def _infer_mutants_impl(self, feature_name, example_index,
inference_addresses, model_names, model_type,
model_versions, model_signatures, use_predict,
predict_input_tensor, predict_output_tensor, x_min,
x_max, feature_index_pattern, custom_predict_fn):
"""Helper for generating PD plots for a feature."""
examples = (self.examples
if example_index == -1 else [self.examples[example_index]])
serving_bundles = []
for model_num in xrange(len(inference_addresses)):
serving_bundles.append(
inference_utils.ServingBundle(
inference_addresses[model_num],
model_names[model_num],
model_type,
model_versions[model_num],
model_signatures[model_num],
use_predict,
predict_input_tensor,
predict_output_tensor,
custom_predict_fn=custom_predict_fn))
viz_params = inference_utils.VizParams(
x_min, x_max, self.examples[0:NUM_EXAMPLES_TO_SCAN], NUM_MUTANTS,
feature_index_pattern)
return inference_utils.mutant_charts_for_feature(
examples, feature_name, serving_bundles, viz_params)
def test_mutant_charts_for_feature(
self, mock_call_servo, mock_make_json_formatted_for_single_chart):
example = self.make_and_write_fake_example()
serving_bundles = [
inference_utils.ServingBundle('', '', 'classification', '', '',
False, '', '')
]
num_mutants = 10
viz_params = inference_utils.VizParams(x_min=1,
x_max=10,
examples=[example],
num_mutants=num_mutants,
feature_index_pattern=None)
mock_call_servo = lambda _, __: None
mock_make_json_formatted_for_single_chart = lambda _, __: {}
charts = inference_utils.mutant_charts_for_feature([example],
'repeated_float',
serving_bundles,
viz_params)
self.assertEqual('numeric', charts['chartType'])
self.assertEqual(4, len(charts['data']))
charts = inference_utils.mutant_charts_for_feature([example],
'repeated_int',
serving_bundles,
viz_params)
self.assertEqual('numeric', charts['chartType'])
self.assertEqual(2, len(charts['data']))
charts = inference_utils.mutant_charts_for_feature([example],
'single_int',
serving_bundles,
viz_params)
self.assertEqual('numeric', charts['chartType'])
self.assertEqual(1, len(charts['data']))
charts = inference_utils.mutant_charts_for_feature([example],
'non_numeric',
serving_bundles,
viz_params)
self.assertEqual('categorical', charts['chartType'])
self.assertEqual(3, len(charts['data']))
def test_mutant_charts_for_feature_with_feature_index_pattern(
self, mock_call_servo, mock_make_json_formatted_for_single_chart):
example = self.make_and_write_fake_example()
serving_bundles = [
inference_utils.ServingBundle('', '', 'classification', '', '',
False, '', '')
]
num_mutants = 10
viz_params = inference_utils.VizParams(x_min=1,
x_max=10,
examples=[example],
num_mutants=num_mutants,
feature_index_pattern='0 , 2-3')
mock_call_servo = lambda _, __: None
mock_make_json_formatted_for_single_chart = lambda _, __: {}
charts = inference_utils.mutant_charts_for_feature([example],
'repeated_float',
serving_bundles,
viz_params)
self.assertEqual('numeric', charts['chartType'])
self.assertEqual(3, len(charts['data']))
# These should return 3 charts even though all fields from the index
# pattern don't exist for the example.
charts = inference_utils.mutant_charts_for_feature([example],
'repeated_int',
serving_bundles,
viz_params)
self.assertEqual('numeric', charts['chartType'])
self.assertEqual(3, len(charts['data']))
charts = inference_utils.mutant_charts_for_feature([example],
'single_int',
serving_bundles,
viz_params)
self.assertEqual('numeric', charts['chartType'])
self.assertEqual(3, len(charts['data']))
def infer_mutants_impl(self, info):
"""Performs mutant inference on specified examples."""
example_index = int(info['example_index'])
feature_name = info['feature_name']
examples = (self.examples
if example_index == -1 else [self.examples[example_index]])
examples = [self.json_to_proto(ex) for ex in examples]
scan_examples = [self.json_to_proto(ex) for ex in self.examples[0:50]]
serving_bundles = []
serving_bundles.append(
inference_utils.ServingBundle(
self.config.get('inference_address'),
self.config.get('model_name'), self.config.get('model_type'),
self.config.get('model_version'),
self.config.get('model_signature'),
self.config.get('uses_predict_api'),
self.config.get('predict_input_tensor'),
self.config.get('predict_output_tensor'),
self.estimator_and_spec.get('estimator'),
self.estimator_and_spec.get('feature_spec'),
self.custom_predict_fn))
if ('inference_address_2' in self.config
or self.compare_estimator_and_spec.get('estimator')
or self.compare_custom_predict_fn):
serving_bundles.append(
inference_utils.ServingBundle(
self.config.get('inference_address_2'),
self.config.get('model_name_2'),
self.config.get('model_type'),
self.config.get('model_version_2'),
self.config.get('model_signature_2'),
self.config.get('uses_predict_api'),
self.config.get('predict_input_tensor'),
self.config.get('predict_output_tensor'),
self.compare_estimator_and_spec.get('estimator'),
self.compare_estimator_and_spec.get('feature_spec'),
self.compare_custom_predict_fn))
viz_params = inference_utils.VizParams(info['x_min'], info['x_max'],
scan_examples, 10,
info['feature_index_pattern'])
self.running_mutant_infer = True
charts = inference_utils.mutant_charts_for_feature(
examples, feature_name, serving_bundles, viz_params)
self.running_mutant_infer = False
return charts