def test_get_numeric_features_to_observed_range(self):
example = test_utils.make_fake_example(single_int_val=2)
data = inference_utils.get_numeric_features_to_observed_range(
[example])
# Returns a sorted list by feature_name.
self.assertDictEqual(
{
'repeated_float': {
'observedMin': 1.,
'observedMax': 4.,
},
'repeated_int': {
'observedMin': 10,
'observedMax': 20,
},
'single_float': {
'observedMin': 24.5,
'observedMax': 24.5,
},
'single_int': {
'observedMin': 2.,
'observedMax': 2.,
},
}, data)
def test_get_numeric_features_to_observed_range(self):
example = test_utils.make_fake_example(single_int_val=2)
data = inference_utils.get_numeric_features_to_observed_range(
[example])
# Returns a sorted list by feature_name.
self.assertDictEqual(
{
"repeated_float": {
"observedMin": 1.0,
"observedMax": 4.0,
},
"repeated_int": {
"observedMin": 10,
"observedMax": 20,
},
"single_float": {
"observedMin": 24.5,
"observedMax": 24.5,
},
"single_int": {
"observedMin": 2.0,
"observedMax": 2.0,
},
},
data,
)
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_dict = (
inference_utils.get_numeric_features_to_observed_range(
self.examples[0:NUM_EXAMPLES_TO_SCAN]))
features_dict.update(
inference_utils.get_categorical_features_to_sampling(
self.examples[0:NUM_EXAMPLES_TO_SCAN], NUM_MUTANTS))
# Massage the features_dict into a sorted list before returning because
# Polymer dom-repeat needs a list.
features_list = []
for k, v in sorted(features_dict.items()):
v['name'] = k
features_list.append(v)
return http_util.Respond(request, features_list, 'application/json')
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_dict = (
inference_utils.get_numeric_features_to_observed_range(
self.examples[0: NUM_EXAMPLES_TO_SCAN]))
features_dict.update(
inference_utils.get_categorical_features_to_sampling(
self.examples[0: NUM_EXAMPLES_TO_SCAN], NUM_MUTANTS))
# Massage the features_dict into a sorted list before returning because
# Polymer dom-repeat needs a list.
features_list = []
for k, v in sorted(features_dict.items()):
v['name'] = k
features_list.append(v)
return http_util.Respond(request, features_list, 'application/json')
