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Description:

mlcompare 1.2.0

MLCompare is a Python package for running model comparison pipelines, with the aim of being both simple and flexible. It supports multiple popular ML libraries, retrieval from multiple online dataset repositories, common data processing steps, and results visualization. Additionally, it allows for using your own models and datasets within the pipelines.

Libraries
Datasets
Data Processing

Scikit-learn
XGBoost

Kaggle
OpenML
Hugging Face
locally saved

train-test split
drop columns
handle NaNs: drop | forward-fill | backward-fill
encoders: OneHot | Ordinal | Target | Label
scalers: Standard | MinMax | MaxAbs | Robust
transformers: Quantile | Power | Normalizer

Installing
It is recommended to create a new virtual environment. Example with Conda:
conda create -n compare_env python==3.11.9
conda activate compare_env

Install this library with pip:
pip install mlcompare

Note that for MacOS, both XGBoost and LightGBM require libomp. It can be installed with Homebrew:
brew install libomp

A Simple Example
Running a pipeline with multiple datasets and models is done by creating a list of dictionaries for each and providing them to a pipeline function.
The below example downloads a dataset from OpenML and Kaggle, one-hot encodes some of the columns in the Kaggle dataset, and trains and evaluates a Random Forest and XGBoost model on them.
import mlcompare

datasets = [
{
"type": "openml",
"id": 8,
"target": "drinks",
},
{
"type": "kaggle",
"user": "gorororororo23",
"dataset": "plant-growth-data-classification",
"file": "plant_growth_data.csv",
"target": "Growth_Milestone",
"oneHotEncode": ["Soil_Type", "Water_Frequency", "Fertilizer_Type"],
}
]

models = [
{
"library": "sklearn",
"name": "RandomForestRegressor",
},
{
"library": "xgboost",
"name": "XGBRegressor",
"params": {"num_leaves": 40, "n_estimators": 200}
}
]

mlcompare.full_pipeline(datasets, models, "regression")

In the case of the XGBoost model some non-default parameter values were used.
Planned Additions
Version 1.3

LightGBM support
CatBoost support
Model results graphing and visualization
Improved documentation
Support for presplit data

Version 1.4

PyTorch support
TensorFlow support
Additional dataset sources
Built-in model and dataset collections for quick testing of similar model types/datasets
Optional pipeline caching
Optional trained model saving

Version 1.5

S3 Support