def generate_quality_report(language: str, report: Mapping[str, Any],
ptr: ReferencePointer,
vnodes: Sequence[VirtualNode], max_files: int,
name: str) -> str:
"""Generate report: classification report, confusion matrix, files with most errors."""
avg_keys = {"macro avg", "micro avg", "weighted avg"}
sorted_report = OrderedDict((key, report["report"][key]) for key in sorted(
report["report"], key=lambda k: -report["report"][k]["support"])
if key not in avg_keys)
for key in avg_keys:
sorted_report[key] = report["report"][key]
# sort files by mispredictions
file_mispred = []
for vnode in vnodes:
if vnode.y != getattr(vnode, "y_old", vnode.y):
file_mispred.append(vnode.path)
file_stat = Counter(file_mispred)
to_show = file_stat.most_common()
if max_files > 0:
to_show = to_show[:max_files]
template = load_jinja2_template(
os.path.join(TEMPLATES_ROOT, "quality_report.md.jinja2"))
# TODO(vmarkovtsev): move all the logic inside the template
res = template.render(language=language,
ptr=ptr,
conf_mat=report["confusion_matrix"],
target_names=report["target_names"],
files=to_show,
cl_report=sorted_report,
ppcr=report["ppcr"],
cl_report_full=report["report_full"],
name=name)
return res
def generate_model_report(
model: FormatModel,
analyze_config: Dict[str, Any],
languages: Optional[Union[str, Iterable[str]]] = None) -> str:
"""
Generate report about model - description for each rule, min/max support, min/max confidence.
:param model: trained format model.
:param analyze_config: config that is used at the analysis stage. It is needed to calculate \
the real number of enabled rules.
:param languages: Languages for which report should be created. You can specify one \
language as string, several as list of strings or None for all languages in \
the model.
:return: report in str format.
"""
languages = languages if languages is not None else model.languages
languages = languages if isinstance(languages, Iterable) else [languages]
for language in languages:
if language not in model:
raise NotFittedError(language)
template = load_jinja2_template(
os.path.join(TEMPLATES_ROOT, "model_report.md.jinja2"))
return template.render(model=model,
languages=languages,
analyze_config=analyze_config,
FeatureExtractor=FeatureExtractor,
describe_rule=describe_rule)
def _finalize(
self, reports: Iterable[Dict[str,
str]]) -> Iterator[Dict[str, str]]:
"""
Summarize all individual reports.
:param reports: Reports generated by `TypoCommitsReporter.generate_commit_dataset_report()`
:return: Summarized final report
"""
scores = self.get_metrics_stub()
reports = list(reports)
for report in reports:
scores += pandas.Series(json.loads(report["report"]))
scores.detection_precision = scores.detection_true_positive / (
scores.detection_true_positive + scores.detection_false_positive)
scores.detection_recall = scores.detection_true_positive / (
scores.detection_true_positive + scores.detection_false_negatives)
scores.fix_accuracy = scores.fix_accuracy / len(reports)
scores.top3_fix_accuracy = scores.top3_fix_accuracy / len(reports)
scores.review_time = scores.review_time / len(reports)
template = load_jinja2_template(self.report_template_path)
report = template.render(scores=scores,
commit=self._get_commit(),
package_version=self._get_package_version(),
failures=self._failures,
tabulate=tabulate)
yield {"report": report}
def generate_report(
data: pandas.DataFrame,
suggestions: Dict[int, List[Tuple[str, float]]],
) -> str:
"""Print scores for suggestions in an easy readable way."""
template = load_jinja2_template(
os.path.join(TEMPLATE_DIR, "scores.md.jinja2"))
return template.render(ScoreMode=ScoreMode,
get_scores=get_scores,
**locals())
def evaluate_typos_on_identifiers(
dataset: str = TYPOS_DATASET,
config: Optional[Mapping[str, Any]] = None,
mistakes_output: Optional[str] = None) -> str:
"""
Run IdTyposAnalyzer on the identifiers from the evaluation dataset.
