def testRawDataOp(self):
with tf.summary.FileWriter(
self.logdir) as writer, tf.Session() as sess:
# We pass raw counts and precision/recall values.
writer.add_summary(
sess.run(
summary.raw_data_op(
tag='foo',
true_positive_counts=tf.constant([75, 64, 21, 5, 0]),
false_positive_counts=tf.constant([150, 105, 18, 0,
0]),
true_negative_counts=tf.constant(
[0, 45, 132, 150, 150]),
false_negative_counts=tf.constant([0, 11, 54, 70, 75]),
precision=tf.constant(
[0.3333333, 0.3786982, 0.5384616, 1.0, 0.0]),
recall=tf.constant(
[1.0, 0.8533334, 0.28, 0.0666667, 0.0]),
num_thresholds=5,
display_name='some_raw_values',
description='We passed raw values into a summary op.'))
)
multiplexer = self.createMultiplexer()
accumulator = multiplexer.GetAccumulator('.')
tag_content_dict = accumulator.PluginTagToContent('pr_curves')
self.assertItemsEqual(['foo/pr_curves'], list(tag_content_dict.keys()))
# Test the metadata.
summary_metadata = multiplexer.SummaryMetadata('.', 'foo/pr_curves')
self.assertEqual('some_raw_values', summary_metadata.display_name)
self.assertEqual('We passed raw values into a summary op.',
summary_metadata.summary_description)
# Test the stored plugin data.
plugin_data = metadata.parse_plugin_metadata(
tag_content_dict['foo/pr_curves'])
self.assertEqual(5, plugin_data.num_thresholds)
# Test the summary contents.
tensor_events = accumulator.Tensors('foo/pr_curves')
self.assertEqual(1, len(tensor_events))
self.validateTensorEvent(
0,
[
[75.0, 64.0, 21.0, 5.0, 0.0], # True positives.
[150.0, 105.0, 18.0, 0.0, 0.0], # False positives.
[0.0, 45.0, 132.0, 150.0, 150.0], # True negatives.
[0.0, 11.0, 54.0, 70.0, 75.0], # False negatives.
[0.3333333, 0.3786982, 0.5384616, 1.0, 0.0], # Precision.
[1.0, 0.8533334, 0.28, 0.0666667, 0.0], # Recall.
],
tensor_events[0])
def test_raw_data(self):
# We pass these raw counts and precision/recall values.
name = "foo"
true_positive_counts = [75, 64, 21, 5, 0]
false_positive_counts = [150, 105, 18, 0, 0]
true_negative_counts = [0, 45, 132, 150, 150]
false_negative_counts = [0, 11, 54, 70, 75]
precision = [0.3333333, 0.3786982, 0.5384616, 1.0, 0.0]
recall = [1.0, 0.8533334, 0.28, 0.0666667, 0.0]
num_thresholds = 5
display_name = "some_raw_values"
description = "We passed raw values into a summary op."
op = summary.raw_data_op(
name=name,
true_positive_counts=tf.constant(true_positive_counts),
false_positive_counts=tf.constant(false_positive_counts),
true_negative_counts=tf.constant(true_negative_counts),
false_negative_counts=tf.constant(false_negative_counts),
precision=tf.constant(precision),
recall=tf.constant(recall),
num_thresholds=num_thresholds,
display_name=display_name,
description=description,
)
pb_via_op = self.normalize_summary_pb(self.pb_via_op(op))
# Call the corresponding method that is decoupled from TensorFlow.
pb = self.normalize_summary_pb(
summary.raw_data_pb(
name=name,
true_positive_counts=true_positive_counts,
false_positive_counts=false_positive_counts,
true_negative_counts=true_negative_counts,
false_negative_counts=false_negative_counts,
precision=precision,
recall=recall,
num_thresholds=num_thresholds,
display_name=display_name,
description=description,
))
# The 2 methods above should write summaries with the same data.
self.assertProtoEquals(pb, pb_via_op)
# Test the metadata.
summary_metadata = pb.value[0].metadata
self.assertEqual("some_raw_values", summary_metadata.display_name)
self.assertEqual(
"We passed raw values into a summary op.",
summary_metadata.summary_description,
)
self.assertEqual(metadata.PLUGIN_NAME,
summary_metadata.plugin_data.plugin_name)
plugin_data = metadata.parse_plugin_metadata(
summary_metadata.plugin_data.content)
self.assertEqual(5, plugin_data.num_thresholds)
# Test the summary contents.
values = tensor_util.make_ndarray(pb.value[0].tensor)
self.verify_float_arrays_are_equal(
[
[75.0, 64.0, 21.0, 5.0, 0.0], # True positives.
[150.0, 105.0, 18.0, 0.0, 0.0], # False positives.
[0.0, 45.0, 132.0, 150.0, 150.0], # True negatives.
[0.0, 11.0, 54.0, 70.0, 75.0], # False negatives.
[0.3333333, 0.3786982, 0.5384616, 1.0, 0.0], # Precision.
[1.0, 0.8533334, 0.28, 0.0666667, 0.0], # Recall.
],
values,
)