# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for metric_ops."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin
from tensorflow.contrib import metrics as metrics_lib
from tensorflow.contrib.metrics.python.ops import metric_ops
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes as dtypes_lib
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test

NAN = float('nan')

metrics = metrics_lib


def _enqueue_vector(sess, queue, values, shape=None):
  if not shape:
    shape = (1, len(values))
  dtype = queue.dtypes[0]
  sess.run(
      queue.enqueue(constant_op.constant(values, dtype=dtype, shape=shape)))


def _binary_2d_label_to_sparse_value(labels):
  """Convert dense 2D binary indicator tensor to sparse tensor.

  Only 1 values in `labels` are included in result.

  Args:
    labels: Dense 2D binary indicator tensor.

  Returns:
    `SparseTensorValue` whose values are indices along the last dimension of
    `labels`.
  """
  indices = []
  values = []
  batch = 0
  for row in labels:
    label = 0
    xi = 0
    for x in row:
      if x == 1:
        indices.append([batch, xi])
        values.append(label)
        xi += 1
      else:
        assert x == 0
      label += 1
    batch += 1
  shape = [len(labels), len(labels[0])]
  return sparse_tensor.SparseTensorValue(
      np.array(indices, np.int64), np.array(values, np.int64),
      np.array(shape, np.int64))


def _binary_2d_label_to_sparse(labels):
  """Convert dense 2D binary indicator tensor to sparse tensor.

  Only 1 values in `labels` are included in result.

  Args:
    labels: Dense 2D binary indicator tensor.

  Returns:
    `SparseTensor` whose values are indices along the last dimension of
    `labels`.
  """
  return sparse_tensor.SparseTensor.from_value(
      _binary_2d_label_to_sparse_value(labels))


def _binary_3d_label_to_sparse_value(labels):
  """Convert dense 3D binary indicator tensor to sparse tensor.

  Only 1 values in `labels` are included in result.

  Args:
    labels: Dense 2D binary indicator tensor.

  Returns:
    `SparseTensorValue` whose values are indices along the last dimension of
    `labels`.
  """
  indices = []
  values = []
  for d0, labels_d0 in enumerate(labels):
    for d1, labels_d1 in enumerate(labels_d0):
      d2 = 0
      for class_id, label in enumerate(labels_d1):
        if label == 1:
          values.append(class_id)
          indices.append([d0, d1, d2])
          d2 += 1
        else:
          assert label == 0
  shape = [len(labels), len(labels[0]), len(labels[0][0])]
  return sparse_tensor.SparseTensorValue(
      np.array(indices, np.int64), np.array(values, np.int64),
      np.array(shape, np.int64))


def _binary_3d_label_to_sparse(labels):
  """Convert dense 3D binary indicator tensor to sparse tensor.

  Only 1 values in `labels` are included in result.

  Args:
    labels: Dense 2D binary indicator tensor.

  Returns:
    `SparseTensor` whose values are indices along the last dimension of
    `labels`.
  """
  return sparse_tensor.SparseTensor.from_value(
      _binary_3d_label_to_sparse_value(labels))


def _assert_nan(test_case, actual):
  test_case.assertTrue(math.isnan(actual), 'Expected NAN, got %s.' % actual)


def _assert_metric_variables(test_case, expected):
  test_case.assertEquals(
      set(expected), set(v.name for v in variables.local_variables()))
  test_case.assertEquals(
      set(expected),
      set(v.name for v in ops.get_collection(ops.GraphKeys.METRIC_VARIABLES)))


class StreamingMeanTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_mean(array_ops.ones([4, 3]))
    _assert_metric_variables(self, ('mean/count:0', 'mean/total:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_mean(
        array_ops.ones([4, 3]), metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_mean(
        array_ops.ones([4, 3]), updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testBasic(self):
    with self.test_session() as sess:
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      mean, update_op = metrics.streaming_mean(values)

      sess.run(variables.local_variables_initializer())
      for _ in range(4):
        sess.run(update_op)
      self.assertAlmostEqual(1.65, sess.run(mean), 5)

  def testUpdateOpsReturnsCurrentValue(self):
    with self.test_session() as sess:
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      mean, update_op = metrics.streaming_mean(values)

      sess.run(variables.local_variables_initializer())

      self.assertAlmostEqual(0.5, sess.run(update_op), 5)
      self.assertAlmostEqual(1.475, sess.run(update_op), 5)
      self.assertAlmostEqual(12.4 / 6.0, sess.run(update_op), 5)
      self.assertAlmostEqual(1.65, sess.run(update_op), 5)

      self.assertAlmostEqual(1.65, sess.run(mean), 5)

  def test1dWeightedValues(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      # Create the queue that populates the weighted labels.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 1))
      _enqueue_vector(sess, weights_queue, [1])
      _enqueue_vector(sess, weights_queue, [0])
      _enqueue_vector(sess, weights_queue, [0])
      _enqueue_vector(sess, weights_queue, [1])
      weights = weights_queue.dequeue()

      mean, update_op = metrics.streaming_mean(values, weights)

      variables.local_variables_initializer().run()
      for _ in range(4):
        update_op.eval()
      self.assertAlmostEqual((0 + 1 - 3.2 + 4.0) / 4.0, mean.eval(), 5)

  def test1dWeightedValues_placeholders(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      feed_values = ((0, 1), (-4.2, 9.1), (6.5, 0), (-3.2, 4.0))
      values = array_ops.placeholder(dtype=dtypes_lib.float32)

      # Create the queue that populates the weighted labels.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1,))
      _enqueue_vector(sess, weights_queue, 1, shape=(1,))
      _enqueue_vector(sess, weights_queue, 0, shape=(1,))
      _enqueue_vector(sess, weights_queue, 0, shape=(1,))
      _enqueue_vector(sess, weights_queue, 1, shape=(1,))
      weights = weights_queue.dequeue()

      mean, update_op = metrics.streaming_mean(values, weights)

      variables.local_variables_initializer().run()
      for i in range(4):
        update_op.eval(feed_dict={values: feed_values[i]})
      self.assertAlmostEqual((0 + 1 - 3.2 + 4.0) / 4.0, mean.eval(), 5)

  def test2dWeightedValues(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      # Create the queue that populates the weighted labels.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, weights_queue, [1, 1])
      _enqueue_vector(sess, weights_queue, [1, 0])
      _enqueue_vector(sess, weights_queue, [0, 1])
      _enqueue_vector(sess, weights_queue, [0, 0])
      weights = weights_queue.dequeue()

      mean, update_op = metrics.streaming_mean(values, weights)

      variables.local_variables_initializer().run()
      for _ in range(4):
        update_op.eval()
      self.assertAlmostEqual((0 + 1 - 4.2 + 0) / 4.0, mean.eval(), 5)

  def test2dWeightedValues_placeholders(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      feed_values = ((0, 1), (-4.2, 9.1), (6.5, 0), (-3.2, 4.0))
      values = array_ops.placeholder(dtype=dtypes_lib.float32)

      # Create the queue that populates the weighted labels.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(2,))
      _enqueue_vector(sess, weights_queue, [1, 1], shape=(2,))
      _enqueue_vector(sess, weights_queue, [1, 0], shape=(2,))
      _enqueue_vector(sess, weights_queue, [0, 1], shape=(2,))
      _enqueue_vector(sess, weights_queue, [0, 0], shape=(2,))
      weights = weights_queue.dequeue()

      mean, update_op = metrics.streaming_mean(values, weights)

      variables.local_variables_initializer().run()
      for i in range(4):
        update_op.eval(feed_dict={values: feed_values[i]})
      self.assertAlmostEqual((0 + 1 - 4.2 + 0) / 4.0, mean.eval(), 5)


class StreamingMeanTensorTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_mean_tensor(array_ops.ones([4, 3]))
    _assert_metric_variables(self,
                             ('mean/total_tensor:0', 'mean/count_tensor:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_mean_tensor(
        array_ops.ones([4, 3]), metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_mean_tensor(
        array_ops.ones([4, 3]), updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testBasic(self):
    with self.test_session() as sess:
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      mean, update_op = metrics.streaming_mean_tensor(values)

      sess.run(variables.local_variables_initializer())
      for _ in range(4):
        sess.run(update_op)
      self.assertAllClose([[-0.9 / 4., 3.525]], sess.run(mean))

  def testMultiDimensional(self):
    with self.test_session() as sess:
      values_queue = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(2, 2, 2))
      _enqueue_vector(
          sess,
          values_queue, [[[1, 2], [1, 2]], [[1, 2], [1, 2]]],
          shape=(2, 2, 2))
      _enqueue_vector(
          sess,
          values_queue, [[[1, 2], [1, 2]], [[3, 4], [9, 10]]],
          shape=(2, 2, 2))
      values = values_queue.dequeue()

      mean, update_op = metrics.streaming_mean_tensor(values)

      sess.run(variables.local_variables_initializer())
      for _ in range(2):
        sess.run(update_op)
      self.assertAllClose([[[1, 2], [1, 2]], [[2, 3], [5, 6]]], sess.run(mean))

  def testUpdateOpsReturnsCurrentValue(self):
    with self.test_session() as sess:
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      mean, update_op = metrics.streaming_mean_tensor(values)

      sess.run(variables.local_variables_initializer())

      self.assertAllClose([[0, 1]], sess.run(update_op), 5)
      self.assertAllClose([[-2.1, 5.05]], sess.run(update_op), 5)
      self.assertAllClose([[2.3 / 3., 10.1 / 3.]], sess.run(update_op), 5)
      self.assertAllClose([[-0.9 / 4., 3.525]], sess.run(update_op), 5)

      self.assertAllClose([[-0.9 / 4., 3.525]], sess.run(mean), 5)

  def testWeighted1d(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      # Create the queue that populates the weights.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 1))
      _enqueue_vector(sess, weights_queue, [[1]])
      _enqueue_vector(sess, weights_queue, [[0]])
      _enqueue_vector(sess, weights_queue, [[1]])
      _enqueue_vector(sess, weights_queue, [[0]])
      weights = weights_queue.dequeue()

      mean, update_op = metrics.streaming_mean_tensor(values, weights)

      sess.run(variables.local_variables_initializer())
      for _ in range(4):
        sess.run(update_op)
      self.assertAllClose([[3.25, 0.5]], sess.run(mean), 5)

  def testWeighted2d_1(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      # Create the queue that populates the weights.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, weights_queue, [1, 1])
      _enqueue_vector(sess, weights_queue, [1, 0])
      _enqueue_vector(sess, weights_queue, [0, 1])
      _enqueue_vector(sess, weights_queue, [0, 0])
      weights = weights_queue.dequeue()

      mean, update_op = metrics.streaming_mean_tensor(values, weights)

      sess.run(variables.local_variables_initializer())
      for _ in range(4):
        sess.run(update_op)
      self.assertAllClose([[-2.1, 0.5]], sess.run(mean), 5)

  def testWeighted2d_2(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      # Create the queue that populates the weights.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, weights_queue, [0, 1])
      _enqueue_vector(sess, weights_queue, [0, 0])
      _enqueue_vector(sess, weights_queue, [0, 1])
      _enqueue_vector(sess, weights_queue, [0, 0])
      weights = weights_queue.dequeue()

      mean, update_op = metrics.streaming_mean_tensor(values, weights)

      sess.run(variables.local_variables_initializer())
      for _ in range(4):
        sess.run(update_op)
      self.assertAllClose([[0, 0.5]], sess.run(mean), 5)


class StreamingAccuracyTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_accuracy(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        name='my_accuracy')
    _assert_metric_variables(self,
                             ('my_accuracy/count:0', 'my_accuracy/total:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_accuracy(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_accuracy(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testPredictionsAndLabelsOfDifferentSizeRaisesValueError(self):
    predictions = array_ops.ones((10, 3))
    labels = array_ops.ones((10, 4))
    with self.assertRaises(ValueError):
      metrics.streaming_accuracy(predictions, labels)

  def testPredictionsAndWeightsOfDifferentSizeRaisesValueError(self):
    predictions = array_ops.ones((10, 3))
    labels = array_ops.ones((10, 3))
    weights = array_ops.ones((9, 3))
    with self.assertRaises(ValueError):
      metrics.streaming_accuracy(predictions, labels, weights)

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=3, dtype=dtypes_lib.int64, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=3, dtype=dtypes_lib.int64, seed=2)
    accuracy, update_op = metrics.streaming_accuracy(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_accuracy = accuracy.eval()
      for _ in range(10):
        self.assertEqual(initial_accuracy, accuracy.eval())

  def testMultipleUpdates(self):
    with self.test_session() as sess:
      # Create the queue that populates the predictions.
      preds_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 1))
      _enqueue_vector(sess, preds_queue, [0])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [2])
      _enqueue_vector(sess, preds_queue, [1])
      predictions = preds_queue.dequeue()

      # Create the queue that populates the labels.
      labels_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 1))
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [2])
      labels = labels_queue.dequeue()

      accuracy, update_op = metrics.streaming_accuracy(predictions, labels)

      sess.run(variables.local_variables_initializer())
      for _ in xrange(3):
        sess.run(update_op)
      self.assertEqual(0.5, sess.run(update_op))
      self.assertEqual(0.5, accuracy.eval())

  def testEffectivelyEquivalentSizes(self):
    predictions = array_ops.ones((40, 1))
    labels = array_ops.ones((40,))
    with self.test_session() as sess:
      accuracy, update_op = metrics.streaming_accuracy(predictions, labels)

      sess.run(variables.local_variables_initializer())
      self.assertEqual(1.0, update_op.eval())
      self.assertEqual(1.0, accuracy.eval())

  def testEffectivelyEquivalentSizesWithStaicShapedWeight(self):
    predictions = ops.convert_to_tensor([1, 1, 1])  # shape 3,
    labels = array_ops.expand_dims(ops.convert_to_tensor([1, 0, 0]),
                                   1)  # shape 3, 1
    weights = array_ops.expand_dims(ops.convert_to_tensor([100, 1, 1]),
                                    1)  # shape 3, 1

    with self.test_session() as sess:
      accuracy, update_op = metrics.streaming_accuracy(predictions, labels,
                                                       weights)

      sess.run(variables.local_variables_initializer())
      # if streaming_accuracy does not flatten the weight, accuracy would be
      # 0.33333334 due to an intended broadcast of weight. Due to flattening,
      # it will be higher than .95
      self.assertGreater(update_op.eval(), .95)
      self.assertGreater(accuracy.eval(), .95)

  def testEffectivelyEquivalentSizesWithDynamicallyShapedWeight(self):
    predictions = ops.convert_to_tensor([1, 1, 1])  # shape 3,
    labels = array_ops.expand_dims(ops.convert_to_tensor([1, 0, 0]),
                                   1)  # shape 3, 1

    weights = [[100], [1], [1]]  # shape 3, 1
    weights_placeholder = array_ops.placeholder(
        dtype=dtypes_lib.int32, name='weights')
    feed_dict = {weights_placeholder: weights}

    with self.test_session() as sess:
      accuracy, update_op = metrics.streaming_accuracy(predictions, labels,
                                                       weights_placeholder)

      sess.run(variables.local_variables_initializer())
      # if streaming_accuracy does not flatten the weight, accuracy would be
      # 0.33333334 due to an intended broadcast of weight. Due to flattening,
      # it will be higher than .95
      self.assertGreater(update_op.eval(feed_dict=feed_dict), .95)
      self.assertGreater(accuracy.eval(feed_dict=feed_dict), .95)

  def testMultipleUpdatesWithWeightedValues(self):
    with self.test_session() as sess:
      # Create the queue that populates the predictions.
      preds_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 1))
      _enqueue_vector(sess, preds_queue, [0])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [2])
      _enqueue_vector(sess, preds_queue, [1])
      predictions = preds_queue.dequeue()

      # Create the queue that populates the labels.
      labels_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 1))
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [2])
      labels = labels_queue.dequeue()

      # Create the queue that populates the weights.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.int64, shapes=(1, 1))
      _enqueue_vector(sess, weights_queue, [1])
      _enqueue_vector(sess, weights_queue, [1])
      _enqueue_vector(sess, weights_queue, [0])
      _enqueue_vector(sess, weights_queue, [0])
      weights = weights_queue.dequeue()

      accuracy, update_op = metrics.streaming_accuracy(predictions, labels,
                                                       weights)

      sess.run(variables.local_variables_initializer())
      for _ in xrange(3):
        sess.run(update_op)
      self.assertEqual(1.0, sess.run(update_op))
      self.assertEqual(1.0, accuracy.eval())


class StreamingTruePositivesTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_true_positives((0, 1, 0), (0, 1, 1))
    _assert_metric_variables(self, ('true_positives/count:0',))

  def testUnweighted(self):
    for expand_predictions in [True, False]:
      for expand_labels in [True, False]:
        for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
          predictions = math_ops.cast(
              constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))),
              dtype=dtype)
          if expand_predictions:
            predictions = array_ops.expand_dims(predictions, 2)
          labels = math_ops.cast(
              constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))),
              dtype=dtype)
          if expand_labels:
            labels = array_ops.expand_dims(labels, 2)
          tp, tp_update_op = metrics.streaming_true_positives(
              predictions, labels)

          with self.test_session() as sess:
            sess.run(variables.local_variables_initializer())
            self.assertEqual(0, tp.eval())
            self.assertEqual(1, tp_update_op.eval())
            self.assertEqual(1, tp.eval())

  def testWeighted(self):
    for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
      predictions = math_ops.cast(
          constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))),
          dtype=dtype)
      labels = math_ops.cast(
          constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))),
          dtype=dtype)
      tp, tp_update_op = metrics.streaming_true_positives(
          predictions, labels, weights=37.0)

      with self.test_session() as sess:
        sess.run(variables.local_variables_initializer())
        self.assertEqual(0, tp.eval())
        self.assertEqual(37.0, tp_update_op.eval())
        self.assertEqual(37.0, tp.eval())


class StreamingFalseNegativesTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_false_negatives((0, 1, 0), (0, 1, 1))
    _assert_metric_variables(self, ('false_negatives/count:0',))

  def testUnweighted(self):
    for expand_predictions in [True, False]:
      for expand_labels in [True, False]:
        for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
          predictions = math_ops.cast(
              constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))),
              dtype=dtype)
          if expand_predictions:
            predictions = array_ops.expand_dims(predictions, 2)
          labels = math_ops.cast(
              constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))),
              dtype=dtype)
          if expand_labels:
            labels = array_ops.expand_dims(labels, 2)
          fn, fn_update_op = metrics.streaming_false_negatives(
              predictions, labels)

          with self.test_session() as sess:
            sess.run(variables.local_variables_initializer())
            self.assertEqual(0, fn.eval())
            self.assertEqual(2, fn_update_op.eval())
            self.assertEqual(2, fn.eval())

  def testWeighted(self):
    for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
      predictions = math_ops.cast(
          constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))),
          dtype=dtype)
      labels = math_ops.cast(
          constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))),
          dtype=dtype)
      fn, fn_update_op = metrics.streaming_false_negatives(
          predictions, labels, weights=((3.0,), (5.0,), (7.0,)))

      with self.test_session() as sess:
        sess.run(variables.local_variables_initializer())
        self.assertEqual(0, fn.eval())
        self.assertEqual(8.0, fn_update_op.eval())
        self.assertEqual(8.0, fn.eval())


class StreamingFalsePositivesTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_false_positives((0, 1, 0), (0, 1, 1))
    _assert_metric_variables(self, ('false_positives/count:0',))

  def testUnweighted(self):
    for expand_predictions in [True, False]:
      for expand_labels in [True, False]:
        for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
          predictions = math_ops.cast(
              constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))),
              dtype=dtype)
          if expand_predictions:
            predictions = array_ops.expand_dims(predictions, 2)
          labels = math_ops.cast(
              constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))),
              dtype=dtype)
          if expand_labels:
            labels = array_ops.expand_dims(labels, 2)
          fp, fp_update_op = metrics.streaming_false_positives(
              predictions, labels)

          with self.test_session() as sess:
            sess.run(variables.local_variables_initializer())
            self.assertEqual(0, fp.eval())
            self.assertEqual(4, fp_update_op.eval())
            self.assertEqual(4, fp.eval())

  def testWeighted(self):
    for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
      predictions = math_ops.cast(
          constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))),
          dtype=dtype)
      labels = math_ops.cast(
          constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))),
          dtype=dtype)
      fp, fp_update_op = metrics.streaming_false_positives(
          predictions,
          labels,
          weights=((1.0, 2.0, 3.0, 5.0), (7.0, 11.0, 13.0, 17.0), (19.0, 23.0,
                                                                   29.0, 31.0)))

      with self.test_session() as sess:
        sess.run(variables.local_variables_initializer())
        self.assertEqual(0, fp.eval())
        self.assertEqual(42.0, fp_update_op.eval())
        self.assertEqual(42.0, fp.eval())


class StreamingTrueNegativesTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_true_negatives((0, 1, 0), (0, 1, 1))
    _assert_metric_variables(self, ('true_negatives/count:0',))

  def testUnweighted(self):
    for expand_predictions in [True, False]:
      for expand_labels in [True, False]:
        for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
          predictions = math_ops.cast(
              constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))),
              dtype=dtype)
          if expand_predictions:
            predictions = array_ops.expand_dims(predictions, 2)
          labels = math_ops.cast(
              constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))),
              dtype=dtype)
          if expand_labels:
            labels = array_ops.expand_dims(labels, 2)
          tn, tn_update_op = metrics.streaming_true_negatives(
              predictions, labels)

          with self.test_session() as sess:
            sess.run(variables.local_variables_initializer())
            self.assertEqual(0, tn.eval())
            self.assertEqual(5, tn_update_op.eval())
            self.assertEqual(5, tn.eval())

  def testWeighted(self):
    for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
      predictions = math_ops.cast(
          constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))),
          dtype=dtype)
      labels = math_ops.cast(
          constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))),
          dtype=dtype)
      tn, tn_update_op = metrics.streaming_true_negatives(
          predictions, labels, weights=((0.0, 2.0, 3.0, 5.0),))

      with self.test_session() as sess:
        sess.run(variables.local_variables_initializer())
        self.assertEqual(0, tn.eval())
        self.assertEqual(15.0, tn_update_op.eval())
        self.assertEqual(15.0, tn.eval())


class StreamingTruePositivesAtThresholdsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_true_positives_at_thresholds(
        (0.0, 1.0, 0.0), (0, 1, 1), thresholds=(0.15, 0.5, 0.85))
    _assert_metric_variables(self, ('true_positives:0',))

  def testUnweighted(self):
    predictions = constant_op.constant(
        ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3)))
    labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0)))
    tp, tp_update_op = metrics.streaming_true_positives_at_thresholds(
        predictions, labels, thresholds=(0.15, 0.5, 0.85))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual((0, 0, 0), tp.eval())
      self.assertAllEqual((3, 1, 0), tp_update_op.eval())
      self.assertAllEqual((3, 1, 0), tp.eval())

  def testWeighted(self):
    predictions = constant_op.constant(
        ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3)))
    labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0)))
    tp, tp_update_op = metrics.streaming_true_positives_at_thresholds(
        predictions, labels, weights=37.0, thresholds=(0.15, 0.5, 0.85))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual((0.0, 0.0, 0.0), tp.eval())
      self.assertAllEqual((111.0, 37.0, 0.0), tp_update_op.eval())
      self.assertAllEqual((111.0, 37.0, 0.0), tp.eval())


class StreamingFalseNegativesAtThresholdsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_false_negatives_at_thresholds(
        (0.0, 1.0, 0.0), (0, 1, 1), thresholds=(
            0.15,
            0.5,
            0.85,
        ))
    _assert_metric_variables(self, ('false_negatives:0',))

  def testUnweighted(self):
    predictions = constant_op.constant(
        ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3)))
    labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0)))
    fn, fn_update_op = metrics.streaming_false_negatives_at_thresholds(
        predictions, labels, thresholds=(0.15, 0.5, 0.85))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual((0, 0, 0), fn.eval())
      self.assertAllEqual((0, 2, 3), fn_update_op.eval())
      self.assertAllEqual((0, 2, 3), fn.eval())

  def testWeighted(self):
    predictions = constant_op.constant(
        ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3)))
    labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0)))
    fn, fn_update_op = metrics.streaming_false_negatives_at_thresholds(
        predictions,
        labels,
        weights=((3.0,), (5.0,), (7.0,)),
        thresholds=(0.15, 0.5, 0.85))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual((0.0, 0.0, 0.0), fn.eval())
      self.assertAllEqual((0.0, 8.0, 11.0), fn_update_op.eval())
      self.assertAllEqual((0.0, 8.0, 11.0), fn.eval())


class StreamingFalsePositivesAtThresholdsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_false_positives_at_thresholds(
        (0.0, 1.0, 0.0), (0, 1, 1), thresholds=(0.15, 0.5, 0.85))
    _assert_metric_variables(self, ('false_positives:0',))

  def testUnweighted(self):
    predictions = constant_op.constant(
        ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3)))
    labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0)))
    fp, fp_update_op = metrics.streaming_false_positives_at_thresholds(
        predictions, labels, thresholds=(0.15, 0.5, 0.85))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual((0, 0, 0), fp.eval())
      self.assertAllEqual((7, 4, 2), fp_update_op.eval())
      self.assertAllEqual((7, 4, 2), fp.eval())

  def testWeighted(self):
    predictions = constant_op.constant(
        ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3)))
    labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0)))
    fp, fp_update_op = metrics.streaming_false_positives_at_thresholds(
        predictions,
        labels,
        weights=((1.0, 2.0, 3.0, 5.0), (7.0, 11.0, 13.0, 17.0), (19.0, 23.0,
                                                                 29.0, 31.0)),
        thresholds=(0.15, 0.5, 0.85))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual((0.0, 0.0, 0.0), fp.eval())
      self.assertAllEqual((125.0, 42.0, 12.0), fp_update_op.eval())
      self.assertAllEqual((125.0, 42.0, 12.0), fp.eval())


class StreamingTrueNegativesAtThresholdsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_true_negatives_at_thresholds(
        (0.0, 1.0, 0.0), (0, 1, 1), thresholds=(0.15, 0.5, 0.85))
    _assert_metric_variables(self, ('true_negatives:0',))

  def testUnweighted(self):
    predictions = constant_op.constant(
        ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3)))
    labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0)))
    tn, tn_update_op = metrics.streaming_true_negatives_at_thresholds(
        predictions, labels, thresholds=(0.15, 0.5, 0.85))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual((0, 0, 0), tn.eval())
      self.assertAllEqual((2, 5, 7), tn_update_op.eval())
      self.assertAllEqual((2, 5, 7), tn.eval())

  def testWeighted(self):
    predictions = constant_op.constant(
        ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3)))
    labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0)))
    tn, tn_update_op = metrics.streaming_true_negatives_at_thresholds(
        predictions,
        labels,
        weights=((0.0, 2.0, 3.0, 5.0),),
        thresholds=(0.15, 0.5, 0.85))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual((0.0, 0.0, 0.0), tn.eval())
      self.assertAllEqual((5.0, 15.0, 23.0), tn_update_op.eval())
      self.assertAllEqual((5.0, 15.0, 23.0), tn.eval())


class StreamingPrecisionTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_precision(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(self, ('precision/false_positives/count:0',
                                    'precision/true_positives/count:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_precision(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_precision(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    precision, update_op = metrics.streaming_precision(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_precision = precision.eval()
      for _ in range(10):
        self.assertEqual(initial_precision, precision.eval())

