Metadata-Version: 1.1
Name: ldr
Version: 0.1
Summary: Latent Dimensionality Reduction in Python
Home-page: https://github.com/ekrekr/pyldr
Author: Elias Kassell
Author-email: eliaskassell@gmail.com
License: UNKNOWN
Description: # PyLDR
        
        Pyldr stands for (Python) Latent Dimensionality Reduction, and is a method for interpreting black box models. It is deployed here as a python module.
        
        Black box models often provide better results than more interpretable methods, and brings some [quite strong opions](https://arxiv.org/abs/1811.10154). This method aims to bridge that gap by providing a generic, reliable algorithmic method for interpreting any model. I define interpretability as:
        
        1. Understanding how a model understands the data, and whether it is similar to how a human would think of it.
        
        2. Interpreting how the value of a feature, or subset of features, affects a model's prediction (feature interpretation).
        
        3. The ability to use a model when not all values for the input features are present.
        
        ## Running the Code
        
        ### Prerequesites
        
        [Python3](https://www.python.org/download/releases/3.0/).
        
        ### Execution
        
        All examples are contained in notebooks, while the LDR module is [ldr.py](ldr.py). The required packages are listed in [requirements.txt](requirements.txt), and their respective distributions and licenses can be found on the [Python Package Index](https://pypi.org/). To run the code use:
        
        1. `python3 -m pip install --requirement requirements.txt`.
        
        2. `jupyter notebook`.
        
        - The generated distribution example can be found [here](distribution_example.ipynb).
        
        - The classification example can be found [here](classification_example.ipynb).
        
        - The regression example can be found [here](regression_example.ipynb).
        
        - The step by step interpolation example can be found [here](interpolation_example.ipynb).
        
        ## Additional Notes
        
        The [style sheet used](style.mplstyle) is from [one of my personal repos](https://github.com/Ekrekr/ekrekr.style).
        
Keywords: sample setuptools development
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
