diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py
index 09485c4fa2a..5a4c0c43585 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py
@@ -22,6 +22,8 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
+import collections as collections_lib
+
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
@@ -1076,6 +1078,9 @@ def streaming_curve_points(labels=None,
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
+
+ TODO(chizeng): Consider rewriting this method to make use of logic within the
+ streaming_precision_recall_at_equal_thresholds method (to improve run time).
"""
with variable_scope.variable_scope(name, 'curve_points',
(labels, predictions, weights)):
@@ -1193,6 +1198,181 @@ def streaming_auc(predictions,
name=name)
+def streaming_precision_recall_at_equal_thresholds(predictions,
+ labels,
+ num_thresholds=None,
+ weights=None,
+ name=None,
+ use_locking=None):
+ """A helper method for creating metrics related to precision-recall curves.
+
+ These values are true positives, false negatives, true negatives, false
+ positives, precision, and recall. This function returns a data structure that
+ contains ops within it.
+
+ Unlike _streaming_confusion_matrix_at_thresholds (which exhibits O(T * N)
+ space and run time), this op exhibits O(T + N) space and run time, where T is
+ the number of thresholds and N is the size of the predictions tensor. Hence,
+ it may be advantageous to use this function when `predictions` is big.
+
+ For instance, prefer this method for per-pixel classification tasks, for which
+ the predictions tensor may be very large.
+
+ Each number in `predictions`, a float in `[0, 1]`, is compared with its
+ corresponding label in `labels`, and counts as a single tp/fp/tn/fn value at
+ each threshold. This is then multiplied with `weights` which can be used to
+ reweight certain values, or more commonly used for masking values.
+
+ Args:
+ predictions: A floating point `Tensor` of arbitrary shape and whose values
+ are in the range `[0, 1]`.
+ labels: A bool `Tensor` whose shape matches `predictions`.
+ num_thresholds: Optional; Number of thresholds, evenly distributed in
+ `[0, 1]`. Should be `>= 2`. Defaults to 201. Note that the number of bins
+ is 1 less than `num_thresholds`. Using an even `num_thresholds` value
+ instead of an odd one may yield unfriendly edges for bins.
+ weights: Optional; If provided, a `Tensor` that has the same dtype as,
+ and broadcastable to, `predictions`. This tensor is multplied by counts.
+ name: Optional; variable_scope name. If not provided, the string
+ 'precision_recall_at_equal_threshold' is used.
+ use_locking: Optional; If True, the op will be protected by a lock.
+ Otherwise, the behavior is undefined, but may exhibit less contention.
+ Defaults to True.
+
+ Returns:
+ result: A named tuple (See PrecisionRecallData within the implementation of
+ this function) with properties that are variables of shape
+ `[num_thresholds]`. The names of the properties are tp, fp, tn, fn,
+ precision, recall, thresholds.
+ update_op: An op that accumulates values.
+
+ Raises:
+ ValueError: If `predictions` and `labels` have mismatched shapes, or if
+ `weights` is not `None` and its shape doesn't match `predictions`, or if
+ `includes` contains invalid keys.
+ """
+ # Disable the invalid-name checker so that we can capitalize the name.
+ # pylint: disable=invalid-name
+ PrecisionRecallData = collections_lib.namedtuple(
+ 'PrecisionRecallData',
+ ['tp', 'fp', 'tn', 'fn', 'precision', 'recall', 'thresholds'])
+ # pylint: enable=invalid-name
+
+ if num_thresholds is None:
+ num_thresholds = 201
+
+ if weights is None:
+ weights = 1.0
+
+ if use_locking is None:
+ use_locking = True
+
+ check_ops.assert_type(labels, dtypes.bool)
+
+ dtype = predictions.dtype
+ with variable_scope.variable_scope(name,
+ 'precision_recall_at_equal_thresholds',
+ (labels, predictions, weights)):
+ # Make sure that predictions are within [0.0, 1.0].
