Lower _MINIMUM_COUNT to 1e-7 (#644)

PR curve summaries had used 1.0 as the minimum division value while computing precision and recall.

That actually does not work because certain weights can cause TP, FP, TN, and FN values to be below 1.0.

This change sets that minimum to 1e-7 and adds a test.

Author: chihuahua

Diff for: tensorboard/plugins/pr_curve/summary.py

@@ -28,8 +28,8 @@
 from tensorboard.plugins.pr_curve import metadata
 
 # A value that we use as the minimum value during division of counts to prevent
-# division by 0. 1 suffices because counts of course must be whole numbers.
-_MINIMUM_COUNT = 1.0
+# division by 0. 1.0 does not work: Certain weights could cause counts below 1.
+_MINIMUM_COUNT = 1e-7
 
 # The default number of thresholds.
 _DEFAULT_NUM_THRESHOLDS = 201

Diff for: tensorboard/plugins/pr_curve/summary_test.py

@@ -239,6 +239,28 @@ def test_exhaustive_random_values(self):
     values = tf.make_ndarray(pb.value[0].tensor)
     self.verify_float_arrays_are_equal(expected, values)
 
+  def test_counts_below_1(self):
+    """Tests support for counts below 1.
+
+    Certain weights cause TP, FP, TN, FN counts to be below 1.
+    """
+    pb = self.compute_and_check_summary_pb(
+        name='foo',
+        labels=np.array([True, False, False, True, True, True]),
+        predictions=np.float32([0.2, 0.3, 0.4, 0.6, 0.7, 0.8]),
+        num_thresholds=3,
+        weights=np.float32([0.0, 0.1, 0.2, 0.1, 0.1, 0.0]))
+    expected = [
+        [0.2, 0.2, 0.0],
+        [0.3, 0.0, 0.0],
+        [0.0, 0.3, 0.3],
+        [0.0, 0.0, 0.2],
+        [0.4, 1.0, 0.0],
+        [1.0, 1.0, 0.0]
+    ]
+    values = tf.make_ndarray(pb.value[0].tensor)
+    self.verify_float_arrays_are_equal(expected, values)
+
   def test_raw_data_op(self):
     # We pass raw counts and precision/recall values.
     op = summary.raw_data_op(