keel-ds 0.2.4

KeelDS is a package to load some datasets from KEEL repository with some normalizations and with split and discretization options.

KeelDS

KeelDS: A Python package for loading datasets from KEEL repository

KeelDS is a Python package that provides easy access to datasets from the KEEL repository, a popular source for machine learning datasets. This package simplifies the process of loading KEEL datasets, offering options for cross-validation and discretization.

Features

• Load KEEL datasets with a single line of code
• Access datasets pre-split into train and test sets
• Discretization option using the Fayyad algorithm (MDLP)
• Support for both balanced and imbalanced datasets
• Easy integration with machine learning workflows

Installation

Dependencies

• Python (>= 3.12)
• pandas (>= 2.2.2)

You can install KeelDS using pip:

pip install keel-ds

Usage

Here's a simple example of how to use KeelDS with a machine learning model:

from keel_ds import load_data
import numpy as np
from catboost import CatBoostClassifier

file_name = 'iris'
folds = load_data(file_name)

evaluations = []
for x_train, y_train, x_test, y_test in folds:
    model = CatBoostClassifier(verbose=False)
    model.fit(x_train, y_train)
    evaluation = model.score(x_test, y_test)
    evaluations.append(evaluation)

print(np.mean(evaluations))  # Output: 0.933333333333