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STT-tensorflow/tensorflow/python/kernel_tests/metrics_test.py
Kibeom Kim 88c2594048 Replace x.eval() with self.evaluate() in tests. 
It makes the line V2 compatible

$ sed -i "s/\([a-z][._a-z0-9]*\).eval()/self.evaluate(\1)/" ./third_party/tensorflow/python/kernel_tests/*.py

And some manual cleaning.

PiperOrigin-RevId: 324169377
Change-Id: I36a8d92c28a46e5a05fb32bdb634a84081951d88
2020-07-31 01:24:08 -07:00

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# 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 metrics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import math
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
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.framework import test_util
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 metrics
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
import tensorflow.python.ops.data_flow_grad # pylint: disable=unused-import
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
from tensorflow.python.platform import test
NAN = float('nan')
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_2d_sparse_value(labels):
"""Convert dense 2D binary indicator to sparse ID.
Only 1 values in `labels` are included in result.
Args:
labels: Dense 2D binary indicator, shape [batch_size, num_classes].
Returns:
`SparseTensorValue` of shape [batch_size, num_classes], where num_classes
is the number of `1` values in each row of `labels`. Values are indices
of `1` values 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_1d_sparse_value(labels):
"""Convert dense 2D binary indicator to sparse ID.
Only 1 values in `labels` are included in result.
Args:
labels: Dense 2D binary indicator, shape [batch_size, num_classes]. Each
row must contain exactly 1 `1` value.
Returns:
`SparseTensorValue` of shape [batch_size]. Values are indices of `1` values
along the last dimension of `labels`.
Raises:
ValueError: if there is not exactly 1 `1` value per row of `labels`.
"""
indices = []
values = []
batch = 0
for row in labels:
label = 0
xi = 0
for x in row:
if x == 1:
indices.append([batch])
values.append(label)
xi += 1
else:
assert x == 0
label += 1
batch += 1
if indices != [[i] for i in range(len(labels))]:
raise ValueError('Expected 1 label/example, got %s.' % indices)
shape = [len(labels)]
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_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 _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.assertEqual(
set(expected), set(v.name for v in variables.local_variables()))
test_case.assertEqual(
set(expected),
set(v.name for v in ops.get_collection(ops.GraphKeys.METRIC_VARIABLES)))
def _test_values(shape):
return np.reshape(np.cumsum(np.ones(shape)), newshape=shape)
class MeanTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.mean(array_ops.ones([4, 3]))
_assert_metric_variables(self, ('mean/count:0', 'mean/total:0'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.mean(
array_ops.ones([4, 3]), metrics_collections=[my_collection_name])
self.assertListEqual(ops.get_collection(my_collection_name), [mean])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.mean(
array_ops.ones([4, 3]), updates_collections=[my_collection_name])
self.assertListEqual(ops.get_collection(my_collection_name), [update_op])
@test_util.run_deprecated_v1
def testBasic(self):
with self.cached_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.mean(values)
self.evaluate(variables.local_variables_initializer())
for _ in range(4):
self.evaluate(update_op)
self.assertAlmostEqual(1.65, self.evaluate(mean), 5)
@test_util.run_deprecated_v1
def testUpdateOpsReturnsCurrentValue(self):
with self.cached_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.mean(values)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.5, self.evaluate(update_op), 5)
self.assertAlmostEqual(1.475, self.evaluate(update_op), 5)
self.assertAlmostEqual(12.4 / 6.0, self.evaluate(update_op), 5)
self.assertAlmostEqual(1.65, self.evaluate(update_op), 5)
self.assertAlmostEqual(1.65, self.evaluate(mean), 5)
@test_util.run_deprecated_v1
def testUnweighted(self):
values = _test_values((3, 2, 4, 1))
mean_results = (
metrics.mean(values),
metrics.mean(values, weights=1.0),
metrics.mean(values, weights=np.ones((1, 1, 1))),
metrics.mean(values, weights=np.ones((1, 1, 1, 1))),
metrics.mean(values, weights=np.ones((1, 1, 1, 1, 1))),
metrics.mean(values, weights=np.ones((1, 1, 4))),
metrics.mean(values, weights=np.ones((1, 1, 4, 1))),
metrics.mean(values, weights=np.ones((1, 2, 1))),
metrics.mean(values, weights=np.ones((1, 2, 1, 1))),
metrics.mean(values, weights=np.ones((1, 2, 4))),
metrics.mean(values, weights=np.ones((1, 2, 4, 1))),
metrics.mean(values, weights=np.ones((3, 1, 1))),
metrics.mean(values, weights=np.ones((3, 1, 1, 1))),
metrics.mean(values, weights=np.ones((3, 1, 4))),
metrics.mean(values, weights=np.ones((3, 1, 4, 1))),
metrics.mean(values, weights=np.ones((3, 2, 1))),
metrics.mean(values, weights=np.ones((3, 2, 1, 1))),
metrics.mean(values, weights=np.ones((3, 2, 4))),
metrics.mean(values, weights=np.ones((3, 2, 4, 1))),
metrics.mean(values, weights=np.ones((3, 2, 4, 1, 1))),)
expected = np.mean(values)
with self.cached_session():
variables.local_variables_initializer().run()
for mean_result in mean_results:
mean, update_op = mean_result
self.assertAlmostEqual(expected, self.evaluate(update_op))
self.assertAlmostEqual(expected, self.evaluate(mean))
def _test_3d_weighted(self, values, weights):
expected = (
np.sum(np.multiply(weights, values)) /
np.sum(np.multiply(weights, np.ones_like(values)))
)
mean, update_op = metrics.mean(values, weights=weights)
with self.cached_session():
variables.local_variables_initializer().run()
self.assertAlmostEqual(expected, self.evaluate(update_op), places=5)
self.assertAlmostEqual(expected, self.evaluate(mean), places=5)
@test_util.run_deprecated_v1
def test1x1x1Weighted(self):
self._test_3d_weighted(
_test_values((3, 2, 4)),
weights=np.asarray((5,)).reshape((1, 1, 1)))
@test_util.run_deprecated_v1
def test1x1xNWeighted(self):
self._test_3d_weighted(
_test_values((3, 2, 4)),
weights=np.asarray((5, 7, 11, 3)).reshape((1, 1, 4)))
@test_util.run_deprecated_v1
def test1xNx1Weighted(self):
self._test_3d_weighted(
_test_values((3, 2, 4)),
weights=np.asarray((5, 11)).reshape((1, 2, 1)))
@test_util.run_deprecated_v1
def test1xNxNWeighted(self):
self._test_3d_weighted(
_test_values((3, 2, 4)),
weights=np.asarray((5, 7, 11, 3, 2, 13, 7, 5)).reshape((1, 2, 4)))
@test_util.run_deprecated_v1
def testNx1x1Weighted(self):
self._test_3d_weighted(
_test_values((3, 2, 4)),
weights=np.asarray((5, 7, 11)).reshape((3, 1, 1)))
@test_util.run_deprecated_v1
def testNx1xNWeighted(self):
self._test_3d_weighted(
_test_values((3, 2, 4)),
weights=np.asarray((
5, 7, 11, 3, 2, 12, 7, 5, 2, 17, 11, 3)).reshape((3, 1, 4)))
@test_util.run_deprecated_v1
def testNxNxNWeighted(self):
self._test_3d_weighted(
_test_values((3, 2, 4)),
weights=np.asarray((
5, 7, 11, 3, 2, 12, 7, 5, 2, 17, 11, 3,
2, 17, 11, 3, 5, 7, 11, 3, 2, 12, 7, 5)).reshape((3, 2, 4)))
@test_util.run_deprecated_v1
def testInvalidWeights(self):
values_placeholder = array_ops.placeholder(dtype=dtypes_lib.float32)
values = _test_values((3, 2, 4, 1))
invalid_weights = (
(1,),
(1, 1),
(3, 2),
(2, 4, 1),
(4, 2, 4, 1),
(3, 3, 4, 1),
(3, 2, 5, 1),
(3, 2, 4, 2),
(1, 1, 1, 1, 1))
expected_error_msg = 'weights can not be broadcast to values'
for invalid_weight in invalid_weights:
# Static shapes.
with self.assertRaisesRegex(ValueError, expected_error_msg):
metrics.mean(values, invalid_weight)
