metrics.py: COSMIT more commets on precision_recall_curve

sklearn/metrics/metrics.py

@@ -12,7 +12,7 @@
 #          Olivier Grisel <[email protected]>
 # License: BSD Style.
 
-import itertools
+from itertools import izip
 import numpy as np
 from scipy.sparse import coo_matrix
 
@@ -855,14 +855,17 @@ def precision_recall_curve(y_true, probas_pred):
         raise ValueError("y_true contains non binary labels: %r" % labels)
 
     # Sort pred_probas (and corresponding true labels) by pred_proba value
-    sort_idxs = np.argsort(probas_pred, kind="mergesort")[::-1]
-    probas_pred = probas_pred[sort_idxs]
-    y_true = y_true[sort_idxs]
-
-    # Get indices where values of probas_pred decreases
-    thresh_idxs = np.r_[0,
-                        np.where(np.diff(probas_pred))[0] + 1,
-                        len(probas_pred)]
+    decreasing_probas_indices = np.argsort(probas_pred, kind="mergesort")[::-1]
+    probas_pred = probas_pred[decreasing_probas_indices]
+    y_true = y_true[decreasing_probas_indices]
+
+    # Probas_pred typically has many tied values. Here we extract
+    # the indices associated with the distinct values. We also
+    # concatenate values onto the ends of the curve.
+    distinct_value_indices = np.where(np.diff(probas_pred))[0] + 1
+    threshold_idxs = np.r_[0,
+                           distinct_value_indices,
+                           len(probas_pred)]
 
     # Initialize true and false positive counts, precision and recall
     total_positive = float(y_true.sum())
@@ -871,20 +874,20 @@ def precision_recall_curve(y_true, probas_pred):
     recall = [0.]
     thresholds = []
 
-    # Iterate over indices which indicate distinct values of probas_pred --
-    # each of these distinct values will be represented in the curve with a
-    # coordinate in precision-recall space. To calculate the precision and
-    # recall associated with each point, we use these indices to select all
-    # labels associated with the predictions. By incrementally keeping track
-    # of the number of positive and negative labels seen so far, we can
-    # calculate precision and recall.
-    for l_idx, r_idx in itertools.izip(thresh_idxs[:-1], thresh_idxs[1:]):
-        thresh_labels = y_true[l_idx:r_idx]
-        n_thresh = r_idx - l_idx
-        n_pos_thresh = thresh_labels.sum()
-        n_neg_thresh = n_thresh - n_pos_thresh
-        tp_count += n_pos_thresh
-        fp_count += n_neg_thresh
+    # Iterate over indices which indicate distinct values (thresholds) of
+    # probas_pred. Each of these threshold values will be represented in the
+    # curve with a coordinate in precision-recall space. To calculate the
+    # precision and recall associated with each point, we use these indices to
+    # select all labels associated with the predictions. By incrementally
+    # keeping track of the number of positive and negative labels seen so far,
+    # we can calculate precision and recall.
+    for l_idx, r_idx in izip(threshold_idxs[:-1], threshold_idxs[1:]):
+        threshold_labels = y_true[l_idx:r_idx]
+        n_at_threshold = r_idx - l_idx
+        n_pos_at_threshold = threshold_labels.sum()
+        n_neg_at_threshold = n_at_threshold - n_pos_at_threshold
+        tp_count += n_pos_at_threshold
+        fp_count += n_neg_at_threshold
         fn_count = total_positive - tp_count
         precision.append(tp_count / (tp_count + fp_count))
         recall.append(tp_count / (tp_count + fn_count))