  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(inputs)
    labels = constant_op.constant(inputs)
    precision, update_op = metrics.streaming_precision(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(1, sess.run(update_op))
      self.assertAlmostEqual(1, precision.eval())

  def testSomeCorrect(self):
    predictions = constant_op.constant([1, 0, 1, 0], shape=(1, 4))
    labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
    precision, update_op = metrics.streaming_precision(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.5, update_op.eval())
      self.assertAlmostEqual(0.5, precision.eval())

  def testWeighted1d(self):
    predictions = constant_op.constant([[1, 0, 1, 0], [1, 0, 1, 0]])
    labels = constant_op.constant([[0, 1, 1, 0], [1, 0, 0, 1]])
    precision, update_op = metrics.streaming_precision(
        predictions, labels, weights=constant_op.constant([[2], [5]]))

    with self.test_session():
      variables.local_variables_initializer().run()
      weighted_tp = 2.0 + 5.0
      weighted_positives = (2.0 + 2.0) + (5.0 + 5.0)
      expected_precision = weighted_tp / weighted_positives
      self.assertAlmostEqual(expected_precision, update_op.eval())
      self.assertAlmostEqual(expected_precision, precision.eval())

  def testWeighted1d_placeholders(self):
    predictions = array_ops.placeholder(dtype=dtypes_lib.float32)
    labels = array_ops.placeholder(dtype=dtypes_lib.float32)
    feed_dict = {
        predictions: ((1, 0, 1, 0), (1, 0, 1, 0)),
        labels: ((0, 1, 1, 0), (1, 0, 0, 1))
    }
    precision, update_op = metrics.streaming_precision(
        predictions, labels, weights=constant_op.constant([[2], [5]]))

    with self.test_session():
      variables.local_variables_initializer().run()
      weighted_tp = 2.0 + 5.0
      weighted_positives = (2.0 + 2.0) + (5.0 + 5.0)
      expected_precision = weighted_tp / weighted_positives
      self.assertAlmostEqual(
          expected_precision, update_op.eval(feed_dict=feed_dict))
      self.assertAlmostEqual(
          expected_precision, precision.eval(feed_dict=feed_dict))

  def testWeighted2d(self):
    predictions = constant_op.constant([[1, 0, 1, 0], [1, 0, 1, 0]])
    labels = constant_op.constant([[0, 1, 1, 0], [1, 0, 0, 1]])
    precision, update_op = metrics.streaming_precision(
        predictions,
        labels,
        weights=constant_op.constant([[1, 2, 3, 4], [4, 3, 2, 1]]))

    with self.test_session():
      variables.local_variables_initializer().run()
      weighted_tp = 3.0 + 4.0
      weighted_positives = (1.0 + 3.0) + (4.0 + 2.0)
      expected_precision = weighted_tp / weighted_positives
      self.assertAlmostEqual(expected_precision, update_op.eval())
      self.assertAlmostEqual(expected_precision, precision.eval())

  def testWeighted2d_placeholders(self):
    predictions = array_ops.placeholder(dtype=dtypes_lib.float32)
    labels = array_ops.placeholder(dtype=dtypes_lib.float32)
    feed_dict = {
        predictions: ((1, 0, 1, 0), (1, 0, 1, 0)),
        labels: ((0, 1, 1, 0), (1, 0, 0, 1))
    }
    precision, update_op = metrics.streaming_precision(
        predictions,
        labels,
        weights=constant_op.constant([[1, 2, 3, 4], [4, 3, 2, 1]]))

    with self.test_session():
      variables.local_variables_initializer().run()
      weighted_tp = 3.0 + 4.0
      weighted_positives = (1.0 + 3.0) + (4.0 + 2.0)
      expected_precision = weighted_tp / weighted_positives
      self.assertAlmostEqual(
          expected_precision, update_op.eval(feed_dict=feed_dict))
      self.assertAlmostEqual(
          expected_precision, precision.eval(feed_dict=feed_dict))

  def testAllIncorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(inputs)
    labels = constant_op.constant(1 - inputs)
    precision, update_op = metrics.streaming_precision(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertAlmostEqual(0, precision.eval())

  def testZeroTrueAndFalsePositivesGivesZeroPrecision(self):
    predictions = constant_op.constant([0, 0, 0, 0])
    labels = constant_op.constant([0, 0, 0, 0])
    precision, update_op = metrics.streaming_precision(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0.0, precision.eval())


class StreamingRecallTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_recall(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(
        self,
        ('recall/false_negatives/count:0', 'recall/true_positives/count:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_recall(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_recall(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    recall, update_op = metrics.streaming_recall(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_recall = recall.eval()
      for _ in range(10):
        self.assertEqual(initial_recall, recall.eval())

  def testAllCorrect(self):
    np_inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(np_inputs)
    labels = constant_op.constant(np_inputs)
    recall, update_op = metrics.streaming_recall(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(1, recall.eval())

  def testSomeCorrect(self):
    predictions = constant_op.constant([1, 0, 1, 0], shape=(1, 4))
    labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
    recall, update_op = metrics.streaming_recall(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.5, update_op.eval())
      self.assertAlmostEqual(0.5, recall.eval())

  def testWeighted1d(self):
    predictions = constant_op.constant([[1, 0, 1, 0], [0, 1, 0, 1]])
    labels = constant_op.constant([[0, 1, 1, 0], [1, 0, 0, 1]])
    weights = constant_op.constant([[2], [5]])
    recall, update_op = metrics.streaming_recall(
        predictions, labels, weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      weighted_tp = 2.0 + 5.0
      weighted_t = (2.0 + 2.0) + (5.0 + 5.0)
      expected_precision = weighted_tp / weighted_t
      self.assertAlmostEqual(expected_precision, update_op.eval())
      self.assertAlmostEqual(expected_precision, recall.eval())

  def testWeighted2d(self):
    predictions = constant_op.constant([[1, 0, 1, 0], [0, 1, 0, 1]])
    labels = constant_op.constant([[0, 1, 1, 0], [1, 0, 0, 1]])
    weights = constant_op.constant([[1, 2, 3, 4], [4, 3, 2, 1]])
    recall, update_op = metrics.streaming_recall(
        predictions, labels, weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      weighted_tp = 3.0 + 1.0
      weighted_t = (2.0 + 3.0) + (4.0 + 1.0)
      expected_precision = weighted_tp / weighted_t
      self.assertAlmostEqual(expected_precision, update_op.eval())
      self.assertAlmostEqual(expected_precision, recall.eval())

  def testAllIncorrect(self):
    np_inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(np_inputs)
    labels = constant_op.constant(1 - np_inputs)
    recall, update_op = metrics.streaming_recall(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0, recall.eval())

  def testZeroTruePositivesAndFalseNegativesGivesZeroRecall(self):
    predictions = array_ops.zeros((1, 4))
    labels = array_ops.zeros((1, 4))
    recall, update_op = metrics.streaming_recall(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0, recall.eval())


class StreamingFPRTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_false_positive_rate(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(self,
                             ('false_positive_rate/false_positives/count:0',
                              'false_positive_rate/true_negatives/count:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_false_positive_rate(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_false_positive_rate(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_fpr = fpr.eval()
      for _ in range(10):
        self.assertEqual(initial_fpr, fpr.eval())

  def testAllCorrect(self):
    np_inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(np_inputs)
    labels = constant_op.constant(np_inputs)
    fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0, fpr.eval())

  def testSomeCorrect(self):
    predictions = constant_op.constant([1, 0, 1, 0], shape=(1, 4))
    labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
    fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.5, update_op.eval())
      self.assertAlmostEqual(0.5, fpr.eval())

  def testWeighted1d(self):
    predictions = constant_op.constant([[1, 0, 1, 0], [0, 1, 0, 1]])
    labels = constant_op.constant([[0, 1, 1, 0], [1, 0, 0, 1]])
    weights = constant_op.constant([[2], [5]])
    fpr, update_op = metrics.streaming_false_positive_rate(
        predictions, labels, weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      weighted_fp = 2.0 + 5.0
      weighted_f = (2.0 + 2.0) + (5.0 + 5.0)
      expected_fpr = weighted_fp / weighted_f
      self.assertAlmostEqual(expected_fpr, update_op.eval())
      self.assertAlmostEqual(expected_fpr, fpr.eval())

  def testWeighted2d(self):
    predictions = constant_op.constant([[1, 0, 1, 0], [0, 1, 0, 1]])
    labels = constant_op.constant([[0, 1, 1, 0], [1, 0, 0, 1]])
    weights = constant_op.constant([[1, 2, 3, 4], [4, 3, 2, 1]])
    fpr, update_op = metrics.streaming_false_positive_rate(
        predictions, labels, weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      weighted_fp = 1.0 + 3.0
      weighted_f = (1.0 + 4.0) + (2.0 + 3.0)
      expected_fpr = weighted_fp / weighted_f
      self.assertAlmostEqual(expected_fpr, update_op.eval())
      self.assertAlmostEqual(expected_fpr, fpr.eval())

  def testAllIncorrect(self):
    np_inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(np_inputs)
    labels = constant_op.constant(1 - np_inputs)
    fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(1, fpr.eval())

  def testZeroFalsePositivesAndTrueNegativesGivesZeroFPR(self):
    predictions = array_ops.ones((1, 4))
    labels = array_ops.ones((1, 4))
    fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0, fpr.eval())


class StreamingFNRTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_false_negative_rate(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(self,
                             ('false_negative_rate/false_negatives/count:0',
                              'false_negative_rate/true_positives/count:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_false_negative_rate(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_false_negative_rate(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_fnr = fnr.eval()
      for _ in range(10):
        self.assertEqual(initial_fnr, fnr.eval())

  def testAllCorrect(self):
    np_inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(np_inputs)
    labels = constant_op.constant(np_inputs)
    fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0, fnr.eval())

  def testSomeCorrect(self):
    predictions = constant_op.constant([1, 0, 1, 0], shape=(1, 4))
    labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
    fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.5, update_op.eval())
      self.assertAlmostEqual(0.5, fnr.eval())

  def testWeighted1d(self):
    predictions = constant_op.constant([[1, 0, 1, 0], [0, 1, 0, 1]])
    labels = constant_op.constant([[0, 1, 1, 0], [1, 0, 0, 1]])
    weights = constant_op.constant([[2], [5]])
    fnr, update_op = metrics.streaming_false_negative_rate(
        predictions, labels, weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      weighted_fn = 2.0 + 5.0
      weighted_t = (2.0 + 2.0) + (5.0 + 5.0)
      expected_fnr = weighted_fn / weighted_t
      self.assertAlmostEqual(expected_fnr, update_op.eval())
      self.assertAlmostEqual(expected_fnr, fnr.eval())

  def testWeighted2d(self):
    predictions = constant_op.constant([[1, 0, 1, 0], [0, 1, 0, 1]])
    labels = constant_op.constant([[0, 1, 1, 0], [1, 0, 0, 1]])
    weights = constant_op.constant([[1, 2, 3, 4], [4, 3, 2, 1]])
    fnr, update_op = metrics.streaming_false_negative_rate(
        predictions, labels, weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      weighted_fn = 2.0 + 4.0
      weighted_t = (2.0 + 3.0) + (1.0 + 4.0)
      expected_fnr = weighted_fn / weighted_t
      self.assertAlmostEqual(expected_fnr, update_op.eval())
      self.assertAlmostEqual(expected_fnr, fnr.eval())

  def testAllIncorrect(self):
    np_inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(np_inputs)
    labels = constant_op.constant(1 - np_inputs)
    fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(1, fnr.eval())

  def testZeroFalseNegativesAndTruePositivesGivesZeroFNR(self):
    predictions = array_ops.zeros((1, 4))
    labels = array_ops.zeros((1, 4))
    fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0, fnr.eval())


class StreamingCurvePointsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metric_ops.streaming_curve_points(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(
        self,
        ('curve_points/true_positives:0', 'curve_points/false_negatives:0',
         'curve_points/false_positives:0', 'curve_points/true_negatives:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    points, _ = metric_ops.streaming_curve_points(
        labels=array_ops.ones((10, 1)),
        predictions=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [points])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metric_ops.streaming_curve_points(
        labels=array_ops.ones((10, 1)),
        predictions=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def _testValueTensorIsIdempotent(self, curve):
    predictions = constant_op.constant(
        np.random.uniform(size=(10, 3)), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        np.random.uniform(high=2, size=(10, 3)), dtype=dtypes_lib.float32)

    points, update_op = metric_ops.streaming_curve_points(
        labels, predictions=predictions, curve=curve)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      sess.run(update_op)
      initial_points = points.eval()

      sess.run(update_op)
      self.assertAllClose(initial_points, points.eval())

  def testValueTensorIsIdempotentROC(self):
    self._testValueTensorIsIdempotent(curve='ROC')

  def testValueTensorIsIdempotentPR(self):
    self._testValueTensorIsIdempotent(curve='PR')

  def _testCase(self, labels, predictions, curve, expected_points):
    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.float32)
      points, update_op = metric_ops.streaming_curve_points(
          labels=labels_tensor,
          predictions=predictions_tensor,
          num_thresholds=3,
          curve=curve)

      sess.run(variables.local_variables_initializer())
      sess.run(update_op)

      self.assertAllClose(expected_points, points.eval())

  def testEdgeCasesROC(self):
    self._testCase([[1]], [[1]], 'ROC', [[0, 1], [0, 1], [0, 0]])
    self._testCase([[0]], [[0]], 'ROC', [[1, 1], [0, 1], [0, 1]])
    self._testCase([[0]], [[1]], 'ROC', [[1, 1], [1, 1], [0, 1]])
    self._testCase([[1]], [[0]], 'ROC', [[0, 1], [0, 0], [0, 0]])

  def testManyValuesROC(self):
    self._testCase([[1.0, 0.0, 0.0, 1.0, 1.0, 1.0]],
                   [[0.2, 0.3, 0.4, 0.6, 0.7, 0.8]], 'ROC',
                   [[1.0, 1.0], [0.0, 0.75], [0.0, 0.0]])

  def testEdgeCasesPR(self):
    self._testCase([[1]], [[1]], 'PR', [[1, 1], [1, 1], [0, 1]])
    self._testCase([[0]], [[0]], 'PR', [[1, 0], [1, 1], [1, 1]])
    self._testCase([[0]], [[1]], 'PR', [[1, 0], [1, 0], [1, 1]])
    self._testCase([[1]], [[0]], 'PR', [[1, 1], [0, 1], [0, 1]])

  def testManyValuesPR(self):
    self._testCase([[1.0, 0.0, 0.0, 1.0, 1.0, 1.0]],
                   [[0.2, 0.3, 0.4, 0.6, 0.7, 0.8]], 'PR',
                   [[1.0, 4.0 / 6.0], [0.75, 1.0], [0.0, 1.0]])


def _np_auc(predictions, labels, weights=None):
  """Computes the AUC explicitly using Numpy.

  Args:
    predictions: an ndarray with shape [N].
    labels: an ndarray with shape [N].
    weights: an ndarray with shape [N].

  Returns:
    the area under the ROC curve.
  """
  if weights is None:
    weights = np.ones(np.size(predictions))
  is_positive = labels > 0
  num_positives = np.sum(weights[is_positive])
  num_negatives = np.sum(weights[~is_positive])

  # Sort descending:
  inds = np.argsort(-predictions)

  sorted_labels = labels[inds]
  sorted_weights = weights[inds]
  is_positive = sorted_labels > 0

  tp = np.cumsum(sorted_weights * is_positive) / num_positives
  return np.sum((sorted_weights * tp)[~is_positive]) / num_negatives


class StreamingAUCTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_auc(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(self,
                             ('auc/true_positives:0', 'auc/false_negatives:0',
                              'auc/false_positives:0', 'auc/true_negatives:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_auc(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_auc(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    auc, update_op = metrics.streaming_auc(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_auc = auc.eval()
      for _ in range(10):
        self.assertAlmostEqual(initial_auc, auc.eval(), 5)

  def testPredictionsOutOfRange(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, -1, 1, -1], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
      _, update_op = metrics.streaming_auc(predictions, labels)
      sess.run(variables.local_variables_initializer())
      self.assertRaises(errors_impl.InvalidArgumentError, update_op.eval)

  def testAllCorrect(self):
    self.allCorrectAsExpected('ROC')

  def allCorrectAsExpected(self, curve):
    inputs = np.random.randint(0, 2, size=(100, 1))

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(inputs)
      auc, update_op = metrics.streaming_auc(predictions, labels, curve=curve)

      sess.run(variables.local_variables_initializer())
      self.assertEqual(1, sess.run(update_op))

      self.assertEqual(1, auc.eval())

  def testSomeCorrect(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
      auc, update_op = metrics.streaming_auc(predictions, labels)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.5, sess.run(update_op))

      self.assertAlmostEqual(0.5, auc.eval())

  def testWeighted1d(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
      weights = constant_op.constant([2], shape=(1, 1))
      auc, update_op = metrics.streaming_auc(
          predictions, labels, weights=weights)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.5, sess.run(update_op), 5)

      self.assertAlmostEqual(0.5, auc.eval(), 5)

  def testWeighted2d(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
      weights = constant_op.constant([1, 2, 3, 4], shape=(1, 4))
      auc, update_op = metrics.streaming_auc(
          predictions, labels, weights=weights)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.7, sess.run(update_op), 5)

      self.assertAlmostEqual(0.7, auc.eval(), 5)

  def testAUCPRSpecialCase(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [0.1, 0.4, 0.35, 0.8], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 0, 1, 1], shape=(1, 4))
      auc, update_op = metrics.streaming_auc(predictions, labels, curve='PR')

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.79166, sess.run(update_op), delta=1e-3)

      self.assertAlmostEqual(0.79166, auc.eval(), delta=1e-3)

  def testAnotherAUCPRSpecialCase(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [0.1, 0.4, 0.35, 0.8, 0.1, 0.135, 0.81],
          shape=(1, 7),
          dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 0, 1, 0, 1, 0, 1], shape=(1, 7))
      auc, update_op = metrics.streaming_auc(predictions, labels, curve='PR')

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.610317, sess.run(update_op), delta=1e-3)

      self.assertAlmostEqual(0.610317, auc.eval(), delta=1e-3)

  def testThirdAUCPRSpecialCase(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [0.0, 0.1, 0.2, 0.33, 0.3, 0.4, 0.5],
          shape=(1, 7),
          dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 0, 0, 0, 1, 1, 1], shape=(1, 7))
      auc, update_op = metrics.streaming_auc(predictions, labels, curve='PR')

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.90277, sess.run(update_op), delta=1e-3)

      self.assertAlmostEqual(0.90277, auc.eval(), delta=1e-3)

  def testAllIncorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(1 - inputs, dtype=dtypes_lib.float32)
      auc, update_op = metrics.streaming_auc(predictions, labels)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0, sess.run(update_op))

      self.assertAlmostEqual(0, auc.eval())

  def testZeroTruePositivesAndFalseNegativesGivesOneAUC(self):
    with self.test_session() as sess:
      predictions = array_ops.zeros([4], dtype=dtypes_lib.float32)
      labels = array_ops.zeros([4])
      auc, update_op = metrics.streaming_auc(predictions, labels)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(1, sess.run(update_op), 6)

      self.assertAlmostEqual(1, auc.eval(), 6)

  def testRecallOneAndPrecisionOneGivesOnePRAUC(self):
    with self.test_session() as sess:
      predictions = array_ops.ones([4], dtype=dtypes_lib.float32)
      labels = array_ops.ones([4])
      auc, update_op = metrics.streaming_auc(predictions, labels, curve='PR')

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(1, sess.run(update_op), 6)

      self.assertAlmostEqual(1, auc.eval(), 6)

  def testWithMultipleUpdates(self):
    num_samples = 1000
    batch_size = 10
    num_batches = int(num_samples / batch_size)

    # Create the labels and data.
    labels = np.random.randint(0, 2, size=num_samples)
    noise = np.random.normal(0.0, scale=0.2, size=num_samples)
    predictions = 0.4 + 0.2 * labels + noise
    predictions[predictions > 1] = 1
    predictions[predictions < 0] = 0

    def _enqueue_as_batches(x, enqueue_ops):
      x_batches = x.astype(np.float32).reshape((num_batches, batch_size))
      x_queue = data_flow_ops.FIFOQueue(
          num_batches, dtypes=dtypes_lib.float32, shapes=(batch_size,))
      for i in range(num_batches):
        enqueue_ops[i].append(x_queue.enqueue(x_batches[i, :]))
      return x_queue.dequeue()

    for weights in (None, np.ones(num_samples),
                    np.random.exponential(scale=1.0, size=num_samples)):
      expected_auc = _np_auc(predictions, labels, weights)

      with self.test_session() as sess:
        enqueue_ops = [[] for i in range(num_batches)]
        tf_predictions = _enqueue_as_batches(predictions, enqueue_ops)
        tf_labels = _enqueue_as_batches(labels, enqueue_ops)
        tf_weights = (
            _enqueue_as_batches(weights, enqueue_ops)
            if weights is not None else None)

        for i in range(num_batches):
          sess.run(enqueue_ops[i])

        auc, update_op = metrics.streaming_auc(
            tf_predictions,
            tf_labels,
            curve='ROC',
            num_thresholds=500,
            weights=tf_weights)

        sess.run(variables.local_variables_initializer())
        for i in range(num_batches):
          sess.run(update_op)

