Update Documentation
Clean up project
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.gitignore
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.gitignore
vendored
@ -132,4 +132,6 @@ dmypy.json
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datasets/
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# PyTorch-Lightning
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lightning_logs/
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lightning_logs/
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.vscode/
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BIN
docs/source/_static/img/logo.png
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docs/source/_static/img/logo.png
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@ -104,7 +104,7 @@ autodoc_inherit_docstrings = False
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# https://sphinx-themes.org/
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html_theme = "sphinx_rtd_theme"
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html_logo = "_static/img/horizontal-lockup.png"
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html_logo = "_static/img/logo.png"
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html_theme_options = {
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"logo_only": True,
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@ -168,8 +168,8 @@ latex_documents = [
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# One entry per manual page. List of tuples
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# (source start file, name, description, authors, manual section).
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man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author],
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1)]
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man_pages = [(master_doc, "ProtoTorch Models",
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"ProtoTorch Models Plugin Documentation", [author], 1)]
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# -- Options for Texinfo output -------------------------------------------
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@ -179,19 +179,22 @@ man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author],
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texinfo_documents = [
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(
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master_doc,
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"prototorch",
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"ProtoTorch Documentation",
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"prototorch models",
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"ProtoTorch Models Plugin Documentation",
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author,
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"prototorch",
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"Prototype-based machine learning in PyTorch.",
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"prototorch models",
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"Prototype-based machine learning Models in ProtoTorch.",
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"Miscellaneous",
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),
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]
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# Example configuration for intersphinx: refer to the Python standard library.
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intersphinx_mapping = {
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"python": ("https://docs.python.org/", None),
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"numpy": ("https://docs.scipy.org/doc/numpy/", None),
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"python": ("https://docs.python.org/3/", None),
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"numpy": ("https://numpy.org/doc/stable/", None),
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"torch": ('https://pytorch.org/docs/stable/', None),
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"pytorch_lightning":
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("https://pytorch-lightning.readthedocs.io/en/stable/", None),
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}
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# -- Options for Epub output ----------------------------------------------
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9
docs/source/custom.rst
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docs/source/custom.rst
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@ -0,0 +1,9 @@
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.. Customize the Models
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Abstract Models
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========================================
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.. autoclass:: prototorch.models.abstract.AbstractPrototypeModel
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:members:
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.. autoclass:: prototorch.models.abstract.PrototypeImageModel
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:members:
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@ -1,25 +1,40 @@
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.. ProtoTorch Models documentation master file
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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About ProtoTorch Models
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========================
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ProtoTorch Models Plugins
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========================================
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.. toctree::
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:hidden:
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:maxdepth: 3
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self
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tutorial.ipynb
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.. toctree::
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:hidden:
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:maxdepth: 3
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:caption: Contents:
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:caption: Library
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self
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models
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tutorial.ipynb
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library
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.. toctree::
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:hidden:
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:maxdepth: 3
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:caption: Customize
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custom
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About
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-----------------------------------------
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`Prototorch Models <https://github.com/si-cim/prototorch_models>`_ is a Plugin
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for `Prototorch <https://github.com/si-cim/prototorch>`_. It implements common
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prototype-based Machine Learning algorithms using `PyTorch-Lightning
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<https://www.pytorchlightning.ai/>`_.
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Indices
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=======
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* :ref:`genindex`
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* :ref:`modindex`
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Library
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-----------------------------------------
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Prototorch Models delivers many application ready models.
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These models have been published in the past and have been adapted to the Prototorch library.
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Customizable
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-----------------------------------------
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Prototorch Models also contains the building blocks to build own models with PyTorch-Lightning and Prototorch.
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@ -1,27 +1,35 @@
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.. Available Models
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Available Models
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Models
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========================================
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Unsupervised Methods
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-----------------------------------------
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.. autoclass:: prototorch.models.knn.KNN
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.. autoclass:: prototorch.models.unsupervised.KNN
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:members:
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.. autoclass:: prototorch.models.neural_gas.NeuralGas
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.. autoclass:: prototorch.models.unsupervised.NeuralGas
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:members:
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Classical Learning Vector Quantization
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-----------------------------------------
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Original LVQ models. Implementations use GLVQ structure as shown in [Sato&Yamada].
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Original LVQ models by Kohonen.
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These heuristic algorithms do not use gradient descent.
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.. autoclass:: prototorch.models.glvq.LVQ1
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:members:
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.. autoclass:: prototorch.models.glvq.LVQ21
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:members:
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It is also possible to use the GLVQ structure as shown in [Sato&Yamada].