# Dynamic shapes.
with self.assertRaisesRegex(errors_impl.OpError, expected_error_msg):
with self.cached_session():
_, update_op = metrics.mean(values_placeholder, invalid_weight)
variables.local_variables_initializer().run()
update_op.eval(feed_dict={values_placeholder: values})
class MeanTensorTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.mean_tensor(array_ops.ones([4, 3]))
_assert_metric_variables(self,
('mean/total_tensor:0', 'mean/count_tensor:0'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.mean_tensor(
array_ops.ones([4, 3]), metrics_collections=[my_collection_name])
self.assertListEqual(ops.get_collection(my_collection_name), [mean])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.mean_tensor(
array_ops.ones([4, 3]), updates_collections=[my_collection_name])
self.assertListEqual(ops.get_collection(my_collection_name), [update_op])
@test_util.run_deprecated_v1
def testBasic(self):
with self.cached_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.mean_tensor(values)
self.evaluate(variables.local_variables_initializer())
for _ in range(4):
self.evaluate(update_op)
self.assertAllClose([[-0.9 / 4., 3.525]], self.evaluate(mean))
@test_util.run_deprecated_v1
def testMultiDimensional(self):
with self.cached_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.mean_tensor(values)
self.evaluate(variables.local_variables_initializer())
for _ in range(2):
self.evaluate(update_op)
self.assertAllClose([[[1, 2], [1, 2]], [[2, 3], [5, 6]]],
self.evaluate(mean))
@test_util.run_deprecated_v1
def testUpdateOpsReturnsCurrentValue(self):
with self.cached_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.mean_tensor(values)
self.evaluate(variables.local_variables_initializer())
self.assertAllClose([[0, 1]], self.evaluate(update_op), 5)
self.assertAllClose([[-2.1, 5.05]], self.evaluate(update_op), 5)
self.assertAllClose([[2.3 / 3., 10.1 / 3.]], self.evaluate(update_op), 5)
self.assertAllClose([[-0.9 / 4., 3.525]], self.evaluate(update_op), 5)
self.assertAllClose([[-0.9 / 4., 3.525]], self.evaluate(mean), 5)
@test_util.run_deprecated_v1
def testBinaryWeighted1d(self):
with self.cached_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.mean_tensor(values, weights)
self.evaluate(variables.local_variables_initializer())
for _ in range(4):
self.evaluate(update_op)
self.assertAllClose([[3.25, 0.5]], self.evaluate(mean), 5)
@test_util.run_deprecated_v1
def testWeighted1d(self):
with self.cached_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, [[0.0025]])
_enqueue_vector(sess, weights_queue, [[0.005]])
_enqueue_vector(sess, weights_queue, [[0.01]])
_enqueue_vector(sess, weights_queue, [[0.0075]])
weights = weights_queue.dequeue()
mean, update_op = metrics.mean_tensor(values, weights)
self.evaluate(variables.local_variables_initializer())
for _ in range(4):
self.evaluate(update_op)
self.assertAllClose([[0.8, 3.52]], self.evaluate(mean), 5)
@test_util.run_deprecated_v1
def testWeighted2d_1(self):
with self.cached_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.mean_tensor(values, weights)
self.evaluate(variables.local_variables_initializer())
for _ in range(4):
self.evaluate(update_op)
self.assertAllClose([[-2.1, 0.5]], self.evaluate(mean), 5)
@test_util.run_deprecated_v1
def testWeighted2d_2(self):
with self.cached_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.mean_tensor(values, weights)
self.evaluate(variables.local_variables_initializer())
for _ in range(4):
self.evaluate(update_op)
self.assertAllClose([[0, 0.5]], self.evaluate(mean), 5)
class AccuracyTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
def testPredictionsAndLabelsOfDifferentSizeRaisesValueError(self):
predictions = array_ops.ones((10, 3))
labels = array_ops.ones((10, 4))
with self.assertRaises(ValueError):
metrics.accuracy(labels, predictions)
@test_util.run_deprecated_v1
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.accuracy(labels, predictions, weights)
@test_util.run_deprecated_v1
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=1)
accuracy, update_op = metrics.accuracy(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_accuracy = self.evaluate(accuracy)
for _ in range(10):
self.assertEqual(initial_accuracy, self.evaluate(accuracy))
@test_util.run_deprecated_v1
def testMultipleUpdates(self):
with self.cached_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.accuracy(labels, predictions)
self.evaluate(variables.local_variables_initializer())
for _ in xrange(3):
self.evaluate(update_op)
self.assertEqual(0.5, self.evaluate(update_op))
self.assertEqual(0.5, self.evaluate(accuracy))
@test_util.run_deprecated_v1
def testEffectivelyEquivalentSizes(self):
predictions = array_ops.ones((40, 1))
labels = array_ops.ones((40,))
with self.cached_session():
accuracy, update_op = metrics.accuracy(labels, predictions)
self.evaluate(variables.local_variables_initializer())
self.assertEqual(1.0, self.evaluate(update_op))
self.assertEqual(1.0, self.evaluate(accuracy))
@test_util.run_deprecated_v1
def testEffectivelyEquivalentSizesWithScalarWeight(self):
predictions = array_ops.ones((40, 1))
labels = array_ops.ones((40,))
with self.cached_session():
accuracy, update_op = metrics.accuracy(labels, predictions, weights=2.0)
self.evaluate(variables.local_variables_initializer())
self.assertEqual(1.0, self.evaluate(update_op))
self.assertEqual(1.0, self.evaluate(accuracy))
@test_util.run_deprecated_v1
def testEffectivelyEquivalentSizesWithStaticShapedWeight(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.cached_session():
accuracy, update_op = metrics.accuracy(labels, predictions, weights)
self.evaluate(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(self.evaluate(update_op), .95)
self.assertGreater(self.evaluate(accuracy), .95)
@test_util.run_deprecated_v1
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.cached_session():
accuracy, update_op = metrics.accuracy(labels, predictions,
weights_placeholder)
self.evaluate(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)
@test_util.run_deprecated_v1
def testMultipleUpdatesWithWeightedValues(self):
with self.cached_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.accuracy(labels, predictions, weights)
self.evaluate(variables.local_variables_initializer())
for _ in xrange(3):
self.evaluate(update_op)
self.assertEqual(1.0, self.evaluate(update_op))
self.assertEqual(1.0, self.evaluate(accuracy))
class PrecisionTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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=1, dtype=dtypes_lib.int64, seed=1)
precision, update_op = metrics.precision(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_precision = self.evaluate(precision)
for _ in range(10):
self.assertEqual(initial_precision, self.evaluate(precision))
@test_util.run_deprecated_v1
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.precision(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(1.0, self.evaluate(update_op), 6)
self.assertAlmostEqual(1.0, self.evaluate(precision), 6)
@test_util.run_deprecated_v1
def testSomeCorrect_multipleInputDtypes(self):
for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
predictions = math_ops.cast(
constant_op.constant([1, 0, 1, 0], shape=(1, 4)), dtype=dtype)
labels = math_ops.cast(
constant_op.constant([0, 1, 1, 0], shape=(1, 4)), dtype=dtype)
precision, update_op = metrics.precision(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.5, self.evaluate(update_op))
self.assertAlmostEqual(0.5, self.evaluate(precision))
@test_util.run_deprecated_v1
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.precision(
labels, predictions, weights=constant_op.constant([[2], [5]]))
with self.cached_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, self.evaluate(update_op))
self.assertAlmostEqual(expected_precision, self.evaluate(precision))
@test_util.run_deprecated_v1
def testWeightedScalar_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.precision(labels, predictions, weights=2)
with self.cached_session():
variables.local_variables_initializer().run()
weighted_tp = 2.0 + 2.0
weighted_positives = (2.0 + 2.0) + (2.0 + 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))
@test_util.run_deprecated_v1
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.precision(
labels, predictions, weights=constant_op.constant([[2], [5]]))
with self.cached_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))
@test_util.run_deprecated_v1
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.precision(
labels,
predictions,
weights=constant_op.constant([[1, 2, 3, 4], [4, 3, 2, 1]]))
with self.cached_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, self.evaluate(update_op))
self.assertAlmostEqual(expected_precision, self.evaluate(precision))
@test_util.run_deprecated_v1
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.precision(
labels,
predictions,
weights=constant_op.constant([[1, 2, 3, 4], [4, 3, 2, 1]]))
with self.cached_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))
@test_util.run_deprecated_v1
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.precision(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.evaluate(update_op)
self.assertAlmostEqual(0, self.evaluate(precision))
@test_util.run_deprecated_v1
def testZeroTrueAndFalsePositivesGivesZeroPrecision(self):
predictions = constant_op.constant([0, 0, 0, 0])
labels = constant_op.constant([0, 0, 0, 0])
precision, update_op = metrics.precision(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.evaluate(update_op)
self.assertEqual(0.0, self.evaluate(precision))
class RecallTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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=1, dtype=dtypes_lib.int64, seed=1)
recall, update_op = metrics.recall(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_recall = self.evaluate(recall)
for _ in range(10):
self.assertEqual(initial_recall, self.evaluate(recall))
@test_util.run_deprecated_v1
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.recall(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.evaluate(update_op)
self.assertAlmostEqual(1.0, self.evaluate(recall), 6)
@test_util.run_deprecated_v1
def testSomeCorrect_multipleInputDtypes(self):
for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
predictions = math_ops.cast(
constant_op.constant([1, 0, 1, 0], shape=(1, 4)), dtype=dtype)
labels = math_ops.cast(
constant_op.constant([0, 1, 1, 0], shape=(1, 4)), dtype=dtype)
recall, update_op = metrics.recall(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.5, self.evaluate(update_op))
self.assertAlmostEqual(0.5, self.evaluate(recall))
@test_util.run_deprecated_v1
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.recall(labels, predictions, weights=weights)
with self.cached_session():
self.evaluate(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, self.evaluate(update_op))
self.assertAlmostEqual(expected_precision, self.evaluate(recall))
@test_util.run_deprecated_v1
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.recall(labels, predictions, weights=weights)
with self.cached_session():
self.evaluate(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, self.evaluate(update_op))
self.assertAlmostEqual(expected_precision, self.evaluate(recall))
@test_util.run_deprecated_v1
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.recall(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.evaluate(update_op)
self.assertEqual(0, self.evaluate(recall))
@test_util.run_deprecated_v1
def testZeroTruePositivesAndFalseNegativesGivesZeroRecall(self):
predictions = array_ops.zeros((1, 4))
labels = array_ops.zeros((1, 4))
recall, update_op = metrics.recall(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.evaluate(update_op)
self.assertEqual(0, self.evaluate(recall))
class AUCTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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=1, dtype=dtypes_lib.int64, seed=1)
auc, update_op = metrics.auc(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_auc = self.evaluate(auc)
for _ in range(10):
self.assertAlmostEqual(initial_auc, self.evaluate(auc), 5)
@test_util.run_deprecated_v1
def testAllCorrect(self):
self.allCorrectAsExpected('ROC')
def allCorrectAsExpected(self, curve):
inputs = np.random.randint(0, 2, size=(100, 1))
with self.cached_session():
predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
labels = constant_op.constant(inputs)
auc, update_op = metrics.auc(labels, predictions, curve=curve)
self.evaluate(variables.local_variables_initializer())
self.assertEqual(1, self.evaluate(update_op))
self.assertEqual(1, self.evaluate(auc))
@test_util.run_deprecated_v1
def testSomeCorrect_multipleLabelDtypes(self):
with self.cached_session():
for label_dtype in (
dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
predictions = constant_op.constant(
[1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
labels = math_ops.cast(
constant_op.constant([0, 1, 1, 0], shape=(1, 4)), dtype=label_dtype)
auc, update_op = metrics.auc(labels, predictions)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.5, self.evaluate(update_op))
self.assertAlmostEqual(0.5, self.evaluate(auc))
@test_util.run_deprecated_v1
def testWeighted1d(self):
with self.cached_session():
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.auc(labels, predictions, weights=weights)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.5, self.evaluate(update_op), 5)
self.assertAlmostEqual(0.5, self.evaluate(auc), 5)
@test_util.run_deprecated_v1
def testWeighted2d(self):
with self.cached_session():
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.auc(labels, predictions, weights=weights)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.7, self.evaluate(update_op), 5)
self.assertAlmostEqual(0.7, self.evaluate(auc), 5)
@test_util.run_deprecated_v1
def testManualThresholds(self):
with self.cached_session():
# Verifies that thresholds passed in to the `thresholds` parameter are
# used correctly.
# The default thresholds do not split the second and third predictions.
# Thus, when we provide manual thresholds which correctly split it, we get
# an accurate AUC value.
predictions = constant_op.constant(
[0.12, 0.3001, 0.3003, 0.72], shape=(1, 4), dtype=dtypes_lib.float32)
labels = constant_op.constant([0, 1, 0, 1], shape=(1, 4))
weights = constant_op.constant([1, 1, 1, 1], shape=(1, 4))
thresholds = [0.0, 0.2, 0.3002, 0.6, 1.0]
default_auc, default_update_op = metrics.auc(labels,
predictions,
weights=weights)
manual_auc, manual_update_op = metrics.auc(labels,
predictions,
weights=weights,
thresholds=thresholds)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.875, self.evaluate(default_update_op), 3)
self.assertAlmostEqual(0.875, self.evaluate(default_auc), 3)
self.assertAlmostEqual(0.75, self.evaluate(manual_update_op), 3)
self.assertAlmostEqual(0.75, self.evaluate(manual_auc), 3)
