Smart selection of hyperparameters
- Free software: MIT license
- Documentation: https://HDI-Project.github.io/BTB
Bayesian Tuning and Bandits is a simple, extensible Auto Machine Learning system that automates model selection and hyperparameter tuning.
selectiondefines Selectors: classes for choosing from a set of discrete options with multi-armed banditstuningdefines Tuners: classes with a fit/predict/propose interface for suggesting sets of hyperparameters
Tuners are specifically designed to speed up the process of selecting the optimal hyper parameter values for a specific machine learning algorithm.
This is done by following a Bayesian Optimization approach and iteratively:
- letting the tuner propose new sets of hyper parameter
- fitting and scoring the model with the proposed hyper parameters
- passing the score obtained back to the tuner
At each iteration the tuner will use the information already obtained to propose the set of hyper parameters that it considers that have the highest probability to obtain the best results.
Selectors apply multiple strategies to decide which models or families of models to train and test next based on how well thay have been performing in the previous test runs. This is an application of what is called the Multi-armed Bandit Problem.
The process works by letting know the selector which models have been already tested and which scores they have obtained, and letting it decide which model to test next.
BTB is not pulished in PyPi yet, but you can already install the latest release using pip
pip install -e git+https://github.com/HDI-Project/BTB.git@v0.1.0#egg=btb
You can also clone the repository and install it from sources
git clone git@github.com:HDI-Project/BTB.git
cd BTB
make install
In order to use a Tuner we will create a Tuner instance indicating which parameters we want to tune, their types and the range of values that we want to try
>>> from btb.tuning import GP
>>> from btb import HyperParameter, ParamTypes
>>> tunables = [
... ('n_estimators', HyperParameter(ParamTypes.INT, [10, 500])),
... ('max_depth', HyperParameter(ParamTypes.INT, [3, 20]))
... ]
>>> tuner = GP(tunables)
Then we into a loop and perform three steps:
>>> parameters = tuner.propose()
>>> parameters
{'n_estimators': 297, 'max_depth': 3}
>>> model = RandomForestClassifier(**parameters)
>>> model.fit(X_train, y_train)
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=3, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=297, n_jobs=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)
>>> score = model.score(X_test, y_test)
>>> score
0.77
tuner.add(parameters, score)
At each iteration, the Tuner will use the information about the previous tests to evaluate and propose the set of parameter values that have the highest probability of obtaining the highest score.
For a more detailed example, check scripts from the examples folder.
The selectors are intended to be used in combination with the Tuners in order to find out and decide which model seems to get the best results once it is properly fine tuned.
In order to use the selector we will create a Tuner instance for each model that we want to try out, as well as the selector instance.
>>> from sklearn.svm import SVC
>>> models = {
... 'RF': RandomForestClassifier,
... 'SVC': SVC
... }
>>> from btb.selection import UCB1
>>> selector = UCB1(['RF', 'SVM'])
>>> tuners = {
... 'RF': GP([
... ('n_estimators', HyperParameter(ParamTypes.INT, [10, 500])),
... ('max_depth', HyperParameter(ParamTypes.INT, [3, 20]))
... ]),
... 'SVM': GP([
... ('c', HyperParameter(ParamTypes.FLOAT_EXP, [0.01, 10.0])),
... ('gamma', HyperParameter(ParamTypes.FLOAT, [0.000000001, 0.0000001]))
... ])
... }
Then, we will go into a loop and, at each iteration, perform the steps:
>>> next_choice = selector.select({'RF': tuners['RF'].y, 'SVM': tuners['SVM'].y})
>>> next_choice
'RF'
>>> parameters = tuners[next_choice].propose()
>>> parameters
{'n_estimators': 289, 'max_depth': 18}
>>> model = models[next_choice](**parameters)
>>> model.fit(X_train, y_train)
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=18, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=289, n_jobs=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)
>>> score = model.score(X_test, y_test)
>>> score
0.89
>>> tuners[next_choice].add(parameters, score)