        # Since this is only approximate, we can't expect a 6 digits match.
        # Although with higher number of samples/thresholds we should see the
        # accuracy improving
        self.assertAlmostEqual(expected_auc, auc.eval(), 2)


class StreamingDynamicAUCTest(test.TestCase):

  def setUp(self):
    super(StreamingDynamicAUCTest, self).setUp()
    np.random.seed(1)
    ops.reset_default_graph()

  def testUnknownCurve(self):
    with self.assertRaisesRegexp(
        ValueError, 'curve must be either ROC or PR, TEST_CURVE unknown'):
      metrics.streaming_dynamic_auc(
          labels=array_ops.ones((10, 1)),
          predictions=array_ops.ones((10, 1)),
          curve='TEST_CURVE')

  def testVars(self):
    metrics.streaming_dynamic_auc(
        labels=array_ops.ones((10, 1)), predictions=array_ops.ones((10, 1)))
    _assert_metric_variables(self, [
        'dynamic_auc/concat_labels/array:0', 'dynamic_auc/concat_labels/size:0',
        'dynamic_auc/concat_preds/array:0', 'dynamic_auc/concat_preds/size:0'
    ])

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    auc, _ = metrics.streaming_dynamic_auc(
        labels=array_ops.ones((10, 1)),
        predictions=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertEqual(ops.get_collection(my_collection_name), [auc])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_dynamic_auc(
        labels=array_ops.ones((10, 1)),
        predictions=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    auc, update_op = metrics.streaming_dynamic_auc(labels, predictions)
    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      # Run several updates.
      for _ in xrange(10):
        sess.run(update_op)
      # Then verify idempotency.
      initial_auc = auc.eval()
      for _ in xrange(10):
        self.assertAlmostEqual(initial_auc, auc.eval(), 5)

  def testAllLabelsOnes(self):
    with self.test_session() as sess:
      predictions = constant_op.constant([1., 1., 1.])
      labels = constant_op.constant([1, 1, 1])
      auc, update_op = metrics.streaming_dynamic_auc(labels, predictions)
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0, auc.eval())

  def testAllLabelsZeros(self):
    with self.test_session() as sess:
      predictions = constant_op.constant([1., 1., 1.])
      labels = constant_op.constant([0, 0, 0])
      auc, update_op = metrics.streaming_dynamic_auc(labels, predictions)
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(0, auc.eval())

  def testNonZeroOnePredictions(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [2.5, -2.5, 2.5, -2.5], dtype=dtypes_lib.float32)
      labels = constant_op.constant([1, 0, 1, 0])
      auc, update_op = metrics.streaming_dynamic_auc(labels, predictions)
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertAlmostEqual(auc.eval(), 1.0)

  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))
    with self.test_session() as sess:
      predictions = constant_op.constant(inputs)
      labels = constant_op.constant(inputs)
      auc, update_op = metrics.streaming_dynamic_auc(labels, predictions)
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertEqual(1, auc.eval())

  def testSomeCorrect(self):
    with self.test_session() as sess:
      predictions = constant_op.constant([1, 0, 1, 0])
      labels = constant_op.constant([0, 1, 1, 0])
      auc, update_op = metrics.streaming_dynamic_auc(labels, predictions)
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertAlmostEqual(0.5, auc.eval())

  def testAllIncorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))
    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(1 - inputs, dtype=dtypes_lib.float32)
      auc, update_op = metrics.streaming_dynamic_auc(labels, predictions)
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertAlmostEqual(0, auc.eval())

  def testExceptionOnIncompatibleShapes(self):
    with self.test_session() as sess:
      predictions = array_ops.ones([5])
      labels = array_ops.zeros([6])
      with self.assertRaisesRegexp(ValueError, 'Shapes .* are incompatible'):
        _, update_op = metrics.streaming_dynamic_auc(labels, predictions)
        sess.run(variables.local_variables_initializer())
        sess.run(update_op)

  def testExceptionOnGreaterThanOneLabel(self):
    with self.test_session() as sess:
      predictions = constant_op.constant([1, 0.5, 0], dtypes_lib.float32)
      labels = constant_op.constant([2, 1, 0])
      _, update_op = metrics.streaming_dynamic_auc(labels, predictions)
      sess.run(variables.local_variables_initializer())
      with self.assertRaisesRegexp(
          errors_impl.InvalidArgumentError,
          '.*labels must be 0 or 1, at least one is >1.*'):
        sess.run(update_op)

  def testExceptionOnNegativeLabel(self):
    with self.test_session() as sess:
      predictions = constant_op.constant([1, 0.5, 0], dtypes_lib.float32)
      labels = constant_op.constant([1, 0, -1])
      _, update_op = metrics.streaming_dynamic_auc(labels, predictions)
      sess.run(variables.local_variables_initializer())
      with self.assertRaisesRegexp(
          errors_impl.InvalidArgumentError,
          '.*labels must be 0 or 1, at least one is <0.*'):
        sess.run(update_op)

  def testWithMultipleUpdates(self):
    batch_size = 10
    num_batches = 100
    labels = np.array([])
    predictions = np.array([])
    tf_labels = variables.Variable(
        array_ops.ones(batch_size, dtypes_lib.int32),
        collections=[ops.GraphKeys.LOCAL_VARIABLES],
        dtype=dtypes_lib.int32)
    tf_predictions = variables.Variable(
        array_ops.ones(batch_size),
        collections=[ops.GraphKeys.LOCAL_VARIABLES],
        dtype=dtypes_lib.float32)
    auc, update_op = metrics.streaming_dynamic_auc(tf_labels, tf_predictions)
    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      for _ in xrange(num_batches):
        new_labels = np.random.randint(0, 2, size=batch_size)
        noise = np.random.normal(0.0, scale=0.2, size=batch_size)
        new_predictions = 0.4 + 0.2 * new_labels + noise
        labels = np.concatenate([labels, new_labels])
        predictions = np.concatenate([predictions, new_predictions])
        sess.run(tf_labels.assign(new_labels))
        sess.run(tf_predictions.assign(new_predictions))
        sess.run(update_op)
        expected_auc = _np_auc(predictions, labels)
        self.assertAlmostEqual(expected_auc, auc.eval())

  def testAUCPRReverseIncreasingPredictions(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [0.1, 0.4, 0.35, 0.8], dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 0, 1, 1])
      auc, update_op = metrics.streaming_dynamic_auc(
          labels, predictions, curve='PR')
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertAlmostEqual(0.79166, auc.eval(), delta=1e-5)

  def testAUCPRJumbledPredictions(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [0.1, 0.4, 0.35, 0.8, 0.1, 0.135, 0.81], dtypes_lib.float32)
      labels = constant_op.constant([0, 0, 1, 0, 1, 0, 1])
      auc, update_op = metrics.streaming_dynamic_auc(
          labels, predictions, curve='PR')
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertAlmostEqual(0.610317, auc.eval(), delta=1e-6)

  def testAUCPRPredictionsLessThanHalf(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [0.0, 0.1, 0.2, 0.33, 0.3, 0.4, 0.5],
          shape=(1, 7),
          dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 0, 0, 0, 1, 1, 1], shape=(1, 7))
      auc, update_op = metrics.streaming_dynamic_auc(
          labels, predictions, curve='PR')
      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertAlmostEqual(0.90277, auc.eval(), delta=1e-5)


class AucWithConfidenceIntervalsTest(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 AucWithConfidenceIntervalData 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.int64)
      weights_tensor = None
      if weights:
        weights_tensor = constant_op.constant(weights, dtype=dtypes_lib.float32)
      gotten_result, update_op = (
          metric_ops.auc_with_confidence_intervals(
              labels=labels_tensor,
              predictions=predictions_tensor,
              weights=weights_tensor))

      sess.run(variables.local_variables_initializer())
      sess.run(update_op)

      self._testResultsEqual(expected_result, gotten_result)

  def testAucAllCorrect(self):
    self._testCase(
        predictions=[0., 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.6, 0.8, 1.0],
        labels=[0, 0, 1, 0, 0, 1, 0, 1, 1, 0],
        expected_result={
            'auc': 0.66666667,
            'lower': 0.27826795,
            'upper': 0.91208512,
        })

  def testAucUnorderedInput(self):
    self._testCase(
        predictions=[1.0, 0.6, 0., 0.3, 0.4, 0.2, 0.5, 0.3, 0.6, 0.8],
        labels=[0, 1, 0, 1, 0, 0, 1, 0, 0, 1],
        expected_result={
            'auc': 0.66666667,
            'lower': 0.27826795,
            'upper': 0.91208512,
        })

  def testAucWithWeights(self):
    self._testCase(
        predictions=[0., 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.6, 0.8, 1.0],
        labels=[0, 0, 1, 0, 0, 1, 0, 1, 1, 0],
        weights=[0.5, 0.6, 1.2, 1.5, 2.0, 2.0, 1.5, 1.2, 0.6, 0.5],
        expected_result={
            'auc': 0.65151515,
            'lower': 0.28918604,
            'upper': 0.89573906,
        })

  def testAucEqualOne(self):
    self._testCase(
        predictions=[0, 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.6, 0.8, 1.0],
        labels=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
        expected_result={
            'auc': 1.0,
            'lower': 1.0,
            'upper': 1.0,
        })

  def testAucEqualZero(self):
    self._testCase(
        predictions=[0, 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.6, 0.8, 1.0],
        labels=[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
        expected_result={
            'auc': 0.0,
            'lower': 0.0,
            'upper': 0.0,
        })

  def testNonZeroOnePredictions(self):
    self._testCase(
        predictions=[2.5, -2.5, .5, -.5, 1],
        labels=[1, 0, 1, 0, 0],
        expected_result={
            'auc': 0.83333333,
            'lower': 0.15229267,
            'upper': 0.99286517,
        })

  def testAllLabelsOnes(self):
    self._testCase(
        predictions=[1., 1., 1., 1., 1.],
        labels=[1, 1, 1, 1, 1],
        expected_result={
            'auc': 0.,
            'lower': 0.,
            'upper': 0.,
        })

  def testAllLabelsZeros(self):
    self._testCase(
        predictions=[0., 0., 0., 0., 0.],
        labels=[0, 0, 0, 0, 0],
        expected_result={
            'auc': 0.,
            'lower': 0.,
            'upper': 0.,
        })

  def testWeightSumLessThanOneAll(self):
    self._testCase(
        predictions=[1., 1., 0., 1., 0., 0.],
        labels=[1, 1, 1, 0, 0, 0],
        weights=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
        expected_result={
            'auc': 0.,
            'lower': 0.,
            'upper': 0.,
        })

  def testWithMultipleUpdates(self):
    batch_size = 50
    num_batches = 100
    labels = np.array([])
    predictions = np.array([])
    tf_labels = variables.Variable(array_ops.ones(batch_size, dtypes_lib.int32),
                                   collections=[ops.GraphKeys.LOCAL_VARIABLES],
                                   dtype=dtypes_lib.int32)
    tf_predictions = variables.Variable(
        array_ops.ones(batch_size),
        collections=[ops.GraphKeys.LOCAL_VARIABLES],
        dtype=dtypes_lib.float32)
    auc, update_op = metrics.auc_with_confidence_intervals(tf_labels,
                                                           tf_predictions)
    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      for _ in xrange(num_batches):
        new_labels = np.random.randint(0, 2, size=batch_size)
        noise = np.random.normal(0.0, scale=0.2, size=batch_size)
        new_predictions = 0.4 + 0.2 * new_labels + noise
        labels = np.concatenate([labels, new_labels])
        predictions = np.concatenate([predictions, new_predictions])
        sess.run(tf_labels.assign(new_labels))
        sess.run(tf_predictions.assign(new_predictions))
        sess.run(update_op)
        expected_auc = _np_auc(predictions, labels)
        self.assertAllClose(expected_auc, auc.auc.eval())

  def testExceptionOnFloatLabels(self):
    with self.test_session() as sess:
      predictions = constant_op.constant([1, 0.5, 0, 1, 0], dtypes_lib.float32)
      labels = constant_op.constant([0.7, 0, 1, 0, 1])
      _, update_op = metrics.auc_with_confidence_intervals(labels, predictions)
      sess.run(variables.local_variables_initializer())
      self.assertRaises(TypeError, sess.run(update_op))

  def testExceptionOnGreaterThanOneLabel(self):
    with self.test_session() as sess:
      predictions = constant_op.constant([1, 0.5, 0, 1, 0], dtypes_lib.float32)
      labels = constant_op.constant([2, 1, 0, 1, 0])
      _, update_op = metrics.auc_with_confidence_intervals(labels, predictions)
      sess.run(variables.local_variables_initializer())
      with self.assertRaisesRegexp(
          errors_impl.InvalidArgumentError,
          '.*labels must be 0 or 1, at least one is >1.*'):
        sess.run(update_op)

  def testExceptionOnNegativeLabel(self):
    with self.test_session() as sess:
      predictions = constant_op.constant([1, 0.5, 0, 1, 0], dtypes_lib.float32)
      labels = constant_op.constant([1, 0, -1, 1, 0])
      _, update_op = metrics.auc_with_confidence_intervals(labels, predictions)
      sess.run(variables.local_variables_initializer())
      with self.assertRaisesRegexp(
          errors_impl.InvalidArgumentError,
          '.*labels must be 0 or 1, at least one is <0.*'):
        sess.run(update_op)


class StreamingPrecisionRecallAtEqualThresholdsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def _testResultsEqual(self, expected_dict, gotten_result, eps=None):
    """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.
      eps: Epsilon value to use for testing output values. If unspecified, use
        default from assertAllClose.
    """
    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():
      if eps is not None:
        self.assertAllClose(expected_values, gotten_dict[key], atol=eps)
      else:
        self.assertAllClose(expected_values, gotten_dict[key])

  def testVars(self):
    metric_ops.precision_recall_at_equal_thresholds(
        labels=constant_op.constant([True], dtype=dtypes_lib.bool),
        predictions=constant_op.constant([0.42], dtype=dtypes_lib.float32))
    _assert_metric_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.precision_recall_at_equal_thresholds(
        labels=constant_op.constant([True], dtype=dtypes_lib.bool),
        predictions=constant_op.constant([0.42], dtype=dtypes_lib.float32),
        name='foo')
    _assert_metric_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.precision_recall_at_equal_thresholds(
        labels=labels, predictions=predictions)

    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 testLargeCase(self):
    self.skipTest("Test consistently timing out")
    shape = [32, 512, 256, 1]
    predictions = random_ops.random_uniform(
        shape, 0.0, 1.0, dtype=dtypes_lib.float32)
    labels = math_ops.greater(random_ops.random_uniform(shape, 0.0, 1.0), 0.5)

    result, update_op = metric_ops.precision_recall_at_equal_thresholds(
        labels=labels, predictions=predictions, num_thresholds=201)
    # Run many updates, enough to cause highly inaccurate values if the
    # code used float32 for accumulation.
    num_updates = 71

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      for _ in xrange(num_updates):
        sess.run(update_op)

      prdata = sess.run(result)

      # Since we use random values, we won't know the tp/fp/tn/fn values, but
      # tp and fp at threshold 0 should be the total number of positive and
      # negative labels, hence their sum should be total number of pixels.
      expected_value = 1.0 * np.product(shape) * num_updates
      got_value = prdata.tp[0] + prdata.fp[0]
      # They should be at least within 1.
      self.assertNear(got_value, expected_value, 1.0)

  def _testCase(self,
                predictions,
                labels,
                expected_result,
                dtype=dtypes_lib.float32,
                eps=None,
                weights=None):
    """Performs a test given a certain scenario of labels, predictions, weights.

    Args:
      predictions: The predictions tensor. Of type dtype.
      labels: The labels tensor. Of type bool.
      expected_result: The expected result (dict) that maps to tensors.
      dtype: Data type to use for predictions and weights tensor. Default
        is float32.
      eps: Epsilon value to use for testing output values. If unspecified, use
        default from assertAllClose.
      weights: Optional weights tensor.
    """
    with self.test_session() as sess:
      predictions_tensor = constant_op.constant(predictions, dtype=dtype)
      labels_tensor = constant_op.constant(labels, dtype=dtypes_lib.bool)
      weights_tensor = None
      if weights:
        weights_tensor = constant_op.constant(weights, dtype=dtype)
      gotten_result, update_op = (
          metric_ops.precision_recall_at_equal_thresholds(
              labels=labels_tensor,
              predictions=predictions_tensor,
              weights=weights_tensor,
              num_thresholds=3))
      self.assertEqual(gotten_result.tp.dtype, dtype)
      self.assertEqual(gotten_result.fp.dtype, dtype)
      self.assertEqual(gotten_result.tn.dtype, dtype)
      self.assertEqual(gotten_result.fn.dtype, dtype)
      self.assertEqual(gotten_result.precision.dtype, dtype)
      self.assertEqual(gotten_result.recall.dtype, dtype)
      self.assertEqual(gotten_result.thresholds.dtype, dtype)

      sess.run(variables.local_variables_initializer())
      sess.run(update_op)

      self._testResultsEqual(expected_result, gotten_result, eps=eps)

  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]])

  def testFloat64(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],
        },
        dtype=dtypes_lib.float64)

  def testFloat16(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],
        },
        dtype=dtypes_lib.float16,
        eps=1e-3)


class StreamingSpecificityAtSensitivityTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_specificity_at_sensitivity(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        sensitivity=0.7)
    _assert_metric_variables(self,
                             ('specificity_at_sensitivity/true_positives:0',
                              'specificity_at_sensitivity/false_negatives:0',
                              'specificity_at_sensitivity/false_positives:0',
                              'specificity_at_sensitivity/true_negatives:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_specificity_at_sensitivity(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        sensitivity=0.7,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_specificity_at_sensitivity(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        sensitivity=0.7,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    specificity, update_op = metrics.streaming_specificity_at_sensitivity(
        predictions, labels, sensitivity=0.7)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_specificity = specificity.eval()
      for _ in range(10):
        self.assertAlmostEqual(initial_specificity, specificity.eval(), 5)

  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
    labels = constant_op.constant(inputs)
    specificity, update_op = metrics.streaming_specificity_at_sensitivity(
        predictions, labels, sensitivity=0.7)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(1, sess.run(update_op))
      self.assertEqual(1, specificity.eval())

  def testSomeCorrectHighSensitivity(self):
    predictions_values = [0.1, 0.2, 0.4, 0.3, 0.0, 0.1, 0.45, 0.5, 0.8, 0.9]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    specificity, update_op = metrics.streaming_specificity_at_sensitivity(
        predictions, labels, sensitivity=0.8)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(1.0, sess.run(update_op))
      self.assertAlmostEqual(1.0, specificity.eval())

  def testSomeCorrectLowSensitivity(self):
    predictions_values = [0.1, 0.2, 0.4, 0.3, 0.0, 0.1, 0.2, 0.2, 0.26, 0.26]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    specificity, update_op = metrics.streaming_specificity_at_sensitivity(
        predictions, labels, sensitivity=0.4)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      self.assertAlmostEqual(0.6, sess.run(update_op))
      self.assertAlmostEqual(0.6, specificity.eval())

  def testWeighted1d(self):
    predictions_values = [0.1, 0.2, 0.4, 0.3, 0.0, 0.1, 0.2, 0.2, 0.26, 0.26]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
    weights_values = [3]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    weights = constant_op.constant(weights_values)
    specificity, update_op = metrics.streaming_specificity_at_sensitivity(
        predictions, labels, weights=weights, sensitivity=0.4)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      self.assertAlmostEqual(0.6, sess.run(update_op))
      self.assertAlmostEqual(0.6, specificity.eval())

  def testWeighted2d(self):
    predictions_values = [0.1, 0.2, 0.4, 0.3, 0.0, 0.1, 0.2, 0.2, 0.26, 0.26]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
    weights_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    weights = constant_op.constant(weights_values)
    specificity, update_op = metrics.streaming_specificity_at_sensitivity(
        predictions, labels, weights=weights, sensitivity=0.4)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      self.assertAlmostEqual(8.0 / 15.0, sess.run(update_op))
      self.assertAlmostEqual(8.0 / 15.0, specificity.eval())


class StreamingSensitivityAtSpecificityTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_sensitivity_at_specificity(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        specificity=0.7)
    _assert_metric_variables(self,
                             ('sensitivity_at_specificity/true_positives:0',
                              'sensitivity_at_specificity/false_negatives:0',
                              'sensitivity_at_specificity/false_positives:0',
                              'sensitivity_at_specificity/true_negatives:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_sensitivity_at_specificity(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        specificity=0.7,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_sensitivity_at_specificity(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        specificity=0.7,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    sensitivity, update_op = metrics.streaming_sensitivity_at_specificity(
        predictions, labels, specificity=0.7)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_sensitivity = sensitivity.eval()
      for _ in range(10):
        self.assertAlmostEqual(initial_sensitivity, sensitivity.eval(), 5)

  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
    labels = constant_op.constant(inputs)
    specificity, update_op = metrics.streaming_sensitivity_at_specificity(
        predictions, labels, specificity=0.7)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(1, sess.run(update_op))
      self.assertEqual(1, specificity.eval())

  def testSomeCorrectHighSpecificity(self):
    predictions_values = [0.0, 0.1, 0.2, 0.3, 0.4, 0.1, 0.45, 0.5, 0.8, 0.9]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    specificity, update_op = metrics.streaming_sensitivity_at_specificity(
        predictions, labels, specificity=0.8)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.8, sess.run(update_op))
      self.assertAlmostEqual(0.8, specificity.eval())

  def testSomeCorrectLowSpecificity(self):
    predictions_values = [0.0, 0.1, 0.2, 0.3, 0.4, 0.01, 0.02, 0.25, 0.26, 0.26]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    specificity, update_op = metrics.streaming_sensitivity_at_specificity(
        predictions, labels, specificity=0.4)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.6, sess.run(update_op))
      self.assertAlmostEqual(0.6, specificity.eval())

  def testWeighted(self):
    predictions_values = [0.0, 0.1, 0.2, 0.3, 0.4, 0.01, 0.02, 0.25, 0.26, 0.26]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
    weights_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    weights = constant_op.constant(weights_values)
    specificity, update_op = metrics.streaming_sensitivity_at_specificity(
        predictions, labels, weights=weights, specificity=0.4)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.675, sess.run(update_op))
      self.assertAlmostEqual(0.675, specificity.eval())


# TODO(nsilberman): Break this up into two sets of tests.
class StreamingPrecisionRecallThresholdsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_precision_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0])
    _assert_metric_variables(self, (
        'precision_at_thresholds/true_positives:0',
        'precision_at_thresholds/false_positives:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    prec, _ = metrics.streaming_precision_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0],
        metrics_collections=[my_collection_name])
    rec, _ = metrics.streaming_recall_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0],
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [prec, rec])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, precision_op = metrics.streaming_precision_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0],
        updates_collections=[my_collection_name])
    _, recall_op = metrics.streaming_recall_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0],
        updates_collections=[my_collection_name])
    self.assertListEqual(
        ops.get_collection(my_collection_name), [precision_op, recall_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (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, thresholds)
    rec, rec_op = metrics.streaming_recall_at_thresholds(
        predictions, labels, thresholds)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # 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):
        self.assertAllClose(initial_prec, prec.eval())
        self.assertAllClose(initial_rec, rec.eval())

  # TODO(nsilberman): fix tests (passing but incorrect).
  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(inputs)
      thresholds = [0.5]
      prec, prec_op = metrics.streaming_precision_at_thresholds(
          predictions, labels, thresholds)
      rec, rec_op = metrics.streaming_recall_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run([prec_op, rec_op])

      self.assertEqual(1, prec.eval())
      self.assertEqual(1, rec.eval())

  def testSomeCorrect(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
      thresholds = [0.5]
      prec, prec_op = metrics.streaming_precision_at_thresholds(
          predictions, labels, thresholds)
      rec, rec_op = metrics.streaming_recall_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run([prec_op, rec_op])

      self.assertAlmostEqual(0.5, prec.eval())
      self.assertAlmostEqual(0.5, rec.eval())

  def testAllIncorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(1 - inputs, dtype=dtypes_lib.float32)
      thresholds = [0.5]
      prec, prec_op = metrics.streaming_precision_at_thresholds(
          predictions, labels, thresholds)
      rec, rec_op = metrics.streaming_recall_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run([prec_op, rec_op])

      self.assertAlmostEqual(0, prec.eval())
      self.assertAlmostEqual(0, rec.eval())

  def testWeights1d(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes_lib.float32)
      labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2))
      weights = constant_op.constant(
          [[0], [1]], shape=(2, 1), dtype=dtypes_lib.float32)
      thresholds = [0.5, 1.1]
      prec, prec_op = metrics.streaming_precision_at_thresholds(
          predictions, labels, thresholds, weights=weights)
      rec, rec_op = metrics.streaming_recall_at_thresholds(
          predictions, labels, thresholds, weights=weights)