# Regarding the AUC-PR tests: note that the preferred method when
# calculating AUC-PR is summation_method='careful_interpolation'.
@test_util.run_deprecated_v1
def testCorrectAUCPRSpecialCase(self):
with self.cached_session():
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.auc(labels, predictions, curve='PR',
summation_method='careful_interpolation')
self.evaluate(variables.local_variables_initializer())
# expected ~= 0.79726744594
expected = 1 - math.log(1.5) / 2
self.assertAlmostEqual(expected, self.evaluate(update_op), delta=1e-3)
self.assertAlmostEqual(expected, self.evaluate(auc), delta=1e-3)
@test_util.run_deprecated_v1
def testCorrectAnotherAUCPRSpecialCase(self):
with self.cached_session():
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.auc(labels, predictions, curve='PR',
summation_method='careful_interpolation')
self.evaluate(variables.local_variables_initializer())
# expected ~= 0.61350593198
expected = (2.5 - 2 * math.log(4./3) - 0.25 * math.log(7./5)) / 3
self.assertAlmostEqual(expected, self.evaluate(update_op), delta=1e-3)
self.assertAlmostEqual(expected, self.evaluate(auc), delta=1e-3)
@test_util.run_deprecated_v1
def testThirdCorrectAUCPRSpecialCase(self):
with self.cached_session():
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.auc(labels, predictions, curve='PR',
summation_method='careful_interpolation')
self.evaluate(variables.local_variables_initializer())
# expected ~= 0.90410597584
expected = 1 - math.log(4./3) / 3
self.assertAlmostEqual(expected, self.evaluate(update_op), delta=1e-3)
self.assertAlmostEqual(expected, self.evaluate(auc), delta=1e-3)
@test_util.run_deprecated_v1
def testIncorrectAUCPRSpecialCase(self):
with self.cached_session():
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.auc(labels, predictions, curve='PR',
summation_method='trapezoidal')
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.79166, self.evaluate(update_op), delta=1e-3)
self.assertAlmostEqual(0.79166, self.evaluate(auc), delta=1e-3)
@test_util.run_deprecated_v1
def testAnotherIncorrectAUCPRSpecialCase(self):
with self.cached_session():
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.auc(labels, predictions, curve='PR',
summation_method='trapezoidal')
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.610317, self.evaluate(update_op), delta=1e-3)
self.assertAlmostEqual(0.610317, self.evaluate(auc), delta=1e-3)
@test_util.run_deprecated_v1
def testThirdIncorrectAUCPRSpecialCase(self):
with self.cached_session():
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.auc(labels, predictions, curve='PR',
summation_method='trapezoidal')
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.90277, self.evaluate(update_op), delta=1e-3)
self.assertAlmostEqual(0.90277, self.evaluate(auc), delta=1e-3)
@test_util.run_deprecated_v1
def testAllIncorrect(self):
inputs = np.random.randint(0, 2, size=(100, 1))
with self.cached_session():
predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
labels = constant_op.constant(1 - inputs, dtype=dtypes_lib.float32)
auc, update_op = metrics.auc(labels, predictions)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0, self.evaluate(update_op))
self.assertAlmostEqual(0, self.evaluate(auc))
@test_util.run_deprecated_v1
def testZeroTruePositivesAndFalseNegativesGivesOneAUC(self):
with self.cached_session():
predictions = array_ops.zeros([4], dtype=dtypes_lib.float32)
labels = array_ops.zeros([4])
auc, update_op = metrics.auc(labels, predictions)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(1, self.evaluate(update_op), 6)
self.assertAlmostEqual(1, self.evaluate(auc), 6)
@test_util.run_deprecated_v1
def testRecallOneAndPrecisionOneGivesOnePRAUC(self):
with self.cached_session():
predictions = array_ops.ones([4], dtype=dtypes_lib.float32)
labels = array_ops.ones([4])
auc, update_op = metrics.auc(labels, predictions, curve='PR')
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(1, self.evaluate(update_op), 6)
self.assertAlmostEqual(1, self.evaluate(auc), 6)
def np_auc(self, predictions, labels, weights):
"""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
@test_util.run_deprecated_v1
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 = self.np_auc(predictions, labels, weights)
with self.cached_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.auc(tf_labels,
tf_predictions,
curve='ROC',
num_thresholds=500,
weights=tf_weights)
self.evaluate(variables.local_variables_initializer())
for i in range(num_batches):
self.evaluate(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, self.evaluate(auc), 2)
class SpecificityAtSensitivityTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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)
specificity, update_op = metrics.specificity_at_sensitivity(
labels, predictions, sensitivity=0.7)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_specificity = self.evaluate(specificity)
for _ in range(10):
self.assertAlmostEqual(initial_specificity, self.evaluate(specificity),
5)
@test_util.run_deprecated_v1
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.specificity_at_sensitivity(
labels, predictions, sensitivity=0.7)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(1, self.evaluate(update_op))
self.assertEqual(1, self.evaluate(specificity))
@test_util.run_deprecated_v1
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.specificity_at_sensitivity(
labels, predictions, sensitivity=0.8)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(1.0, self.evaluate(update_op))
self.assertAlmostEqual(1.0, self.evaluate(specificity))
@test_util.run_deprecated_v1
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.specificity_at_sensitivity(
labels, predictions, sensitivity=0.4)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.6, self.evaluate(update_op))
self.assertAlmostEqual(0.6, self.evaluate(specificity))
@test_util.run_deprecated_v1
def testWeighted1d_multipleLabelDtypes(self):
for label_dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
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 = math_ops.cast(labels_values, dtype=label_dtype)
weights = constant_op.constant(weights_values)
specificity, update_op = metrics.specificity_at_sensitivity(
labels, predictions, weights=weights, sensitivity=0.4)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.6, self.evaluate(update_op))
self.assertAlmostEqual(0.6, self.evaluate(specificity))
@test_util.run_deprecated_v1
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.specificity_at_sensitivity(
labels, predictions, weights=weights, sensitivity=0.4)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(8.0 / 15.0, self.evaluate(update_op))
self.assertAlmostEqual(8.0 / 15.0, self.evaluate(specificity))
class SensitivityAtSpecificityTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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)
sensitivity, update_op = metrics.sensitivity_at_specificity(
labels, predictions, specificity=0.7)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_sensitivity = self.evaluate(sensitivity)
for _ in range(10):
self.assertAlmostEqual(initial_sensitivity, self.evaluate(sensitivity),
5)
@test_util.run_deprecated_v1
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.sensitivity_at_specificity(
labels, predictions, specificity=0.7)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(1.0, self.evaluate(update_op), 6)
self.assertAlmostEqual(1.0, self.evaluate(specificity), 6)
@test_util.run_deprecated_v1
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.sensitivity_at_specificity(
labels, predictions, specificity=0.8)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.8, self.evaluate(update_op))
self.assertAlmostEqual(0.8, self.evaluate(specificity))
@test_util.run_deprecated_v1
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.sensitivity_at_specificity(
labels, predictions, specificity=0.4)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.6, self.evaluate(update_op))
self.assertAlmostEqual(0.6, self.evaluate(specificity))
@test_util.run_deprecated_v1
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, 0.01, 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 = math_ops.cast(labels_values, dtype=label_dtype)
weights = constant_op.constant(weights_values)
specificity, update_op = metrics.sensitivity_at_specificity(
labels, predictions, weights=weights, specificity=0.4)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(0.675, self.evaluate(update_op))
self.assertAlmostEqual(0.675, self.evaluate(specificity))
# TODO(nsilberman): Break this up into two sets of tests.
class PrecisionRecallThresholdsTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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',
))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
prec, _ = metrics.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.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, precision_op = metrics.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.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])
@test_util.run_deprecated_v1
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=1, dtype=dtypes_lib.int64, seed=1)
thresholds = [0, 0.5, 1.0]
prec, prec_op = metrics.precision_at_thresholds(labels, predictions,
thresholds)
rec, rec_op = metrics.recall_at_thresholds(labels, predictions, thresholds)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates, then verify idempotency.
self.evaluate([prec_op, rec_op])
initial_prec = self.evaluate(prec)
initial_rec = self.evaluate(rec)
for _ in range(10):
self.evaluate([prec_op, rec_op])
self.assertAllClose(initial_prec, prec)
self.assertAllClose(initial_rec, rec)
