85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
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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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from csv import QUOTE_NONE
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from nltk.tokenize import word_tokenize
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import gensim.downloader
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#Based on source material from classes
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class MyNeuralNetwork(torch.nn.Module):
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def __init__(self, input_size, hidden_size, num_classes):
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super(MyNeuralNetwork, self).__init__()
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self.fc1 = torch.nn.Linear(input_size, hidden_size)
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self.fc2 = torch.nn.Linear(hidden_size, num_classes)
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def forward(self, x):
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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x = torch.sigmoid(x)
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return x
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word2vec = gensim.downloader.load('word2vec-google-news-300')
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def get_word2vec(document):
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return np.mean([word2vec[token] for token in document if token in word2vec] or [np.zeros(300)], axis=0)
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#Basic paths + reading from files
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XtrainingData = pd.read_table('train/in.tsv.xz', error_bad_lines=False, header=None, quoting=QUOTE_NONE, names=['content', 'id'])
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YtrainingData = pd.read_table('train/expected.tsv', error_bad_lines=False, header=None, quoting=QUOTE_NONE, names=['label'])['label']
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XtestData = pd.read_table('test-A/in.tsv.xz', error_bad_lines=False, header=None, quoting=QUOTE_NONE, names=['content', 'id'])
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XdevData = pd.read_table('dev-0/in.tsv.xz', error_bad_lines=False, header=None, quoting=QUOTE_NONE, names=['content', 'id'])
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#Data filltering and preprocessing
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XtrainingData = [word_tokenize(row) for row in XtrainingData['content'].str.lower()]
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XtestData = [word_tokenize(row) for row in XtestData['content'].str.lower()]
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XdevData = [word_tokenize(row) for row in XdevData['content'].str.lower()]
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XtrainingData = [get_word2vec(document) for document in XtrainingData]
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XtestData = [get_word2vec(document) for document in XtestData]
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XdevData = [get_word2vec(document) for document in XdevData]
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#Basic parameters for the model
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eph = 30
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batches = 5
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network = MyNeuralNetwork(300, 600, 1)
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(network.parameters(), lr=0.02)
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#Model training according to source files from classes
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for epoch in range(eph):
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network.train()
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for i in range(0, YtrainingData.shape[0], batches):
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x = XtrainingData[i :i + batches]
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x = torch.tensor(x)
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y = YtrainingData[i :i + batches]
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y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
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outputs = network(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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#Basic evaluation
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YpredDev = []
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YtestPred = []
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with torch.no_grad():
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for i in range(0, len(XdevData), batches):
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x = XdevData[i :i + batches]
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x = torch.tensor(x)
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outputs = network(x.float())
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prediction = outputs > 0.5
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YpredDev += prediction.tolist()
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for i in range(0, len(XtestData), batches):
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x = XtestData[i :i + batches]
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x = torch.tensor(x)
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outputs = network(x.float())
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prediction = outputs > 0.5
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YtestPred += prediction.tolist()
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#Saving outputs
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np.asarray(YpredDev, dtype=np.int32).tofile('./dev-0/out.tsv', sep='\n')
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np.asarray(YtestPred, dtype=np.int32).tofile('./test-A/out.tsv', sep='\n')
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