grid search参数调优

一、简介

之前看论文的时候，看到过grid search进行超参调优，一开始觉得应该是个比较高大上的东西，其实后来一看就是个暴力搜索。如果一个模型一共有3个参数，分别是A、B、C，假如A有3个选择，B有4个选择，C有5个选择，为了选出最好的模型，就需要一个参数一个参数试过去，从这60（$3\ast 4\ast 5$）个模型里面找出一个符合自己需求的。

注意：grid search是一种调优的思路，和实现方式是没啥关系的。

你既可以通过简单的for循环，也可以直接用sklearn里面的类sklearn.model_selection.GridSearchCV。

这篇博客我觉得讲得挺好的：调参必备–Grid Search网格搜索

grid search缺点也很明显，比较耗时，所以在实际应用中，需要减少调优参数的个数并且缩小每个参数的搜索范围。

二、代码实现

sklearn官网提供的代码就挺好。sklearn官网参数调优示例

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC

print(__doc__)

# Loading the Digits dataset
digits = datasets.load_digits()

# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target

# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.5, random_state=0)

# Set the parameters by cross-validation
tuned_parameters = [{
    
    'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                     'C': [1, 10, 100, 1000]},
                    {
    
    'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]

scores = ['precision', 'recall']

for score in scores:
    print("# Tuning hyper-parameters for %s" % score)
    print()

    clf = GridSearchCV(
        SVC(), tuned_parameters, scoring='%s_macro' % score
    )
    clf.fit(X_train, y_train)

    print("Best parameters set found on development set:")
    print()
    print(clf.best_params_)
    print()
    print("Grid scores on development set:")
    print()
    means = clf.cv_results_['mean_test_score']
    stds = clf.cv_results_['std_test_score']
    for mean, std, params in zip(means, stds, clf.cv_results_['params']):
        print("%0.3f (+/-%0.03f) for %r"
              % (mean, std * 2, params))
    print()

    print("Detailed classification report:")
    print()
    print("The model is trained on the full development set.")
    print("The scores are computed on the full evaluation set.")
    print()
    y_true, y_pred = y_test, clf.predict(X_test)
    print(classification_report(y_true, y_pred))
    print()

# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality.

三、其他

官网是全英文的，如果嫌麻烦，可以看看这篇博客：【GridSearchCV，CV调优超参数使用】【K最近邻分类器 KNeighborsClassifier 使用】【交叉验证】