.. _sphx_glr_auto_examples_model_selection_randomized_search.py:


=========================================================================
Comparing randomized search and grid search for hyperparameter estimation
=========================================================================

Compare randomized search and grid search for optimizing hyperparameters of a
random forest.
All parameters that influence the learning are searched simultaneously
(except for the number of estimators, which poses a time / quality tradeoff).

The randomized search and the grid search explore exactly the same space of
parameters. The result in parameter settings is quite similar, while the run
time for randomized search is drastically lower.

The performance is slightly worse for the randomized search, though this
is most likely a noise effect and would not carry over to a held-out test set.

Note that in practice, one would not search over this many different parameters
simultaneously using grid search, but pick only the ones deemed most important.


.. code-block:: python

    print(__doc__)

    import numpy as np

    from time import time
    from scipy.stats import randint as sp_randint

    from sklearn.model_selection import GridSearchCV
    from sklearn.model_selection import RandomizedSearchCV
    from sklearn.datasets import load_digits
    from sklearn.ensemble import RandomForestClassifier

    # get some data
    digits = load_digits()
    X, y = digits.data, digits.target

    # build a classifier
    clf = RandomForestClassifier(n_estimators=20)


    # Utility function to report best scores
    def report(results, n_top=3):
        for i in range(1, n_top + 1):
            candidates = np.flatnonzero(results['rank_test_score'] == i)
            for candidate in candidates:
                print("Model with rank: {0}".format(i))
                print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
                      results['mean_test_score'][candidate],
                      results['std_test_score'][candidate]))
                print("Parameters: {0}".format(results['params'][candidate]))
                print("")


    # specify parameters and distributions to sample from
    param_dist = {"max_depth": [3, None],
                  "max_features": sp_randint(1, 11),
                  "min_samples_split": sp_randint(1, 11),
                  "min_samples_leaf": sp_randint(1, 11),
                  "bootstrap": [True, False],
                  "criterion": ["gini", "entropy"]}

    # run randomized search
    n_iter_search = 20
    random_search = RandomizedSearchCV(clf, param_distributions=param_dist,
                                       n_iter=n_iter_search)

    start = time()
    random_search.fit(X, y)
    print("RandomizedSearchCV took %.2f seconds for %d candidates"
          " parameter settings." % ((time() - start), n_iter_search))
    report(random_search.cv_results_)

    # use a full grid over all parameters
    param_grid = {"max_depth": [3, None],
                  "max_features": [1, 3, 10],
                  "min_samples_split": [1, 3, 10],
                  "min_samples_leaf": [1, 3, 10],
                  "bootstrap": [True, False],
                  "criterion": ["gini", "entropy"]}

    # run grid search
    grid_search = GridSearchCV(clf, param_grid=param_grid)
    start = time()
    grid_search.fit(X, y)

    print("GridSearchCV took %.2f seconds for %d candidate parameter settings."
          % (time() - start, len(grid_search.cv_results_['params'])))
    report(grid_search.cv_results_)

**Total running time of the script:**
(0 minutes 0.000 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`randomized_search.py <randomized_search.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`randomized_search.ipynb <randomized_search.ipynb>`