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import pandas as pd
import numpy as np
import torch
import gensim.downloader as gensim
from nltk.tokenize import word_tokenize
x_train = pd.read_table('train/in.tsv', sep='\t', header = None, error_bad_lines = False, quoting = 3)
y_train = pd.read_table('train/expected.tsv', sep='\t', header = None, quoting = 3)
y_train = y_train[0]
x_dev = pd.read_table('dev-0/in.tsv', sep='\t', header = None, quoting = 3)
x_test = pd.read_table('test-A/in.tsv', sep='\t', header = None, quoting = 3)
x_train = x_train[0].str.lower()
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]
word2vec = gensim.load('glove-wiki-gigaword-50')
def document_vector(doc):
return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(50)], axis=0)
x_train = [document_vector(doc) for doc in x_train]
x_dev = [document_vector(doc) for doc in x_dev]
x_test = [document_vector(doc) for doc in x_test]
class NeuralNetworkModel(torch.nn.Module):
def __init__(self, features):
super(NeuralNetworkModel, self).__init__()
self.fc1 = torch.nn.Linear(50, features)
self.fc2 = torch.nn.Linear(features, 1)
def forward(self, x):
x = self.fc1(x)
x = torch.relu(x)
x = self.fc2(x)
x = torch.sigmoid(x)
return x
nn_model = NeuralNetworkModel(100)
BATCH_SIZE = 5
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
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()
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())
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 = nn_model(X.float())
y = (outputs > 0.5)
y_test.extend(y)
y_dev = np.asarray(y_dev, dtype=np.int32)
y_test = np.asarray(y_test, dtype=np.int32)
Y_dev = pd.DataFrame({'label':y_dev})
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)

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Likelihood 0.0000
Accuracy 0.7289
F1.0 0.5594
Precision 0.6587
Recall 0.4861

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