10000 [MRG] Ensure that ROC curve starts at (0, 0) (#10093) · jwjohnson314/scikit-learn@3ed115b · GitHub
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qinhanmin2014Jeremiah Johnson
qinhanmin2014
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[MRG] Ensure that ROC curve starts at (0, 0) (scikit-learn#10093)
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‎doc/modules/model_evaluation.rst

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1137,11 +1137,11 @@ Here is a small example of how to use the :func:`roc_curve` function::
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>>> scores = np.array([0.1, 0.4, 0.35, 0.8])
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>>> fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2)
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>>> fpr
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array([ 0. , 0.5, 0.5, 1. ])
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array([ 0. , 0. , 0.5, 0.5, 1. ])
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>>> tpr
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array([ 0.5, 0.5, 1. , 1. ])
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array([ 0. , 0.5, 0.5, 1. , 1. ])
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>>> thresholds
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array([ 0.8 , 0.4 , 0.35, 0.1 ])
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array([ 1.8 , 0.8 , 0.4 , 0.35, 0.1 ])
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This figure shows an example of such an ROC curve:
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‎doc/whats_new/v0.20.rst

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@@ -18,6 +18,7 @@ random sampling procedures.
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- :class:`decomposition.IncrementalPCA` in Python 2 (bug fix)
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- :class:`isotonic.IsotonicRegression` (bug fix)
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- :class:`metrics.roc_auc_score` (bug fix)
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- :class:`metrics.roc_curve` (bug fix)
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- :class:`neural_network.BaseMultilayerPerceptron` (bug fix)
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- :class:`neural_network.MLPRegressor` (bug fix)
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- :class:`neural_network.MLPClassifier` (bug fix)
@@ -160,6 +161,12 @@ Metrics
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- Fixed a bug due to floating point error in :func:`metrics.roc_auc_score` with
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non-integer sample weights. :issue:`9786` by :user:`Hanmin Qin <qinhanmin2014>`.
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- Fixed a bug where :func:`metrics.roc_curve` sometimes starts on y-axis instead
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of (0, 0), which is inconsistent with the document and other implementations.
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Note that this will not influence the result from :func:`metrics.roc_auc_score`
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:issue:`10093` by :user:`alexryndin <alexryndin>`
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and :user:`Hanmin Qin <qinhanmin2014>`.
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API changes summary
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-------------------
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‎sklearn/metrics/ranking.py

Lines changed: 10 additions & 5 deletions
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@@ -217,7 +217,6 @@ def _binary_uninterpolated_average_precision(
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sample_weight=sample_weight)
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220-
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def roc_auc_score(y_true, y_score, average="macro", sample_weight=None):
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"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
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from prediction scores.
@@ -267,6 +266,9 @@ def roc_auc_score(y_true, y_score, average="macro", sample_weight=None):
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.. [1] `Wikipedia entry for the Receiver operating characteristic
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<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
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.. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
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Letters, 2006, 27(8):861-874.
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See also
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--------
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average_precision_score : Area under the precision-recall curve
@@ -541,6 +543,8 @@ def roc_curve(y_true, y_score, pos_label=None, sample_weight=None,
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.. [1] `Wikipedia entry for the Receiver operating characteristic
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<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
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546+
.. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
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Letters, 2006, 27(8):861-874.
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Examples
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--------
@@ -550,11 +554,11 @@ def roc_curve(y_true, y_score, pos_label=None, sample_weight=None,
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>>> scores = np.array([0.1, 0.4, 0.35, 0.8])
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>>> fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)
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>>> fpr
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array([ 0. , 0.5, 0.5, 1. ])
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array([ 0. , 0. , 0.5, 0.5, 1. ])
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>>> tpr
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array([ 0.5, 0.5, 1. , 1. ])
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array([ 0. , 0.5, 0.5, 1. , 1. ])
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>>> thresholds
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array([ 0.8 , 0.4 , 0.35, 0.1 ])
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array([ 1.8 , 0.8 , 0.4 , 0.35, 0.1 ])
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"""
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fps, tps, thresholds = _binary_clf_curve(
@@ -578,8 +582,9 @@ def roc_curve(y_true, y_score, pos_label=None, sample_weight=None,
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tps = tps[optimal_idxs]
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thresholds = thresholds[optimal_idxs]
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if tps.size == 0 or fps[0] != 0:
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if tps.size == 0 or fps[0] != 0 or tps[0] != 0:
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# Add an extra threshold position if necessary
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# to make sure that the curve starts at (0, 0)
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tps = np.r_[0, tps]
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fps = np.r_[0, fps]
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thresholds = np.r_[thresholds[0] + 1, thresholds]

‎sklearn/metrics/tests/test_ranking.py

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -270,8 +270,8 @@ def test_roc_curve_toydata():
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y_score = [0, 1]
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tpr, fpr, _ = roc_curve(y_true, y_score)
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roc_auc = roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 1])
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assert_array_almost_equal(fpr, [1, 1])
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assert_array_almost_equal(tpr, [0, 0, 1])
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assert_array_almost_equal(fpr, [0, 1, 1])
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assert_almost_equal(roc_auc, 1.)
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y_true = [0, 1]
@@ -294,8 +294,8 @@ def test_roc_curve_toydata():
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y_score = [1, 0]
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tpr, fpr, _ = roc_curve(y_true, y_score)
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roc_auc = roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 1])
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assert_array_almost_equal(fpr, [1, 1])
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assert_array_almost_equal(tpr, [0, 0, 1])
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assert_array_almost_equal(fpr, [0, 1, 1])
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assert_almost_equal(roc_auc, 1.)
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y_true = [1, 0]
@@ -319,8 +319,8 @@ def test_roc_curve_toydata():
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# assert UndefinedMetricWarning because of no negative sample in y_true
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tpr, fpr, _ = assert_warns(UndefinedMetricWarning, roc_curve, y_true, y_score)
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assert_raises(ValueError, roc_auc_score, y_true, y_score)
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assert_array_almost_equal(tpr, [np.nan, np.nan])
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assert_array_almost_equal(fpr, [0.5, 1.])
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assert_array_almost_equal(tpr, [np.nan, np.nan, np.nan])
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assert_array_almost_equal(fpr, [0., 0.5, 1.])
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# Multi-label classification task
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y_true = np.array([[0, 1], [0, 1]])
@@ -359,7 +359,7 @@ def test_roc_curve_drop_intermediate():
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y_true = [0, 0, 0, 0, 1, 1]
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y_score = [0., 0.2, 0.5, 0.6, 0.7, 1.0]
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tpr, fpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=True)
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assert_array_almost_equal(thresholds, [1., 0.7, 0.])
362+
assert_array_almost_equal(thresholds, [2., 1., 0.7, 0.])
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# Test dropping thresholds with repeating scores
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y_true = [0, 0, 0, 0, 0, 0, 0,
@@ -368,7 +368,7 @@ def test_roc_curve_drop_intermediate():
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0.6, 0.7, 0.8, 0.9, 0.9, 1.0]
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tpr, fpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=True)
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assert_array_almost_equal(thresholds,
371-
[1.0, 0.9, 0.7, 0.6, 0.])
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[2.0, 1.0, 0.9, 0.7, 0.6, 0.])
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def test_roc_curve_fpr_tpr_increasing():

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