Merge pull request #68 from ayrna/development

New package version 2.0.0

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -39,8 +39,8 @@ body:
 
 
     placeholder: |
-      ```python
-      Your code here. Placing the code snippet here will help us reproduce the bug and identify the issue.
+      ```
+      Place your code here. Placing the code snippet here will help us reproduce the bug and identify the issue.
       ```
 - type: textarea
   attributes:

.github/ISSUE_TEMPLATE/other_issue.yml

@@ -1,6 +1,7 @@
 name: ❗ Other issue
 description: Other issue not covered by the other templates.
 title: "[MNT] "
+labels: ["maintenance"]
 
 body:
 - type: markdown

.github/workflows/run_tests.yml

@@ -11,6 +11,7 @@ on:
   pull_request:
     branches:
       - main
+      - development
     paths:
       - "tutorials/**"
       - "dlordinal/**"
@@ -22,10 +23,10 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: 3.8
 
@@ -46,10 +47,10 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: 3.8
 
@@ -63,6 +64,7 @@ jobs:
     - name: Run tests for codecov
       run: |
         pytest --cov=dlordinal --cov-report=xml
+      timeout-minutes: 20
 
     - name: Upload coverage reports to Codecov
       uses: codecov/codecov-action@v3

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "dlordinal"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

README.md

@@ -1,23 +1,100 @@
 # Deep learning utilities library
 
-`dlordinal` is an open-source Python toolkit focused on deep learning with ordinal methodologies. It is compatible with
-[scikit-learn](https://scikit-learn.org).
-
-The library includes various modules such as loss functions, models, layers, metrics, and an estimator.
+`dlordinal` is an open-source Python toolkit focused on deep learning with ordinal methodologies.
 
 | Overview  |                                                                                                                                          |
 |-----------|------------------------------------------------------------------------------------------------------------------------------------------|
 | **CI/CD** | [![!codecov](https://img.shields.io/codecov/c/github/ayrna/dlordinal?label=codecov&logo=codecov)](https://codecov.io/gh/ayrna/dlordinal) [![!docs](https://readthedocs.org/projects/dlordinal/badge/?version=latest&style=flat)](https://dlordinal.readthedocs.io/en/latest/)  [![!python](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue)](https://www.python.org/) |
-| **Code**  | [![![pypi]](https://img.shields.io/pypi/v/dlordinal)](https://pypi.org/project/dlordinal/1.0.1/) [![![binder]](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ayrna/dlordinal/main?filepath=tutorials) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Linter: Ruff](https://img.shields.io/badge/Linter-Ruff-brightgreen?style=flat-square)](https://github.com/charliermarsh/ruff)                     |
+| **Code**  | [![![pypi]](https://img.shields.io/pypi/v/dlordinal)](https://pypi.org/project/dlordinal/2.0.0/) [![![binder]](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ayrna/dlordinal/main?filepath=tutorials) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Linter: Ruff](https://img.shields.io/badge/Linter-Ruff-brightgreen?style=flat-square)](https://github.com/charliermarsh/ruff)                     |
+
+
+## Table of Contents
+- [⚙️ Installation](#%EF%B8%8F-installation)
+- [📖 Documentation](#-documentation)
+- [Collaborating](#collaborating)
+    - [Guidelines for code contributions](#guidelines-for-code-contributions)
 
 ## ⚙️ Installation
 
-`dlordinal v1.0.1` is the last version supported by Python 3.8, Python 3.9 and Python 3.10.
+`dlordinal v2.0.0` is the last version supported by Python 3.8, Python 3.9 and Python 3.10.
 
 The easiest way to install `dlordinal` is via `pip`:
 
-    pip install dlordinal
+```bash
+pip install dlordinal
+```
+
+## 📖 Documentation
+
+`Sphinx` is a documentation generator tool that is commonly used in the Python ecosystem. It allows developers to write documentation in a markup language called reStructuredText (reST) and generates HTML, PDF, and other formats from it. Sphinx provides a powerful and flexible way to document code, making it easier for developers to create comprehensive and user-friendly documentation for their projects.
+
+To document `dlordinal`, it is necessary to install all documentation dependencies:
+
+```bash
+pip install -e '.[docs]'
+```
+
+Then access the `docs/` directory:
+
+```bash
+docs/
+↳ api.rst
+↳ conf.py
+↳ distributions.rst
+↳ references.bib
+↳ ...
+```
+
+If a new module is created in the software project, the `api.rst` file must be modified to include the name of the new module:
+
+```plaintext
+.. _api:
+
+=============
+API Reference
+=============
+
+This is the API for the **dlordinal** package.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   losses
+   datasets
+   distributions
+   layers
+   metrics
+   sklearn_integration
+   ***NEW_MODULE***
+```
+
+Afterwards, a new file in `.rst` format associated to the new module must be created, specifying the automatic inclusion of documentation from the module files containing a docstring, and the inclusion of the bibliography if it exists within any of them.
+
+```bash
+docs/
+↳ api.rst
+↳ conf.py
+↳ distributions.rst
+↳ new_module.rst
+↳ references.bib
+↳ ...
+```
+
+```plaintext
+.. _new_module:
+
+New Module
+==========
+
+.. automodule:: dlordinal.new_module
+    :members:
+
+.. footbibliography::
+
+```
 
+Finally, if any new bibliographic citations have been added, they should be included in the `references.bib` file.
 
