def test_full_data_check(self):
print("Boost model folder: ", self.boost_model_folder)
decision_maker = BoostingDecisionMaker(self.boost_model_folder)
boost_model_results = self.get_fixture(self.boost_model_results)
tests = [
{
"elastic_results":
[(self.get_fixture(self.log_message),
self.get_fixture(self.one_hit_search_rs_explained))],
"config":
TestBoostingModel.get_default_config(),
},
{
"elastic_results":
[(self.get_fixture(self.log_message),
self.get_fixture(self.two_hits_search_rs_explained))],
"config":
TestBoostingModel.get_default_config(),
},
{
"elastic_results":
[(self.get_fixture(self.log_message),
self.get_fixture(self.two_hits_search_rs_explained)),
(self.get_fixture(self.log_message),
self.get_fixture(self.one_hit_search_rs_explained))],
"config":
TestBoostingModel.get_default_config(),
},
{
"elastic_results":
[(self.get_fixture(self.log_message_only_small_logs),
self.get_fixture(self.two_hits_search_rs_small_logs))],
"config":
TestBoostingModel.get_default_config(),
},
]
for idx, test in enumerate(tests):
_boosting_featurizer = BoostingFeaturizer(
test["elastic_results"], test["config"],
decision_maker.get_feature_ids())
with sure.ensure('Error in the test case number: {0}', idx):
gathered_data, issue_type_names = _boosting_featurizer.gather_features_info(
)
gathered_data.should.equal(boost_model_results[str(idx)][0],
epsilon=self.epsilon)
predict_label, predict_probability = decision_maker.predict(
gathered_data)
predict_label.tolist().should.equal(
boost_model_results[str(idx)][1], epsilon=self.epsilon)
predict_probability.tolist().should.equal(
boost_model_results[str(idx)][2], epsilon=self.epsilon)
def test_full_data_check(self):
print("Boost model folder : ", self.boost_model_folder)
print("Weights model folder : ", self.weights_folder)
print("Global defect type model folder : ",
self.global_defect_type_model_folder)
decision_maker = BoostingDecisionMaker(folder=self.boost_model_folder)
boost_model_results = utils.get_fixture(self.boost_model_results,
to_json=True)
tests = []
for log_lines, filter_fields, _decision_maker in [
(-1, ["detected_message", "stacktrace"], decision_maker),
(2, ["message"], decision_maker)
]:
tests.extend([
{
"elastic_results":
[(utils.get_fixture(self.log_message, to_json=True),
utils.get_fixture(self.one_hit_search_rs_explained,
to_json=True))],
"config":
self.get_default_config(number_of_log_lines=log_lines,
filter_fields=filter_fields),
"decision_maker":
_decision_maker
},
{
"elastic_results":
[(utils.get_fixture(self.log_message, to_json=True),
utils.get_fixture(self.two_hits_search_rs_explained,
to_json=True))],
"config":
self.get_default_config(number_of_log_lines=log_lines,
filter_fields=filter_fields),
"decision_maker":
_decision_maker
},
{
"elastic_results":
[(utils.get_fixture(self.log_message, to_json=True),
utils.get_fixture(self.two_hits_search_rs_explained,
to_json=True)),
(utils.get_fixture(self.log_message, to_json=True),
utils.get_fixture(self.one_hit_search_rs_explained,
to_json=True))],
"config":
self.get_default_config(number_of_log_lines=log_lines,
filter_fields=filter_fields),
"decision_maker":
_decision_maker
},
{
"elastic_results":
[(utils.get_fixture(self.log_message_only_small_logs,
to_json=True),
utils.get_fixture(self.two_hits_search_rs_small_logs,
to_json=True))],
"config":
self.get_default_config(number_of_log_lines=log_lines,
filter_fields=filter_fields),
"decision_maker":
_decision_maker
},
])
for idx, test in enumerate(tests):
feature_ids = test["decision_maker"].get_feature_ids()
weight_log_sim = None
if self.weights_folder.strip():
weight_log_sim = weighted_similarity_calculator.\
WeightedSimilarityCalculator(folder=self.weights_folder)
_boosting_featurizer = BoostingFeaturizer(
test["elastic_results"],
test["config"],
feature_ids,
weighted_log_similarity_calculator=weight_log_sim)
if self.global_defect_type_model_folder.strip():
_boosting_featurizer.set_defect_type_model(
defect_type_model.DefectTypeModel(
folder=self.global_defect_type_model_folder))
with sure.ensure('Error in the test case number: {0}', idx):
gathered_data, issue_type_names = _boosting_featurizer.gather_features_info(
)
gathered_data.should.equal(boost_model_results[str(idx)][0],
epsilon=self.epsilon)
predict_label, predict_probability = test[
"decision_maker"].predict(gathered_data)
predict_label.tolist().should.equal(
boost_model_results[str(idx)][1], epsilon=self.epsilon)
predict_probability.tolist().should.equal(
boost_model_results[str(idx)][2], epsilon=self.epsilon)