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Dask parallelized bayesian optimization toolbox

Project description

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

Let’s start with the below dummy training data:

import pandas as pd
import numpy as np

data = np.arange(30*4).reshape(30, 4)
df = pd.DataFrame(data=data, columns=['target', 'f1', 'f2', 'f3'])

features = ['f1', 'f2', 'f3']
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")  # "xgboost_regression" or "xgboost_classification" or callable estimator (more on this later)

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()
https://github.com/mozjay0619/scikit-optimize-adapter/blob/master/media/graph.png

Let’s run the code. num_initial is the number of random initial searches and num_iter is the total number of search steps taken, including the num_initial step counts. (Example: num_initial=5, num_iter=15 means 5 random search and 10 Bayesian search)

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!

https://github.com/mozjay0619/scikit-optimize-adapter/blob/master/media/daskdashboard.png

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
https://github.com/mozjay0619/scikit-optimize-adapter/blob/master/media/improvement.png

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

adapt.cleanup()

Cross-validation schemes

There are 5 different cross-validation schemes supported by the adapter:

  • random_shuffle: create K cross-validation folds from randomly shuffled rows
    • Default mode for most regression tasks .
  • ordered: create K cross-validation folds after sorting the train data by a certain column
    • Used for regression tasks where data has time series nature with high temporal auto-correlation.
    • Must supply orderby argument.
  • binary_classification: create K cross-validation folds where positive/negative label proportion is preserved
    • Used for classification task.
    • This mode will preserve the positive and negative label proportions in each fold.
  • stratified_sampling: create K cross-validation folds such that the skew distribution of response is preserved
    • Used for regression task where the continuous response variable is highly skewed.
    • This mode will preserve the skew distribution of the response values by sampling from stratification.
    • Must supply num_partition argument.
  • expanding_window: mainly for time series modeling
    • Refer to:

Tuning for multiple models in parallel

Again, let’s take a look at a specific example data:

import pandas as pd
import numpy as np

group_col = np.asarray([1]*10 + [2]*10 + [3]*10 + [4]*10 + [5]*10 + [6]*10).reshape(-1, 1)  # this time we have a column specifying group
data = np.arange(60*4).reshape(60, 4)
data = np.hstack((data, group_col))
df = pd.DataFrame(data=data, columns=['target', 'f1', 'f2', 'f3', 'groups'])

features = ['f1', 'f2', 'f3']
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)

We can tune the models for each group by passing by groupby argument.

from adapter import Adapter

adapt = Adapter(df, features, target, K, groupby='groups',
        cross_validation_scheme='random_shuffle',
        search_method="bayesian_optimization",
        estimator="xgboost_regression")

Run the adapter the same way:

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

You can visualize the Dask delayed computation graph:

https://github.com/mozjay0619/scikit-optimize-adapter/blob/master/media/multigraph_dashboard.png

Passing in an arbitrary callable estimator

You can pass in an arbitrary callable estimator as long as it implements the standard scikit-learn estimator API:

from abc import ABCMeta, abstractmethod

class BaseEstimator(object, metaclass=ABCMeta):
    """
    Base class for all Algorithm classes.
    """

    def __init__(self, **kwargs):
        pass

    @abstractmethod
    def fit(self, X, y, params):
        pass

    @abstractmethod
    def score(self, X, y):
        pass

    @abstractmethod
    def predict(self, X):
        pass

For example, we can even do something like:

from adapter import BaseEstimator  # import BaseEstimator!

class DummyEstimator(BaseEstimator):

    def __init__(self):
        pass

    def fit(self, train_X, train_y, params):
        a = len(train_X)/10.

        for i in range(int(a*5000000)):
            i + 1

        print(len(train_X), len(train_y))

    def score(self, validation_X, validation_y):

        print(len(validation_X), len(validation_y))

        return 1.5

    def predict(self, test_X):

        return len(test_X)

my_estimator = DummyEstimator()

Then you can use it with the Adapter:

from adapter import Adapter

adapt = Adapter(df, features, target, K, groupby='groups',
        cross_validation_scheme='random_shuffle',
        search_method="bayesian_optimization",
        estimator=my_estimator)  # your own estimator

Tuning multiple models with highly skewed training data sizes

When the data size for each group is highly skewed, a suboptimal resource allocation can occur. In this case, it is more advantageous to throttle the feeding of delayed graphs to the Dask client by using multiple thread instances. Let’s again look at an example case:

import pandas as pd
import numpy as np
import time

group_col = np.asarray([1]*100 + [2]*2 + [3]*2 + [4]*2 + [5]*2 + [6]*2 + [7]*2 + [8]*2 + [9]*2 + [16]*2 + [26]*2 + [17]*2 + [18]*2 + [19]*2 + [116]*2 + [126]*2).reshape(-1, 1)
data = np.arange(130*4).reshape(130, 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

from adapter import BaseEstimator  # import BaseEstimator!

class DummyEstimator(BaseEstimator):

    def __init__(self):
        pass

    def fit(self, train_X, train_y, params):
        a = len(train_X)/10.

        for i in range(int(a*5000000)):
            i + 1

        print(len(train_X), len(train_y))

    def score(self, validation_X, validation_y):

        print(len(validation_X), len(validation_y))

        return 1.5

    def predict(self, test_X):

        return len(test_X)

my_estimator = DummyEstimator()

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)

In such a case, we use run_with_threads method call, where we pass an additional argument of num_threads:

from adapter import Adapter

adapt = Adapter(df, features, target, K, groupby='groups',
        cross_validation_scheme='random_shuffle',
        search_method="bayesian_optimization",
        estimator=my_estimator)  # your own estimator

res = adapt.run(num_initial=5, num_iter=15, search_space=space, num_threads=2)  # num_threads

You can check fromt the Dask dashboard that only two delayed computation graphs are worked on at the same time, achieving a dynamic resource allocation in effect:

https://github.com/mozjay0619/scikit-optimize-adapter/blob/master/media/twograph_dashboard.png

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

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