binary stats

tensorpack/utils/stat.py

@@ -3,6 +3,8 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 import numpy as np
 
+__all__ = ['StatCounter', 'Accuracy', 'BinaryStatistics']
+
 class StatCounter(object):
     def __init__(self):
         self.reset()
@@ -35,7 +37,61 @@ class Accuracy(object):
 
     @property
     def accuracy(self):
-        if self.tot < 0.001:
+        if self.tot == 0:
             return 0
         return self.corr * 1.0 / self.tot
 
+
+class BinaryStatistics(object):
+    """
+    Statistics for binary decision,
+    including precision, recall, false positive, false negative
+    """
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.nr_pos = 0  # positive label
+        self.nr_neg = 0  # negative label
+        self.nr_pred_pos = 0
+        self.nr_pred_neg = 0
+        self.corr_pos = 0   # correct predict positive
+        self.corr_neg = 0   # correct predict negative
+
+    def feed(self, pred, label):
+        """
+        :param pred: 0/1 np array
+        :param label: 0/1 np array of the same size
+        """
+        nr = label.size
+        assert pred.size == label.size
+        self.nr_pos += (label == 1).sum()
+        self.nr_neg += (label == 0).sum()
+        self.nr_pred_pos += (pred == 1).sum()
+        self.nr_pred_neg += (pred == 0).sum()
+        self.corr_pos += ((pred == 1) & (pred == label)).sum()
+        self.corr_neg += ((pred == 0) & (pred == label)).sum()
+
+    @property
+    def precision(self):
+        if self.nr_pred_pos == 0:
+            return 0
+        return self.corr_pos * 1. / self.nr_pred_pos
+
+    @property
+    def recall(self):
+        if self.nr_pos == 0:
+            return 0
+        return self.corr_pos * 1. / self.nr_pos
+
+    @property
+    def false_positive(self):
+        if self.nr_pred_pos == 0:
+            return 0
+        return 1 - self.precision
+
+    @property
+    def false_negative(self):
+        if self.nr_pos == 0:
+            return 0
+        return 1 - self.recall