:param dataset: Dataset of misspelled identifiers.
:param config: Configuration for the IdTyposAnalyzer.
:param mistakes_output: Path to the file for printing the wrong corrections.
:return: Quality report.
"""
identifiers = pandas.read_csv(dataset,
header=0,
usecols=[0, 1],
names=["wrong", "correct"],
keep_default_na=False)
analyzer = IdTyposAnalyzer(IdTyposModel(), "",
{} if config is None else config)
suggestions = analyzer.check_identifiers(identifiers["wrong"].tolist())
corrections = []
for i, identifier in enumerate(identifiers["wrong"]):
candidates = list(
analyzer.generate_identifier_suggestions(suggestions[i],
identifier))
corrections.append(candidates if len(candidates) > 0 else
[Candidate(identifier, 1.0)])
for pos in range(analyzer.config["n_candidates"]):
identifiers["sugg " + str(pos)] = [
correction[pos][0] if pos < len(correction) else ""
for correction in corrections
]
if mistakes_output is not None:
identifiers[identifiers["sugg 0"] != identifiers["correct"]][[
"wrong", "sugg 0", "correct"
]].to_csv(mistakes_output)
template = load_jinja2_template(
os.path.join(TEMPLATE_DIR, "quality_on_identifiers.md.jinja2"))
return template.render(
identifiers=identifiers,
suggestions=suggestions,
vocabulary_tokens=analyzer.corrector.generator.tokens,
n_candidates=analyzer.config["n_candidates"],
IDENTIFIER_INDEX_COLUMN=IDENTIFIER_INDEX_COLUMN,
Candidate=Candidate,
Columns=Columns,
tokenize=lambda x: list(analyzer.parser.split(x)),
flatten_df_by_column=flatten_df_by_column,
generate_report=generate_report)
def __init__(self, model: IdTyposModel, url: str, config: Mapping[str,
Any]):
"""
Initialize a new instance of IdTyposAnalyzer.
:param model: The instance of the model loaded from the repository or freshly trained.
:param url: The analyzed project's Git remote.
:param config: Configuration of the analyzer of unspecified structure.
"""
super().__init__(model, url, config)
self.config = self._load_config(config)
self.corrector = self.corrector_manager.get(self.config["corrector"])
self.parser = self.create_token_parser()
self.comment_template = load_jinja2_template(
self.config["comment_template"])
for identifier in model.identifiers:
self.corrector.expand_vocabulary(set(
self.parser.split(identifier)))
self.allowed_identifiers = set() if self.config["check_all_identifiers"] else \
model.identifiers
def _finalize(
self, reports: Iterable[Dict[str,
str]]) -> Iterator[Dict[str, str]]:
"""
Summarize all individual reports.
:param reports: Reports generated by `TypoCommitsReporter.generate_commit_dataset_report()`
:return: Summarized final report
"""
def format_series(series: pandas.Series,
formatting: Tuple[str, str]) -> pandas.Series:
series = series.copy()
keys = []
for key, fmt in formatting:
keys.append(key)
series.loc[key] = ("%" + fmt) % scores[key]
return series[keys]
scores = self.get_metrics_stub()
reports = list(reports)
for report in reports:
scores += pandas.Series(json.loads(report["report"]))
scores.detection_precision = scores.detection_true_positive / (
scores.detection_true_positive + scores.detection_false_positive)
scores.detection_recall = scores.detection_true_positive / (
scores.detection_true_positive + scores.detection_false_negatives)
scores.fix_accuracy = scores.fix_accuracy / scores.detection_true_positive
scores.top3_fix_accuracy = scores.top3_fix_accuracy / scores.detection_true_positive
scores.review_time = scores.review_time / len(reports)
self._log.info("final scores are\n%s", repr(scores))
template = load_jinja2_template(self.report_template_path)
report = template.render(scores=scores,
commit=self._get_commit(),
package_version=self._get_package_version(),
failures=self._failures,
tabulate=tabulate,
format_series=format_series)
yield {"report": report}