forked from moreo/QuaPy
352 lines
14 KiB
Python
352 lines
14 KiB
Python
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
|
|
from data import LabelledCollection
|
|
from util import EarlyStop
|
|
import quapy as qp
|
|
|
|
|
|
|
|
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_
|
|
|
|
|
|
|
|
|
|
|