exists
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sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
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multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
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multi_target_linear.fit(X, y)
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assert hasattr(multi_target_linear, "predict_proba")
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# check predict_proba passes
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def test_multi_output_predict_proba():
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sgd_linear_clf = SGDClassifier(random_state=1, max_iter=5)
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param = {'loss': ('hinge', 'log', 'modified_huber')}
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# inner function for custom scoring
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def custom_scorer(estimator, X, y):
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if hasattr(estimator, "predict_proba"):
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return 1.0
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else:
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return 0.0
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grid_clf = GridSearchCV(sgd_linear_clf, param_grid=param,
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scoring=custom_scorer, cv=3)
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multi_target_linear = MultiOutputClassifier(grid_clf)
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multi_target_linear.fit(X, y)
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multi_target_linear.predict_proba(X)
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# SGDClassifier defaults to loss='hinge' which is not a probabilistic
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# loss function; therefore it does not expose a predict_proba method
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sgd_linear_clf = SGDClassifier(random_state=1, max_iter=5)
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multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
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multi_target_linear.fit(X, y)
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err_msg = "The base estimator should implement predict_proba method"
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with pytest.raises(AttributeError, match=err_msg):
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multi_target_linear.predict_proba(X)
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def test_multi_output_classification_partial_fit():
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# test if multi_target initializes correctly with base estimator and fit
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# assert predictions work as expected for predict
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sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
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multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
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# train the multi_target_linear and also get the predictions.
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half_index = X.shape[0] // 2
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multi_target_linear.partial_fit(
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X[:half_index], y[:half_index], classes=classes)
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first_predictions = multi_target_linear.predict(X)
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assert (n_samples, n_outputs) == first_predictions.shape
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multi_target_linear.partial_fit(X[half_index:], y[half_index:])
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second_predictions = multi_target_linear.predict(X)
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assert (n_samples, n_outputs) == second_predictions.shape
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# train the linear classification with each column and assert that
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# predictions are equal after first partial_fit and second partial_fit
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for i in range(3):
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# create a clone with the same state
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sgd_linear_clf = clone(sgd_linear_clf)
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sgd_linear_clf.partial_fit(
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X[:half_index], y[:half_index, i], classes=classes[i])
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assert_array_equal(sgd_linear_clf.predict(X), first_predictions[:, i])
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sgd_linear_clf.partial_fit(X[half_index:], y[half_index:, i])
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assert_array_equal(sgd_linear_clf.predict(X), second_predictions[:, i])
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def test_multi_output_classification_partial_fit_no_first_classes_exception():
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sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
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multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
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assert_raises_regex(ValueError, "classes must be passed on the first call "
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"to partial_fit.",
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multi_target_linear.partial_fit, X, y)
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def test_multi_output_classification():
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# test if multi_target initializes correctly with base estimator and fit
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# assert predictions work as expected for predict, prodict_proba and score
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forest = RandomForestClassifier(n_estimators=10, random_state=1)
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multi_target_forest = MultiOutputClassifier(forest)
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# train the multi_target_forest and also get the predictions.
