Add flake8 linter and isort workflow to aucmedi

.github/.isort.cfg

@@ -0,0 +1,8 @@
+[settings]
+src_paths=aucmedi
+line_length=120
+known_first_party=aucmedi
+import_heading_stdlib=Python Standard Library
+import_heading_thirdparty=Third Party Libraries
+import_heading_firstparty=Internal Libraries
+skip=__init__.py

.github/flake8.cfg

@@ -0,0 +1,17 @@
+[flake8]
+extend-ignore =
+    E124, 
+    E127,
+    E128,
+    # all ignore visual indent errors
+    E701,
+    # ignore multi-statement per line (because of 'name: type' error)
+    E731,
+    # allow lambdas
+    E265,
+    # ignore block comment should start with '# '
+    E231,
+    # ignore missing whitespace after ','
+    W503
+    # ignore line break before binary operator
+max-line-length = 120

.github/workflows/code-quality.yml

@@ -1,5 +1,6 @@
 # This workflow will install Python dependencies and run tests for computing code coverage
-# Results will be uploaded to codecov
+# The results will be uploaded to codecov
+# The workflow further executes commitlint, isort, and flake8 linters to ensure a common code style
 
 name: Code Quality
 
@@ -14,7 +15,7 @@ jobs:
     name: Coverage (codecov)
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
     - name: Set up Python 3.10
       uses: actions/setup-python@v2
       with:
@@ -44,9 +45,46 @@ jobs:
     name: Commit Convention
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
       - uses: wagoid/commitlint-github-action@v4
         with:
           configFile: .github/commitlint.config.js
+
+  isort-lint:
+    runs-on: ubuntu-latest
+    name: Import Order
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.10'
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install isort
+      - name: isort Lint
+        run: |
+          python -m isort --settings-path .github --check-only aucmedi
+
+  flake8-lint:
+    runs-on: ubuntu-latest
+    name: Lint
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.10'
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install flake8
+      - name: Install package
+        run: |
+          python -m pip install .
+      - name: Run flake8 linter
+        run: |
+          python -m flake8 --config .github/flake8.cfg aucmedi

aucmedi/__init__.py

@@ -68,7 +68,7 @@
     # Run model inference for unknown samples
     preds = model.predict(test_gen)
     ```
-"""
+""" # noqa E501
 #-----------------------------------------------------#
 #                   Library imports                   #
 #-----------------------------------------------------#
@@ -78,3 +78,13 @@
                                                  VolumeAugmentation, \
                                                  BatchgeneratorsAugmentation
 from aucmedi.neural_network.model import NeuralNetwork
+
+
+__all__ = [
+    "input_interface",
+    "DataGenerator",
+    "ImageAugmentation",
+    "VolumeAugmentation",
+    "BatchgeneratorsAugmentation",
+    "NeuralNetwork"
+]

aucmedi/automl/__init__.py

@@ -41,7 +41,7 @@
     | `evaluation`  | [CLI - Evaluation][aucmedi.automl.cli.cli_evaluation] | [Block - Evaluate][aucmedi.automl.block_eval] |
 
 More information can be found in the docs: [Documentation - AutoML](../../automl/overview/)
-"""
+""" # noqa E501
 #-----------------------------------------------------#
 #                   Library imports                   #
 #-----------------------------------------------------#
@@ -52,3 +52,12 @@
 # Parser
 from aucmedi.automl.parser_yaml import parse_yaml
 from aucmedi.automl.parser_cli import parse_cli
+
+
+__all__ = [
+    "block_train",
+    "block_predict",
+    "block_evaluate",
+    "parse_yaml",
+    "parse_cli"
+]

aucmedi/automl/block_eval.py

@@ -19,15 +19,19 @@
 #-----------------------------------------------------#
 #                   Library imports                   #
 #-----------------------------------------------------#
-# External libraries
+# Python Standard Library
 import os
-import pandas as pd
-import numpy as np
 import re
-# Internal libraries
-from aucmedi import *
+
+# Third Party Libraries
+import numpy as np
+import pandas as pd
+
+# Internal Libraries
+from aucmedi import input_interface
 from aucmedi.evaluation import evaluate_performance
 
