94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
import numpy as np
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import pandas as pd
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import torch
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import csv
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import gensim.downloader
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import torch
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from nltk import word_tokenize
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class NeuralNetwork(torch.nn.Module):
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def __init__(self, input_size, hidden_size, num_classes):
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super(NeuralNetwork, self).__init__()
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self.l1 = torch.nn.Linear(input_size, hidden_size)
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self.l2 = torch.nn.Linear(hidden_size, num_classes)
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def forward(self, x):
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x = self.l1(x)
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x = torch.relu(x)
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x = self.l2(x)
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x = torch.sigmoid(x)
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return x
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print('STEP 1 - LOAD DATA')
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names = ['content', 'id', 'label']
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train_data_content = pd.read_table('train/in.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=names[:2])
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train_data_labels = pd.read_table('train/expected.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=names[2:])
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dev_data = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=names[:2])
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test_data = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=names[:2])
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print('STEP 2 - SET PARAMS')
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hidden_size = int(input('Hidden units size: ') or '600')
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epochs = int(input("Epochs: ") or '5')
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batch_size = int(input("Batch size: ") or '15')
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print('STEP 3 - PREPROCESSING')
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# lowercase all content
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X_train = train_data_content['content'].str.lower()
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y_train = train_data_labels['label']
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X_dev = dev_data['content'].str.lower()
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X_test = test_data['content'].str.lower()
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# tokenize datasets
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X_train = [word_tokenize(content) for content in X_train]
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X_dev = [word_tokenize(content) for content in X_dev]
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X_test = [word_tokenize(content) for content in X_test]
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# use Google word2vec algorithm
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word2vec = gensim.downloader.load('word2vec-google-news-300')
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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]
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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]
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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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print('STEP 4 - MODEL TRAINING')
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#prepare neural model
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model = NeuralNetwork(300, hidden_size, 1)
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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for epoch in range(epochs):
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model.train()
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for i in range(0, y_train.shape[0], batch_size):
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X = X_train[i:i+batch_size]
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X = torch.tensor(X)
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y = y_train[i:i+batch_size]
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y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
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outputs = model(X.float())
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loss = criterion(outputs, y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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print('STEP 5 - PREDICTION')
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y_dev, y_test = [], []
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model.eval()
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with torch.no_grad():
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for i in range(0, len(X_dev), batch_size):
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X = X_dev[i:i+batch_size]
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X = torch.tensor(X)
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outputs = model(X.float())
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prediction = (outputs > 0.5)
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y_dev += prediction.tolist()
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for i in range(0, len(X_test), batch_size):
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X = X_test[i:i+batch_size]
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X = torch.tensor(X)
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outputs = model(X.float())
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y = (outputs > 0.5)
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y_test += prediction.tolist()
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print('STEP 6 - EXPORT RESULTS')
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# export results to tsv
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y_dev = np.asarray(y_dev, dtype=np.int32)
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y_test = np.asarray(y_test, dtype=np.int32)
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y_dev.tofile('./dev-0/out.tsv', sep='\n')
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y_test.tofile('./test-A/out.tsv', sep='\n') |