def __init__(self, lemmatization=False):
BugModel.__init__(self, lemmatization, commit_data=True)
self.cross_validation_enabled = False
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords({"dev-doc-needed", "dev-doc-complete"}),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.product(),
bug_features.component(),
bug_features.commit_added(),
bug_features.commit_deleted(),
bug_features.commit_types(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline(
[
(
"bug_extractor",
bug_features.BugExtractor(
feature_extractors,
cleanup_functions,
rollback=True,
rollback_when=self.rollback,
commit_data=True,
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(), "title"),
("comments", self.text_vectorizer(), "comments"),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=utils.get_physical_cpu_count())
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False):
BugModel.__init__(self, lemmatization)
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords({"dev-doc-needed", "dev-doc-complete"}),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.product(),
bug_features.component(),
bug_features.commit_added(),
bug_features.commit_deleted(),
bug_features.commit_types(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline(
[
(
"bug_extractor",
bug_features.BugExtractor(
feature_extractors,
cleanup_functions,
rollback=True,
rollback_when=self.rollback,
commit_data=True,
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(), "title"),
("comments", self.text_vectorizer(), "comments"),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False, historical=False):
BugModel.__init__(self, lemmatization)
self.calculate_importance = False
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
# Ignore keywords that would make the ML completely skewed
# (we are going to use them as 100% rules in the evaluation phase).
bug_features.keywords(set(KEYWORD_DICT.keys())),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.blocked_bugs_number(),
bug_features.ever_affected(),
bug_features.affected_then_unaffected(),
bug_features.product(),
bug_features.component(),
]
cleanup_functions = [
feature_cleanup.url(),
feature_cleanup.fileref(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline([
(
"bug_extractor",
bug_features.BugExtractor(feature_extractors,
cleanup_functions),
),
(
"union",
ColumnTransformer([
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(min_df=0.001), "title"),
(
"first_comment",
self.text_vectorizer(min_df=0.001),
"first_comment",
),
(
"comments",
self.text_vectorizer(min_df=0.001),
"comments",
),
]),
),
])
self.clf = OneVsRestClassifier(xgboost.XGBClassifier(n_jobs=16))
def __init__(self, lemmatization=False):
BugModel.__init__(self, lemmatization)
self.cross_validation_enabled = False
self.calculate_importance = False
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
bug_features.keywords(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline(
[
(
"bug_extractor",
bug_features.BugExtractor(
feature_extractors, cleanup_functions, rollback=True
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(min_df=0.0001), "title"),
(
"comments",
self.text_vectorizer(min_df=0.0001),
"comments",
),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
self.CONFLATED_COMPONENTS_INVERSE_MAPPING = {
v: k for k, v in self.CONFLATED_COMPONENTS_MAPPING.items()
}
def __init__(self, lemmatization=False):
BugModel.__init__(self, lemmatization)
self.cross_validation_enabled = False
self.calculate_importance = False
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
bug_features.keywords(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline(
[
(
"bug_extractor",
bug_features.BugExtractor(
feature_extractors, cleanup_functions, rollback=True
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(min_df=0.0001), "title"),
(
"comments",
self.text_vectorizer(min_df=0.0001),
"comments",
),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
self.CONFLATED_COMPONENTS_INVERSE_MAPPING = {
v: k for k, v in self.CONFLATED_COMPONENTS_MAPPING.items()
}
def __init__(self, lemmatization=False):
BugModel.__init__(self, lemmatization)
self.sampler = RandomUnderSampler(random_state=0)
self.calculate_importance = False
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.product(),
bug_features.component(),
