def count_predictions(label):
"""
count all predictions or all predictions associated to a specific label made.
:param label:
:return: total amount of classifications
"""
sess = Database().session()
if label:
rows = sess.query(
func.count('*').filter(DBResult.label == "{label}".format(
label=label))).scalar()
else:
rows = sess.query(func.count(DBResult.result_id)).scalar()
sess.close()
return rows
def calc_accuracies():
"""
Calculates the accuracy with which the class was predicted.
:param label: the class with which the associated precisions of the classifications are calculated
:return: accuracy of associated predictions
"""
accuracies = {}
sess = Database().session()
for label in sess.query(DBResult.label):
label = list(label)[0]
acc_sum = sess.query(func.sum(DBResult.accuracy)).filter(
DBResult.label == "{label}".format(label=label)).scalar()
accuracies.update({
label:
round(acc_sum / DBClassification.count_predictions(label), 2)
})
sess.close()
return accuracies
def total_predictions():
"""
counts the number of predictions of the corresponding classes
:return: The amount of predictions belong to each corresponding class
"""
predictions = {}
sess = Database().session()
amounts = []
for label in sess.query(DBResult.label):
amounts.append(label)
sess.close()
predictions.update({
list(label)[0]: amount
for label, amount in Counter(amounts).items()
})
total_amount = reduce(lambda x, y: x + y, Counter(amounts).values())
return predictions, total_amount
def resolve_answers(self, *args, **kwargs):
return Database.query(AnswerModel).all()
def resolve_answer(self, *args, **kwargs):
return Database.query(AnswerModel).first()