# TODO(nsilberman): fix tests (passing but incorrect).
@test_util.run_deprecated_v1
def testAllCorrect(self):
inputs = np.random.randint(0, 2, size=(100, 1))
with self.cached_session():
predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32)
labels = constant_op.constant(inputs)
thresholds = [0.5]
prec, prec_op = metrics.precision_at_thresholds(labels, predictions,
thresholds)
rec, rec_op = metrics.recall_at_thresholds(labels, predictions,
thresholds)
self.evaluate(variables.local_variables_initializer())
self.evaluate([prec_op, rec_op])
self.assertEqual(1, self.evaluate(prec))
self.assertEqual(1, self.evaluate(rec))
@test_util.run_deprecated_v1
def testSomeCorrect_multipleLabelDtypes(self):
with self.cached_session():
for label_dtype in (
dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32):
predictions = constant_op.constant(
[1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32)
labels = math_ops.cast(
constant_op.constant([0, 1, 1, 0], shape=(1, 4)), dtype=label_dtype)
thresholds = [0.5]
prec, prec_op = metrics.precision_at_thresholds(labels, predictions,
thresholds)
rec, rec_op = metrics.recall_at_thresholds(labels, predictions,
thresholds)
self.evaluate(variables.local_variables_initializer())
self.evaluate([prec_op, rec_op])
self.assertAlmostEqual(0.5, self.evaluate(prec))
self.assertAlmostEqual(0.5, self.evaluate(rec))
@test_util.run_deprecated_v1
def testAllIncorrect(self):
inputs = np.random.randint(0, 2, size=(100, 1))
with self.cached_session():
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.precision_at_thresholds(labels, predictions,
thresholds)
rec, rec_op = metrics.recall_at_thresholds(labels, predictions,
thresholds)
self.evaluate(variables.local_variables_initializer())
self.evaluate([prec_op, rec_op])
self.assertAlmostEqual(0, self.evaluate(prec))
self.assertAlmostEqual(0, self.evaluate(rec))
@test_util.run_deprecated_v1
def testWeights1d(self):
with self.cached_session():
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.precision_at_thresholds(
labels, predictions, thresholds, weights=weights)
rec, rec_op = metrics.recall_at_thresholds(
labels, predictions, thresholds, weights=weights)
[prec_low, prec_high] = array_ops.split(
value=prec, num_or_size_splits=2, axis=0)
prec_low = array_ops.reshape(prec_low, shape=())
prec_high = array_ops.reshape(prec_high, shape=())
[rec_low, rec_high] = array_ops.split(
value=rec, num_or_size_splits=2, axis=0)
rec_low = array_ops.reshape(rec_low, shape=())
rec_high = array_ops.reshape(rec_high, shape=())
self.evaluate(variables.local_variables_initializer())
self.evaluate([prec_op, rec_op])
self.assertAlmostEqual(1.0, self.evaluate(prec_low), places=5)
self.assertAlmostEqual(0.0, self.evaluate(prec_high), places=5)
self.assertAlmostEqual(1.0, self.evaluate(rec_low), places=5)
self.assertAlmostEqual(0.0, self.evaluate(rec_high), places=5)
@test_util.run_deprecated_v1
def testWeights2d(self):
with self.cached_session():
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.precision_at_thresholds(
labels, predictions, thresholds, weights=weights)
rec, rec_op = metrics.recall_at_thresholds(
labels, predictions, thresholds, weights=weights)
[prec_low, prec_high] = array_ops.split(
value=prec, num_or_size_splits=2, axis=0)
prec_low = array_ops.reshape(prec_low, shape=())
prec_high = array_ops.reshape(prec_high, shape=())
[rec_low, rec_high] = array_ops.split(
value=rec, num_or_size_splits=2, axis=0)
rec_low = array_ops.reshape(rec_low, shape=())
rec_high = array_ops.reshape(rec_high, shape=())
self.evaluate(variables.local_variables_initializer())
self.evaluate([prec_op, rec_op])
self.assertAlmostEqual(1.0, self.evaluate(prec_low), places=5)
self.assertAlmostEqual(0.0, self.evaluate(prec_high), places=5)
self.assertAlmostEqual(1.0, self.evaluate(rec_low), places=5)
self.assertAlmostEqual(0.0, self.evaluate(rec_high), places=5)
@test_util.run_deprecated_v1
def testExtremeThresholds(self):
with self.cached_session():
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.precision_at_thresholds(labels, predictions,
thresholds)
rec, rec_op = metrics.recall_at_thresholds(labels, predictions,
thresholds)
[prec_low, prec_high] = array_ops.split(
value=prec, num_or_size_splits=2, axis=0)
[rec_low, rec_high] = array_ops.split(
value=rec, num_or_size_splits=2, axis=0)
self.evaluate(variables.local_variables_initializer())
self.evaluate([prec_op, rec_op])
self.assertAlmostEqual(0.75, self.evaluate(prec_low))
self.assertAlmostEqual(0.0, self.evaluate(prec_high))
self.assertAlmostEqual(1.0, self.evaluate(rec_low))
self.assertAlmostEqual(0.0, self.evaluate(rec_high))
@test_util.run_deprecated_v1
def testZeroLabelsPredictions(self):
with self.cached_session():
predictions = array_ops.zeros([4], dtype=dtypes_lib.float32)
labels = array_ops.zeros([4])
thresholds = [0.5]
prec, prec_op = metrics.precision_at_thresholds(labels, predictions,
thresholds)
rec, rec_op = metrics.recall_at_thresholds(labels, predictions,
thresholds)
self.evaluate(variables.local_variables_initializer())
self.evaluate([prec_op, rec_op])
self.assertAlmostEqual(0, self.evaluate(prec), 6)
self.assertAlmostEqual(0, self.evaluate(rec), 6)
@test_util.run_deprecated_v1
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.cached_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.precision_at_thresholds(tf_labels, tf_predictions,
thresholds)
rec, rec_op = metrics.recall_at_thresholds(tf_labels, tf_predictions,
thresholds)
self.evaluate(variables.local_variables_initializer())
for _ in range(int(num_samples / batch_size)):
self.evaluate([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, self.evaluate(prec), 2)
self.assertAlmostEqual(expected_rec, self.evaluate(rec), 2)
def _test_precision_at_k(predictions,
labels,
k,
expected,
class_id=None,
weights=None,
test_case=None):
with ops.Graph().as_default() as g, test_case.test_session(g):
if weights is not None:
weights = constant_op.constant(weights, dtypes_lib.float32)
metric, update = metrics.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.
test_case.assertRaises(errors_impl.OpError, metric.eval)
test_case.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(test_case, update.eval())
_assert_nan(test_case, metric.eval())
else:
test_case.assertEqual(expected, update.eval())
test_case.assertEqual(expected, metric.eval())
def _test_precision_at_top_k(
predictions_idx,
labels,
expected,
k=None,
class_id=None,
weights=None,
test_case=None):
with ops.Graph().as_default() as g, test_case.test_session(g):
if weights is not None:
weights = constant_op.constant(weights, dtypes_lib.float32)
metric, update = metrics.precision_at_top_k(
predictions_idx=constant_op.constant(predictions_idx, dtypes_lib.int32),
labels=labels,
k=k,
class_id=class_id,
weights=weights)
# Fails without initialized vars.
test_case.assertRaises(errors_impl.OpError, metric.eval)
test_case.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):
test_case.assertTrue(math.isnan(update.eval()))
test_case.assertTrue(math.isnan(metric.eval()))
else:
test_case.assertEqual(expected, update.eval())
test_case.assertEqual(expected, metric.eval())
def _test_average_precision_at_k(predictions,
labels,
k,
expected,
weights=None,
test_case=None):
with ops.Graph().as_default() as g, test_case.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.average_precision_at_k(
labels, predictions, k, weights=weights)
# Fails without initialized vars.
test_case.assertRaises(errors_impl.OpError, metric.eval)
test_case.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(test_case, update.eval())
_assert_nan(test_case, metric.eval())
else:
test_case.assertAlmostEqual(expected, update.eval())
test_case.assertAlmostEqual(expected, metric.eval())
class SingleLabelPrecisionAtKTest(test.TestCase):
def setUp(self):
self._predictions = ((0.1, 0.3, 0.2, 0.4), (0.1, 0.2, 0.3, 0.4))
self._predictions_idx = [[3], [3]]
indicator_labels = ((0, 0, 0, 1), (0, 0, 1, 0))
class_labels = (3, 2)
# Sparse vs dense, and 1d vs 2d labels should all be handled the same.
self._labels = (
_binary_2d_label_to_1d_sparse_value(indicator_labels),
_binary_2d_label_to_2d_sparse_value(indicator_labels), np.array(
class_labels, dtype=np.int64), np.array(
[[class_id] for class_id in class_labels], dtype=np.int64))
self._test_precision_at_k = functools.partial(
_test_precision_at_k, test_case=self)
self._test_precision_at_top_k = functools.partial(
_test_precision_at_top_k, test_case=self)
self._test_average_precision_at_k = functools.partial(
_test_average_precision_at_k, test_case=self)
@test_util.run_deprecated_v1
def test_at_k1_nan(self):
for labels in self._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_precision_at_k(
self._predictions, labels, k=1, expected=NAN, class_id=class_id)
self._test_precision_at_top_k(
self._predictions_idx, labels, k=1, expected=NAN, class_id=class_id)
@test_util.run_deprecated_v1
def test_at_k1(self):
for labels in self._labels:
# Class 3: 1 label, 2 predictions, 1 correct.
self._test_precision_at_k(
self._predictions, labels, k=1, expected=1.0 / 2, class_id=3)
self._test_precision_at_top_k(
self._predictions_idx, labels, k=1, expected=1.0 / 2, class_id=3)
# All classes: 2 labels, 2 predictions, 1 correct.
self._test_precision_at_k(
self._predictions, labels, k=1, expected=1.0 / 2)
self._test_precision_at_top_k(
self._predictions_idx, labels, k=1, expected=1.0 / 2)
self._test_average_precision_at_k(
self._predictions, labels, k=1, expected=1.0 / 2)
class MultiLabelPrecisionAtKTest(test.TestCase):
def setUp(self):
self._test_precision_at_k = functools.partial(
_test_precision_at_k, test_case=self)
self._test_precision_at_top_k = functools.partial(
_test_precision_at_top_k, test_case=self)
self._test_average_precision_at_k = functools.partial(
_test_average_precision_at_k, test_case=self)
@test_util.run_deprecated_v1
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_idx_ex1 = (5, 3, 6, 0, 1)
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_precision_at_k(
predictions, labels, k, expected=precision_ex1[i])
self._test_precision_at_top_k(
(predictions_idx_ex1[:k],), labels, k=k, expected=precision_ex1[i])
self._test_average_precision_at_k(
predictions, labels, k, 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_idx_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_precision_at_k(
predictions, labels, k, expected=precision_ex2[i])
self._test_precision_at_top_k(
(predictions_idx_ex2[:k],), labels, k=k, expected=precision_ex2[i])
self._test_average_precision_at_k(
predictions, labels, k, 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
predictions_idx = (predictions_idx_ex1[:k], predictions_idx_ex2[:k])
self._test_precision_at_k(
predictions, labels, k, expected=streaming_precision[i])
self._test_precision_at_top_k(
predictions_idx, labels, k=k, expected=streaming_precision[i])
self._test_average_precision_at_k(
predictions, labels, k, 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_average_precision_at_k(
predictions,
labels,
k,
expected=streaming_average_precision[i],
weights=weights)
@test_util.run_deprecated_v1
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_idx_ex1 = (5, 3, 6, 0, 1)
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_precision_at_k(
predictions, labels, k, expected=precision_ex1[i])
self._test_precision_at_top_k(
(predictions_idx_ex1[:k],), labels, k=k, expected=precision_ex1[i])
self._test_average_precision_at_k(
predictions, labels, k, expected=avg_precision_ex1[i])
@test_util.run_deprecated_v1
def test_average_precision_different_num_labels(self):
"""Tests the case where the numbers of labels differ across examples."""
predictions = [[0.4, 0.3, 0.2, 0.1], [0.1, 0.2, 0.3, 0.4]]
sparse_labels = _binary_2d_label_to_2d_sparse_value(
[[0, 0, 1, 1], [0, 0, 0, 1]])
dense_labels = np.array([[2, 3], [3, -1]], dtype=np.int64)
predictions_idx_ex1 = np.array(((0, 1, 2, 3), (3, 2, 1, 0)))
precision_ex1 = ((0.0 / 1, 0.0 / 2, 1.0 / 3, 2.0 / 4),
(1.0 / 1, 1.0 / 2, 1.0 / 3, 1.0 / 4))
mean_precision_ex1 = np.mean(precision_ex1, axis=0)
avg_precision_ex1 = (
(0.0 / 1, 0.0 / 2, 1.0 / 3 / 2, (1.0 / 3 + 2.0 / 4) / 2),
(1.0 / 1, 1.0 / 1, 1.0 / 1, 1.0 / 1))
mean_avg_precision_ex1 = np.mean(avg_precision_ex1, axis=0)
for labels in (sparse_labels, dense_labels):
for i in xrange(4):
k = i + 1
self._test_precision_at_k(
predictions, labels, k, expected=mean_precision_ex1[i])
self._test_precision_at_top_k(
predictions_idx_ex1[:, :k], labels, k=k,
expected=mean_precision_ex1[i])
self._test_average_precision_at_k(
predictions, labels, k, expected=mean_avg_precision_ex1[i])
@test_util.run_deprecated_v1
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]]
predictions_idx = [[9, 4, 6, 2, 0], [5, 7, 2, 9, 6]]
sparse_labels = _binary_2d_label_to_2d_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_precision_at_k(
predictions, labels, k=5, expected=NAN, class_id=class_id)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=NAN, class_id=class_id)
@test_util.run_deprecated_v1
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]]
predictions_idx = [[9, 4, 6, 2, 0], [5, 7, 2, 9, 6]]
sparse_labels = _binary_2d_label_to_2d_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_precision_at_k(
predictions, labels, k=5, expected=0.0, class_id=class_id)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=0.0, class_id=class_id)
@test_util.run_deprecated_v1
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]]
predictions_idx = [[9, 4, 6, 2, 0], [5, 7, 2, 9, 6]]
sparse_labels = _binary_2d_label_to_2d_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_precision_at_k(
predictions, labels, k=5, expected=2.0 / 2, class_id=2)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=2.0 / 2, class_id=2)
