def test_pr_curves(self):
old_event = event_pb2.Event()
old_event.step = 123
old_event.wall_time = 456.75
pr_curve_pb = pr_curve_summary.pb(
"foo",
labels=np.array([True, False, True, False]),
predictions=np.array([0.75, 0.25, 0.85, 0.15]),
num_thresholds=10,
display_name="bar",
description="baz",
)
old_event.summary.ParseFromString(pr_curve_pb.SerializeToString())
new_events = self._migrate_event(old_event)
self.assertLen(new_events, 1)
self.assertLen(new_events[0].summary.value, 1)
value = new_events[0].summary.value[0]
tensor = tensor_util.make_ndarray(value.tensor)
self.assertEqual(tensor.shape, (6, 10))
np.testing.assert_array_equal(
tensor, tensor_util.make_ndarray(pr_curve_pb.value[0].tensor)
)
self.assertEqual(
value.metadata.data_class, summary_pb2.DATA_CLASS_TENSOR
)
self.assertEqual(
value.metadata.plugin_data.plugin_name,
pr_curve_metadata.PLUGIN_NAME,
)
def compute_and_check_summary_pb(self,
name,
labels,
predictions,
num_thresholds,
weights=None,
display_name=None,
description=None,
feed_dict=None):
"""Use both `op` and `pb` to get a summary, asserting equality.
Returns:
a `Summary` protocol buffer
"""
labels_tensor = tf.constant(labels)
predictions_tensor = tf.constant(predictions)
weights_tensor = None if weights is None else tf.constant(weights)
op = summary.op(name=name,
labels=labels_tensor,
predictions=predictions_tensor,
num_thresholds=num_thresholds,
weights=weights_tensor,
display_name=display_name,
description=description)
pb = self.normalize_summary_pb(
summary.pb(name=name,
labels=labels,
predictions=predictions,
num_thresholds=num_thresholds,
weights=weights,
display_name=display_name,
description=description))
pb_via_op = self.normalize_summary_pb(
self.pb_via_op(op, feed_dict=feed_dict))
self.assertProtoEquals(pb, pb_via_op)
return pb