import os
from abc import ABCMeta, abstractmethod
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, f1_score
from torch.nn.utils.rnn import pad_sequence
from tqdm import tqdm
import quapy as qp
from data import LabelledCollection
from util import EarlyStop
class NeuralClassifierTrainer:
def __init__(self,
net, # TextClassifierNet
lr=1e-3,
weight_decay=0,
patience=10,
epochs=200,
batch_size=64,
batch_size_test=512,
padding_length=300,
device='cpu',
checkpointpath='../checkpoint/classifier_net.dat'):
super().__init__()
assert isinstance(net, TextClassifierNet), f'net is not an instance of {TextClassifierNet.__name__}'
self.net = net
self.vocab_size = self.net.vocabulary_size
self.trainer_hyperparams={
'lr': lr,
'weight_decay': weight_decay,
'patience': patience,
'epochs': epochs,
'batch_size': batch_size,
'batch_size_test': batch_size_test,
'padding_length': padding_length,
'device': torch.device(device)
}
self.learner_hyperparams = self.net.get_params()
self.checkpointpath = checkpointpath
self.classes_ = np.asarray([0, 1])
print(f'[NeuralNetwork running on {device}]')
os.makedirs(Path(checkpointpath).parent, exist_ok=True)
def reset_net_params(self, vocab_size, n_classes):
self.net = self.net.__class__(vocab_size, n_classes, **self.learner_hyperparams)
self.net.xavier_uniform()
def get_params(self):
return {**self.net.get_params(), **self.trainer_hyperparams}
def set_params(self, **params):
trainer_hyperparams = self.trainer_hyperparams
learner_hyperparams = self.net.get_params()
for key, val in params.items():
if key in trainer_hyperparams and key in learner_hyperparams:
raise ValueError(f'the use of parameter {key} is ambiguous since it can refer to '
f'a parameters of the Trainer or the learner {self.netclass.__name__}')
elif key not in trainer_hyperparams and key not in learner_hyperparams:
raise ValueError(f'parameter {key} is not valid')
if key in trainer_hyperparams:
trainer_hyperparams[key] = val
else:
learner_hyperparams[key] = val
self.trainer_hyperparams = trainer_hyperparams
self.learner_hyperparams = learner_hyperparams
@property
def device(self):
return next(self.net.parameters()).device
def __update_progress_bar(self, pbar):
pbar.set_description(f'[{self.net.__class__.__name__}] training epoch={self.current_epoch} '
f'tr-loss={self.status["tr"]["loss"]:.5f} '
f'tr-acc={100 * self.status["tr"]["acc"]:.2f}% '
f'tr-macroF1={100 * self.status["tr"]["f1"]:.2f}% '
f'patience={self.early_stop.patience}/{self.early_stop.PATIENCE_LIMIT} '
f'val-loss={self.status["va"]["loss"]:.5f} '
f'val-acc={100 * self.status["va"]["acc"]:.2f}% '
f'macroF1={100 * self.status["va"]["f1"]:.2f}%')
def _train_epoch(self, data, status, pbar):
self.net.train()
criterion = torch.nn.CrossEntropyLoss()
losses, predictions, true_labels = [], [], []
for xi, yi in data:
self.optim.zero_grad()
logits = self.net.forward(xi)
loss = criterion(logits, yi)
loss.backward()
self.optim.step()
losses.append(loss.item())
preds = torch.softmax(logits, dim=-1).detach().cpu().numpy().argmax(axis=-1)
status["loss"] = np.mean(losses)
predictions.extend(preds.tolist())
true_labels.extend(yi.detach().cpu().numpy().tolist())
status["acc"] = accuracy_score(true_labels, predictions)
status["f1"] = f1_score(true_labels, predictions, average='macro')
self.__update_progress_bar(pbar)
def _test_epoch(self, data, status, pbar):
self.net.eval()
criterion = torch.nn.CrossEntropyLoss()
losses, predictions, true_labels = [], [], []
with torch.no_grad():
for xi, yi in data:
logits = self.net.forward(xi)
loss = criterion(logits, yi)
losses.append(loss.item())
preds = torch.softmax(logits, dim=-1).detach().cpu().numpy().argmax(axis=-1)
predictions.extend(preds.tolist())
true_labels.extend(yi.detach().cpu().numpy().tolist())
status["loss"] = np.mean(losses)
status["acc"] = accuracy_score(true_labels, predictions)
status["f1"] = f1_score(true_labels, predictions, average='macro')
