hgboost 1.1.1

hgboost is a python package for hyperparameter optimization for xgboost, catboost and lightboost for both classification and regression tasks.

hgboost - Hyperoptimized Gradient Boosting

hgboost is short for Hyperoptimized Gradient Boosting and is a python package for hyperparameter optimization for xgboost, catboost and lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.

hgboost is fun because:

* 1. Hyperoptimization of the Parameter-space using bayesian approach.
* 2. Determines the best scoring model(s) using k-fold cross validation.
* 3. Evaluates best model on independent evaluation set.
* 4. Fit model on entire input-data using the best model.
* 5. Works for classification and regression
* 6. Creating a super-hyperoptimized model by an ensemble of all individual optimized models.
* 7. Return model, space and test/evaluation results.
* 8. Makes insightful plots.

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Documentation pages

On the documentation pages you can find detailed information about the working of the hgboost with many examples.

Colab Notebooks

• Regression example

• Classification example

Schematic overview of hgboost

Installation Environment

conda create -n env_hgboost python=3.8
conda activate env_hgboost

Install from pypi

pip install hgboost
pip install -U hgboost # Force update

Import hgboost package

import hgboost as hgboost

Examples

• Example: Fit catboost by hyperoptimization and cross-validation

• Example: Fit lightboost by hyperoptimization and cross-validation

• Example: Fit xgboost by hyperoptimization and cross-validation

• Example: Plot searched parameter space

• Example: plot summary

• Example: Tree plot

• Example: Plot the validation results

• Example: Plot the cross-validation results

• Example: use the learned model to make new predictions

• Example: Create ensemble model for Classification

• Example: Create ensemble model for Regression

Classification example for xgboost, catboost and lightboost:

# Load library
from hgboost import hgboost

# Initialization
hgb = hgboost(max_eval=10, threshold=0.5, cv=5, test_size=0.2, val_size=0.2, top_cv_evals=10, random_state=42)

# Fit xgboost by hyperoptimization and cross-validation
results = hgb.xgboost(X, y, pos_label='survived')

# [hgboost] >Start hgboost classification..
# [hgboost] >Collecting xgb_clf parameters.
# [hgboost] >Number of variables in search space is [11], loss function: [auc].
# [hgboost] >method: xgb_clf
# [hgboost] >eval_metric: auc
# [hgboost] >greater_is_better: True
# [hgboost] >pos_label: True
# [hgboost] >Total dataset: (891, 204) 
# [hgboost] >Hyperparameter optimization..
#  100% |----| 500/500 [04:39<05:21,  1.33s/trial, best loss: -0.8800619834710744]
# [hgboost] >Best performing [xgb_clf] model: auc=0.881198
# [hgboost] >5-fold cross validation for the top 10 scoring models, Total nr. tests: 50
# 100%|██████████| 10/10 [00:42<00:00,  4.27s/it]
# [hgboost] >Evalute best [xgb_clf] model on independent validation dataset (179 samples, 20.00%).
# [hgboost] >[auc] on independent validation dataset: -0.832
# [hgboost] >Retrain [xgb_clf] on the entire dataset with the optimal parameters settings.

# Plot the ensemble classification validation results
hgb.plot_validation()

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References

* http://hyperopt.github.io/hyperopt/
* https://github.com/dmlc/xgboost
* https://github.com/microsoft/LightGBM
* https://github.com/catboost/catboost

Maintainers

• Erdogan Taskesen, github: erdogant

Contribute

• Contributions are welcome.

Licence See LICENSE for details.

Coffee

• If you wish to buy me a Coffee for this work, it is very appreciated :)