def calculate_area_of_testing_predictions(text: str, classes: list,
archive_path: Path) -> dict:
""" Calculates area of testing predictions.
Parameters:
----------
text:
Bug description.
classes:
Model's classes.
archive_path:
Path to user archive.
Returns:
----------
Calculated predictions.
"""
prediction = {
parameter: get_probabilities(
text,
BINARY_CLASSES,
read_from_archive(archive_path, parameter + ".sav"),
)[1]
for parameter in classes
}
return prediction
def calculate_predictions(df: pd.DataFrame, training_parameters: dict,
archive_path: Path) -> pd.DataFrame:
""" Calculates predictions for received DataFrame.
Parameters:
----------
df:
Bugs.
training_parameters:
Training parameters.
archive_path:
Path to user archive.
Returns:
----------
DataFrame with calculated predictions.
"""
for parameter in training_parameters:
if parameter == "Time to Resolve":
df[parameter + "_prediction"] = df["Description_tr"].apply(
lambda descr: get_probabilities(
descr,
training_parameters[parameter],
read_from_archive(archive_path, parameter + ".sav"),
))
elif parameter == "Resolution":
df[parameter + "_prediction"] = df["Description_tr"].apply(
lambda descr: calculate_resolution_predictions(
descr, training_parameters[parameter], archive_path))
elif parameter == "areas_of_testing":
df[parameter + "_prediction"] = df["Description_tr"].apply(
lambda descr: calculate_area_of_testing_predictions(
descr, training_parameters[parameter], archive_path))
return df
def calculate_predictions(issues: pd.DataFrame, training_parameters: dict,
models: dict) -> pd.DataFrame:
"""Calculates predictions for received DataFrame and writes them to the DB.
Parameters:
----------
issues:
Bug reports.
training_parameters:
Training parameters.
models:
Models' pipelines.
Returns:
----------
Predictions.
"""
for parameter in training_parameters:
if parameter == "Time to Resolve":
issues[parameter + "_prediction"] = issues["Description_tr"].apply(
lambda descr: get_probabilities(
descr,
training_parameters[parameter],
models[parameter],
))
elif parameter == "Resolution":
issues[parameter + "_prediction"] = issues["Description_tr"].apply(
lambda descr: calculate_resolution_predictions(
descr, training_parameters[parameter], models[parameter]))
elif parameter == "areas_of_testing":
issues[parameter + "_prediction"] = issues["Description_tr"].apply(
lambda descr: calculate_area_of_testing_predictions(
descr, training_parameters[parameter], models[parameter]))
return issues
def post(self, request):
description = clean_text(request.data.get("description"))
if not description.strip():
raise CannotAnalyzeDescriptionWarning
training_parameters = get_training_parameters(request.user)
models = load_models(request.user)
probabilities = dict()
for parameter in training_parameters:
if parameter in ["Time to Resolve", "Priority"]:
probabilities[parameter] = get_probabilities(
description,
training_parameters[parameter],
models[parameter],
)
elif parameter == "Resolution":
probabilities["resolution"] = calculate_resolution_predictions(
description,
training_parameters[parameter],
models[parameter],
)
elif parameter == "areas_of_testing":
probabilities[
parameter] = calculate_area_of_testing_predictions(
description,
training_parameters[parameter],
models[parameter],
)
for probability in probabilities:
if probability == "resolution":
for resolution in probabilities[probability]:
resolution_obj = probabilities[probability][resolution]
for metric in resolution_obj:
resolution_obj[metric] = convert_to_integer(
resolution_obj[metric])
else:
for metric in probabilities[probability]:
probabilities[probability][metric] = convert_to_integer(
probabilities[probability][metric])
redis_conn.set(
f"user:{request.user.id}:description_assessment:description",
dumps(description),
)
redis_conn.set(
f"user:{request.user.id}:description_assessment:probabilities",
dumps(probabilities),
)
context = {"probabilities": probabilities}
return Response(context)
def post(self, request):
def _convert_to_integer(value):
return int(floor((value * 100) + 0.5))
description = clean_text(request.data.get("description"))
if not description.strip():
raise DescriptionCantAnalyzedWarning
archive_path = get_archive_path(request.user)
training_parameters = read_from_archive(
archive_path, TRAINING_PARAMETERS_FILENAME
)
probabilities = {}
probabilities["resolution"] = calculate_resolution_predictions(
description, training_parameters["Resolution"], archive_path
)
probabilities[
"areas_of_testing"
] = calculate_area_of_testing_predictions(
description, training_parameters["areas_of_testing"], archive_path
)
for metric in ["Time to Resolve", "Priority"]:
probabilities[metric] = get_probabilities(
description,
training_parameters[metric],
read_from_archive(archive_path, metric + ".sav"),
)
for probability in probabilities:
if probability == "resolution":
for resolution in probabilities[probability]:
resolution_obj = probabilities[probability][resolution]
for metric in resolution_obj:
resolution_obj[metric] = _convert_to_integer(
resolution_obj[metric]
)
else:
for metric in probabilities[probability]:
probabilities[probability][metric] = _convert_to_integer(
probabilities[probability][metric]
)
redis_conn.set(f"description:{request.user.id}", dumps(description))
redis_conn.set(
f"probabilities:{request.user.id}", dumps(probabilities)
)
context = {"probabilities": probabilities}
return Response(context)
def calculate_resolution_predictions(text: str, classes: dict,
models: dict) -> dict:
"""Calculates predictions for defect resolutions.
Parameters:
----------
text:
Bug description.
classes:
Model's classes.
archive_path:
Path to user archive.
Returns:
----------
Calculated predictions.
"""
prediction = {
parameter: get_probabilities(text, classes, models[parameter])
for parameter, classes in classes.items()
}
return prediction
def calculate_resolution_predictions(text: str, classes: list,
archive_path: Path) -> dict:
""" Calculates predictions for defect resolutions.
Parameters:
----------
text:
Bug description.
classes:
Model's classes.
archive_path:
Path to user archive.
Returns:
----------
Calculated predictions.
"""
prediction = {
parameter:
get_probabilities(text, classes,
read_from_archive(archive_path, parameter + ".sav"))
for parameter, classes in classes.items()
}
return prediction