# Class 5: 1 label, 1 correct prediction.
self._test_precision_at_k(
predictions, labels, k=5, expected=1.0 / 1, class_id=5)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=1.0 / 1, class_id=5)
# Class 7: 1 label, 1 incorrect prediction.
self._test_precision_at_k(
predictions, labels, k=5, expected=0.0 / 1, class_id=7)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=0.0 / 1, class_id=7)
# All classes: 10 predictions, 3 correct.
self._test_precision_at_k(
predictions, labels, k=5, expected=3.0 / 10)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=3.0 / 10)
@test_util.run_deprecated_v1
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]]
predictions_idx = [[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_precision_at_k(
predictions, sp_labels, k=5, expected=2.0 / 2, class_id=2)
self._test_precision_at_top_k(
predictions_idx, sp_labels, k=5, expected=2.0 / 2, class_id=2)
# Class 5: 1 label, 1 correct prediction.
self._test_precision_at_k(
predictions, sp_labels, k=5, expected=1.0 / 1, class_id=5)
self._test_precision_at_top_k(
predictions_idx, sp_labels, k=5, expected=1.0 / 1, class_id=5)
# Class 7: 1 label, 1 incorrect prediction.
self._test_precision_at_k(
predictions, sp_labels, k=5, expected=0.0 / 1, class_id=7)
self._test_precision_at_top_k(
predictions_idx, sp_labels, k=5, expected=0.0 / 1, class_id=7)
# All classes: 10 predictions, 3 correct.
self._test_precision_at_k(
predictions, sp_labels, k=5, expected=3.0 / 10)
self._test_precision_at_top_k(
predictions_idx, sp_labels, k=5, expected=3.0 / 10)
@test_util.run_deprecated_v1
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]]]
predictions_idx = [[[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_precision_at_k(
predictions, labels, k=5, expected=NAN, class_id=class_id)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=NAN, class_id=class_id)
@test_util.run_deprecated_v1
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]]]
predictions_idx = [[[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_precision_at_k(
predictions, labels, k=5, expected=0.0, class_id=class_id)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=0.0, class_id=class_id)
@test_util.run_deprecated_v1
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]]]
predictions_idx = [[[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_precision_at_k(
predictions, labels, k=5, expected=4.0 / 4, class_id=2)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=4.0 / 4, class_id=2)
# Class 5: 2 predictions, both correct.
self._test_precision_at_k(
predictions, labels, k=5, expected=2.0 / 2, class_id=5)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=2.0 / 2, class_id=5)
# Class 7: 2 predictions, 1 correct.
self._test_precision_at_k(
predictions, labels, k=5, expected=1.0 / 2, class_id=7)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=1.0 / 2, class_id=7)
# All classes: 20 predictions, 7 correct.
self._test_precision_at_k(
predictions, labels, k=5, expected=7.0 / 20)
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=7.0 / 20)
@test_util.run_deprecated_v1
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]]]
predictions_idx = [[[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_precision_at_k(
predictions, labels, k=5, expected=2.0 / 2.0, class_id=2,
weights=[[1], [0]])
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=2.0 / 2.0, class_id=2,
weights=[[1], [0]])
# Class 2: 2 predictions, both correct.
self._test_precision_at_k(
predictions, labels, k=5, expected=2.0 / 2.0, class_id=2,
weights=[[0], [1]])
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=2.0 / 2.0, class_id=2,
weights=[[0], [1]])
# Class 7: 1 incorrect prediction.
self._test_precision_at_k(
predictions, labels, k=5, expected=0.0 / 1.0, class_id=7,
weights=[[1], [0]])
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=0.0 / 1.0, class_id=7,
weights=[[1], [0]])
# Class 7: 1 correct prediction.
self._test_precision_at_k(
predictions, labels, k=5, expected=1.0 / 1.0, class_id=7,
weights=[[0], [1]])
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=1.0 / 1.0, class_id=7,
weights=[[0], [1]])
# Class 7: no predictions.
self._test_precision_at_k(
predictions, labels, k=5, expected=NAN, class_id=7,
weights=[[1, 0], [0, 1]])
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=NAN, class_id=7,
weights=[[1, 0], [0, 1]])
# Class 7: 2 predictions, 1 correct.
self._test_precision_at_k(
predictions, labels, k=5, expected=1.0 / 2.0, class_id=7,
weights=[[0, 1], [1, 0]])
self._test_precision_at_top_k(
predictions_idx, labels, k=5, expected=1.0 / 2.0, class_id=7,
weights=[[0, 1], [1, 0]])
def _test_recall_at_k(predictions,
labels,
k,
expected,
class_id=None,
weights=None,
test_case=None):
with ops.Graph().as_default() as g, test_case.test_session(g):
if weights is not None:
weights = constant_op.constant(weights, dtypes_lib.float32)
metric, update = metrics.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.
test_case.assertRaises(errors_impl.OpError, metric.eval)
test_case.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(test_case, update.eval())
_assert_nan(test_case, metric.eval())
else:
test_case.assertEqual(expected, update.eval())
test_case.assertEqual(expected, metric.eval())
def _test_recall_at_top_k(
predictions_idx,
labels,
expected,
k=None,
class_id=None,
weights=None,
test_case=None):
with ops.Graph().as_default() as g, test_case.test_session(g):
if weights is not None:
weights = constant_op.constant(weights, dtypes_lib.float32)
metric, update = metrics.recall_at_top_k(
predictions_idx=constant_op.constant(predictions_idx, dtypes_lib.int32),
labels=labels,
k=k,
class_id=class_id,
weights=weights)
# Fails without initialized vars.
test_case.assertRaises(errors_impl.OpError, metric.eval)
test_case.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(test_case, update.eval())
_assert_nan(test_case, metric.eval())
else:
test_case.assertEqual(expected, update.eval())
test_case.assertEqual(expected, metric.eval())
class SingleLabelRecallAtKTest(test.TestCase):
def setUp(self):
self._predictions = ((0.1, 0.3, 0.2, 0.4), (0.1, 0.2, 0.3, 0.4))
self._predictions_idx = [[3], [3]]
indicator_labels = ((0, 0, 0, 1), (0, 0, 1, 0))
class_labels = (3, 2)
# Sparse vs dense, and 1d vs 2d labels should all be handled the same.
self._labels = (
_binary_2d_label_to_1d_sparse_value(indicator_labels),
_binary_2d_label_to_2d_sparse_value(indicator_labels), np.array(
class_labels, dtype=np.int64), np.array(
[[class_id] for class_id in class_labels], dtype=np.int64))
self._test_recall_at_k = functools.partial(
_test_recall_at_k, test_case=self)
self._test_recall_at_top_k = functools.partial(
_test_recall_at_top_k, test_case=self)
@test_util.run_deprecated_v1
def test_at_k1_nan(self):
# Classes 0,1 have 0 labels, 0 predictions, classes -1 and 4 are out of
# range.
for labels in self._labels:
for class_id in (-1, 0, 1, 4):
self._test_recall_at_k(
self._predictions, labels, k=1, expected=NAN, class_id=class_id)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=1, expected=NAN, class_id=class_id)
@test_util.run_deprecated_v1
def test_at_k1_no_predictions(self):
for labels in self._labels:
# Class 2: 0 predictions.
self._test_recall_at_k(
self._predictions, labels, k=1, expected=0.0, class_id=2)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=1, expected=0.0, class_id=2)
@test_util.run_deprecated_v1
def test_one_label_at_k1(self):
for labels in self._labels:
# Class 3: 1 label, 2 predictions, 1 correct.
self._test_recall_at_k(
self._predictions, labels, k=1, expected=1.0 / 1, class_id=3)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=1, expected=1.0 / 1, class_id=3)
# All classes: 2 labels, 2 predictions, 1 correct.
self._test_recall_at_k(self._predictions, labels, k=1, expected=1.0 / 2)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=1, expected=1.0 / 2)
@test_util.run_deprecated_v1
def test_one_label_at_k1_weighted_class_id3(self):
predictions = self._predictions
predictions_idx = self._predictions_idx
for labels in self._labels:
# Class 3: 1 label, 2 predictions, 1 correct.
self._test_recall_at_k(
predictions, labels, k=1, expected=NAN, class_id=3, weights=(0.0,))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=NAN, class_id=3,
weights=(0.0,))
self._test_recall_at_k(
predictions, labels, k=1, expected=1.0 / 1, class_id=3,
weights=(1.0,))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=1.0 / 1, class_id=3,
weights=(1.0,))
self._test_recall_at_k(
predictions, labels, k=1, expected=1.0 / 1, class_id=3,
weights=(2.0,))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=1.0 / 1, class_id=3,
weights=(2.0,))
self._test_recall_at_k(
predictions, labels, k=1, expected=NAN, class_id=3,
weights=(0.0, 1.0))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=NAN, class_id=3,
weights=(0.0, 1.0))
self._test_recall_at_k(
predictions, labels, k=1, expected=1.0 / 1, class_id=3,
weights=(1.0, 0.0))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=1.0 / 1, class_id=3,
weights=(1.0, 0.0))
self._test_recall_at_k(
predictions, labels, k=1, expected=2.0 / 2, class_id=3,
weights=(2.0, 3.0))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=2.0 / 2, class_id=3,
weights=(2.0, 3.0))
@test_util.run_deprecated_v1
def test_one_label_at_k1_weighted(self):
predictions = self._predictions
predictions_idx = self._predictions_idx
for labels in self._labels:
# All classes: 2 labels, 2 predictions, 1 correct.
self._test_recall_at_k(
predictions, labels, k=1, expected=NAN, weights=(0.0,))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=NAN, weights=(0.0,))
self._test_recall_at_k(
predictions, labels, k=1, expected=1.0 / 2, weights=(1.0,))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=1.0 / 2, weights=(1.0,))
self._test_recall_at_k(
predictions, labels, k=1, expected=1.0 / 2, weights=(2.0,))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=1.0 / 2, weights=(2.0,))
self._test_recall_at_k(
predictions, labels, k=1, expected=1.0 / 1, weights=(1.0, 0.0))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=1.0 / 1, weights=(1.0, 0.0))
self._test_recall_at_k(
predictions, labels, k=1, expected=0.0 / 1, weights=(0.0, 1.0))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=0.0 / 1, weights=(0.0, 1.0))
self._test_recall_at_k(
predictions, labels, k=1, expected=2.0 / 5, weights=(2.0, 3.0))
self._test_recall_at_top_k(
predictions_idx, labels, k=1, expected=2.0 / 5, weights=(2.0, 3.0))
class MultiLabel2dRecallAtKTest(test.TestCase):
def setUp(self):
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))
self._predictions_idx = ((9, 4, 6, 2, 0), (5, 7, 2, 9, 6))
indicator_labels = ((0, 0, 1, 0, 0, 0, 0, 1, 1, 0),
(0, 1, 1, 0, 0, 1, 0, 0, 0, 0))
class_labels = ((2, 7, 8), (1, 2, 5))
