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src/s/c/scikit-learn-0.14.1/sklearn/metrics/tests/test_metrics.py   scikit-learn(Download)
from sklearn.utils import check_random_state, shuffle
from sklearn.utils.multiclass import unique_labels
from sklearn.utils.testing import (assert_true,
                                   assert_raises,
                                   assert_raise_message,
    y_true = [0, 1, 1]
    y_score = [.5, .5, .6]
    assert_not_equal(average_precision_score(y_true, y_score), 1.)
 
 

src/s/c/scikit-learn-0.14.1/sklearn/decomposition/tests/test_kernel_pca.py   scikit-learn(Download)
import numpy as np
import scipy.sparse as sp
 
from sklearn.utils.testing import (assert_array_almost_equal, assert_less,
                                   assert_equal, assert_not_equal,
            # non-regression test: previously, gamma would be 0 by default,
            # forcing all eigenvalues to 0 under the poly kernel
            assert_not_equal(X_fit_transformed, [])
 
            # transform new data