HyperGBM is a library that supports full-pipeline AutoML, which completely covers the end-to-end stages of data cleaning, preprocessing, feature generation and selection, model selection and hyperparameter optimization.It is a real-AutoML tool for tabular data.
Unlike most AutoML approaches that focus on tackling the hyperparameter optimization problem of machine learning algorithms, HyperGBM can put the entire process from data cleaning to algorithm selection in one search space for optimization. End-to-end pipeline optimization is more like a sequential decision process, thereby HyperGBM uses reinforcement learning, Monte Carlo Tree Search, evolution algorithm combined with a meta-learner to efficiently solve such problems.
As the name implies, the ML algorithms used in HyperGBM are all GBM models, and more precisely the gradient boosting tree model, which currently includes XGBoost, LightGBM and Catboost.
The underlying search space representation and search algorithm in HyperGBM are powered by the Hypernets project a general AutoML framework.
pip install hypergbmUser can create experiment instance with make_experiment and run it quickly。train_data is the only required parameter, all others are optional. The target is also required if your target feature name isn't y。
Codes:
from hypergbm import make_experiment
from tabular_toolbox.datasets import dsutils
train_data = dsutils.load_blood()
experiment = make_experiment(train_data, target='Class')
estimator = experiment.run()
print(estimator)Outputs：
Pipeline(steps=[('data_clean',
DataCleanStep(...),
('estimator',
GreedyEnsemble(...)])
Process finished with exit code 0
Hypergbm also provides command line tools to train models and predict data:
hypergm -h
usage: hypergbm [-h] --train_file TRAIN_FILE [--eval_file EVAL_FILE]
[--eval_size EVAL_SIZE] [--test_file TEST_FILE] --target
TARGET [--pos_label POS_LABEL] [--max_trials MAX_TRIALS]
[--model_output MODEL_OUTPUT]
[--prediction_output PREDICTION_OUTPUT] [--searcher SEARCHER]
...
For example, train dataset blood.csv:
hypergbm --train_file=blood.csv --test_file=blood.csv --target=Class --pos_label=1 --model_output=model.pkl- HyperGBM: A full pipeline AutoML tool integrated various GBM models.
- HyperDT/DeepTables: An AutoDL tool for tabular data.
- HyperKeras: An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.
- Cooka: Lightweight interactive AutoML system.
- Hypernets: A general automated machine learning framework.
HyperGBM is an open source project created by DataCanvas.