def test_random_run(self):
print("Boost model folder: ", self.boost_model_folder)
decision_maker = BoostingDecisionMaker(self.boost_model_folder)
test_data_size = 5
random_data = np.random.rand(test_data_size,
len(decision_maker.get_feature_ids()))
result, result_probability = decision_maker.predict(random_data)
result.should.have.length_of(test_data_size)
result_probability.should.have.length_of(test_data_size)
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)
def test_suggest_items(self):
"""Test suggesting test items"""
tests = [
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
],
"test_item_info":
launch_objects.TestItemInfo(testItemId=1,
uniqueId="341",
testCaseHash=123,
launchId=1,
launchName="Launch",
project=1,
logs=[]),
"expected_result": [],
"boost_predict": ([], [])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.NOT_FOUND,
},
],
"test_item_info":
launch_objects.TestItemInfo(testItemId=1,
uniqueId="341",
testCaseHash=123,
launchId=1,
launchName="Launch",
project=2,
logs=[
launch_objects.Log(
logId=1,
message="error found",
logLevel=40000)
]),
"expected_result": [],
"boost_predict": ([], [])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
],
"test_item_info":
launch_objects.TestItemInfo(testItemId=1,
uniqueId="341",
testCaseHash=123,
launchId=1,
launchName="Launch",
project=1,
logs=[
launch_objects.Log(
logId=1,
message=" ",
logLevel=40000)
]),
"expected_result": [],
"boost_predict": ([], [])
},
{
"test_calls": [{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
}, {
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_first),
"rs": utils.get_fixture(self.no_hits_search_rs),
}, {
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_second),
"rs": utils.get_fixture(self.no_hits_search_rs),
}, {
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_third),
"rs": utils.get_fixture(self.no_hits_search_rs),
}],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_logs, to_json=True)),
"expected_result": [],
"boost_predict": ([], [])
},
{
"test_calls": [{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
}, {
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_first),
"rs": utils.get_fixture(self.no_hits_search_rs),
}, {
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_second),
"rs": utils.get_fixture(self.no_hits_search_rs),
}, {
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_third),
"rs": utils.get_fixture(self.no_hits_search_rs),
}],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_logs, to_json=True)),
"expected_result": [],
"boost_predict": ([], [])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_first),
"rs": utils.get_fixture(self.no_hits_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_second),
"rs": utils.get_fixture(self.one_hit_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_third),
"rs": utils.get_fixture(self.one_hit_search_rs),
},
],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_logs, to_json=True)),
"expected_result": [
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='AB001',
relevantItem=1,
relevantLogId=1,
matchScore=80.0,
esScore=10.0,
esPosition=0,
modelFeatureNames='0',
modelFeatureValues='1.0',
modelInfo='',
resultPosition=0,
usedLogLines=-1,
minShouldMatch=80)
],
"boost_predict": ([1], [[0.2, 0.8]])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_first),
"rs": utils.get_fixture(self.one_hit_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_second),
"rs": utils.get_fixture(self.one_hit_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_third),
"rs": utils.get_fixture(self.one_hit_search_rs),
},
],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_logs, to_json=True)),
"expected_result": [
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='AB001',
relevantItem=1,
relevantLogId=1,
matchScore=70.0,
esScore=10.0,
esPosition=0,
modelFeatureNames='0',
modelFeatureValues='1.0',
modelInfo='',
resultPosition=0,
usedLogLines=-1,
minShouldMatch=80)
],
"boost_predict": ([1], [[0.3, 0.7]])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_first),
"rs": utils.get_fixture(self.one_hit_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_second),
"rs": utils.get_fixture(self.two_hits_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_third),
"rs": utils.get_fixture(self.two_hits_search_rs),
},
],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_logs, to_json=True)),
"expected_result": [
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='AB001',
relevantItem=1,
relevantLogId=1,
matchScore=70.0,
esScore=15.0,
esPosition=0,
modelFeatureNames='0',
modelFeatureValues='1.0',
modelInfo='',
resultPosition=0,
usedLogLines=-1,
