112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
import pandas as pd
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import numpy as np
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import torch
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from nltk.tokenize import word_tokenize
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import gensim.downloader as api
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# Wczytanie X i Y do Train oraz X do Dev i Test
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X_train = pd.read_table('train/in.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])
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y_train = pd.read_table('train/expected.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['label'])
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X_dev = pd.read_table('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
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X_test = pd.read_table('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
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# lowercase-ing zbiorów
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# https://www.datacamp.com/community/tutorials/case-conversion-python
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X_train = X_train.content.str.lower()
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X_dev = X_dev.content.str.lower()
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X_test = X_test.content.str.lower()
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y_train = y_train['label'] #Df do Series?
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# tokenizacja zbiorów
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#https://www.nltk.org/_modules/nltk/tokenize.html
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X_train = [word_tokenize(doc) for doc in X_train]
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X_dev = [word_tokenize(doc) for doc in X_dev]
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X_test = [word_tokenize(doc) for doc in X_test]
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# word2vec zgodnie z poradą Pana Jakuba
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# https://radimrehurek.com/gensim/auto_examples/howtos/run_downloader_api.html
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# https://www.kaggle.com/kstathou/word-embeddings-logistic-regression
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w2v = api.load('word2vec-google-news-300')
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def document_vector(doc):
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"""Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
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return np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis=0)
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X_train = [document_vector(doc) for doc in X_train]
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X_dev = [document_vector(doc) for doc in X_dev]
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X_test = [document_vector(doc) for doc in X_test]
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#Sieć neuronowa z ćwiczeń 8
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#https://git.wmi.amu.edu.pl/filipg/aitech-eks-pub/src/branch/master/cw/08_regresja_logistyczna.ipynb
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class NeuralNetwork(torch.nn.Module):
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def __init__(self, hidden_size):
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super(NeuralNetwork, self).__init__()
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self.l1 = torch.nn.Linear(300, hidden_size) #Korzystamy z word2vec-google-news-300 który ma zawsze na wejściu wymiar 300
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self.l2 = torch.nn.Linear(hidden_size, 1)
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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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model = NeuralNetwork(600)
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)
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batch_size = 15
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# Trening modelu z ćwiczeń 8
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#https://git.wmi.amu.edu.pl/filipg/aitech-eks-pub/src/branch/master/cw/08_regresja_logistyczna.ipynb
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for epoch in range(5):
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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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y_dev = []
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y_test = []
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#Predykcje
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#model.eval() will notify all your layers that you are in eval mode
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model.eval()
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#torch.no_grad() impacts the autograd engine and deactivate it. It will reduce memory usage and speed up
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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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y = (outputs > 0.5)
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y_dev.extend(y)
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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.extend(y)
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#Wygenerowanie plików outputowych
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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_df = pd.DataFrame({'label':y_dev})
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y_test_df = pd.DataFrame({'label':y_test})
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y_dev_df.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
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y_test_df.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False) |