583 lines
21 KiB
Python
583 lines
21 KiB
Python
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"""Testing for the boost module (sklearn.ensemble.boost)."""
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import numpy as np
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import pytest
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from scipy.sparse import csc_matrix
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from scipy.sparse import csr_matrix
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from scipy.sparse import coo_matrix
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from scipy.sparse import dok_matrix
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from scipy.sparse import lil_matrix
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from sklearn.utils._testing import assert_array_equal, assert_array_less
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn.utils._testing import assert_raises, assert_raises_regexp
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from sklearn.utils._testing import ignore_warnings
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from sklearn.base import BaseEstimator
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from sklearn.base import clone
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from sklearn.dummy import DummyClassifier, DummyRegressor
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import GridSearchCV
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.ensemble import AdaBoostRegressor
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from sklearn.ensemble._weight_boosting import _samme_proba
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from sklearn.svm import SVC, SVR
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from sklearn.utils import shuffle
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from sklearn.utils._mocking import NoSampleWeightWrapper
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from sklearn import datasets
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# Common random state
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rng = np.random.RandomState(0)
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# Toy sample
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
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y_class = ["foo", "foo", "foo", 1, 1, 1] # test string class labels
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y_regr = [-1, -1, -1, 1, 1, 1]
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T = [[-1, -1], [2, 2], [3, 2]]
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y_t_class = ["foo", 1, 1]
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y_t_regr = [-1, 1, 1]
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# Load the iris dataset and randomly permute it
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iris = datasets.load_iris()
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perm = rng.permutation(iris.target.size)
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iris.data, iris.target = shuffle(iris.data, iris.target, random_state=rng)
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# Load the boston dataset and randomly permute it
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boston = datasets.load_boston()
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boston.data, boston.target = shuffle(boston.data, boston.target,
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random_state=rng)
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def test_samme_proba():
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# Test the `_samme_proba` helper function.
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# Define some example (bad) `predict_proba` output.
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probs = np.array([[1, 1e-6, 0],
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[0.19, 0.6, 0.2],
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[-999, 0.51, 0.5],
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[1e-6, 1, 1e-9]])
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probs /= np.abs(probs.sum(axis=1))[:, np.newaxis]
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# _samme_proba calls estimator.predict_proba.
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# Make a mock object so I can control what gets returned.
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class MockEstimator:
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def predict_proba(self, X):
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assert_array_equal(X.shape, probs.shape)
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return probs
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mock = MockEstimator()
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samme_proba = _samme_proba(mock, 3, np.ones_like(probs))
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assert_array_equal(samme_proba.shape, probs.shape)
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assert np.isfinite(samme_proba).all()
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# Make sure that the correct elements come out as smallest --
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# `_samme_proba` should preserve the ordering in each example.
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assert_array_equal(np.argmin(samme_proba, axis=1), [2, 0, 0, 2])
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assert_array_equal(np.argmax(samme_proba, axis=1), [0, 1, 1, 1])
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def test_oneclass_adaboost_proba():
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# Test predict_proba robustness for one class label input.
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# In response to issue #7501
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# https://github.com/scikit-learn/scikit-learn/issues/7501
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y_t = np.ones(len(X))
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clf = AdaBoostClassifier().fit(X, y_t)
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assert_array_almost_equal(clf.predict_proba(X), np.ones((len(X), 1)))
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@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
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def test_classification_toy(algorithm):
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# Check classification on a toy dataset.
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clf = AdaBoostClassifier(algorithm=algorithm, random_state=0)
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clf.fit(X, y_class)
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assert_array_equal(clf.predict(T), y_t_class)
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assert_array_equal(np.unique(np.asarray(y_t_class)), clf.classes_)
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assert clf.predict_proba(T).shape == (len(T), 2)
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assert clf.decision_function(T).shape == (len(T),)
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def test_regression_toy():
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# Check classification on a toy dataset.
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clf = AdaBoostRegressor(random_state=0)
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clf.fit(X, y_regr)
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assert_array_equal(clf.predict(T), y_t_regr)
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def test_iris():
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# Check consistency on dataset iris.