      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])

      self.assertAlmostEqual(1.0, prec_low.eval(), places=5)
      self.assertAlmostEqual(0.0, prec_high.eval(), places=5)
      self.assertAlmostEqual(1.0, rec_low.eval(), places=5)
      self.assertAlmostEqual(0.0, rec_high.eval(), places=5)

  def testWeights2d(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes_lib.float32)
      labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2))
      weights = constant_op.constant(
          [[0, 0], [1, 1]], shape=(2, 2), dtype=dtypes_lib.float32)
      thresholds = [0.5, 1.1]
      prec, prec_op = metrics.streaming_precision_at_thresholds(
          predictions, labels, thresholds, weights=weights)
      rec, rec_op = metrics.streaming_recall_at_thresholds(
          predictions, labels, thresholds, weights=weights)

      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])

      self.assertAlmostEqual(1.0, prec_low.eval(), places=5)
      self.assertAlmostEqual(0.0, prec_high.eval(), places=5)
      self.assertAlmostEqual(1.0, rec_low.eval(), places=5)
      self.assertAlmostEqual(0.0, rec_high.eval(), places=5)

  def testExtremeThresholds(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 1], shape=(1, 4))
      thresholds = [-1.0, 2.0]  # lower/higher than any values
      prec, prec_op = metrics.streaming_precision_at_thresholds(
          predictions, labels, thresholds)
      rec, rec_op = metrics.streaming_recall_at_thresholds(
          predictions, labels, thresholds)

      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])

      self.assertAlmostEqual(0.75, prec_low.eval())
      self.assertAlmostEqual(0.0, prec_high.eval())
      self.assertAlmostEqual(1.0, rec_low.eval())
      self.assertAlmostEqual(0.0, rec_high.eval())

  def testZeroLabelsPredictions(self):
    with self.test_session() as sess:
      predictions = array_ops.zeros([4], dtype=dtypes_lib.float32)
      labels = array_ops.zeros([4])
      thresholds = [0.5]
      prec, prec_op = metrics.streaming_precision_at_thresholds(
          predictions, labels, thresholds)
      rec, rec_op = metrics.streaming_recall_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run([prec_op, rec_op])

      self.assertAlmostEqual(0, prec.eval(), 6)
      self.assertAlmostEqual(0, rec.eval(), 6)

  def testWithMultipleUpdates(self):
    num_samples = 1000
    batch_size = 10
    num_batches = int(num_samples / batch_size)

    # Create the labels and data.
    labels = np.random.randint(0, 2, size=(num_samples, 1))
    noise = np.random.normal(0.0, scale=0.2, size=(num_samples, 1))
    predictions = 0.4 + 0.2 * labels + noise
    predictions[predictions > 1] = 1
    predictions[predictions < 0] = 0
    thresholds = [0.3]

    tp = 0
    fp = 0
    fn = 0
    tn = 0
    for i in range(num_samples):
      if predictions[i] > thresholds[0]:
        if labels[i] == 1:
          tp += 1
        else:
          fp += 1
      else:
        if labels[i] == 1:
          fn += 1
        else:
          tn += 1
    epsilon = 1e-7
    expected_prec = tp / (epsilon + tp + fp)
    expected_rec = tp / (epsilon + tp + fn)

    labels = labels.astype(np.float32)
    predictions = predictions.astype(np.float32)

    with self.test_session() as sess:
      # Reshape the data so its easy to queue up:
      predictions_batches = predictions.reshape((batch_size, num_batches))
      labels_batches = labels.reshape((batch_size, num_batches))

      # Enqueue the data:
      predictions_queue = data_flow_ops.FIFOQueue(
          num_batches, dtypes=dtypes_lib.float32, shapes=(batch_size,))
      labels_queue = data_flow_ops.FIFOQueue(
          num_batches, dtypes=dtypes_lib.float32, shapes=(batch_size,))

      for i in range(int(num_batches)):
        tf_prediction = constant_op.constant(predictions_batches[:, i])
        tf_label = constant_op.constant(labels_batches[:, i])
        sess.run([
            predictions_queue.enqueue(tf_prediction),
            labels_queue.enqueue(tf_label)
        ])

      tf_predictions = predictions_queue.dequeue()
      tf_labels = labels_queue.dequeue()

      prec, prec_op = metrics.streaming_precision_at_thresholds(
          tf_predictions, tf_labels, thresholds)
      rec, rec_op = metrics.streaming_recall_at_thresholds(
          tf_predictions, tf_labels, thresholds)

      sess.run(variables.local_variables_initializer())
      for _ in range(int(num_samples / batch_size)):
        sess.run([prec_op, rec_op])
      # Since this is only approximate, we can't expect a 6 digits match.
      # Although with higher number of samples/thresholds we should see the
      # accuracy improving
      self.assertAlmostEqual(expected_prec, prec.eval(), 2)
      self.assertAlmostEqual(expected_rec, rec.eval(), 2)


class StreamingFPRThresholdsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_false_positive_rate_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0])
    _assert_metric_variables(self, (
        'false_positive_rate_at_thresholds/false_positives:0',
        'false_positive_rate_at_thresholds/true_negatives:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    fpr, _ = metrics.streaming_false_positive_rate_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0],
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [fpr])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_false_positive_rate_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0],
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (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)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(fpr_op)

      # Then verify idempotency.
      initial_fpr = fpr.eval()
      for _ in range(10):
        self.assertAllClose(initial_fpr, fpr.eval())

  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(inputs)
      thresholds = [0.5]
      fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run(fpr_op)

      self.assertEqual(0, fpr.eval())

  def testSomeCorrect(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
      thresholds = [0.5]
      fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run(fpr_op)

      self.assertAlmostEqual(0.5, fpr.eval())

  def testAllIncorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(1 - inputs, dtype=dtypes_lib.float32)
      thresholds = [0.5]
      fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run(fpr_op)

      self.assertAlmostEqual(1, fpr.eval())

  def testWeights1d(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes_lib.float32)
      labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2))
      weights = constant_op.constant(
          [[0], [1]], shape=(2, 1), dtype=dtypes_lib.float32)
      thresholds = [0.5, 1.1]
      fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
          predictions, labels, thresholds, weights=weights)

      fpr_low = fpr[0]
      fpr_high = fpr[1]

      sess.run(variables.local_variables_initializer())
      sess.run(fpr_op)

      self.assertAlmostEqual(0.0, fpr_low.eval(), places=5)
      self.assertAlmostEqual(0.0, fpr_high.eval(), places=5)

  def testWeights2d(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes_lib.float32)
      labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2))
      weights = constant_op.constant(
          [[0, 0], [1, 1]], shape=(2, 2), dtype=dtypes_lib.float32)
      thresholds = [0.5, 1.1]
      fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
          predictions, labels, thresholds, weights=weights)

      fpr_low = fpr[0]
      fpr_high = fpr[1]

      sess.run(variables.local_variables_initializer())
      sess.run(fpr_op)

      self.assertAlmostEqual(0.0, fpr_low.eval(), places=5)
      self.assertAlmostEqual(0.0, fpr_high.eval(), places=5)

  def testExtremeThresholds(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 1], shape=(1, 4))
      thresholds = [-1.0, 2.0]  # lower/higher than any values
      fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
          predictions, labels, thresholds)

      fpr_low = fpr[0]
      fpr_high = fpr[1]

      sess.run(variables.local_variables_initializer())
      sess.run(fpr_op)

      self.assertAlmostEqual(1.0, fpr_low.eval(), places=5)
      self.assertAlmostEqual(0.0, fpr_high.eval(), places=5)

  def testZeroLabelsPredictions(self):
    with self.test_session() as sess:
      predictions = array_ops.zeros([4], dtype=dtypes_lib.float32)
      labels = array_ops.zeros([4])
      thresholds = [0.5]
      fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run(fpr_op)

      self.assertAlmostEqual(0, fpr.eval(), 6)

  def testWithMultipleUpdates(self):
    num_samples = 1000
    batch_size = 10
    num_batches = int(num_samples / batch_size)

    # Create the labels and data.
    labels = np.random.randint(0, 2, size=(num_samples, 1))
    noise = np.random.normal(0.0, scale=0.2, size=(num_samples, 1))
    predictions = 0.4 + 0.2 * labels + noise
    predictions[predictions > 1] = 1
    predictions[predictions < 0] = 0
    thresholds = [0.3]

    fp = 0
    tn = 0
    for i in range(num_samples):
      if predictions[i] > thresholds[0]:
        if labels[i] == 0:
          fp += 1
      else:
        if labels[i] == 0:
          tn += 1
    epsilon = 1e-7
    expected_fpr = fp / (epsilon + fp + tn)

    labels = labels.astype(np.float32)
    predictions = predictions.astype(np.float32)

    with self.test_session() as sess:
      # Reshape the data so its easy to queue up:
      predictions_batches = predictions.reshape((batch_size, num_batches))
      labels_batches = labels.reshape((batch_size, num_batches))

      # Enqueue the data:
      predictions_queue = data_flow_ops.FIFOQueue(
          num_batches, dtypes=dtypes_lib.float32, shapes=(batch_size,))
      labels_queue = data_flow_ops.FIFOQueue(
          num_batches, dtypes=dtypes_lib.float32, shapes=(batch_size,))

      for i in range(int(num_batches)):
        tf_prediction = constant_op.constant(predictions_batches[:, i])
        tf_label = constant_op.constant(labels_batches[:, i])
        sess.run([
            predictions_queue.enqueue(tf_prediction),
            labels_queue.enqueue(tf_label)
        ])

      tf_predictions = predictions_queue.dequeue()
      tf_labels = labels_queue.dequeue()

      fpr, fpr_op = metrics.streaming_false_positive_rate_at_thresholds(
          tf_predictions, tf_labels, thresholds)

      sess.run(variables.local_variables_initializer())
      for _ in range(int(num_samples / batch_size)):
        sess.run(fpr_op)
      # Since this is only approximate, we can't expect a 6 digits match.
      # Although with higher number of samples/thresholds we should see the
      # accuracy improving
      self.assertAlmostEqual(expected_fpr, fpr.eval(), 2)


class RecallAtPrecisionTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.recall_at_precision(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        precision=0.7)
    _assert_metric_variables(self, ('recall_at_precision/true_positives:0',
                                    'recall_at_precision/false_negatives:0',
                                    'recall_at_precision/false_positives:0',
                                    'recall_at_precision/true_negatives:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.recall_at_precision(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        precision=0.7,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.recall_at_precision(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        precision=0.7,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2)
    recall, update_op = metrics.recall_at_precision(
        labels, predictions, precision=0.7)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_recall = recall.eval()
      for _ in range(10):
        self.assertAlmostEqual(initial_recall, recall.eval(), 5)

  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
    labels = constant_op.constant(inputs)
    recall, update_op = metrics.recall_at_precision(
        labels, predictions, precision=1.0)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(1, sess.run(update_op))
      self.assertEqual(1, recall.eval())

  def testSomeCorrectHighPrecision(self):
    predictions_values = [1, .9, .8, .7, .6, .5, .4, .3]
    labels_values = [1, 1, 1, 1, 0, 0, 0, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    recall, update_op = metrics.recall_at_precision(
        labels, predictions, precision=0.8)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.8, sess.run(update_op))
      self.assertAlmostEqual(0.8, recall.eval())

  def testSomeCorrectLowPrecision(self):
    predictions_values = [1, .9, .8, .7, .6, .5, .4, .3, .2, .1]
    labels_values = [1, 1, 0, 0, 0, 0, 0, 0, 0, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    recall, update_op = metrics.recall_at_precision(
        labels, predictions, precision=0.4)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      target_recall = 2.0 / 3.0
      self.assertAlmostEqual(target_recall, sess.run(update_op))
      self.assertAlmostEqual(target_recall, recall.eval())

  def testWeighted(self):
    predictions_values = [1, .9, .8, .7, .6]
    labels_values = [1, 1, 0, 0, 1]
    weights_values = [1, 1, 3, 4, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    weights = constant_op.constant(weights_values)
    recall, update_op = metrics.recall_at_precision(
        labels, predictions, weights=weights, precision=0.4)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      target_recall = 2.0 / 3.0
      self.assertAlmostEqual(target_recall, sess.run(update_op))
      self.assertAlmostEqual(target_recall, recall.eval())


class PrecisionAtRecallTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.precision_at_recall(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        target_recall=0.7)
    _assert_metric_variables(self,
                             ('precision_at_recall/true_positives:0',
                              'precision_at_recall/false_negatives:0',
                              'precision_at_recall/false_positives:0',
                              'precision_at_recall/true_negatives:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.precision_at_recall(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        target_recall=0.7,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.precision_at_recall(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        target_recall=0.7,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=1)
    precision, update_op = metrics.precision_at_recall(
        labels, predictions, target_recall=0.7)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_precision = precision.eval()
      for _ in range(10):
        self.assertAlmostEqual(initial_precision, precision.eval(), places=5)

  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
    labels = constant_op.constant(inputs)
    precision, update_op = metrics.precision_at_recall(
        labels, predictions, target_recall=0.7)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(1, sess.run(update_op))
      self.assertEqual(1, precision.eval())

  def testAllIncorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
    labels = 1.0 - predictions
    label_prior = math_ops.reduce_mean(labels)
    precision, update_op = metrics.precision_at_recall(
        labels, predictions, target_recall=0.2)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(sess.run(label_prior), sess.run(update_op))
      self.assertEqual(sess.run(label_prior), precision.eval())

  def testSomeCorrectHighRecall(self):
    predictions_values = [0.1, 0.2, 0.5, 0.3, 0.0, 0.1, 0.45, 0.5, 0.8, 0.9]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    precision, update_op = metrics.precision_at_recall(
        labels, predictions, target_recall=0.8)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(0.8, sess.run(update_op))
      self.assertAlmostEqual(0.8, precision.eval())

  def testSomeCorrectLowRecall(self):
    predictions_values = [0.1, 0.2, 0.7, 0.3, 0.0, 0.1, 0.45, 0.5, 0.6, 0.9]
    labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]

    predictions = constant_op.constant(
        predictions_values, dtype=dtypes_lib.float32)
    labels = constant_op.constant(labels_values)
    precision, update_op = metrics.precision_at_recall(
        labels, predictions, target_recall=0.4)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(2.0/3, sess.run(update_op))
      self.assertAlmostEqual(2.0/3, precision.eval())

  def testWeighted_multipleLabelDtypes(self):
    for label_dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
      predictions_values = [
          0.0, 0.1, 0.2, 0.3, 0.4, 0.1, 0.22, 0.25, 0.31, 0.35]
      labels_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
      weights_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

      predictions = constant_op.constant(
          predictions_values, dtype=dtypes_lib.float32)
      labels = math_ops.cast(labels_values, dtype=label_dtype)
      weights = constant_op.constant(weights_values)
      precision, update_op = metrics.precision_at_recall(
          labels, predictions, target_recall=0.8, weights=weights)

      with self.test_session() as sess:
        sess.run(variables.local_variables_initializer())
        self.assertAlmostEqual(34.0/43, sess.run(update_op))
        self.assertAlmostEqual(34.0/43, precision.eval())


class StreamingFNRThresholdsTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_false_negative_rate_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0])
    _assert_metric_variables(self, (
        'false_negative_rate_at_thresholds/false_negatives:0',
        'false_negative_rate_at_thresholds/true_positives:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    fnr, _ = metrics.streaming_false_negative_rate_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0],
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [fnr])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_false_negative_rate_at_thresholds(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        thresholds=[0, 0.5, 1.0],
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes_lib.float32, seed=1)
    labels = random_ops.random_uniform(
        (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)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(fnr_op)

      # Then verify idempotency.
      initial_fnr = fnr.eval()
      for _ in range(10):
        self.assertAllClose(initial_fnr, fnr.eval())

  def testAllCorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(inputs)
      thresholds = [0.5]
      fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run(fnr_op)

      self.assertEqual(0, fnr.eval())

  def testSomeCorrect(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
      thresholds = [0.5]
      fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run(fnr_op)

      self.assertAlmostEqual(0.5, fnr.eval())

  def testAllIncorrect(self):
    inputs = np.random.randint(0, 2, size=(100, 1))

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(1 - inputs, dtype=dtypes_lib.float32)
      thresholds = [0.5]
      fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run(fnr_op)

      self.assertAlmostEqual(1, fnr.eval())

  def testWeights1d(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes_lib.float32)
      labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2))
      weights = constant_op.constant(
          [[0], [1]], shape=(2, 1), dtype=dtypes_lib.float32)
      thresholds = [0.5, 1.1]
      fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
          predictions, labels, thresholds, weights=weights)

      fnr_low = fnr[0]
      fnr_high = fnr[1]

      sess.run(variables.local_variables_initializer())
      sess.run(fnr_op)

      self.assertAlmostEqual(0.0, fnr_low.eval(), places=5)
      self.assertAlmostEqual(1.0, fnr_high.eval(), places=5)

  def testWeights2d(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes_lib.float32)
      labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2))
      weights = constant_op.constant(
          [[0, 0], [1, 1]], shape=(2, 2), dtype=dtypes_lib.float32)
      thresholds = [0.5, 1.1]
      fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
          predictions, labels, thresholds, weights=weights)

      fnr_low = fnr[0]
      fnr_high = fnr[1]

      sess.run(variables.local_variables_initializer())
      sess.run(fnr_op)

      self.assertAlmostEqual(0.0, fnr_low.eval(), places=5)
      self.assertAlmostEqual(1.0, fnr_high.eval(), places=5)

  def testExtremeThresholds(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant([0, 1, 1, 1], shape=(1, 4))
      thresholds = [-1.0, 2.0]  # lower/higher than any values
      fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
          predictions, labels, thresholds)

      fnr_low = fnr[0]
      fnr_high = fnr[1]

      sess.run(variables.local_variables_initializer())
      sess.run(fnr_op)

      self.assertAlmostEqual(0.0, fnr_low.eval())
      self.assertAlmostEqual(1.0, fnr_high.eval())

  def testZeroLabelsPredictions(self):
    with self.test_session() as sess:
      predictions = array_ops.zeros([4], dtype=dtypes_lib.float32)
      labels = array_ops.zeros([4])
      thresholds = [0.5]
      fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
          predictions, labels, thresholds)

      sess.run(variables.local_variables_initializer())
      sess.run(fnr_op)

      self.assertAlmostEqual(0, fnr.eval(), 6)

  def testWithMultipleUpdates(self):
    num_samples = 1000
    batch_size = 10
    num_batches = int(num_samples / batch_size)

    # Create the labels and data.
    labels = np.random.randint(0, 2, size=(num_samples, 1))
    noise = np.random.normal(0.0, scale=0.2, size=(num_samples, 1))
    predictions = 0.4 + 0.2 * labels + noise
    predictions[predictions > 1] = 1
    predictions[predictions < 0] = 0
    thresholds = [0.3]

    fn = 0
    tp = 0
    for i in range(num_samples):
      if predictions[i] > thresholds[0]:
        if labels[i] == 1:
          tp += 1
      else:
        if labels[i] == 1:
          fn += 1
    epsilon = 1e-7
    expected_fnr = fn / (epsilon + fn + tp)

    labels = labels.astype(np.float32)
    predictions = predictions.astype(np.float32)

    with self.test_session() as sess:
      # Reshape the data so its easy to queue up:
      predictions_batches = predictions.reshape((batch_size, num_batches))
      labels_batches = labels.reshape((batch_size, num_batches))

      # Enqueue the data:
      predictions_queue = data_flow_ops.FIFOQueue(
          num_batches, dtypes=dtypes_lib.float32, shapes=(batch_size,))
      labels_queue = data_flow_ops.FIFOQueue(
          num_batches, dtypes=dtypes_lib.float32, shapes=(batch_size,))

      for i in range(int(num_batches)):
        tf_prediction = constant_op.constant(predictions_batches[:, i])
        tf_label = constant_op.constant(labels_batches[:, i])
        sess.run([
            predictions_queue.enqueue(tf_prediction),
            labels_queue.enqueue(tf_label)
        ])

      tf_predictions = predictions_queue.dequeue()
      tf_labels = labels_queue.dequeue()

      fnr, fnr_op = metrics.streaming_false_negative_rate_at_thresholds(
          tf_predictions, tf_labels, thresholds)

      sess.run(variables.local_variables_initializer())
      for _ in range(int(num_samples / batch_size)):
        sess.run(fnr_op)
      # Since this is only approximate, we can't expect a 6 digits match.
      # Although with higher number of samples/thresholds we should see the
      # accuracy improving
      self.assertAlmostEqual(expected_fnr, fnr.eval(), 2)


# TODO(ptucker): Remove when we remove `streaming_recall_at_k`.
# This op will be deprecated soon in favor of `streaming_sparse_recall_at_k`.
# Until then, this test validates that both ops yield the same results.
class StreamingRecallAtKTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

    self._batch_size = 4
    self._num_classes = 3
    self._np_predictions = np.matrix(('0.1 0.2 0.7;'
                                      '0.6 0.2 0.2;'
                                      '0.0 0.9 0.1;'
                                      '0.2 0.0 0.8'))
    self._np_labels = [0, 0, 0, 0]

  def testVars(self):
    metrics.streaming_recall_at_k(
        predictions=array_ops.ones((self._batch_size, self._num_classes)),
        labels=array_ops.ones((self._batch_size,), dtype=dtypes_lib.int32),
        k=1)
    _assert_metric_variables(self,
                             ('recall_at_1/count:0', 'recall_at_1/total:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_recall_at_k(
        predictions=array_ops.ones((self._batch_size, self._num_classes)),
        labels=array_ops.ones((self._batch_size,), dtype=dtypes_lib.int32),
        k=1,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_recall_at_k(
        predictions=array_ops.ones((self._batch_size, self._num_classes)),
        labels=array_ops.ones((self._batch_size,), dtype=dtypes_lib.int32),
        k=1,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testSingleUpdateKIs1(self):
    predictions = constant_op.constant(
        self._np_predictions,
        shape=(self._batch_size, self._num_classes),
        dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        self._np_labels, shape=(self._batch_size,), dtype=dtypes_lib.int64)
    recall, update_op = metrics.streaming_recall_at_k(predictions, labels, k=1)
    sp_recall, sp_update_op = metrics.streaming_sparse_recall_at_k(
        predictions, array_ops.reshape(labels, (self._batch_size, 1)), k=1)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(0.25, sess.run(update_op))
      self.assertEqual(0.25, recall.eval())
      self.assertEqual(0.25, sess.run(sp_update_op))
      self.assertEqual(0.25, sp_recall.eval())

  def testSingleUpdateKIs2(self):
    predictions = constant_op.constant(
        self._np_predictions,
        shape=(self._batch_size, self._num_classes),
        dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        self._np_labels, shape=(self._batch_size,), dtype=dtypes_lib.int64)
    recall, update_op = metrics.streaming_recall_at_k(predictions, labels, k=2)
    sp_recall, sp_update_op = metrics.streaming_sparse_recall_at_k(
        predictions, array_ops.reshape(labels, (self._batch_size, 1)), k=2)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(0.5, sess.run(update_op))
      self.assertEqual(0.5, recall.eval())
      self.assertEqual(0.5, sess.run(sp_update_op))
      self.assertEqual(0.5, sp_recall.eval())

  def testSingleUpdateKIs3(self):
    predictions = constant_op.constant(
        self._np_predictions,
        shape=(self._batch_size, self._num_classes),
        dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        self._np_labels, shape=(self._batch_size,), dtype=dtypes_lib.int64)
    recall, update_op = metrics.streaming_recall_at_k(predictions, labels, k=3)
    sp_recall, sp_update_op = metrics.streaming_sparse_recall_at_k(
        predictions, array_ops.reshape(labels, (self._batch_size, 1)), k=3)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(1.0, sess.run(update_op))
      self.assertEqual(1.0, recall.eval())
      self.assertEqual(1.0, sess.run(sp_update_op))
      self.assertEqual(1.0, sp_recall.eval())

  def testSingleUpdateSomeMissingKIs2(self):
    predictions = constant_op.constant(
        self._np_predictions,
        shape=(self._batch_size, self._num_classes),
        dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        self._np_labels, shape=(self._batch_size,), dtype=dtypes_lib.int64)
    weights = constant_op.constant(
        [0, 1, 0, 1], shape=(self._batch_size,), dtype=dtypes_lib.float32)
    recall, update_op = metrics.streaming_recall_at_k(
        predictions, labels, k=2, weights=weights)
    sp_recall, sp_update_op = metrics.streaming_sparse_recall_at_k(
        predictions,
        array_ops.reshape(labels, (self._batch_size, 1)),
        k=2,
        weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(1.0, sess.run(update_op))
      self.assertEqual(1.0, recall.eval())
      self.assertEqual(1.0, sess.run(sp_update_op))
      self.assertEqual(1.0, sp_recall.eval())


class StreamingSparsePrecisionTest(test.TestCase):

  def _test_streaming_sparse_precision_at_k(self,
                                            predictions,
                                            labels,
                                            k,
                                            expected,
                                            class_id=None,
                                            weights=None):
    with ops.Graph().as_default() as g, self.test_session(g):
      if weights is not None:
        weights = constant_op.constant(weights, dtypes_lib.float32)
      metric, update = metrics.streaming_sparse_precision_at_k(
          predictions=constant_op.constant(predictions, dtypes_lib.float32),
          labels=labels,
          k=k,
          class_id=class_id,
          weights=weights)

