def get_eligible_features_impl(self):
"""Returns information about features eligible for mutant inference."""
examples = [
self.json_to_proto(ex)
for ex in self.examples[0:NUM_EXAMPLES_FOR_MUTANT_ANALYSIS]
]
return inference_utils.get_eligible_features(examples,
NUM_MUTANTS_TO_GENERATE)
def _sort_eligible_features_handler(self, request):
"""Returns a sorted list of JSON objects for each feature in the
example.
The list is sorted by interestingness in terms of the resulting change in
inference values across feature values, for partial dependence plots.
Args:
request: A request for sorted features.
Returns:
A sorted list with a JSON object for each feature.
Numeric features are represented as
{name: observedMin: observedMax: interestingness:}.
Categorical features are repesented as
{name: samples:[] interestingness:}.
"""
try:
features_list = inference_utils.get_eligible_features(
self.examples[0:NUM_EXAMPLES_TO_SCAN], NUM_MUTANTS)
example_index = int(request.args.get("example_index", "0"))
(
inference_addresses,
model_names,
model_versions,
model_signatures,
) = self._parse_request_arguments(request)
chart_data = {}
for feat in features_list:
chart_data[feat["name"]] = self._infer_mutants_impl(
feat["name"],
example_index,
inference_addresses,
model_names,
request.args.get("model_type"),
model_versions,
model_signatures,
request.args.get("use_predict") == "true",
request.args.get("predict_input_tensor"),
request.args.get("predict_output_tensor"),
feat["observedMin"] if "observedMin" in feat else 0,
feat["observedMax"] if "observedMin" in feat else 0,
None,
)
features_list = inference_utils.sort_eligible_features(
features_list, chart_data)
return http_util.Respond(request, features_list,
"application/json")
except common_utils.InvalidUserInputError as e:
return http_util.Respond(request, {"error": e.message},
"application/json",
code=400)
def _eligible_features_from_example_handler(self, request):
"""Returns a list of JSON objects for each feature in the example.
Args:
request: A request for features.
Returns:
A list with a JSON object for each feature.
Numeric features are represented as {name: observedMin: observedMax:}.
Categorical features are repesented as {name: samples:[]}.
"""
features_list = inference_utils.get_eligible_features(
self.examples[0:NUM_EXAMPLES_TO_SCAN], NUM_MUTANTS)
return http_util.Respond(request, features_list, "application/json")
def get_eligible_features(self):
examples = [self.json_to_proto(ex) for ex in self.examples[0:50]]
features_list = inference_utils.get_eligible_features(
examples, 10)
output.eval_js("""eligibleFeaturesCallback('{features_list}')""".format(
features_list=json.dumps(features_list)))
def _get_eligible_features(self, change):
examples = [self.json_to_proto(ex) for ex in self.examples[0:50]]
features_list = inference_utils.get_eligible_features(
examples, 10)
self.eligible_features = features_list
