test(githooks): Add githooks for automatic commit checks

.bumpversion.cfg

@@ -3,7 +3,7 @@ current_version = 0.1.7
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
-serialize = 
+serialize =
 	{major}.{minor}.{patch}
 
 [bumpversion:file:setup.py]

.gitignore

@@ -138,4 +138,4 @@ lightning_logs/
 
 #  Pytorch Models or Weights
 #  If necessary make exceptions for single pretrained models
-*.pt
+*.pt

.pre-commit-config.yaml

@@ -0,0 +1,54 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+    -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+    -   id: check-yaml
+    -   id: check-added-large-files
+    -   id: check-ast
+    -   id: check-case-conflict
+
+
+- repo: https://github.com/myint/autoflake
+  rev: v1.4
+  hooks:
+  -   id: autoflake
+
+- repo: http://github.com/PyCQA/isort
+  rev: 5.8.0
+  hooks:
+  -   id: isort
+
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: 'v0.902'
+    hooks:
+    -   id: mypy
+        files: prototorch
+        additional_dependencies: [types-pkg_resources]
+
+-   repo: https://github.com/pre-commit/mirrors-yapf
+    rev: 'v0.31.0'  # Use the sha / tag you want to point at
+    hooks:
+    -   id: yapf
+
+-   repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.9.0  # Use the ref you want to point at
+    hooks:
+    -   id: python-use-type-annotations
+    -   id: python-no-log-warn
+    -   id: python-check-blanket-noqa
+
+
+-   repo: https://github.com/asottile/pyupgrade
+    rev: v2.19.4
+    hooks:
+    -   id: pyupgrade
+
+-   repo: https://github.com/jorisroovers/gitlint
+    rev: "v0.15.1"
+    hooks:
+    -   id: gitlint
+        args: [--contrib=CT1, --ignore=B6, --msg-filename]

README.md

@@ -18,6 +18,19 @@ pip install prototorch_models
 of** [ProtoTorch](https://github.com/si-cim/prototorch). The plugin should then
 be available for use in your Python environment as `prototorch.models`.
 
+## Contribution
+
+This repository contains definition for [git hooks](https://githooks.com).
+[Pre-commit](https://pre-commit.com) gets installed as development dependency with prototorch.
+Please install the hooks by running
+```bash
+pre-commit install
+pre-commit install --hook-type commit-msg
+```
+before creating the first commit.
+
+
+
 ## Available models
 
 ### LVQ Family

docs/source/custom.rst

@@ -4,4 +4,4 @@ Abstract Models
 ========================================
 .. automodule:: prototorch.models.abstract
    :members:
-   :undoc-members:
+   :undoc-members:

docs/source/index.rst

@@ -37,4 +37,4 @@ These models have been published in the past and have been adapted to the Protot
 
 Customizable
 -----------------------------------------
-Prototorch Models also contains the building blocks to build own models with PyTorch-Lightning and Prototorch.
+Prototorch Models also contains the building blocks to build own models with PyTorch-Lightning and Prototorch.

docs/source/library.rst

@@ -71,7 +71,7 @@ Probabilistic Models
 Probabilistic variants assume, that the prototypes generate a probability distribution over the classes.
 For a test sample they return a distribution instead of a class assignment.
 
-The following two algorihms were presented by :cite:t:`seo2003` . 
+The following two algorihms were presented by :cite:t:`seo2003` .
 Every prototypes is a center of a gaussian distribution of its class, generating a mixture model.
 
 .. autoclass:: prototorch.models.probabilistic.SLVQ
@@ -114,4 +114,4 @@ The visulizations can be shown in a seperate window and inside a tensorboard.
 
