mlcompare 1.1.0

Quickly compare machine learning models across libraries and datasets.

This library is still in early development. Expect many more features to come :D

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 Hugging Face OpenML Locally saved	Encode: One-hot | Label Drop Columns

Installing

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 models and datasets is done by making 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)

In the case of the XGBoost model we passed in our own parameter values rather than using the defaults.