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multi_target_forest.fit(X, y)
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predictions = multi_target_forest.predict(X)
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assert (n_samples, n_outputs) == predictions.shape
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predict_proba = multi_target_forest.predict_proba(X)
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assert len(predict_proba) == n_outputs
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for class_probabilities in predict_proba:
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assert (n_samples, n_classes) == class_probabilities.shape
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assert_array_equal(np.argmax(np.dstack(predict_proba), axis=1),
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predictions)
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# train the forest with each column and assert that predictions are equal
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for i in range(3):
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forest_ = clone(forest) # create a clone with the same state
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forest_.fit(X, y[:, i])
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assert list(forest_.predict(X)) == list(predictions[:, i])
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assert_array_equal(list(forest_.predict_proba(X)),
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list(predict_proba[i]))
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def test_multiclass_multioutput_estimator():
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# test to check meta of meta estimators
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svc = LinearSVC(random_state=0)
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multi_class_svc = OneVsRestClassifier(svc)
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multi_target_svc = MultiOutputClassifier(multi_class_svc)
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multi_target_svc.fit(X, y)
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predictions = multi_target_svc.predict(X)
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assert (n_samples, n_outputs) == predictions.shape
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# train the forest with each column and assert that predictions are equal
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for i in range(3):
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multi_class_svc_ = clone(multi_class_svc) # create a clone
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multi_class_svc_.fit(X, y[:, i])
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assert (list(multi_class_svc_.predict(X)) ==
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list(predictions[:, i]))
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def test_multiclass_multioutput_estimator_predict_proba():
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seed = 542
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# make test deterministic
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rng = np.random.RandomState(seed)
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# random features
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X = rng.normal(size=(5, 5))
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# random labels
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y1 = np.array(['b', 'a', 'a', 'b', 'a']).reshape(5, 1) # 2 classes
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y2 = np.array(['d', 'e', 'f', 'e', 'd']).reshape(5, 1) # 3 classes
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Y = np.concatenate([y1, y2], axis=1)
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clf = MultiOutputClassifier(LogisticRegression(
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solver='liblinear', random_state=seed))
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clf.fit(X, Y)
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y_result = clf.predict_proba(X)
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y_actual = [np.array([[0.23481764, 0.76518236],
|
||
|
|
[0.67196072, 0.32803928],
|
||
|
|
[0.54681448, 0.45318552],
|
||
|
|
[0.34883923, 0.65116077],
|
||
|
|
[0.73687069, 0.26312931]]),
|
||
|
|
np.array([[0.5171785, 0.23878628, 0.24403522],
|
||
|
|
[0.22141451, 0.64102704, 0.13755846],
|
||
|
|
[0.16751315, 0.18256843, 0.64991843],
|
||
|
|
[0.27357372, 0.55201592, 0.17441036],
|
||
|
|
[0.65745193, 0.26062899, 0.08191907]])]
|
||
|
|
|
||
|
|
for i in range(len(y_actual)):
|
||
|
|
assert_almost_equal(y_result[i], y_actual[i])
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_classification_sample_weights():
|
||
|
|
# weighted classifier
|
||
|
|
Xw = [[1, 2, 3], [4, 5, 6]]
|
||
|
|
yw = [[3, 2], [2, 3]]
|
||
|
|
w = np.asarray([2., 1.])
|
||
|
|
forest = RandomForestClassifier(n_estimators=10, random_state=1)
|
||
|
|
clf_w = MultiOutputClassifier(forest)
|
||
|
|
clf_w.fit(Xw, yw, w)
|
||
|
|
|
||
|
|
# unweighted, but with repeated samples
|
||
|
|
X = [[1, 2, 3], [1, 2, 3], [4, 5, 6]]
|
||
|
|
y = [[3, 2], [3, 2], [2, 3]]
|
||
|
|
forest = RandomForestClassifier(n_estimators=10, random_state=1)
|
||
|
|
clf = MultiOutputClassifier(forest)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]
|
||
|
|
assert_almost_equal(clf.predict(X_test), clf_w.predict(X_test))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_classification_partial_fit_sample_weights():
|
||
|
|
# weighted classifier
|
||
|
|
Xw = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
|
||
|
|
yw = [[3, 2], [2, 3], [3, 2]]
|
||
|
|
w = np.asarray([2., 1., 1.])
|
||
|
|
sgd_linear_clf = SGDClassifier(random_state=1, max_iter=20)
|
||
|
|
clf_w = MultiOutputClassifier(sgd_linear_clf)
|
||
|
|
clf_w.fit(Xw, yw, w)
|
||
|
|
|
||
|
|
# unweighted, but with repeated samples
|
||
|
|
X = [[1, 2, 3], [1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
|
||
|
|
y = [[3, 2], [3, 2], [2, 3], [3, 2]]
|
||
|
|
sgd_linear_clf = SGDClassifier(random_state=1, max_iter=20)
|
||
|
|
clf = MultiOutputClassifier(sgd_linear_clf)
|
||
|
|
clf.fit(X, y)
|
||
|
|
X_test = [[1.5, 2.5, 3.5]]
|
||
|
|
assert_array_almost_equal(clf.predict(X_test), clf_w.predict(X_test))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_exceptions():
|
||
|
|
# NotFittedError when fit is not done but score, predict and
|
||
|
|
# and predict_proba are called
|
||
|
|
moc = MultiOutputClassifier(LinearSVC(random_state=0))
|
||
|
|
assert_raises(NotFittedError, moc.predict, y)
|
||
|
|
with pytest.raises(NotFittedError):
|
||
|
|
moc.predict_proba
|
||
|
|
assert_raises(NotFittedError, moc.score, X, y)
|
||
|
|
# ValueError when number of outputs is different
|
||
|
|
# for fit and score
|
||
|
|
y_new = np.column_stack((y1, y2))
|
||
|
|
moc.fit(X, y)
|
||
|
|
assert_raises(ValueError, moc.score, X, y_new)
|
||
|
|
# ValueError when y is continuous
|
||
|
|
assert_raise_message(ValueError, "Unknown label type", moc.fit, X, X[:, 1])
|
||
|
|
|
||
|
|
|
||
|
|
def generate_multilabel_dataset_with_correlations():
