hgboost 0.1.0

hgboost is a Python package to minimize a function from the model xgboost, catboost or lightboost over a hyperparameter space for both classification and regression.

hgboost

hgboost is Python package to minimize the function for xgboost, catboost or lightboost over a hyperparameter space by using cross-validation, and evaluating the results on an indepdendent validation set. hgboost can be applied for classification and regression tasks.

Documentation

• API Documentation: https://erdogant.github.io/hgboost/

Installation Environment

• Install hgboost from PyPI (recommended). hgboost is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.
• A new environment is recommended and created as following:

conda create -n env_hgboost python=3.6
conda activate env_hgboost

Install newest version hgboost from pypi

pip install hgboost

Install from github-source

pip install git+https://github.com/erdogant/hgboost#egg=master

Import hgboost package

import hgboost as hgboost

Classification example for xgboost, catboost and lightboost:

# Load libray
from hgboost import hgboost

# Initizalization
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)

# Import data
df = hgb.import_example()
y = df['Survived'].values
y = y.astype(str)
y[y=='1']='survived'
y[y=='0']='dead'

# Preprocessing by encoding variables
del df['Survived']
X = hgb.preprocessing(df)

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

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

# 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 datset: (891, 204) 
# [hgboost] >Hyperparameter optimization..
#  100% |----| 500/500 [04:39<05:21,  1.33s/trial, best loss: -0.8800619834710744]
# [hgboost] >Best peforming [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 searched parameter space 
hgb.plot_params()

# Plot summary results
hgb.plot()

# Plot the best tree
hgb.treeplot()

# Plot the validation results
hgb.plot_validation()

# Plot the cross-validation results
hgb.plot_cv()

# use the learned model to make new predictions.
y_pred, y_proba = hgb.predict(X)

Citation

Please cite hgboost in your publications if this is useful for your research. Here is an example BibTeX entry:

@misc{erdogant2020hgboost,
  title={hgboost},
  author={Erdogan Taskesen},
  year={2019},
  howpublished={\url{https://github.com/erdogant/hgboost}},
}

References

Maintainers

• Erdogan Taskesen, github: erdogant

Contribute

• Contributions are welcome.

Licence

See LICENSE for details.

Coffee

• This work is created and maintained in my free time. If you wish to buy me a Coffee for this work, it is very appreciated.