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 = 1
BATCH = 1
SEQ_LEN = 5

# Functions from jupyter


def build_vocab(dataset):
    counter = Counter()
    for document in dataset:
        counter.update(document)
    v = vocab(counter)
    v.set_default_index(0)
    return v


def data_process(dt, vocab):
    return [torch.tensor([vocab[token] for token in document], 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


class GRU(torch.nn.Module):
    def __init__(self, vocab_len):
        super(GRU, self).__init__()
        self.emb = torch.nn.Embedding(vocab_len, 100)
        self.rec = torch.nn.GRU(100, 256, 1, batch_first=True, dropout=0.2)
        self.fc1 = torch.nn.Linear(256, 9)

    def forward(self, x):
        emb = torch.relu(self.emb(x))
        gru_output, h_n = self.rec(emb)
        out_weights = self.fc1(gru_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 = -crf(predicted_tags, tags)

            loss.backward()
            optimizer.step()


def data_translate(dt, vocab):
    return [[vocab.itos[token] for token in document] for document in dt]


def dev_eval(model, crf, dev_tokens, dev_labels_tokens, vocab):
    Y_true = []
    Y_pred = []
    model.eval()
    crf.eval()
    for i in tqdm(range(len(dev_labels_tokens))):
        batch_tokens = dev_tokens[i].unsqueeze(0)
        tags = list(dev_labels_tokens[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())
        Y_pred += [crf.decode(Y_batch_pred)[0]]

    # print(Y_pred)
    # Y_pred_translated = data_translate(Y_pred, vocab)
    # with open('dev-0/out.tsv', "w+") as file:
    #     temp_str = ""
    #     for i in Y_pred_translated:
    #         for j in i:
    #             temp_str += str(j)
    #             temp_str += " "
    #         temp_str = temp_str[:-1]
    #         temp_str += "\n"
    #     temp_str = temp_str[:-1]
    #     file.write(temp_str)

    precision, recall, f1 = get_scores(Y_true, Y_pred)
    print(f'precision: {0}, recall: {1}, f1: {2}', precision, recall, f1)


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)

    # train
    print(len(vocab_x.get_itos()))
    model = GRU(len(vocab_x.get_itos()))
    crf = CRF(9)
    p = list(model.parameters()) + list(crf.parameters())
    optimizer = torch.optim.Adam(p)
    # # mask = torch.ByteTensor([1, 1])  # (batch_size. sequence_size)
    train(model, crf, train_tokens, labels_tokens)

    # eval dev
    dev_tokens = data_process(X_dev, vocab_x)
    dev_labels_tokens = data_process(Y_dev, vocab_y)
    dev_eval(model, crf, dev_tokens, dev_labels_tokens, vocab_x)