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word2vec_model_resample_to_agree_length.py
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word2vec_model_resample_to_agree_length.py
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# -*- coding: utf-8 -*-
"""word2vec model resample to agree length
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1wET4XbXm2s03ocHwk_9wdjn_YS56Pe4k
#WORD2VEC
"""
from google.colab import drive
drive.mount('/content/drive')
!wget https://raw.githubusercontent.com/FakeNewsChallenge/fnc-1/master/train_stances.csv -P /content/drive/MyDrive/Georgia\ Tech/Clubs/Big\ Data/Datasets
!wget https://raw.githubusercontent.com/FakeNewsChallenge/fnc-1/master/train_bodies.csv -P /content/drive/MyDrive/Georgia\ Tech/Clubs/Big\ Data/Datasets
!wget https://raw.githubusercontent.com/FakeNewsChallenge/fnc-1/master/competition_test_bodies.csv -P /content/drive/MyDrive/Georgia\ Tech/Clubs/Big\ Data/Datasets
!wget https://raw.githubusercontent.com/FakeNewsChallenge/fnc-1/master/competition_test_stances.csv -P /content/drive/MyDrive/Georgia\ Tech/Clubs/Big\ Data/Datasets
import pandas as pandas
import numpy as numpy
import tensorflow as tf
from keras_preprocessing.text import Tokenizer
from gensim.models import KeyedVectors
from keras_preprocessing.sequence import pad_sequences
from keras import Sequential, Model
from keras.layers import Conv1D, Dropout, Dense, Embedding, MaxPooling1D, Concatenate, Flatten, Input
from keras.layers.merge import concatenate
from sklearn.utils import resample
PATH = '/content/drive/MyDrive/Georgia Tech/Clubs/Big Data/Datasets/'
RANDOM_SEED = 42
numpy.random.seed(RANDOM_SEED)
tf.random.set_seed(RANDOM_SEED)
# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline
import matplotlib.pyplot as plt
def load_test_data():
#create Pandas dataframes from the two csv files
train_bodies = pandas.read_csv(PATH + "competition_test_bodies.csv", encoding='utf-8')
train_headlines = pandas.read_csv(PATH + "competition_test_stances.csv", encoding='utf-8')
#merge the csv files on Body ID
test_data_set = pandas.merge(train_bodies, train_headlines, how='left', on='Body ID')
stances = {
'Stance': {
'agree': 0,
'disagree': 1,
'discuss': 2,
'unrelated': 3,
}
}
test_data_set.replace(stances, inplace=True)
print(test_data_set)
return test_data_set
# load the data set from the train csv files
def load_train_data():
#create Pandas dataframes from the two csv files
train_bodies = pandas.read_csv(PATH + "train_bodies.csv", encoding='utf-8')
train_headlines = pandas.read_csv(PATH + "train_stances.csv", encoding='utf-8')
#merge the csv files on Body ID
train_data_set = pandas.merge(train_bodies, train_headlines, how='left', on='Body ID')
stances = {
'Stance': {
'agree': 0,
'disagree': 1,
'discuss': 2,
'unrelated': 3,
}
}
train_data_set.replace(stances, inplace=True)
print(train_data_set)
print(train_data_set['Stance'].value_counts())
# average to 8909 or 3678? because 36545 is a lot but 840 is very small
# 3 - 36545, 2 - 8909, 0 - 3678, 1 - 840
print(train_data_set['Stance'].value_counts())
data_length = 8909
unrelated_downsampled = resample(train_data_set.loc[train_data_set['Stance'] == 3], replace = False, n_samples = data_length, random_state = RANDOM_SEED)
discuss_downsampled = resample(train_data_set.loc[train_data_set['Stance'] == 2], replace = False, n_samples = data_length, random_state = RANDOM_SEED)
# agree_upsampled = resample(train_data_set.loc[train_data_set['Stance'] == 0], replace=True, n_samples=data_length, random_state=RANDOM_SEED)
agree = train_data_set.loc[train_data_set['Stance'] == 0]
disagree_upsampled = resample(train_data_set.loc[train_data_set['Stance'] == 1], replace=True, n_samples=data_length, random_state=RANDOM_SEED)