# Sparse vs dense labels should be handled the same.
self._labels = (_binary_2d_label_to_2d_sparse_value(indicator_labels),
np.array(
class_labels, dtype=np.int64))
self._test_recall_at_k = functools.partial(
_test_recall_at_k, test_case=self)
self._test_recall_at_top_k = functools.partial(
_test_recall_at_top_k, test_case=self)
@test_util.run_deprecated_v1
def test_at_k5_nan(self):
for labels in self._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_recall_at_k(
self._predictions, labels, k=5, expected=NAN, class_id=class_id)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=NAN, class_id=class_id)
@test_util.run_deprecated_v1
def test_at_k5_no_predictions(self):
for labels in self._labels:
# Class 8: 1 label, no predictions.
self._test_recall_at_k(
self._predictions, labels, k=5, expected=0.0 / 1, class_id=8)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=0.0 / 1, class_id=8)
@test_util.run_deprecated_v1
def test_at_k5(self):
for labels in self._labels:
# Class 2: 2 labels, both correct.
self._test_recall_at_k(
self._predictions, labels, k=5, expected=2.0 / 2, class_id=2)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=2.0 / 2, class_id=2)
# Class 5: 1 label, incorrect.
self._test_recall_at_k(
self._predictions, labels, k=5, expected=1.0 / 1, class_id=5)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=1.0 / 1, class_id=5)
# Class 7: 1 label, incorrect.
self._test_recall_at_k(
self._predictions, labels, k=5, expected=0.0 / 1, class_id=7)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=0.0 / 1, class_id=7)
# All classes: 6 labels, 3 correct.
self._test_recall_at_k(self._predictions, labels, k=5, expected=3.0 / 6)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=3.0 / 6)
@test_util.run_deprecated_v1
def test_at_k5_some_out_of_range(self):
"""Tests that labels outside the [0, n_classes) count in denominator."""
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_recall_at_k(
self._predictions, labels, k=5, expected=2.0 / 2, class_id=2)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=2.0 / 2, class_id=2)
# Class 5: 1 label, incorrect.
self._test_recall_at_k(
self._predictions, labels, k=5, expected=1.0 / 1, class_id=5)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=1.0 / 1, class_id=5)
# Class 7: 1 label, incorrect.
self._test_recall_at_k(
self._predictions, labels, k=5, expected=0.0 / 1, class_id=7)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=0.0 / 1, class_id=7)
# All classes: 8 labels, 3 correct.
self._test_recall_at_k(self._predictions, labels, k=5, expected=3.0 / 8)
self._test_recall_at_top_k(
self._predictions_idx, labels, k=5, expected=3.0 / 8)
class MultiLabel3dRecallAtKTest(test.TestCase):
def setUp(self):
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)))
self._predictions_idx = (((9, 4, 6, 2, 0), (5, 7, 2, 9, 6)),
((5, 7, 2, 9, 6), (9, 4, 6, 2, 0)))
# Note: We don't test dense labels here, since examples have different
# numbers of labels.
self._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))))
self._test_recall_at_k = functools.partial(
_test_recall_at_k, test_case=self)
self._test_recall_at_top_k = functools.partial(
_test_recall_at_top_k, test_case=self)
@test_util.run_deprecated_v1
def test_3d_nan(self):
# 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_recall_at_k(
self._predictions, self._labels, k=5, expected=NAN, class_id=class_id)
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=NAN,
class_id=class_id)
@test_util.run_deprecated_v1
def test_3d_no_predictions(self):
# Classes 1,8 have 0 predictions, >=1 label.
for class_id in (1, 8):
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=0.0, class_id=class_id)
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=0.0,
class_id=class_id)
@test_util.run_deprecated_v1
def test_3d(self):
# Class 2: 4 labels, all correct.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=4.0 / 4, class_id=2)
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=4.0 / 4,
class_id=2)
# Class 5: 2 labels, both correct.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=2.0 / 2, class_id=5)
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=2.0 / 2,
class_id=5)
# Class 7: 2 labels, 1 incorrect.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=1.0 / 2, class_id=7)
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=1.0 / 2,
class_id=7)
# All classes: 12 labels, 7 correct.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=7.0 / 12)
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=7.0 / 12)
@test_util.run_deprecated_v1
def test_3d_ignore_all(self):
for class_id in xrange(10):
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=NAN, class_id=class_id,
weights=[[0], [0]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=NAN,
class_id=class_id, weights=[[0], [0]])
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=NAN, class_id=class_id,
weights=[[0, 0], [0, 0]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=NAN,
class_id=class_id, weights=[[0, 0], [0, 0]])
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=NAN, weights=[[0], [0]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=NAN,
weights=[[0], [0]])
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=NAN,
weights=[[0, 0], [0, 0]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=NAN,
weights=[[0, 0], [0, 0]])
@test_util.run_deprecated_v1
def test_3d_ignore_some(self):
# Class 2: 2 labels, both correct.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=2.0 / 2.0, class_id=2,
weights=[[1], [0]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=2.0 / 2.0,
class_id=2, weights=[[1], [0]])
# Class 2: 2 labels, both correct.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=2.0 / 2.0, class_id=2,
weights=[[0], [1]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=2.0 / 2.0,
class_id=2, weights=[[0], [1]])
# Class 7: 1 label, correct.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=1.0 / 1.0, class_id=7,
weights=[[0], [1]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=1.0 / 1.0,
class_id=7, weights=[[0], [1]])
# Class 7: 1 label, incorrect.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=0.0 / 1.0, class_id=7,
weights=[[1], [0]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=0.0 / 1.0,
class_id=7, weights=[[1], [0]])
# Class 7: 2 labels, 1 correct.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=1.0 / 2.0, class_id=7,
weights=[[1, 0], [1, 0]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=1.0 / 2.0,
class_id=7, weights=[[1, 0], [1, 0]])
# Class 7: No labels.
self._test_recall_at_k(
self._predictions, self._labels, k=5, expected=NAN, class_id=7,
weights=[[0, 1], [0, 1]])
self._test_recall_at_top_k(
self._predictions_idx, self._labels, k=5, expected=NAN, class_id=7,
weights=[[0, 1], [0, 1]])
class MeanAbsoluteErrorTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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.mean_absolute_error(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_error = self.evaluate(error)
for _ in range(10):
self.assertEqual(initial_error, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_absolute_error(labels, predictions, weights)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(3, self.evaluate(update_op))
self.assertEqual(3, self.evaluate(error))
class MeanRelativeErrorTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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.mean_relative_error(labels, predictions,
normalizer)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_error = self.evaluate(error)
for _ in range(10):
self.assertEqual(initial_error, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_relative_error(
labels, predictions, normalizer=labels)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(expected_error, self.evaluate(update_op))
self.assertEqual(expected_error, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_relative_error(
labels, predictions, normalizer=array_ops.zeros_like(labels))
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(0.0, self.evaluate(update_op))
self.assertEqual(0.0, self.evaluate(error))
class MeanSquaredErrorTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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.mean_squared_error(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_error = self.evaluate(error)
for _ in range(10):
self.assertEqual(initial_error, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_squared_error(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(0, self.evaluate(update_op))
self.assertEqual(0, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_squared_error(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(6, self.evaluate(update_op))
self.assertEqual(6, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_squared_error(labels, predictions, weights)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(13, self.evaluate(update_op))
self.assertEqual(13, self.evaluate(error))
@test_util.run_deprecated_v1
def testMultipleBatchesOfSizeOne(self):
with self.cached_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.mean_squared_error(labels, predictions)
self.evaluate(variables.local_variables_initializer())
self.evaluate(update_op)
self.assertAlmostEqual(208.0 / 6, self.evaluate(update_op), 5)
self.assertAlmostEqual(208.0 / 6, self.evaluate(error), 5)
@test_util.run_deprecated_v1
def testMetricsComputedConcurrently(self):
with self.cached_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.mean_squared_error(
labels0, predictions0, name='msd0')
mse1, update_op1 = metrics.mean_squared_error(
labels1, predictions1, name='msd1')
self.evaluate(variables.local_variables_initializer())
self.evaluate([update_op0, update_op1])
self.evaluate([update_op0, update_op1])
mse0, mse1 = self.evaluate([mse0, mse1])
self.assertAlmostEqual(208.0 / 6, mse0, 5)
self.assertAlmostEqual(79.0 / 6, mse1, 5)
@test_util.run_deprecated_v1
def testMultipleMetricsOnMultipleBatchesOfSizeOne(self):
with self.cached_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.mean_absolute_error(labels, predictions)
mse, ms_update_op = metrics.mean_squared_error(labels, predictions)
self.evaluate(variables.local_variables_initializer())
self.evaluate([ma_update_op, ms_update_op])
self.evaluate([ma_update_op, ms_update_op])
self.assertAlmostEqual(32.0 / 6, self.evaluate(mae), 5)
self.assertAlmostEqual(208.0 / 6, self.evaluate(mse), 5)
class RootMeanSquaredErrorTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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'))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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.root_mean_squared_error(labels, predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_error = self.evaluate(error)
for _ in range(10):
self.assertEqual(initial_error, self.evaluate(error))
@test_util.run_deprecated_v1
def testSingleUpdateZeroError(self):
with self.cached_session():
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.root_mean_squared_error(labels, predictions)
self.evaluate(variables.local_variables_initializer())
self.assertEqual(0, self.evaluate(update_op))
self.assertEqual(0, self.evaluate(rmse))
@test_util.run_deprecated_v1
def testSingleUpdateWithError(self):
with self.cached_session():
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.root_mean_squared_error(labels, predictions)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(math.sqrt(6), self.evaluate(update_op), 5)
self.assertAlmostEqual(math.sqrt(6), self.evaluate(rmse), 5)
@test_util.run_deprecated_v1
def testSingleUpdateWithErrorAndWeights(self):
with self.cached_session():
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.root_mean_squared_error(labels, predictions,
weights)
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(math.sqrt(13), self.evaluate(update_op))
self.assertAlmostEqual(math.sqrt(13), self.evaluate(rmse), 5)
def _reweight(predictions, labels, weights):
return (np.concatenate([[p] * int(w) for p, w in zip(predictions, weights)]),
