implement data reading and basic Neural Network

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Dawid 2021-06-08 21:31:19 +02:00
parent a6bbd87b3b
commit ff95d0bcc7

102
seq_lab.py Normal file
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# imports
import torch
import pandas as pd
import csv
from torchtext.vocab import Vocab
from collections import Counter
class NERModel(torch.nn.Module):
def __init__(self,):
super(NERModel, self).__init__()
self.emb = torch.nn.Embedding(23627,200)
self.fc1 = torch.nn.Linear(600,9)
def forward(self, x):
x = self.emb(x)
x = x.reshape(600)
x = self.fc1(x)
return x
class NeuralNetworkModel(torch.nn.Module):
def __init__(self, output_size):
super(NeuralNetworkModel, self).__init__()
self.fc1 = torch.nn.Linear(10_000, output_size)
self.softmax = torch.nn.Softmax(dim=0)
def forward(self, x):
x = self.fc1(x)
x = self.softmax(x)
return x
def build_vocab(dataset):
counter = Counter()
for document in dataset:
counter.update(document)
return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])
def data_process(dt):
return [torch.tensor([vocab['<bos>']] + [vocab[token] for token in document] + [vocab['<eos>']], dtype=torch.long)
for document in dt]
def labels_process(dt):
return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]
LABELS = ['O','B-LOC', 'I-LOC','B-MISC', 'I-MISC', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER']
train = pd.read_csv("./train/train.tsv.xz", error_bad_lines=False, compression='xz', sep='\t', header=None, quoting=csv.QUOTE_NONE)
dev = pd.read_csv('./dev-0/in.tsv', error_bad_lines=False, sep='\t', header=None, quoting=csv.QUOTE_NONE)
test = pd.read_csv('./test-A/in.tsv', error_bad_lines=False, sep='\t', header=None, quoting=csv.QUOTE_NONE)
tags = train[0].apply(lambda x: [LABELS.index(y) for y in x.split()])
tokens = train[1].apply(lambda x: x.split())
dev_tokens = dev[0].apply(lambda x: x.split())
test_tokens = dev[0].apply(lambda x: x.split())
vocab = build_vocab(tokens)
train_labels = labels_process(tags)
train_tokens_ids = data_process(tokens)
ner_model = NERModel()
nn_model = NeuralNetworkModel(len(train_tokens_ids))
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(ner_model.parameters())
for epoch in range(2):
loss_score = 0
acc_score = 0
prec_score = 0
selected_items = 0
recall_score = 0
relevant_items = 0
items_total = 0
nn_model.train()
for i in range(100):
for j in range(1, len(train_labels[i]) - 1):
X = train_tokens_ids[i][j-1: j+2]
Y = train_labels[i][j: j+1]
Y_predictions = ner_model(X)
acc_score += int(torch.argmax(Y_predictions) == Y)
if torch.argmax(Y_predictions) != 0:
selected_items +=1
if torch.argmax(Y_predictions) != 0 and torch.argmax(Y_predictions) == Y.item():
prec_score += 1
if Y.item() != 0:
relevant_items +=1
if Y.item() != 0 and torch.argmax(Y_predictions) == Y.item():
recall_score += 1
items_total += 1
optimizer.zero_grad()
loss = criterion(Y_predictions.unsqueeze(0), Y)
loss.backward()
optimizer.step()
loss_score += loss.item()
precision = prec_score / selected_items
recall = recall_score / relevant_items
f1_score = (2*precision * recall) / (precision + recall)
print('epoch: ', epoch)
print('loss: ', loss_score / items_total)
print('acc: ', acc_score / items_total)
print('prec: ', precision)
print('recall: : ', recall)
print('f1: ', f1_score)