paranormal-or-skeptic-ISI-p.../main.py
VanillaHellen 955e53646f out
2021-07-05 23:49:47 +02:00

120 lines
3.9 KiB
Python

from nltk.util import pr
import pandas as pd
import numpy as np
import torch
from gensim import downloader
from nltk.tokenize import word_tokenize
import csv
import os
dir_path = os.path.dirname(os.path.realpath(__file__)) + '\\'
IN_HEADER_PATH = dir_path + 'in-header.tsv'
OUT_HEADER_PATH = dir_path + 'out-header.tsv'
IN_TRAIN_TABLE_PATH = dir_path + 'train\\in.tsv'
IN_EXPECTED_TABLE_PATH = dir_path + 'train\\expected.tsv'
X_DEV_PATH = dir_path + 'dev-0\\in.tsv'
X_TEST_PATH = dir_path + 'test-A\\in.tsv'
DEV_OUT_PATH = dir_path + 'dev-0\\out.tsv'
TEST_OUT_PATH = dir_path + 'test-A\\out.tsv'
BATCH_SIZE = 5
class NeuralNetworkModel(torch.nn.Module):
def __init__(self):
dim = 200
super(NeuralNetworkModel, self).__init__()
self.one = torch.nn.Linear(dim, 500)
self.two = torch.nn.Linear(500, 1)
def forward(self, x):
x = self.one(x)
x = torch.relu(x)
x = self.two(x)
x = torch.sigmoid(x)
return x
def read():
x_labels = (pd.read_csv(IN_HEADER_PATH, sep='\t')).columns
y_labels = (pd.read_csv(OUT_HEADER_PATH, sep='\t')).columns
x_train = pd.read_table(IN_TRAIN_TABLE_PATH, header=None, quoting=csv.QUOTE_NONE, names=x_labels)
y_train = pd.read_table(IN_EXPECTED_TABLE_PATH, header=None, quoting=csv.QUOTE_NONE, names=y_labels)
x_dev = pd.read_table(X_DEV_PATH, header=None, quoting=csv.QUOTE_NONE, names=x_labels)
x_test = pd.read_table(X_TEST_PATH, header=None, quoting=csv.QUOTE_NONE, names=x_labels)
remove_n = 200000
drop_indices = np.random.choice(x_train.index, remove_n, replace=False)
x_train = x_train.drop(drop_indices)
y_train = y_train.drop(drop_indices)
return x_labels, y_labels, x_train, y_train, x_dev, x_test
def process(x_labels, y_labels, x_train, y_train, x_dev, x_test):
x_train = x_train[x_labels[0]].str.lower()
x_dev = x_dev[x_labels[0]].str.lower()
x_test = x_test[x_labels[0]].str.lower()
y_train = y_train[y_labels[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]
word2vec = downloader.load('glove-wiki-gigaword-200')
x_train = [np.mean([word2vec[w] for w in d if w in word2vec] or [np.zeros(200)], axis=0) for d in x_train]
x_dev = [np.mean([word2vec[w] for w in d if w in word2vec] or [np.zeros(200)], axis=0) for d in x_dev]
x_test = [np.mean([word2vec[w] for w in d if w in word2vec] or [np.zeros(200)], axis=0) for d in x_test]
return x_train, y_train, x_dev, x_test
def predict(model, x_data, out_path):
y_out = []
model.eval()
with torch.no_grad():
for i in range(0, len(x_data), BATCH_SIZE):
x = x_data[i:i+BATCH_SIZE]
x = torch.tensor(x)
pred = model(x.float())
y_pred = (pred > 0.5)
y_out.extend(y_pred)
y_data = np.asarray(y_out, dtype=np.int32)
pd.DataFrame(y_data).to_csv(out_path, sep='\t', index=False, header=False)
def main():
x_labels, y_labels, x_train, y_train, x_dev, x_test = read()
x_train, y_train, x_dev, x_test = process(x_labels, y_labels, x_train, y_train, x_dev, x_test)
nn_model = NeuralNetworkModel()
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)
Y_predictions = nn_model(X.float())
loss = criterion(Y_predictions, Y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
predict(nn_model, x_dev, DEV_OUT_PATH)
predict(nn_model, x_test, TEST_OUT_PATH)
if __name__ == "__main__":
main()