main.py

@@ -1,102 +0,0 @@
-import pandas as pd
-import numpy as np
-import torch
-import csv
-from nltk.tokenize import word_tokenize
-from gensim.models import Word2Vec
-import gensim.downloader
-
-class NeuralNetwork(torch.nn.Module):
-    def __init__(self, input_size, hidden_size, num_classes):
-        super(NeuralNetwork, self).__init__()
-        self.l1 = torch.nn.Linear(input_size, hidden_size)
-        self.l2 = torch.nn.Linear(hidden_size, num_classes)
-
-    def forward(self, x):
-        x = self.l1(x)
-        x = torch.relu(x)
-        x = self.l2(x)
-        x = torch.sigmoid(x)
-        return x
-
-col_names = ['content', 'id', 'label']
-
-print('Wczytanie danych...')
-# loading dataset
-train_set_features = pd.read_table('train/in.tsv.xz', error_bad_lines=False, quoting=csv.QUOTE_NONE, header=None, names=col_names[:2])
-train_set_labels = pd.read_table('train/expected.tsv', error_bad_lines=False, quoting=csv.QUOTE_NONE, header=None, names=col_names[2:])
-dev_set = pd.read_table('dev-0/in.tsv.xz', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=col_names[:2])
-test_set = pd.read_table('test-A/in.tsv.xz', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=col_names[:2])
-
-print('Preprocessing danych...')
-# lowercase
-X_train = train_set_features['content'].str.lower()
-y_train = train_set_labels['label']
-
-X_dev = dev_set['content'].str.lower()
-X_test = test_set['content'].str.lower()
-
-# tokenize
-X_train = [word_tokenize(content) for content in X_train]
-X_dev = [word_tokenize(content) for content in X_dev]
-X_test = [word_tokenize(content) for content in X_test]
-
-# 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]
-
-model = NeuralNetwork(300, 600, 1)
-
-criterion = torch.nn.BCELoss()
-optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
-
-batch_size = 10
-
-print('Trenowanie modelu...')
-for epoch in range(6):
-    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 = model(X.float())
-        loss = criterion(outputs, y)
-
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-
-print('Predykcje...')
-dev_prediction = []
-test_prediction = []
-
-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)
-        dev_prediction = dev_prediction + 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())
-
-        prediction = (outputs > 0.5)
-        test_prediction = test_prediction + prediction.tolist()
-
-dev_prediction = np.asarray(dev_prediction, dtype=np.int32)
-test_prediction = np.asarray(test_prediction, dtype=np.int32)
-
-dev_prediction.tofile('./dev-0/out.tsv', sep='\n')
-test_prediction.tofile('./test-A/out.tsv', sep='\n')