bug_features.num_words_title(),
bug_features.num_words_comments(),
bug_features.keywords(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline(
[
(
"bug_extractor",
bug_features.BugExtractor(
feature_extractors, cleanup_functions, rollback=True
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(), "title"),
("comments", self.text_vectorizer(), "comments"),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
self.sampler = InstanceHardnessThreshold(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.product(),
bug_features.component(),
bug_features.is_mozillian(),
bug_features.bug_reporter(),
bug_features.blocked_bugs_number(),
bug_features.priority(),
bug_features.has_cve_in_alias(),
bug_features.comment_count(),
bug_features.comment_length(),
bug_features.reporter_experience(),
bug_features.number_of_bug_dependencies()
]
cleanup_functions = [
bug_features.cleanup_url,
bug_features.cleanup_fileref,
bug_features.cleanup_hex,
bug_features.cleanup_dll,
bug_features.cleanup_synonyms,
bug_features.cleanup_crash,
]
self.extraction_pipeline = Pipeline([
('bug_extractor', bug_features.BugExtractor(feature_extractors, cleanup_functions, rollback=True, rollback_when=self.rollback)),
('union', ColumnTransformer([
('data', DictVectorizer(), 'data'),
('title', self.text_vectorizer(min_df=0.0001), 'title'),
('comments', self.text_vectorizer(min_df=0.0001), 'comments'),
])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor='cpu_predictor')
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords({"qawanted"}),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
]
cleanup_functions = [
bug_features.cleanup_fileref,
bug_features.cleanup_url,
bug_features.cleanup_synonyms,
]
self.extraction_pipeline = Pipeline(
[
(
"bug_extractor",
bug_features.BugExtractor(
feature_extractors,
cleanup_functions,
rollback=True,
rollback_when=self.rollback,
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(), "title"),
("comments", self.text_vectorizer(), "comments"),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords({'dev-doc-needed', 'dev-doc-complete'}),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.product(),
bug_features.component(),
bug_features.commit_added(),
bug_features.commit_deleted(),
bug_features.commit_types(),
]
cleanup_functions = [
bug_features.cleanup_fileref,
bug_features.cleanup_url,
bug_features.cleanup_synonyms,
]
self.extraction_pipeline = Pipeline([
('bug_extractor',
bug_features.BugExtractor(feature_extractors,
cleanup_functions,
rollback=True,
rollback_when=self.rollback,
commit_data=True)),
('union',
ColumnTransformer([
('data', DictVectorizer(), 'data'),
('title', self.text_vectorizer(stop_words='english'),
'title'),
('comments', self.text_vectorizer(stop_words='english'),
'comments'),
])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor='cpu_predictor')
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
self.sampler = BorderlineSMOTE(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
# Ignore keywords that would make the ML completely skewed
# (we are going to use them as 100% rules in the evaluation phase).
bug_features.keywords(
{'regression', 'talos-regression', 'feature'}),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.blocked_bugs_number(),
bug_features.ever_affected(),
bug_features.affected_then_unaffected(),
bug_features.product(),
bug_features.component(),
]
cleanup_functions = [
bug_features.cleanup_url,
bug_features.cleanup_fileref,
bug_features.cleanup_synonyms,
]
self.extraction_pipeline = Pipeline([
('bug_extractor',
bug_features.BugExtractor(feature_extractors, cleanup_functions)),
('union',
ColumnTransformer([
('data', DictVectorizer(), 'data'),
('title', self.text_vectorizer(min_df=0.001), 'title'),
('first_comment', self.text_vectorizer(min_df=0.001),
'first_comment'),
('comments', self.text_vectorizer(min_df=0.001), 'comments'),
])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor='cpu_predictor')
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
self.undersampling_enabled = False
self.cross_validation_enabled = False
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
bug_features.keywords(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
]
cleanup_functions = [
bug_features.cleanup_fileref,
bug_features.cleanup_url,
bug_features.cleanup_synonyms,
]