      # Fails without initialized vars.
      self.assertRaises(errors_impl.OpError, metric.eval)
      self.assertRaises(errors_impl.OpError, update.eval)
      variables.variables_initializer(variables.local_variables()).run()

      # Run per-step op and assert expected values.
      if math.isnan(expected):
        _assert_nan(self, update.eval())
        _assert_nan(self, metric.eval())
      else:
        self.assertEqual(expected, update.eval())
        self.assertEqual(expected, metric.eval())

  def _test_streaming_sparse_precision_at_top_k(self,
                                                top_k_predictions,
                                                labels,
                                                expected,
                                                class_id=None,
                                                weights=None):
    with ops.Graph().as_default() as g, self.test_session(g):
      if weights is not None:
        weights = constant_op.constant(weights, dtypes_lib.float32)
      metric, update = metrics.streaming_sparse_precision_at_top_k(
          top_k_predictions=constant_op.constant(top_k_predictions,
                                                 dtypes_lib.int32),
          labels=labels,
          class_id=class_id,
          weights=weights)

      # Fails without initialized vars.
      self.assertRaises(errors_impl.OpError, metric.eval)
      self.assertRaises(errors_impl.OpError, update.eval)
      variables.variables_initializer(variables.local_variables()).run()

      # Run per-step op and assert expected values.
      if math.isnan(expected):
        self.assertTrue(math.isnan(update.eval()))
        self.assertTrue(math.isnan(metric.eval()))
      else:
        self.assertEqual(expected, update.eval())
        self.assertEqual(expected, metric.eval())

  def _test_streaming_sparse_average_precision_at_k(self,
                                                    predictions,
                                                    labels,
                                                    k,
                                                    expected,
                                                    weights=None):
    with ops.Graph().as_default() as g, self.test_session(g):
      if weights is not None:
        weights = constant_op.constant(weights, dtypes_lib.float32)
      predictions = constant_op.constant(predictions, dtypes_lib.float32)
      metric, update = metrics.streaming_sparse_average_precision_at_k(
          predictions, labels, k, weights=weights)

      # Fails without initialized vars.
      self.assertRaises(errors_impl.OpError, metric.eval)
      self.assertRaises(errors_impl.OpError, update.eval)
      local_variables = variables.local_variables()
      variables.variables_initializer(local_variables).run()

      # Run per-step op and assert expected values.
      if math.isnan(expected):
        _assert_nan(self, update.eval())
        _assert_nan(self, metric.eval())
      else:
        self.assertAlmostEqual(expected, update.eval())
        self.assertAlmostEqual(expected, metric.eval())

  def _test_streaming_sparse_average_precision_at_top_k(self,
                                                        top_k_predictions,
                                                        labels,
                                                        expected,
                                                        weights=None):
    with ops.Graph().as_default() as g, self.test_session(g):
      if weights is not None:
        weights = constant_op.constant(weights, dtypes_lib.float32)
      metric, update = metrics.streaming_sparse_average_precision_at_top_k(
          top_k_predictions, labels, weights=weights)

      # Fails without initialized vars.
      self.assertRaises(errors_impl.OpError, metric.eval)
      self.assertRaises(errors_impl.OpError, update.eval)
      local_variables = variables.local_variables()
      variables.variables_initializer(local_variables).run()

      # Run per-step op and assert expected values.
      if math.isnan(expected):
        _assert_nan(self, update.eval())
        _assert_nan(self, metric.eval())
      else:
        self.assertAlmostEqual(expected, update.eval())
        self.assertAlmostEqual(expected, metric.eval())

  def test_top_k_rank_invalid(self):
    with self.test_session():
      # top_k_predictions has rank < 2.
      top_k_predictions = [9, 4, 6, 2, 0]
      sp_labels = sparse_tensor.SparseTensorValue(
          indices=np.array([[
              0,
          ], [
              1,
          ], [
              2,
          ]], np.int64),
          values=np.array([2, 7, 8], np.int64),
          dense_shape=np.array([
              10,
          ], np.int64))

      with self.assertRaises(ValueError):
        precision, _ = metrics.streaming_sparse_precision_at_top_k(
            top_k_predictions=constant_op.constant(top_k_predictions,
                                                   dtypes_lib.int64),
            labels=sp_labels)
        variables.variables_initializer(variables.local_variables()).run()
        precision.eval()

  def test_average_precision(self):
    # Example 1.
    # Matches example here:
    # fastml.com/what-you-wanted-to-know-about-mean-average-precision
    labels_ex1 = (0, 1, 2, 3, 4)
    labels = np.array([labels_ex1], dtype=np.int64)
    predictions_ex1 = (0.2, 0.1, 0.0, 0.4, 0.0, 0.5, 0.3)
    predictions = (predictions_ex1,)
    predictions_top_k_ex1 = (5, 3, 6, 0, 1, 2)
    precision_ex1 = (0.0 / 1, 1.0 / 2, 1.0 / 3, 2.0 / 4)
    avg_precision_ex1 = (0.0 / 1, precision_ex1[1] / 2, precision_ex1[1] / 3,
                         (precision_ex1[1] + precision_ex1[3]) / 4)
    for i in xrange(4):
      k = i + 1
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k, expected=precision_ex1[i])
      self._test_streaming_sparse_precision_at_top_k(
          (predictions_top_k_ex1[:k],), labels, expected=precision_ex1[i])
      self._test_streaming_sparse_average_precision_at_k(
          predictions, labels, k, expected=avg_precision_ex1[i])
      self._test_streaming_sparse_average_precision_at_top_k(
          (predictions_top_k_ex1[:k],), labels, expected=avg_precision_ex1[i])

    # Example 2.
    labels_ex2 = (0, 2, 4, 5, 6)
    labels = np.array([labels_ex2], dtype=np.int64)
    predictions_ex2 = (0.3, 0.5, 0.0, 0.4, 0.0, 0.1, 0.2)
    predictions = (predictions_ex2,)
    predictions_top_k_ex2 = (1, 3, 0, 6, 5)
    precision_ex2 = (0.0 / 1, 0.0 / 2, 1.0 / 3, 2.0 / 4)
    avg_precision_ex2 = (0.0 / 1, 0.0 / 2, precision_ex2[2] / 3,
                         (precision_ex2[2] + precision_ex2[3]) / 4)
    for i in xrange(4):
      k = i + 1
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k, expected=precision_ex2[i])
      self._test_streaming_sparse_precision_at_top_k(
          (predictions_top_k_ex2[:k],), labels, expected=precision_ex2[i])
      self._test_streaming_sparse_average_precision_at_k(
          predictions, labels, k, expected=avg_precision_ex2[i])
      self._test_streaming_sparse_average_precision_at_top_k(
          (predictions_top_k_ex2[:k],), labels, expected=avg_precision_ex2[i])

    # Both examples, we expect both precision and average precision to be the
    # average of the 2 examples.
    labels = np.array([labels_ex1, labels_ex2], dtype=np.int64)
    predictions = (predictions_ex1, predictions_ex2)
    streaming_precision = [
        (ex1 + ex2) / 2 for ex1, ex2 in zip(precision_ex1, precision_ex2)
    ]
    streaming_average_precision = [
        (ex1 + ex2) / 2
        for ex1, ex2 in zip(avg_precision_ex1, avg_precision_ex2)
    ]
    for i in xrange(4):
      k = i + 1
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k, expected=streaming_precision[i])
      predictions_top_k = (predictions_top_k_ex1[:k], predictions_top_k_ex2[:k])
      self._test_streaming_sparse_precision_at_top_k(
          predictions_top_k, labels, expected=streaming_precision[i])
      self._test_streaming_sparse_average_precision_at_k(
          predictions, labels, k, expected=streaming_average_precision[i])
      self._test_streaming_sparse_average_precision_at_top_k(
          predictions_top_k, labels, expected=streaming_average_precision[i])

    # Weighted examples, we expect streaming average precision to be the
    # weighted average of the 2 examples.
    weights = (0.3, 0.6)
    streaming_average_precision = [
        (weights[0] * ex1 + weights[1] * ex2) / (weights[0] + weights[1])
        for ex1, ex2 in zip(avg_precision_ex1, avg_precision_ex2)
    ]
    for i in xrange(4):
      k = i + 1
      self._test_streaming_sparse_average_precision_at_k(
          predictions,
          labels,
          k,
          expected=streaming_average_precision[i],
          weights=weights)
      self._test_streaming_sparse_average_precision_at_top_k(
          (predictions_top_k_ex1[:k], predictions_top_k_ex2[:k]),
          labels,
          expected=streaming_average_precision[i],
          weights=weights)

  def test_average_precision_some_labels_out_of_range(self):
    """Tests that labels outside the [0, n_classes) range are ignored."""
    labels_ex1 = (-1, 0, 1, 2, 3, 4, 7)
    labels = np.array([labels_ex1], dtype=np.int64)
    predictions_ex1 = (0.2, 0.1, 0.0, 0.4, 0.0, 0.5, 0.3)
    predictions = (predictions_ex1,)
    predictions_top_k_ex1 = (5, 3, 6, 0, 1, 2)
    precision_ex1 = (0.0 / 1, 1.0 / 2, 1.0 / 3, 2.0 / 4)
    avg_precision_ex1 = (0.0 / 1, precision_ex1[1] / 2, precision_ex1[1] / 3,
                         (precision_ex1[1] + precision_ex1[3]) / 4)
    for i in xrange(4):
      k = i + 1
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k, expected=precision_ex1[i])
      self._test_streaming_sparse_precision_at_top_k(
          (predictions_top_k_ex1[:k],), labels, expected=precision_ex1[i])
      self._test_streaming_sparse_average_precision_at_k(
          predictions, labels, k, expected=avg_precision_ex1[i])
      self._test_streaming_sparse_average_precision_at_top_k(
          (predictions_top_k_ex1[:k],), labels, expected=avg_precision_ex1[i])

  def test_average_precision_at_top_k_static_shape_check(self):
    predictions_top_k = array_ops.placeholder(
        shape=(2, None), dtype=dtypes_lib.int64)
    labels = np.array(((1,), (2,)), dtype=np.int64)
    # Fails due to non-static predictions_idx shape.
    with self.assertRaises(ValueError):
      metric_ops.streaming_sparse_average_precision_at_top_k(
          predictions_top_k, labels)

    predictions_top_k = (2, 1)
    # Fails since rank of predictions_idx is less than one.
    with self.assertRaises(ValueError):
      metric_ops.streaming_sparse_average_precision_at_top_k(
          predictions_top_k, labels)
    predictions_top_k = ((2,), (1,))
    # Valid static shape.
    metric_ops.streaming_sparse_average_precision_at_top_k(
        predictions_top_k, labels)

  def test_one_label_at_k1_nan(self):
    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
    top_k_predictions = [[3], [3]]
    sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1],
                                                      [0, 0, 1, 0]])
    dense_labels = np.array([[3], [2]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Classes 0,1,2 have 0 predictions, classes -1 and 4 are out of range.
      for class_id in (-1, 0, 1, 2, 4):
        self._test_streaming_sparse_precision_at_k(
            predictions, labels, k=1, expected=NAN, class_id=class_id)
        self._test_streaming_sparse_precision_at_top_k(
            top_k_predictions, labels, expected=NAN, class_id=class_id)

  def test_one_label_at_k1(self):
    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
    top_k_predictions = [[3], [3]]
    sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1],
                                                      [0, 0, 1, 0]])
    dense_labels = np.array([[3], [2]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Class 3: 1 label, 2 predictions, 1 correct.
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=1, expected=1.0 / 2, class_id=3)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=1.0 / 2, class_id=3)

      # All classes: 2 labels, 2 predictions, 1 correct.
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=1, expected=1.0 / 2)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=1.0 / 2)

  def test_three_labels_at_k5_no_predictions(self):
    predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                   [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
    top_k_predictions = [
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ]
    sparse_labels = _binary_2d_label_to_sparse_value(
        [[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]])
    dense_labels = np.array([[2, 7, 8], [1, 2, 5]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Classes 1,3,8 have 0 predictions, classes -1 and 10 are out of range.
      for class_id in (-1, 1, 3, 8, 10):
        self._test_streaming_sparse_precision_at_k(
            predictions, labels, k=5, expected=NAN, class_id=class_id)
        self._test_streaming_sparse_precision_at_top_k(
            top_k_predictions, labels, expected=NAN, class_id=class_id)

  def test_three_labels_at_k5_no_labels(self):
    predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                   [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
    top_k_predictions = [
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ]
    sparse_labels = _binary_2d_label_to_sparse_value(
        [[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]])
    dense_labels = np.array([[2, 7, 8], [1, 2, 5]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Classes 0,4,6,9: 0 labels, >=1 prediction.
      for class_id in (0, 4, 6, 9):
        self._test_streaming_sparse_precision_at_k(
            predictions, labels, k=5, expected=0.0, class_id=class_id)
        self._test_streaming_sparse_precision_at_top_k(
            top_k_predictions, labels, expected=0.0, class_id=class_id)

  def test_three_labels_at_k5(self):
    predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                   [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
    top_k_predictions = [
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ]
    sparse_labels = _binary_2d_label_to_sparse_value(
        [[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]])
    dense_labels = np.array([[2, 7, 8], [1, 2, 5]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Class 2: 2 labels, 2 correct predictions.
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=5, expected=2.0 / 2, class_id=2)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=2.0 / 2, class_id=2)

      # Class 5: 1 label, 1 correct prediction.
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=5, expected=1.0 / 1, class_id=5)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=1.0 / 1, class_id=5)

      # Class 7: 1 label, 1 incorrect prediction.
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=5, expected=0.0 / 1, class_id=7)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=0.0 / 1, class_id=7)

      # All classes: 10 predictions, 3 correct.
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=5, expected=3.0 / 10)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=3.0 / 10)

  def test_three_labels_at_k5_some_out_of_range(self):
    """Tests that labels outside the [0, n_classes) range are ignored."""
    predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                   [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
    top_k_predictions = [
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ]
    sp_labels = sparse_tensor.SparseTensorValue(
        indices=[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 1], [1, 2], [1,
                                                                          3]],
        # values -1 and 10 are outside the [0, n_classes) range and are ignored.
        values=np.array([2, 7, -1, 8, 1, 2, 5, 10], np.int64),
        dense_shape=[2, 4])

    # Class 2: 2 labels, 2 correct predictions.
    self._test_streaming_sparse_precision_at_k(
        predictions, sp_labels, k=5, expected=2.0 / 2, class_id=2)
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, sp_labels, expected=2.0 / 2, class_id=2)

    # Class 5: 1 label, 1 correct prediction.
    self._test_streaming_sparse_precision_at_k(
        predictions, sp_labels, k=5, expected=1.0 / 1, class_id=5)
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, sp_labels, expected=1.0 / 1, class_id=5)

    # Class 7: 1 label, 1 incorrect prediction.
    self._test_streaming_sparse_precision_at_k(
        predictions, sp_labels, k=5, expected=0.0 / 1, class_id=7)
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, sp_labels, expected=0.0 / 1, class_id=7)

    # All classes: 10 predictions, 3 correct.
    self._test_streaming_sparse_precision_at_k(
        predictions, sp_labels, k=5, expected=3.0 / 10)
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, sp_labels, expected=3.0 / 10)

  def test_3d_nan(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    # Classes 1,3,8 have 0 predictions, classes -1 and 10 are out of range.
    for class_id in (-1, 1, 3, 8, 10):
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=5, expected=NAN, class_id=class_id)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=NAN, class_id=class_id)

  def test_3d_no_labels(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    # Classes 0,4,6,9: 0 labels, >=1 prediction.
    for class_id in (0, 4, 6, 9):
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=5, expected=0.0, class_id=class_id)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=0.0, class_id=class_id)

  def test_3d(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    # Class 2: 4 predictions, all correct.
    self._test_streaming_sparse_precision_at_k(
        predictions, labels, k=5, expected=4.0 / 4, class_id=2)
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, labels, expected=4.0 / 4, class_id=2)

    # Class 5: 2 predictions, both correct.
    self._test_streaming_sparse_precision_at_k(
        predictions, labels, k=5, expected=2.0 / 2, class_id=5)
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, labels, expected=2.0 / 2, class_id=5)

    # Class 7: 2 predictions, 1 correct.
    self._test_streaming_sparse_precision_at_k(
        predictions, labels, k=5, expected=1.0 / 2, class_id=7)
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, labels, expected=1.0 / 2, class_id=7)

    # All classes: 20 predictions, 7 correct.
    self._test_streaming_sparse_precision_at_k(
        predictions, labels, k=5, expected=7.0 / 20)
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, labels, expected=7.0 / 20)

  def test_3d_ignore_all(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    for class_id in xrange(10):
      self._test_streaming_sparse_precision_at_k(
          predictions,
          labels,
          k=5,
          expected=NAN,
          class_id=class_id,
          weights=[[0], [0]])
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions,
          labels,
          expected=NAN,
          class_id=class_id,
          weights=[[0], [0]])
      self._test_streaming_sparse_precision_at_k(
          predictions,
          labels,
          k=5,
          expected=NAN,
          class_id=class_id,
          weights=[[0, 0], [0, 0]])
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions,
          labels,
          expected=NAN,
          class_id=class_id,
          weights=[[0, 0], [0, 0]])
    self._test_streaming_sparse_precision_at_k(
        predictions, labels, k=5, expected=NAN, weights=[[0], [0]])
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, labels, expected=NAN, weights=[[0], [0]])
    self._test_streaming_sparse_precision_at_k(
        predictions, labels, k=5, expected=NAN, weights=[[0, 0], [0, 0]])
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions, labels, expected=NAN, weights=[[0, 0], [0, 0]])

  def test_3d_ignore_some(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    # Class 2: 2 predictions, both correct.
    self._test_streaming_sparse_precision_at_k(
        predictions,
        labels,
        k=5,
        expected=2.0 / 2.0,
        class_id=2,
        weights=[[1], [0]])
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions,
        labels,
        expected=2.0 / 2.0,
        class_id=2,
        weights=[[1], [0]])

    # Class 2: 2 predictions, both correct.
    self._test_streaming_sparse_precision_at_k(
        predictions,
        labels,
        k=5,
        expected=2.0 / 2.0,
        class_id=2,
        weights=[[0], [1]])
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions,
        labels,
        expected=2.0 / 2.0,
        class_id=2,
        weights=[[0], [1]])

    # Class 7: 1 incorrect prediction.
    self._test_streaming_sparse_precision_at_k(
        predictions,
        labels,
        k=5,
        expected=0.0 / 1.0,
        class_id=7,
        weights=[[1], [0]])
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions,
        labels,
        expected=0.0 / 1.0,
        class_id=7,
        weights=[[1], [0]])

    # Class 7: 1 correct prediction.
    self._test_streaming_sparse_precision_at_k(
        predictions,
        labels,
        k=5,
        expected=1.0 / 1.0,
        class_id=7,
        weights=[[0], [1]])
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions,
        labels,
        expected=1.0 / 1.0,
        class_id=7,
        weights=[[0], [1]])

    # Class 7: no predictions.
    self._test_streaming_sparse_precision_at_k(
        predictions,
        labels,
        k=5,
        expected=NAN,
        class_id=7,
        weights=[[1, 0], [0, 1]])
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions,
        labels,
        expected=NAN,
        class_id=7,
        weights=[[1, 0], [0, 1]])

    # Class 7: 2 predictions, 1 correct.
    self._test_streaming_sparse_precision_at_k(
        predictions,
        labels,
        k=5,
        expected=1.0 / 2.0,
        class_id=7,
        weights=[[0, 1], [1, 0]])
    self._test_streaming_sparse_precision_at_top_k(
        top_k_predictions,
        labels,
        expected=1.0 / 2.0,
        class_id=7,
        weights=[[0, 1], [1, 0]])

  def test_sparse_tensor_value(self):
    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
    labels = [[0, 0, 0, 1], [0, 0, 1, 0]]
    expected_precision = 0.5
    with self.test_session():
      _, precision = metrics.streaming_sparse_precision_at_k(
          predictions=constant_op.constant(predictions, dtypes_lib.float32),
          labels=_binary_2d_label_to_sparse_value(labels),
          k=1)

      variables.variables_initializer(variables.local_variables()).run()

      self.assertEqual(expected_precision, precision.eval())


class StreamingSparseRecallTest(test.TestCase):

  def _test_streaming_sparse_recall_at_k(self,
                                         predictions,
                                         labels,
                                         k,
                                         expected,
                                         class_id=None,
                                         weights=None):
    with ops.Graph().as_default() as g, self.test_session(g):
      if weights is not None:
        weights = constant_op.constant(weights, dtypes_lib.float32)
      metric, update = metrics.streaming_sparse_recall_at_k(
          predictions=constant_op.constant(predictions, dtypes_lib.float32),
          labels=labels,
          k=k,
          class_id=class_id,
          weights=weights)

      # Fails without initialized vars.
      self.assertRaises(errors_impl.OpError, metric.eval)
      self.assertRaises(errors_impl.OpError, update.eval)
      variables.variables_initializer(variables.local_variables()).run()

      # Run per-step op and assert expected values.
      if math.isnan(expected):
        _assert_nan(self, update.eval())
        _assert_nan(self, metric.eval())
      else:
        self.assertEqual(expected, update.eval())
        self.assertEqual(expected, metric.eval())

  def _test_sparse_recall_at_top_k(self,
                                   labels,
                                   top_k_predictions,
                                   expected,
                                   class_id=None,
                                   weights=None):
    with ops.Graph().as_default() as g, self.test_session(g):
      if weights is not None:
        weights = constant_op.constant(weights, dtypes_lib.float32)
      metric, update = metric_ops.sparse_recall_at_top_k(
          labels=labels,
          top_k_predictions=constant_op.constant(top_k_predictions,
                                                 dtypes_lib.int32),
          class_id=class_id,
          weights=weights)

      # Fails without initialized vars.
      self.assertRaises(errors_impl.OpError, metric.eval)
      self.assertRaises(errors_impl.OpError, update.eval)
      variables.variables_initializer(variables.local_variables()).run()

      # Run per-step op and assert expected values.
      if math.isnan(expected):
        self.assertTrue(math.isnan(update.eval()))
        self.assertTrue(math.isnan(metric.eval()))
      else:
        self.assertEqual(expected, update.eval())
        self.assertEqual(expected, metric.eval())

  def test_one_label_at_k1_nan(self):
    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
    top_k_predictions = [[3], [3]]
    sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1],
                                                      [0, 0, 1, 0]])
    dense_labels = np.array([[3], [2]], dtype=np.int64)

    # Classes 0,1 have 0 labels, 0 predictions, classes -1 and 4 are out of
    # range.
    for labels in (sparse_labels, dense_labels):
      for class_id in (-1, 0, 1, 4):
        self._test_streaming_sparse_recall_at_k(
            predictions, labels, k=1, expected=NAN, class_id=class_id)
        self._test_sparse_recall_at_top_k(
            labels, top_k_predictions, expected=NAN, class_id=class_id)

  def test_one_label_at_k1_no_predictions(self):
    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
    top_k_predictions = [[3], [3]]
    sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1],
                                                      [0, 0, 1, 0]])
    dense_labels = np.array([[3], [2]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Class 2: 0 predictions.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=0.0, class_id=2)
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=0.0, class_id=2)

  def test_one_label_at_k1(self):
    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
    top_k_predictions = [[3], [3]]
    sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1],
                                                      [0, 0, 1, 0]])
    dense_labels = np.array([[3], [2]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Class 3: 1 label, 2 predictions, 1 correct.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=1.0 / 1, class_id=3)
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=1.0 / 1, class_id=3)

