Adds pylint check

.travis.yml

@@ -1,10 +1,22 @@
 language: python
 python:
   - "3.9"
+before_install:
+  - export PYTHONPATH=$PYTHONPATH:$(pwd)/src
+
 install:
   - pip install pipenv
-  - pipenv install
+  - pipenv install --dev
+
+stages:
+  - style
+  - test
 
-script:
-  # Your test script goes here
-  - pytest -v --color=yes src # Runs in pipenv
+jobs:
+  include:
+    - stage: style
+      name: "Style check"
+      script: pylint **/*.py
+    - stage: test
+      name: "Unit tests"
+      script: pytest -v --color=yes src # Runs in pipenv

Makefile

@@ -1,17 +1,20 @@
+activate:
+	pipenv shell
+
 test: 
 	pytest
 
 lint:
-	pylint src
+	pylint **/*.py
 
 lintfix:
-	autopep8 src --recursive --in-place --aggressive
+	autopep8 **/*.py --recursive --in-place --aggressive
 
 lintfixhard:
-	autopep8 src --recursive --in-place --aggressive --aggressive
+	autopep8 **/*.py --recursive --in-place --aggressive --aggressive
 
 install:
-	pipenv install
+	pipenv install --dev
 
 lock:
 	pipenv lock

README.md

@@ -22,9 +22,9 @@ make install
 ```
 This calls `pipenv`, which will create a virtual environment for this project.
 
-To enter a shell on this environment run
+To activate this environment run
 ```bash
-make shell
+make activate
 ```
 
 ## Tests
@@ -44,11 +44,11 @@ make lint
 
 We use `autopep8` to automatically fix errors.
 
-User
+Use
 ```bash
 make lintfix
 ```
-of
+or
 ```bash
 make lintfixhard
 ```

cli/cnn_fashion_mnist_classifier.py

@@ -1,22 +1,24 @@
-
+"""CLI to use the cnn.FashionMNISTClassifier"""
 import argparse
 from computer_vision.cnn import FashionMNISTClassifier
 
+
 def build_arg_parser():
+    """Parses the user's arguments"""
     parser = argparse.ArgumentParser(
-        description="Use a CNN classifier to classify the "+
+        description="Use a CNN classifier to classify the " +
         "Fashion MNIST training dataset",
         epilog="Built with <3 by Emmanuel Byrd at 8th Light Ltd."
     )
     parser.add_argument(
         "--model_path", metavar="./model.h5", type=str, default="./model.h5",
-        help="The read/write path of the trained model " + 
+        help="The read/write path of the trained model " +
         "(default: ./model.h5)"
     )
     parser.add_argument(
-        "--history_path", metavar="./train_hist", type=str, 
+        "--history_path", metavar="./train_hist", type=str,
         default="./train_hist",
-        help="The read/write path of the training history "+
+        help="The read/write path of the training history " +
         "(default: ./hist)"
     )
     parser.add_argument(
@@ -49,49 +51,54 @@ def build_arg_parser():
         help="Path to store the generated plot. Leave blank to ignore."
     )
     parser.add_argument(
-        "--save_model", action=argparse.BooleanOptionalAction,type=bool,
+        "--save_model", action=argparse.BooleanOptionalAction, type=bool,
         help="Save the model"
     )
     return parser
 
+
 def execute_cnn(args):
-    cnn_trainer = FashionMNISTClassifier(args.model_path, args.history_path)
+    """Execute the FashionMNISTClassifier functions as requested"""
+    cnn_trainer = FashionMNISTClassifier()
 
     if args.use_stored:
-        cnn_trainer.load_model()
-        cnn_trainer.load_train_history()
+        cnn_trainer.load_model(args.model_path)
+        cnn_trainer.load_train_history(args.history_path)
         cnn_trainer.show_summary()
     elif args.train:
         cnn_trainer.load_dataset()
         cnn_trainer.build_model()
         cnn_trainer.show_summary()
         cnn_trainer.compile()
         cnn_trainer.train(
-            args.mini_batch_size, 
-            args.epochs, 
+            args.mini_batch_size,
+            args.epochs,
             args.validation_split
         )
     else:
-        print (
-            "Do you want to load a model or train a new one? "+
+        print(
+            "Do you want to load a model or train a new one? " +
             "(--use_stored, --train)"
         )
         return
-    
+
     if args.plot or args.save_plot:
         cnn_trainer.plot_hist(args.plot, args.save_plot)
 
     if args.save_model:
-        cnn_trainer.save_model()
-        cnn_trainer.save_train_history()
+        cnn_trainer.save_model(args.model_path)
+        cnn_trainer.save_train_history(args.history_path)
+
 
 def main():
+    """Main function"""
     arg_parser = build_arg_parser()
     args = arg_parser.parse_args()
 
     execute_cnn(args)
 
     print("Finished.")
-
+
+
 if __name__ == "__main__":
-    main()
+    main()

path_adder.py

@@ -1,23 +1,33 @@
+"""Adds this repository's modules to the Python's PATH"""
 import os
 import sys
 
 import pathlib
 
+
 def main():
-    for elem in sys.path:
-        this_file_path = pathlib.Path(__file__).parent.resolve()
-        src_path = os.path.join(this_file_path, "src")
+    """
+    Creates a `.pth` file under the corresponding `site-packages` dir.
 
+    This function works in both a virtual and a native environment.
+    """
+    this_file_path = pathlib.Path(__file__).parent.resolve()
+    src_path = os.path.join(this_file_path, "src")
+
+    for elem in sys.path:
         if elem.endswith("site-packages"):
             site_packages_dir = elem
         elif elem == src_path:
             print(src_path + " path is already added. Aborting...")
             return
-
-    file_name = 'extra_python_folders.pth'
-    with open(os.path.join(site_packages_dir, file_name), 'w') as f:
-        f.write(src_path)
+
+    file_path = os.path.join(site_packages_dir,
+                             'extra_python_folders.pth')
+
+    with open(file_path, 'w', encoding="utf-8") as file:
+        file.write(src_path)
         print("Added to path: " + src_path)
 
+
 if __name__ == "__main__":
-    main()
+    main()

src/computer_vision/cnn/__init__.py

@@ -1 +1,2 @@
+"""Convolutional Neural Network (CNN) implementations"""
 from .fashion_mnist_classifier import FashionMNISTClassifier