build: bump version 1.0.0a7 → 1.0.0a8

omega trace normalization and matrix profile visualization

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.0.0a6
+current_version = 1.0.0a8
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)((?P<release>[a-zA-Z0-9_.-]+))?

.github/workflows/pythonapp.yml

@@ -21,7 +21,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install .[all]
-    - uses: pre-commit/action@v2.0.3
+    - uses: pre-commit/action@v3.0.0
   compatibility:
     needs: style
     strategy:
@@ -36,7 +36,8 @@ jobs:
           python-version: "3.8"
         - os: windows-latest
           python-version: "3.9"
-
+        - os: windows-latest
+          python-version: "3.11"
     runs-on: ${{ matrix.os }}
     steps:
     - uses: actions/checkout@v2

.pre-commit-config.yaml

@@ -3,7 +3,7 @@
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
+  rev: v4.3.0
   hooks:
   - id: trailing-whitespace
     exclude: (^\.bumpversion\.cfg$|cli_messages\.py)
@@ -14,7 +14,7 @@ repos:
   - id: check-case-conflict
 
 - repo: https://github.com/myint/autoflake
-  rev: v1.4
+  rev: v1.7.7
   hooks:
   - id: autoflake
 
@@ -24,7 +24,7 @@ repos:
   - id: isort
 
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.950
+  rev: v0.982
   hooks:
   - id: mypy
     files: prototorch
@@ -43,7 +43,7 @@ repos:
   - id: python-check-blanket-noqa
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v2.32.1
+  rev: v3.1.0
   hooks:
   - id: pyupgrade
 
@@ -52,3 +52,8 @@ repos:
   hooks:
   - id: gitlint
     args: [--contrib=CT1, --ignore=B6, --msg-filename]
+
+- repo: https://github.com/dosisod/refurb
+  rev: v1.4.0
+  hooks:
+    - id: refurb

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "1.0.0-a6"
+release = "1.0.0-a8"
 
 # -- General configuration ---------------------------------------------------
 

examples/gmlvq_iris.py

@@ -97,6 +97,13 @@ def main():
         step=Steps.VALIDATION,
     )
 
+    accuracy = LogTorchmetricCallback(
+        'validation_accuracy',
+        torchmetrics.Accuracy,
+        num_classes=3,
+        step=Steps.VALIDATION,
+    )
+
     es = EarlyStopping(
         monitor=stopping_criterion.name,
         mode="max",
@@ -111,6 +118,7 @@ def main():
         callbacks=[
             vis,
             recall,
+            accuracy,
             stopping_criterion,
             es,
             PlotLambdaMatrixToTensorboard(),

prototorch/models/__init__.py

@@ -22,4 +22,4 @@ __all__ = [
     "GLVQLossMixin",
 ]
 
-__version__ = "1.0.0-a6"
+__version__ = "1.0.0-a8"

prototorch/models/architectures/base.py

@@ -46,15 +46,15 @@ class BaseYArchitecture(pl.LightningModule):
     components_layer: torch.nn.Module
 
     def __init__(self, hparams) -> None:
-        if type(hparams) is dict:
+        if isinstance(hparams, dict):
             self.save_hyperparameters(hparams)
             # TODO: => Move into Component Child
             del hparams["initialized_proto_shape"]
             hparams = self.HyperParameters(**hparams)
         else:
-            hparam_dict = asdict(hparams)
+            hparams_dict = asdict(hparams)
-            hparam_dict["component_initializer"] = None
+            hparams_dict["component_initializer"] = None
-            self.save_hyperparameters(hparam_dict, )
+            self.save_hyperparameters(hparams_dict, )
 
         super().__init__()
 
@@ -72,6 +72,9 @@ class BaseYArchitecture(pl.LightningModule):
 