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This allows the use of gradient descent methods.
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.. autoclass:: prototorch.models.glvq.GLVQ1
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:members:
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.. autoclass:: prototorch.models.glvq.GLVQ21
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:members:
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Generalized Learning Vector Quantization
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-----------------------------------------
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@ -43,10 +51,17 @@ Generalized Learning Vector Quantization
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.. autoclass:: prototorch.models.glvq.LVQMLN
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:members:
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CBC
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Classification by Component
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-----------------------------------------
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.. autoclass:: prototorch.models.cbc.CBC
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:members:
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.. autoclass:: prototorch.models.cbc.ImageCBC
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:members:
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:members:
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Visualization
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========================================
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.. automodule:: prototorch.models.vis
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:members:
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:undoc-members:
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@ -3,8 +3,7 @@ from importlib.metadata import PackageNotFoundError, version
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from .cbc import CBC, ImageCBC
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from .glvq import (GLVQ, GLVQ1, GLVQ21, GMLVQ, GRLVQ, LVQ1, LVQ21, LVQMLN,
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ImageGLVQ, ImageGMLVQ, SiameseGLVQ)
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from .knn import KNN
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from .neural_gas import NeuralGas
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from .unsupervised import KNN, NeuralGas
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from .vis import *
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__version__ = "0.1.7"
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@ -1,14 +0,0 @@
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"""Callbacks for Pytorch Lighning Modules"""
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import pytorch_lightning as pl
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import torch
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class StopOnNaN(pl.Callback):
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def __init__(self, param):
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super().__init__()
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self.param = param
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def on_epoch_end(self, trainer, pl_module, logs={}):
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if torch.isnan(self.param).any():
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raise ValueError("NaN encountered. Stopping.")
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@ -1,3 +1,4 @@
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"""Models based on the GLVQ Framework"""
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import torch
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import torchmetrics
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from prototorch.components import LabeledComponents
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@ -6,15 +7,8 @@ from prototorch.functions.competitions import wtac
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from prototorch.functions.distances import (euclidean_distance, omega_distance,
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sed)
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from prototorch.functions.helper import get_flat
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from prototorch.functions.losses import (_get_dp_dm, _get_matcher, glvq_loss,
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lvq1_loss, lvq21_loss)
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from .abstract import AbstractPrototypeModel, PrototypeImageModel
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class GLVQ(AbstractPrototypeModel):
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"""Generalized Learning Vector Quantization."""
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from prototorch.functions.losses import (_get_dp_dm, glvq_loss, lvq1_loss,
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lvq21_loss)
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from .abstract import AbstractPrototypeModel, PrototypeImageModel
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@ -192,11 +186,14 @@ class GRLVQ(SiameseGLVQ):
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self.relevances = torch.nn.parameter.Parameter(
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torch.ones(self.hparams.input_dim))
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# Overwrite backbone
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self.backbone = self._backbone
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@property
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def relevance_profile(self):
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return self.relevances.detach().cpu()
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def backbone(self, x):
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def _backbone(self, x):
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"""Namespace hook for the visualization callbacks to work."""
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return x @ torch.diag(self.relevances)
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@ -262,6 +259,7 @@ class LVQMLN(SiameseGLVQ):
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class NonGradientGLVQ(GLVQ):
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"""Abstract Model for Models that do not use gradients in their update phase."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.automatic_optimization = False
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@ -271,6 +269,7 @@ class NonGradientGLVQ(GLVQ):
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class LVQ1(NonGradientGLVQ):
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"""Learning Vector Quantization 1."""
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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protos = self.proto_layer.components
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plabels = self.proto_layer.component_labels
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@ -299,6 +298,7 @@ class LVQ1(NonGradientGLVQ):
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class LVQ21(NonGradientGLVQ):
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"""Learning Vector Quantization 2.1."""
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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protos = self.proto_layer.components
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plabels = self.proto_layer.component_labels
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@ -311,8 +311,7 @@ class LVQ21(NonGradientGLVQ):
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xi = xi.view(1, -1)
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yi = yi.view(1, )
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d = self(xi)
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preds = wtac(d, plabels)
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(dp, wp), (dn, wn) = _get_dp_dm(d, yi, plabels, with_indices=True)
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(_, wp), (_, wn) = _get_dp_dm(d, yi, plabels, with_indices=True)
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shiftp = xi - protos[wp]
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shiftn = protos[wn] - xi
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updated_protos = protos + 0.0
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@ -328,11 +327,11 @@ class LVQ21(NonGradientGLVQ):
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class MedianLVQ(NonGradientGLVQ):
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...
