prototorch_models/prototorch/models/probabilistic.py
2021-06-03 14:05:44 +02:00

74 lines
2.6 KiB
Python

"""Probabilistic GLVQ methods"""
import torch
from prototorch.functions.competitions import stratified_min, stratified_sum
from prototorch.functions.losses import (log_likelihood_ratio_loss,
robust_soft_loss)
from prototorch.functions.transforms import gaussian
from .glvq import GLVQ
class CELVQ(GLVQ):
"""Cross-Entropy Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.loss = torch.nn.CrossEntropyLoss()
def shared_step(self, batch, batch_idx, optimizer_idx=None):
x, y = batch
out = self._forward(x) # [None, num_protos]
plabels = self.proto_layer.component_labels
probs = -1.0 * stratified_min(out, plabels) # [None, num_classes]
batch_loss = self.loss(probs, y.long())
loss = batch_loss.sum(dim=0)
return out, loss
class ProbabilisticLVQ(GLVQ):
def __init__(self, hparams, rejection_confidence=0.0, **kwargs):
super().__init__(hparams, **kwargs)
self.conditional_distribution = gaussian
self.rejection_confidence = rejection_confidence
def forward(self, x):
distances = self._forward(x)
conditional = self.conditional_distribution(distances,
self.hparams.variance)
prior = (1. / self.num_prototypes) * torch.ones(self.num_prototypes,
device=self.device)
posterior = conditional * prior
plabels = self.proto_layer._labels
y_pred = stratified_sum(posterior, plabels)
return y_pred
def predict(self, x):
y_pred = self.forward(x)
confidence, prediction = torch.max(y_pred, dim=1)
prediction[confidence < self.rejection_confidence] = -1
return prediction
def training_step(self, batch, batch_idx, optimizer_idx=None):
X, y = batch
out = self.forward(X)
plabels = self.proto_layer.component_labels
batch_loss = self.loss_fn(out, y, plabels)
loss = batch_loss.sum(dim=0)
return loss
class LikelihoodRatioLVQ(ProbabilisticLVQ):
"""Learning Vector Quantization based on Likelihood Ratios."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_fn = log_likelihood_ratio_loss
class RSLVQ(ProbabilisticLVQ):
"""Robust Soft Learning Vector Quantization."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_fn = robust_soft_loss