     # external API
     def get_competition(self, batch, components):
+        '''
+        Returns the output of the competition layer.
+        '''
         latent_batch, latent_components = self.backbone(batch, components)
         # TODO: => Latent Hook
         comparison_tensor = self.comparison(latent_batch, latent_components)
@@ -79,6 +82,9 @@ class BaseYArchitecture(pl.LightningModule):
         return comparison_tensor
 
     def forward(self, batch):
+        '''
+        Returns the prediction.
+        '''
         if isinstance(batch, torch.Tensor):
             batch = (batch, None)
         # TODO: manage different datatypes?
@@ -95,6 +101,9 @@ class BaseYArchitecture(pl.LightningModule):
         return self.forward(batch)
 
     def forward_comparison(self, batch):
+        '''
+        Returns the Output of the comparison layer.
+        '''
         if isinstance(batch, torch.Tensor):
             batch = (batch, None)
         # TODO: manage different datatypes?
@@ -103,6 +112,9 @@ class BaseYArchitecture(pl.LightningModule):
         return self.get_competition(batch, components)
 
     def loss_forward(self, batch):
+        '''
+        Returns the output of the loss layer.
+        '''
         # TODO: manage different datatypes?
         components = self.components_layer()
         # TODO: => Component Hook
@@ -115,37 +127,31 @@ class BaseYArchitecture(pl.LightningModule):
         """
         All initialization necessary for the components step.
         """
-        ...
 
     def init_backbone(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the backbone step.
         """
-        ...
 
     def init_comparison(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the comparison step.
         """
-        ...
 
     def init_competition(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the competition step.
         """
-        ...
 
     def init_loss(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the loss step.
         """
-        ...
 
     def init_inference(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the inference step.
         """
-        ...
 
     # Empty Steps
     def components(self):
@@ -162,7 +168,8 @@ class BaseYArchitecture(pl.LightningModule):
         The backbone step receives the data batch and the components.
         It can transform both by an arbitrary function.
 
-        It returns the transformed batch and components, each of the same length as the original input.
+        It returns the transformed batch and components,
+        each of the same length as the original input.
         """
         return batch, components
 
@@ -211,6 +218,9 @@ class BaseYArchitecture(pl.LightningModule):
         step: str = Steps.TRAINING,
         **metric_kwargs,
     ):
+        '''
+        Register a callback for evaluating a torchmetric.
+        '''
         if step == Steps.PREDICT:
             raise ValueError("Prediction metrics are not supported.")
 
@@ -224,10 +234,10 @@ class BaseYArchitecture(pl.LightningModule):
         # Prediction Metrics
         preds = self(batch)
 
-        x, y = batch
+        _, y = batch
         for metric in self.registered_metrics[step]:
             instance = self.registered_metrics[step][metric].to(self.device)
-            instance(y, preds)
+            instance(y, preds.reshape(y.shape))
 
     def update_metrics_epoch(self, step):
         for metric in self.registered_metrics[step]:
@@ -247,7 +257,7 @@ class BaseYArchitecture(pl.LightningModule):
 
         return self.loss_forward(batch)
 
-    def training_epoch_end(self, outs) -> None:
+    def training_epoch_end(self, outputs) -> None:
         self.update_metrics_epoch(Steps.TRAINING)
 
     # >>>> Validation
@@ -256,7 +266,7 @@ class BaseYArchitecture(pl.LightningModule):
 
         return self.loss_forward(batch)
 
-    def validation_epoch_end(self, outs) -> None:
+    def validation_epoch_end(self, outputs) -> None:
         self.update_metrics_epoch(Steps.VALIDATION)
 
     # >>>> Test
@@ -264,7 +274,7 @@ class BaseYArchitecture(pl.LightningModule):
         self.update_metrics_step(batch, Steps.TEST)
         return self.loss_forward(batch)
 
-    def test_epoch_end(self, outs) -> None:
+    def test_epoch_end(self, outputs) -> None:
         self.update_metrics_epoch(Steps.TEST)
 
     # >>>> Prediction

prototorch/models/architectures/comparison.py

@@ -32,9 +32,9 @@ class SimpleComparisonMixin(BaseYArchitecture):
         comparison_args: Keyword arguments for the comparison function. Default: {}.
         """
         comparison_fn: Callable = euclidean_distance
-        comparison_args: dict = field(default_factory=lambda: dict())
+        comparison_args: dict = field(default_factory=dict)
 
-        comparison_parameters: dict = field(default_factory=lambda: dict())
+        comparison_parameters: dict = field(default_factory=dict)
 
