Prześlij pliki do ''
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run.py
196
run.py
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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 lzma
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import gensim.downloader
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from nltk import word_tokenize
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#print('wczytanie danych')
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x_train = pd.read_table('train/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
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y_train = pd.read_table('train/expected.tsv', sep='\t', header=None, quoting=3)
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x_dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
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x_test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
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#print('inicjalizacja modelu')
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class NeuralNetworkModel(torch.nn.Module):
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def __init__(self):
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super(NeuralNetworkModel, self).__init__()
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self.l01 = torch.nn.Linear(300, 300)
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self.l02 = torch.nn.Linear(300, 1)
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def forward(self, x):
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x = self.l01(x)
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x = torch.relu(x)
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x = self.l02(x)
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x = torch.sigmoid(x)
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return x
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#print('przygotowanie danych')
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x_train = x_train[0].str.lower()
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y_train = y_train[0]
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x_dev = x_dev[0].str.lower()
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x_test = x_test[0].str.lower()
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x_train = [word_tokenize(x) for x in x_train]
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x_dev = [word_tokenize(x) for x in x_dev]
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x_test = [word_tokenize(x) for x in x_test]
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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('trenowanie modelu')
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model = NeuralNetworkModel()
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BATCH_SIZE = 5
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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(BATCH_SIZE):
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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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optimizer.zero_grad()
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outputs = model(X.float())
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loss = criterion(outputs, y)
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loss.backward()
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optimizer.step()
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#print('predykcja wynikow')
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y_dev = []
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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('eksportowanie do plików')
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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')
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torchvision import transforms
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import pickle
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import numpy as np
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import pandas as pd
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from word2vec import Word2Vec
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class FFN(nn.Module):
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def __init__(self, input_dim, output_dim, hidden1_size, hidden2_size, lr, epochs, batch_size):
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super(FFN, self).__init__()
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self.path = 'model1.pickle'
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self.lr = lr
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self.epochs = epochs
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self.output_dim = output_dim
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self.word2vec = Word2Vec()
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self.word2vec.load()
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self.batch_size = batch_size
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self.input_dim = input_dim
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self.fc1 = nn.Linear(batch_size, hidden1_size)
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self.fc2 = nn.Linear(hidden1_size, hidden2_size)
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self.fc3 = nn.Linear(hidden2_size, hidden2_size)
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self.fc4 = nn.Linear(hidden2_size, hidden2_size)
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self.fc5 = nn.Linear(hidden2_size, batch_size)
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def forward(self, data):
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data = F.relu(self.fc1(data))
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data = F.relu(self.fc2(data))
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data = F.relu(self.fc3(data))
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data = F.relu(self.fc4(data))
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data = F.sigmoid(self.fc5(data))
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return data
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def serialize(self):
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with open(self.path, 'wb') as file:
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pickle.dump(self, file)
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def load(self):
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with open(self.path, 'rb') as file:
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self = pickle.load(file)
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def batch(self, iterable, n=1):
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l = len(iterable)
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for ndx in range(0, l, n):
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yield iterable[ndx:min(ndx + n, l)]
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"""
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data is a tuple of embedding vector and a label of 0/1
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"""
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def train(self, data, expected):
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self.zero_grad()
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criterion = torch.nn.BCELoss()
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optimizer = optim.Adam(self.parameters(), lr=self.lr)
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batch_size = self.batch_size
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num_of_classes = self.output_dim
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for epoch in range(self.epochs):
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epoch_loss = 0.0
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idx = 0
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for i in range(0, int(len(data) / batch_size) * batch_size, batch_size):
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inputs = data[i:i + batch_size]
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labels = expected[i:i + batch_size]
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optimizer.zero_grad()
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outputs = self.forward(torch.tensor(self.word2vec.list_of_sentences2vec(inputs)))
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target = torch.tensor(labels.values).double()
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loss = criterion(outputs.view(batch_size), target.view(-1, ))
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loss.backward()
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optimizer.step()
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epoch_loss += loss.item()
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if (idx % 1000 == 0):
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print('epoch: {}, idx: {}, loss: {}'.format(epoch, idx, epoch_loss / 1000))
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epoch_loss = 0
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idx += 1
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self.serialize()
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def test(self, data, expected, path):
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correct = 0
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incorrect = 0
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total = 0
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predictions = []
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batch_size = self.batch_size
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for i in range(0, int(len(data) / batch_size) * batch_size, batch_size):
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inputs = data[i:i + batch_size]
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labels = expected[i:i + batch_size]
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predicted = self.forward(torch.tensor(self.word2vec.list_of_sentences2vec(inputs)))
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score = [1 if x > 0.5 else 0 for x in predicted]
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for x, y in zip(score, labels):
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if (x == y):
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correct += 1
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else:
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incorrect += 1
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predictions.append(score)
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print(correct)
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print(incorrect)
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print(correct / (incorrect + correct))
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df = pd.DataFrame(np.asarray(predictions).reshape(int(len(data) / batch_size) * batch_size))
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df.reset_index(drop=True, inplace=True)
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df.to_csv(path, sep="\t", index=False)
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