np.concatenate([[l] * int(w) for l, w in zip(labels, weights)]))
class MeanCosineDistanceTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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',
))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
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.mean_cosine_distance(labels, predictions, dim=1)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_error = self.evaluate(error)
for _ in range(10):
self.assertEqual(initial_error, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_cosine_distance(labels, predictions, dim=2)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(0, self.evaluate(update_op))
self.assertEqual(0, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_cosine_distance(labels, predictions, dim=2)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(1, self.evaluate(update_op), 5)
self.assertAlmostEqual(1, self.evaluate(error), 5)
@test_util.run_deprecated_v1
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.mean_cosine_distance(labels, predictions, dim=2)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAlmostEqual(1.0, self.evaluate(update_op), 5)
self.assertAlmostEqual(1.0, self.evaluate(error), 5)
@test_util.run_deprecated_v1
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.mean_cosine_distance(
labels, predictions, dim=2, weights=weights)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(0, self.evaluate(update_op))
self.assertEqual(0, self.evaluate(error))
@test_util.run_deprecated_v1
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.mean_cosine_distance(
labels, predictions, dim=2, weights=weights)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertEqual(1.5, self.evaluate(update_op))
self.assertEqual(1.5, self.evaluate(error))
class PcntBelowThreshTest(test.TestCase):
def setUp(self):
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.percentage_below(values=array_ops.ones((10,)), threshold=2)
_assert_metric_variables(self, (
'percentage_below_threshold/count:0',
'percentage_below_threshold/total:0',
))
@test_util.run_deprecated_v1
def testMetricsCollection(self):
my_collection_name = '__metrics__'
mean, _ = metrics.percentage_below(
values=array_ops.ones((10,)),
threshold=2,
metrics_collections=[my_collection_name])
self.assertListEqual(ops.get_collection(my_collection_name), [mean])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.percentage_below(
values=array_ops.ones((10,)),
threshold=2,
updates_collections=[my_collection_name])
self.assertListEqual(ops.get_collection(my_collection_name), [update_op])
@test_util.run_deprecated_v1
def testOneUpdate(self):
with self.cached_session():
values = constant_op.constant(
[2, 4, 6, 8], shape=(1, 4), dtype=dtypes_lib.float32)
pcnt0, update_op0 = metrics.percentage_below(values, 100, name='high')
pcnt1, update_op1 = metrics.percentage_below(values, 7, name='medium')
pcnt2, update_op2 = metrics.percentage_below(values, 1, name='low')
self.evaluate(variables.local_variables_initializer())
self.evaluate([update_op0, update_op1, update_op2])
pcnt0, pcnt1, pcnt2 = self.evaluate([pcnt0, pcnt1, pcnt2])
self.assertAlmostEqual(1.0, pcnt0, 5)
self.assertAlmostEqual(0.75, pcnt1, 5)
self.assertAlmostEqual(0.0, pcnt2, 5)
@test_util.run_deprecated_v1
def testSomePresentOneUpdate(self):
with self.cached_session():
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.percentage_below(
values, 100, weights=weights, name='high')
pcnt1, update_op1 = metrics.percentage_below(
values, 7, weights=weights, name='medium')
pcnt2, update_op2 = metrics.percentage_below(
values, 1, weights=weights, name='low')
self.evaluate(variables.local_variables_initializer())
self.assertListEqual([1.0, 0.5, 0.0],
self.evaluate([update_op0, update_op1, update_op2]))
pcnt0, pcnt1, pcnt2 = self.evaluate([pcnt0, pcnt1, pcnt2])
self.assertAlmostEqual(1.0, pcnt0, 5)
self.assertAlmostEqual(0.5, pcnt1, 5)
self.assertAlmostEqual(0.0, pcnt2, 5)
class MeanIOUTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.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',))
@test_util.run_deprecated_v1
def testMetricsCollections(self):
my_collection_name = '__metrics__'
mean_iou, _ = metrics.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])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.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])
@test_util.run_deprecated_v1
def testPredictionsAndLabelsOfDifferentSizeRaisesValueError(self):
predictions = array_ops.ones([10, 3])
labels = array_ops.ones([10, 4])
with self.assertRaises(ValueError):
metrics.mean_iou(labels, predictions, num_classes=2)
@test_util.run_deprecated_v1
def testLabelsAndWeightsOfDifferentSizeRaisesValueError(self):
predictions = array_ops.ones([10])
labels = array_ops.ones([10])
weights = array_ops.zeros([9])
with self.assertRaises(ValueError):
metrics.mean_iou(labels, predictions, num_classes=2, weights=weights)
@test_util.run_deprecated_v1
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=1)
mean_iou, update_op = metrics.mean_iou(
labels, predictions, num_classes=num_classes)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_mean_iou = self.evaluate(mean_iou)
for _ in range(10):
self.assertEqual(initial_mean_iou, self.evaluate(mean_iou))
@test_util.run_deprecated_v1
def testMultipleUpdates(self):
num_classes = 3
with self.cached_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.mean_iou(labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
for _ in range(5):
self.evaluate(update_op)
desired_output = np.mean([1.0 / 2.0, 1.0 / 4.0, 0.])
self.assertEqual(desired_output, self.evaluate(miou))
@test_util.run_deprecated_v1
def testMultipleUpdatesWithWeights(self):
num_classes = 2
with self.cached_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()
mean_iou, update_op = metrics.mean_iou(
labels, predictions, num_classes, weights=weights)
variables.local_variables_initializer().run()
for _ in range(6):
self.evaluate(update_op)
desired_output = np.mean([2.0 / 3.0, 1.0 / 2.0])
self.assertAlmostEqual(desired_output, self.evaluate(mean_iou))
@test_util.run_deprecated_v1
def testMultipleUpdatesWithMissingClass(self):
# Test the case where there are no predictions 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.cached_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.mean_iou(labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
for _ in range(5):
self.evaluate(update_op)
desired_output = np.mean([1.0 / 3.0, 2.0 / 4.0])
self.assertAlmostEqual(desired_output, self.evaluate(miou))
@test_util.run_deprecated_v1
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.cached_session():
miou, update_op = metrics.mean_iou(labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
confusion_matrix = self.evaluate(update_op)
self.assertAllEqual([[3, 0], [2, 5]], confusion_matrix)
desired_miou = np.mean([3. / 5., 5. / 7.])
self.assertAlmostEqual(desired_miou, self.evaluate(miou))
@test_util.run_deprecated_v1
def testAllCorrect(self):
predictions = array_ops.zeros([40])
labels = array_ops.zeros([40])
num_classes = 1
with self.cached_session():
miou, update_op = metrics.mean_iou(labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
self.assertEqual(40, self.evaluate(update_op)[0])
self.assertEqual(1.0, self.evaluate(miou))
@test_util.run_deprecated_v1
def testAllWrong(self):
predictions = array_ops.zeros([40])
labels = array_ops.ones([40])
num_classes = 2
with self.cached_session():
miou, update_op = metrics.mean_iou(labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual([[0, 0], [40, 0]], update_op)
self.assertEqual(0., self.evaluate(miou))
@test_util.run_deprecated_v1
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.cached_session():
miou, update_op = metrics.mean_iou(
labels, predictions, num_classes, weights=weights)
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual([[2, 0], [2, 4]], update_op)
desired_miou = np.mean([2. / 4., 4. / 6.])
self.assertAlmostEqual(desired_miou, self.evaluate(miou))
@test_util.run_deprecated_v1
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.cached_session():
miou, update_op = metrics.mean_iou(labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual([[7, 4, 3], [3, 5, 2], [0, 0, 0]], update_op)
self.assertAlmostEqual(
1 / 3 * (7 / (7 + 3 + 7) + 5 / (5 + 4 + 5) + 0 / (0 + 5 + 0)),
self.evaluate(miou))
@test_util.run_deprecated_v1
def testMissingClassOverallSmall(self):
labels = constant_op.constant([0])
predictions = constant_op.constant([0])
num_classes = 2
with self.cached_session():
miou, update_op = metrics.mean_iou(labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual([[1, 0], [0, 0]], update_op)
self.assertAlmostEqual(1, self.evaluate(miou))
@test_util.run_deprecated_v1
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.cached_session():
miou, update_op = metrics.mean_iou(labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual([[9, 5, 0], [3, 7, 0], [0, 0, 0]], update_op)
self.assertAlmostEqual(1 / 2 * (9 / (9 + 3 + 5) + 7 / (7 + 5 + 3)),
self.evaluate(miou))
class MeanPerClassAccuracyTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.mean_per_class_accuracy(
predictions=array_ops.ones([10, 1]),
labels=array_ops.ones([10, 1]),
num_classes=2)
_assert_metric_variables(self, ('mean_accuracy/count:0',
'mean_accuracy/total:0'))
@test_util.run_deprecated_v1
def testMetricsCollections(self):
my_collection_name = '__metrics__'
mean_accuracy, _ = metrics.mean_per_class_accuracy(
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_accuracy])
@test_util.run_deprecated_v1
def testUpdatesCollection(self):
my_collection_name = '__updates__'
_, update_op = metrics.mean_per_class_accuracy(
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])
@test_util.run_deprecated_v1
def testPredictionsAndLabelsOfDifferentSizeRaisesValueError(self):
predictions = array_ops.ones([10, 3])
labels = array_ops.ones([10, 4])
with self.assertRaises(ValueError):
metrics.mean_per_class_accuracy(labels, predictions, num_classes=2)
@test_util.run_deprecated_v1
def testLabelsAndWeightsOfDifferentSizeRaisesValueError(self):
predictions = array_ops.ones([10])
labels = array_ops.ones([10])
weights = array_ops.zeros([9])
with self.assertRaises(ValueError):
metrics.mean_per_class_accuracy(
labels, predictions, num_classes=2, weights=weights)
@test_util.run_deprecated_v1
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=1)
mean_accuracy, update_op = metrics.mean_per_class_accuracy(
labels, predictions, num_classes=num_classes)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_mean_accuracy = self.evaluate(mean_accuracy)
for _ in range(10):
self.assertEqual(initial_mean_accuracy, self.evaluate(mean_accuracy))
num_classes = 3
with self.cached_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()
mean_accuracy, update_op = metrics.mean_per_class_accuracy(
labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
for _ in range(5):
self.evaluate(update_op)
desired_output = np.mean([1.0, 1.0 / 3.0, 0.0])
self.assertAlmostEqual(desired_output, self.evaluate(mean_accuracy))
@test_util.run_deprecated_v1
def testMultipleUpdatesWithWeights(self):
num_classes = 2
with self.cached_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, [0.5])
_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()
mean_accuracy, update_op = metrics.mean_per_class_accuracy(
labels, predictions, num_classes, weights=weights)
variables.local_variables_initializer().run()
for _ in range(6):
self.evaluate(update_op)
desired_output = np.mean([2.0 / 2.0, 0.5 / 1.5])
self.assertAlmostEqual(desired_output, self.evaluate(mean_accuracy))
@test_util.run_deprecated_v1
def testMultipleUpdatesWithMissingClass(self):
# Test the case where there are no predictions 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.cached_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()
mean_accuracy, update_op = metrics.mean_per_class_accuracy(
labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
for _ in range(5):
self.evaluate(update_op)
desired_output = np.mean([1.0 / 2.0, 2.0 / 3.0, 0.])
