def testEvaluateMultiLabelsPredictions(self):
# Test case where we have multiple labels/predictions
# Have 6 labels of values 3, 5, 23, 12, 16, 31 and
# 6 predictions of values 2, 2, 2, 4, 4, 4
# This should give sum = 108 and mean = 9.
examples = [
self._makeExample(age=1.0,
prediction=2,
prediction_2=4,
label=3,
label_2=5),
self._makeExample(age=1.0,
prediction=2,
prediction_2=4,
label=23,
label_2=12),
self._makeExample(age=1.0,
prediction=2,
prediction_2=4,
label=16,
label_2=31),
]
serialized_examples = [e.SerializeToString() for e in examples]
# Set up a model agnostic config so we can get the FPLConfig.
feature_map = {
'age': tf.io.FixedLenFeature([], tf.float32),
'prediction': tf.io.FixedLenFeature([], tf.int64),
'prediction_2': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'label_2': tf.io.FixedLenFeature([], tf.int64)
}
model_agnostic_config = agnostic_predict.ModelAgnosticConfig(
label_keys=['label', 'label_2'],
prediction_keys=['prediction', 'prediction_2'],
feature_spec=feature_map)
# Create a Model Anostic Evaluate graph handler and feed in the FPL list.
evaluate_graph = model_agnostic_evaluate_graph.ModelAgnosticEvaluateGraph(
[add_mean_callback], model_agnostic_config)
evaluate_graph.metrics_reset_update_get_list(serialized_examples)
outputs = evaluate_graph.get_metric_values()
# Verify that we got the right metrics out.
self.assertEqual(2, len(outputs))
self.assertEqual(outputs['tf_metric_mean'], 9.0)
self.assertEqual(outputs['py_func_total_label'], 108.0)
def testEvaluateGraph(self):
# Have 3 labels of values 3, 23, 16 and predictions of values 2, 2, 2.
# This should give sum = 48 and mean = 8.
examples = [
self._makeExample(age=3.0,
language='english',
predictions=2.0,
labels=3.0),
self._makeExample(age=3.0,
language='chinese',
predictions=2.0,
labels=23.0),
self._makeExample(age=4.0,
language='english',
predictions=2.0,
labels=16.0),
]
serialized_examples = [e.SerializeToString() for e in examples]
# Set up a model agnostic config so we can get the FPLConfig.
feature_map = {
'age': tf.io.FixedLenFeature([], tf.float32),
'language': tf.io.VarLenFeature(tf.string),
'predictions': tf.io.FixedLenFeature([], tf.float32),
'labels': tf.io.FixedLenFeature([], tf.float32)
}
model_agnostic_config = agnostic_predict.ModelAgnosticConfig(
label_keys=['labels'],
prediction_keys=['predictions'],
feature_spec=feature_map)
# Create a Model Anostic Evaluate graph handler and feed in the FPL list.
evaluate_graph = model_agnostic_evaluate_graph.ModelAgnosticEvaluateGraph(
[add_mean_callback], model_agnostic_config)
evaluate_graph.metrics_reset_update_get_list(serialized_examples)
outputs = evaluate_graph.get_metric_values()
# Verify that we got the right metrics out.
self.assertEqual(2, len(outputs))
self.assertEqual(outputs['tf_metric_mean'], 8.0)
self.assertEqual(outputs['py_func_total_label'], 48.0)
def testEvaluateGraph(self):
# Create some FPLs. The Features aren't terribly useful for these metrics.
# Just make sure they can be processed correctly by the feed/feedlist
# generation logic by having one dense tensor and one sparse tensor.
features = {
'age': {
encoding.NODE_SUFFIX: np.array([1])
},
'language': {
encoding.NODE_SUFFIX:
tf.SparseTensorValue(indices=np.array([[0, 0]]),
values=np.array(['english']),
dense_shape=np.array([1, 1]))