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"""Median LVQ"""
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class GLVQ1(GLVQ):
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"""Learning Vector Quantization 1."""
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"""Generalized Learning Vector Quantization 1."""
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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self.loss = lvq1_loss
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@ -340,7 +339,7 @@ class GLVQ1(GLVQ):
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class GLVQ21(GLVQ):
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"""Learning Vector Quantization 2.1."""
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"""Generalized Learning Vector Quantization 2.1."""
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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self.loss = lvq21_loss
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@ -354,7 +353,6 @@ class ImageGLVQ(PrototypeImageModel, GLVQ):
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after updates.
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"""
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pass
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class ImageGMLVQ(PrototypeImageModel, GMLVQ):
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@ -364,4 +362,3 @@ class ImageGMLVQ(PrototypeImageModel, GMLVQ):
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after updates.
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"""
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pass
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"""The popular K-Nearest-Neighbors classification algorithm."""
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import warnings
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import torch
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import torchmetrics
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from prototorch.components import LabeledComponents
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from prototorch.components.initializers import parse_data_arg
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from prototorch.functions.competitions import knnc
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from prototorch.functions.distances import euclidean_distance
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from .abstract import AbstractPrototypeModel
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class KNN(AbstractPrototypeModel):
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"""K-Nearest-Neighbors classification algorithm."""
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def __init__(self, hparams, **kwargs):
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super().__init__()
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self.save_hyperparameters(hparams)
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# Default Values
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self.hparams.setdefault("k", 1)
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self.hparams.setdefault("distance", euclidean_distance)
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data = kwargs.get("data")
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x_train, y_train = parse_data_arg(data)
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self.proto_layer = LabeledComponents(initialized_components=(x_train,
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y_train))
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self.train_acc = torchmetrics.Accuracy()
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@property
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def prototype_labels(self):
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return self.proto_layer.component_labels.detach()
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def forward(self, x):
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protos, _ = self.proto_layer()
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dis = self.hparams.distance(x, protos)
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return dis
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def predict(self, x):
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# model.eval() # ?!
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with torch.no_grad():
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d = self(x)
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plabels = self.proto_layer.component_labels
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y_pred = knnc(d, plabels, k=self.hparams.k)
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return y_pred
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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return 1
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def on_train_batch_start(self,
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train_batch,
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batch_idx,
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dataloader_idx=None):
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warnings.warn("k-NN has no training, skipping!")
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return -1
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def configure_optimizers(self):
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return None
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"""Unsupervised prototype learning algorithms."""
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import warnings
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import torch
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from prototorch.components import Components
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import torchmetrics
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from prototorch.components import Components, LabeledComponents
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from prototorch.components import initializers as cinit
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from prototorch.components.initializers import ZerosInitializer
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from prototorch.components.initializers import ZerosInitializer, parse_data_arg
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from prototorch.functions.competitions import knnc
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from prototorch.functions.distances import euclidean_distance
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from prototorch.modules.losses import NeuralGasEnergy
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@ -36,6 +42,56 @@ class ConnectionTopology(torch.nn.Module):
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return f"agelimit: {self.agelimit}"
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class KNN(AbstractPrototypeModel):
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"""K-Nearest-Neighbors classification algorithm."""
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def __init__(self, hparams, **kwargs):
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super().__init__()
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self.save_hyperparameters(hparams)
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# Default Values
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self.hparams.setdefault("k", 1)
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self.hparams.setdefault("distance", euclidean_distance)
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data = kwargs.get("data")
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x_train, y_train = parse_data_arg(data)
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self.proto_layer = LabeledComponents(initialized_components=(x_train,
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y_train))
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self.train_acc = torchmetrics.Accuracy()
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@property
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def prototype_labels(self):
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return self.proto_layer.component_labels.detach()
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def forward(self, x):
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protos, _ = self.proto_layer()
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dis = self.hparams.distance(x, protos)
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return dis
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def predict(self, x):
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# model.eval() # ?!
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with torch.no_grad():
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d = self(x)
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plabels = self.proto_layer.component_labels
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y_pred = knnc(d, plabels, k=self.hparams.k)
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return y_pred
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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return 1
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def on_train_batch_start(self,
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train_batch,
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batch_idx,
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dataloader_idx=None):
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warnings.warn("k-NN has no training, skipping!")
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return -1
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def configure_optimizers(self):
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return None
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class NeuralGas(AbstractPrototypeModel):
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def __init__(self, hparams, **kwargs):
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super().__init__()
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