|
||
|
|
# Generate a multilabel data set from a multiclass dataset as a way of
|
||
|
|
# by representing the integer number of the original class using a binary
|
||
|
|
# encoding.
|
||
|
|
X, y = make_classification(n_samples=1000,
|
||
|
|
n_features=100,
|
||
|
|
n_classes=16,
|
||
|
|
n_informative=10,
|
||
|
|
random_state=0)
|
||
|
|
|
||
|
|
Y_multi = np.array([[int(yyy) for yyy in format(yy, '#06b')[2:]]
|
||
|
|
for yy in y])
|
||
|
|
return X, Y_multi
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_fit_and_predict_with_linear_svc():
|
||
|
|
# Fit classifier chain and verify predict performance using LinearSVC
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
classifier_chain = ClassifierChain(LinearSVC())
|
||
|
|
classifier_chain.fit(X, Y)
|
||
|
|
|
||
|
|
Y_pred = classifier_chain.predict(X)
|
||
|
|
assert Y_pred.shape == Y.shape
|
||
|
|
|
||
|
|
Y_decision = classifier_chain.decision_function(X)
|
||
|
|
|
||
|
|
Y_binary = (Y_decision >= 0)
|
||
|
|
assert_array_equal(Y_binary, Y_pred)
|
||
|
|
assert not hasattr(classifier_chain, 'predict_proba')
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_fit_and_predict_with_sparse_data():
|
||
|
|
# Fit classifier chain with sparse data
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
X_sparse = sp.csr_matrix(X)
|
||
|
|
|
||
|
|
classifier_chain = ClassifierChain(LogisticRegression())
|
||
|
|
classifier_chain.fit(X_sparse, Y)
|
||
|
|
Y_pred_sparse = classifier_chain.predict(X_sparse)
|
||
|
|
|
||
|
|
classifier_chain = ClassifierChain(LogisticRegression())
|
||
|
|
classifier_chain.fit(X, Y)
|
||
|
|
Y_pred_dense = classifier_chain.predict(X)
|
||
|
|
|
||
|
|
assert_array_equal(Y_pred_sparse, Y_pred_dense)
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_vs_independent_models():
|
||
|
|
# Verify that an ensemble of classifier chains (each of length
|
||
|
|
# N) can achieve a higher Jaccard similarity score than N independent
|
||
|
|
# models
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
X_train = X[:600, :]
|
||
|
|
X_test = X[600:, :]
|
||
|
|
Y_train = Y[:600, :]
|
||
|
|
Y_test = Y[600:, :]
|
||
|
|
|
||
|
|
ovr = OneVsRestClassifier(LogisticRegression())
|
||
|
|
ovr.fit(X_train, Y_train)
|
||
|
|
Y_pred_ovr = ovr.predict(X_test)
|
||
|
|
|
||
|
|
chain = ClassifierChain(LogisticRegression())
|
||
|
|
chain.fit(X_train, Y_train)
|
||
|
|
Y_pred_chain = chain.predict(X_test)
|
||
|
|
|
||
|
|
assert (jaccard_score(Y_test, Y_pred_chain, average='samples') >
|
||
|
|
jaccard_score(Y_test, Y_pred_ovr, average='samples'))
|
||
|
|
|
||
|
|
|
||
|
|
def test_base_chain_fit_and_predict():
|
||
|
|
# Fit base chain and verify predict performance
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
chains = [RegressorChain(Ridge()),
|
||
|
|
ClassifierChain(LogisticRegression())]
|
||
|
|
for chain in chains:
|
||
|
|
chain.fit(X, Y)
|
||
|
|
Y_pred = chain.predict(X)
|
||
|
|
assert Y_pred.shape == Y.shape
|
||
|
|
assert ([c.coef_.size for c in chain.estimators_] ==
|
||
|
|
list(range(X.shape[1], X.shape[1] + Y.shape[1])))
|
||
|
|
|
||
|
|
Y_prob = chains[1].predict_proba(X)
|
||
|
|
Y_binary = (Y_prob >= .5)
|
||
|
|
assert_array_equal(Y_binary, Y_pred)
|
||
|
|
|
||
|
|
assert isinstance(chains[1], ClassifierMixin)
|
||
|
|
|
||
|
|
|
||
|
|