minShouldMatch=80)
],
"boost_predict": ([1, 0], [[0.3, 0.7], [0.9, 0.1]])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_first),
"rs": utils.get_fixture(self.one_hit_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_second),
"rs": utils.get_fixture(self.two_hits_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_third),
"rs": utils.get_fixture(self.no_hits_search_rs),
},
],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_logs, to_json=True)),
"expected_result": [
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='AB001',
relevantItem=1,
relevantLogId=1,
matchScore=70.0,
esScore=15.0,
esPosition=0,
modelFeatureNames='0',
modelFeatureValues='1.0',
modelInfo='',
resultPosition=0,
usedLogLines=-1,
minShouldMatch=80),
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='PB001',
relevantItem=2,
relevantLogId=2,
matchScore=45.0,
esScore=10.0,
esPosition=1,
modelFeatureNames='0',
modelFeatureValues='0.67',
modelInfo='',
resultPosition=1,
usedLogLines=-1,
minShouldMatch=80)
],
"boost_predict": ([1, 0], [[0.3, 0.7], [0.55, 0.45]])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_first),
"rs": utils.get_fixture(self.two_hits_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_second),
"rs": utils.get_fixture(self.three_hits_search_rs),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_third),
"rs": utils.get_fixture(self.no_hits_search_rs),
},
],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_logs, to_json=True)),
"expected_result": [
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='PB001',
relevantItem=3,
relevantLogId=3,
matchScore=80.0,
esScore=10.0,
esPosition=2,
modelFeatureNames='0',
modelFeatureValues='0.67',
modelInfo='',
resultPosition=0,
usedLogLines=-1,
minShouldMatch=80),
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='AB001',
relevantItem=1,
relevantLogId=1,
matchScore=70.0,
esScore=15.0,
esPosition=0,
modelFeatureNames='0',
modelFeatureValues='1.0',
modelInfo='',
resultPosition=1,
usedLogLines=-1,
minShouldMatch=80),
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='PB001',
relevantItem=2,
relevantLogId=2,
matchScore=45.0,
esScore=10.0,
esPosition=1,
modelFeatureNames='0',
modelFeatureValues='0.67',
modelInfo='',
resultPosition=2,
usedLogLines=-1,
minShouldMatch=80)
],
"boost_predict": ([1, 0, 1], [[0.3, 0.7], [0.55, 0.45],
[0.2, 0.8]])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_first),
"rs": utils.get_fixture(self.two_hits_search_rs),
},
{
"method":
httpretty.GET,
"uri":
"/1/_search",
"status":
HTTPStatus.OK,
"content_type":
"application/json",
"rq":
utils.get_fixture(self.search_rq_second),
"rs":
utils.get_fixture(
self.three_hits_search_rs_with_duplicate),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_third),
"rs": utils.get_fixture(self.no_hits_search_rs),
},
],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_logs, to_json=True)),
"expected_result": [
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='AB001',
relevantItem=3,
relevantLogId=3,
matchScore=70.0,
esScore=15.0,
esPosition=0,
modelFeatureNames='0',
modelFeatureValues='1.0',
modelInfo='',
resultPosition=0,
usedLogLines=-1,
minShouldMatch=80),
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='AB001',
relevantItem=1,
relevantLogId=1,
matchScore=70.0,
esScore=15.0,
esPosition=0,
modelFeatureNames='0',
modelFeatureValues='1.0',
modelInfo='',
resultPosition=1,
usedLogLines=-1,
minShouldMatch=80),
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=178,
issueType='PB001',
relevantItem=2,
relevantLogId=2,
matchScore=70.0,
esScore=10.0,
esPosition=1,
modelFeatureNames='0',
modelFeatureValues='0.67',
modelInfo='',
resultPosition=2,
usedLogLines=-1,
minShouldMatch=80)
],
"boost_predict": ([1, 1, 1], [[0.3, 0.7], [0.3, 0.7],
[0.3, 0.7]])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_merged_first),
"rs": utils.get_fixture(self.one_hit_search_rs_merged),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_merged_second),
"rs": utils.get_fixture(self.one_hit_search_rs_merged),
},
{
"method": httpretty.GET,
"uri": "/1/_search",
"status": HTTPStatus.OK,
"content_type": "application/json",
"rq": utils.get_fixture(self.search_rq_merged_third),
"rs": utils.get_fixture(self.one_hit_search_rs_merged),
},
],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_merged_logs, to_json=True)),
"expected_result": [
launch_objects.SuggestAnalysisResult(
testItem=123,
testItemLogId=-1,
issueType='AB001',
relevantItem=1,
relevantLogId=1,
matchScore=90.0,
esScore=10.0,
esPosition=0,
modelFeatureNames='0',
modelFeatureValues='1.0',
modelInfo='',
resultPosition=0,
usedLogLines=-1,
minShouldMatch=80)
],
