def test_named_args_with_weights(self):
features = {"f1": "f1_value", "f2": "f2_value"}
labels_ = {"l1": "l1_value", "l2": "l2_value"}
predictions_ = {"p1": "p1_value", "p2": "p2_value"}
def _fn0(predictions, labels, weights=None):
self.assertEqual("p1_value", predictions)
self.assertEqual("l1_value", labels)
self.assertEqual("f2_value", weights)
return "metric_fn_result"
def _fn1(predictions, targets, weights=None):
self.assertEqual("p1_value", predictions)
self.assertEqual("l1_value", targets)
self.assertEqual("f2_value", weights)
return "metric_fn_result"
def _fn2(prediction, label, weight=None):
self.assertEqual("p1_value", prediction)
self.assertEqual("l1_value", label)
self.assertEqual("f2_value", weight)
return "metric_fn_result"
def _fn3(prediction, target, weight=None):
self.assertEqual("p1_value", prediction)
self.assertEqual("l1_value", target)
self.assertEqual("f2_value", weight)
return "metric_fn_result"
for fn in (_fn0, _fn1, _fn2, _fn3):
spec = MetricSpec(
metric_fn=fn, prediction_key="p1", label_key="l1", weight_key="f2")
self.assertEqual(
"metric_fn_result",
spec.create_metric_ops(features, labels_, predictions_))
def testCustomMetrics(self):
"""Tests custom evaluation metrics."""
def _input_fn(num_epochs=None):
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
labels = constant_op.constant([[1], [0], [0], [0]])
features = {
'x':
input_lib.limit_epochs(array_ops.ones(shape=[4, 1],
dtype=dtypes.float32),
num_epochs=num_epochs),
}
return features, labels
def _my_metric_op(predictions, labels):
# For the case of binary classification, the 2nd column of "predictions"
# denotes the model predictions.
labels = math_ops.cast(labels, dtypes.float32)
predictions = array_ops.strided_slice(predictions, [0, 1], [-1, 2],
end_mask=1)
labels = math_ops.cast(labels, predictions.dtype)
return math_ops.reduce_sum(math_ops.multiply(predictions, labels))
classifier = debug.DebugClassifier(config=run_config.RunConfig(
tf_random_seed=1))
classifier.fit(input_fn=_input_fn, steps=5)
scores = classifier.evaluate(
input_fn=_input_fn,
steps=5,
metrics={
'my_accuracy':
MetricSpec(metric_fn=metric_ops.streaming_accuracy,
prediction_key='classes'),
'my_precision':
MetricSpec(metric_fn=metric_ops.streaming_precision,
prediction_key='classes'),
'my_metric':
MetricSpec(metric_fn=_my_metric_op,
prediction_key='probabilities')
})
self.assertTrue(
set(['loss', 'my_accuracy', 'my_precision',
'my_metric']).issubset(set(scores.keys())))
predict_input_fn = functools.partial(_input_fn, num_epochs=1)
predictions = np.array(
list(classifier.predict_classes(input_fn=predict_input_fn)))
self.assertEqual(_sklearn.accuracy_score([1, 0, 0, 0], predictions),
scores['my_accuracy'])
# Test the case where the 2nd element of the key is neither "classes" nor
# "probabilities".
with self.assertRaisesRegexp(KeyError, 'bad_type'):
classifier.evaluate(input_fn=_input_fn,
steps=5,
metrics={
'bad_name':
MetricSpec(
metric_fn=metric_ops.streaming_auc,
prediction_key='bad_type')
})
def test_no_args(self):
def _fn():
self.fail("Expected failure before metric_fn.")
spec = MetricSpec(metric_fn=_fn)
with self.assertRaises(TypeError):
spec.create_metric_ops(
{"f1": "f1_value"}, "labels_value", "predictions_value")
def test_no_named_labels_or_predictions_2args(self):
features = {"f1": "f1_value"}
labels_ = "labels_value"
predictions_ = "predictions_value"
def _fn(a, b):
del a, b
self.fail("Expected failure before metric_fn.")