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classes = np.unique(iris.target)
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clf_samme = prob_samme = None
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for alg in ['SAMME', 'SAMME.R']:
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clf = AdaBoostClassifier(algorithm=alg)
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clf.fit(iris.data, iris.target)
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assert_array_equal(classes, clf.classes_)
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proba = clf.predict_proba(iris.data)
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if alg == "SAMME":
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clf_samme = clf
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prob_samme = proba
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assert proba.shape[1] == len(classes)
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assert clf.decision_function(iris.data).shape[1] == len(classes)
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score = clf.score(iris.data, iris.target)
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assert score > 0.9, "Failed with algorithm %s and score = %f" % \
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(alg, score)
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# Check we used multiple estimators
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assert len(clf.estimators_) > 1
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# Check for distinct random states (see issue #7408)
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assert (len(set(est.random_state for est in clf.estimators_)) ==
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len(clf.estimators_))
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# Somewhat hacky regression test: prior to
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# ae7adc880d624615a34bafdb1d75ef67051b8200,
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# predict_proba returned SAMME.R values for SAMME.
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clf_samme.algorithm = "SAMME.R"
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assert_array_less(0,
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np.abs(clf_samme.predict_proba(iris.data) - prob_samme))
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@pytest.mark.parametrize('loss', ['linear', 'square', 'exponential'])
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def test_boston(loss):
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# Check consistency on dataset boston house prices.
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reg = AdaBoostRegressor(loss=loss, random_state=0)
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reg.fit(boston.data, boston.target)
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score = reg.score(boston.data, boston.target)
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assert score > 0.85
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# Check we used multiple estimators
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assert len(reg.estimators_) > 1
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# Check for distinct random states (see issue #7408)
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assert (len(set(est.random_state for est in reg.estimators_)) ==
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len(reg.estimators_))
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@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
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def test_staged_predict(algorithm):
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# Check staged predictions.
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rng = np.random.RandomState(0)
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iris_weights = rng.randint(10, size=iris.target.shape)
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boston_weights = rng.randint(10, size=boston.target.shape)
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clf = AdaBoostClassifier(algorithm=algorithm, n_estimators=10)
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clf.fit(iris.data, iris.target, sample_weight=iris_weights)
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predictions = clf.predict(iris.data)
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staged_predictions = [p for p in clf.staged_predict(iris.data)]
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proba = clf.predict_proba(iris.data)
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staged_probas = [p for p in clf.staged_predict_proba(iris.data)]
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score = clf.score(iris.data, iris.target, sample_weight=iris_weights)
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staged_scores = [
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s for s in clf.staged_score(
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iris.data, iris.target, sample_weight=iris_weights)]
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assert len(staged_predictions) == 10
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assert_array_almost_equal(predictions, staged_predictions[-1])
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assert len(staged_probas) == 10
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assert_array_almost_equal(proba, staged_probas[-1])
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assert len(staged_scores) == 10
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assert_array_almost_equal(score, staged_scores[-1])
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# AdaBoost regression
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clf = AdaBoostRegressor(n_estimators=10, random_state=0)
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clf.fit(boston.data, boston.target, sample_weight=boston_weights)
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predictions = clf.predict(boston.data)
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staged_predictions = [p for p in clf.staged_predict(boston.data)]
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score = clf.score(boston.data, boston.target, sample_weight=boston_weights)
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staged_scores = [
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s for s in clf.staged_score(
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boston.data, boston.target, sample_weight=boston_weights)]
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assert len(staged_predictions) == 10
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assert_array_almost_equal(predictions, staged_predictions[-1])
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assert len(staged_scores) == 10
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assert_array_almost_equal(score, staged_scores[-1])
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def test_gridsearch():
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# Check that base trees can be grid-searched.
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# AdaBoost classification
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boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())
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parameters = {'n_estimators': (1, 2),
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'base_estimator__max_depth': (1, 2),
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'algorithm': ('SAMME', 'SAMME.R')}
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clf = GridSearchCV(boost, parameters)
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clf.fit(iris.data, iris.target)
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# AdaBoost regression
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boost = AdaBoostRegressor(base_estimator=DecisionTreeRegressor(),
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random_state=0)
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parameters = {'n_estimators': (1, 2),
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'base_estimator__max_depth': (1, 2)}
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clf = GridSearchCV(boost, parameters)
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clf.fit(boston.data, boston.target)
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def test_pickle():
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# Check pickability.