self.assertAlmostEqual(desired_output, self.evaluate(mean_accuracy))
@test_util.run_deprecated_v1
def testAllCorrect(self):
predictions = array_ops.zeros([40])
labels = array_ops.zeros([40])
num_classes = 1
with self.cached_session():
mean_accuracy, update_op = metrics.mean_per_class_accuracy(
labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
self.assertEqual(1.0, self.evaluate(update_op)[0])
self.assertEqual(1.0, self.evaluate(mean_accuracy))
@test_util.run_deprecated_v1
def testAllWrong(self):
predictions = array_ops.zeros([40])
labels = array_ops.ones([40])
num_classes = 2
with self.cached_session():
mean_accuracy, update_op = metrics.mean_per_class_accuracy(
labels, predictions, num_classes)
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual([0.0, 0.0], update_op)
self.assertEqual(0., self.evaluate(mean_accuracy))
@test_util.run_deprecated_v1
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.cached_session():
mean_accuracy, update_op = metrics.mean_per_class_accuracy(
labels, predictions, num_classes, weights=weights)
self.evaluate(variables.local_variables_initializer())
desired_accuracy = np.array([2. / 2., 4. / 6.], dtype=np.float32)
self.assertAllEqual(desired_accuracy, update_op)
desired_mean_accuracy = np.mean(desired_accuracy)
self.assertAlmostEqual(desired_mean_accuracy,
self.evaluate(mean_accuracy))
class FalseNegativesTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.false_negatives(
labels=(0, 1, 0, 1),
predictions=(0, 0, 1, 1))
_assert_metric_variables(self, ('false_negatives/count:0',))
@test_util.run_deprecated_v1
def testUnweighted(self):
labels = constant_op.constant(((0, 1, 0, 1, 0),
(0, 0, 1, 1, 1),
(1, 1, 1, 1, 0),
(0, 0, 0, 0, 1)))
predictions = constant_op.constant(((0, 0, 1, 1, 0),
(1, 1, 1, 1, 1),
(0, 1, 0, 1, 0),
(1, 1, 1, 1, 1)))
tn, tn_update_op = metrics.false_negatives(
labels=labels, predictions=predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllClose(0., tn)
self.assertAllClose(3., tn_update_op)
self.assertAllClose(3., tn)
@test_util.run_deprecated_v1
def testWeighted(self):
labels = constant_op.constant(((0, 1, 0, 1, 0),
(0, 0, 1, 1, 1),
(1, 1, 1, 1, 0),
(0, 0, 0, 0, 1)))
predictions = constant_op.constant(((0, 0, 1, 1, 0),
(1, 1, 1, 1, 1),
(0, 1, 0, 1, 0),
(1, 1, 1, 1, 1)))
weights = constant_op.constant((1., 1.5, 2., 2.5))
tn, tn_update_op = metrics.false_negatives(
labels=labels, predictions=predictions, weights=weights)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllClose(0., tn)
self.assertAllClose(5., tn_update_op)
self.assertAllClose(5., tn)
class FalseNegativesAtThresholdsTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.false_negatives_at_thresholds(
predictions=array_ops.ones((10, 1)),
labels=array_ops.ones((10, 1)),
thresholds=[0.15, 0.5, 0.85])
_assert_metric_variables(self, ('false_negatives/false_negatives:0',))
@test_util.run_deprecated_v1
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.false_negatives_at_thresholds(
predictions=predictions, labels=labels, thresholds=[0.15, 0.5, 0.85])
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual((0, 0, 0), fn)
self.assertAllEqual((0, 2, 3), fn_update_op)
self.assertAllEqual((0, 2, 3), fn)
@test_util.run_deprecated_v1
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.false_negatives_at_thresholds(
predictions=predictions,
labels=labels,
weights=((3.0,), (5.0,), (7.0,)),
thresholds=[0.15, 0.5, 0.85])
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual((0.0, 0.0, 0.0), fn)
self.assertAllEqual((0.0, 8.0, 11.0), fn_update_op)
self.assertAllEqual((0.0, 8.0, 11.0), fn)
class FalsePositivesTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.false_positives(
labels=(0, 1, 0, 1),
predictions=(0, 0, 1, 1))
_assert_metric_variables(self, ('false_positives/count:0',))
@test_util.run_deprecated_v1
def testUnweighted(self):
labels = constant_op.constant(((0, 1, 0, 1, 0),
(0, 0, 1, 1, 1),
(1, 1, 1, 1, 0),
(0, 0, 0, 0, 1)))
predictions = constant_op.constant(((0, 0, 1, 1, 0),
(1, 1, 1, 1, 1),
(0, 1, 0, 1, 0),
(1, 1, 1, 1, 1)))
tn, tn_update_op = metrics.false_positives(
labels=labels, predictions=predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllClose(0., tn)
self.assertAllClose(7., tn_update_op)
self.assertAllClose(7., tn)
@test_util.run_deprecated_v1
def testWeighted(self):
labels = constant_op.constant(((0, 1, 0, 1, 0),
(0, 0, 1, 1, 1),
(1, 1, 1, 1, 0),
(0, 0, 0, 0, 1)))
predictions = constant_op.constant(((0, 0, 1, 1, 0),
(1, 1, 1, 1, 1),
(0, 1, 0, 1, 0),
(1, 1, 1, 1, 1)))
weights = constant_op.constant((1., 1.5, 2., 2.5))
tn, tn_update_op = metrics.false_positives(
labels=labels, predictions=predictions, weights=weights)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllClose(0., tn)
self.assertAllClose(14., tn_update_op)
self.assertAllClose(14., tn)
class FalsePositivesAtThresholdsTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.false_positives_at_thresholds(
predictions=array_ops.ones((10, 1)),
labels=array_ops.ones((10, 1)),
thresholds=[0.15, 0.5, 0.85])
_assert_metric_variables(self, ('false_positives/false_positives:0',))
@test_util.run_deprecated_v1
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.false_positives_at_thresholds(
predictions=predictions, labels=labels, thresholds=[0.15, 0.5, 0.85])
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual((0, 0, 0), fp)
self.assertAllEqual((7, 4, 2), fp_update_op)
self.assertAllEqual((7, 4, 2), fp)
@test_util.run_deprecated_v1
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.false_positives_at_thresholds(
predictions=predictions,
labels=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.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual((0.0, 0.0, 0.0), fp)
self.assertAllEqual((125.0, 42.0, 12.0), fp_update_op)
self.assertAllEqual((125.0, 42.0, 12.0), fp)
class TrueNegativesTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.true_negatives(
labels=(0, 1, 0, 1),
predictions=(0, 0, 1, 1))
_assert_metric_variables(self, ('true_negatives/count:0',))
@test_util.run_deprecated_v1
def testUnweighted(self):
labels = constant_op.constant(((0, 1, 0, 1, 0),
(0, 0, 1, 1, 1),
(1, 1, 1, 1, 0),
(0, 0, 0, 0, 1)))
predictions = constant_op.constant(((0, 0, 1, 1, 0),
(1, 1, 1, 1, 1),
(0, 1, 0, 1, 0),
(1, 1, 1, 1, 1)))
tn, tn_update_op = metrics.true_negatives(
labels=labels, predictions=predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllClose(0., tn)
self.assertAllClose(3., tn_update_op)
self.assertAllClose(3., tn)
@test_util.run_deprecated_v1
def testWeighted(self):
labels = constant_op.constant(((0, 1, 0, 1, 0),
(0, 0, 1, 1, 1),
(1, 1, 1, 1, 0),
(0, 0, 0, 0, 1)))
predictions = constant_op.constant(((0, 0, 1, 1, 0),
(1, 1, 1, 1, 1),
(0, 1, 0, 1, 0),
(1, 1, 1, 1, 1)))
weights = constant_op.constant((1., 1.5, 2., 2.5))
tn, tn_update_op = metrics.true_negatives(
labels=labels, predictions=predictions, weights=weights)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllClose(0., tn)
self.assertAllClose(4., tn_update_op)
self.assertAllClose(4., tn)
class TrueNegativesAtThresholdsTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.true_negatives_at_thresholds(
predictions=array_ops.ones((10, 1)),
labels=array_ops.ones((10, 1)),
thresholds=[0.15, 0.5, 0.85])
_assert_metric_variables(self, ('true_negatives/true_negatives:0',))
@test_util.run_deprecated_v1
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.true_negatives_at_thresholds(
predictions=predictions, labels=labels, thresholds=[0.15, 0.5, 0.85])
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual((0, 0, 0), tn)
self.assertAllEqual((2, 5, 7), tn_update_op)
self.assertAllEqual((2, 5, 7), tn)
@test_util.run_deprecated_v1
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.true_negatives_at_thresholds(
predictions=predictions,
labels=labels,
weights=((0.0, 2.0, 3.0, 5.0),),
thresholds=[0.15, 0.5, 0.85])
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual((0.0, 0.0, 0.0), tn)
self.assertAllEqual((5.0, 15.0, 23.0), tn_update_op)
self.assertAllEqual((5.0, 15.0, 23.0), tn)
class TruePositivesTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.true_positives(
labels=(0, 1, 0, 1),
predictions=(0, 0, 1, 1))
_assert_metric_variables(self, ('true_positives/count:0',))
@test_util.run_deprecated_v1
def testUnweighted(self):
labels = constant_op.constant(((0, 1, 0, 1, 0),
(0, 0, 1, 1, 1),
(1, 1, 1, 1, 0),
(0, 0, 0, 0, 1)))
predictions = constant_op.constant(((0, 0, 1, 1, 0),
(1, 1, 1, 1, 1),
(0, 1, 0, 1, 0),
(1, 1, 1, 1, 1)))
tn, tn_update_op = metrics.true_positives(
labels=labels, predictions=predictions)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllClose(0., tn)
self.assertAllClose(7., tn_update_op)
self.assertAllClose(7., tn)
@test_util.run_deprecated_v1
def testWeighted(self):
labels = constant_op.constant(((0, 1, 0, 1, 0),
(0, 0, 1, 1, 1),
(1, 1, 1, 1, 0),
(0, 0, 0, 0, 1)))
predictions = constant_op.constant(((0, 0, 1, 1, 0),
(1, 1, 1, 1, 1),
(0, 1, 0, 1, 0),
(1, 1, 1, 1, 1)))
weights = constant_op.constant((1., 1.5, 2., 2.5))
tn, tn_update_op = metrics.true_positives(
labels=labels, predictions=predictions, weights=weights)
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllClose(0., tn)
self.assertAllClose(12., tn_update_op)
self.assertAllClose(12., tn)
class TruePositivesAtThresholdsTest(test.TestCase):
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
@test_util.run_deprecated_v1
def testVars(self):
metrics.true_positives_at_thresholds(
predictions=array_ops.ones((10, 1)),
labels=array_ops.ones((10, 1)),
thresholds=[0.15, 0.5, 0.85])
_assert_metric_variables(self, ('true_positives/true_positives:0',))
@test_util.run_deprecated_v1
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.true_positives_at_thresholds(
predictions=predictions, labels=labels, thresholds=[0.15, 0.5, 0.85])
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual((0, 0, 0), tp)
self.assertAllEqual((3, 1, 0), tp_update_op)
self.assertAllEqual((3, 1, 0), tp)
@test_util.run_deprecated_v1
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.true_positives_at_thresholds(
predictions=predictions, labels=labels, weights=37.0,
thresholds=[0.15, 0.5, 0.85])
with self.cached_session():
self.evaluate(variables.local_variables_initializer())
self.assertAllEqual((0.0, 0.0, 0.0), tp)
self.assertAllEqual((111.0, 37.0, 0.0), tp_update_op)
self.assertAllEqual((111.0, 37.0, 0.0), tp)
if __name__ == '__main__':
test.main()
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