     # Steps
     # ----------------------------------------------------------------------------------------------
@@ -44,7 +44,7 @@ class SimpleComparisonMixin(BaseYArchitecture):
             **hparams.comparison_args,
         )
 
-        self.comparison_kwargs: dict[str, Tensor] = dict()
+        self.comparison_kwargs: dict[str, Tensor] = {}
 
     def comparison(self, batch, components):
         comp_tensor, _ = components
@@ -86,7 +86,7 @@ class OmegaComparisonMixin(SimpleComparisonMixin):
         latent_dim: int = 2
         omega_initializer: type[
             AbstractLinearTransformInitializer] = EyeLinearTransformInitializer
-        omega_initializer_kwargs: dict = field(default_factory=lambda: dict())
+        omega_initializer_kwargs: dict = field(default_factory=dict)
 
     # Steps
     # ----------------------------------------------------------------------------------------------
@@ -137,3 +137,12 @@ class OmegaComparisonMixin(SimpleComparisonMixin):
         '''
         lam = self.lambda_matrix
         return lam.abs().sum(0)
+
+    @property
+    def parameter_omega(self):
+        return self._omega
+
+    @parameter_omega.setter
+    def parameter_omega(self, new_omega):
+        with torch.no_grad():
+            self._omega.data.copy_(new_omega)

prototorch/models/architectures/optimization.py

@@ -46,7 +46,7 @@ class MultipleLearningRateMixin(BaseYArchitecture):
         lr: The learning rate. Default: 0.1.
         optimizer: The optimizer to use. Default: torch.optim.Adam.
         """
-        lr: dict = field(default_factory=lambda: dict())
+        lr: dict = field(default_factory=dict)
         optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
 
     # Hooks

prototorch/models/callbacks.py

@@ -1,13 +1,15 @@
 import logging
 import warnings
+from enum import Enum
 from typing import Optional, Type
 
+import matplotlib.pyplot as plt
 import numpy as np
 import pytorch_lightning as pl
 import torch
 import torchmetrics
-from matplotlib import pyplot as plt
 from prototorch.models.architectures.base import BaseYArchitecture, Steps
+from prototorch.models.architectures.comparison import OmegaComparisonMixin
 from prototorch.models.library.gmlvq import GMLVQ
 from prototorch.models.vis import Vis2DAbstract
 from prototorch.utils.utils import mesh2d
@@ -36,12 +38,14 @@ class LogTorchmetricCallback(pl.Callback):
         name,
         metric: Type[torchmetrics.Metric],
         step: str = Steps.TRAINING,
+        on_epoch=True,
         **metric_kwargs,
     ) -> None:
         self.name = name
         self.metric = metric
         self.metric_kwargs = metric_kwargs
         self.step = step
+        self.on_epoch = on_epoch
 
     def setup(
         self,
@@ -57,7 +61,12 @@ class LogTorchmetricCallback(pl.Callback):
         )
 
     def __call__(self, value, pl_module: BaseYArchitecture):
-        pl_module.log(self.name, value)
+        pl_module.log(
+            self.name,
+            value,
+            on_epoch=self.on_epoch,
+            on_step=(not self.on_epoch),
+        )
 
