en-ner-conll-2003/seq.py

from numpy.lib.shape_base import split
import pandas as pd
import numpy as np
import gensim
import torch
import pandas as pd
from sklearn.model_selection import train_test_split
from collections import Counter
from torchtext.vocab import Vocab
from TorchCRF import CRF
from tqdm import tqdm

EPOCHS = 5
BATCH = 1

# Functions from jupyter


def build_vocab(dataset):
    counter = Counter()
    for document in dataset:
        counter.update(document)
    return Vocab(counter)


def data_process(dt, vocab):
    return [torch.tensor([vocab['<bos>']] + [vocab[token] for token in document] + [vocab['<eos>']], dtype=torch.long) for document in dt]


def get_scores(y_true, y_pred):
    acc_score = 0
    tp = 0
    fp = 0
    selected_items = 0
    relevant_items = 0

    for p, t in zip(y_pred, y_true):
        if p == t:
            acc_score += 1

        if p > 0 and p == t:
            tp += 1

        if p > 0:
            selected_items += 1

        if t > 0:
            relevant_items += 1

    if selected_items == 0:
        precision = 1.0
    else:
        precision = tp / selected_items

    if relevant_items == 0:
        recall = 1.0
    else:
        recall = tp / relevant_items

    if precision + recall == 0.0:
        f1 = 0.0
    else:
        f1 = 2 * precision * recall / (precision + recall)

    return precision, recall, f1


def eval_model(dataset_tokens, dataset_labels, model):
    Y_true = []
    Y_pred = []
    for i in tqdm(range(len(dataset_labels))):
        batch_tokens = dataset_tokens[i].unsqueeze(0)
        tags = list(dataset_labels[i].numpy())
        Y_true += tags

        Y_batch_pred_weights = model(batch_tokens).squeeze(0)
        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)
        Y_pred += list(Y_batch_pred.numpy())

    return get_scores(Y_true, Y_pred)


class LSTM(torch.nn.Module):
    def __init__(self, vocab_len):
        super(LSTM, self).__init__()
        self.emb = torch.nn.Embedding(vocab_len, 100)
        self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)
        self.fc1 = torch.nn.Linear(256, 9)

    def forward(self, x):
        emb = torch.relu(self.emb(x))
        lstm_output, (h_n, c_n) = self.rec(emb)
        out_weights = self.fc1(lstm_output)

        return out_weights

# Load data


def load_data():
    train = pd.read_csv('train/train.tsv', sep='\t',
                        names=['labels', 'document'])

    Y_train = [y.split(sep=" ") for y in train['labels'].values]
    X_train = [x.split(sep=" ") for x in train['document'].values]

    dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=['document'])
    exp = pd.read_csv('dev-0/expected.tsv', sep='\t', names=['labels'])
    X_dev = [x.split(sep=" ") for x in dev['document'].values]
    Y_dev = [y.split(sep=" ") for y in exp['labels'].values]

    test = pd.read_csv('test-A/in.tsv', sep='\t', names=['document'])
    X_test = test['document'].values

    return X_train, Y_train, X_dev, Y_dev, X_test


def train(model, crf, train_tokens, labels_tokens):
    for i in range(EPOCHS):
        crf.train()
        model.train()
        for i in tqdm(range(len(labels_tokens))):
            batch_tokens = train_tokens[i].unsqueeze(0)
            tags = labels_tokens[i].unsqueeze(1)

            predicted_tags = model(batch_tokens).squeeze(0).unsqueeze(1)

            optimizer.zero_grad()
            loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))

            loss.backward()
            optimizer.step()


if __name__ == "__main__":
    X_train, Y_train, X_dev, Y_dev, X_test = load_data()
    vocab_x = build_vocab(X_train)
    vocab_y = build_vocab(Y_train)
    train_tokens = data_process(X_train, vocab_x)
    labels_tokens = data_process(Y_train, vocab_y)
    print(train_tokens[0])

    # model
    model = LSTM(len(vocab_x))
    crf = CRF(9)
    p = list(model.parameters()) + list(crf.parameters())
    optimizer = torch.optim.Adam(p)
    criterion = torch.nn.CrossEntropyLoss()
    train(model, crf, train_tokens, labels_tokens)