比较用于超参数估计的随机搜索和网格搜索#

比较随机搜索和网格搜索在SGD训练的线性SVM中优化超参数。所有影响学习的参数同时进行搜索（除了估计器数量，这涉及到时间/质量权衡）。

随机搜索和网格搜索探索完全相同的参数空间。参数设置的结果非常相似，但随机搜索的运行时间大大缩短。

随机搜索的性能可能略差，这很可能是由于噪声效应，并且不会延续到保留的测试集。

请注意，在实践中，人们不会使用网格搜索同时搜索如此多的不同参数，而只会选择那些被认为最重要的参数。

RandomizedSearchCV took 0.58 seconds for 15 candidates parameter settings.
Model with rank: 1
Mean validation score: 0.987 (std: 0.011)
Parameters: {'alpha': np.float64(0.01001911984591966), 'average': False, 'l1_ratio': np.float64(0.7665012035905148)}

Model with rank: 2
Mean validation score: 0.987 (std: 0.011)
Parameters: {'alpha': np.float64(0.40134964872774576), 'average': False, 'l1_ratio': np.float64(0.05033776045421079)}

Model with rank: 3
Mean validation score: 0.983 (std: 0.011)
Parameters: {'alpha': np.float64(0.1352374671440465), 'average': False, 'l1_ratio': np.float64(0.6719936995475292)}

GridSearchCV took 3.28 seconds for 60 candidate parameter settings.
Model with rank: 1
Mean validation score: 0.994 (std: 0.005)
Parameters: {'alpha': np.float64(0.1), 'average': False, 'l1_ratio': np.float64(0.1111111111111111)}

Model with rank: 2
Mean validation score: 0.991 (std: 0.008)
Parameters: {'alpha': np.float64(0.1), 'average': False, 'l1_ratio': np.float64(0.0)}

Model with rank: 3
Mean validation score: 0.989 (std: 0.018)
Parameters: {'alpha': np.float64(1.0), '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_)

脚本总运行时间： (0分钟 3.871秒)

比较用于超参数估计的随机搜索和网格搜索#

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