This package offers implementations of several black-box optimisation methods to tune hyperparameters of machine learning models. Its purpose is to enable data scientists to use optimization techniques for rapid protyping. Simply import OptML and supply it with a model and the parameters to optimize.
OptML offers a unified interface for models built with Scikit-Learn, Keras, XGBoost (and hopefully soon Statsmodels).
This package requires scikit-learn with version 0.19.0 or higher. If Scikit-Learn is not yet installed run
pip install scikit-learn==0.19.0. If you want to make use of the
HyperoptOptimizer then you also need to install hyperopt (e.g. by
pip install hyperopt).
If Scikit-Learn is version 0.19 or higher simply install optml using
pip install optml and you're ready to go.
Specify your ML model and the parameters you want to optimize over. For the parameters you have to choose the type (such as integer, categorical, boolean, etc.) and the range of values it can take.
model = SomeMLModel() params = [Parameter(name='param1', param_type='continuous', lower=0.1, upper=5), Parameter(name='param2', param_type='integer', lower=1, upper=5), Parameter(name='param3', param_type='categorical', possible_values=['val1','val2','val3'])]
Then define the evaluation function. This can be anything from RMSE to crossentropy to custom functions. The first argument of the evaluation function is the array of true labels and the second argument is an array with model predictions.
def clf_score(y_true,y_pred): return np.sum(y_true==y_pred)/float(len(y_true))
Import and initialize an optimizer and optimize the model for some training data.
from optml.bayesian_optimizer import BayesianOptimizer bayesOpt = BayesianOptimizer(model=model, hyperparams=params, eval_func=clf_score) bayes_best_params, bayes_best_model = bayesOpt.fit(X_train=X_train, y_train=y_train, n_iters=50)
At the moment this library includes:
OptML implements several optimization methods to address a range of requirements that can arise in data science problems. One of the main concerns is the effort required to evaluate a model for a set of parameters: If a model takes a long time to train we should choose an optimizer that maximises the potential improvement with every new set of parameters. In this case Bayesian Optimization and Hyperopt are more applicable. If a model is cheap to train then we can seek to parallelise the evaluations.
Also consider the number of parameters and their ranges. Clearly, it is more difficult to optimize over a large search space. It is advised to only include parameters in the optimization if they are expected to improve the final model.
Please also note that all of OptML's optimizers require parameters to be bounded.
|number of evaluations||works with large search space||can use training in parallel||handles categorical parameters||stochastic optimisation|
|Genetic Algorithm||high||yes||not implemented||yes||yes|
|Bayesian Optimizer||low||yes||not implemented||yes||yes|
This project is licensed under the MIT License - see the LICENSE.md file for details