      # All classes: 2 labels, 2 predictions, 1 correct.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=1.0 / 2)
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=1.0 / 2)

  def test_one_label_at_k1_weighted(self):
    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
    top_k_predictions = [[3], [3]]
    sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1],
                                                      [0, 0, 1, 0]])
    dense_labels = np.array([[3], [2]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Class 3: 1 label, 2 predictions, 1 correct.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=NAN, class_id=3, weights=(0.0,))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=NAN, class_id=3, weights=(0.0,))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=1.0 / 1,
          class_id=3,
          weights=(1.0,))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=1.0 / 1,
          class_id=3,
          weights=(1.0,))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=1.0 / 1,
          class_id=3,
          weights=(2.0,))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=1.0 / 1,
          class_id=3,
          weights=(2.0,))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=NAN,
          class_id=3,
          weights=(0.0, 0.0))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=NAN,
          class_id=3,
          weights=(0.0, 0.0))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=NAN,
          class_id=3,
          weights=(0.0, 1.0))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=NAN,
          class_id=3,
          weights=(0.0, 1.0))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=1.0 / 1,
          class_id=3,
          weights=(1.0, 0.0))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=1.0 / 1,
          class_id=3,
          weights=(1.0, 0.0))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=1.0 / 1,
          class_id=3,
          weights=(1.0, 1.0))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=1.0 / 1,
          class_id=3,
          weights=(1.0, 1.0))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=2.0 / 2,
          class_id=3,
          weights=(2.0, 3.0))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=2.0 / 2,
          class_id=3,
          weights=(2.0, 3.0))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=3.0 / 3,
          class_id=3,
          weights=(3.0, 2.0))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=3.0 / 3,
          class_id=3,
          weights=(3.0, 2.0))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=0.3 / 0.3,
          class_id=3,
          weights=(0.3, 0.6))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=0.3 / 0.3,
          class_id=3,
          weights=(0.3, 0.6))
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=1,
          expected=0.6 / 0.6,
          class_id=3,
          weights=(0.6, 0.3))
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=0.6 / 0.6,
          class_id=3,
          weights=(0.6, 0.3))

      # All classes: 2 labels, 2 predictions, 1 correct.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=NAN, weights=(0.0,))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=NAN, weights=(0.0,))
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=1.0 / 2, weights=(1.0,))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=1.0 / 2, weights=(1.0,))

      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=1.0 / 2, weights=(2.0,))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=1.0 / 2, weights=(2.0,))

      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=1.0 / 1, weights=(1.0, 0.0))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=1.0 / 1, weights=(1.0, 0.0))

      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=0.0 / 1, weights=(0.0, 1.0))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=0.0 / 1, weights=(0.0, 1.0))

      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=1.0 / 2, weights=(1.0, 1.0))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=1.0 / 2, weights=(1.0, 1.0))

      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=2.0 / 5, weights=(2.0, 3.0))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=2.0 / 5, weights=(2.0, 3.0))

      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=3.0 / 5, weights=(3.0, 2.0))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=3.0 / 5, weights=(3.0, 2.0))

      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=0.3 / 0.9, weights=(0.3, 0.6))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=0.3 / 0.9, weights=(0.3, 0.6))

      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=1, expected=0.6 / 0.9, weights=(0.6, 0.3))
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=0.6 / 0.9, weights=(0.6, 0.3))

  def test_three_labels_at_k5_nan(self):
    predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                   [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
    top_k_predictions = [
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ]
    sparse_labels = _binary_2d_label_to_sparse_value(
        [[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]])
    dense_labels = np.array([[2, 7, 8], [1, 2, 5]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Classes 0,3,4,6,9 have 0 labels, class 10 is out of range.
      for class_id in (0, 3, 4, 6, 9, 10):
        self._test_streaming_sparse_recall_at_k(
            predictions, labels, k=5, expected=NAN, class_id=class_id)
        self._test_sparse_recall_at_top_k(
            labels, top_k_predictions, expected=NAN, class_id=class_id)

  def test_three_labels_at_k5_no_predictions(self):
    predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                   [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
    top_k_predictions = [
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ]
    sparse_labels = _binary_2d_label_to_sparse_value(
        [[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]])
    dense_labels = np.array([[2, 7, 8], [1, 2, 5]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Class 8: 1 label, no predictions.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=5, expected=0.0 / 1, class_id=8)
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=0.0 / 1, class_id=8)

  def test_three_labels_at_k5(self):
    predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                   [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
    top_k_predictions = [
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ]
    sparse_labels = _binary_2d_label_to_sparse_value(
        [[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]])
    dense_labels = np.array([[2, 7, 8], [1, 2, 5]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Class 2: 2 labels, both correct.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=5, expected=2.0 / 2, class_id=2)
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=2.0 / 2, class_id=2)

      # Class 5: 1 label, incorrect.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=5, expected=1.0 / 1, class_id=5)
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=1.0 / 1, class_id=5)

      # Class 7: 1 label, incorrect.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=5, expected=0.0 / 1, class_id=7)
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=0.0 / 1, class_id=7)

      # All classes: 6 labels, 3 correct.
      self._test_streaming_sparse_recall_at_k(
          predictions, labels, k=5, expected=3.0 / 6)
      self._test_sparse_recall_at_top_k(
          labels, top_k_predictions, expected=3.0 / 6)

  def test_three_labels_at_k5_some_out_of_range(self):
    """Tests that labels outside the [0, n_classes) count in denominator."""
    predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                   [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
    top_k_predictions = [
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ]
    sp_labels = sparse_tensor.SparseTensorValue(
        indices=[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 1], [1, 2], [1,
                                                                          3]],
        # values -1 and 10 are outside the [0, n_classes) range.
        values=np.array([2, 7, -1, 8, 1, 2, 5, 10], np.int64),
        dense_shape=[2, 4])

    # Class 2: 2 labels, both correct.
    self._test_streaming_sparse_recall_at_k(
        predictions=predictions,
        labels=sp_labels,
        k=5,
        expected=2.0 / 2,
        class_id=2)
    self._test_sparse_recall_at_top_k(
        sp_labels, top_k_predictions, expected=2.0 / 2, class_id=2)

    # Class 5: 1 label, incorrect.
    self._test_streaming_sparse_recall_at_k(
        predictions=predictions,
        labels=sp_labels,
        k=5,
        expected=1.0 / 1,
        class_id=5)
    self._test_sparse_recall_at_top_k(
        sp_labels, top_k_predictions, expected=1.0 / 1, class_id=5)

    # Class 7: 1 label, incorrect.
    self._test_streaming_sparse_recall_at_k(
        predictions=predictions,
        labels=sp_labels,
        k=5,
        expected=0.0 / 1,
        class_id=7)
    self._test_sparse_recall_at_top_k(
        sp_labels, top_k_predictions, expected=0.0 / 1, class_id=7)

    # All classes: 8 labels, 3 correct.
    self._test_streaming_sparse_recall_at_k(
        predictions=predictions, labels=sp_labels, k=5, expected=3.0 / 8)
    self._test_sparse_recall_at_top_k(
        sp_labels, top_k_predictions, expected=3.0 / 8)

  def test_3d_nan(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    sparse_labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 1, 0]]])
    dense_labels = np.array(
        [[[2, 7, 8], [1, 2, 5]], [
            [1, 2, 5],
            [2, 7, 8],
        ]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Classes 0,3,4,6,9 have 0 labels, class 10 is out of range.
      for class_id in (0, 3, 4, 6, 9, 10):
        self._test_streaming_sparse_recall_at_k(
            predictions, labels, k=5, expected=NAN, class_id=class_id)
        self._test_sparse_recall_at_top_k(
            labels, top_k_predictions, expected=NAN, class_id=class_id)

  def test_3d_no_predictions(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    sparse_labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 1, 0]]])
    dense_labels = np.array(
        [[[2, 7, 8], [1, 2, 5]], [
            [1, 2, 5],
            [2, 7, 8],
        ]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Classes 1,8 have 0 predictions, >=1 label.
      for class_id in (1, 8):
        self._test_streaming_sparse_recall_at_k(
            predictions, labels, k=5, expected=0.0, class_id=class_id)
        self._test_sparse_recall_at_top_k(
            labels, top_k_predictions, expected=0.0, class_id=class_id)

  def test_3d(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    # Class 2: 4 labels, all correct.
    self._test_streaming_sparse_recall_at_k(
        predictions, labels, k=5, expected=4.0 / 4, class_id=2)
    self._test_sparse_recall_at_top_k(
        labels, top_k_predictions, expected=4.0 / 4, class_id=2)

    # Class 5: 2 labels, both correct.
    self._test_streaming_sparse_recall_at_k(
        predictions, labels, k=5, expected=2.0 / 2, class_id=5)
    self._test_sparse_recall_at_top_k(
        labels, top_k_predictions, expected=2.0 / 2, class_id=5)

    # Class 7: 2 labels, 1 incorrect.
    self._test_streaming_sparse_recall_at_k(
        predictions, labels, k=5, expected=1.0 / 2, class_id=7)
    self._test_sparse_recall_at_top_k(
        labels, top_k_predictions, expected=1.0 / 2, class_id=7)

    # All classes: 12 labels, 7 correct.
    self._test_streaming_sparse_recall_at_k(
        predictions, labels, k=5, expected=7.0 / 12)
    self._test_sparse_recall_at_top_k(
        labels, top_k_predictions, expected=7.0 / 12)

  def test_3d_ignore_all(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    for class_id in xrange(10):
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=5,
          expected=NAN,
          class_id=class_id,
          weights=[[0], [0]])
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=NAN,
          class_id=class_id,
          weights=[[0], [0]])
      self._test_streaming_sparse_recall_at_k(
          predictions,
          labels,
          k=5,
          expected=NAN,
          class_id=class_id,
          weights=[[0, 0], [0, 0]])
      self._test_sparse_recall_at_top_k(
          labels,
          top_k_predictions,
          expected=NAN,
          class_id=class_id,
          weights=[[0, 0], [0, 0]])
    self._test_streaming_sparse_recall_at_k(
        predictions, labels, k=5, expected=NAN, weights=[[0], [0]])
    self._test_sparse_recall_at_top_k(
        labels, top_k_predictions, expected=NAN, weights=[[0], [0]])
    self._test_streaming_sparse_recall_at_k(
        predictions, labels, k=5, expected=NAN, weights=[[0, 0], [0, 0]])
    self._test_sparse_recall_at_top_k(
        labels, top_k_predictions, expected=NAN, weights=[[0, 0], [0, 0]])

  def test_3d_ignore_some(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    # Class 2: 2 labels, both correct.
    self._test_streaming_sparse_recall_at_k(
        predictions,
        labels,
        k=5,
        expected=2.0 / 2.0,
        class_id=2,
        weights=[[1], [0]])
    self._test_sparse_recall_at_top_k(
        labels,
        top_k_predictions,
        expected=2.0 / 2.0,
        class_id=2,
        weights=[[1], [0]])

    # Class 2: 2 labels, both correct.
    self._test_streaming_sparse_recall_at_k(
        predictions,
        labels,
        k=5,
        expected=2.0 / 2.0,
        class_id=2,
        weights=[[0], [1]])
    self._test_sparse_recall_at_top_k(
        labels,
        top_k_predictions,
        expected=2.0 / 2.0,
        class_id=2,
        weights=[[0], [1]])

    # Class 7: 1 label, correct.
    self._test_streaming_sparse_recall_at_k(
        predictions,
        labels,
        k=5,
        expected=1.0 / 1.0,
        class_id=7,
        weights=[[0], [1]])
    self._test_sparse_recall_at_top_k(
        labels,
        top_k_predictions,
        expected=1.0 / 1.0,
        class_id=7,
        weights=[[0], [1]])

    # Class 7: 1 label, incorrect.
    self._test_streaming_sparse_recall_at_k(
        predictions,
        labels,
        k=5,
        expected=0.0 / 1.0,
        class_id=7,
        weights=[[1], [0]])
    self._test_sparse_recall_at_top_k(
        labels,
        top_k_predictions,
        expected=0.0 / 1.0,
        class_id=7,
        weights=[[1], [0]])

    # Class 7: 2 labels, 1 correct.
    self._test_streaming_sparse_recall_at_k(
        predictions,
        labels,
        k=5,
        expected=1.0 / 2.0,
        class_id=7,
        weights=[[1, 0], [1, 0]])
    self._test_sparse_recall_at_top_k(
        labels,
        top_k_predictions,
        expected=1.0 / 2.0,
        class_id=7,
        weights=[[1, 0], [1, 0]])

    # Class 7: No labels.
    self._test_streaming_sparse_recall_at_k(
        predictions,
        labels,
        k=5,
        expected=NAN,
        class_id=7,
        weights=[[0, 1], [0, 1]])
    self._test_sparse_recall_at_top_k(
        labels,
        top_k_predictions,
        expected=NAN,
        class_id=7,
        weights=[[0, 1], [0, 1]])

  def test_sparse_tensor_value(self):
    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
    labels = [[0, 0, 1, 0], [0, 0, 0, 1]]
    expected_recall = 0.5
    with self.test_session():
      _, recall = metrics.streaming_sparse_recall_at_k(
          predictions=constant_op.constant(predictions, dtypes_lib.float32),
          labels=_binary_2d_label_to_sparse_value(labels),
          k=1)

      variables.variables_initializer(variables.local_variables()).run()

      self.assertEqual(expected_recall, recall.eval())


class StreamingMeanAbsoluteErrorTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_mean_absolute_error(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(
        self, ('mean_absolute_error/count:0', 'mean_absolute_error/total:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_mean_absolute_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_mean_absolute_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_normal((10, 3), seed=1)
    labels = random_ops.random_normal((10, 3), seed=2)
    error, update_op = metrics.streaming_mean_absolute_error(
        predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_error = error.eval()
      for _ in range(10):
        self.assertEqual(initial_error, error.eval())

  def testSingleUpdateWithErrorAndWeights(self):
    predictions = constant_op.constant(
        [2, 4, 6, 8], shape=(1, 4), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        [1, 3, 2, 3], shape=(1, 4), dtype=dtypes_lib.float32)
    weights = constant_op.constant([0, 1, 0, 1], shape=(1, 4))

    error, update_op = metrics.streaming_mean_absolute_error(
        predictions, labels, weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(3, sess.run(update_op))
      self.assertEqual(3, error.eval())


class StreamingMeanRelativeErrorTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_mean_relative_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        normalizer=array_ops.ones((10, 1)))
    _assert_metric_variables(
        self, ('mean_relative_error/count:0', 'mean_relative_error/total:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_mean_relative_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        normalizer=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_mean_relative_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        normalizer=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_normal((10, 3), seed=1)
    labels = random_ops.random_normal((10, 3), seed=2)
    normalizer = random_ops.random_normal((10, 3), seed=3)
    error, update_op = metrics.streaming_mean_relative_error(
        predictions, labels, normalizer)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_error = error.eval()
      for _ in range(10):
        self.assertEqual(initial_error, error.eval())

  def testSingleUpdateNormalizedByLabels(self):
    np_predictions = np.asarray([2, 4, 6, 8], dtype=np.float32)
    np_labels = np.asarray([1, 3, 2, 3], dtype=np.float32)
    expected_error = np.mean(
        np.divide(np.absolute(np_predictions - np_labels), np_labels))

    predictions = constant_op.constant(
        np_predictions, shape=(1, 4), dtype=dtypes_lib.float32)
    labels = constant_op.constant(np_labels, shape=(1, 4))

    error, update_op = metrics.streaming_mean_relative_error(
        predictions, labels, normalizer=labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(expected_error, sess.run(update_op))
      self.assertEqual(expected_error, error.eval())

  def testSingleUpdateNormalizedByZeros(self):
    np_predictions = np.asarray([2, 4, 6, 8], dtype=np.float32)

    predictions = constant_op.constant(
        np_predictions, shape=(1, 4), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        [1, 3, 2, 3], shape=(1, 4), dtype=dtypes_lib.float32)

    error, update_op = metrics.streaming_mean_relative_error(
        predictions, labels, normalizer=array_ops.zeros_like(labels))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(0.0, sess.run(update_op))
      self.assertEqual(0.0, error.eval())


class StreamingMeanSquaredErrorTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_mean_squared_error(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(
        self, ('mean_squared_error/count:0', 'mean_squared_error/total:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_mean_squared_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_mean_squared_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_normal((10, 3), seed=1)
    labels = random_ops.random_normal((10, 3), seed=2)
    error, update_op = metrics.streaming_mean_squared_error(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_error = error.eval()
      for _ in range(10):
        self.assertEqual(initial_error, error.eval())

  def testSingleUpdateZeroError(self):
    predictions = array_ops.zeros((1, 3), dtype=dtypes_lib.float32)
    labels = array_ops.zeros((1, 3), dtype=dtypes_lib.float32)

    error, update_op = metrics.streaming_mean_squared_error(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(0, sess.run(update_op))
      self.assertEqual(0, error.eval())

  def testSingleUpdateWithError(self):
    predictions = constant_op.constant(
        [2, 4, 6], shape=(1, 3), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        [1, 3, 2], shape=(1, 3), dtype=dtypes_lib.float32)

    error, update_op = metrics.streaming_mean_squared_error(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(6, sess.run(update_op))
      self.assertEqual(6, error.eval())

  def testSingleUpdateWithErrorAndWeights(self):
    predictions = constant_op.constant(
        [2, 4, 6, 8], shape=(1, 4), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        [1, 3, 2, 3], shape=(1, 4), dtype=dtypes_lib.float32)
    weights = constant_op.constant([0, 1, 0, 1], shape=(1, 4))

    error, update_op = metrics.streaming_mean_squared_error(
        predictions, labels, weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(13, sess.run(update_op))
      self.assertEqual(13, error.eval())

  def testMultipleBatchesOfSizeOne(self):
    with self.test_session() as sess:
      # Create the queue that populates the predictions.
      preds_queue = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(1, 3))
      _enqueue_vector(sess, preds_queue, [10, 8, 6])
      _enqueue_vector(sess, preds_queue, [-4, 3, -1])
      predictions = preds_queue.dequeue()

      # Create the queue that populates the labels.
      labels_queue = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(1, 3))
      _enqueue_vector(sess, labels_queue, [1, 3, 2])
      _enqueue_vector(sess, labels_queue, [2, 4, 6])
      labels = labels_queue.dequeue()

      error, update_op = metrics.streaming_mean_squared_error(
          predictions, labels)

      sess.run(variables.local_variables_initializer())
      sess.run(update_op)
      self.assertAlmostEqual(208.0 / 6, sess.run(update_op), 5)

      self.assertAlmostEqual(208.0 / 6, error.eval(), 5)

  def testMetricsComputedConcurrently(self):
    with self.test_session() as sess:
      # Create the queue that populates one set of predictions.
      preds_queue0 = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(1, 3))
      _enqueue_vector(sess, preds_queue0, [10, 8, 6])
      _enqueue_vector(sess, preds_queue0, [-4, 3, -1])
      predictions0 = preds_queue0.dequeue()

      # Create the queue that populates one set of predictions.
      preds_queue1 = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(1, 3))
      _enqueue_vector(sess, preds_queue1, [0, 1, 1])
      _enqueue_vector(sess, preds_queue1, [1, 1, 0])
      predictions1 = preds_queue1.dequeue()

      # Create the queue that populates one set of labels.
      labels_queue0 = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(1, 3))
      _enqueue_vector(sess, labels_queue0, [1, 3, 2])
      _enqueue_vector(sess, labels_queue0, [2, 4, 6])
      labels0 = labels_queue0.dequeue()

      # Create the queue that populates another set of labels.
      labels_queue1 = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(1, 3))
      _enqueue_vector(sess, labels_queue1, [-5, -3, -1])
      _enqueue_vector(sess, labels_queue1, [5, 4, 3])
      labels1 = labels_queue1.dequeue()

      mse0, update_op0 = metrics.streaming_mean_squared_error(
          predictions0, labels0, name='msd0')
      mse1, update_op1 = metrics.streaming_mean_squared_error(
          predictions1, labels1, name='msd1')

      sess.run(variables.local_variables_initializer())
      sess.run([update_op0, update_op1])
      sess.run([update_op0, update_op1])

      mse0, mse1 = sess.run([mse0, mse1])
      self.assertAlmostEqual(208.0 / 6, mse0, 5)
      self.assertAlmostEqual(79.0 / 6, mse1, 5)

  def testMultipleMetricsOnMultipleBatchesOfSizeOne(self):
    with self.test_session() as sess:
      # Create the queue that populates the predictions.
      preds_queue = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(1, 3))
      _enqueue_vector(sess, preds_queue, [10, 8, 6])
      _enqueue_vector(sess, preds_queue, [-4, 3, -1])
      predictions = preds_queue.dequeue()

      # Create the queue that populates the labels.
      labels_queue = data_flow_ops.FIFOQueue(
          2, dtypes=dtypes_lib.float32, shapes=(1, 3))
      _enqueue_vector(sess, labels_queue, [1, 3, 2])
      _enqueue_vector(sess, labels_queue, [2, 4, 6])
      labels = labels_queue.dequeue()

      mae, ma_update_op = metrics.streaming_mean_absolute_error(
          predictions, labels)
      mse, ms_update_op = metrics.streaming_mean_squared_error(
          predictions, labels)

      sess.run(variables.local_variables_initializer())
      sess.run([ma_update_op, ms_update_op])
      sess.run([ma_update_op, ms_update_op])

      self.assertAlmostEqual(32.0 / 6, mae.eval(), 5)
      self.assertAlmostEqual(208.0 / 6, mse.eval(), 5)


class StreamingRootMeanSquaredErrorTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_root_mean_squared_error(
        predictions=array_ops.ones((10, 1)), labels=array_ops.ones((10, 1)))
    _assert_metric_variables(
        self,
        ('root_mean_squared_error/count:0', 'root_mean_squared_error/total:0'))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_root_mean_squared_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_root_mean_squared_error(
        predictions=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_normal((10, 3), seed=1)
    labels = random_ops.random_normal((10, 3), seed=2)
    error, update_op = metrics.streaming_root_mean_squared_error(
        predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_error = error.eval()
      for _ in range(10):
        self.assertEqual(initial_error, error.eval())

  def testSingleUpdateZeroError(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          0.0, shape=(1, 3), dtype=dtypes_lib.float32)
      labels = constant_op.constant(0.0, shape=(1, 3), dtype=dtypes_lib.float32)

      rmse, update_op = metrics.streaming_root_mean_squared_error(
          predictions, labels)

      sess.run(variables.local_variables_initializer())
      self.assertEqual(0, sess.run(update_op))

      self.assertEqual(0, rmse.eval())

  def testSingleUpdateWithError(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [2, 4, 6], shape=(1, 3), dtype=dtypes_lib.float32)
      labels = constant_op.constant(
          [1, 3, 2], shape=(1, 3), dtype=dtypes_lib.float32)

      rmse, update_op = metrics.streaming_root_mean_squared_error(
          predictions, labels)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(math.sqrt(6), update_op.eval(), 5)
      self.assertAlmostEqual(math.sqrt(6), rmse.eval(), 5)

  def testSingleUpdateWithErrorAndWeights(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [2, 4, 6, 8], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant(
          [1, 3, 2, 3], shape=(1, 4), dtype=dtypes_lib.float32)
      weights = constant_op.constant([0, 1, 0, 1], shape=(1, 4))

      rmse, update_op = metrics.streaming_root_mean_squared_error(
          predictions, labels, weights)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(math.sqrt(13), sess.run(update_op))

      self.assertAlmostEqual(math.sqrt(13), rmse.eval(), 5)


class StreamingCovarianceTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_covariance(
        predictions=math_ops.to_float(math_ops.range(10)) +
        array_ops.ones([10, 10]),
        labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]))
    _assert_metric_variables(self, (
        'covariance/comoment:0',
        'covariance/count:0',
        'covariance/mean_label:0',
        'covariance/mean_prediction:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    cov, _ = metrics.streaming_covariance(
        predictions=math_ops.to_float(math_ops.range(10)) +
        array_ops.ones([10, 10]),
        labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [cov])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_covariance(
        predictions=math_ops.to_float(math_ops.range(10)) +
        array_ops.ones([10, 10]),
        labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    labels = random_ops.random_normal((10, 3), seed=2)
    predictions = labels * 0.5 + random_ops.random_normal((10, 3), seed=1) * 0.5
    cov, update_op = metrics.streaming_covariance(predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_cov = cov.eval()
      for _ in range(10):
        self.assertEqual(initial_cov, cov.eval())

  def testSingleUpdateIdentical(self):
    with self.test_session() as sess:
      predictions = math_ops.to_float(math_ops.range(10))
      labels = math_ops.to_float(math_ops.range(10))

      cov, update_op = metrics.streaming_covariance(predictions, labels)

      expected_cov = np.cov(np.arange(10), np.arange(10))[0, 1]
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expected_cov, sess.run(update_op), 5)
      self.assertAlmostEqual(expected_cov, cov.eval(), 5)

  def testSingleUpdateNonIdentical(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [2, 4, 6], shape=(1, 3), dtype=dtypes_lib.float32)
      labels = constant_op.constant(
          [1, 3, 2], shape=(1, 3), dtype=dtypes_lib.float32)

