scikit-optimize-adapter 0.0b0

Dask parallelized bayesian optimization toolbox

Scikit-Optimize-Adapter (Adapter: “A DAsk Parallel TunER”) is an efficient light weight library built on top of Scikit-Optimize and Dask that lets the user do Bayesian optimization hyperparameter tuning with different schemes of parallelized cross-validations.

Install

pip install --index-url https://test.pypi.org/simple/ --no-deps scikit-optimize-adapter --upgrade

Getting started

This is a very quick and basic tutorial. More detailed tutorials will be written soon!

Let’s start with the below dummy training data:

import pandas as pd
import numpy as np

group_col = np.asarray([1]*10 + [2]*10 + [3]*10).reshape(-1, 1)
data = np.arange(30*4).reshape(30, 4)
data = np.hstack((data, group_col))
df = pd.DataFrame(data=data, columns=['target', 'f1', 'f2', 'f3', 'groups'])

features = ['f1', 'f2', 'f3']
groupby = 'groups'
target = 'target'

K = 5

orderby=None
num_partition=None
window_size=None

from skopt.space import Space, Categorical, Integer, Real, Dimension

space  = [Real(0.5, 10),      # learning rate       (learn_rate)
          Real(0, 1),         # gamma               (min_split_improvement)
          Integer(3, 4),      # max_depth           (max_depth)
          Integer(11, 13),    # n_estimators        (ntrees)
          Integer(2, 4),      # min_child_weight    (min_rows)
          Real(0, 1),         # colsample_bytree    (col_sample_rate_per_tree)
          Real(0, 1)]         # subsample           (sample_rate)

Adapter is shipped with XGBoost regressor and classifier, but you can pass in a callable estimator of your design if you wish to customize it.

from adapter import Adapter

adapt = Adapter(df, features, target, K, groupby=None,
        cross_validation_scheme='random_shuffle',
        search_method="bayesian_optimization",
        estimator="xgboost_regression")

Try copying the link to the web browser to check out the dask dashboard: http://127.0.0.1:8789/status.

You can visualize the Dask delayed computation graph:

delayed_graph = adapt.construct_delayed_graph(num_iter=3, search_space=space)  # we will set n_iter to 3 to make visualizing manageable.
delayed_graph.visualize()

Let’s run the code:

res = adapt.run(num_initial=5, num_iter=15, search_space=space)

While it runs, checkout the dashboard again, and click on the Graph tab. You will see the above computation graph being worked on in real time!

Now you can retrieve the results:

adapt.plot_improvements()  # to show the improvements
optimal_params = adapt.get_optimal_params()  # which you can use to train your final model

If you are running this in a local machine, you must take responsibility of removing the temporary directory:

adapt.cleanup()

Todo:

1. rest of the cross validation schemes

2. testing hard thresholded submit process (and testing speed without it)

3. supervised encodings

4. add unit tests

5. continuous integration set up

6. random search method

7. multi GPU environment

8. documentations

9. ~~getting the results of the optimization~~

10. ~~visualization of optimizations~~

11. early stop criterion using callbacks

12. ~~beta readme.rst for install and tutorial~~

13. full readme.rst for install and tutorial

14. periodic training

15. bayesian warm start training

16. dependency managements

17. active per worker threadpool managements