Rename rslvq example
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@ -13,6 +13,9 @@ if __name__ == "__main__":
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parser = pl.Trainer.add_argparse_args(parser)
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parser = pl.Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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args = parser.parse_args()
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# Reproducibility
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pl.utilities.seed.seed_everything(seed=42)
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# Dataset
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# Dataset
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train_ds = pt.datasets.Iris(dims=[0, 2])
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train_ds = pt.datasets.Iris(dims=[0, 2])
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@ -20,20 +23,17 @@ if __name__ == "__main__":
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
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# Hyperparameters
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# Hyperparameters
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num_classes = 3
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prototypes_per_class = 2
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hparams = dict(
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hparams = dict(
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distribution=(num_classes, prototypes_per_class),
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distribution=[2, 2, 3],
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lr=0.05,
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lr=0.05,
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variance=1.0,
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variance=0.3,
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)
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)
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# Initialize the model
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# Initialize the model
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model = pt.models.probabilistic.LikelihoodRatioLVQ(
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model = pt.models.probabilistic.RSLVQ(
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hparams,
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hparams,
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optimizer=torch.optim.Adam,
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optimizer=torch.optim.Adam,
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# prototype_initializer=pt.components.UniformInitializer(2),
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prototype_initializer=pt.components.SSI(train_ds, noise=0.2),
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prototype_initializer=pt.components.SMI(train_ds),
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)
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)
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print(model)
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print(model)
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@ -45,6 +45,9 @@ if __name__ == "__main__":
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trainer = pl.Trainer.from_argparse_args(
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trainer = pl.Trainer.from_argparse_args(
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args,
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args,
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callbacks=[vis],
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callbacks=[vis],
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terminate_on_nan=True,
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weights_summary=None,
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# accelerator="ddp",
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)
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)
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# Training loop
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# Training loop
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@ -1,6 +1,6 @@
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from importlib.metadata import PackageNotFoundError, version
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from importlib.metadata import PackageNotFoundError, version
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from . import probabilistic
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from .probabilistic import LikelihoodRatioLVQ, RSLVQ
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from .cbc import CBC, ImageCBC
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from .cbc import CBC, ImageCBC
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from .glvq import (CELVQ, GLVQ, GLVQ1, GLVQ21, GMLVQ, GRLVQ, LVQMLN, ImageGLVQ,
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from .glvq import (CELVQ, GLVQ, GLVQ1, GLVQ21, GMLVQ, GRLVQ, LVQMLN, ImageGLVQ,
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ImageGMLVQ, SiameseGLVQ)
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ImageGMLVQ, SiameseGLVQ)
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@ -2,15 +2,14 @@
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import torch
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import torch
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from prototorch.functions.competitions import stratified_sum
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from prototorch.functions.competitions import stratified_sum
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from prototorch.functions.losses import (log_likelihood_ratio_loss,
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from prototorch.functions.losses import log_likelihood_ratio_loss, robust_soft_loss
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robust_soft_loss)
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from prototorch.functions.transform import gaussian
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from prototorch.functions.transform import gaussian
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from .glvq import GLVQ
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from .glvq import GLVQ
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class ProbabilisticLVQ(GLVQ):
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class ProbabilisticLVQ(GLVQ):
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def __init__(self, hparams, rejection_confidence=1.0, **kwargs):
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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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super().__init__(hparams, **kwargs)
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self.conditional_distribution = gaussian
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self.conditional_distribution = gaussian
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@ -45,19 +44,14 @@ class ProbabilisticLVQ(GLVQ):
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class LikelihoodRatioLVQ(ProbabilisticLVQ):
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class LikelihoodRatioLVQ(ProbabilisticLVQ):
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"""Learning Vector Quantization based on Likelihood Ratios
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"""Learning Vector Quantization based on Likelihood Ratios."""
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"""
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def __init__(self, *args, **kwargs):
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def __init__(self, *args, **kwargs):
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super().__init__(*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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self.loss_fn = log_likelihood_ratio_loss
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class RSLVQ(ProbabilisticLVQ):
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class RSLVQ(ProbabilisticLVQ):
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"""Learning Vector Quantization based on Likelihood Ratios
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"""Robust Soft Learning Vector Quantization."""
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"""
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def __init__(self, *args, **kwargs):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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super().__init__(*args, **kwargs)
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self.loss_fn = robust_soft_loss
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self.loss_fn = robust_soft_loss
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__all__ = ["LikelihoodRatioLVQ", "RSLVQ"]
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