forked from kubapok/en-ner-conll-2003
150 lines
3.9 KiB
Python
150 lines
3.9 KiB
Python
from numpy.lib.shape_base import split
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import pandas as pd
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import numpy as np
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import gensim
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import torch
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from collections import Counter
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from torchtext.vocab import Vocab
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from TorchCRF import CRF
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from tqdm import tqdm
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EPOCHS = 5
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BATCH = 1
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# Functions from jupyter
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def build_vocab(dataset):
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counter = Counter()
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for document in dataset:
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counter.update(document)
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return Vocab(counter)
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def data_process(dt, vocab):
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return [torch.tensor([vocab['<bos>']] + [vocab[token] for token in document] + [vocab['<eos>']], dtype=torch.long) for document in dt]
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def get_scores(y_true, y_pred):
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acc_score = 0
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tp = 0
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fp = 0
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selected_items = 0
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relevant_items = 0
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for p, t in zip(y_pred, y_true):
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if p == t:
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acc_score += 1
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if p > 0 and p == t:
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tp += 1
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if p > 0:
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selected_items += 1
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if t > 0:
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relevant_items += 1
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if selected_items == 0:
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precision = 1.0
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else:
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precision = tp / selected_items
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if relevant_items == 0:
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recall = 1.0
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else:
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recall = tp / relevant_items
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if precision + recall == 0.0:
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f1 = 0.0
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else:
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f1 = 2 * precision * recall / (precision + recall)
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return precision, recall, f1
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def eval_model(dataset_tokens, dataset_labels, model):
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Y_true = []
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Y_pred = []
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for i in tqdm(range(len(dataset_labels))):
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batch_tokens = dataset_tokens[i].unsqueeze(0)
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tags = list(dataset_labels[i].numpy())
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Y_true += tags
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Y_batch_pred_weights = model(batch_tokens).squeeze(0)
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Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)
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Y_pred += list(Y_batch_pred.numpy())
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return get_scores(Y_true, Y_pred)
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class LSTM(torch.nn.Module):
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def __init__(self, vocab_len):
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super(LSTM, self).__init__()
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self.emb = torch.nn.Embedding(vocab_len, 100)
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self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)
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self.fc1 = torch.nn.Linear(256, 9)
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def forward(self, x):
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emb = torch.relu(self.emb(x))
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lstm_output, (h_n, c_n) = self.rec(emb)
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out_weights = self.fc1(lstm_output)
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return out_weights
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# Load data
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def load_data():
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train = pd.read_csv('train/train.tsv', sep='\t',
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names=['labels', 'document'])
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Y_train = [y.split(sep=" ") for y in train['labels'].values]
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X_train = [x.split(sep=" ") for x in train['document'].values]
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dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=['document'])
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exp = pd.read_csv('dev-0/expected.tsv', sep='\t', names=['labels'])
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X_dev = [x.split(sep=" ") for x in dev['document'].values]
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Y_dev = [y.split(sep=" ") for y in exp['labels'].values]
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test = pd.read_csv('test-A/in.tsv', sep='\t', names=['document'])
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X_test = test['document'].values
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return X_train, Y_train, X_dev, Y_dev, X_test
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def train(model, crf, train_tokens, labels_tokens):
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for i in range(EPOCHS):
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crf.train()
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model.train()
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for i in tqdm(range(len(labels_tokens))):
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batch_tokens = train_tokens[i].unsqueeze(0)
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tags = labels_tokens[i].unsqueeze(1)
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predicted_tags = model(batch_tokens).squeeze(0).unsqueeze(1)
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optimizer.zero_grad()
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loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))
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loss.backward()
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optimizer.step()
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if __name__ == "__main__":
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X_train, Y_train, X_dev, Y_dev, X_test = load_data()
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vocab_x = build_vocab(X_train)
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vocab_y = build_vocab(Y_train)
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train_tokens = data_process(X_train, vocab_x)
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labels_tokens = data_process(Y_train, vocab_y)
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print(train_tokens[0])
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# model
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model = LSTM(len(vocab_x))
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crf = CRF(9)
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p = list(model.parameters()) + list(crf.parameters())
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optimizer = torch.optim.Adam(p)
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criterion = torch.nn.CrossEntropyLoss()
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train(model, crf, train_tokens, labels_tokens)
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