 
 class LogConfusionMatrix(LogTorchmetricCallback):
@@ -207,7 +216,7 @@ class PlotLambdaMatrixToTensorboard(pl.Callback):
         # add to tensorboard
         if isinstance(trainer.logger, TensorBoardLogger):
             trainer.logger.experiment.add_figure(
-                f"lambda_matrix",
+                "lambda_matrix",
                 self.fig,
                 trainer.global_step,
             )
@@ -215,3 +224,84 @@ class PlotLambdaMatrixToTensorboard(pl.Callback):
             warnings.warn(
                 f"{self.__class__.__name__} is not compatible with {trainer.logger.__class__.__name__} as logger. Use TensorBoardLogger instead."
             )
+
+
+class Profiles(Enum):
+    '''
+    Available Profiles
+    '''
+    RELEVANCE = 'relevance'
+    INFLUENCE = 'influence'
+
+    def __str__(self):
+        return str(self.value)
+
+
+class PlotMatrixProfiles(pl.Callback):
+
+    def __init__(self, profile=Profiles.INFLUENCE, cmap='seismic') -> None:
+        super().__init__()
+        self.cmap = cmap
+        self.profile = profile
+
+    def on_train_start(self, trainer, pl_module: GMLVQ):
+        '''
+        Plot initial profile.
+        '''
+        self._plot_profile(trainer, pl_module)
+
+    def on_train_epoch_end(self, trainer, pl_module: GMLVQ):
+        '''
+        Plot after every epoch.
+        '''
+        self._plot_profile(trainer, pl_module)
+
+    def _plot_profile(self, trainer, pl_module: GMLVQ):
+
+        fig, ax = plt.subplots(1, 1)
+
+        # plot lambda matrix
+        l_matrix = torch.abs(pl_module.lambda_matrix)
+
+        if self.profile == Profiles.RELEVANCE:
+            profile_value = l_matrix.diag()
+        elif self.profile == Profiles.INFLUENCE:
+            profile_value = l_matrix.sum(0)
+
+        # plot lambda matrix
+        ax.plot(profile_value.detach().numpy())
+
+        # add title
+        ax.set_title(f'{self.profile} profile')
+
+        # add to tensorboard
+        if isinstance(trainer.logger, TensorBoardLogger):
+            trainer.logger.experiment.add_figure(
+                f"{self.profile}_matrix",
+                fig,
+                trainer.global_step,
+            )
+        else:
+            class_name = self.__class__.__name__
+            logger_name = trainer.logger.__class__.__name__
+            warnings.warn(
+                f"{class_name} is not compatible with {logger_name} as logger. Use TensorBoardLogger instead."
+            )
+
+
+class OmegaTraceNormalization(pl.Callback):
+    '''
+    Trace normalization of the Omega Matrix.
+    '''
+    __epsilon = torch.finfo(torch.float32).eps
+
+    def on_train_epoch_end(self, trainer: "pl.Trainer",
+                           pl_module: OmegaComparisonMixin) -> None:
+
+        omega = pl_module.parameter_omega
+        denominator = torch.sqrt(torch.trace(omega.T @ omega))
+        logging.debug(
+            "Apply Omega Trace Normalization: demoninator=%f",
+            denominator.item(),
+        )
+        pl_module.parameter_omega = omega / (denominator + self.__epsilon)

prototorch/models/library/gmlvq.py

@@ -41,7 +41,7 @@ class GMLVQ(
         comparison_args: Keyword arguments for the comparison function. Override Default: {}.
         """
         comparison_fn: Callable = omega_distance
-        comparison_args: dict = field(default_factory=lambda: dict())
+        comparison_args: dict = field(default_factory=dict)
         optimizer: type[torch.optim.Optimizer] = torch.optim.Adam
 
         lr: dict = field(default_factory=lambda: dict(

setup.py

@@ -10,6 +10,8 @@
 
 ProtoTorch models Plugin Package
 """
+from pathlib import Path
+
 from pkg_resources import safe_name
 from setuptools import find_namespace_packages, setup
 
@@ -18,8 +20,7 @@ PLUGIN_NAME = "models"
 PROJECT_URL = "https://github.com/si-cim/prototorch_models"
 DOWNLOAD_URL = "https://github.com/si-cim/prototorch_models.git"
 
-with open("README.md", "r") as fh:
+long_description = Path("README.md").read_text(encoding='utf8')
-    long_description = fh.read()
 
 INSTALL_REQUIRES = [
     "prototorch>=0.7.3",
@@ -55,7 +56,7 @@ ALL = CLI + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name=safe_name("prototorch_" + PLUGIN_NAME),
-    version="1.0.0-a6",
+    version="1.0.0-a8",
     description="Pre-packaged prototype-based "
     "machine learning models using ProtoTorch and PyTorch-Lightning.",
     long_description=long_description,

tests/test_models.py

@@ -0,0 +1,13 @@
+"""prototorch.models test suite."""
+
+import prototorch as pt
+from prototorch.models.library import GLVQ
+
+
+def test_glvq_model_build():
+    hparams = GLVQ.HyperParameters(
+        distribution=dict(num_classes=2, per_class=1),
+        component_initializer=pt.initializers.RNCI(2),
+    )
+
+    model = GLVQ(hparams=hparams)