+ with ops.control_dependencies([
+ check_ops.assert_greater_equal(
+ predictions,
+ math_ops.cast(0.0, dtype=predictions.dtype),
+ message='predictions must be in [0, 1]'),
+ check_ops.assert_less_equal(
+ predictions,
+ math_ops.cast(1.0, dtype=predictions.dtype),
+ message='predictions must be in [0, 1]')
+ ]):
+ predictions, labels, weights = _remove_squeezable_dimensions(
+ predictions=predictions, labels=labels, weights=weights)
+
+ predictions.get_shape().assert_is_compatible_with(labels.get_shape())
+
+ # We cast to float to ensure we have 0.0 or 1.0.
+ f_labels = math_ops.cast(labels, dtype)
+
+ # Get weighted true/false labels.
+ true_labels = f_labels * weights
+ false_labels = (1.0 - f_labels) * weights
+
+ # Flatten predictions and labels.
+ predictions = array_ops.reshape(predictions, [-1])
+ true_labels = array_ops.reshape(true_labels, [-1])
+ false_labels = array_ops.reshape(false_labels, [-1])
+
+ # To compute TP/FP/TN/FN, we are measuring a binary classifier
+ # C(t) = (predictions >= t)
+ # at each threshold 't'. So we have
+ # TP(t) = sum( C(t) * true_labels )
+ # FP(t) = sum( C(t) * false_labels )
+ #
+ # But, computing C(t) requires computation for each t. To make it fast,
+ # observe that C(t) is a cumulative integral, and so if we have
+ # thresholds = [t_0, ..., t_{n-1}]; t_0 < ... < t_{n-1}
+ # where n = num_thresholds, and if we can compute the bucket function
+ # B(i) = Sum( (predictions == t), t_i <= t < t{i+1} )
+ # then we get
+ # C(t_i) = sum( B(j), j >= i )
+ # which is the reversed cumulative sum in tf.cumsum().
+ #
+ # We can compute B(i) efficiently by taking advantage of the fact that
+ # our thresholds are evenly distributed, in that
+ # width = 1.0 / (num_thresholds - 1)
+ # thresholds = [0.0, 1*width, 2*width, 3*width, ..., 1.0]
+ # Given a prediction value p, we can map it to its bucket by
+ # bucket_index(p) = floor( p * (num_thresholds - 1) )
+ # so we can use tf.scatter_add() to update the buckets in one pass.
+ #
+ # This implementation exhibits a run time and space complexity of O(T + N),
+ # where T is the number of thresholds and N is the size of predictions.
+ # Metrics that rely on _streaming_confusion_matrix_at_thresholds instead
+ # exhibit a complexity of O(T * N).
+
+ # Compute the bucket indices for each prediction value.
+ bucket_indices = math_ops.cast(
+ math_ops.floor(predictions * (num_thresholds - 1)), dtypes.int32)
+
+ with ops.name_scope('variables'):
+ tp_buckets_v = _create_local(
+ 'tp_buckets', shape=[num_thresholds], dtype=dtype)
+ fp_buckets_v = _create_local(
+ 'fp_buckets', shape=[num_thresholds], dtype=dtype)
+
+ with ops.name_scope('update_op'):
+ update_tp = state_ops.scatter_add(
+ tp_buckets_v, bucket_indices, true_labels, use_locking=use_locking)
+ update_fp = state_ops.scatter_add(
+ fp_buckets_v, bucket_indices, false_labels, use_locking=use_locking)
+
+ # Set up the cumulative sums to compute the actual metrics.
+ tp = math_ops.cumsum(tp_buckets_v, reverse=True, name='tp')
+ fp = math_ops.cumsum(fp_buckets_v, reverse=True, name='fp')
+ # fn = sum(true_labels) - tp
+ # = sum(tp_buckets) - tp
+ # = tp[0] - tp
+ # Similarly,
+ # tn = fp[0] - fp
+ tn = fp[0] - fp
+ fn = tp[0] - tp
+
+ # We use a minimum to prevent division by 0.
