140 lines
4.5 KiB
Python
140 lines
4.5 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Callable
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import torch
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from prototorch.core.distances import euclidean_distance
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from prototorch.core.initializers import (
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AbstractLinearTransformInitializer,
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EyeLinearTransformInitializer,
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)
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from prototorch.models.architectures.base import BaseYArchitecture
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from prototorch.nn.wrappers import LambdaLayer
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from torch import Tensor
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from torch.nn.parameter import Parameter
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class SimpleComparisonMixin(BaseYArchitecture):
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"""
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Simple Comparison
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A comparison layer that only uses the positions of the components
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and the batch for dissimilarity computation.
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"""
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# HyperParameters
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# ----------------------------------------------------------------------------------------------
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@dataclass
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class HyperParameters(BaseYArchitecture.HyperParameters):
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"""
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comparison_fn: The comparison / dissimilarity function to use. Default: euclidean_distance.
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comparison_args: Keyword arguments for the comparison function. Default: {}.
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"""
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comparison_fn: Callable = euclidean_distance
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comparison_args: dict = field(default_factory=lambda: dict())
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comparison_parameters: dict = field(default_factory=lambda: dict())
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# Steps
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# ----------------------------------------------------------------------------------------------
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def init_comparison(self, hparams: HyperParameters):
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self.comparison_layer = LambdaLayer(
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fn=hparams.comparison_fn,
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**hparams.comparison_args,
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)
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self.comparison_kwargs: dict[str, Tensor] = dict()
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def comparison(self, batch, components):
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comp_tensor, _ = components
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batch_tensor, _ = batch
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comp_tensor = comp_tensor.unsqueeze(1)
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distances = self.comparison_layer(
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batch_tensor,
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comp_tensor,
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**self.comparison_kwargs,
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)
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return distances
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class OmegaComparisonMixin(SimpleComparisonMixin):
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"""
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Omega Comparison
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A comparison layer that uses the positions of the components
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and the batch for dissimilarity computation.
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"""
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_omega: torch.Tensor
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# HyperParameters
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# ----------------------------------------------------------------------------------------------
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@dataclass
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class HyperParameters(SimpleComparisonMixin.HyperParameters):
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"""
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input_dim: Necessary Field: The dimensionality of the input.
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latent_dim:
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The dimensionality of the latent space. Default: 2.
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omega_initializer:
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The initializer to use for the omega matrix. Default: EyeLinearTransformInitializer.
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"""
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input_dim: int | None = None
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latent_dim: int = 2
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omega_initializer: type[
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AbstractLinearTransformInitializer] = EyeLinearTransformInitializer
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omega_initializer_kwargs: dict = field(default_factory=lambda: dict())
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# Steps
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# ----------------------------------------------------------------------------------------------
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def init_comparison(self, hparams: HyperParameters) -> None:
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super().init_comparison(hparams)
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# Initialize the omega matrix
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if hparams.input_dim is None:
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raise ValueError("input_dim must be specified.")
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else:
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omega = hparams.omega_initializer(
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**hparams.omega_initializer_kwargs).generate(
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hparams.input_dim,
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hparams.latent_dim,
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)
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self.register_parameter("_omega", Parameter(omega))
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self.comparison_kwargs = dict(omega=self._omega)
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# Properties
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# ----------------------------------------------------------------------------------------------
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@property
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def omega_matrix(self):
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'''
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Omega Matrix. Mapping applied to data and prototypes.
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'''
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return self._omega.detach().cpu()
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@property
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def lambda_matrix(self):
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'''
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Lambda Matrix.
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'''
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omega = self._omega.detach()
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lam = omega @ omega.T
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return lam.detach().cpu()
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@property
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def relevance_profile(self):
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'''
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Relevance Profile. Main Diagonal of the Lambda Matrix.
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'''
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return self.lambda_matrix.diag().abs()
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@property
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def classification_influence_profile(self):
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'''
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Classification Influence Profile. Influence of each dimension.
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'''
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lam = self.lambda_matrix
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return lam.abs().sum(0)
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