Added README.md documentation to PyPI

mlforall.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: mlforall
+Version: 0.2
+Summary: Library that easily allows to create machine learning progress for more unexperiencied programmers.
+Home-page: /~https://github.com/UnaiTorrecilla/MLForAll
+Download-URL: /~https://github.com/UnaiTorrecilla/MLForAll/archive/refs/tags/v_02.tar.gz
+Author: Unai Torrecilla
+Author-email: unai.torrecilla@alumni.mondragon.edu
+License: MIT
+Keywords: Machine learning,Easy to use
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Topic :: Software Development :: Build Tools
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3.9
+Description-Content-Type: text/markdown
+License-File: LICENSE
+
+# MLForAll: Easy ML projects from scratch
+[![PyPI Latest Release](https://img.shields.io/pypi/v/mlforall.svg)](https://pypi.org/project/mlforall/)
+[![Package Status](https://img.shields.io/pypi/status/mlforall.svg)](https://pypi.org/project/mlforall/)
+[![License](https://img.shields.io/pypi/l/mlforall.svg)](/~https://github.com/mlforall-dev/mlforall/blob/main/LICENSE)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](/~https://github.com/psf/black)
+
+
+## Description
+**mlforall** is an open-source library aimed to developers that are beginners in the data analysis area but want to build powerful machine learning projects from the very beginning. The package offers a reliable, easy to use and well documented set of functions that drive the user through the most common steps of any machine learning projects, from data reading to model testing.
+
+## Main features
+These are some of the functionalities that mlforall offers:
+1. File extension asbtraction when reading data (only supported for `.csv`, `.txt`, `.xlsx`, `.xlsx`, `.parquet` and `.npy`)
+2. Automatic handling of non-numeric features and missing values.
+3. A pool with almost all the data-scaling methods available and the most common ML models.
+4. Automatic model evaluation and reporting.
+
+## Usage options
+The **mlforall** package can be used by command line interface or with Interactive Python Notebooks (`.ipynb`). An example of the first usage method is the following:
+
+```sh
+# MLForAll usage through command line
+python -m mlforall --kwargs
+```
+With this option, the library will execute the full ML pipeline, from data reading to model testing. The available keyword arguments are:
+- --data_path: **Mandatory**. *str*.
+  - The absolute or relative route to the data for which the ML pipelin wants to be built.
+- --target_var: **Mandatory**. *str* or *int*.
+  - The name or the position of the target column in the original data.
+- --test_size: **Optional**. *float*
+  - Proportion of the data that wants to be kept as test. The defualt is 0.2.
+- --cv: **Optional**. *int*
+  - The number of k-folds that will be performed when evaluating the model. The deault is 5.
+- --path_to_save_metrics: **Optional**. *str*
+  - The absolute or relative path to save the final metrics of the model. It must be a file with the `.csv` extension. If not provided, the metrics will be printed in the console but not saved.
+
+
+As mentioned, usage from `.ipynb` files is also possible. As of version 0.1, the way to use this module from `.ipynb` files is by importing the submodules and using their methods separatedly. To import the different modules the following code snippet can be used:
+
+```sh
+# MLForAll usage through .ipynb files
+from mlforall.DataReading import DataReader
+from mlforall.DataScaling import DataScaler
+from mlforall.DataModeling import DataModeler
+
+
+data_reader = DataReader.ReadData(*args, **kwargs)
+data_scaler = DataScaler.ScaleData(*args, **kwargs)
+data_modeler = DataModeler.ModelData(*args, **kwargs)
+```
+
+
+## Dependencies
+- [Numpy. It offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more](https://numpy.org)
+- [Pandas. a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,
+built on top of the Python programming language](https://pandas.pydata.org)
+- [Scikit-learn. accesible, reusable, simple and efficient tools for predictive data analysis](https://scikit-learn.org/stable/)
+
+## Where to get it
+The source code is currently hosted on GitHub at:
+[/~https://github.com/UnaiTorrecilla/MLForAll](/~https://github.com/UnaiTorrecilla/MLForAll)
+
+Binary installers for the latest released version are available at the [Python
+Package Index (PyPI)](https://pypi.org/project/mlforall)
+
+
+```sh
+# PyPI
+pip install mlforall
+```

mlforall.egg-info/SOURCES.txt

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+LICENSE
+README.md
+setup.cfg
+setup.py
+mlforall/__init__.py
+mlforall/__main__.py
+mlforall.egg-info/PKG-INFO
+mlforall.egg-info/SOURCES.txt
+mlforall.egg-info/dependency_links.txt
+mlforall.egg-info/requires.txt
+mlforall.egg-info/top_level.txt

mlforall.egg-info/dependency_links.txt

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+

mlforall.egg-info/requires.txt

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+pandas
+numpy
+scikit-learn

mlforall.egg-info/top_level.txt

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+mlforall

setup.py

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 from distutils.core import setup
+from pathlib import Path
+
+this_directory = Path(__file__).parent
+long_description = (this_directory / "README.md").read_text()
+
+
+
 setup(
   name = 'mlforall',         # How you named your package folder (MyLib)
   packages = ['mlforall'],   # Chose the same as "name"
-  version = '0.1',      # Start with a small number and increase it with every change you make
+  version = '0.3',      # Start with a small number and increase it with every change you make
   license='MIT',        # Chose a license from here: https://help.github.com/articles/licensing-a-repository
   description = 'Library that easily allows to create machine learning progress for more unexperiencied programmers.',   # Give a short description about your library
   author = 'Unai Torrecilla',                   # Type in your name
   author_email = 'unai.torrecilla@alumni.mondragon.edu',      # Type in your E-Mail
   url = '/~https://github.com/UnaiTorrecilla/MLForAll',   # Provide either the link to your github or to your website
-  download_url = '/~https://github.com/UnaiTorrecilla/MLForAll/archive/refs/tags/v_01.tar.gz',    # I explain this later on
+  download_url = '/~https://github.com/UnaiTorrecilla/MLForAll/archive/refs/tags/v_03.tar.gz',    # I explain this later on
+  long_description = long_description,
+  long_description_content_type = 'text/markdown',
   keywords = ['Machine learning', 'Easy to use'],   # Keywords that define your package best
-  install_requires=[            # I get to this in a second
+  install_requires = [            # I get to this in a second
           'pandas',
           'numpy',
           'scikit-learn'
       ],
   classifiers=[
-    'Development Status :: 4 - Beta',      # Chose either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package
+    'Development Status :: 5 - Production/Stable',      # Chose either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package
     'Intended Audience :: Developers',      # Define that your audience are developers
     'Topic :: Software Development :: Build Tools',
     'License :: OSI Approved :: MIT License',   # Again, pick a license