}
}
predictions = {'predictions': {encoding.NODE_SUFFIX: np.array([2])}}
# Have 3 labels of values 3, 23, 16 and predictions of values 2, 2, 2.
# This should give sum = 48 and mean = 8.
labels = {'labels': {encoding.NODE_SUFFIX: np.array([3])}}
labels_2 = {'labels': {encoding.NODE_SUFFIX: np.array([23])}}
labels_3 = {'labels': {encoding.NODE_SUFFIX: np.array([16])}}
# Compile the actual FPLs
fpl = types.FeaturesPredictionsLabels(input_ref=0,
features=features,
predictions=predictions,
labels=labels)
fpl_2 = types.FeaturesPredictionsLabels(input_ref=0,
features=features,
predictions=predictions,
labels=labels_2)
fpl_3 = types.FeaturesPredictionsLabels(input_ref=0,
features=features,
predictions=predictions,
labels=labels_3)
# Set up a model agnostic config so we can get the FPLConfig.
feature_map = {
'age': tf.FixedLenFeature([], tf.float32),
'language': tf.VarLenFeature(tf.string),
'predictions': tf.FixedLenFeature([], tf.float32),
'labels': tf.FixedLenFeature([], tf.float32)
}
model_agnostic_config = agnostic_predict.ModelAgnosticConfig(
label_keys=['labels'],
prediction_keys=['predictions'],
feature_spec=feature_map)
# Create a Model Anostic Evaluate graph handler and feed in the FPL list.
evaluate_graph = model_agnostic_evaluate_graph.ModelAgnosticEvaluateGraph(
[add_mean_callback],
model_agnostic_extractor.ModelAgnosticGetFPLFeedConfig(
model_agnostic_config))
evaluate_graph.metrics_reset_update_get_list([fpl, fpl_2, fpl_3])
outputs = evaluate_graph.get_metric_values()
# Verify that we got the right metrics out.
self.assertEqual(2, len(outputs))
self.assertEqual(outputs['tf_metric_mean'], 8.0)
self.assertEqual(outputs['py_func_total_label'], 48.0)
def testEvaluateMultiLabelsPredictions(self):
# Test case where we have multiple labels/predictions
features = {'age': {encoding.NODE_SUFFIX: np.array([1])}}
predictions = {
'prediction': {
encoding.NODE_SUFFIX: np.array([2])
},
'prediction_2': {
encoding.NODE_SUFFIX: np.array([4])
}
}
# Have 6 labels of values 3, 5, 23, 12, 16, 31 and
# 6 predictions of values 2, 2, 2, 4, 4, 4
# This should give sum = 108 and mean = 9.
labels = {
'label': {
encoding.NODE_SUFFIX: np.array([3])
},
'label_2': {
encoding.NODE_SUFFIX: np.array([5])
}
}
labels_2 = {
'label': {
encoding.NODE_SUFFIX: np.array([23])
},
'label_2': {
encoding.NODE_SUFFIX: np.array([12])
}
}
labels_3 = {
'label': {
encoding.NODE_SUFFIX: np.array([16])
},
'label_2': {
encoding.NODE_SUFFIX: np.array([31])
}
}
# Compile the actual FPLs
fpl = types.FeaturesPredictionsLabels(input_ref=0,
features=features,
predictions=predictions,
labels=labels)
fpl_2 = types.FeaturesPredictionsLabels(input_ref=0,
features=features,
predictions=predictions,
labels=labels_2)
fpl_3 = types.FeaturesPredictionsLabels(input_ref=0,
features=features,
predictions=predictions,
labels=labels_3)
# Set up a model agnostic config so we can get the FPLConfig.
feature_map = {
'age': tf.FixedLenFeature([], tf.float32),
'prediction': tf.FixedLenFeature([], tf.int64),
'prediction_2': tf.FixedLenFeature([], tf.int64),
'label': tf.FixedLenFeature([], tf.int64),
'label_2': tf.FixedLenFeature([], tf.int64)
}
model_agnostic_config = agnostic_predict.ModelAgnosticConfig(
label_keys=['label', 'label_2'],
prediction_keys=['prediction', 'prediction_2'],
feature_spec=feature_map)
# Create a Model Anostic Evaluate graph handler and feed in the FPL list.
evaluate_graph = model_agnostic_evaluate_graph.ModelAgnosticEvaluateGraph(
[add_mean_callback],
model_agnostic_extractor.ModelAgnosticGetFPLFeedConfig(
model_agnostic_config))
evaluate_graph.metrics_reset_update_get_list([fpl, fpl_2, fpl_3])
outputs = evaluate_graph.get_metric_values()
# Verify that we got the right metrics out.
self.assertEqual(2, len(outputs))
self.assertEqual(outputs['tf_metric_mean'], 9.0)
self.assertEqual(outputs['py_func_total_label'], 108.0)