all_resampled = [unrelated_downsampled, discuss_downsampled, agree, disagree_upsampled]
result = pandas.concat(all_resampled)
return result
def prepare_data(data_set, length=None):
#tokenize the data set
bodies_tokenizer, headlines_tokenizer = (Tokenizer(), Tokenizer())
#find the max length of each dataset
bodies_max_length = 0
headlines_max_length = 0
if not length:
bodies_max_length = data_set['articleBody'].map(lambda x : len(x.split())).max()
headlines_max_length = data_set['Headline'].map(lambda x : len(x.split())).max()
else:
bodies_max_length = length[0]
headlines_max_length = length[1]
#fit the tokenizer on the data set
bodies_tokenizer.fit_on_texts(data_set['articleBody'])
headlines_tokenizer.fit_on_texts(data_set['Headline'])
#convert the texts to sequences
bodies_sequences = bodies_tokenizer.texts_to_sequences(data_set['articleBody'])
headlines_sequences = headlines_tokenizer.texts_to_sequences(data_set['Headline'])
#pad the data to be the max length
bodies_sequences = pad_sequences(bodies_sequences, maxlen=bodies_max_length, padding='post', truncating='post')
headlines_sequences = pad_sequences(headlines_sequences, maxlen=headlines_max_length, padding='post', truncating='post')
return bodies_sequences, headlines_sequences, bodies_tokenizer.word_index, headlines_tokenizer.word_index, data_set['Stance']
def create_embeddings(bodies_word_index, headlines_word_index):
# create empty dictionaries for the embeddings
bodies_embeddings_index, headlines_embeddings_index = ({},{})
word2vec_model = KeyedVectors.load_word2vec_format(PATH + "GoogleNews-vectors-negative300.bin", binary=True)
def getVector(str):
if str in word2vec_model:
return word2vec_model[str]
else:
return None;
#save the vector for each word to the matrix
bodies_embeddings_matrix = numpy.zeros((len(bodies_word_index)+1, 300))
for word, i in bodies_word_index.items():
embedding_vector = getVector(word)
if embedding_vector is not None:
bodies_embeddings_matrix[i] = embedding_vector
headlines_embeddings_matrix = numpy.zeros((len(headlines_word_index)+1, 300))
for word, i in headlines_word_index.items():
embedding_vector = getVector(word)
if embedding_vector is not None:
headlines_embeddings_matrix[i] = embedding_vector
return bodies_embeddings_matrix, headlines_embeddings_matrix
#save the wector for each word to the matrix
bodies_embeddings_matrix = numpy.zeros((len(bodies_word_index)+1, 100))
for word, i in bodies_word_index.items():
embedding_vector = bodies_embeddings_index.get(word)
if embedding_vector is not None:
bodies_embeddings_matrix[i] = embedding_vector
headlines_embeddings_matrix = numpy.zeros((len(headlines_word_index)+1, 100))
for word, i in headlines_word_index.items():
embedding_vector = headlines_embeddings_index.get(word)
if embedding_vector is not None:
headlines_embeddings_matrix[i] = embedding_vector
return bodies_embeddings_matrix, headlines_embeddings_matrix
if __name__ == '__main__':
train_data = load_train_data()
# train_data = train_data[train_data['Stance'] != 3]
# g = train_data.groupby('Stance')
# train_data = g.apply(lambda x: x.sample(g.size().min()).reset_index(drop=True))
test_data = load_test_data()
# f = test_data.groupby('Stance')
# test_data = f.apply(lambda x: x.sample(f.size().min()).reset_index(drop=True))
# test_data = test_data[test_data['Stance'] != 3]
bodies_sequences, headlines_sequences, bodies_word_index, headlines_word_index, stances = prepare_data(train_data)
test_bodies_sequences, test_headlines_sequences, test_bodies_word_index, test_headlines_word_index, test_stances = prepare_data(test_data,[bodies_sequences.shape[1],headlines_sequences.shape[1]])