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import pickle
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# Adaboost classifier
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for alg in ['SAMME', 'SAMME.R']:
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obj = AdaBoostClassifier(algorithm=alg)
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obj.fit(iris.data, iris.target)
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score = obj.score(iris.data, iris.target)
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s = pickle.dumps(obj)
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obj2 = pickle.loads(s)
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assert type(obj2) == obj.__class__
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score2 = obj2.score(iris.data, iris.target)
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assert score == score2
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# Adaboost regressor
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obj = AdaBoostRegressor(random_state=0)
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obj.fit(boston.data, boston.target)
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score = obj.score(boston.data, boston.target)
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s = pickle.dumps(obj)
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obj2 = pickle.loads(s)
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assert type(obj2) == obj.__class__
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score2 = obj2.score(boston.data, boston.target)
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assert score == score2
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def test_importances():
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# Check variable importances.
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X, y = datasets.make_classification(n_samples=2000,
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n_features=10,
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n_informative=3,
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n_redundant=0,
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n_repeated=0,
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shuffle=False,
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random_state=1)
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for alg in ['SAMME', 'SAMME.R']:
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clf = AdaBoostClassifier(algorithm=alg)
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clf.fit(X, y)
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importances = clf.feature_importances_
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assert importances.shape[0] == 10
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assert (importances[:3, np.newaxis] >= importances[3:]).all()
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def test_error():
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# Test that it gives proper exception on deficient input.
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assert_raises(ValueError,
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AdaBoostClassifier(learning_rate=-1).fit,
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X, y_class)
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assert_raises(ValueError,
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AdaBoostClassifier(algorithm="foo").fit,
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X, y_class)
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assert_raises(ValueError,
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AdaBoostClassifier().fit,
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X, y_class, sample_weight=np.asarray([-1]))
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def test_base_estimator():
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# Test different base estimators.
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from sklearn.ensemble import RandomForestClassifier
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# XXX doesn't work with y_class because RF doesn't support classes_
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# Shouldn't AdaBoost run a LabelBinarizer?
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clf = AdaBoostClassifier(RandomForestClassifier())
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clf.fit(X, y_regr)
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clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
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clf.fit(X, y_class)
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from sklearn.ensemble import RandomForestRegressor
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clf = AdaBoostRegressor(RandomForestRegressor(), random_state=0)
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clf.fit(X, y_regr)
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clf = AdaBoostRegressor(SVR(), random_state=0)
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clf.fit(X, y_regr)
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# Check that an empty discrete ensemble fails in fit, not predict.
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X_fail = [[1, 1], [1, 1], [1, 1], [1, 1]]
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y_fail = ["foo", "bar", 1, 2]
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clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
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assert_raises_regexp(ValueError, "worse than random",
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clf.fit, X_fail, y_fail)
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def test_sparse_classification():
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# Check classification with sparse input.
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class CustomSVC(SVC):
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"""SVC variant that records the nature of the training set."""
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def fit(self, X, y, sample_weight=None):
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"""Modification on fit caries data type for later verification."""