 Bibliography
 ========================================
-.. bibliography::
+.. bibliography::

docs/source/refs.bib

@@ -70,4 +70,3 @@
     year="2018",
     publisher="Springer International Publishing",
 }
-

examples/cli/README.md

@@ -5,4 +5,4 @@ Examples in this folder use the experimental [Lightning CLI](https://pytorch-lig
 To use the example run
 ```
 python gmlvq.py --config gmlvq.yaml
-```
+```

examples/cli/gmlvq.py

@@ -1,12 +1,20 @@
 """GMLVQ example using the MNIST dataset."""
 
-from prototorch.models import ImageGMLVQ
-from prototorch.models.data import train_on_mnist
+import torch
 from pytorch_lightning.utilities.cli import LightningCLI
 
-
-class GMLVQMNIST(train_on_mnist(batch_size=64), ImageGMLVQ):
-    """Model Definition."""
+import prototorch as pt
+from prototorch.models import ImageGMLVQ
+from prototorch.models.abstract import PrototypeModel
+from prototorch.models.data import MNISTDataModule
 
 
-cli = LightningCLI(GMLVQMNIST)
+class ExperimentClass(ImageGMLVQ):
+    def __init__(self, hparams, **kwargs):
+        super().__init__(hparams,
+                         optimizer=torch.optim.Adam,
+                         prototype_initializer=pt.components.zeros(28 * 28),
+                         **kwargs)
+
+
+cli = LightningCLI(ImageGMLVQ, MNISTDataModule)

examples/cli/gmlvq.yaml

@@ -1,9 +1,11 @@
 model:
   hparams:
+    input_dim: 784
+    latent_dim: 784
     distribution:
       num_classes: 10
       prototypes_per_class: 2
-    input_dim: 784
-    latent_dim: 784
     proto_lr: 0.01
     bb_lr: 0.01
+data:
+  batch_size: 32

examples/dynamic_pruning.py

@@ -2,10 +2,11 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
+import prototorch as pt
+
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

examples/glvq_iris.py

@@ -2,11 +2,12 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from torch.optim.lr_scheduler import ExponentialLR
 
+import prototorch as pt
+
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

examples/glvq_spiral.py

@@ -2,10 +2,11 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
+import prototorch as pt
+
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

examples/gng_iris.py

@@ -2,10 +2,11 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
+import prototorch as pt
+
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

examples/knn_iris.py

@@ -2,11 +2,12 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from sklearn.datasets import load_iris
 
+import prototorch as pt
+
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

examples/ksom_colors.py

@@ -2,11 +2,12 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
 
+import prototorch as pt
+
 
 def hex_to_rgb(hex_values):
     for v in hex_values:

examples/lgmlvq_moons.py

@@ -2,10 +2,11 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
+import prototorch as pt
+
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

examples/liramlvq_tecator.py

@@ -3,10 +3,11 @@
 import argparse
 
 import matplotlib.pyplot as plt
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
+import prototorch as pt
+
 
 def plot_matrix(matrix):
     title = "Lambda matrix"

examples/ng_iris.py

@@ -2,13 +2,14 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
 from torch.optim.lr_scheduler import ExponentialLR
 
+import prototorch as pt
+
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

examples/rslvq_iris.py

@@ -2,11 +2,12 @@
 
 import argparse
 
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from torchvision.transforms import Lambda
 
+import prototorch as pt
+
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

prototorch/models/data.py

@@ -1,10 +1,19 @@
-import prototorch as pt
+"""Prototorch Data Modules
+
+This allows to store the used dataset inside a Lightning Module.
+Mainly used for PytorchLightningCLI configurations.
+"""
+from typing import Any, Optional, Type
+
 import pytorch_lightning as pl
-from torch.utils.data import DataLoader, random_split
+from torch.utils.data import DataLoader, Dataset, random_split
 from torchvision import transforms
 from torchvision.datasets import MNIST
 
+import prototorch as pt
 
+
+# MNIST
 class MNISTDataModule(pl.LightningDataModule):
     def __init__(self, batch_size=32):
         super().__init__()