def test_base_chain_fit_and_predict_with_sparse_data_and_cv():
|
||
|
|
# Fit base chain with sparse data cross_val_predict
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
X_sparse = sp.csr_matrix(X)
|
||
|
|
base_chains = [ClassifierChain(LogisticRegression(), cv=3),
|
||
|
|
RegressorChain(Ridge(), cv=3)]
|
||
|
|
for chain in base_chains:
|
||
|
|
chain.fit(X_sparse, Y)
|
||
|
|
Y_pred = chain.predict(X_sparse)
|
||
|
|
assert Y_pred.shape == Y.shape
|
||
|
|
|
||
|
|
|
||
|
|
def test_base_chain_random_order():
|
||
|
|
# Fit base chain with random order
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
for chain in [ClassifierChain(LogisticRegression()),
|
||
|
|
RegressorChain(Ridge())]:
|
||
|
|
chain_random = clone(chain).set_params(order='random', random_state=42)
|
||
|
|
chain_random.fit(X, Y)
|
||
|
|
chain_fixed = clone(chain).set_params(order=chain_random.order_)
|
||
|
|
chain_fixed.fit(X, Y)
|
||
|
|
assert_array_equal(chain_fixed.order_, chain_random.order_)
|
||
|
|
assert list(chain_random.order) != list(range(4))
|
||
|
|
assert len(chain_random.order_) == 4
|
||
|
|
assert len(set(chain_random.order_)) == 4
|
||
|
|
# Randomly ordered chain should behave identically to a fixed order
|
||
|
|
# chain with the same order.
|
||
|
|
for est1, est2 in zip(chain_random.estimators_,
|
||
|
|
chain_fixed.estimators_):
|
||
|
|
assert_array_almost_equal(est1.coef_, est2.coef_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_base_chain_crossval_fit_and_predict():
|
||
|
|
# Fit chain with cross_val_predict and verify predict
|
||
|
|
# performance
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
|
||
|
|
for chain in [ClassifierChain(LogisticRegression()),
|
||
|
|
RegressorChain(Ridge())]:
|
||
|
|
chain.fit(X, Y)
|
||
|
|
chain_cv = clone(chain).set_params(cv=3)
|
||
|
|
chain_cv.fit(X, Y)
|
||
|
|
Y_pred_cv = chain_cv.predict(X)
|
||
|
|
Y_pred = chain.predict(X)
|
||
|
|
|
||
|
|
assert Y_pred_cv.shape == Y_pred.shape
|
||
|
|
assert not np.all(Y_pred == Y_pred_cv)
|
||
|
|
if isinstance(chain, ClassifierChain):
|
||
|
|
assert jaccard_score(Y, Y_pred_cv, average='samples') > .4
|
||
|
|
else:
|
||
|
|
assert mean_squared_error(Y, Y_pred_cv) < .25
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
'estimator',
|
||
|
|
[RandomForestClassifier(n_estimators=2),
|
||
|
|
MultiOutputClassifier(RandomForestClassifier(n_estimators=2)),
|
||
|
|
ClassifierChain(RandomForestClassifier(n_estimators=2))]
|
||
|
|
)
|
||
|
|
def test_multi_output_classes_(estimator):
|
||
|
|
# Tests classes_ attribute of multioutput classifiers
|
||
|
|
# RandomForestClassifier supports multioutput out-of-the-box
|
||
|
|
estimator.fit(X, y)
|
||
|
|
assert isinstance(estimator.classes_, list)
|
||
|
|
assert len(estimator.classes_) == n_outputs
|
||
|
|
for estimator_classes, expected_classes in zip(classes,
|
||
|
|
estimator.classes_):
|
||
|
|
assert_array_equal(estimator_classes, expected_classes)
|
||
|
|
|
||
|
|
|
||
|
|
class DummyRegressorWithFitParams(DummyRegressor):
|
||
|
|
def fit(self, X, y, sample_weight=None, **fit_params):
|
||
|
|
self._fit_params = fit_params
|
||
|
|
return super().fit(X, y, sample_weight)
|
||
|
|
|
||
|
|
|
||
|
|
class DummyClassifierWithFitParams(DummyClassifier):
|
||
|
|
def fit(self, X, y, sample_weight=None, **fit_params):
