main.py

@@ -1,86 +0,0 @@
-import numpy as np
-import pandas as pd
-import torch
-from nltk.tokenize import word_tokenize
-from gensim.models import Word2Vec
-import gensim.downloader as gensim_downloader
-
-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
-
-def doc2vec(doc):
-    return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(300)], axis=0)
-
-train = pd.read_table('train/train.tsv', error_bad_lines=False, sep='\t', header=None, quoting=3)
-X_dev = pd.read_table('dev-0/in.tsv', error_bad_lines=False, sep='\t', header=None, quoting=3)
-Y_dev = pd.read_table('dev-0/expected.tsv', error_bad_lines=False, sep='\t', header=None, quoting=3)
-X_test = pd.read_table('test-A/in.tsv', error_bad_lines=False, sep='\t', header=None, quoting=3)
-
-X_train = train[1].str.lower()
-Y_train = 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]
-
-word2vec = gensim_downloader.load('word2vec-google-news-300')
-
-X_train = [doc2vec(doc) for doc in X_train]
-X_dev = [doc2vec(doc) for doc in X_dev]
-X_test = [doc2vec(doc) for doc in X_test]
-
-model = NeuralNetworkModel()
-BATCH_SIZE = 5
-criterion = torch.nn.BCELoss()
-optimizer = torch.optim.Adam(model.parameters())
-
-for epoch in range(5):
-    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()
-
-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())
-        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 = 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)
-dev = pd.DataFrame({'label': Y_dev})
-test = pd.DataFrame({'label': Y_test})
-dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
-test.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)