#!/usr/bin/env python
# coding: utf-8

# In[1]:


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


# In[ ]:





# In[2]:


def predict_year(x, path_out, model):
    results = model.predict(x)
    with open(path_out, 'wt') as file:
        for r in results:
            file.write(str(r) + '\n') 


# In[3]:


def read_file(filename):
    result = []
    with open(filename, 'r', encoding="utf-8") as file:
        for line in file:
            text = line.split("\t")[0].strip()
            result.append(text)
    return result


# In[4]:


x_train = pd.read_table('train/in.tsv', sep='\t', header=None, quoting=3)
x_train = x_train[0:200000]
x_train


# In[5]:


with open('train/expected.tsv', 'r', encoding='utf8') as file:
    y_train = pd.read_csv(file, sep='\t', header=None)
y_train = y_train[0:200000]
y_train


# In[6]:


with open('dev-0/in.tsv', 'r', encoding='utf8') as file:
    x_dev = pd.read_csv(file, sep='\t', header=None)
x_dev


# In[7]:


with open('test-A/in.tsv', 'r', encoding='utf8') as file:
    x_test = pd.read_csv(file, sep='\t', header=None)
x_test


# In[8]:


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


# In[9]:


x_train = x_train[0].str.lower()
y_train = y_train[0]
x_dev = x_dev[0].str.lower()
x_test = x_test[0].str.lower()

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]


# In[11]:


from gensim.test.utils import common_texts
from gensim.models import Word2Vec

word2vec = gensim.downloader.load('word2vec-google-news-300')
x_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_train]
x_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_dev]
x_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_test]


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model = NeuralNetworkModel()
BATCH_SIZE = 5
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

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()


# In[ ]:


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())
        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 = model(X.float())
        y = (outputs >= 0.5)
        y_test += prediction.tolist()


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y_dev = np.asarray(y_dev, dtype=np.int32)
y_test = np.asarray(y_test, dtype=np.int32)


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with open('./dev-0/out.tsv', 'wt') as file:
    for r in y_dev:
        file.write(str(r) + '\n')   


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with open('./test-A/out.tsv', 'wt') as file:
    for r in y_test:
        file.write(str(r) + '\n')   


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get_ipython().system('jupyter nbconvert --to script run.ipynb')