def load_train_validation_data(self):
self.train = dh.loaddata(self._train_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
print('Training data loading finished...')
self.validation = dh.loaddata(self._validation_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
print('Validation data loading finished...')
if (self._test_file != None):
self.test = dh.loaddata(self._test_file,
self._word_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=True)
def load_train_validation_test_data(self):
print("Loading resource...")
self.train = dh.loaddata(self._train_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
self.validation = dh.loaddata(self._validation_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
if (self._test_file != None):
self.test = dh.loaddata(self._test_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=True)
def predict(self, test_file, verbose=False):
try:
start = time.time()
self.test = dh.loaddata(test_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False,
lowercase=False,
n_grams=3,
at_character=True)
end = time.time()
if (verbose == True):
print('test resource loading time::', (end - start))
self._vocab = self.load_vocab()
print('vocab loaded...')
start = time.time()
tX, tY, tD, tC, tA = dh.vectorize_word_dimension(
self.test, self._vocab)
tX = dh.pad_sequence_1d(tX, maxlen=self._line_maxlen)
end = time.time()
if (verbose == True):
print('test resource preparation time::', (end - start))
self.__predict_model(tX, self.test)
except Exception as e:
print('Error:', e)
def predict(self, test_file, verbose=False):
start = time.time()
self.test = dh.loaddata(test_file,
self._word_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
end = time.time()
if (verbose == True):
print('test resource loading time::', (end - start))
self._vocab = self.load_vocab()
start = time.time()
tX, tY, tD, tC, tA = dh.vectorize_word_dimension(
self.test, self._vocab)
tX = dh.pad_sequence_1d(tX, maxlen=self._line_maxlen)
tC = dh.pad_sequence_1d(tC, maxlen=self._line_maxlen)
tD = dh.pad_sequence_1d(tD, maxlen=11)
end = time.time()
if (verbose == True):
print('test resource preparation time::', (end - start))
self.__predict_model([tC, tX, tD], self.test)
def load_train_validation_test_data(self):
self.train = dh.loaddata(self._train_file,
self._word_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
self.validation = dh.loaddata(self._validation_file,
self._word_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
if (self._test_file != None):
self.test = dh.loaddata(self._test_file,
self._word_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=True)
def load_train_data(self):
self.train = dh.loaddata(self._train_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
print('Training data loading finished...')
def load_train_validation_data(self,
ignore_profiles=True,
lowercase=True,
at_character=False):
self.train = dh.loaddata(self._train_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
lowercase=lowercase,
ignore_profiles=ignore_profiles,
at_character=at_character)
self.validation = dh.loaddata(self._validation_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
lowercase=lowercase,
ignore_profiles=ignore_profiles,
at_character=at_character)
def load_train_validation_data(self):
self.train = dh.loaddata(self._train_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False,
lowercase=False,
n_grams=3,
at_character=True)
print('Training data loading finished...')
self.validation = dh.loaddata(self._validation_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=False,
ignore_profiles=False,
lowercase=False,
n_grams=3,
at_character=True)
print('Validation data loading finished...')
def test_predict(self, verbose=False):
start = time.time()
self.test = dh.loaddata(self._test_file,
self._word_file_path,
self._split_word_file_path,
self._emoji_file_path,
normalize_text=True,
split_hashtag=True,
ignore_profiles=False)
end = time.time()
if (verbose == True):
print('test resource loading time::', (end - start))
self._vocab = dh.build_vocab(self.test, min_freq=1)
if ('unk' not in self._vocab):
self._vocab['unk'] = len(self._vocab.keys()) + 1
dh.write_vocab(self._vocab_file_path, self._vocab)
tX, tY, D, C, A = dh.vectorize_word_dimension(self.test, self._vocab)
tX = dh.pad_sequence_1d(tX, maxlen=self._line_maxlen)
dimension_size = 300
emb_weights = load_glove_model(
self._vocab,
n=dimension_size,
glove_path='/content/SarcasmDetection/src/glove.6B.300d.txt')
label_dict = {
0: 'EXTRAVERSION',
1: 'NEUROTICISM',
2: 'AGREEABLENESS',
3: 'CONSCIENTIOUSNESS',
4: 'OPENNESS'
}
predictions = self.model.predict(tX)
total_pred = np.array([0, 0, 0, 0, 0])
for i in predictions:
total_pred = np.add(total_pred, np.array(i))
pos = np.where(total_pred == max(total_pred))
l_pos = pos[0].tolist()
RESULT = l_pos[0]
print("THE RESULT IS " + str(label_dict[RESULT]))
