def __init__(self, lemmatization=False, granularity="label"):
Model.__init__(self, lemmatization)
self.granularity = granularity
self.required_dbs = [repository.COMMITS_DB]
if granularity == "label":
self.required_dbs.append(test_scheduling.TEST_LABEL_SCHEDULING_DB)
elif granularity == "group":
self.required_dbs.append(test_scheduling.TEST_GROUP_SCHEDULING_DB)
self.cross_validation_enabled = False
self.entire_dataset_training = True
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
test_scheduling_features.name(),
test_scheduling_features.prev_failures(),
]
if granularity == "label":
feature_extractors += [
test_scheduling_features.platform(),
test_scheduling_features.chunk(),
test_scheduling_features.suite(),
]
elif granularity == "group":
feature_extractors += [
test_scheduling_features.path_distance(),
test_scheduling_features.common_path_components(),
]
self.extraction_pipeline = Pipeline([
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors, []),
),
("union", ColumnTransformer([("data", DictVectorizer(), "data")])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
self.required_dbs = [
repository.COMMITS_DB, test_scheduling.TEST_SCHEDULING_DB
]
self.calculate_importance = False
self.cross_validation_enabled = False
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
test_scheduling_features.name(),
test_scheduling_features.platform(),
test_scheduling_features.chunk(),
test_scheduling_features.suite(),
test_scheduling_features.prev_failures(),
]
self.extraction_pipeline = Pipeline([
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors, []),
),
("union", ColumnTransformer([("data", DictVectorizer(), "data")])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")