autoboost 20.8.15

A thin wrapper for step-wise parameter optimization of boosting algorithms.

autoboost

Automatic step-wise parameter optimization for xgboost, lightgbm and sklearn's GradingBoosting.

Implemented Strategy

The optimization strategy is taken from SylwiaOliwia. We only incorporate slight changes to the implementation, e.g. we base all decision on the cross-validation test folds and not the entire data set.

The following excerpt is also taken from the readme:

General note

Full GridSearch is time- and memory-demanding, so xgboost-AutoTune tunes parameters in the following steps (one by one, from the most robust to the less):

1. n_estimators

2. max_depth, min_child_weight

3. Gamma

4. n_estimators

5. Subsample, colsample_bytree

6. reg_alpha, reg_lambda

7. n_estimators and learning_rate

8. Some of them are related only to xgboost, LightGBM or GBM. Algorithm picks parameters valid for given model and skip the rest.

Model is updated by newly chosen parameters in each step.

Detailed notes

Algorithm make GridsearchCV for each in seven steps (see General note section) and choose the best value. It uses domian values:

{'n_estimators': [30, 50, 70, 100, 150, 200, 300]},
{'max_depth': [3, 5, 7, 9], 'min_child_weight': [0.001, 0.1, 1, 5, 10, 20], 'min_samples_split': [1, 2, 5, 10, 20, 30],
 'num_leaves': [15, 35, 50, 75, 100, 150]},
{'gamma': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], 'min_samples_leaf': [1, 2, 5, 10, 20, 30],
 'min_child_samples': [2, 7, 15, 25, 45], 'min_split_gain': [0, 0.001, 0.1, 1, 5, 20]},
{'n_estimators': [30, 50, 70, 100, 150, 200, 300], 'max_features': range(10, 25, 3)},
{'subsample': [i / 10 for i in range(4, 10)], 'colsample_bytree': [i / 10 for i in range(4, 10)],
 'feature_fraction': [i / 10 for i in range(4, 10)]},
{'reg_alpha': [1e-5, 1e-2, 0.1, 1, 25, 100], 'reg_lambda': [1e-5, 1e-2, 0.1, 1, 25, 100]}

Unless user will provide his own dictionary of values in initial_params_dict.

In each iteration, if chosing the best value from array has improved scoring by min_loss, algorithm continue searching. It creates new array from the best value, and 2 values in the neighbourhood:

• If the best value in the previous array had neighbours, then new neighbours will be average between best value and it's previous neighbours. Example: if the best value from n_estimators: [30, 50, 70, 100, 150, 200, 300] will be 70, than the new array to search will be [60, 70, 85].

• If the best value is the lowest from the array, it's new value will be 2*best_value- following_value unless it's bigger then minimal (otherwise minimal posible value).

• The the best value was the biggest in the array, it will be treated in the similar way, as the lowest one.

If new values are float and int is required, values are rounded.

n_estimators and learning_rate are chosen pairwise. Algorithm takes its values from model and train them pairwise: ( n* n_estimators , learning_rate/ n ).

Installation

autoboost is available on PyPi and conda. You can easily install the package via:

conda install -c conda-forge autoboost

or alternatively via pip:

pip install autoboost

Usage

The standard usage can be summarized as follows:

from autoboost import optimizer

bo = optimizer.BoostingOptimizer(initial_model=xgboost.XGBRegressor(), scorer=mse_scorer)
clf = bo.fit(x_train, y_train)

Please the example file for a full working example for regression and classification.

Sources

The following list of sources is taking from xgboost-AutoTune.

• autoboost is based on xgboost-AutoTune
• https://xgboost.readthedocs.io/en/stable/tutorials/param_tuning.html
• https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
• https://machinelearningmastery.com/tune-number-size-decision-trees-xgboost-python/
• https://www.kaggle.com/prasunmishra/parameter-tuning-for-xgboost-sklearn/notebook
• https://cambridgespark.com/content/tutorials/hyperparameter-tuning-in-xgboost/index.html