Fix precision/recall test.

Precision and Recall have as the numerator TP: true positives.
The labels generated in the test were only negative, and hence the test passed before because all updates were 0.

PiperOrigin-RevId: 172812994

1 files changed, 26 insertions, 32 deletions

diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
index cc0ad155fa..f288fceef6 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
@@ -1101,7 +1101,7 @@ class StreamingPrecisionTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     precision, update_op = metrics.streaming_precision(predictions, labels)
 
     with self.test_session() as sess:
@@ -1265,7 +1265,7 @@ class StreamingRecallTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     recall, update_op = metrics.streaming_recall(predictions, labels)
 
     with self.test_session() as sess:
@@ -1388,7 +1388,7 @@ class StreamingFPRTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     fpr, update_op = metrics.streaming_false_positive_rate(
         predictions, labels)
 
@@ -1516,7 +1516,7 @@ class StreamingFNRTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     fnr, update_op = metrics.streaming_false_negative_rate(
         predictions, labels)
 
@@ -1737,7 +1737,7 @@ class StreamingAUCTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     auc, update_op = metrics.streaming_auc(predictions, labels)
 
     with self.test_session() as sess:
@@ -2009,7 +2009,7 @@ class StreamingSpecificityAtSensitivityTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     specificity, update_op = metrics.streaming_specificity_at_sensitivity(
         predictions, labels, sensitivity=0.7)
 
@@ -2271,7 +2271,7 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     thresholds = [0, 0.5, 1.0]
     prec, prec_op = metrics.streaming_precision_at_thresholds(predictions,
                                                               labels,
@@ -2282,12 +2282,14 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase):
     with self.test_session() as sess:
       sess.run(variables.local_variables_initializer())
 
-      # Run several updates, then verify idempotency.
-      sess.run([prec_op, rec_op])
+      # Run several updates.
+      for _ in range(10):
+        sess.run([prec_op, rec_op])
+
+      # Then verify idempotency.
       initial_prec = prec.eval()
       initial_rec = rec.eval()
       for _ in range(10):
-        sess.run([prec_op, rec_op])
         self.assertAllClose(initial_prec, prec.eval())
         self.assertAllClose(initial_rec, rec.eval())
 
@@ -2361,14 +2363,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase):
       rec, rec_op = metrics.streaming_recall_at_thresholds(
           predictions, labels, thresholds, weights=weights)
 
-      [prec_low, prec_high] = array_ops.split(
-          value=prec, num_or_size_splits=2, axis=0)
-      prec_low = array_ops.reshape(prec_low, shape=())
-      prec_high = array_ops.reshape(prec_high, shape=())
-      [rec_low, rec_high] = array_ops.split(
-          value=rec, num_or_size_splits=2, axis=0)
-      rec_low = array_ops.reshape(rec_low, shape=())
-      rec_high = array_ops.reshape(rec_high, shape=())
+      prec_low = prec[0]
+      prec_high = prec[1]
+      rec_low = rec[0]
+      rec_high = rec[1]
 
       sess.run(variables.local_variables_initializer())
       sess.run([prec_op, rec_op])
@@ -2391,14 +2389,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase):
       rec, rec_op = metrics.streaming_recall_at_thresholds(
           predictions, labels, thresholds, weights=weights)
 
-      [prec_low, prec_high] = array_ops.split(
-          value=prec, num_or_size_splits=2, axis=0)
-      prec_low = array_ops.reshape(prec_low, shape=())
-      prec_high = array_ops.reshape(prec_high, shape=())
-      [rec_low, rec_high] = array_ops.split(
-          value=rec, num_or_size_splits=2, axis=0)
-      rec_low = array_ops.reshape(rec_low, shape=())
-      rec_high = array_ops.reshape(rec_high, shape=())
+      prec_low = prec[0]
+      prec_high = prec[1]
+      rec_low = rec[0]
+      rec_high = rec[1]
 
       sess.run(variables.local_variables_initializer())
       sess.run([prec_op, rec_op])
@@ -2420,10 +2414,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase):
       rec, rec_op = metrics.streaming_recall_at_thresholds(predictions, labels,
                                                            thresholds)
 
-      [prec_low, prec_high] = array_ops.split(
-          value=prec, num_or_size_splits=2, axis=0)
-      [rec_low, rec_high] = array_ops.split(
-          value=rec, num_or_size_splits=2, axis=0)
+      prec_low = prec[0]
+      prec_high = prec[1]
+      rec_low = rec[0]
+      rec_high = rec[1]
 
       sess.run(variables.local_variables_initializer())
       sess.run([prec_op, rec_op])
@@ -2562,7 +2556,7 @@ class StreamingFPRThresholdsTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     thresholds = [0, 0.5, 1.0]
     fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
         predictions, labels, thresholds)
@@ -2794,7 +2788,7 @@ class StreamingFNRThresholdsTest(test.TestCase):
     predictions = random_ops.random_uniform(
         (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
     labels = random_ops.random_uniform(
-        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=2)
+        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
     thresholds = [0, 0.5, 1.0]
     fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
         predictions, labels, thresholds)