 ## Collaborating
 

build_tools/run_tutorials.sh

@@ -7,6 +7,7 @@ CMD="jupyter nbconvert --to notebook --inplace --execute --ExecutePreprocessor.t
 
 excluded=(
     "tutorials/datasets_tutorial.ipynb"
+    "tutorials/dlordinal_with_skorch_tutorial.ipynb"
 )
 
 shopt -s lastpipe

dlordinal/.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+    // Use IntelliSense para saber los atributos posibles.
+    // Mantenga el puntero para ver las descripciones de los existentes atributos.
+    // Para más información, visite: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: archivo actual",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": true
+        }
+    ]
+}

dlordinal/.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "datasets"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

dlordinal/__init__.py

@@ -1,2 +1,2 @@
 __all__ = []
-__version__ = "1.0.1"
+__version__ = "2.0.0"

dlordinal/datasets/adience.py

@@ -13,6 +13,31 @@
 class Adience:
     """
     Base class for the Adience dataset.
+
+    Parameters
+    ----------
+    extract_file_path : Union[str, Path]
+        Path to the tar.gz file containing the dataset.
+    folds_path : Union[str, Path]
+        Path to the folder containing the folds.
+    images_path : Union[str, Path]
+        Path to the folder containing the images.
+    transformed_images_path : Union[str, Path]
+        Path to the folder containing the transformed images.
+    partition_path : Union[str, Path]
+        Path to the folder containing the partitions.
+    number_partitions : int, optional
+        Number of partitions to create, by default 20.
+    ranges : list, optional
+        List of age ranges to use, by default [(0, 2), (4, 6), (8, 13),
+        (15, 20), (25, 32), (38, 43), (48, 53), (60, 100)].
+    test_size : float, optional
+        Test size, by default 0.2.
+    extract : bool, optional
+        Boolean indicating if the tar.gz file should be extracted, by default True.
+    transfrom : bool, optional
+        Boolean indicating if the images should be transformed and the partitions
+        created, by default True.
     """
 
     def __init__(
@@ -37,33 +62,6 @@ def __init__(
         extract: bool = True,
         transfrom: bool = True,
     ) -> None:
-        """
-        Parameters
-        ----------
-        extract_file_path : Union[str, Path]
-            Path to the tar.gz file containing the dataset.
-        folds_path : Union[str, Path]
-            Path to the folder containing the folds.
-        images_path : Union[str, Path]
-            Path to the folder containing the images.
-        transformed_images_path : Union[str, Path]
-            Path to the folder containing the transformed images.
-        partition_path : Union[str, Path]
-            Path to the folder containing the partitions.
-        number_partitions : int, optional
-            Number of partitions to create, by default 20.
-        ranges : list, optional
-            List of age ranges to use, by default [(0, 2), (4, 6), (8, 13),
-            (15, 20), (25, 32), (38, 43), (48, 53), (60, 100)].
-        test_size : float, optional
-            Test size, by default 0.2.
-        extract : bool, optional
-            Boolean indicating if the tar.gz file should be extracted, by default True.
-        transfrom : bool, optional
-            Boolean indicating if the images should be transformed and the partitions
-            created, by default True.
-        """
-
         super().__init__()
 
         self.extract_file_path = Path(extract_file_path)

dlordinal/datasets/featuredataset.py

@@ -57,7 +57,12 @@ def get_valid_shape_array(self, v: ArrayLike):
         Parameters
         ----------
         v : ArrayLike
+            Input array.
 
+        Returns
+        -------
+        v : np.ndarray
+            2D numpy array with shape (n, 1).
         """
 
         if isinstance(v, pd.Series):

dlordinal/datasets/fgnet.py

@@ -17,6 +17,25 @@
 class FGNet(VisionDataset):
     """
     Base class for FGNet dataset.
+
+    Parameters
+    ----------
+    root : str or Path
+        Root directory of dataset
+    download : bool, optional
+        If True, downloads the dataset from the internet and puts it in root directory.
+        If dataset is already downloaded, it is not downloaded again.
+    process_data : bool, optional
+        If True, processes the dataset and puts it in root directory.
+        If dataset is already processed, it is not processed again.
+    target_size : tuple, optional
+        Size of the images after resizing.
+    categories : list, optional
+        List of categories to be used.
+    test_size : float, optional
+        Size of the test set.
+    validation_size : float, optional
+        Size of the validation set.
     """
 
     def __init__(
@@ -29,29 +48,6 @@ def __init__(
         test_size: float = 0.2,
         validation_size: float = 0.15,
     ) -> None:
-        """
-        FGNet dataset.
-
-        Parameters
-        ----------
-        root : str or Path
-            Root directory of dataset
-        download : bool, optional
-            If True, downloads the dataset from the internet and puts it in root directory.
-            If dataset is already downloaded, it is not downloaded again.
-        process_data : bool, optional
-            If True, processes the dataset and puts it in root directory.
-            If dataset is already processed, it is not processed again.
-        target_size : tuple, optional
-            Size of the images after resizing.
-        categories : list, optional
-            List of categories to be used.
-        test_size : float, optional
-            Size of the test set.
-        validation_size : float, optional
-            Size of the validation set.
-        """
-
         super(FGNet, self).__init__(root)
 
         self.root = Path(self.root)

dlordinal/datasets/tests/test_adience.py

@@ -8,7 +8,7 @@
 import pytest
 from PIL import Image
 
-from ..adience import Adience
+from dlordinal.datasets import Adience
 
 temp_dir = None
 

dlordinal/datasets/tests/test_featuredataset.py

@@ -2,7 +2,7 @@
 import pytest
 import torch
 
-from ..featuredataset import FeatureDataset
+from dlordinal.datasets import FeatureDataset
 
 
 @pytest.fixture

dlordinal/datasets/tests/test_fgnet.py

@@ -3,7 +3,7 @@
 
 import pytest
 
-from ..fgnet import FGNet
+from dlordinal.datasets import FGNet
 
 TMP_DIR = "./tmp_test_dir_fgnet"