+ epsilon = 1e-7
+ precision = tp / math_ops.maximum(epsilon, tp + fp)
+ recall = tp / math_ops.maximum(epsilon, tp + fn)
+
+ result = PrecisionRecallData(
+ tp=tp,
+ fp=fp,
+ tn=tn,
+ fn=fn,
+ precision=precision,
+ recall=recall,
+ thresholds=math_ops.lin_space(0.0, 1.0, num_thresholds))
+ update_op = control_flow_ops.group(update_tp, update_fp)
+ return result, update_op
+
+
def streaming_specificity_at_sensitivity(predictions,
labels,
sensitivity,
diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
index f288fceef6c..f24bec7f115 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
@@ -1970,6 +1970,170 @@ class StreamingAUCTest(test.TestCase):
self.assertAlmostEqual(expected_auc, auc.eval(), 2)
+class StreamingPrecisionRecallAtEqualThresholdsTest(test.TestCase):
+
+ def setUp(self):
+ np.random.seed(1)
+ ops.reset_default_graph()
+
+ def _testResultsEqual(self, expected_dict, gotten_result):
+ """Tests that 2 results (dicts) represent the same data.
+
+ Args:
+ expected_dict: A dictionary with keys that are the names of properties
+ of PrecisionRecallData and whose values are lists of floats.
+ gotten_result: A PrecisionRecallData object.
+ """
+ gotten_dict = {k: t.eval() for k, t in gotten_result._asdict().items()}
+ self.assertItemsEqual(
+ list(expected_dict.keys()), list(gotten_dict.keys()))
+
+ for key, expected_values in expected_dict.items():
+ self.assertAllClose(expected_values, gotten_dict[key])
+
+ def _testCase(self, predictions, labels, expected_result, weights=None):
+ """Performs a test given a certain scenario of labels, predictions, weights.
+
+ Args:
+ predictions: The predictions tensor. Of type float32.
+ labels: The labels tensor. Of type bool.
+ expected_result: The expected result (dict) that maps to tensors.
+ weights: Optional weights tensor.
+ """
+ with self.test_session() as sess:
+ predictions_tensor = constant_op.constant(
+ predictions, dtype=dtypes_lib.float32)
+ labels_tensor = constant_op.constant(labels, dtype=dtypes_lib.bool)
+ weights_tensor = None
+ if weights:
+ weights_tensor = constant_op.constant(weights, dtype=dtypes_lib.float32)
+ gotten_result, update_op = (
+ metric_ops.streaming_precision_recall_at_equal_thresholds(
+ predictions=predictions_tensor,
+ labels=labels_tensor,
+ num_thresholds=3,
+ weights=weights_tensor))
+
+ sess.run(variables.local_variables_initializer())
+ sess.run(update_op)
+
+ self._testResultsEqual(expected_result, gotten_result)
+
+ def testVars(self):
+ metric_ops.streaming_precision_recall_at_equal_thresholds(
+ predictions=constant_op.constant([0.42], dtype=dtypes_lib.float32),
+ labels=constant_op.constant([True], dtype=dtypes_lib.bool))
+ _assert_local_variables(
+ self,
+ (
+ 'precision_recall_at_equal_thresholds/variables/tp_buckets:0',
+ 'precision_recall_at_equal_thresholds/variables/fp_buckets:0'
+ ))
+
+ def testVarsWithName(self):
+ metric_ops.streaming_precision_recall_at_equal_thresholds(
+ predictions=constant_op.constant([0.42], dtype=dtypes_lib.float32),
+ labels=constant_op.constant([True], dtype=dtypes_lib.bool),
+ name='foo')
+ _assert_local_variables(
+ self, ('foo/variables/tp_buckets:0', 'foo/variables/fp_buckets:0'))
+
+ def testValuesAreIdempotent(self):
+ predictions = constant_op.constant(
+ np.random.uniform(size=(10, 3)), dtype=dtypes_lib.float32)
+ labels = constant_op.constant(
+ np.random.uniform(size=(10, 3)) > 0.5, dtype=dtypes_lib.bool)
+
+ result, update_op = (
+ metric_ops.streaming_precision_recall_at_equal_thresholds(
+ predictions=predictions, labels=labels))
+
+ with self.test_session() as sess:
+ # Run several updates.