spec = MetricSpec(metric_fn=_fn)
with self.assertRaises(TypeError):
spec.create_metric_ops(features, labels_, predictions_)
def test_named_labels_no_predictions(self):
features = {"f1": "f1_value"}
labels_ = "labels_value"
predictions_ = "predictions_value"
def _fn(labels):
self.assertEqual(labels_, labels)
return "metric_fn_result"
spec = MetricSpec(metric_fn=_fn)
with self.assertRaises(TypeError):
spec.create_metric_ops(features, labels_, predictions_)
def test_no_named_labels_or_predictions_1arg(self):
features = {"f1": "f1_value"}
labels_ = "labels_value"
predictions_ = "predictions_value"
def _fn(a):
self.assertEqual(predictions_, a)
return "metric_fn_result"
spec = MetricSpec(metric_fn=_fn)
self.assertEqual(
"metric_fn_result",
spec.create_metric_ops(features, labels_, predictions_))
def test_no_named_predictions_named_labels_second_arg(self):
features = {"f1": "f1_value"}
labels_ = "labels_value"
predictions_ = "predictions_value"
def _fn(predictions_by_another_name, labels):
self.assertEqual(predictions_, predictions_by_another_name)
self.assertEqual(labels_, labels)
return "metric_fn_result"
spec = MetricSpec(metric_fn=_fn)
self.assertEqual(
"metric_fn_result",
spec.create_metric_ops(features, labels_, predictions_))
def testCustomMetrics(self):
"""Tests custom evaluation metrics."""
def _input_fn(num_epochs=None):
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {
'x':
input_lib.limit_epochs(array_ops.ones(shape=[4, 1],
dtype=dtypes.float32),
num_epochs=num_epochs),
}
return features, labels
def _my_metric_op(predictions, labels):
return math_ops.reduce_sum(math_ops.multiply(predictions, labels))
regressor = debug.DebugRegressor(config=run_config.RunConfig(
tf_random_seed=1))
regressor.fit(input_fn=_input_fn, steps=5)
scores = regressor.evaluate(
input_fn=_input_fn,
steps=1,
metrics={
'my_error':
MetricSpec(metric_fn=metric_ops.streaming_mean_squared_error,
prediction_key='scores'),
'my_metric':
MetricSpec(metric_fn=_my_metric_op, prediction_key='scores')
})
self.assertIn('loss', set(scores.keys()))
self.assertIn('my_error', set(scores.keys()))
self.assertIn('my_metric', set(scores.keys()))
predict_input_fn = functools.partial(_input_fn, num_epochs=1)
predictions = np.array(
list(regressor.predict_scores(input_fn=predict_input_fn)))
self.assertAlmostEqual(
_sklearn.mean_squared_error(np.array([1, 0, 0, 0]), predictions),
scores['my_error'])
# Tests the case where the prediction_key is not "scores".
with self.assertRaisesRegexp(KeyError, 'bad_type'):
regressor.evaluate(input_fn=_input_fn,
steps=1,
metrics={
'bad_name':
MetricSpec(
metric_fn=metric_ops.streaming_auc,
prediction_key='bad_type')
})
def test_single_labels_with_key(self):
features = {"f1": "f1_value", "f2": "f2_value"}
labels = "l1_value"
predictions = {"p1": "p1_value", "p2": "p2_value"}
def _fn(predictions, labels, weights=None):
del labels, predictions, weights
self.fail("Expected failure before metric_fn.")
spec = MetricSpec(
metric_fn=_fn, prediction_key="p1", label_key="l1", weight_key="f2")
with self.assertRaisesRegexp(
ValueError, "MetricSpec with label_key specified requires labels dict"):
spec.create_metric_ops(features, labels, predictions)
def test_single_label(self):
features = {"f1": "f1_value", "f2": "f2_value"}
labels_ = "l1_value"
predictions_ = {"p1": "p1_value", "p2": "p2_value"}
def _fn(predictions, labels, weights=None):
self.assertEqual("p1_value", predictions)
self.assertEqual(labels_, labels)
self.assertEqual("f2_value", weights)
return "metric_fn_result"
spec = MetricSpec(metric_fn=_fn, prediction_key="p1", weight_key="f2")
self.assertEqual(
"metric_fn_result",
spec.create_metric_ops(features, labels_, predictions_))
def test_labels_dict_no_key(self):
features = {"f1": "f1_value", "f2": "f2_value"}
labels = {"l1": "l1_value", "l2": "l2_value"}
predictions = {"p1": "p1_value", "p2": "p2_value"}
def _fn(labels, predictions, weights=None):
del labels, predictions, weights
self.fail("Expected failure before metric_fn.")