self.__update_progress_bar(pbar)
def fit(self, instances, labels, val_split=0.3):
train, val = LabelledCollection(instances, labels).split_stratified(1-val_split)
opt = self.trainer_hyperparams
checkpoint = self.checkpointpath
self.reset_net_params(self.vocab_size, train.n_classes)
train_generator = TorchDataset(train.instances, train.labels).asDataloader(
opt['batch_size'], shuffle=True, pad_length=opt['padding_length'], device=opt['device'])
valid_generator = TorchDataset(val.instances, val.labels).asDataloader(
opt['batch_size_test'], shuffle=False, pad_length=opt['padding_length'], device=opt['device'])
self.status = {'tr': {'loss': -1, 'acc': -1, 'f1': -1},
'va': {'loss': -1, 'acc': -1, 'f1': -1}}
self.optim = torch.optim.Adam(self.net.parameters(), lr=opt['lr'], weight_decay=opt['weight_decay'])
self.early_stop = EarlyStop(opt['patience'], lower_is_better=False)
with tqdm(range(1, opt['epochs'] + 1)) as pbar:
for self.current_epoch in pbar:
self._train_epoch(train_generator, self.status['tr'], pbar)
self._test_epoch(valid_generator, self.status['va'], pbar)
self.early_stop(self.status['va']['f1'], self.current_epoch)
if self.early_stop.IMPROVED:
torch.save(self.net.state_dict(), checkpoint)
elif self.early_stop.STOP:
print(f'training ended by patience exhasted; loading best model parameters in {checkpoint} '
f'for epoch {self.early_stop.best_epoch}')
self.net.load_state_dict(torch.load(checkpoint))
break
print('performing one training pass over the validation set...')
self._train_epoch(valid_generator, self.status['tr'], pbar)
print('[done]')
return self
def predict(self, instances):
return np.argmax(self.predict_proba(instances), axis=-1)
def predict_proba(self, instances):
return self.net.predict_proba(instances)
def predict_probability_positive(self, instances):
self.net.eval()
opt = self.trainer_hyperparams
with torch.no_grad():
positive_probs = []
for xi in TorchDataset(instances).asDataloader(
opt['batch_size_test'], shuffle=False, pad_length=opt['padding_length'], device=opt['device']):
positive_probs.append(self.net.predict_proba(xi))
return np.concatenate(positive_probs)
def transform(self, instances):
self.net.eval()
embeddings = []
with torch.no_grad():
for xi in TorchDataset(instances).asDataloader(
self.batch_size_test, shuffle=False, pad_length=self.padding_length, device=self.device):
embeddings.append(self.net.document_embedding(xi).detach().cpu().numpy())
return np.concatenate(embeddings)
class TorchDataset(torch.utils.data.Dataset):
def __init__(self, instances, labels=None):
self.instances = instances
self.labels = labels
def __len__(self):
return len(self.instances)
def __getitem__(self, index):
return {'doc': self.instances[index], 'label': self.labels[index] if self.labels is not None else None}
def asDataloader(self, batch_size, shuffle, pad_length, device):
def collate(batch):
data = [torch.LongTensor(item['doc'][:pad_length]) for item in batch]
data = pad_sequence(data, batch_first=True, padding_value=qp.environ['PAD_INDEX']).to(device)
targets = [item['label'] for item in batch]
if targets[0] is None:
return data
else:
targets = torch.as_tensor(targets, dtype=torch.long).to(device)
return [data, targets]
torchDataset = TorchDataset(self.instances, self.labels)
return torch.utils.data.DataLoader(torchDataset, batch_size=batch_size, shuffle=shuffle, collate_fn=collate)
class TextClassifierNet(torch.nn.Module, metaclass=ABCMeta):
@abstractmethod
def document_embedding(self, x): ...
def forward(self, x):
doc_embedded = self.document_embedding(x)
return self.output(doc_embedded)
def dimensions(self):
return self.dim
def predict_proba(self, x):
logits = self(x)
return torch.softmax(logits).detach().cpu().numpy()
def xavier_uniform(self):
for p in self.parameters():
if p.dim() > 1 and p.requires_grad:
torch.nn.init.xavier_uniform_(p)
@abstractmethod
def get_params(self): ...
