paranormal-or-skeptic-ISI-public/Untitled.ipynb

def getInput(path):
    with open(path,encoding='utf-8') as f:
          return f.readlines()

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

word2vec = gensim.load('word2vec-google-news-300')

# train_in=getInput('./train/in.tsv')
# train_expected=getInput('./train/expected.tsv')
# test_in=getInput('./test-A/in.tsv')
# dev_in=getInput('./dev-0/in.tsv')
# dev_expected=getInput('./dev-0/expected.tsv')

class NeuralNetworkModel(torch.nn.Module):
    def __init__(self):
        super(NeuralNetworkModel, self).__init__()
        self.l01 = torch.nn.Linear(300, 300)
        self.l02 = torch.nn.Linear(300, 1)

    def forward(self, x):
        x = self.l01(x)
        x = torch.relu(x)
        x = self.l02(x)
        x = torch.sigmoid(x)
        return x

def d2v(doc):
    return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(300)], axis=0)
x_train = pd.read_table('train/in.tsv.xz', compression='xz', sep='\t', header=None, error_bad_lines=False, quoting=3)
x_train = x_train[0].str.lower()
x_dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
x_dev = x_dev[0].str.lower()
x_test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
x_test = x_test[0].str.lower()
y_train = pd.read_table('train/expected.tsv', sep='\t', header=None, quoting=3)
y_train = y_train[0]
x_train = [word_tokenize(x) for x in x_train]
x_dev = [word_tokenize(x) for x in x_dev]
x_test = [word_tokenize(x) for x in x_test]
x_train = [d2v(doc) for doc in x_train]
x_dev = [d2v(doc) for doc in x_dev]
x_test = [d2v(doc) for doc in x_test]
model = NeuralNetworkModel()
BATCH_SIZE = 10
criterion = torch.nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters())
for epoch in range(BATCH_SIZE):
    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)
        optimizer.zero_grad()
        outputs = model(X.float())
        loss = criterion(outputs, y)
        loss.backward()
        optimizer.step()
y_dev = []
y_test = []
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 = model(X.float())
        y = (outputs > 0.5)
        y_dev.extend(y)

    for i in range(0, len(x_test), BATCH_SIZE):
        X = x_test[i:i + BATCH_SIZE]
        X = torch.tensor(X)
        outputs = model(X.float())
        y = (outputs >= 0.5)
        y_test.extend(y)

y_dev = np.asarray(y_dev, dtype=np.int32)
Y_dev = pd.DataFrame({'label': y_dev})
y_test = np.asarray(y_test, dtype=np.int32)
Y_test = pd.DataFrame({'label': y_test})
Y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
Y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)