spec = MetricSpec(metric_fn=_fn, prediction_key="p1", weight_key="f2")
with self.assertRaisesRegexp(
ValueError,
"MetricSpec without specified label_key requires labels tensor or"
" single element dict"):
spec.create_metric_ops(features, labels, predictions)
def test_multiple_weight_args(self):
def _fn(predictions, labels, weights=None, weight=None):
del predictions, labels, weights, weight
self.fail("Expected failure before metric_fn.")
with self.assertRaisesRegexp(ValueError, "provide only one of.*weight"):
MetricSpec(metric_fn=_fn)
def test_multiple_prediction_args(self):
def _fn(predictions, prediction, labels):
del predictions, prediction, labels
self.fail("Expected failure before metric_fn.")
with self.assertRaisesRegexp(ValueError, "provide only one of.*prediction"):
MetricSpec(metric_fn=_fn)
def test_partial(self):
features = {"f1": "f1_value", "f2": "f2_value"}
labels = {"l1": "l1_value"}
predictions = {"p1": "p1_value", "p2": "p2_value"}
def custom_metric(predictions, labels, stuff, weights=None):
self.assertEqual("p1_value", predictions)
self.assertEqual("l1_value", labels)
self.assertEqual("f2_value", weights)
if stuff:
return "metric_fn_result"
raise ValueError("No stuff.")
spec = MetricSpec(
metric_fn=functools.partial(custom_metric, stuff=5),
label_key="l1",
prediction_key="p1",
weight_key="f2")
self.assertEqual(
"metric_fn_result",
spec.create_metric_ops(features, labels, predictions))
spec = MetricSpec(
metric_fn=functools.partial(custom_metric, stuff=None),
prediction_key="p1", label_key="l1", weight_key="f2")
with self.assertRaisesRegexp(ValueError, "No stuff."):
spec.create_metric_ops(features, labels, predictions)
def test_multiple_label_args(self):
def _fn0(predictions, labels, targets):
del predictions, labels, targets
self.fail("Expected failure before metric_fn.")
def _fn1(prediction, label, target):
del prediction, label, target
self.fail("Expected failure before metric_fn.")
for fn in (_fn0, _fn1):
with self.assertRaisesRegexp(ValueError, "provide only one of.*label"):
MetricSpec(metric_fn=fn)
def test_label_key_without_label_arg(self):
def _fn0(predictions, weights=None):
del predictions, weights
self.fail("Expected failure before metric_fn.")
def _fn1(prediction, weight=None):
del prediction, weight
self.fail("Expected failure before metric_fn.")
for fn in (_fn0, _fn1):
with self.assertRaisesRegexp(ValueError, "label.*missing"):
MetricSpec(metric_fn=fn, label_key="l1")
def test_str(self):
def _metric_fn(labels, predictions, weights=None):
return predictions, labels, weights
string = str(MetricSpec(
metric_fn=_metric_fn,
label_key="my_label",
prediction_key="my_prediction",
weight_key="my_weight"))
self.assertIn("_metric_fn", string)
self.assertIn("my_label", string)
self.assertIn("my_prediction", string)
self.assertIn("my_weight", string)
def test_partial_str(self):
def custom_metric(predictions, labels, stuff, weights=None):
return predictions, labels, weights, stuff
string = str(MetricSpec(
metric_fn=functools.partial(custom_metric, stuff=5),
label_key="my_label",
prediction_key="my_prediction",
weight_key="my_weight"))
self.assertIn("custom_metric", string)
self.assertIn("my_label", string)
self.assertIn("my_prediction", string)
self.assertIn("my_weight", string)
def test_weight_key_without_weight_arg(self):
def _fn0(predictions, labels):
del predictions, labels
self.fail("Expected failure before metric_fn.")
def _fn1(prediction, label):
del prediction, label
self.fail("Expected failure before metric_fn.")
def _fn2(predictions, targets):
del predictions, targets
self.fail("Expected failure before metric_fn.")
def _fn3(prediction, target):
del prediction, target
self.fail("Expected failure before metric_fn.")
for fn in (_fn0, _fn1, _fn2, _fn3):
with self.assertRaisesRegexp(ValueError, "weight.*missing"):
MetricSpec(metric_fn=fn, weight_key="f2")
