def predict(self, test_input, input_type, test_case_count=25):
normalize = Normalize()
if input_type == 'RANDOM_INPUT':
input_count = 0
for question in test_input:
input_count += 1
question_ = normalize.normalize(question)
logging.debug('Test Case No.{}: {}'.format(
input_count, str(question)))
logging.debug('-' * (len(question) + 16))
logging.debug('Predicted Tags: {}'.format(
self.tag_predictor(question_)))
logging.debug('')
else:
test_idx = np.random.randint(len(test_input), size=test_case_count)
logging.debug("Predicted Vs Ground Truth for {} sample".format(
test_case_count))
logging.debug('-' * 50)
logging.debug('')
input_count = 0
for idx in test_idx:
input_count += 1
test_case = idx
question = str(X_test[test_case])
logging.debug('Test Case No.{}: {}'.format(
input_count, question))
logging.debug('-' * 100)
logging.debug("Question ID: {}".format(test_case))
logging.debug('Predicted: ' + str(
self.tag_predictor(normalize.normalize_(
X_test[test_case]))))
logging.debug('Ground Truth: ' + str(
self._tag_encoder.inverse_transform(
np.array([y_test[test_case]]))))
logging.debug('\n')
def input(self):
fin = open('F:\\data\\ml\\2\\page_blocks_test_feature.txt', 'r')
lines = fin.readlines()
row = 0
for line in lines:
list = line.strip('\n').split(' ')
self.matx[row][0:10] = list
row += 1
Normalize.normalize(self.matx)
fin = open('F:\\data\\ml\\2\\page_blocks_test_label.txt', 'r')
lines = fin.readlines()
row = 0
for line in lines:
list = line.strip('\n')
self.label[row] = list[0]
row += 1
def predict(self, test_input, custom_input, test_case_count):
normalize = Normalize()
if custom_input:
input_count = 0
#prediction_df = pd.DataFrame(columns = ["Que No","Questions", "Predicted_Tags"])
prediction_list = []
for question in test_input:
input_count += 1
question_ = normalize.normalize(question)
logging.debug('-' * (len(question) + 16))
logging.debug('Test Case No.{}: {}'.format(
input_count, str(question)))
predicted_tag = self.tag_predictor(question_)
logging.debug('Predicted Tags: {}'.format(predicted_tag))
prediction_list.append({
'que_no': input_count,
'questions': str(question),
'predicted_tags': predicted_tag
})
#logging.debug('')
logging.debug('')
return prediction_list
else:
test_idx = np.random.randint(len(test_input), size=test_case_count)
logging.debug("Predicted Vs Ground Truth for {} sample(s)".format(
test_case_count))
logging.debug('-' * 50)
logging.debug('')
input_count = 0
input_predicted_list = []
prediction_score = 0
predicted_tag_list = []
prediction_list = []
#pd.DataFrame(columns = ["Que No", "Questions", "Ground_Truth","Predicted_Tags"])
for idx in test_idx:
input_count += 1
test_case = idx
question = str(test_input[test_case])
logging.debug('')
logging.debug('-' * 100)
logging.debug('Test Case No.{}:'.format(input_count))
logging.debug("Question ID: {}".format(test_case))
logging.debug('Question: {}'.format(question))
predicted_tag = self.tag_predictor(
normalize.normalize_(question))
predicted_tag_list.append(predicted_tag)
ground_truth = self._tag_encoder.inverse_transform(
np.array([self._y_test[test_case]]))
score = 0
ground_truth_ = [*ground_truth[0]]
#predicted_tag_ = [*predicted_tag]
for tag in predicted_tag:
tags = [*tag]
for tag in tags:
if tag in ground_truth_:
if (len(tag) > 0):
score = 1
prediction_score += 1
break
else:
for gt_tag in ground_truth_:
if (gt_tag.startswith(tag)
or tag.startswith(gt_tag)
) and len(gt_tag) > 0:
score = 1
prediction_score += 1
break
prediction_current = {
'que_no': input_count,
'questions': question,
'ground_truth': str(ground_truth),
'predicted_tags': str(predicted_tag)
}
prediction_list.append(prediction_current)
# append row to the dataframe
input_predicted_list.append(
[input_count, ground_truth, predicted_tag, score])
# log the ground truth & prediction
logging.debug('Predicted: ' + str(predicted_tag))
logging.debug('Ground Truth: ' + str(ground_truth))
logging.debug('\n')
accuracy = prediction_score / input_count
self._accuracy = accuracy
return prediction_list
