389 lines
12 KiB
Python
389 lines
12 KiB
Python
import os
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from collections import defaultdict
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader, Dataset
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from sklearn.decomposition import TruncatedSVD
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.preprocessing import normalize
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from torch.optim import AdamW
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from transformers.modeling_outputs import SequenceClassifierOutput
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from evaluation.evaluate import evaluate, log_eval
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PRINT_ON_EPOCH = 10
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def _normalize(lX, l2=True):
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return {lang: normalize(np.asarray(X)) for lang, X in lX.items()} if l2 else lX
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def XdotM(X, M, sif):
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E = X.dot(M)
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if sif:
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E = remove_pc(E, npc=1)
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return E
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def remove_pc(X, npc=1):
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"""
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Remove the projection on the principal components
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:param X: X[i,:] is a data point
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:param npc: number of principal components to remove
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:return: XX[i, :] is the data point after removing its projection
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"""
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pc = compute_pc(X, npc)
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if npc == 1:
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XX = X - X.dot(pc.transpose()) * pc
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else:
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XX = X - X.dot(pc.transpose()).dot(pc)
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return XX
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def compute_pc(X, npc=1):
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"""
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Compute the principal components.
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:param X: X[i,:] is a data point
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:param npc: number of principal components to remove
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:return: component_[i,:] is the i-th pc
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"""
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if isinstance(X, np.matrix):
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X = np.asarray(X)
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svd = TruncatedSVD(n_components=npc, n_iter=7, random_state=0)
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svd.fit(X)
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return svd.components_
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def predict(logits, classification_type="multilabel"):
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"""
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Converts soft precictions to hard predictions [0,1]
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"""
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if classification_type == "multilabel":
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prediction = torch.sigmoid(logits) > 0.5
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elif classification_type == "singlelabel":
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prediction = torch.argmax(logits, dim=1).view(-1, 1)
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else:
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print("unknown classification type")
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return prediction.detach().cpu().numpy()
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class TfidfVectorizerMultilingual:
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def __init__(self, **kwargs):
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self.kwargs = kwargs
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def fit(self, lX, ly=None):
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self.langs = sorted(lX.keys())
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self.vectorizer = {
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l: TfidfVectorizer(**self.kwargs).fit(lX[l]) for l in self.langs
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}
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return self
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def transform(self, lX):
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return {l: self.vectorizer[l].transform(lX[l]) for l in self.langs}
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def fit_transform(self, lX, ly=None):
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return self.fit(lX, ly).transform(lX)
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def vocabulary(self, l=None):
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if l is None:
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return {l: self.vectorizer[l].vocabulary_ for l in self.langs}
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else:
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return self.vectorizer[l].vocabulary_
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def get_analyzer(self, l=None):
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if l is None:
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return {l: self.vectorizer[l].build_analyzer() for l in self.langs}
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else:
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return self.vectorizer[l].build_analyzer()
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class Trainer:
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def __init__(
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self,
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model,
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optimizer_name,
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device,
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loss_fn,
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lr,
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print_steps,
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evaluate_step,
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patience,
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experiment_name,
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checkpoint_path,
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):
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self.device = device
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self.model = model.to(device)
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self.optimizer = self.init_optimizer(optimizer_name, lr)
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self.evaluate_steps = evaluate_step
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self.loss_fn = loss_fn.to(device)
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self.print_steps = print_steps
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self.experiment_name = experiment_name
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self.patience = patience
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self.earlystopping = EarlyStopping(
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patience=patience,
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checkpoint_path=checkpoint_path,
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verbose=True,
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experiment_name=experiment_name,
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)
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def init_optimizer(self, optimizer_name, lr):
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if optimizer_name.lower() == "adamw":
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return AdamW(self.model.parameters(), lr=lr)
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else:
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raise ValueError(f"Optimizer {optimizer_name} not supported")
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def train(self, train_dataloader, eval_dataloader, epochs=10):
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print(
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f"""- Training params for {self.experiment_name}:
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- epochs: {epochs}
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- learning rate: {self.optimizer.defaults['lr']}
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- train batch size: {train_dataloader.batch_size}
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- eval batch size: {eval_dataloader.batch_size}
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- max len: {train_dataloader.dataset.X.shape[-1]}
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- patience: {self.earlystopping.patience}\n"""
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)
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for epoch in range(epochs):
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self.train_epoch(train_dataloader, epoch)
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if (epoch + 1) % self.evaluate_steps == 0:
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metric_watcher = self.evaluate(eval_dataloader)
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stop = self.earlystopping(metric_watcher, self.model, epoch + 1)
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if stop:
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print(
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f"- restoring best model from epoch {self.earlystopping.best_epoch}"
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)
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self.model = self.earlystopping.load_model(self.model).to(
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self.device
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)
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break
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# TODO: maybe a lower lr?
