main.py

@@ -1,94 +0,0 @@
-import gensim.downloader as gensim
-import numpy as np
-import pandas as pd
-import torch
-from nltk.tokenize import word_tokenize
-
-
-class NeuralNetworkModel(torch.nn.Module):
-    def __init__(self):
-        super(NeuralNetworkModel, self).__init__()
-        self.1 = torch.nn.Linear(300, 300)
-        self.2 = torch.nn.Linear(300, 1)
-
-    def forward(self, x):
-        x = self.1(x)
-        x = torch.relu(x)
-        x = self.2(x)
-        x = torch.sigmoid(x)
-        return x
-        
-nm = NeuralNetworkModel()
-dev_train = []
-test_train = []
-word2vec = gensim.load('word2vec-google-news-300')
-
-
-np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(300)], axis=0)
-
-
-def model_train():
-
-train = pd.read_table('train/in.tsv.xz', compression='xz', sep='\t', quoting=3)
-trainy = pd.read_table('train/expected.tsv', sep='\t', quoting=3)
-trainy = trainy[0]
-
-def model_prepare():
-dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\t', quoting=3)
-test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\t', quoting=3)
-
-
-
-train = [word_tokenize(x) for x in train]
-dev = [word_tokenize(x) for x in dev]
-test = [word_tokenize(x) for x in test]
-
-
-
-def word_2_voc():
-train = [d2v(doc) for doc in train]
-dev = [d2v(doc) for doc in dev]
-test = [d2v(doc) for doc in test]
-
-
-criterion = torch.nn.BCELoss()
-optimizer = torch.optim.Adam(model.parameters())
-
-print ("1")
-
-for epoch in range(5):
-    model.train()
-    for i in range(0, y_train.shape[0], 5):
-        X = train[i:i + 5]
-        X = torch.tensor(X)
-        y = trainy[i:i + 5]
-        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
-        optimizer.zero_grad()
-        outputs = nm(X.float())
-        loss = criterion(outputs, y)
-        loss.backward()
-        optimizer.step()
-
-
-
-print ("2")
-
-with torch.no_grad():
-    for i in range(0, len(dev), 5):
-        X = dev[i:i + 5]
-        X = torch.tensor(X)
-        outputs = nm(X.float())
-        y = (outputs > 0.5)
-        dev_train.extend(y)
-
-    for i in range(0, len(test), 5):
-        X = test[i:i + 5]
-        X = torch.tensor(X)
-        outputs = nm(X.float())
-        y = (outputs >= 0.5)
-        testy.extend(y)
-
-
-dev_train.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
-test_train.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)
-