scikit-learn · haochunchang · Nov 2, 2019 · Nov 2, 2019 · Nov 2, 2019 · Nov 2, 2019
diff --git a/sklearn/metrics/__init__.py b/sklearn/metrics/__init__.py
@@ -34,6 +34,7 @@
 from ._classification import zero_one_loss
 from ._classification import brier_score_loss
 from ._classification import multilabel_confusion_matrix
+from ._classification import tpr_fpr_tnr_fnr_scores
 
 from . import cluster
 from .cluster import adjusted_mutual_info_score
@@ -160,6 +161,7 @@
     'SCORERS',
     'silhouette_samples',
     'silhouette_score',
+    'tpr_fpr_tnr_fnr_scores',
     'v_measure_score',
     'zero_one_loss',
     'brier_score_loss',

diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -1538,6 +1538,209 @@ def precision_recall_fscore_support(y_true, y_pred, *, beta=1.0, labels=None,
     return precision, recall, f_score, true_sum
 
 
+@_deprecate_positional_args
+def tpr_fpr_tnr_fnr_scores(y_true, y_pred, *, labels=None, pos_label=1,
+                           average=None, warn_for=('tpr', 'fpr',
+                                                   'tnr', 'fnr'),
+                           sample_weight=None, zero_division="warn"):
+    """Compute True Positive Rate (TPR), False Positive Rate (FPR),\
+    True Negative Rate (TNR), False Negative Rate (FNR) for each class
+
+    The TPR is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
+    true positives and ``fn`` the number of false negatives.
+
+    The FPR is the ratio ``fp / (tn + fp)`` where ``tn`` is the number of
+    true negatives and ``fp`` the number of false positives.
+
+    The TNR is the ratio ``tn / (tn + fp)`` where ``tn`` is the number of
+    true negatives and ``fp`` the number of false positives.
+
+    The FNR is the ratio ``fn / (tp + fn)`` where ``tp`` is the number of
+    true positives and ``fn`` the number of false negatives.
+
+    If ``pos_label is None`` and in binary classification, this function
+    returns the true positive rate, false positive rate, true negative rate
+    and false negative rate if ``average`` is one of ``'micro'``, ``'macro'``,
+    ``'weighted'`` or ``'samples'``.
+
+    Parameters
+    ----------
+    y_true : {array-like, label indicator array, sparse matrix} \
+        of shape (n_samples,)
+        Ground truth (correct) target values.
+
+    y_pred : {array-like, label indicator array, sparse matrix} \
+        of shape (n_samples,)
+        Estimated targets as returned by a classifier.
+
+    labels : list, default=None
+        The set of labels to include when ``average != 'binary'``, and their
+        order if ``average is None``. Labels present in the data can be
+        excluded, for example to calculate a multiclass average ignoring a
+        majority negative class, while labels not present in the data will
+        result in 0 components in a macro average. For multilabel targets,
+        labels are column indices. By default, all labels in ``y_true`` and
+        ``y_pred`` are used in sorted order.
+
+    pos_label : str or int, default=1
+        The class to report if ``average='binary'`` and the data is binary.
+        If the data are multiclass or multilabel, this will be ignored;
+        setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
+        scores for that label only.
+
+    average : str, {None, 'binary', 'micro', 'macro', 'samples', 'weighted'}, \
+        default=None
+        If ``None``, the scores for each class are returned. Otherwise, this
+        determines the type of averaging performed on the data:
+
+        ``'binary'``:
+            Only report results for the class specified by ``pos_label``.
+            This is applicable only if targets (``y_{true,pred}``) are binary.
+        ``'micro'``:
+            Calculate metrics globally by counting the total true positives,
+            false negatives and false positives.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average weighted
+            by support (the number of true instances for each label). This
+            alters 'macro' to account for label imbalance.
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average (only
+            meaningful for multilabel classification where this differs from
+            :func:`accuracy_score`).
+
+    warn_for : tuple or set, for internal use
+        This determines which warnings will be made in the case that this
+        function is being used to return only one of its metrics.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    zero_division : str or int, {'warn', 0, 1}, default="warn"
+        Sets the value to return when there is a zero division:
+           - tpr, fnr: when there are no positive labels
+           - fpr, tnr: when there are no negative labels
+
+        If set to "warn", this acts as 0, but warnings are also raised.
+
+    Returns
+    -------
+    tpr : float (if average is not None), \
+        or ndarray of shape (n_unique_labels,)
+
+    fpr : float (if average is not None), \
+        or ndarray of shape (n_unique_labels,)
+
+    tnr : float (if average is not None), \