self.extraction_pipeline = Pipeline([
('bug_extractor',
bug_features.BugExtractor(feature_extractors, cleanup_functions)),
(
'union',
ColumnTransformer([
# TODO: Re-enable when we'll support bug snapshotting (#5).
# ('data', DictVectorizer(), 'data'),
('title', self.text_vectorizer(stop_words='english'),
'title'),
# TODO: Re-enable when we'll support bug snapshotting (#5).
# ('comments', self.text_vectorizer(stop_words='english'), 'comments'),
('first_comment',
self.text_vectorizer(stop_words='english'),
'first_comment'),
])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor='cpu_predictor')
def __init__(self, lemmatization=False):
BugModel.__init__(self, lemmatization)
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords({"stepswanted"}),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline([
(
"bug_extractor",
bug_features.BugExtractor(feature_extractors,
cleanup_functions),
),
(
"union",
ColumnTransformer([
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(), "title"),
("comments", self.text_vectorizer(), "comments"),
]),
),
])
self.clf = xgboost.XGBClassifier(n_jobs=utils.get_physical_cpu_count())
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
self.cross_validation_enabled = False
self.calculate_importance = False
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
bug_features.keywords(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
]
cleanup_functions = [
bug_features.cleanup_fileref,
bug_features.cleanup_url,
bug_features.cleanup_synonyms,
]
self.extraction_pipeline = Pipeline([
('bug_extractor',
bug_features.BugExtractor(feature_extractors,
cleanup_functions,
rollback=True)),
('union',
ColumnTransformer([
('data', DictVectorizer(), 'data'),
('title', self.text_vectorizer(min_df=0.0001), 'title'),
('comments', self.text_vectorizer(min_df=0.0001), 'comments'),
])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor='cpu_predictor')
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
]
cleanup_functions = [
bug_features.cleanup_fileref,
bug_features.cleanup_url,
bug_features.cleanup_synonyms,
]
self.extraction_pipeline = Pipeline([
('bug_extractor',
bug_features.BugExtractor(feature_extractors, cleanup_functions)),
('union',
ColumnTransformer([
('data', DictVectorizer(), 'data'),
('title', self.text_vectorizer(), 'title'),
('comments', self.text_vectorizer(), 'comments'),
])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor='cpu_predictor')
def __init__(self, lemmatization=False):
BugModel.__init__(self, lemmatization)
self.sampler = InstanceHardnessThreshold(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.product(),
bug_features.component(),
bug_features.is_mozillian(),
bug_features.bug_reporter(),
bug_features.blocked_bugs_number(),
bug_features.priority(),
bug_features.has_cve_in_alias(),
bug_features.comment_count(),
bug_features.comment_length(),
bug_features.reporter_experience(),
bug_features.number_of_bug_dependencies(),
]
cleanup_functions = [
feature_cleanup.url(),
feature_cleanup.fileref(),
feature_cleanup.hex(),
feature_cleanup.dll(),
feature_cleanup.synonyms(),
feature_cleanup.crash(),
]
self.extraction_pipeline = Pipeline(
[
(
"bug_extractor",
bug_features.BugExtractor(
feature_extractors,
cleanup_functions,
rollback=True,
rollback_when=self.rollback,
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(min_df=0.0001), "title"),
(
"comments",
self.text_vectorizer(min_df=0.0001),
"comments",
),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self,
lemmatization=False,
historical=False,
rca_subcategories_enabled=False):
BugModel.__init__(self, lemmatization)
self.calculate_importance = False
self.rca_subcategories_enabled = rca_subcategories_enabled
# should we consider only the main category or all sub categories
self.RCA_TYPES = (RCA_SUBCATEGORIES + RCA_CATEGORIES
if rca_subcategories_enabled else RCA_CATEGORIES)
self.RCA_LIST = sorted(set(self.RCA_TYPES))
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
# Ignore whiteboards that would make the ML completely skewed
# bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.blocked_bugs_number(),
bug_features.ever_affected(),
bug_features.affected_then_unaffected(),
bug_features.product(),
bug_features.component(),
]
cleanup_functions = [
