prototorch_models/prototorch/models/probabilistic.py

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"""Probabilistic GLVQ methods"""
import torch
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from prototorch.functions.competitions import stratified_sum
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from prototorch.functions.losses import log_likelihood_ratio_loss, 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 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
self.rejection_confidence = rejection_confidence
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def forward(self, x):
distances = self._forward(x)
conditional = self.conditional_distribution(distances,
self.hparams.variance)
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prior = (1. / self.num_prototypes) * torch.ones(self.num_prototypes)
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posterior = conditional * prior
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plabels = self.proto_layer._labels
y_pred = stratified_sum(posterior, plabels)
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return y_pred
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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
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def training_step(self, batch, batch_idx, optimizer_idx=None):
X, y = batch
out = self.forward(X)
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)
return loss
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class LikelihoodRatioLVQ(ProbabilisticLVQ):
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"""Learning Vector Quantization based on Likelihood Ratios."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_fn = log_likelihood_ratio_loss
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class RSLVQ(ProbabilisticLVQ):
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"""Robust Soft Learning Vector Quantization."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_fn = robust_soft_loss