@property
def vocabulary_size(self): ...
class LSTMnet(TextClassifierNet):
def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, lstm_nlayers=1,
drop_p=0.5):
super().__init__()
self.vocabulary_size_ = vocabulary_size
self.n_classes = n_classes
self.hyperparams={
'embedding_size': embedding_size,
'hidden_size': hidden_size,
'repr_size': repr_size,
'lstm_nlayers': lstm_nlayers,
'drop_p': drop_p
}
self.word_embedding = torch.nn.Embedding(vocabulary_size, embedding_size)
self.lstm = torch.nn.LSTM(embedding_size, hidden_size, lstm_nlayers, dropout=drop_p, batch_first=True)
self.dropout = torch.nn.Dropout(drop_p)
self.dim = repr_size
self.doc_embedder = torch.nn.Linear(hidden_size, self.dim)
self.output = torch.nn.Linear(self.dim, n_classes)
def init_hidden(self, set_size):
opt = self.hyperparams
var_hidden = torch.zeros(opt['lstm_nlayers'], set_size, opt['lstm_hidden_size'])
var_cell = torch.zeros(opt['lstm_nlayers'], set_size, opt['lstm_hidden_size'])
if next(self.lstm.parameters()).is_cuda:
var_hidden, var_cell = var_hidden.cuda(), var_cell.cuda()
return var_hidden, var_cell
def document_embedding(self, x):
embedded = self.word_embedding(x)
rnn_output, rnn_hidden = self.lstm(embedded, self.init_hidden(x.size()[0]))
abstracted = self.dropout(F.relu(rnn_hidden[0][-1]))
abstracted = self.doc_embedder(abstracted)
return abstracted
def get_params(self):
return self.hyperparams
@property
def vocabulary_size(self):
return self.vocabulary_size_
class CNNnet(TextClassifierNet):
def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100,
kernel_heights=[3, 5, 7], stride=1, padding=0, drop_p=0.5):
super(CNNnet, self).__init__()
self.vocabulary_size_ = vocabulary_size
self.n_classes = n_classes
self.hyperparams={
'embedding_size': embedding_size,
'hidden_size': hidden_size,
'repr_size': repr_size,
'kernel_heights':kernel_heights,
'stride': stride,
'drop_p': drop_p
}
self.word_embedding = torch.nn.Embedding(vocabulary_size, embedding_size)
in_channels = 1
self.conv1 = nn.Conv2d(in_channels, hidden_size, (kernel_heights[0], embedding_size), stride, padding)
self.conv2 = nn.Conv2d(in_channels, hidden_size, (kernel_heights[1], embedding_size), stride, padding)
self.conv3 = nn.Conv2d(in_channels, hidden_size, (kernel_heights[2], embedding_size), stride, padding)
self.dropout = nn.Dropout(drop_p)
self.dim = repr_size
self.doc_embedder = torch.nn.Linear(len(kernel_heights) * hidden_size, self.dim)
self.output = nn.Linear(self.dim, n_classes)
def conv_block(self, input, conv_layer):
conv_out = conv_layer(input) # conv_out.size() = (batch_size, out_channels, dim, 1)
activation = F.relu(conv_out.squeeze(3)) # activation.size() = (batch_size, out_channels, dim1)
max_out = F.max_pool1d(activation, activation.size()[2]).squeeze(2) # maxpool_out.size() = (batch_size, out_channels)
return max_out
def document_embedding(self, input):
input = self.word_embedding(input)
input = input.unsqueeze(1) # input.size() = (batch_size, 1, num_seq, embedding_length)
max_out1 = self.conv_block(input, self.conv1)
max_out2 = self.conv_block(input, self.conv2)
max_out3 = self.conv_block(input, self.conv3)
all_out = torch.cat((max_out1, max_out2, max_out3), 1) # all_out.size() = (batch_size, num_kernels*out_channels)
abstracted = self.dropout(F.relu(all_out)) # (batch_size, num_kernels*out_channels)
abstracted = self.doc_embedder(abstracted)
return abstracted
def get_params(self):
return self.hyperparams
@property
def vocabulary_size(self):
return self.vocabulary_size_