"boost_predict": ([1], [[0.1, 0.9]])
},
{
"test_calls": [
{
"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
},
{
"method":
httpretty.GET,
"uri":
"/1/_search",
"status":
HTTPStatus.OK,
"content_type":
"application/json",
"rq":
utils.get_fixture(self.search_rq_merged_first),
"rs":
utils.get_fixture(self.one_hit_search_rs_merged_wrong),
},
{
"method":
httpretty.GET,
"uri":
"/1/_search",
"status":
HTTPStatus.OK,
"content_type":
"application/json",
"rq":
utils.get_fixture(self.search_rq_merged_second),
"rs":
utils.get_fixture(self.one_hit_search_rs_merged_wrong),
},
{
"method":
httpretty.GET,
"uri":
"/1/_search",
"status":
HTTPStatus.OK,
"content_type":
"application/json",
"rq":
utils.get_fixture(self.search_rq_merged_third),
"rs":
utils.get_fixture(self.one_hit_search_rs_merged_wrong),
},
],
"test_item_info":
launch_objects.TestItemInfo(**utils.get_fixture(
self.suggest_test_item_info_w_merged_logs, to_json=True)),
"expected_result": [],
"boost_predict": ([], [])
},
]
for idx, test in enumerate(tests):
with sure.ensure('Error in the test case number: {0}', idx):
self._start_server(test["test_calls"])
config = self.get_default_search_config()
suggest_service = SuggestService(app_config=self.app_config,
search_cfg=config)
_boosting_decision_maker = BoostingDecisionMaker()
_boosting_decision_maker.get_feature_ids = MagicMock(
return_value=[0])
_boosting_decision_maker.predict = MagicMock(
return_value=test["boost_predict"])
suggest_service.suggest_decision_maker = _boosting_decision_maker
response = suggest_service.suggest_items(
test["test_item_info"])
response.should.have.length_of(len(test["expected_result"]))
for real_resp, expected_resp in zip(response,
test["expected_result"]):
real_resp.should.equal(expected_resp)
TestSuggestService.shutdown_server(test["test_calls"])
def test_full_data_check_suggests(self):
print("Boost model folder suggests: ", self.suggest_boost_model_folder)
print("Weights model folder suggests: ", self.weights_folder)
decision_maker = BoostingDecisionMaker(
folder=self.suggest_boost_model_folder)
boost_model_results = utils.get_fixture(
self.suggest_boost_model_results, to_json=True)
tests = []
all_configs = [(-1, [
"detected_message_extended",
"detected_message_without_params_extended",
"detected_message_without_params_and_brackets"
], decision_maker),
(2, [
"message_extended",
"message_without_params_extended",
"message_without_params_and_brackets"
], decision_maker)]
for log_lines, filter_fields_any, _decision_maker in all_configs:
tests.extend([
{
"elastic_results":
[(utils.get_fixture(self.log_message_suggest,
to_json=True),
utils.get_fixture(self.one_hit_search_rs_explained,
to_json=True))],
"config":
TestBoostingModel.get_default_config(
number_of_log_lines=log_lines,
filter_fields=[],
filter_fields_any=filter_fields_any,
min_should_match=0.4),
"decision_maker":
_decision_maker
},
{
"elastic_results":
[(utils.get_fixture(self.log_message_suggest,
to_json=True),
utils.get_fixture(self.two_hits_search_rs_explained,
to_json=True))],
"config":
TestBoostingModel.get_default_config(
number_of_log_lines=log_lines,
filter_fields=[],
filter_fields_any=filter_fields_any,
min_should_match=0.4),
"decision_maker":
_decision_maker
},
{
"elastic_results":
[(utils.get_fixture(self.log_message_suggest,
to_json=True),
utils.get_fixture(self.two_hits_search_rs_explained,
to_json=True)),
(utils.get_fixture(self.log_message_suggest,
to_json=True),
utils.get_fixture(self.one_hit_search_rs_explained,
to_json=True))],
"config":
TestBoostingModel.get_default_config(
number_of_log_lines=log_lines,
filter_fields=[],
filter_fields_any=filter_fields_any,
min_should_match=0.4),
"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":
TestBoostingModel.get_default_config(
number_of_log_lines=log_lines,
filter_fields=[],
filter_fields_any=filter_fields_any,
min_should_match=0.4),
"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 = SuggestBoostingFeaturizer(
test["elastic_results"],
test["config"],
feature_ids,
weighted_log_similarity_calculator=weight_log_sim)
with sure.ensure('Error in the test case number: {0}', idx):
gathered_data, test_item_ids = _boosting_featurizer.gather_features_info(
)
predict_label, predict_probability = test[
"decision_maker"].predict(gathered_data)
gathered_data.should.equal(boost_model_results[str(idx)][0],
epsilon=self.epsilon)
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_analyze_logs(self):
"""Test analyzing logs"""
tests = [
{
"test_calls": [{"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
}, ],
"index_rq": utils.get_fixture(self.launch_wo_test_items),
"expected_count": 0,
