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# 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
    from sklearn.datasets import fetch_california_housing
    from sklearn.ensemble import GradientBoostingRegressor
    from sklearn.model_selection import cross_val_score

## 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.

    california_housing = fetch_california_housing()
    X, y = california_housing.data, california_housing.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)

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 Integer, Real
    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

    'Best score=0.4477'

    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])
    )

    Best parameters:
    - max_depth=5
    - learning_rate=0.147511
    - max_features=8
    - min_samples_split=100
    - min_samples_leaf=46

## Convergence plot

    from skopt.plots import plot_convergence

    plot_convergence(res_gp)

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

Total running time of the script: (2 minutes 21.302 seconds)

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