+        or ndarray of shape (n_unique_labels,)
+
+    fnr : float (if average is not None), \
+        or ndarray of shape (n_unique_labels,)
+        The number of occurrences of each label in ``y_true``.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for confusion matrix
+           <https://en.wikipedia.org/wiki/Confusion_matrix>`_
+
+    .. [2] `Discriminative Methods for Multi-labeled Classification Advances
+           in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu
+           Godbole, Sunita Sarawagi
+           <http://www.godbole.net/shantanu/pubs/multilabelsvm-pakdd04.pdf>`_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig'])
+    >>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog'])
+    >>> tpr_fpr_tnr_fnr_scores(y_true, y_pred, average='macro')
+    (0.3333333333333333, 0.3333333333333333, 0.6666666666666666,
+     0.6666666666666666)
+    >>> tpr_fpr_tnr_fnr_scores(y_true, y_pred, average='micro')
+    (0.3333333333333333, 0.3333333333333333, 0.6666666666666666,
+     0.6666666666666666)
+    >>> tpr_fpr_tnr_fnr_scores(y_true, y_pred, average='weighted')
+    (0.3333333333333333, 0.3333333333333333, 0.6666666666666666,
+    0.6666666666666666)
+
+    It is possible to compute per-label fpr, fnr, tnr, tpr and
+    supports instead of averaging:
+
+    >>> tpr_fpr_tnr_fnr_scores(y_true, y_pred, average=None,
+    ... labels=['pig', 'dog', 'cat'])
+    (array([0., 0., 1.]), array([0.25, 0.5 , 0.25]),
+    array([0.75, 0.5 , 0.75]), array([1., 1., 0.]))
+
+    Notes
+    -----
+    When ``true positive + false negative == 0``, TPR, FNR are undefined;
+    When ``true negative + false positive == 0``, FPR, TNR are undefined.
+    In such cases, by default the metric will be set to 0, as will f-score,
+    and ``UndefinedMetricWarning`` will be raised. This behavior can be
+    modified with ``zero_division``.
+    """
+    _check_zero_division(zero_division)
+
+    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
+
+    # Calculate tp_sum, fp_sum, tn_sum, fn_sum, pos_sum, neg_sum
+    samplewise = average == 'samples'
+    MCM = multilabel_confusion_matrix(y_true, y_pred,
+                                      sample_weight=sample_weight,
+                                      labels=labels, samplewise=samplewise)
+    tn_sum = MCM[:, 0, 0]
+    fp_sum = MCM[:, 0, 1]
+    fn_sum = MCM[:, 1, 0]
+    tp_sum = MCM[:, 1, 1]
+    neg_sum = tn_sum + fp_sum
+    pos_sum = fn_sum + tp_sum
+
+    if average == 'micro':
+        tp_sum = np.array([tp_sum.sum()])
+        fp_sum = np.array([fp_sum.sum()])
+        tn_sum = np.array([tn_sum.sum()])
+        fn_sum = np.array([fn_sum.sum()])
+        neg_sum = np.array([neg_sum.sum()])
+        pos_sum = np.array([pos_sum.sum()])
+
+    # Divide, and on zero-division, set scores and/or warn according to
+    # zero_division:
+    tpr = _prf_divide(tp_sum, pos_sum, 'tpr', 'positives',
+                      average, warn_for, zero_division)
+    fpr = _prf_divide(fp_sum, neg_sum, 'fpr', 'negatives',
+                      average, warn_for, zero_division)
+    tnr = _prf_divide(tn_sum, neg_sum, 'tnr', 'negatives',
+                      average, warn_for, zero_division)
+    fnr = _prf_divide(fn_sum, pos_sum, 'fnr', 'positives',
+                      average, warn_for, zero_division)
+    # Average the results
+    if average == 'weighted':
+        weights = pos_sum
+        if weights.sum() == 0:
+            zero_division_value = 0.0 if zero_division in ["warn", 0] else 1.0
+            # TPR and FNR is zero_division if there are no positive labels
+            # FPR and TNR is zero_division if there are no negative labels
+            return (zero_division_value if pos_sum.sum() == 0 else 0,
+                    zero_division_value if neg_sum.sum() == 0 else 0,
+                    zero_division_value if neg_sum.sum() == 0 else 0,
+                    zero_division_value if pos_sum.sum() == 0 else 0)
+
+    elif average == 'samples':
+        weights = sample_weight
+    else:
+        weights = None
+
+    if average is not None:
+        assert average != 'binary' or len(fpr) == 1
+        fpr = np.average(fpr, weights=weights)
+        tnr = np.average(tnr, weights=weights)
+        fnr = np.average(fnr, weights=weights)
+        tpr = np.average(tpr, weights=weights)
+    return tpr, fpr, tnr, fnr
+
+
 @_deprecate_positional_args
 def precision_score(y_true, y_pred, *, labels=None, pos_label=1,
                     average='binary', sample_weight=None,
@@ -2174,7 +2377,8 @@ def log_loss(y_true, y_pred, *, eps=1e-15, normalize=True, sample_weight=None,
     y_true : array-like or label indicator matrix
         Ground truth (correct) labels for n_samples samples.
 
-    y_pred : array-like of float, shape = (n_samples, n_classes) or (n_samples,)
+    y_pred : array-like of float, shape = (n_samples, n_classes) \
+    or (n_samples,)
         Predicted probabilities, as returned by a classifier's
         predict_proba method. If ``y_pred.shape = (n_samples,)``
         the probabilities provided are assumed to be that of the