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@ -338,7 +338,7 @@ class Scorer(object):
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# c. Neither the name of the Scikit-learn Developers nor the names of
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# its contributors may be used to endorse or promote products
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# derived from this software without specific prior written
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# permission.
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# permission.
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#
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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@ -353,6 +353,7 @@ class Scorer(object):
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# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
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# DAMAGE.
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def _roc_auc_score(y_true, y_score):
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"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
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from prediction scores.
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@ -490,19 +491,19 @@ def _binary_clf_curve(y_true, y_score):
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thresholds : array, shape = [n_thresholds]
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Decreasing score values.
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"""
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pos_label = 1.
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pos_label = 1.0
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y_true = np.ravel(y_true)
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y_score = np.ravel(y_score)
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# make y_true a boolean vector
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y_true = (y_true == pos_label)
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y_true = y_true == pos_label
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# sort scores and corresponding truth values
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desc_score_indices = np.argsort(y_score, kind="mergesort")[::-1]
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y_score = y_score[desc_score_indices]
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y_true = y_true[desc_score_indices]
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weight = 1.
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weight = 1.0
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# y_score typically has many tied values. Here we extract
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# the indices associated with the distinct values. We also
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@ -533,8 +534,11 @@ def _stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
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"""
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out = np.cumsum(arr, axis=axis, dtype=np.float64)
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expected = np.sum(arr, axis=axis, dtype=np.float64)
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if not np.all(np.isclose(out.take(-1, axis=axis), expected, rtol=rtol,
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atol=atol, equal_nan=True)):
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if not np.all(
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np.isclose(
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out.take(-1, axis=axis), expected, rtol=rtol, atol=atol, equal_nan=True
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)
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):
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raise ValueError(Errors.E163)
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return out
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