[QA] Add more pre commit checks

.pre-commit-config.yaml

@@ -9,12 +9,24 @@ repos:
     -   id: check-yaml
     -   id: check-added-large-files
     -   id: check-ast
-    -   id: check-byte-order-marker
     -   id: check-case-conflict
-#-   repo: https://github.com/pre-commit/mirrors-mypy
+
-#    rev: 'v0.902'
+
-#    hooks:
+- repo: https://github.com/myint/autoflake
-#    -   id: mypy
+  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
+        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

docs/source/conf.py

@@ -68,7 +68,7 @@ master_doc = "index"
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = []
+#exclude_patterns = []
 
 # The name of the Pygments (syntax highlighting) style to use. Choose from:
 # ["default", "emacs", "friendly", "colorful", "autumn", "murphy", "manni",
@@ -124,7 +124,7 @@ htmlhelp_basename = "protoflowdoc"
 
 # -- Options for LaTeX output ---------------------------------------------
 
-latex_elements = {
+#latex_elements = {
 # The paper size ("letterpaper" or "a4paper").
 #
 # "papersize": "letterpaper",
@@ -137,7 +137,7 @@ latex_elements = {
 # Latex figure (float) alignment
 #
 # "figure_align": "htbp",
-}
+#}
 
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title,

examples/glvq_iris.py

@@ -3,13 +3,14 @@
 import numpy as np
 import torch
 from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+from torchinfo import summary
+
 from prototorch.components import LabeledComponents, StratifiedMeanInitializer
 from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import euclidean_distance
 from prototorch.modules.losses import GLVQLoss
-from sklearn.datasets import load_iris
-from sklearn.preprocessing import StandardScaler
-from torchinfo import summary
 
 # Prepare and preprocess the data
 scaler = StandardScaler()

examples/gmlvq_tecator.py

@@ -2,12 +2,13 @@
 
 import matplotlib.pyplot as plt
 import torch
+from torch.utils.data import DataLoader
+
 from prototorch.components import LabeledComponents, StratifiedMeanInitializer
 from prototorch.datasets.tecator import Tecator
 from prototorch.functions.distances import sed
 from prototorch.modules.losses import GLVQLoss
 from prototorch.utils.colors import get_legend_handles
-from torch.utils.data import DataLoader
 
 # Prepare the dataset and dataloader
 train_data = Tecator(root="./artifacts", train=True)

examples/gtlvq_mnist.py

@@ -12,10 +12,11 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torchvision
+from torchvision import transforms
+
 from prototorch.functions.helper import calculate_prototype_accuracy
 from prototorch.modules.losses import GLVQLoss
 from prototorch.modules.models import GTLVQ
-from torchvision import transforms
 
 # Parameters and options
 num_epochs = 50

examples/lgmlvq_iris.py

@@ -3,13 +3,14 @@
 import numpy as np
 import torch
 from matplotlib import pyplot as plt
-from prototorch.components import LabeledComponents, StratifiedMeanInitializer
-from prototorch.functions.competitions import stratified_min
-from prototorch.functions.distances import lomega_distance
-from prototorch.modules.losses import GLVQLoss
 from sklearn.datasets import load_iris
 from sklearn.metrics import accuracy_score
 
+from prototorch.components import LabeledComponents, StratifiedMeanInitializer
+from prototorch.functions.distances import lomega_distance
+from prototorch.functions.pooling import stratified_min_pooling
+from prototorch.modules.losses import GLVQLoss
+
 # Prepare training data
 x_train, y_train = load_iris(True)
 x_train = x_train[:, [0, 2]]
@@ -55,7 +56,8 @@ for epoch in range(100):
     # Compute loss
     dis, plabels = model(x_in)
     loss = criterion([dis, plabels], y_in)
-    y_pred = np.argmin(stratified_min(dis, plabels).detach().numpy(), axis=1)
+    y_pred = np.argmin(stratified_min_pooling(dis, plabels).detach().numpy(),
+                       axis=1)
     acc = accuracy_score(y_train, y_pred)
     log_string = f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} "
     log_string += f"Acc: {acc * 100:05.02f}%"
@@ -96,7 +98,8 @@ for epoch in range(100):
     mesh_input = np.c_[xx.ravel(), yy.ravel()]
 
     d, plabels = model(torch.Tensor(mesh_input))
-    y_pred = np.argmin(stratified_min(d, plabels).detach().numpy(), axis=1)
+    y_pred = np.argmin(stratified_min_pooling(d, plabels).detach().numpy(),
+                       axis=1)
     y_pred = y_pred.reshape(xx.shape)
 