+ sess.run(variables.local_variables_initializer())
+ for _ in range(3):
+ sess.run(update_op)
+
+ # Then verify idempotency.
+ initial_result = {k: value.eval().tolist() for k, value in
+ result._asdict().items()}
+ for _ in range(3):
+ self._testResultsEqual(initial_result, result)
+
+ def testAllTruePositives(self):
+ self._testCase([[1]], [[True]], {
+ 'tp': [1, 1, 1],
+ 'fp': [0, 0, 0],
+ 'tn': [0, 0, 0],
+ 'fn': [0, 0, 0],
+ 'precision': [1.0, 1.0, 1.0],
+ 'recall': [1.0, 1.0, 1.0],
+ 'thresholds': [0.0, 0.5, 1.0],
+ })
+
+ def testAllTrueNegatives(self):
+ self._testCase([[0]], [[False]], {
+ 'tp': [0, 0, 0],
+ 'fp': [1, 0, 0],
+ 'tn': [0, 1, 1],
+ 'fn': [0, 0, 0],
+ 'precision': [0.0, 0.0, 0.0],
+ 'recall': [0.0, 0.0, 0.0],
+ 'thresholds': [0.0, 0.5, 1.0],
+ })
+
+ def testAllFalsePositives(self):
+ self._testCase([[1]], [[False]], {
+ 'tp': [0, 0, 0],
+ 'fp': [1, 1, 1],
+ 'tn': [0, 0, 0],
+ 'fn': [0, 0, 0],
+ 'precision': [0.0, 0.0, 0.0],
+ 'recall': [0.0, 0.0, 0.0],
+ 'thresholds': [0.0, 0.5, 1.0],
+ })
+
+ def testAllFalseNegatives(self):
+ self._testCase([[0]], [[True]], {
+ 'tp': [1, 0, 0],
+ 'fp': [0, 0, 0],
+ 'tn': [0, 0, 0],
+ 'fn': [0, 1, 1],
+ 'precision': [1.0, 0.0, 0.0],
+ 'recall': [1.0, 0.0, 0.0],
+ 'thresholds': [0.0, 0.5, 1.0],
+ })
+
+ def testManyValues(self):
+ self._testCase(
+ [[0.2, 0.3, 0.4, 0.6, 0.7, 0.8]],
+ [[True, False, False, True, True, True]],
+ {
+ 'tp': [4, 3, 0],
+ 'fp': [2, 0, 0],
+ 'tn': [0, 2, 2],
+ 'fn': [0, 1, 4],
+ 'precision': [2.0 / 3.0, 1.0, 0.0],
+ 'recall': [1.0, 0.75, 0.0],
+ 'thresholds': [0.0, 0.5, 1.0],
+ })
+
+ def testManyValuesWithWeights(self):
+ self._testCase(
+ [[0.2, 0.3, 0.4, 0.6, 0.7, 0.8]],
+ [[True, False, False, True, True, True]],
+ {
+ 'tp': [1.5, 1.5, 0.0],
+ 'fp': [2.5, 0.0, 0.0],
+ 'tn': [0.0, 2.5, 2.5],
+ 'fn': [0.0, 0.0, 1.5],
+ 'precision': [0.375, 1.0, 0.0],
+ 'recall': [1.0, 1.0, 0.0],
+ 'thresholds': [0.0, 0.5, 1.0],
+ },
+ weights=[0.0, 0.5, 2.0, 0.0, 0.5, 1.0])
+
+
class StreamingSpecificityAtSensitivityTest(test.TestCase):
def setUp(self):