def test_convert_to_TFIDF(self):
import pickle
import pandas as pd
import numpy as np
from keras.models import model_from_json
from keras.preprocessing.sequence import pad_sequences
revModel = "review_model_gpu.json"
revModelWeights = "review_model_gpu.h5"
# load json and create model (review predicts rating)
json_file = open(revModel, 'r')
loaded_model_json = json_file.read()
json_file.close()
review_model = model_from_json(loaded_model_json)
review_model.load_weights(revModelWeights)
review_model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['categorical_accuracy'])
pros_rev = input("Enter a pros review: ")
cons_rev = input("Enter a cons review: ")
combine_rev = preProcessing(pros_rev + " " + cons_rev)
combine_rev = pd.Series(combine_rev)
# loading
with open('tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
maxlen = 200
tokenized_rev = tokenizer.texts_to_sequences(combine_rev)
user_rev = pad_sequences(tokenized_rev,
maxlen=maxlen,
padding='post',
truncating='post')
# Predict rating based on user review (LSTM-CNN)
model_pred = review_model.predict([user_rev],
batch_size=1024,
verbose=1)
print("LSTM-CNN Overall Rating:", np.argmax(model_pred[0]))
self.assertEqual(type(np.argmax(model_pred[0])) == str, True)
self.assertEqual(type(np.argmax(model_pred[0])) == bool, False)
self.assertEqual(type(np.argmax(model_pred[0])) == int, False)
# for i in x_train_rev.index]
#
#x_test_rev["review"] = [x_test_rev["pros"][i] if rating_pred_test[i] == 5 or rating_pred_test[i] == 3
# else x_test_rev["pros"][i] + ". " + x_test_rev["cons"][i] if rating_pred_test[i] == 4 or rating_pred_test[i] == 2
# else x_test_rev["cons"][i] for i in x_test_rev.index]
"""""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """
Data preprocessing (remove emoticons, remove non-alphabetic characters, remove digit,
one hot encode labels, tokenization)
""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """"""
x_train_rev = x_train_rev["review"]
x_test_rev = x_test_rev["review"]
y_train_val = y_train.value_counts()
y_test_val = y_test.value_counts()
x_train_rev = x_train_rev.apply(lambda x: preProcessing(x)).reset_index(
drop=True)
x_test_rev = x_test_rev.apply(lambda x: preProcessing(x)).reset_index(
drop=True)
# One hot encode y
y_train_rev = to_categorical(ytrain_arr)
y_test_rev = to_categorical(ytest_arr)
max_features = 20000
maxlen = 200
embed_size = 300
tokenizer = Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(list(x_train_rev))
tokenized_train = tokenizer.texts_to_sequences(x_train_rev)
tokenized_test = tokenizer.texts_to_sequences(x_test_rev)
]
"""""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """
Data preprocessing (train test split, remove non-alphabetic characters, remove digit,
remove emoticons, one hot encode labels, tokenization)
""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """"""
reviews = reviews[["review", "rating"]]
X = reviews["review"]
Y = reviews["rating"]
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
#y_train_val = y_train.value_counts()
#y_test_val = y_test.value_counts()
#print (y_train_val)
#print (y_test_val)
x_train = x_train.apply(lambda x: preProcessing(x)).reset_index(drop=True)
x_test = x_test.apply(lambda x: preProcessing(x)).reset_index(drop=True)
# Array created for resampling purpose (data imbalanced) and one hot encoding
ytrain_arr = np.array(y_train)
ytest_arr = np.array(y_test)
# One hot encode y
y_train = to_categorical(ytrain_arr)
y_test = to_categorical(ytest_arr)
max_features = 20000
maxlen = 200
embed_size = 300
batch_size = 128
epochs = 20
ratings_model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['categorical_accuracy'])
# load svm models
svm_model_rev = pickle.load(open("svm_model_rev.sav", "rb"))
svm_model_rat = pickle.load(open("svm_model_rat.sav", "rb"))
# Ask user to enter pros and cons reviews or aspect rating
print()
mode = input(
"Use review or aspect ratings to predict overall rating (r/a)? ")
if mode.strip().lower() == "r":
pros_rev = input("Enter pros review: ")
cons_rev = input("Enter cons review: ")
combine_rev = preProcessing(pros_rev + " " + cons_rev)
combine_rev = pd.Series(combine_rev)
# loading
with open('tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
maxlen = 200
tokenized_rev = tokenizer.texts_to_sequences(combine_rev)
user_rev = pad_sequences(tokenized_rev,
maxlen=maxlen,
padding='post',
truncating='post')
# SVM
with open("tfidfVectorizer.pickle", "rb") as handle:
def test_removeBoth(self):
self.assertTrue(preProcessing("1@#$$abc23456") == "abc")
self.assertTrue(preProcessing("asd12bcd*@#") == "asdbcd")
self.assertTrue(preProcessing(" ") == "")
self.assertTrue(preProcessing(" ") == "")
self.assertTrue(preProcessing("abcdef") == "abcdef")
def test_removeDigits(self):
self.assertTrue(preProcessing("123456") == "")
self.assertTrue(preProcessing("asd12bcd") == "asdbcd")
self.assertTrue(preProcessing("") == "")
def test_removeNonAlphabetics(self):
self.assertTrue(preProcessing("#@$&@#)($*@)") == "")
self.assertTrue(preProcessing("abc#@$&@#") == "abc")
self.assertTrue(preProcessing("") == "")
