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https://github.com/zygmuntz/hyperband

Tuning hyperparams fast with Hyperband
https://github.com/zygmuntz/hyperband

gradient-boosting gradient-boosting-classifier hyperparameter-optimization hyperparameter-tuning hyperparameters machine-learning

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Tuning hyperparams fast with Hyperband

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README 

        
hyperband

=========



Code for tuning hyperparams with Hyperband, adapted from [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://people.eecs.berkeley.edu/~kjamieson/hyperband.html). 



	defs/ - functions and search space definitions for various classifiers

	defs_regression/ - the same for regression models

	common_defs.py - imports and definitions shared by defs files

	hyperband.py - from hyperband import Hyperband

	

	load_data.py - classification defs import data from this file

	load_data_regression.py - regression defs import data from this file

	

	main.py - a complete example for classification

	main_regression.py - the same, for regression

	main_simple.py - a simple, bare-bones, example	



The goal is to provide a fully functional implementation of Hyperband, as well as a number of ready to use functions for a number of models (classifiers and regressors). Currently these include four from _scikit-learn_ and four others:



* gradient boosting (GB)

* random forest (RF)

* extremely randomized trees (XT)

* linear SGD

* factorization machines from polylearn

* polynomial networks from polylearn

* a multilayer perceptron from Keras

* gradient boosting from XGBoost (classification only)



Meta-classifier/regressor

-------------------------



Use `defs.meta`/`defs_regression.meta` to try many models in one Hyperband run. This is an automatic alternative to constructing search spaces with multiple models (like `defs.rf_xt`, or `defs.polylearn_fm_pn`) by hand.



Loading data

------------



Definitions files in `defs`/`defs_regression` import data from `load_data.py` and `load_data_regression.py`, respectively.



Edit these files, or a definitions file directly, to make your data available for tuning.



Regression defs use the _kin8nm_ dataset in `data/kin8nm`. There is no attached data for classification.



For the provided models data format follows _scikit-learn_ conventions, that is, there are _x_train_, _y_train_, _x_test_ and _y_test_ Numpy arrays.



Usage

-----



Run `main.py` (with your own data), or `main_regression.py`. The essence of it is



```python

from hyperband import Hyperband

from defs.gb import get_params, try_params



hb = Hyperband( get_params, try_params )

results = hb.run()

```



Here's a sample output from a run (three configurations tested) using `defs.xt`:



	3 | Tue Feb 28 15:39:54 2017 | best so far: 0.5777 (run 2)



	n_estimators: 5

	{'bootstrap': False,

	'class_weight': 'balanced',

	'criterion': 'entropy',

	'max_depth': 5,

	'max_features': 'sqrt',

	'min_samples_leaf': 5,

	'min_samples_split': 6}



	# training | log loss: 62.21%, AUC: 75.25%, accuracy: 67.20%

	# testing  | log loss: 62.64%, AUC: 74.81%, accuracy: 66.78%



	7 seconds.



	4 | Tue Feb 28 15:40:01 2017 | best so far: 0.5777 (run 2)



	n_estimators: 5

	{'bootstrap': False,

	'class_weight': None,

	'criterion': 'gini',

	'max_depth': 5,

	'max_features': 'sqrt',

	'min_samples_leaf': 1,

	'min_samples_split': 2}



	# training | log loss: 53.39%, AUC: 75.69%, accuracy: 72.37%

	# testing  | log loss: 53.96%, AUC: 75.29%, accuracy: 71.89%



	7 seconds.



	5 | Tue Feb 28 15:40:07 2017 | best so far: 0.5396 (run 4)



	n_estimators: 5

	{'bootstrap': True,

	'class_weight': None,

	'criterion': 'gini',

	'max_depth': 3,

	'max_features': None,

	'min_samples_leaf': 7,

	'min_samples_split': 8}



	# training | log loss: 50.20%, AUC: 77.04%, accuracy: 75.39%

	# testing  | log loss: 50.67%, AUC: 76.77%, accuracy: 75.12%



	8 seconds.

	

Early stopping

--------------



Some models may use early stopping (as the Keras MLP example does). If a configuration  stopped early, it doesn't make sense to run it with more iterations (duh). To indicate this, make `try_params()`



```python

return { 'loss': loss, 'early_stop': True }

```

	

This way, Hyperband will know not to select that configuration for any further runs.



Moar

----



See [http://fastml.com/tuning-hyperparams-fast-with-hyperband/](http://fastml.com/tuning-hyperparams-fast-with-hyperband/) for a detailed description.