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super().fit(X, y, sample_weight=sample_weight)
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self.data_type_ = type(X)
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return self
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X, y = datasets.make_multilabel_classification(n_classes=1, n_samples=15,
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n_features=5,
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random_state=42)
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# Flatten y to a 1d array
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y = np.ravel(y)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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for sparse_format in [csc_matrix, csr_matrix, lil_matrix, coo_matrix,
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dok_matrix]:
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X_train_sparse = sparse_format(X_train)
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X_test_sparse = sparse_format(X_test)
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# Trained on sparse format
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sparse_classifier = AdaBoostClassifier(
|
||
|
|
base_estimator=CustomSVC(probability=True),
|
||
|
|
random_state=1,
|
||
|
|
algorithm="SAMME"
|
||
|
|
).fit(X_train_sparse, y_train)
|
||
|
|
|
||
|
|
# Trained on dense format
|
||
|
|
dense_classifier = AdaBoostClassifier(
|
||
|
|
base_estimator=CustomSVC(probability=True),
|
||
|
|
random_state=1,
|
||
|
|
algorithm="SAMME"
|
||
|
|
).fit(X_train, y_train)
|
||
|
|
|
||
|
|
# predict
|
||
|
|
sparse_results = sparse_classifier.predict(X_test_sparse)
|
||
|
|
dense_results = dense_classifier.predict(X_test)
|
||
|
|
assert_array_equal(sparse_results, dense_results)
|
||
|
|
|
||
|
|
# decision_function
|
||
|
|
sparse_results = sparse_classifier.decision_function(X_test_sparse)
|
||
|
|
dense_results = dense_classifier.decision_function(X_test)
|
||
|
|
assert_array_almost_equal(sparse_results, dense_results)
|
||
|
|
|
||
|
|
# predict_log_proba
|
||
|
|
sparse_results = sparse_classifier.predict_log_proba(X_test_sparse)
|
||
|
|
dense_results = dense_classifier.predict_log_proba(X_test)
|
||
|
|
assert_array_almost_equal(sparse_results, dense_results)
|
||
|
|
|
||
|
|
# predict_proba
|
||
|
|
sparse_results = sparse_classifier.predict_proba(X_test_sparse)
|
||
|
|
dense_results = dense_classifier.predict_proba(X_test)
|
||
|
|
assert_array_almost_equal(sparse_results, dense_results)
|
||
|
|
|
||
|
|
# score
|
||
|
|
sparse_results = sparse_classifier.score(X_test_sparse, y_test)
|
||
|
|
dense_results = dense_classifier.score(X_test, y_test)
|
||
|
|
assert_array_almost_equal(sparse_results, dense_results)
|
||
|
|
|
||
|
|
# staged_decision_function
|
||
|
|
sparse_results = sparse_classifier.staged_decision_function(
|
||
|
|
X_test_sparse)
|
||
|
|
dense_results = dense_classifier.staged_decision_function(X_test)
|
||
|
|
for sprase_res, dense_res in zip(sparse_results, dense_results):
|
||
|
|
assert_array_almost_equal(sprase_res, dense_res)
|
||
|
|
|
||
|
|
# staged_predict
|
||
|
|
sparse_results = sparse_classifier.staged_predict(X_test_sparse)
|
||
|
|
dense_results = dense_classifier.staged_predict(X_test)
|
||
|
|
for sprase_res, dense_res in zip(sparse_results, dense_results):
|
||
|
|
assert_array_equal(sprase_res, dense_res)
|
||
|
|
|
||
|
|
# staged_predict_proba
|
||
|
|
sparse_results = sparse_classifier.staged_predict_proba(X_test_sparse)
|
||
|
|
dense_results = dense_classifier.staged_predict_proba(X_test)
|
||
|
|
for sprase_res, dense_res in zip(sparse_results, dense_results):
|
||
|
|
assert_array_almost_equal(sprase_res, dense_res)
|
||
|
|
|
||
|
|
# staged_score
|
||
|
|
sparse_results = sparse_classifier.staged_score(X_test_sparse,
|
||
|
|
y_test)
|
||
|
|
dense_results = dense_classifier.staged_score(X_test, y_test)
|
||
|
|
for sprase_res, dense_res in zip(sparse_results, dense_results):
|
||
|
|
assert_array_equal(sprase_res, dense_res)
|
||
|
|
|
||
|
|
# Verify sparsity of data is maintained during training
|
||
|
|
types = [i.data_type_ for i in sparse_classifier.estimators_]
|
||
|
|
|
||
|
|
assert all([(t == csc_matrix or t == csr_matrix)
|
||
|
|
for t in types])
|
||
|
|
|
||
|
|
|
||
|
|
def test_sparse_regression():
|
||
|
|
# Check regression with sparse input.
|
||
|
|
|
||
|
|
class CustomSVR(SVR):
|
||
|
|
"""SVR variant that records the nature of the training set."""