"expected_issue_type": "",
"boost_predict": ([], [])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/1",
"status": HTTPStatus.OK,
}, ],
"index_rq": utils.get_fixture(
self.launch_w_test_items_wo_logs),
"expected_count": 0,
"expected_issue_type": "",
"boost_predict": ([], [])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}, ],
"index_rq": utils.get_fixture(
self.launch_w_test_items_w_empty_logs),
"expected_count": 0,
"expected_issue_type": "",
"boost_predict": ([], [])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}, ],
"msearch_results": [utils.get_fixture(self.no_hits_search_rs, to_json=True),
utils.get_fixture(self.no_hits_search_rs, to_json=True)],
"index_rq": utils.get_fixture(self.launch_w_test_items_w_logs),
"expected_count": 0,
"expected_issue_type": "",
"boost_predict": ([], [])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.NOT_FOUND,
}, ],
"index_rq": utils.get_fixture(self.launch_w_test_items_w_logs),
"expected_count": 0,
"expected_issue_type": "",
"boost_predict": ([], [])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}],
"msearch_results": [utils.get_fixture(self.no_hits_search_rs, to_json=True),
utils.get_fixture(self.one_hit_search_rs, to_json=True)],
"index_rq": utils.get_fixture(self.launch_w_test_items_w_logs),
"expected_count": 1,
"expected_issue_type": "AB001",
"boost_predict": ([1], [[0.2, 0.8]])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}],
"msearch_results": [utils.get_fixture(self.one_hit_search_rs, to_json=True),
utils.get_fixture(self.two_hits_search_rs, to_json=True)],
"index_rq": utils.get_fixture(self.launch_w_test_items_w_logs),
"expected_count": 1,
"expected_issue_type": "AB001",
"boost_predict": ([1, 0], [[0.2, 0.8], [0.7, 0.3]])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}],
"msearch_results": [utils.get_fixture(self.two_hits_search_rs, to_json=True),
utils.get_fixture(self.three_hits_search_rs, to_json=True)],
"index_rq": utils.get_fixture(self.launch_w_test_items_w_logs),
"expected_count": 1,
"expected_issue_type": "AB001",
"boost_predict": ([1, 1], [[0.2, 0.8], [0.3, 0.7]])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}],
"msearch_results": [utils.get_fixture(self.no_hits_search_rs, to_json=True),
utils.get_fixture(self.three_hits_search_rs, to_json=True)],
"index_rq": utils.get_fixture(self.launch_w_test_items_w_logs),
"expected_count": 1,
"expected_issue_type": "PB001",
"boost_predict": ([0, 1], [[0.8, 0.2], [0.3, 0.7]])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}],
"msearch_results": [utils.get_fixture(self.no_hits_search_rs, to_json=True),
utils.get_fixture(self.three_hits_search_rs, to_json=True)],
"index_rq": utils.get_fixture(self.launch_w_test_items_w_logs),
"expected_count": 1,
"expected_issue_type": "AB001",
"boost_predict": ([1, 0], [[0.2, 0.8], [0.7, 0.3]])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}],
"msearch_results": [utils.get_fixture(self.two_hits_search_rs, to_json=True)],
"index_rq": utils.get_fixture(
self.launch_w_test_items_w_logs_to_be_merged),
"expected_count": 0,
"expected_issue_type": "",
"boost_predict": ([], [])
},
{
"test_calls": [{"method": httpretty.GET,
"uri": "/2",
"status": HTTPStatus.OK,
}],
"msearch_results": [
utils.get_fixture(self.no_hits_search_rs, to_json=True),
utils.get_fixture(self.three_hits_search_rs_with_one_unique_id, to_json=True)],
"index_rq": utils.get_fixture(
self.launch_w_test_items_w_logs),
"expected_count": 1,
"expected_issue_type": "AB001",
"boost_predict": ([1], [[0.2, 0.8]])
}
]
for idx, test in enumerate(tests):
with sure.ensure('Error in the test case number: {0}', idx):
self._start_server(test["test_calls"])
config = self.get_default_search_config()
analyzer_service = AutoAnalyzerService(app_config=self.app_config,
search_cfg=config)
_boosting_decision_maker = BoostingDecisionMaker()
_boosting_decision_maker.get_feature_ids = MagicMock(return_value=[0])
_boosting_decision_maker.predict = MagicMock(return_value=test["boost_predict"])
if "msearch_results" in test:
analyzer_service.es_client.es_client.msearch = MagicMock(
return_value={"responses": test["msearch_results"]})
analyzer_service.boosting_decision_maker = _boosting_decision_maker
launches = [launch_objects.Launch(**launch)
for launch in json.loads(test["index_rq"])]
response = analyzer_service.analyze_logs(launches)
response.should.have.length_of(test["expected_count"])
if test["expected_issue_type"] != "":
test["expected_issue_type"].should.equal(response[0].issueType)
if "expected_id" in test:
test["expected_id"].should.equal(response[0].relevantItem)
TestAutoAnalyzerService.shutdown_server(test["test_calls"])