+
 #-----------------------------------------------------#
 #            Building Blocks for Evaluation           #
 #-----------------------------------------------------#
@@ -44,14 +48,20 @@ def block_evaluate(config):
 
     Attributes:
         path_imagedir (str):                Path to the directory containing the ground truth images.
-        path_gt (str):                      Path to the index/class annotation file if required. (only for 'csv' interface).
+        path_gt (str):                      Path to the index/class annotation file if required
+                                            (only for 'csv' interface).
         path_pred (str):                    Path to the input file in which predicted csv file is stored.
-        path_evaldir (str):                 Path to the directory in which evaluation figures and tables should be stored.
-        ohe (bool):                         Boolean option whether annotation data is sparse categorical or one-hot encoded.
+        path_evaldir (str):                 Path to the directory in which evaluation figures and tables should be
+                                            stored.
+        ohe (bool):                         Boolean option whether annotation data is sparse categorical or one-hot
+                                            encoded.
     """
     # Obtain interface
-    if config["path_gt"] is None : config["interface"] = "directory"
-    else : config["interface"] = "csv"
+    if config["path_gt"] is None:
+        config["interface"] = "directory"
+    else:
+        config["interface"] = "csv"
+
     # Peak into the dataset via the input interface
     ds = input_interface(config["interface"],
                          config["path_imagedir"],
@@ -73,7 +83,6 @@ def block_evaluate(config):
     df_gt_data = pd.DataFrame(data=class_ohe, columns=class_names)
     df_gt = pd.concat([df_index, df_gt_data], axis=1, sort=False)
 
-
     # Verify - maybe there is a file path encoded in the index?
     if os.path.sep in df_gt.iloc[0,0]:
         samples_split = df_gt["SAMPLE"].str.split(pat=os.path.sep,
@@ -94,8 +103,10 @@ def block_evaluate(config):
     data_gt = df_merged.iloc[:, (class_n+1):].to_numpy()
 
     # Identify task (multi-class vs multi-label)
-    if np.sum(data_pd) > (class_ohe.shape[0] + 1.5) : multi_label = True
-    else : multi_label = False
+    if np.sum(data_pd) > (class_ohe.shape[0] + 1.5):
+        multi_label = True
+    else:
+        multi_label = False
 
     # Evaluate performance via AUCMEDI evaluation submodule
     evaluate_performance(data_pd, data_gt,

aucmedi/automl/block_pred.py

@@ -19,17 +19,21 @@
 #-----------------------------------------------------#
 #                   Library imports                   #
 #-----------------------------------------------------#
-# External libraries
-import os
+# Python Standard Library
 import json
+import os
+
+# Third Party Libraries
 import pandas as pd
-# Internal libraries
-from aucmedi import *
+
+# Internal Libraries
+from aucmedi import DataGenerator, NeuralNetwork, input_interface
 from aucmedi.data_processing.io_loader import image_loader, sitk_loader
-from aucmedi.data_processing.subfunctions import *
-from aucmedi.ensemble import *
+from aucmedi.data_processing.subfunctions import Chromer, Crop, Padding, Standardize
+from aucmedi.ensemble import Composite, predict_augmenting
 from aucmedi.xai import xai_decoder
 
+
 #-----------------------------------------------------#
 #            Building Blocks for Inference            #
 #-----------------------------------------------------#
@@ -46,10 +50,12 @@ def block_predict(config):
 