|
||
|
|
self._fit_params = fit_params
|
||
|
|
return super().fit(X, y, sample_weight)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"estimator, dataset",
|
||
|
|
[(MultiOutputClassifier(DummyClassifierWithFitParams(strategy="prior")),
|
||
|
|
datasets.make_multilabel_classification()),
|
||
|
|
(MultiOutputRegressor(DummyRegressorWithFitParams()),
|
||
|
|
datasets.make_regression(n_targets=3))])
|
||
|
|
def test_multioutput_estimator_with_fit_params(estimator, dataset):
|
||
|
|
X, y = dataset
|
||
|
|
some_param = np.zeros_like(X)
|
||
|
|
estimator.fit(X, y, some_param=some_param)
|
||
|
|
for dummy_estimator in estimator.estimators_:
|
||
|
|
assert 'some_param' in dummy_estimator._fit_params
|
||
|
|
|
||
|
|
|
||
|
|
def test_regressor_chain_w_fit_params():
|
||
|
|
# Make sure fit_params are properly propagated to the sub-estimators
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X, y = datasets.make_regression(n_targets=3)
|
||
|
|
weight = rng.rand(y.shape[0])
|
||
|
|
|
||
|
|
class MySGD(SGDRegressor):
|
||
|
|
|
||
|
|
def fit(self, X, y, **fit_params):
|
||
|
|
self.sample_weight_ = fit_params['sample_weight']
|
||
|
|
super().fit(X, y, **fit_params)
|
||
|
|
|
||
|
|
model = RegressorChain(MySGD())
|
||
|
|
|
||
|
|
# Fitting with params
|
||
|
|
fit_param = {'sample_weight': weight}
|
||
|
|
model.fit(X, y, **fit_param)
|
||
|
|
|
||
|
|
for est in model.estimators_:
|
||
|
|
assert est.sample_weight_ is weight
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
'MultiOutputEstimator, Estimator',
|
||
|
|
[(MultiOutputClassifier, LogisticRegression),
|
||
|
|
(MultiOutputRegressor, Ridge)]
|
||
|
|
)
|
||
|
|
# FIXME: we should move this test in `estimator_checks` once we are able
|
||
|
|
# to construct meta-estimator instances
|
||
|
|
def test_support_missing_values(MultiOutputEstimator, Estimator):
|
||
|
|
# smoke test to check that pipeline MultioutputEstimators are letting
|
||
|
|
# the validation of missing values to
|
||
|
|
# the underlying pipeline, regressor or classifier
|
||
|
|
rng = np.random.RandomState(42)
|
||
|
|
X, y = rng.randn(50, 2), rng.binomial(1, 0.5, (50, 3))
|
||
|
|
mask = rng.choice([1, 0], X.shape, p=[.01, .99]).astype(bool)
|
||
|
|
X[mask] = np.nan
|
||
|
|
|
||
|
|
pipe = make_pipeline(SimpleImputer(), Estimator())
|
||
|
|
MultiOutputEstimator(pipe).fit(X, y).score(X, y)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("order_type", [list, np.array, tuple])
|
||
|
|
def test_classifier_chain_tuple_order(order_type):
|
||
|
|
X = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
|
||
|
|
y = [[3, 2], [2, 3], [3, 2]]
|
||
|
|
order = order_type([1, 0])
|
||
|
|
|
||
|
|
chain = ClassifierChain(RandomForestClassifier(), order=order)
|
||
|
|
|
||
|
|
chain.fit(X, y)
|
||
|
|
X_test = [[1.5, 2.5, 3.5]]
|
||
|
|
y_test = [[3, 2]]
|
||
|
|
assert_array_almost_equal(chain.predict(X_test), y_test)
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_tuple_invalid_order():
|
||
|
|
X = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
|
||
|
|
y = [[3, 2], [2, 3], [3, 2]]
|
||
|
|
order = tuple([1, 2])
|
||
|
|
|
||
|
|
chain = ClassifierChain(RandomForestClassifier(), order=order)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match='invalid order'):
|
||
|
|
chain.fit(X, y)
|