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 _infer(self, request):
"""Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_type, 'model_version', 'model_signature' and 'label_vocab_path'.
Returns:
A list of JSON objects, one for each chart.
"""
label_vocab = inference_utils.get_label_vocab(
request.args.get('label_vocab_path'))
try:
if request.method != 'GET':
logger.error('%s requests are forbidden.', request.method)
return http_util.Respond(request,
{'error': 'invalid non-GET request'},
'application/json',
code=405)
(inference_addresses, model_names, model_versions,
model_signatures) = self._parse_request_arguments(request)
indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [
self.examples[index] for index in indices_to_infer
]
infer_objs = []
for model_num in xrange(len(inference_addresses)):
serving_bundle = inference_utils.ServingBundle(
inference_addresses[model_num],
model_names[model_num],
request.args.get('model_type'),
model_versions[model_num],
model_signatures[model_num],
request.args.get('use_predict') == 'true',
request.args.get('predict_input_tensor'),
request.args.get('predict_output_tensor'),
custom_predict_fn=self.custom_predict_fn)
(predictions,
_) = inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle)
infer_objs.append(predictions)
resp = {'indices': indices_to_infer, 'results': infer_objs}
self.updated_example_indices = set()
return http_util.Respond(request, {
'inferences': json.dumps(resp),
'vocab': json.dumps(label_vocab)
}, 'application/json')
except common_utils.InvalidUserInputError as e:
return http_util.Respond(request, {'error': e.message},
'application/json',
code=400)
except AbortionError as e:
return http_util.Respond(request, {'error': e.details},
'application/json',
code=400)
def _infer(self, request):
"""Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_type, 'model_version', 'model_signature' and 'label_vocab_path'.
Returns:
A list of JSON objects, one for each chart.
"""
start_example = (int(request.args.get('start_example'))
if request.args.get('start_example') else 0)
if not start_example:
label_vocab = inference_utils.get_label_vocab(
request.args.get('label_vocab_path'))
try:
if request.method != 'GET':
logger.error('%s requests are forbidden.', request.method)
return http_util.Respond(request, 'invalid non-GET request',
'application/json', code=405)
(inference_addresses, model_names, model_versions,
model_signatures) = self._parse_request_arguments(request)
self.indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [self.examples[index] for index in self.indices_to_infer]
self.infer_objs = []
for model_num in xrange(len(inference_addresses)):
serving_bundle = inference_utils.ServingBundle(
inference_addresses[model_num],
model_names[model_num],
request.args.get('model_type'),
model_versions[model_num],
model_signatures[model_num],
request.args.get('use_predict') == 'true',
request.args.get('predict_input_tensor'),
request.args.get('predict_output_tensor'),
custom_predict_fn=self.custom_predict_fn)
(predictions, _) = inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle)
self.infer_objs.append(predictions)
self.updated_example_indices = set()
except AbortionError as e:
logging.error(str(e))
return http_util.Respond(request, e.details,
'application/json', code=400)
except Exception as e:
logging.error(str(e))
return http_util.Respond(request, str(e),
'application/json', code=400)
# Split results from start_example to + max_examples
# Send next start_example if necessary
end_example = start_example + MAX_EXAMPLES_TO_SEND
def get_inferences_resp():
sliced_infer_objs = [
copy.deepcopy(infer_obj) for infer_obj in self.infer_objs]
if request.args.get('model_type') == 'classification':
for obj in sliced_infer_objs:
obj['classificationResult']['classifications'][:] = obj[
'classificationResult']['classifications'][
start_example:end_example]
else:
for obj in sliced_infer_objs:
obj['regressionResult']['regressions'][:] = obj['regressionResult'][
'regressions'][start_example:end_example]
return {'indices': self.indices_to_infer[start_example:end_example],
'results': sliced_infer_objs}
try:
inferences_resp = get_inferences_resp()
resp = {'inferences': json.dumps(inferences_resp)}
if end_example >= len(self.examples):
end_example = -1
if start_example == 0:
resp['vocab'] = json.dumps(label_vocab)
resp['next'] = end_example
return http_util.Respond(request, resp, 'application/json')
except Exception as e:
logging.error(e)
return http_util.Respond(request, str(e),
'application/json', code=400)