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self.train_epoch(eval_dataloader, epoch=epoch)
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print(f"\n- last swipe on eval set")
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self.earlystopping.save_model(self.model)
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return self.model
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def train_epoch(self, dataloader, epoch):
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self.model.train()
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for b_idx, (x, y, lang) in enumerate(dataloader):
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self.optimizer.zero_grad()
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y_hat = self.model(x.to(self.device))
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if isinstance(y_hat, SequenceClassifierOutput):
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loss = self.loss_fn(y_hat.logits, y.to(self.device))
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else:
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loss = self.loss_fn(y_hat, y.to(self.device))
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loss.backward()
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self.optimizer.step()
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if (epoch + 1) % PRINT_ON_EPOCH == 0:
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if b_idx % self.print_steps == 0:
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print(f"Epoch: {epoch+1} Step: {b_idx+1} Loss: {loss:.4f}")
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return self
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def evaluate(self, dataloader):
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self.model.eval()
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lY = defaultdict(list)
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lY_hat = defaultdict(list)
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for b_idx, (x, y, lang) in enumerate(dataloader):
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y_hat = self.model(x.to(self.device))
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if isinstance(y_hat, SequenceClassifierOutput):
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loss = self.loss_fn(y_hat.logits, y.to(self.device))
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predictions = predict(y_hat.logits, classification_type="multilabel")
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else:
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loss = self.loss_fn(y_hat, y.to(self.device))
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predictions = predict(y_hat, classification_type="multilabel")
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for l, _true, _pred in zip(lang, y, predictions):
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lY[l].append(_true.detach().cpu().numpy())
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lY_hat[l].append(_pred)
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for lang in lY:
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lY[lang] = np.vstack(lY[lang])
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lY_hat[lang] = np.vstack(lY_hat[lang])
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l_eval = evaluate(lY, lY_hat)
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average_metrics = log_eval(l_eval, phase="validation")
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return average_metrics[0] # macro-F1
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class EarlyStopping:
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# TODO: add checkpointing + restore model if early stopping + last swipe on validation set
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def __init__(
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self,
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patience,
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checkpoint_path,
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experiment_name,
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min_delta=0,
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verbose=True,
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):
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self.patience = patience
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self.min_delta = min_delta
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self.counter = 0
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self.best_score = 0
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self.best_epoch = None
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self.verbose = verbose
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self.checkpoint_path = checkpoint_path
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self.experiment_name = experiment_name
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def __call__(self, validation, model, epoch):
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if validation > self.best_score:
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print(
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f"- earlystopping: Validation score improved from {self.best_score:.3f} to {validation:.3f}"
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)
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self.best_score = validation
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self.counter = 0
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self.best_epoch = epoch
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self.save_model(model)
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elif validation < (self.best_score + self.min_delta):
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self.counter += 1
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print(
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f"- earlystopping: Validation score decreased from {self.best_score:.3f} to {validation:.3f}, current patience: {self.patience - self.counter}"
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)
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if self.counter >= self.patience:
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if self.verbose:
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print(f"- earlystopping: Early stopping at epoch {epoch}")
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return True
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def save_model(self, model):
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_checkpoint_dir = os.path.join(self.checkpoint_path, self.experiment_name)
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os.makedirs(_checkpoint_dir, exist_ok=True)
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model.save_pretrained(_checkpoint_dir)
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def load_model(self, model):
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_checkpoint_dir = os.path.join(self.checkpoint_path, self.experiment_name)
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return model.from_pretrained(_checkpoint_dir)
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class AttentionModule(nn.Module):
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def __init__(self, embed_dim, num_heads, out_dim):
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super().__init__()
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self.attn = nn.MultiheadAttention(embed_dim, num_heads)
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self.linear = nn.Linear(embed_dim, out_dim)
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def __call__(self, X):
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attn_out, attn_weights = self.attn(query=X, key=X, value=X)
