Metadata-Version: 2.1
Name: pytalite
Version: 1.0.0
Summary: Python/Pyspark Package for Model-agnostic Evaluation and Diagnosis.
Home-page: https://github.com/rallyhealth/pytalite/
Author: Jinghao Jia, Lun Yu
Author-email: jiajinghao1998@gmail.com, lun.yu@rallyhealth.com
License: MIT
Keywords: pytalite
Platform: UNKNOWN
Description-Content-Type: text/markdown
License-File: LICENSE

# <img src="icons/logo_thumb.png" alt="logo" height="100" />
# Pytalite: Model-agnostic diagnostics and evaluation tool for Python and Pyspark

## Overview
Evaluating model performance and diagnosing models are important steps in machine learning model development. Pytalite provides a straightforward wrapper for visualizing model performance and diagnosing machine learning models. It enables users to visualize key snapshots of model performance, e.g. discrete precision-recall curves, probability density plots, model-agnostic feature importance, partial dependence plots, accumulative local effect plots, and feature correlation plots.

## Requirements
Pytalite for python is developed under python 3.7, but is compatible with python 2.7. Pytalite for pyspark is developed to support spark 2.0 and above.

matplotlib &ge; 2.2.x (although 1.4.3 also works, higher version is recommended)

numpy &ge; 1.9.x

scipy &ge; 0.15.x

multiprocess &ge; 0.70.4

## Package Structure
<center><img src='resources/package_structure.png'></center>

## Highlights
Pytalite provides the following model evaluation and diagnostic algorithms:

- Discrete Precision/Recall Plot (Binary-Classification only)
- Feature Correlation Plot (Binary-Classification only)
- Probability Density Plot (Binary-Classification only)
- Feature Importance Plot (Binary-Classification only)
- Accumulated Local Effect Plot (Binary-Classification / Regression, numerical feature)
- Partial Dependence Plot (Binary-Classification / Regression)

## Examples
See `examples` folder for usage examples.

## Gallery
### Discrete Precision/Recall Plot

<center><img src='resources/decile_plot.png'></center>

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### Feature Importance Plot

<center><img src='resources/feature_importance_plot.png'></center>

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### Feature ALE Plot for numerical features

<center><img src='resources/feature_ale_plot.png'></center>