|
||
|
|
|
||
|
|
def fit(self, X, y, sample_weight=None):
|
||
|
|
"""Modification on fit caries data type for later verification."""
|
||
|
|
super().fit(X, y, sample_weight=sample_weight)
|
||
|
|
self.data_type_ = type(X)
|
||
|
|
return self
|
||
|
|
|
||
|
|
X, y = datasets.make_regression(n_samples=15, n_features=50, n_targets=1,
|
||
|
|
random_state=42)
|
||
|
|
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
|
||
|
|
|
||
|
|
for sparse_format in [csc_matrix, csr_matrix, lil_matrix, coo_matrix,
|
||
|
|
dok_matrix]:
|
||
|
|
X_train_sparse = sparse_format(X_train)
|
||
|
|
X_test_sparse = sparse_format(X_test)
|
||
|
|
|
||
|
|
# Trained on sparse format
|
||
|
|
sparse_classifier = AdaBoostRegressor(
|
||
|
|
base_estimator=CustomSVR(),
|
||
|
|
random_state=1
|
||
|
|
).fit(X_train_sparse, y_train)
|
||
|
|
|
||
|
|
# Trained on dense format
|
||
|
|
dense_classifier = dense_results = AdaBoostRegressor(
|
||
|
|
base_estimator=CustomSVR(),
|
||
|
|
random_state=1
|
||
|
|
).fit(X_train, y_train)
|
||
|
|
|
||
|
|
# predict
|
||
|
|
sparse_results = sparse_classifier.predict(X_test_sparse)
|
||
|
|
dense_results = dense_classifier.predict(X_test)
|
||
|
|
assert_array_almost_equal(sparse_results, dense_results)
|
||
|
|
|
||
|
|
# staged_predict
|
||
|
|
sparse_results = sparse_classifier.staged_predict(X_test_sparse)
|
||
|
|
dense_results = dense_classifier.staged_predict(X_test)
|
||
|
|
for sprase_res, dense_res in zip(sparse_results, dense_results):
|
||
|
|
assert_array_almost_equal(sprase_res, dense_res)
|
||
|
|
|
||
|
|
types = [i.data_type_ for i in sparse_classifier.estimators_]
|
||
|
|
|
||
|
|
assert all([(t == csc_matrix or t == csr_matrix)
|
||
|
|
for t in types])
|
||
|
|
|
||
|
|
|
||
|
|
def test_sample_weight_adaboost_regressor():
|
||
|
|
"""
|
||
|
|
AdaBoostRegressor should work without sample_weights in the base estimator
|
||
|
|
The random weighted sampling is done internally in the _boost method in
|
||
|
|
AdaBoostRegressor.
|
||
|
|
"""
|
||
|
|
class DummyEstimator(BaseEstimator):
|
||
|
|
|
||
|
|
def fit(self, X, y):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def predict(self, X):
|
||
|
|
return np.zeros(X.shape[0])
|
||
|
|
|
||
|
|
boost = AdaBoostRegressor(DummyEstimator(), n_estimators=3)
|
||
|
|
boost.fit(X, y_regr)
|
||
|
|
assert len(boost.estimator_weights_) == len(boost.estimator_errors_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multidimensional_X():
|
||
|
|
"""
|
||
|
|
Check that the AdaBoost estimators can work with n-dimensional
|
||
|
|
data matrix
|
||
|
|
"""
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
|
||
|
|
X = rng.randn(50, 3, 3)
|
||
|
|
yc = rng.choice([0, 1], 50)
|
||
|
|
yr = rng.randn(50)
|
||
|
|
|
||
|
|
boost = AdaBoostClassifier(DummyClassifier(strategy='most_frequent'))
|
||
|
|
boost.fit(X, yc)
|
||
|
|
boost.predict(X)
|
||
|
|
boost.predict_proba(X)
|
||
|
|
|
||
|
|
boost = AdaBoostRegressor(DummyRegressor())
|
||
|
|
boost.fit(X, yr)
|
||
|
|
boost.predict(X)
|
||
|
|
|
||
|
|
|
||
|
|
# TODO: Remove in 0.24 when DummyClassifier's `strategy` default changes
|
||
|
|
@ignore_warnings
|
||
|
|
