paranormal-or-skeptic-ISI-public/logistic-regression.py

import pandas as pd
import numpy as np
import csv
import torch
from nltk.tokenize import word_tokenize
from gensim import downloader

FEATURES = ['content', 'id', 'label']
PATHS = ['train/in.tsv', 'train/expected.tsv', 'dev-0/in.tsv', 'test-A/in.tsv', './dev-0/out.tsv', './test-A/out.tsv']
PRE_TRAINED = 'word2vec-google-news-300'

class NeuralNetwork(torch.nn.Module):
    def __init__(self, INPUT_DIM):
        super(NeuralNetwork, self).__init__()
        self.l1 = torch.nn.Linear(INPUT_DIM, 500)
        self.l2 = torch.nn.Linear(500, 1)

    def forward(self, x):
        x = self.l1(x)
        x = torch.relu(x)
        x = self.l2(x)
        x = torch.sigmoid(x)
        return x

def get_data(FEATURES, PATHS):
  x_train = pd.read_table(PATHS[0], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])
  y_train = pd.read_table(PATHS[1], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[2:])
  x_dev = pd.read_table(PATHS[2], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])
  x_test = pd.read_table(PATHS[3], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])

  return x_train, y_train, x_dev, x_test

def preprocess(x_train, y_train, x_dev, x_test):
  x_train = x_train[FEATURES[0]].str.lower()
  x_dev = x_dev[FEATURES[0]].str.lower()
  x_test = x_test[FEATURES[0]].str.lower()
  y_train = y_train[FEATURES[2]]

  return x_train, y_train, x_dev, x_test

def tokenize(x_train, x_dev, x_test):
  x_train = [word_tokenize(i) for i in x_train]
  x_dev = [word_tokenize(i) for i in x_dev]
  x_test = [word_tokenize(i) for i in x_test]

  return x_train, x_dev, x_test

def use_word2vec():
  w2v = downloader.load(PRE_TRAINED)

  return w2v

def document_vector(w2v, x_train, x_dev, x_test):
  x_train = [np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis = 0) for doc in x_train]
  x_dev = [np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis = 0) for doc in x_dev]
  x_test = [np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis = 0) for doc in x_test]

  return x_train, x_dev, x_test

def basic_config():
  INPUT_DIM = 300
  BATCH_SIZE = 5

  return INPUT_DIM, BATCH_SIZE

def init_model(INPUT_DIM):
  nn_model = NeuralNetwork(INPUT_DIM)
  criterion = torch.nn.BCELoss()
  optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)

  return nn_model, optimizer, criterion

def train(nn_model, BATCH_SIZE, criterion, optimizer, x_train, y_train):
  for epoch in range(5):
    nn_model.train()
    for i in range(0, y_train.shape[0], BATCH_SIZE):
        X = x_train[i:i+BATCH_SIZE]
        X = torch.tensor(X)
        y = y_train[i:i+BATCH_SIZE]
        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
        outputs = nn_model(X.float())
        loss = criterion(outputs, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

def prediction(nn_model, BATCH_SIZE, x_dev, x_test):
  y_dev, y_test = [], []
  nn_model.eval()
  with torch.no_grad():
      for i in range(0, len(x_dev), BATCH_SIZE):
          X = x_dev[i:i+BATCH_SIZE]
          X = torch.tensor(X)
          outputs = nn_model(X.float())
          prediction = (outputs > 0.5)
          y_dev += prediction.tolist()
      for i in range(0, len(x_test), BATCH_SIZE):
          X = x_test[i:i+BATCH_SIZE]
          X = torch.tensor(X)
          outputs = nn_model(X.float())
          prediction = (outputs > 0.5)
          y_test += prediction.tolist()

  return y_dev, y_test

def get_result(y_dev, y_test):
  np.asarray(y_dev, dtype = np.int32).tofile(PATHS[4], sep='\n')
  np.asarray(y_test, dtype = np.int32).tofile(PATHS[5], sep='\n')

def main():
  x_train, y_train, x_dev, x_test = get_data(FEATURES, PATHS)
  x_train, y_train, x_dev, x_test = preprocess(x_train, y_train, x_dev, x_test)
  x_train, x_dev, x_test = tokenize(x_train, x_dev, x_test)
  w2v = use_word2vec()
  x_train, x_dev, x_test = document_vector(w2v, x_train, x_dev, x_test)
  INPUT_DIM, BATCH_SIZE = basic_config()
  nn_model, optimizer, criterion = init_model(INPUT_DIM)
  train(nn_model, BATCH_SIZE, criterion, optimizer, x_train, y_train)
  y_dev, y_test = prediction(nn_model, BATCH_SIZE, x_dev, x_test)
  get_result(y_dev, y_test)

if __name__ == '__main__':
  main()