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