@pytest.mark.parametrize("algorithm", ['SAMME', 'SAMME.R'])
|
||
|
|
def test_adaboostclassifier_without_sample_weight(algorithm):
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
base_estimator = NoSampleWeightWrapper(DummyClassifier())
|
||
|
|
clf = AdaBoostClassifier(
|
||
|
|
base_estimator=base_estimator, algorithm=algorithm
|
||
|
|
)
|
||
|
|
err_msg = ("{} doesn't support sample_weight"
|
||
|
|
.format(base_estimator.__class__.__name__))
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_adaboostregressor_sample_weight():
|
||
|
|
# check that giving weight will have an influence on the error computed
|
||
|
|
# for a weak learner
|
||
|
|
rng = np.random.RandomState(42)
|
||
|
|
X = np.linspace(0, 100, num=1000)
|
||
|
|
y = (.8 * X + 0.2) + (rng.rand(X.shape[0]) * 0.0001)
|
||
|
|
X = X.reshape(-1, 1)
|
||
|
|
|
||
|
|
# add an arbitrary outlier
|
||
|
|
X[-1] *= 10
|
||
|
|
y[-1] = 10000
|
||
|
|
|
||
|
|
# random_state=0 ensure that the underlying bootstrap will use the outlier
|
||
|
|
regr_no_outlier = AdaBoostRegressor(
|
||
|
|
base_estimator=LinearRegression(), n_estimators=1, random_state=0
|
||
|
|
)
|
||
|
|
regr_with_weight = clone(regr_no_outlier)
|
||
|
|
regr_with_outlier = clone(regr_no_outlier)
|
||
|
|
|
||
|
|
# fit 3 models:
|
||
|
|
# - a model containing the outlier
|
||
|
|
# - a model without the outlier
|
||
|
|
# - a model containing the outlier but with a null sample-weight
|
||
|
|
regr_with_outlier.fit(X, y)
|
||
|
|
regr_no_outlier.fit(X[:-1], y[:-1])
|
||
|
|
sample_weight = np.ones_like(y)
|
||
|
|
sample_weight[-1] = 0
|
||
|
|
regr_with_weight.fit(X, y, sample_weight=sample_weight)
|
||
|
|
|
||
|
|
score_with_outlier = regr_with_outlier.score(X[:-1], y[:-1])
|
||
|
|
score_no_outlier = regr_no_outlier.score(X[:-1], y[:-1])
|
||
|
|
score_with_weight = regr_with_weight.score(X[:-1], y[:-1])
|
||
|
|
|
||
|
|
assert score_with_outlier < score_no_outlier
|
||
|
|
assert score_with_outlier < score_with_weight
|
||
|
|
assert score_no_outlier == pytest.approx(score_with_weight)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
|
||
|
|
def test_adaboost_consistent_predict(algorithm):
|
||
|
|
# check that predict_proba and predict give consistent results
|
||
|
|
# regression test for:
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/14084
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
||
|
|
*datasets.load_digits(return_X_y=True), random_state=42
|
||
|
|
)
|
||
|
|
model = AdaBoostClassifier(algorithm=algorithm, random_state=42)
|
||
|
|
model.fit(X_train, y_train)
|
||
|
|
|
||
|
|
assert_array_equal(
|
||
|
|
np.argmax(model.predict_proba(X_test), axis=1),
|
||
|
|
model.predict(X_test)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
'model, X, y',
|
||
|
|
[(AdaBoostClassifier(), iris.data, iris.target),
|
||
|
|
(AdaBoostRegressor(), boston.data, boston.target)]
|
||
|
|
)
|
||
|
|
def test_adaboost_negative_weight_error(model, X, y):
|
||
|
|
sample_weight = np.ones_like(y)
|
||
|
|
sample_weight[-1] = -10
|
||
|
|
|
||
|
|
err_msg = "sample_weight cannot contain negative weight"
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
model.fit(X, y, sample_weight=sample_weight)
|