比較用於超參數估計的隨機搜尋和網格搜尋#

比較隨機搜尋和網格搜尋，以使用 SGD 訓練來最佳化線性 SVM 的超參數。同時搜尋所有影響學習的參數（估計器的數量除外，這會造成時間/品質的權衡）。

隨機搜尋和網格搜尋會探索完全相同的參數空間。參數設定的結果非常相似，而隨機搜尋的執行時間則大幅縮短。

隨機搜尋的效能可能略差，這可能是由於雜訊效應，並且不會延續到保留的測試集。

請注意，在實務上，不會使用網格搜尋同時搜尋這麼多不同的參數，而是只選擇那些被認為最重要的參數。

RandomizedSearchCV took 1.29 seconds for 15 candidates parameter settings.
Model with rank: 1
Mean validation score: 0.991 (std: 0.006)
Parameters: {'alpha': np.float64(0.05063247886572012), 'average': False, 'l1_ratio': np.float64(0.13822072286080167)}

Model with rank: 2
Mean validation score: 0.987 (std: 0.014)
Parameters: {'alpha': np.float64(0.010877306503748912), 'average': True, 'l1_ratio': np.float64(0.9226260871125187)}

Model with rank: 3
Mean validation score: 0.976 (std: 0.023)
Parameters: {'alpha': np.float64(0.727148206404819), 'average': False, 'l1_ratio': np.float64(0.25183501383331797)}

GridSearchCV took 4.06 seconds for 60 candidate parameter settings.
Model with rank: 1
Mean validation score: 0.993 (std: 0.011)
Parameters: {'alpha': np.float64(0.1), 'average': False, 'l1_ratio': np.float64(0.1111111111111111)}

Model with rank: 2
Mean validation score: 0.987 (std: 0.013)
Parameters: {'alpha': np.float64(0.01), 'average': False, 'l1_ratio': np.float64(0.5555555555555556)}

Model with rank: 3
Mean validation score: 0.987 (std: 0.007)
Parameters: {'alpha': np.float64(0.01), 'average': False, 'l1_ratio': np.float64(0.0)}

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

from time import time

import numpy as np
import scipy.stats as stats

from sklearn.datasets import load_digits
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

# get some data
X, y = load_digits(return_X_y=True, n_class=3)

# build a classifier
clf = SGDClassifier(loss="hinge", penalty="elasticnet", fit_intercept=True)


# 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 = {
    "average": [True, False],
    "l1_ratio": stats.uniform(0, 1),
    "alpha": stats.loguniform(1e-2, 1e0),
}

# run randomized search
n_iter_search = 15
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 = {
    "average": [True, False],
    "l1_ratio": np.linspace(0, 1, num=10),
    "alpha": np.power(10, np.arange(-2, 1, dtype=float)),
}

# 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_)