bodies_embeddings_matrix, headlines_embeddings_matrix = create_embeddings(bodies_word_index=bodies_word_index, headlines_word_index=headlines_word_index)
bodies_vocab_size, headlines_vocab_size = len(bodies_word_index), len(headlines_word_index)
def create_model(embedding_matrix, vocab_size, input_length):
model = Sequential()
# model.add(Input())
model.add(Embedding(vocab_size + 1,300, weights = [embedding_matrix], trainable=False, input_length=input_length))
model.add(Conv1D(256, 3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2, padding="same"))
model.add(Conv1D(256, 3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2,padding="same"))
model.add(Conv1D(512, 3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2,padding="same"))
model.add(Conv1D(512, 3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2,padding="same"))
# if input_length >= 512:
# model.add(Conv1D(512, 3, activation='relu'))
# model.add(Dropout(0.5))
# model.add(MaxPooling1D(pool_size=2,padding="same"))
model.add(Flatten())
print("issue here3")
# model.add(Conv1D(768, 3, activation='relu'))
# print("issue here4")
# model.add(Dropout(0.5))
# model.add(MaxPooling1D(pool_size=2, padding="same"))
return model
bodies_model = create_model(embedding_matrix=bodies_embeddings_matrix, vocab_size=bodies_vocab_size, input_length=bodies_sequences.shape[1])
headlines_model = create_model(embedding_matrix=headlines_embeddings_matrix, vocab_size=headlines_vocab_size, input_length=headlines_sequences.shape[1])
print(bodies_vocab_size)
print(headlines_vocab_size)
#bodies_model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
#print(bodies_model.summary())
#headlines_model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
#print(headlines_model.summary())
finalModel = Sequential()
print(bodies_model.input)
print(headlines_model.input)
print(bodies_model.output)
print(headlines_model.output)
finalModel = Concatenate()([bodies_model.output, headlines_model.output])
finalModel = Flatten()(finalModel)
finalModel = Dense(1024, activation='relu') (finalModel)
finalModel = Dense(1024, activation='relu') (finalModel)
finalModel = Dense(1024, activation='relu') (finalModel)
finalModel = Dense(4, activation='softmax') (finalModel)
#0,1,2,3
#0: [1,0,0,0]
#1: [0,1,0,0]
#2: [0,0,1,0]
model = Model(inputs=[bodies_model.input, headlines_model.input], outputs = finalModel)
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
print(model.summary())
from keras.utils.vis_utils import plot_model
plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
# print(headlines_sequences[4].size)
from keras.utils import to_categorical
print(bodies_sequences.shape)
print(headlines_sequences.shape)
print(stances)
onehot_stances = to_categorical(stances)
print(onehot_stances)
model.fit([bodies_sequences, headlines_sequences],
onehot_stances,batch_size=16,
epochs=100,
validation_split=0.05,
shuffle=True,
)
model.save(PATH)
test_onehot_stances = to_categorical(test_stances)
print(len(bodies_sequences))
print(len(test_bodies_sequences), test_headlines_sequences[0], test_onehot_stances[0])
model.evaluate([test_bodies_sequences, test_headlines_sequences], test_onehot_stances)
import pandas as pd
import numpy as np
def test(headline, body):
data = {'Headline': [headline], 'articleBody':[body], 'Stance': [None]}
df = pd.DataFrame.from_dict(data)
bodies_sequences, headlines_sequences, bodies_word_index, headlines_word_index, stances = prepare_data(df, [2243,40])
stances = {
0: "agree",
1: "disagree",
2: "discuss",
3: "unrelated"
}
prediction = model.predict([bodies_sequences, headlines_sequences])
print(prediction)
print(stances[np.argmax(prediction)])
test("Pope Francis loves Donald Trump", '''Pope Francis hates Donlad Trump''')