     Attributes:
         path_imagedir (str):                Path to the directory containing the images for prediction.
-        path_modeldir (str):                Path to the model directory in which fitted model weights and metadata are stored.
+        path_modeldir (str):                Path to the model directory in which fitted model weights and metadata are
+                                            stored.
         path_pred (str):                    Path to the output file in which predicted csv file should be stored.
         xai_method (str or None):           Key for XAI method.
-        xai_directory (str or None):        Path to the output directory in which predicted image xai heatmaps should be stored.
+        xai_directory (str or None):        Path to the output directory in which predicted image xai heatmaps should be
+                                            stored.
         batch_size (int):                   Number of samples inside a single batch.
         workers (int):                      Number of workers/threads which preprocess batches during runtime.
     """
@@ -102,23 +108,26 @@ def block_predict(config):
         "shuffle": False,
         "grayscale": False,
     }
-    if not meta_training["three_dim"] : paras_datagen["loader"] = image_loader
-    else : paras_datagen["loader"] = sitk_loader
+    if not meta_training["three_dim"]:
+        paras_datagen["loader"] = image_loader
+    else:
+        paras_datagen["loader"] = sitk_loader
 
     # Apply MIC pipelines
     if meta_training["analysis"] == "minimal":
         # Setup neural network
         if not meta_training["three_dim"]:
             arch_dim = "2D." + meta_training["architecture"]
-        else : arch_dim = "3D." + meta_training["architecture"]
+        else:
+            arch_dim = "3D." + meta_training["architecture"]
         model = NeuralNetwork(architecture=arch_dim, **nn_paras)
 
         # Build DataGenerator
         pred_gen = DataGenerator(samples=index_list,
-                                 labels=None,
-                                 resize=model.meta_input,
-                                 standardize_mode=model.meta_standardize,
-                                 **paras_datagen)
+                                labels=None,
+                                resize=model.meta_input,
+                                standardize_mode=model.meta_standardize,
+                                **paras_datagen)
         # Load model
         path_model = os.path.join(config["path_modeldir"], "model.last.keras")
         model.load(path_model)
@@ -128,15 +137,16 @@ def block_predict(config):
         # Setup neural network
         if not meta_training["three_dim"]:
             arch_dim = "2D." + meta_training["architecture"]
-        else : arch_dim = "3D." + meta_training["architecture"]
+        else:
+            arch_dim = "3D." + meta_training["architecture"]
         model = NeuralNetwork(architecture=arch_dim, **nn_paras)
 
         # Build DataGenerator
         pred_gen = DataGenerator(samples=index_list,
-                                 labels=None,
-                                 resize=model.meta_input,
-                                 standardize_mode=model.meta_standardize,
-                                 **paras_datagen)
+                                labels=None,
+                                resize=model.meta_input,
+                                standardize_mode=model.meta_standardize,
+                                **paras_datagen)
         # Load model
         path_model = os.path.join(config["path_modeldir"],
                                   "model.best_loss.keras")
@@ -147,19 +157,21 @@ def block_predict(config):
         # Build multi-model list
         model_list = []
         for arch in meta_training["architecture"]:
-            if not meta_training["three_dim"] : arch_dim = "2D." + arch
-            else : arch_dim = "3D." + arch
+            if not meta_training["three_dim"]:
+                arch_dim = "2D." + arch
+            else:
+                arch_dim = "3D." + arch
             model_part = NeuralNetwork(architecture=arch_dim, **nn_paras)
             model_list.append(model_part)
         el = Composite(model_list, metalearner=meta_training["metalearner"],
-                       k_fold=len(meta_training["architecture"]))
+                    k_fold=len(meta_training["architecture"]))
 
         # Build DataGenerator
         pred_gen = DataGenerator(samples=index_list,
-                                 labels=None,
-                                 resize=None,
-                                 standardize_mode=None,
-                                 **paras_datagen)
+                                labels=None,
+                                resize=None,
+                                standardize_mode=None,
+                                **paras_datagen)
         # Load composite model directory
         el.load(config["path_modeldir"])
         # Start model inference via ensemble learning