@@ -49,15 +58,67 @@ class MNISTDataModule(pl.LightningDataModule):
         return mnist_test
 
 
-def train_on_mnist(batch_size=256) -> type:
-    class DataClass(pl.LightningModule):
-        datamodule = MNISTDataModule(batch_size=batch_size)
+# def train_on_mnist(batch_size=256) -> type:
+#     class DataClass(pl.LightningModule):
+#         datamodule = MNISTDataModule(batch_size=batch_size)
 
-        def __init__(self, *args, **kwargs):
-            prototype_initializer = kwargs.pop(
-                "prototype_initializer", pt.components.Zeros((28, 28, 1)))
-            super().__init__(*args,
-                             prototype_initializer=prototype_initializer,
-                             **kwargs)
+#         def __init__(self, *args, **kwargs):
+#             prototype_initializer = kwargs.pop(
+#                 "prototype_initializer", pt.components.Zeros((28, 28, 1)))
+#             super().__init__(*args,
+#                              prototype_initializer=prototype_initializer,
+#                              **kwargs)
 
-    return DataClass
+#     dc: Type[DataClass] = DataClass
+#     return dc
+
+
+# ABSTRACT
+class GeneralDataModule(pl.LightningDataModule):
+    def __init__(self, dataset: Dataset, batch_size: int = 32) -> None:
+        super().__init__()
+        self.train_dataset = dataset
+        self.batch_size = batch_size
+
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(self.train_dataset, batch_size=self.batch_size)
+
+
+# def train_on_dataset(dataset: Dataset, batch_size: int = 256):
+#     class DataClass(pl.LightningModule):
+#         datamodule = GeneralDataModule(dataset, batch_size)
+#         datashape = dataset[0][0].shape
+#         example_input_array = torch.zeros_like(dataset[0][0]).unsqueeze(0)
+
+#         def __init__(self, *args: Any, **kwargs: Any) -> None:
+#             prototype_initializer = kwargs.pop(
+#                 "prototype_initializer",
+#                 pt.components.Zeros(self.datashape),
+#             )
+#             super().__init__(*args,
+#                              prototype_initializer=prototype_initializer,
+#                              **kwargs)
+
+#     return DataClass
+
+# if __name__ == "__main__":
+#     from prototorch.models import GLVQ
+
+#     demo_dataset = pt.datasets.Iris()
+
+#     TrainingClass: Type = train_on_dataset(demo_dataset)
+
+#     class DemoGLVQ(TrainingClass, GLVQ):
+#         """Model Definition."""
+
+#     # Hyperparameters
+#     hparams = dict(
+#         distribution={
+#             "num_classes": 3,
+#             "prototypes_per_class": 4
+#         },
+#         lr=0.01,
+#     )
+
+#     initialized = DemoGLVQ(hparams)
+#     print(initialized)

prototorch/models/glvq.py

@@ -3,11 +3,8 @@
 import torch
 from prototorch.functions.activations import get_activation
 from prototorch.functions.competitions import wtac
-from prototorch.functions.distances import (
-    lomega_distance,
-    omega_distance,
-    squared_euclidean_distance,
-)
+from prototorch.functions.distances import (lomega_distance, omega_distance,
+                                            squared_euclidean_distance)
 from prototorch.functions.helper import get_flat
 from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
 from prototorch.modules import LambdaLayer, LossLayer

setup.py

@@ -1,10 +1,12 @@
 """
-  _____           _     _______             _
- |  __ \         | |   |__   __|           | |
- | |__) | __ ___ | |_ ___ | | ___  _ __ ___| |__
- |  ___/ '__/ _ \| __/ _ \| |/ _ \| '__/ __| '_ \
- | |   | | | (_) | || (_) | | (_) | | | (__| | | |
- |_|   |_|  \___/ \__\___/|_|\___/|_|  \___|_| |_|Plugin
+
+ ######
+ #     # #####   ####  #####  ####  #####  ####  #####   ####  #    #
+ #     # #    # #    #   #   #    #   #   #    # #    # #    # #    #
+ ######  #    # #    #   #   #    #   #   #    # #    # #      ######
+ #       #####  #    #   #   #    #   #   #    # #####  #      #    #
+ #       #   #  #    #   #   #    #   #   #    # #   #  #    # #    #
+ #       #    #  ####    #    ####    #    ####  #    #  ####  #    #Plugin
 
 ProtoTorch models Plugin Package
 """
@@ -29,6 +31,7 @@ CLI = [
 ]
 DEV = [
     "bumpversion",
+    "pre-commit",
 ]
 DOCS = [
     "recommonmark",