4_text_classification.py

#%%
!pip install torchtext
!pip install spacy==2.3.5
!python -m spacy download en 
#%%

import torch 
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from torchtext import data 
import torchtext
from pathlib import Path
import pandas as pd
import spacy
# %%

# CPU or GPU
if torch.cuda.is_available():
    device = "cuda"         
else:
    device = "cpu"

# %%

#学習用のデータをここからダウンロード
# https://www.kaggle.com/kazanova/sentiment140
# 解凍してできる　training.1600000.processed.noemoticon.csv を
# ./data/text/ に入れる
tweetsDF = pd.read_csv("./data/text/training.1600000.processed.noemoticon.csv", 
engine="python", header=None)

# %%
# 最初のいくつかを表示
tweetsDF.head() 
# %%

# エントリー数
tweetsDF[0].value_counts()
# %%
tweetsDF["sentiment_cat"] = tweetsDF[0].astype('category')
tweetsDF["sentiment"] = tweetsDF["sentiment_cat"].cat.codes
tweetsDF.to_csv("train-processed.csv", header=None, index=None)      
tweetsDF.sample(10000).to_csv("train-processed-sample.csv", header=None, index=None) 

tweetsDF.tail()
# %%
LABEL = data.LabelField()
TWEET = data.Field(tokenize='spacy', lower=True)

fields = [('score',None), ('id',None),('date',None),('query',None),
      ('name',None),
      ('tweet', TWEET),('category',None),('label',LABEL)] 
# %%
twitterDataset = torchtext.data.TabularDataset(
        path="train-processed-sample.csv", 
        format="CSV", 
        fields=fields,
        skip_header=False)
# %%

(train, test, valid)=twitterDataset.split(split_ratio=[0.6,0.2,0.2],stratified=True, strata_field='label')

(len(train),len(test),len(valid))
# %%

vocab_size = 20000
TWEET.build_vocab(train, max_size = vocab_size)
LABEL.build_vocab(train)
TWEET.vocab.freqs.most_common(10)
# %%
train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
(train, valid, test), 
batch_size = 32,
device = device,
sort_key = lambda x: len(x.tweet),
sort_within_batch = False)


# %%
class OurFirstLSTM(nn.Module):
    def __init__(self, hidden_size, embedding_dim, vocab_size):
        super(OurFirstLSTM, self).__init__()
    
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.encoder = nn.LSTM(input_size=embedding_dim,  
                hidden_size=hidden_size, num_layers=1)
        self.predictor = nn.Linear(hidden_size, 2)

    def forward(self, seq):
        print(seq.shape)
        output, (hidden,_) = self.encoder(self.embedding(seq))
        preds = self.predictor(hidden.squeeze(0))
        return preds

model = OurFirstLSTM(100,300, 20002)
model.to(device)
# %%


optimizer = optim.Adam(model.parameters(), lr=2e-2)
criterion = nn.CrossEntropyLoss()

def train(epochs, model, optimizer, criterion, train_iterator, valid_iterator):
    for epoch in range(1, epochs + 1):
     
        training_loss = 0.0
        valid_loss = 0.0
        model.train()
        for batch_idx, batch in enumerate(train_iterator):
            optimizer.zero_grad()
            predict = model(batch.tweet)
            loss = criterion(predict,batch.label)
            loss.backward()
            optimizer.step()
            training_loss += loss.data.item() * batch.tweet.size(0)
        training_loss /= len(train_iterator)
 
        
        model.eval()
        for batch_idx,batch in enumerate(valid_iterator):
            predict = model(batch.tweet)
            loss = criterion(predict,batch.label)
            valid_loss += loss.data.item() * batch.tweet.size(0)
 
        valid_loss /= len(valid_iterator)
        print('Epoch: {}, Training Loss: {:.2f}, Validation Loss: {:.2f}'.format(epoch, training_loss, valid_loss))

# %%

train(5, model, optimizer, criterion, train_iterator, valid_iterator)    
# %%