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out = self.linear(attn_out)
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return out
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def transform(self, X):
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attn_out, attn_weights = self.attn(query=X, key=X, value=X)
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return attn_out
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def save_pretrained(self, path):
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torch.save(self.state_dict(), f"{path}.pt")
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def _wtf(self):
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print("wtf")
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class AttentionAggregator:
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def __init__(self, embed_dim, out_dim, epochs, num_heads=1, device="cpu"):
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.device = device
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self.epochs = epochs
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self.attn = AttentionModule(embed_dim, num_heads, out_dim).to(self.device)
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def fit(self, X, Y):
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print("- fitting Attention-based aggregating function")
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hstacked_X = self.stack(X)
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dataset = AggregatorDatasetTorch(hstacked_X, Y)
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tra_dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
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experiment_name = "attention_aggregator"
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trainer = Trainer(
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self.attn,
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optimizer_name="adamW",
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lr=1e-3,
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loss_fn=torch.nn.CrossEntropyLoss(),
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print_steps=100,
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evaluate_step=1000,
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patience=10,
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experiment_name=experiment_name,
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device=self.device,
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checkpoint_path="models/aggregator",
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)
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trainer.train(
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train_dataloader=tra_dataloader,
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eval_dataloader=tra_dataloader,
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epochs=self.epochs,
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)
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return self
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def transform(self, X):
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# TODO: implement transform
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h_stacked = self.stack(X)
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dataset = AggregatorDatasetTorch(h_stacked, lY=None, split="whole")
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dataloader = DataLoader(dataset, batch_size=32, shuffle=False)
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_embeds = []
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l_embeds = defaultdict(list)
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self.attn.eval()
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with torch.no_grad():
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for input_ids, lang in dataloader:
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input_ids = input_ids.to(self.device)
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out = self.attn.transform(input_ids)
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_embeds.append((out.cpu().numpy(), lang))
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for embed, lang in _embeds:
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for sample_embed, sample_lang in zip(embed, lang):
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l_embeds[sample_lang].append(sample_embed)
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l_embeds = {lang: np.array(preds) for lang, preds in l_embeds.items()}
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return l_embeds
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def stack(self, data):
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hstack = self._hstack(data)
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return hstack
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def _hstack(self, data):
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_langs = data[0].keys()
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l_projections = {}
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for l in _langs:
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l_projections[l] = torch.tensor(
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np.hstack([view[l] for view in data]), dtype=torch.float32
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)
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return l_projections
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def _vstack(self, data):
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return torch.vstack()
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class AggregatorDatasetTorch(Dataset):
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def __init__(self, lX, lY, split="train"):
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self.lX = lX
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self.lY = lY
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self.split = split
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self.langs = []
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self.init()
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def init(self):
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self.X = torch.vstack([data for data in self.lX.values()])
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if self.split != "whole":
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self.Y = torch.vstack([torch.Tensor(data) for data in self.lY.values()])
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self.langs = sum(
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[
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v
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for v in {
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lang: [lang] * len(data) for lang, data in self.lX.items()
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}.values()
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],
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[],
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)
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return self
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def __len__(self):
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return len(self.X)
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def __getitem__(self, index):
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if self.split == "whole":
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return self.X[index], self.langs[index]
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return self.X[index], self.Y[index], self.langs[index]
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