     # Plot voronoi regions

prototorch/__init__.py

@@ -1,6 +1,7 @@
 """ProtoTorch package."""
 
 import pkgutil
+from typing import List
 
 import pkg_resources
 
@@ -19,7 +20,7 @@ __all_core__ = [
 ]
 
 # Plugin Loader
-__path__ = pkgutil.extend_path(__path__, __name__)
+__path__: List[str] = pkgutil.extend_path(__path__, __name__)
 
 
 def discover_plugins():

prototorch/components/components.py

@@ -3,12 +3,13 @@
 import warnings
 
 import torch
+from torch.nn.parameter import Parameter
+
 from prototorch.components.initializers import (ClassAwareInitializer,
                                                 ComponentsInitializer,
                                                 EqualLabelsInitializer,
                                                 UnequalLabelsInitializer,
                                                 ZeroReasoningsInitializer)
-from torch.nn.parameter import Parameter
 
 from .initializers import parse_data_arg
 

prototorch/datasets/sklearn.py

@@ -8,11 +8,11 @@ URL:
 import warnings
 from typing import Sequence, Union
 
-from prototorch.datasets.abstract import NumpyDataset
-
 from sklearn.datasets import (load_iris, make_blobs, make_circles,
                               make_classification, make_moons)
 
+from prototorch.datasets.abstract import NumpyDataset
+
 
 class Iris(NumpyDataset):
     """Iris Dataset by Ronald Fisher introduced in 1936.

prototorch/datasets/tecator.py

@@ -40,9 +40,10 @@ import os
 
 import numpy as np
 import torch
-from prototorch.datasets.abstract import ProtoDataset
 from torchvision.datasets.utils import download_file_from_google_drive
 
+from prototorch.datasets.abstract import ProtoDataset
+
 
 class Tecator(ProtoDataset):
     """

prototorch/functions/distances.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 import torch
+
 from prototorch.functions.helper import (_check_shapes, _int_and_mixed_shape,
                                          equal_int_shape, get_flat)
 

prototorch/modules/competitions.py

@@ -1,6 +1,7 @@
 """ProtoTorch Competition Modules."""
 
 import torch
+
 from prototorch.functions.competitions import knnc, wtac
 
 

prototorch/modules/losses.py

@@ -1,6 +1,7 @@
 """ProtoTorch losses."""
 
 import torch
+
 from prototorch.functions.activations import get_activation
 from prototorch.functions.losses import glvq_loss
 

prototorch/modules/models.py

@@ -1,8 +1,9 @@
 import torch
+from torch import nn
+
 from prototorch.components import LabeledComponents, StratifiedMeanInitializer
 from prototorch.functions.distances import euclidean_distance_matrix
 from prototorch.functions.normalization import orthogonalization
-from torch import nn
 
 
 class GTLVQ(nn.Module):

prototorch/modules/pooling.py

@@ -1,6 +1,7 @@
 """ProtoTorch Pooling Modules."""
 
 import torch
+
 from prototorch.functions.pooling import (stratified_max_pooling,
                                           stratified_min_pooling,
                                           stratified_prod_pooling,

tests/test_components.py

@@ -1,8 +1,9 @@
 """ProtoTorch components test suite."""
 
-import prototorch as pt
 import torch
 
+import prototorch as pt
+
 
 def test_labcomps_zeros_init():
     protos = torch.zeros(3, 2)

tests/test_functions.py

@@ -4,6 +4,7 @@ import unittest
 
 import numpy as np
 import torch
+
 from prototorch.functions import (activations, competitions, distances,
                                   initializers, losses, pooling)