Are you sick and tired of the endless cycle of model comparisons and hyperparameter tuning? Meet RektGBM, your ultimate no-brainer solution to building good enough performance machine learning models. RektGBM seamlessly combines LightGBM and XGBoost, two leading gradient boosting frameworks available today.

RektGBM simplifies the complexities of model building and optimization, delivering a robust, yet easy-to-use platform that empowers you to achieve superior results without the hassle. With the integration of Optuna, a cutting-edge hyperparameter optimization framework, RektGBM fine-tunes your models to perfection, ensuring optimal performance with minimal effort.

Getting started with RektGBM is a breeze. Install it using pip:

pip install rektgbm

• RektDataset: A wrapper for datasets used in RektGBM and RektOptimizer.
• RektGBM: A versatile gradient boosting model that leverages the power of both LightGBM and XGBoost.
• RektOptimizer: An Optuna-based optimizer that fine-tunes RektGBM hyperparameters for maximum efficiency.

Here’s a quick example to showcase how you can use RektGBM in your machine learning pipeline:

$ rektgbm --help
Usage: rektgbm [OPTIONS] DATA_PATH TEST_DATA_PATH TARGET [RESULT_PATH] [N_TRIALS]
╭─ Arguments ───────────────────────────────────────────────────────────────────────────────────────╮
│ *    data_path           TEXT           Path to the training data file.  [required]
│ *    test_data_path      TEXT           Path to the test data file.  [required]
│ *    target              TEXT           Name of the target column.  [required]
│      result_path         [RESULT_PATH]  Path to the prediction results. [default: predict.csv]
│      n_trials            [N_TRIALS]     Number of optimization trials. [default: 100]
╰───────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────╮
│ --help          Show this message and exit.
╰───────────────────────────────────────────────────────────────────────────────────────────────────╯

$ rektgbm train.csv test.csv target predict.csv 100

Alternatively, the same functionality is available through Python.

import pandas as pd
from rektgbm import RektDataset, RektGBM, RektOptimizer

# Prepare your datasets
X_train = pd.read_csv("train.csv")
X_test = pd.read_csv("test.csv")
y_train = X_train.pop("target")

dtrain = RektDataset(data=X_train, label=y_train)
dtest = RektDataset(data=X_test)

# Initialize RektOptimizer to automatically detect task type, objective, and metric
rekt_optimizer = RektOptimizer()

# Optimize hyperparameters over 100 trials
rekt_optimizer.optimize_params(dataset=dtrain, n_trials=100)

# Initialize RektGBM with the optimized hyperparameters
rekt_gbm = RektGBM(**rekt_optimizer.best_params)

# Train the model
rekt_gbm.fit(dataset=dtrain)

# Make predictions on the test set
preds = rekt_gbm.predict(dataset=dtest)
pd.DataFrame(preds).to_csv("predict.csv")

# DONE!

For more detailed examples, check out the Examples section.

Welcome all contributions! Please note that this project might have some bugs, so your contributions in identifying and fixing them are especially valuable! Whether you want to create an issue or submit a pull request, your input is appreciated. Feel free to get involved in any way that suits you.