      cov, update_op = metrics.streaming_covariance(predictions, labels)

      expected_cov = np.cov([2, 4, 6], [1, 3, 2])[0, 1]
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expected_cov, update_op.eval())
      self.assertAlmostEqual(expected_cov, cov.eval())

  def testSingleUpdateWithErrorAndWeights(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [2, 4, 6, 8], shape=(1, 4), dtype=dtypes_lib.float32)
      labels = constant_op.constant(
          [1, 3, 2, 7], shape=(1, 4), dtype=dtypes_lib.float32)
      weights = constant_op.constant(
          [0, 1, 3, 1], shape=(1, 4), dtype=dtypes_lib.float32)

      cov, update_op = metrics.streaming_covariance(
          predictions, labels, weights=weights)

      expected_cov = np.cov(
          [2, 4, 6, 8], [1, 3, 2, 7], fweights=[0, 1, 3, 1])[0, 1]
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expected_cov, sess.run(update_op))
      self.assertAlmostEqual(expected_cov, cov.eval())

  def testMultiUpdateWithErrorNoWeights(self):
    with self.test_session() as sess:
      np.random.seed(123)
      n = 100
      predictions = np.random.randn(n)
      labels = 0.5 * predictions + np.random.randn(n)

      stride = 10
      predictions_t = array_ops.placeholder(dtypes_lib.float32, [stride])
      labels_t = array_ops.placeholder(dtypes_lib.float32, [stride])

      cov, update_op = metrics.streaming_covariance(predictions_t, labels_t)

      sess.run(variables.local_variables_initializer())
      prev_expected_cov = NAN
      for i in range(n // stride):
        feed_dict = {
            predictions_t: predictions[stride * i:stride * (i + 1)],
            labels_t: labels[stride * i:stride * (i + 1)]
        }
        self.assertEqual(
            np.isnan(prev_expected_cov),
            np.isnan(sess.run(cov, feed_dict=feed_dict)))
        if not np.isnan(prev_expected_cov):
          self.assertAlmostEqual(prev_expected_cov,
                                 sess.run(cov, feed_dict=feed_dict), 5)
        expected_cov = np.cov(predictions[:stride * (i + 1)],
                              labels[:stride * (i + 1)])[0, 1]
        self.assertAlmostEqual(expected_cov,
                               sess.run(update_op, feed_dict=feed_dict), 5)
        self.assertAlmostEqual(expected_cov, sess.run(cov, feed_dict=feed_dict),
                               5)
        prev_expected_cov = expected_cov

  def testMultiUpdateWithErrorAndWeights(self):
    with self.test_session() as sess:
      np.random.seed(123)
      n = 100
      predictions = np.random.randn(n)
      labels = 0.5 * predictions + np.random.randn(n)
      weights = np.tile(np.arange(n // 10), n // 10)
      np.random.shuffle(weights)

      stride = 10
      predictions_t = array_ops.placeholder(dtypes_lib.float32, [stride])
      labels_t = array_ops.placeholder(dtypes_lib.float32, [stride])
      weights_t = array_ops.placeholder(dtypes_lib.float32, [stride])

      cov, update_op = metrics.streaming_covariance(
          predictions_t, labels_t, weights=weights_t)

      sess.run(variables.local_variables_initializer())
      prev_expected_cov = NAN
      for i in range(n // stride):
        feed_dict = {
            predictions_t: predictions[stride * i:stride * (i + 1)],
            labels_t: labels[stride * i:stride * (i + 1)],
            weights_t: weights[stride * i:stride * (i + 1)]
        }
        self.assertEqual(
            np.isnan(prev_expected_cov),
            np.isnan(sess.run(cov, feed_dict=feed_dict)))
        if not np.isnan(prev_expected_cov):
          self.assertAlmostEqual(prev_expected_cov,
                                 sess.run(cov, feed_dict=feed_dict), 5)
        expected_cov = np.cov(
            predictions[:stride * (i + 1)],
            labels[:stride * (i + 1)],
            fweights=weights[:stride * (i + 1)])[0, 1]
        self.assertAlmostEqual(expected_cov,
                               sess.run(update_op, feed_dict=feed_dict), 5)
        self.assertAlmostEqual(expected_cov, sess.run(cov, feed_dict=feed_dict),
                               5)
        prev_expected_cov = expected_cov


class StreamingPearsonRTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_pearson_correlation(
        predictions=math_ops.to_float(math_ops.range(10)) +
        array_ops.ones([10, 10]),
        labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]))
    _assert_metric_variables(self, (
        'pearson_r/covariance/comoment:0',
        'pearson_r/covariance/count:0',
        'pearson_r/covariance/mean_label:0',
        'pearson_r/covariance/mean_prediction:0',
        'pearson_r/variance_labels/count:0',
        'pearson_r/variance_labels/comoment:0',
        'pearson_r/variance_labels/mean_label:0',
        'pearson_r/variance_labels/mean_prediction:0',
        'pearson_r/variance_predictions/comoment:0',
        'pearson_r/variance_predictions/count:0',
        'pearson_r/variance_predictions/mean_label:0',
        'pearson_r/variance_predictions/mean_prediction:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    pearson_r, _ = metrics.streaming_pearson_correlation(
        predictions=math_ops.to_float(math_ops.range(10)) +
        array_ops.ones([10, 10]),
        labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]),
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [pearson_r])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_pearson_correlation(
        predictions=math_ops.to_float(math_ops.range(10)) +
        array_ops.ones([10, 10]),
        labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]),
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    labels = random_ops.random_normal((10, 3), seed=2)
    predictions = labels * 0.5 + random_ops.random_normal((10, 3), seed=1) * 0.5
    pearson_r, update_op = metrics.streaming_pearson_correlation(
        predictions, labels)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_r = pearson_r.eval()
      for _ in range(10):
        self.assertEqual(initial_r, pearson_r.eval())

  def testSingleUpdateIdentical(self):
    with self.test_session() as sess:
      predictions = math_ops.to_float(math_ops.range(10))
      labels = math_ops.to_float(math_ops.range(10))

      pearson_r, update_op = metrics.streaming_pearson_correlation(
          predictions, labels)

      expected_r = np.corrcoef(np.arange(10), np.arange(10))[0, 1]
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expected_r, sess.run(update_op), 5)
      self.assertAlmostEqual(expected_r, pearson_r.eval(), 5)

  def testSingleUpdateNonIdentical(self):
    with self.test_session() as sess:
      predictions = constant_op.constant(
          [2, 4, 6], shape=(1, 3), dtype=dtypes_lib.float32)
      labels = constant_op.constant(
          [1, 3, 2], shape=(1, 3), dtype=dtypes_lib.float32)

      pearson_r, update_op = metrics.streaming_pearson_correlation(
          predictions, labels)

      expected_r = np.corrcoef([2, 4, 6], [1, 3, 2])[0, 1]
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expected_r, update_op.eval())
      self.assertAlmostEqual(expected_r, pearson_r.eval())

  def testSingleUpdateWithErrorAndWeights(self):
    with self.test_session() as sess:
      predictions = np.array([2, 4, 6, 8])
      labels = np.array([1, 3, 2, 7])
      weights = np.array([0, 1, 3, 1])
      predictions_t = constant_op.constant(
          predictions, shape=(1, 4), dtype=dtypes_lib.float32)
      labels_t = constant_op.constant(
          labels, shape=(1, 4), dtype=dtypes_lib.float32)
      weights_t = constant_op.constant(
          weights, shape=(1, 4), dtype=dtypes_lib.float32)

      pearson_r, update_op = metrics.streaming_pearson_correlation(
          predictions_t, labels_t, weights=weights_t)

      cmat = np.cov(predictions, labels, fweights=weights)
      expected_r = cmat[0, 1] / np.sqrt(cmat[0, 0] * cmat[1, 1])
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expected_r, sess.run(update_op))
      self.assertAlmostEqual(expected_r, pearson_r.eval())

  def testMultiUpdateWithErrorNoWeights(self):
    with self.test_session() as sess:
      np.random.seed(123)
      n = 100
      predictions = np.random.randn(n)
      labels = 0.5 * predictions + np.random.randn(n)

      stride = 10
      predictions_t = array_ops.placeholder(dtypes_lib.float32, [stride])
      labels_t = array_ops.placeholder(dtypes_lib.float32, [stride])

      pearson_r, update_op = metrics.streaming_pearson_correlation(
          predictions_t, labels_t)

      sess.run(variables.local_variables_initializer())
      prev_expected_r = NAN
      for i in range(n // stride):
        feed_dict = {
            predictions_t: predictions[stride * i:stride * (i + 1)],
            labels_t: labels[stride * i:stride * (i + 1)]
        }
        self.assertEqual(
            np.isnan(prev_expected_r),
            np.isnan(sess.run(pearson_r, feed_dict=feed_dict)))
        if not np.isnan(prev_expected_r):
          self.assertAlmostEqual(prev_expected_r,
                                 sess.run(pearson_r, feed_dict=feed_dict), 5)
        expected_r = np.corrcoef(predictions[:stride * (i + 1)],
                                 labels[:stride * (i + 1)])[0, 1]
        self.assertAlmostEqual(expected_r,
                               sess.run(update_op, feed_dict=feed_dict), 5)
        self.assertAlmostEqual(expected_r,
                               sess.run(pearson_r, feed_dict=feed_dict), 5)
        prev_expected_r = expected_r

  def testMultiUpdateWithErrorAndWeights(self):
    with self.test_session() as sess:
      np.random.seed(123)
      n = 100
      predictions = np.random.randn(n)
      labels = 0.5 * predictions + np.random.randn(n)
      weights = np.tile(np.arange(n // 10), n // 10)
      np.random.shuffle(weights)

      stride = 10
      predictions_t = array_ops.placeholder(dtypes_lib.float32, [stride])
      labels_t = array_ops.placeholder(dtypes_lib.float32, [stride])
      weights_t = array_ops.placeholder(dtypes_lib.float32, [stride])

      pearson_r, update_op = metrics.streaming_pearson_correlation(
          predictions_t, labels_t, weights=weights_t)

      sess.run(variables.local_variables_initializer())
      prev_expected_r = NAN
      for i in range(n // stride):
        feed_dict = {
            predictions_t: predictions[stride * i:stride * (i + 1)],
            labels_t: labels[stride * i:stride * (i + 1)],
            weights_t: weights[stride * i:stride * (i + 1)]
        }
        self.assertEqual(
            np.isnan(prev_expected_r),
            np.isnan(sess.run(pearson_r, feed_dict=feed_dict)))
        if not np.isnan(prev_expected_r):
          self.assertAlmostEqual(prev_expected_r,
                                 sess.run(pearson_r, feed_dict=feed_dict), 5)
        cmat = np.cov(
            predictions[:stride * (i + 1)],
            labels[:stride * (i + 1)],
            fweights=weights[:stride * (i + 1)])
        expected_r = cmat[0, 1] / np.sqrt(cmat[0, 0] * cmat[1, 1])
        self.assertAlmostEqual(expected_r,
                               sess.run(update_op, feed_dict=feed_dict), 5)
        self.assertAlmostEqual(expected_r,
                               sess.run(pearson_r, feed_dict=feed_dict), 5)
        prev_expected_r = expected_r

  def testMultiUpdateWithErrorAndSingletonBatches(self):
    with self.test_session() as sess:
      np.random.seed(123)
      n = 100
      predictions = np.random.randn(n)
      labels = 0.5 * predictions + np.random.randn(n)
      stride = 10
      weights = (np.arange(n).reshape(n // stride, stride) % stride == 0)
      for row in weights:
        np.random.shuffle(row)
      # Now, weights is one-hot by row - one item per batch has non-zero weight.
      weights = weights.reshape((n,))

      predictions_t = array_ops.placeholder(dtypes_lib.float32, [stride])
      labels_t = array_ops.placeholder(dtypes_lib.float32, [stride])
      weights_t = array_ops.placeholder(dtypes_lib.float32, [stride])

      pearson_r, update_op = metrics.streaming_pearson_correlation(
          predictions_t, labels_t, weights=weights_t)

      sess.run(variables.local_variables_initializer())
      for i in range(n // stride):
        feed_dict = {
            predictions_t: predictions[stride * i:stride * (i + 1)],
            labels_t: labels[stride * i:stride * (i + 1)],
            weights_t: weights[stride * i:stride * (i + 1)]
        }
        cmat = np.cov(
            predictions[:stride * (i + 1)],
            labels[:stride * (i + 1)],
            fweights=weights[:stride * (i + 1)])
        expected_r = cmat[0, 1] / np.sqrt(cmat[0, 0] * cmat[1, 1])
        actual_r = sess.run(update_op, feed_dict=feed_dict)
        self.assertEqual(np.isnan(expected_r), np.isnan(actual_r))
        self.assertEqual(
            np.isnan(expected_r),
            np.isnan(sess.run(pearson_r, feed_dict=feed_dict)))
        if not np.isnan(expected_r):
          self.assertAlmostEqual(expected_r, actual_r, 5)
          self.assertAlmostEqual(expected_r,
                                 sess.run(pearson_r, feed_dict=feed_dict), 5)


class StreamingMeanCosineDistanceTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_mean_cosine_distance(
        predictions=array_ops.ones((10, 3)),
        labels=array_ops.ones((10, 3)),
        dim=1)
    _assert_metric_variables(self, (
        'mean_cosine_distance/count:0',
        'mean_cosine_distance/total:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_mean_cosine_distance(
        predictions=array_ops.ones((10, 3)),
        labels=array_ops.ones((10, 3)),
        dim=1,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_mean_cosine_distance(
        predictions=array_ops.ones((10, 3)),
        labels=array_ops.ones((10, 3)),
        dim=1,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_normal((10, 3), seed=1)
    labels = random_ops.random_normal((10, 3), seed=2)
    error, update_op = metrics.streaming_mean_cosine_distance(
        predictions, labels, dim=1)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_error = error.eval()
      for _ in range(10):
        self.assertEqual(initial_error, error.eval())

  def testSingleUpdateZeroError(self):
    np_labels = np.matrix(('1 0 0;' '0 0 1;' '0 1 0'))

    predictions = constant_op.constant(
        np_labels, shape=(1, 3, 3), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        np_labels, shape=(1, 3, 3), dtype=dtypes_lib.float32)

    error, update_op = metrics.streaming_mean_cosine_distance(
        predictions, labels, dim=2)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(0, sess.run(update_op))
      self.assertEqual(0, error.eval())

  def testSingleUpdateWithError1(self):
    np_labels = np.matrix(('1 0 0;' '0 0 1;' '0 1 0'))
    np_predictions = np.matrix(('1 0 0;' '0 0 -1;' '1 0 0'))

    predictions = constant_op.constant(
        np_predictions, shape=(3, 1, 3), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        np_labels, shape=(3, 1, 3), dtype=dtypes_lib.float32)

    error, update_op = metrics.streaming_mean_cosine_distance(
        predictions, labels, dim=2)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(1, sess.run(update_op), 5)
      self.assertAlmostEqual(1, error.eval(), 5)

  def testSingleUpdateWithError2(self):
    np_predictions = np.matrix(
        ('0.819031913261206 0.567041924552012 0.087465312324590;'
         '-0.665139432070255 -0.739487441769973 -0.103671883216994;'
         '0.707106781186548 -0.707106781186548 0'))
    np_labels = np.matrix(
        ('0.819031913261206 0.567041924552012 0.087465312324590;'
         '0.665139432070255 0.739487441769973 0.103671883216994;'
         '0.707106781186548 0.707106781186548 0'))

    predictions = constant_op.constant(
        np_predictions, shape=(3, 1, 3), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        np_labels, shape=(3, 1, 3), dtype=dtypes_lib.float32)
    error, update_op = metrics.streaming_mean_cosine_distance(
        predictions, labels, dim=2)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(1.0, sess.run(update_op), 5)
      self.assertAlmostEqual(1.0, error.eval(), 5)

  def testSingleUpdateWithErrorAndWeights1(self):
    np_predictions = np.matrix(('1 0 0;' '0 0 -1;' '1 0 0'))
    np_labels = np.matrix(('1 0 0;' '0 0 1;' '0 1 0'))

    predictions = constant_op.constant(
        np_predictions, shape=(3, 1, 3), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        np_labels, shape=(3, 1, 3), dtype=dtypes_lib.float32)
    weights = constant_op.constant(
        [1, 0, 0], shape=(3, 1, 1), dtype=dtypes_lib.float32)

    error, update_op = metrics.streaming_mean_cosine_distance(
        predictions, labels, dim=2, weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(0, sess.run(update_op))
      self.assertEqual(0, error.eval())

  def testSingleUpdateWithErrorAndWeights2(self):
    np_predictions = np.matrix(('1 0 0;' '0 0 -1;' '1 0 0'))
    np_labels = np.matrix(('1 0 0;' '0 0 1;' '0 1 0'))

    predictions = constant_op.constant(
        np_predictions, shape=(3, 1, 3), dtype=dtypes_lib.float32)
    labels = constant_op.constant(
        np_labels, shape=(3, 1, 3), dtype=dtypes_lib.float32)
    weights = constant_op.constant(
        [0, 1, 1], shape=(3, 1, 1), dtype=dtypes_lib.float32)

    error, update_op = metrics.streaming_mean_cosine_distance(
        predictions, labels, dim=2, weights=weights)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(1.5, update_op.eval())
      self.assertEqual(1.5, error.eval())


class PcntBelowThreshTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_percentage_less(values=array_ops.ones((10,)), threshold=2)
    _assert_metric_variables(self, (
        'percentage_below_threshold/count:0',
        'percentage_below_threshold/total:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.streaming_percentage_less(
        values=array_ops.ones((10,)),
        threshold=2,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_percentage_less(
        values=array_ops.ones((10,)),
        threshold=2,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testOneUpdate(self):
    with self.test_session() as sess:
      values = constant_op.constant(
          [2, 4, 6, 8], shape=(1, 4), dtype=dtypes_lib.float32)

      pcnt0, update_op0 = metrics.streaming_percentage_less(
          values, 100, name='high')
      pcnt1, update_op1 = metrics.streaming_percentage_less(
          values, 7, name='medium')
      pcnt2, update_op2 = metrics.streaming_percentage_less(
          values, 1, name='low')

      sess.run(variables.local_variables_initializer())
      sess.run([update_op0, update_op1, update_op2])

      pcnt0, pcnt1, pcnt2 = sess.run([pcnt0, pcnt1, pcnt2])
      self.assertAlmostEqual(1.0, pcnt0, 5)
      self.assertAlmostEqual(0.75, pcnt1, 5)
      self.assertAlmostEqual(0.0, pcnt2, 5)

  def testSomePresentOneUpdate(self):
    with self.test_session() as sess:
      values = constant_op.constant(
          [2, 4, 6, 8], shape=(1, 4), dtype=dtypes_lib.float32)
      weights = constant_op.constant(
          [1, 0, 0, 1], shape=(1, 4), dtype=dtypes_lib.float32)

      pcnt0, update_op0 = metrics.streaming_percentage_less(
          values, 100, weights=weights, name='high')
      pcnt1, update_op1 = metrics.streaming_percentage_less(
          values, 7, weights=weights, name='medium')
      pcnt2, update_op2 = metrics.streaming_percentage_less(
          values, 1, weights=weights, name='low')

      sess.run(variables.local_variables_initializer())
      self.assertListEqual([1.0, 0.5, 0.0],
                           sess.run([update_op0, update_op1, update_op2]))

      pcnt0, pcnt1, pcnt2 = sess.run([pcnt0, pcnt1, pcnt2])
      self.assertAlmostEqual(1.0, pcnt0, 5)
      self.assertAlmostEqual(0.5, pcnt1, 5)
      self.assertAlmostEqual(0.0, pcnt2, 5)


class StreamingMeanIOUTest(test.TestCase):

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_mean_iou(
        predictions=array_ops.ones([10, 1]),
        labels=array_ops.ones([10, 1]),
        num_classes=2)
    _assert_metric_variables(self, ('mean_iou/total_confusion_matrix:0',))

  def testMetricsCollections(self):
    my_collection_name = '__metrics__'
    mean_iou, _ = metrics.streaming_mean_iou(
        predictions=array_ops.ones([10, 1]),
        labels=array_ops.ones([10, 1]),
        num_classes=2,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean_iou])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_mean_iou(
        predictions=array_ops.ones([10, 1]),
        labels=array_ops.ones([10, 1]),
        num_classes=2,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testPredictionsAndLabelsOfDifferentSizeRaisesValueError(self):
    predictions = array_ops.ones([10, 3])
    labels = array_ops.ones([10, 4])
    with self.assertRaises(ValueError):
      metrics.streaming_mean_iou(predictions, labels, num_classes=2)

  def testLabelsAndWeightsOfDifferentSizeRaisesValueError(self):
    predictions = array_ops.ones([10])
    labels = array_ops.ones([10])
    weights = array_ops.zeros([9])
    with self.assertRaises(ValueError):
      metrics.streaming_mean_iou(
          predictions, labels, num_classes=2, weights=weights)

  def testValueTensorIsIdempotent(self):
    num_classes = 3
    predictions = random_ops.random_uniform(
        [10], maxval=num_classes, dtype=dtypes_lib.int64, seed=1)
    labels = random_ops.random_uniform(
        [10], maxval=num_classes, dtype=dtypes_lib.int64, seed=2)
    miou, update_op = metrics.streaming_mean_iou(
        predictions, labels, num_classes=num_classes)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_miou = miou.eval()
      for _ in range(10):
        self.assertEqual(initial_miou, miou.eval())

  def testMultipleUpdates(self):
    num_classes = 3
    with self.test_session() as sess:
      # Create the queue that populates the predictions.
      preds_queue = data_flow_ops.FIFOQueue(
          5, dtypes=dtypes_lib.int32, shapes=(1, 1))
      _enqueue_vector(sess, preds_queue, [0])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [2])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [0])
      predictions = preds_queue.dequeue()

      # Create the queue that populates the labels.
      labels_queue = data_flow_ops.FIFOQueue(
          5, dtypes=dtypes_lib.int32, shapes=(1, 1))
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [2])
      _enqueue_vector(sess, labels_queue, [1])
      labels = labels_queue.dequeue()

      miou, update_op = metrics.streaming_mean_iou(predictions, labels,
                                                   num_classes)

      sess.run(variables.local_variables_initializer())
      for _ in range(5):
        sess.run(update_op)
      desired_output = np.mean([1.0 / 2.0, 1.0 / 4.0, 0.])
      self.assertEqual(desired_output, miou.eval())

  def testMultipleUpdatesWithWeights(self):
    num_classes = 2
    with self.test_session() as sess:
      # Create the queue that populates the predictions.
      preds_queue = data_flow_ops.FIFOQueue(
          6, dtypes=dtypes_lib.int32, shapes=(1, 1))
      _enqueue_vector(sess, preds_queue, [0])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [0])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [0])
      _enqueue_vector(sess, preds_queue, [1])
      predictions = preds_queue.dequeue()

      # Create the queue that populates the labels.
      labels_queue = data_flow_ops.FIFOQueue(
          6, dtypes=dtypes_lib.int32, shapes=(1, 1))
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [1])
      labels = labels_queue.dequeue()

      # Create the queue that populates the weights.
      weights_queue = data_flow_ops.FIFOQueue(
          6, dtypes=dtypes_lib.float32, shapes=(1, 1))
      _enqueue_vector(sess, weights_queue, [1.0])
      _enqueue_vector(sess, weights_queue, [1.0])
      _enqueue_vector(sess, weights_queue, [1.0])
      _enqueue_vector(sess, weights_queue, [0.0])
      _enqueue_vector(sess, weights_queue, [1.0])
      _enqueue_vector(sess, weights_queue, [0.0])
      weights = weights_queue.dequeue()

      miou, update_op = metrics.streaming_mean_iou(
          predictions, labels, num_classes, weights=weights)

      sess.run(variables.local_variables_initializer())
      for _ in range(6):
        sess.run(update_op)
      desired_output = np.mean([2.0 / 3.0, 1.0 / 2.0])
      self.assertAlmostEqual(desired_output, miou.eval())

  def testMultipleUpdatesWithMissingClass(self):
    # Test the case where there are no predicions and labels for
    # one class, and thus there is one row and one column with
    # zero entries in the confusion matrix.
    num_classes = 3
    with self.test_session() as sess:
      # Create the queue that populates the predictions.
      # There is no prediction for class 2.
      preds_queue = data_flow_ops.FIFOQueue(
          5, dtypes=dtypes_lib.int32, shapes=(1, 1))
      _enqueue_vector(sess, preds_queue, [0])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [0])
      predictions = preds_queue.dequeue()

