Tuning a scikit-learn estimator with skopt

Gilles Louppe, July 2016 Katie Malone, August 2016 Reformatted by Holger Nahrstaedt 2020

If you are looking for a sklearn.model_selection.GridSearchCV replacement checkout Scikit-learn hyperparameter search wrapper instead.

Problem statement

Tuning the hyper-parameters of a machine learning model is often carried out using an exhaustive exploration of (a subset of) the space all hyper-parameter configurations (e.g., using sklearn.model_selection.GridSearchCV), which often results in a very time consuming operation.

In this notebook, we illustrate how to couple gp_minimize with sklearn’s estimators to tune hyper-parameters using sequential model-based optimisation, hopefully resulting in equivalent or better solutions, but within fewer evaluations.

Note: scikit-optimize provides a dedicated interface for estimator tuning via BayesSearchCV class which has a similar interface to those of sklearn.model_selection.GridSearchCV. This class uses functions of skopt to perform hyperparameter search efficiently. For example usage of this class, see Scikit-learn hyperparameter search wrapper example notebook.

print(__doc__)
import numpy as np

Objective

To tune the hyper-parameters of our model we need to define a model, decide which parameters to optimize, and define the objective function we want to minimize.

from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import cross_val_score

boston = load_boston()
X, y = boston.data, boston.target
n_features = X.shape[1]

# gradient boosted trees tend to do well on problems like this
reg = GradientBoostingRegressor(n_estimators=50, random_state=0)

Out:

/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.

    The Boston housing prices dataset has an ethical problem. You can refer to
    the documentation of this function for further details.

    The scikit-learn maintainers therefore strongly discourage the use of this
    dataset unless the purpose of the code is to study and educate about
    ethical issues in data science and machine learning.

    In this case special case, you can fetch the dataset from the original
    source::

        import pandas as pd
        import numpy as np


        data_url = "http://lib.stat.cmu.edu/datasets/boston"
        raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
        target = raw_df.values[1::2, 2]

    Alternative datasets include the California housing dataset (i.e.
    func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
    dataset. You can load the datasets as follows:

        from sklearn.datasets import fetch_california_housing
        housing = fetch_california_housing()

    for the California housing dataset and:

        from sklearn.datasets import fetch_openml
        housing = fetch_openml(name="house_prices", as_frame=True)

    for the Ames housing dataset.

  warnings.warn(msg, category=FutureWarning)

Next, we need to define the bounds of the dimensions of the search space we want to explore and pick the objective. In this case the cross-validation mean absolute error of a gradient boosting regressor over the Boston dataset, as a function of its hyper-parameters.

from skopt.space import Real, Integer
from skopt.utils import use_named_args


# The list of hyper-parameters we want to optimize. For each one we define the
# bounds, the corresponding scikit-learn parameter name, as well as how to
# sample values from that dimension (`'log-uniform'` for the learning rate)
space  = [Integer(1, 5, name='max_depth'),
          Real(10**-5, 10**0, "log-uniform", name='learning_rate'),
          Integer(1, n_features, name='max_features'),
          Integer(2, 100, name='min_samples_split'),
          Integer(1, 100, name='min_samples_leaf')]

# this decorator allows your objective function to receive a the parameters as
# keyword arguments. This is particularly convenient when you want to set
# scikit-learn estimator parameters
@use_named_args(space)
def objective(**params):
    reg.set_params(**params)

    return -np.mean(cross_val_score(reg, X, y, cv=5, n_jobs=-1,
                                    scoring="neg_mean_absolute_error"))

Optimize all the things!

With these two pieces, we are now ready for sequential model-based optimisation. Here we use gaussian process-based optimisation.

from skopt import gp_minimize
res_gp = gp_minimize(objective, space, n_calls=50, random_state=0)

"Best score=%.4f" % res_gp.fun

Out:

'Best score=2.9062'

print("""Best parameters:
- max_depth=%d
- learning_rate=%.6f
- max_features=%d
- min_samples_split=%d
- min_samples_leaf=%d""" % (res_gp.x[0], res_gp.x[1],
                            res_gp.x[2], res_gp.x[3],
                            res_gp.x[4]))

Out:

Best parameters:
- max_depth=5
- learning_rate=0.143650
- max_features=9
- min_samples_split=100
- min_samples_leaf=1

Convergence plot

from skopt.plots import plot_convergence

plot_convergence(res_gp)

Out:

<AxesSubplot:title={'center':'Convergence plot'}, xlabel='Number of calls $n$', ylabel='$\\min f(x)$ after $n$ calls'>

Estimated memory usage: 35 MB