feature_cleanup.url(),
feature_cleanup.fileref(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline([
(
"bug_extractor",
bug_features.BugExtractor(feature_extractors,
cleanup_functions),
),
(
"union",
ColumnTransformer([
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(min_df=0.001), "title"),
(
"first_comment",
self.text_vectorizer(min_df=0.001),
"first_comment",
),
(
"comments",
self.text_vectorizer(min_df=0.001),
"comments",
),
]),
),
])
self.clf = OneVsRestClassifier(xgboost.XGBClassifier(n_jobs=16))
def __init__(self, lemmatization=False):
BugModel.__init__(self, lemmatization)
self.calculate_importance = False
self.sampler = InstanceHardnessThreshold(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.has_regression_range(),
bug_features.severity(),
bug_features.keywords(),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.product(),
bug_features.component(),
bug_features.is_mozillian(),
bug_features.bug_reporter(),
bug_features.blocked_bugs_number(),
bug_features.priority(),
bug_features.has_cve_in_alias(),
bug_features.comment_count(),
bug_features.comment_length(),
bug_features.reporter_experience(),
bug_features.number_of_bug_dependencies(),
]
cleanup_functions = [
feature_cleanup.url(),
feature_cleanup.fileref(),
feature_cleanup.hex(),
feature_cleanup.dll(),
feature_cleanup.synonyms(),
feature_cleanup.crash(),
]
self.extraction_pipeline = Pipeline([
(
"bug_extractor",
bug_features.BugExtractor(
feature_extractors,
cleanup_functions,
rollback=True,
rollback_when=self.rollback,
),
),
(
"union",
ColumnTransformer([
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(min_df=0.0001), "title"),
(
"comments",
self.text_vectorizer(min_df=0.0001),
"comments",
),
]),
),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False, historical=False):
BugModel.__init__(self, lemmatization)
self.sampler = BorderlineSMOTE(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
# Ignore keywords that would make the ML completely skewed
# (we are going to use them as 100% rules in the evaluation phase).
bug_features.keywords(
{"regression", "talos-regression", "feature"}),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.blocked_bugs_number(),
bug_features.ever_affected(),
bug_features.affected_then_unaffected(),
bug_features.product(),
bug_features.component(),
]
if historical:
feature_extractors.append(bug_features.had_severity_enhancement())
cleanup_functions = [
feature_cleanup.url(),
feature_cleanup.fileref(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline([
(
"bug_extractor",
bug_features.BugExtractor(feature_extractors,
cleanup_functions),
),
(
"union",
ColumnTransformer([
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(min_df=0.001), "title"),
(
"first_comment",
self.text_vectorizer(min_df=0.001),
"first_comment",
),
(
"comments",
self.text_vectorizer(min_df=0.001),
"comments",
),
]),
),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False, historical=False):
BugModel.__init__(self, lemmatization)
self.sampler = BorderlineSMOTE(random_state=0)
feature_extractors = [
bug_features.has_str(),
bug_features.severity(),
# Ignore keywords that would make the ML completely skewed
# (we are going to use them as 100% rules in the evaluation phase).
bug_features.keywords(set(keyword_dict.keys())),
bug_features.is_coverity_issue(),
bug_features.has_crash_signature(),
bug_features.has_url(),
bug_features.has_w3c_url(),
bug_features.has_github_url(),
bug_features.whiteboard(),
bug_features.patches(),
bug_features.landings(),
bug_features.title(),
bug_features.blocked_bugs_number(),
bug_features.ever_affected(),
bug_features.affected_then_unaffected(),
bug_features.product(),
bug_features.component(),
]
cleanup_functions = [
feature_cleanup.url(),
feature_cleanup.fileref(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline(
[
(
"bug_extractor",
bug_features.BugExtractor(feature_extractors, cleanup_functions),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("title", self.text_vectorizer(min_df=0.001), "title"),
(
"first_comment",
self.text_vectorizer(min_df=0.001),
"first_comment",
),
(
"comments",
self.text_vectorizer(min_df=0.001),
"comments",
),
]
),
),
]
)
self.clf = OneVsRestClassifier(xgboost.XGBClassifier(n_jobs=16))