      # Create the queue that populates the labels.
      # There is label for class 2.
      labels_queue = data_flow_ops.FIFOQueue(
          5, dtypes=dtypes_lib.int32, shapes=(1, 1))
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [1])
      labels = labels_queue.dequeue()

      miou, update_op = metrics.streaming_mean_iou(predictions, labels,
                                                   num_classes)

      sess.run(variables.local_variables_initializer())
      for _ in range(5):
        sess.run(update_op)
      desired_output = np.mean([1.0 / 3.0, 2.0 / 4.0])
      self.assertAlmostEqual(desired_output, miou.eval())

  def testUpdateOpEvalIsAccumulatedConfusionMatrix(self):
    predictions = array_ops.concat([
        constant_op.constant(0, shape=[5]),
        constant_op.constant(1, shape=[5])
    ], 0)
    labels = array_ops.concat([
        constant_op.constant(0, shape=[3]),
        constant_op.constant(1, shape=[7])
    ], 0)
    num_classes = 2
    with self.test_session() as sess:
      miou, update_op = metrics.streaming_mean_iou(predictions, labels,
                                                   num_classes)
      sess.run(variables.local_variables_initializer())
      confusion_matrix = update_op.eval()
      self.assertAllEqual([[3, 0], [2, 5]], confusion_matrix)
      desired_miou = np.mean([3. / 5., 5. / 7.])
      self.assertAlmostEqual(desired_miou, miou.eval())

  def testAllCorrect(self):
    predictions = array_ops.zeros([40])
    labels = array_ops.zeros([40])
    num_classes = 1
    with self.test_session() as sess:
      miou, update_op = metrics.streaming_mean_iou(predictions, labels,
                                                   num_classes)
      sess.run(variables.local_variables_initializer())
      self.assertEqual(40, update_op.eval()[0])
      self.assertEqual(1.0, miou.eval())

  def testAllWrong(self):
    predictions = array_ops.zeros([40])
    labels = array_ops.ones([40])
    num_classes = 2
    with self.test_session() as sess:
      miou, update_op = metrics.streaming_mean_iou(predictions, labels,
                                                   num_classes)
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual([[0, 0], [40, 0]], update_op.eval())
      self.assertEqual(0., miou.eval())

  def testResultsWithSomeMissing(self):
    predictions = array_ops.concat([
        constant_op.constant(0, shape=[5]),
        constant_op.constant(1, shape=[5])
    ], 0)
    labels = array_ops.concat([
        constant_op.constant(0, shape=[3]),
        constant_op.constant(1, shape=[7])
    ], 0)
    num_classes = 2
    weights = array_ops.concat([
        constant_op.constant(0, shape=[1]),
        constant_op.constant(1, shape=[8]),
        constant_op.constant(0, shape=[1])
    ], 0)
    with self.test_session() as sess:
      miou, update_op = metrics.streaming_mean_iou(
          predictions, labels, num_classes, weights=weights)
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual([[2, 0], [2, 4]], update_op.eval())
      desired_miou = np.mean([2. / 4., 4. / 6.])
      self.assertAlmostEqual(desired_miou, miou.eval())

  def testMissingClassInLabels(self):
    labels = constant_op.constant([[[0, 0, 1, 1, 0, 0], [1, 0, 0, 0, 0, 1]],
                                   [[1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]]])
    predictions = constant_op.constant(
        [[[0, 0, 2, 1, 1, 0], [0, 1, 2, 2, 0, 1]], [[0, 0, 2, 1, 1, 1],
                                                    [1, 1, 2, 0, 0, 0]]])
    num_classes = 3
    with self.test_session() as sess:
      miou, update_op = metrics.streaming_mean_iou(predictions, labels,
                                                   num_classes)
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual([[7, 4, 3], [3, 5, 2], [0, 0, 0]], update_op.eval())
      self.assertAlmostEqual(1 / 3 * (7 / (7 + 3 + 7) + 5 / (5 + 4 + 5) + 0 /
                                      (0 + 5 + 0)), miou.eval())

  def testMissingClassOverallSmall(self):
    labels = constant_op.constant([0])
    predictions = constant_op.constant([0])
    num_classes = 2
    with self.test_session() as sess:
      miou, update_op = metrics.streaming_mean_iou(predictions, labels,
                                                   num_classes)
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual([[1, 0], [0, 0]], update_op.eval())
      self.assertAlmostEqual(1, miou.eval())

  def testMissingClassOverallLarge(self):
    labels = constant_op.constant([[[0, 0, 1, 1, 0, 0], [1, 0, 0, 0, 0, 1]],
                                   [[1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]]])
    predictions = constant_op.constant(
        [[[0, 0, 1, 1, 0, 0], [1, 1, 0, 0, 1, 1]], [[0, 0, 0, 1, 1, 1],
                                                    [1, 1, 1, 0, 0, 0]]])
    num_classes = 3
    with self.test_session() as sess:
      miou, update_op = metrics.streaming_mean_iou(predictions, labels,
                                                   num_classes)
      sess.run(variables.local_variables_initializer())
      self.assertAllEqual([[9, 5, 0], [3, 7, 0], [0, 0, 0]], update_op.eval())
      self.assertAlmostEqual(1 / 2 * (9 / (9 + 3 + 5) + 7 / (7 + 5 + 3)),
                             miou.eval())


class StreamingConcatTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.streaming_concat(values=array_ops.ones((10,)))
    _assert_metric_variables(self, (
        'streaming_concat/array:0',
        'streaming_concat/size:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    value, _ = metrics.streaming_concat(
        values=array_ops.ones((10,)), metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [value])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.streaming_concat(
        values=array_ops.ones((10,)), updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testNextArraySize(self):
    next_array_size = metric_ops._next_array_size  # pylint: disable=protected-access
    with self.test_session():
      self.assertEqual(next_array_size(2, growth_factor=2).eval(), 2)
      self.assertEqual(next_array_size(3, growth_factor=2).eval(), 4)
      self.assertEqual(next_array_size(4, growth_factor=2).eval(), 4)
      self.assertEqual(next_array_size(5, growth_factor=2).eval(), 8)
      self.assertEqual(next_array_size(6, growth_factor=2).eval(), 8)

  def testStreamingConcat(self):
    with self.test_session() as sess:
      values = array_ops.placeholder(dtypes_lib.int32, [None])
      concatenated, update_op = metrics.streaming_concat(values)
      sess.run(variables.local_variables_initializer())

      self.assertAllEqual([], concatenated.eval())

      sess.run([update_op], feed_dict={values: [0, 1, 2]})
      self.assertAllEqual([0, 1, 2], concatenated.eval())

      sess.run([update_op], feed_dict={values: [3, 4]})
      self.assertAllEqual([0, 1, 2, 3, 4], concatenated.eval())

      sess.run([update_op], feed_dict={values: [5, 6, 7, 8, 9]})
      self.assertAllEqual(np.arange(10), concatenated.eval())

  def testStreamingConcatStringValues(self):
    with self.test_session() as sess:
      values = array_ops.placeholder(dtypes_lib.string, [None])
      concatenated, update_op = metrics.streaming_concat(values)
      sess.run(variables.local_variables_initializer())

      self.assertItemsEqual([], concatenated.eval())

      sess.run([update_op], feed_dict={values: ['a', 'b', 'c']})
      self.assertItemsEqual([b'a', b'b', b'c'], concatenated.eval())

      sess.run([update_op], feed_dict={values: ['d', 'e']})
      self.assertItemsEqual([b'a', b'b', b'c', b'd', b'e'], concatenated.eval())

      sess.run([update_op], feed_dict={values: ['f', 'g', 'h', 'i', 'j']})
      self.assertItemsEqual(
          [b'a', b'b', b'c', b'd', b'e', b'f', b'g', b'h', b'i', b'j'],
          concatenated.eval())

  def testStreamingConcatMaxSize(self):
    with self.test_session() as sess:
      values = math_ops.range(3)
      concatenated, update_op = metrics.streaming_concat(values, max_size=5)
      sess.run(variables.local_variables_initializer())

      self.assertAllEqual([], concatenated.eval())

      sess.run([update_op])
      self.assertAllEqual([0, 1, 2], concatenated.eval())

      sess.run([update_op])
      self.assertAllEqual([0, 1, 2, 0, 1], concatenated.eval())

      sess.run([update_op])
      self.assertAllEqual([0, 1, 2, 0, 1], concatenated.eval())

  def testStreamingConcat2D(self):
    with self.test_session() as sess:
      values = array_ops.reshape(math_ops.range(3), (3, 1))
      concatenated, update_op = metrics.streaming_concat(values, axis=-1)
      sess.run(variables.local_variables_initializer())
      for _ in range(10):
        sess.run([update_op])
      self.assertAllEqual([[0] * 10, [1] * 10, [2] * 10], concatenated.eval())

  def testStreamingConcatErrors(self):
    with self.assertRaises(ValueError):
      metrics.streaming_concat(array_ops.placeholder(dtypes_lib.float32))

    values = array_ops.zeros((2, 3))
    with self.assertRaises(ValueError):
      metrics.streaming_concat(values, axis=-3, max_size=3)
    with self.assertRaises(ValueError):
      metrics.streaming_concat(values, axis=2, max_size=3)

    with self.assertRaises(ValueError):
      metrics.streaming_concat(
          array_ops.placeholder(dtypes_lib.float32, [None, None]))

  def testStreamingConcatReset(self):
    with self.test_session() as sess:
      values = array_ops.placeholder(dtypes_lib.int32, [None])
      concatenated, update_op = metrics.streaming_concat(values)
      sess.run(variables.local_variables_initializer())

      self.assertAllEqual([], concatenated.eval())

      sess.run([update_op], feed_dict={values: [0, 1, 2]})
      self.assertAllEqual([0, 1, 2], concatenated.eval())

      sess.run(variables.local_variables_initializer())

      sess.run([update_op], feed_dict={values: [3, 4]})
      self.assertAllEqual([3, 4], concatenated.eval())


class AggregateMetricsTest(test.TestCase):

  def testAggregateNoMetricsRaisesValueError(self):
    with self.assertRaises(ValueError):
      metrics.aggregate_metrics()

  def testAggregateSingleMetricReturnsOneItemLists(self):
    values = array_ops.ones((10, 4))
    value_tensors, update_ops = metrics.aggregate_metrics(
        metrics.streaming_mean(values))
    self.assertEqual(len(value_tensors), 1)
    self.assertEqual(len(update_ops), 1)
    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(1, update_ops[0].eval())
      self.assertEqual(1, value_tensors[0].eval())

  def testAggregateMultipleMetricsReturnsListsInOrder(self):
    predictions = array_ops.ones((10, 4))
    labels = array_ops.ones((10, 4)) * 3
    value_tensors, update_ops = metrics.aggregate_metrics(
        metrics.streaming_mean_absolute_error(predictions, labels),
        metrics.streaming_mean_squared_error(predictions, labels))
    self.assertEqual(len(value_tensors), 2)
    self.assertEqual(len(update_ops), 2)
    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(2, update_ops[0].eval())
      self.assertEqual(4, update_ops[1].eval())
      self.assertEqual(2, value_tensors[0].eval())
      self.assertEqual(4, value_tensors[1].eval())


class AggregateMetricMapTest(test.TestCase):

  def testAggregateMultipleMetricsReturnsListsInOrder(self):
    predictions = array_ops.ones((10, 4))
    labels = array_ops.ones((10, 4)) * 3
    names_to_values, names_to_updates = metrics.aggregate_metric_map({
        'm1': metrics.streaming_mean_absolute_error(predictions, labels),
        'm2': metrics.streaming_mean_squared_error(predictions, labels),
    })

    self.assertEqual(2, len(names_to_values))
    self.assertEqual(2, len(names_to_updates))

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      self.assertEqual(2, names_to_updates['m1'].eval())
      self.assertEqual(4, names_to_updates['m2'].eval())
      self.assertEqual(2, names_to_values['m1'].eval())
      self.assertEqual(4, names_to_values['m2'].eval())


class CountTest(test.TestCase):

  def setUp(self):
    ops.reset_default_graph()

  def testVars(self):
    metrics.count(array_ops.ones([4, 3]))
    _assert_metric_variables(self, ['count/count:0'])

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    mean, _ = metrics.count(
        array_ops.ones([4, 3]), metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [mean])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.count(
        array_ops.ones([4, 3]), updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testBasic(self):
    with self.test_session() as sess:
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      result, update_op = metrics.count(values)

      sess.run(variables.local_variables_initializer())
      for _ in range(4):
        sess.run(update_op)
      self.assertAlmostEqual(8.0, sess.run(result), 5)

  def testUpdateOpsReturnsCurrentValue(self):
    with self.test_session() as sess:
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      result, update_op = metrics.count(values)

      sess.run(variables.local_variables_initializer())

      self.assertAlmostEqual(2.0, sess.run(update_op), 5)
      self.assertAlmostEqual(4.0, sess.run(update_op), 5)
      self.assertAlmostEqual(6.0, sess.run(update_op), 5)
      self.assertAlmostEqual(8.0, sess.run(update_op), 5)

      self.assertAlmostEqual(8.0, sess.run(result), 5)

  def test1dWeightedValues(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      # Create the queue that populates the weighted labels.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 1))
      _enqueue_vector(sess, weights_queue, [0.5])
      _enqueue_vector(sess, weights_queue, [0])
      _enqueue_vector(sess, weights_queue, [0])
      _enqueue_vector(sess, weights_queue, [1.2])
      weights = weights_queue.dequeue()

      result, update_op = metrics.count(values, weights)

      variables.local_variables_initializer().run()
      for _ in range(4):
        update_op.eval()
      self.assertAlmostEqual(3.4, result.eval(), 5)

  def test1dWeightedValues_placeholders(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      feed_values = ((0, 1), (-4.2, 9.1), (6.5, 0), (-3.2, 4.0))
      values = array_ops.placeholder(dtype=dtypes_lib.float32)

      # Create the queue that populates the weighted labels.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1,))
      _enqueue_vector(sess, weights_queue, 0.5, shape=(1,))
      _enqueue_vector(sess, weights_queue, 0, shape=(1,))
      _enqueue_vector(sess, weights_queue, 0, shape=(1,))
      _enqueue_vector(sess, weights_queue, 1.2, shape=(1,))
      weights = weights_queue.dequeue()

      result, update_op = metrics.count(values, weights)

      variables.local_variables_initializer().run()
      for i in range(4):
        update_op.eval(feed_dict={values: feed_values[i]})
      self.assertAlmostEqual(3.4, result.eval(), 5)

  def test2dWeightedValues(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      values_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, values_queue, [0, 1])
      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
      _enqueue_vector(sess, values_queue, [6.5, 0])
      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
      values = values_queue.dequeue()

      # Create the queue that populates the weighted labels.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
      _enqueue_vector(sess, weights_queue, [1.1, 1])
      _enqueue_vector(sess, weights_queue, [1, 0])
      _enqueue_vector(sess, weights_queue, [0, 1])
      _enqueue_vector(sess, weights_queue, [0, 0])
      weights = weights_queue.dequeue()

      result, update_op = metrics.count(values, weights)

      variables.local_variables_initializer().run()
      for _ in range(4):
        update_op.eval()
      self.assertAlmostEqual(4.1, result.eval(), 5)

  def test2dWeightedValues_placeholders(self):
    with self.test_session() as sess:
      # Create the queue that populates the values.
      feed_values = ((0, 1), (-4.2, 9.1), (6.5, 0), (-3.2, 4.0))
      values = array_ops.placeholder(dtype=dtypes_lib.float32)

      # Create the queue that populates the weighted labels.
      weights_queue = data_flow_ops.FIFOQueue(
          4, dtypes=dtypes_lib.float32, shapes=(2,))
      _enqueue_vector(sess, weights_queue, [1.1, 1], shape=(2,))
      _enqueue_vector(sess, weights_queue, [1, 0], shape=(2,))
      _enqueue_vector(sess, weights_queue, [0, 1], shape=(2,))
      _enqueue_vector(sess, weights_queue, [0, 0], shape=(2,))
      weights = weights_queue.dequeue()

      result, update_op = metrics.count(values, weights)

      variables.local_variables_initializer().run()
      for i in range(4):
        update_op.eval(feed_dict={values: feed_values[i]})
      self.assertAlmostEqual(4.1, result.eval(), 5)


class CohenKappaTest(test.TestCase):

  def _confusion_matrix_to_samples(self, confusion_matrix):
    x, y = confusion_matrix.shape
    pairs = []
    for label in range(x):
      for feature in range(y):
        pairs += [label, feature] * confusion_matrix[label, feature]
    pairs = np.array(pairs).reshape((-1, 2))
    return pairs[:, 0], pairs[:, 1]

  def setUp(self):
    np.random.seed(1)
    ops.reset_default_graph()

  def testVars(self):
    metrics.cohen_kappa(
        predictions_idx=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        num_classes=2)
    _assert_metric_variables(self, (
        'cohen_kappa/po:0',
        'cohen_kappa/pe_row:0',
        'cohen_kappa/pe_col:0',
    ))

  def testMetricsCollection(self):
    my_collection_name = '__metrics__'
    kappa, _ = metrics.cohen_kappa(
        predictions_idx=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        num_classes=2,
        metrics_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [kappa])

  def testUpdatesCollection(self):
    my_collection_name = '__updates__'
    _, update_op = metrics.cohen_kappa(
        predictions_idx=array_ops.ones((10, 1)),
        labels=array_ops.ones((10, 1)),
        num_classes=2,
        updates_collections=[my_collection_name])
    self.assertListEqual(ops.get_collection(my_collection_name), [update_op])

  def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 1), maxval=3, dtype=dtypes_lib.int64, seed=1)
    labels = random_ops.random_uniform(
        (10, 1), maxval=3, dtype=dtypes_lib.int64, seed=2)
    kappa, update_op = metrics.cohen_kappa(labels, predictions, 3)

    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run(update_op)

      # Then verify idempotency.
      initial_kappa = kappa.eval()
      for _ in range(10):
        self.assertAlmostEqual(initial_kappa, kappa.eval(), 5)

  def testBasic(self):
    confusion_matrix = np.array([[9, 3, 1], [4, 8, 2], [2, 1, 6]])
    # overall total = 36
    # po = [9, 8, 6], sum(po) = 23
    # pe_row = [15, 12, 9], pe_col = [13, 14, 9], so pe = [5.42, 4.67, 2.25]
    # finally, kappa = (sum(po) - sum(pe)) / (N - sum(pe))
    #                = (23 - 12.34) / (36 - 12.34)
    #                = 0.45
    # see: http://psych.unl.edu/psycrs/handcomp/hckappa.PDF
    expect = 0.45
    labels, predictions = self._confusion_matrix_to_samples(confusion_matrix)

    dtypes = [dtypes_lib.int16, dtypes_lib.int32, dtypes_lib.int64]
    shapes = [
        (len(labels,)),  # 1-dim
        (len(labels), 1)
    ]  # 2-dim
    weights = [None, np.ones_like(labels)]

    for dtype in dtypes:
      for shape in shapes:
        for weight in weights:
          with self.test_session() as sess:
            predictions_tensor = constant_op.constant(
                np.reshape(predictions, shape), dtype=dtype)
            labels_tensor = constant_op.constant(
                np.reshape(labels, shape), dtype=dtype)
            kappa, update_op = metrics.cohen_kappa(
                labels_tensor, predictions_tensor, 3, weights=weight)

            sess.run(variables.local_variables_initializer())
            self.assertAlmostEqual(expect, sess.run(update_op), 2)
            self.assertAlmostEqual(expect, kappa.eval(), 2)

  def testAllCorrect(self):
    inputs = np.arange(0, 100) % 4
    # confusion matrix
    # [[25, 0, 0],
    #  [0, 25, 0],
    #  [0, 0, 25]]
    # Calculated by v0.19: sklearn.metrics.cohen_kappa_score(inputs, inputs)
    expect = 1.0

    with self.test_session() as sess:
      predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
      labels = constant_op.constant(inputs)
      kappa, update_op = metrics.cohen_kappa(labels, predictions, 4)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expect, sess.run(update_op), 5)
      self.assertAlmostEqual(expect, kappa.eval(), 5)

  def testAllIncorrect(self):
    labels = np.arange(0, 100) % 4
    predictions = (labels + 1) % 4
    # confusion matrix
    # [[0, 25, 0],
    #  [0, 0, 25],
    #  [25, 0, 0]]
    # Calculated by v0.19: sklearn.metrics.cohen_kappa_score(labels, predictions)
    expect = -0.333333333333

    with self.test_session() as sess:
      predictions = constant_op.constant(predictions, dtype=dtypes_lib.float32)
      labels = constant_op.constant(labels)
      kappa, update_op = metrics.cohen_kappa(labels, predictions, 4)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expect, sess.run(update_op), 5)
      self.assertAlmostEqual(expect, kappa.eval(), 5)

  def testWeighted(self):
    confusion_matrix = np.array([[9, 3, 1], [4, 8, 2], [2, 1, 6]])
    labels, predictions = self._confusion_matrix_to_samples(confusion_matrix)
    num_samples = np.sum(confusion_matrix, dtype=np.int32)
    weights = (np.arange(0, num_samples) % 5) / 5.0
    # Calculated by v0.19: sklearn.metrics.cohen_kappa_score(
    #                          labels, predictions, sample_weight=weights)
    expect = 0.453466583385

    with self.test_session() as sess:
      predictions = constant_op.constant(predictions, dtype=dtypes_lib.float32)
      labels = constant_op.constant(labels)
      kappa, update_op = metrics.cohen_kappa(
          labels, predictions, 4, weights=weights)

      sess.run(variables.local_variables_initializer())
      self.assertAlmostEqual(expect, sess.run(update_op), 5)
      self.assertAlmostEqual(expect, kappa.eval(), 5)

  def testWithMultipleUpdates(self):
    confusion_matrix = np.array([[90, 30, 10, 20], [40, 80, 20, 30],
                                 [20, 10, 60, 35], [15, 25, 30, 25]])
    labels, predictions = self._confusion_matrix_to_samples(confusion_matrix)
    num_samples = np.sum(confusion_matrix, dtype=np.int32)
    weights = (np.arange(0, num_samples) % 5) / 5.0
    num_classes = confusion_matrix.shape[0]

    batch_size = num_samples // 10
    predictions_t = array_ops.placeholder(
        dtypes_lib.float32, shape=(batch_size,))
    labels_t = array_ops.placeholder(dtypes_lib.int32, shape=(batch_size,))
    weights_t = array_ops.placeholder(dtypes_lib.float32, shape=(batch_size,))
    kappa, update_op = metrics.cohen_kappa(
        labels_t, predictions_t, num_classes, weights=weights_t)
    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())

      for idx in range(0, num_samples, batch_size):
        batch_start, batch_end = idx, idx + batch_size
        sess.run(
            update_op,
            feed_dict={
                labels_t: labels[batch_start:batch_end],
                predictions_t: predictions[batch_start:batch_end],
                weights_t: weights[batch_start:batch_end]
            })
      # Calculated by v0.19: sklearn.metrics.cohen_kappa_score(
      #                          labels_np, predictions_np, sample_weight=weights_np)
      expect = 0.289965397924
      self.assertAlmostEqual(expect, kappa.eval(), 5)

  def testInvalidNumClasses(self):
    predictions = array_ops.placeholder(dtypes_lib.float32, shape=(4, 1))
    labels = array_ops.placeholder(dtypes_lib.int32, shape=(4, 1))
    with self.assertRaisesRegexp(ValueError, 'num_classes'):
      metrics.cohen_kappa(labels, predictions, 1)

  def testInvalidDimension(self):
    predictions = array_ops.placeholder(dtypes_lib.float32, shape=(4, 1))
    invalid_labels = array_ops.placeholder(dtypes_lib.int32, shape=(4, 2))
    with self.assertRaises(ValueError):
      metrics.cohen_kappa(invalid_labels, predictions, 3)

    invalid_predictions = array_ops.placeholder(
        dtypes_lib.float32, shape=(4, 2))
    labels = array_ops.placeholder(dtypes_lib.int32, shape=(4, 1))
    with self.assertRaises(ValueError):
      metrics.cohen_kappa(labels, invalid_predictions, 3)

  def testConditionalPackingOptimization(self):
    placeholder = array_ops.placeholder(dtypes_lib.float32, [None])
    values, update_op = metric_ops.streaming_concat(placeholder)
    with self.test_session() as sess:
      sess.run(variables.local_variables_initializer())
      for feed in range(10):
        sess.run(update_op, feed_dict={placeholder: [feed]})
        print(sess.run(values))

if __name__ == '__main__':
  test.main()
