在 gridsearchcv 期间打印用于网格搜索的参数答案

【问题标题】：Print Parameters Used in Grid Search During gridsearchcv在 gridsearchcv 期间打印用于网格搜索的参数
【发布时间】：2019-04-17 17:57:05
【问题描述】：

我正在尝试在执行网格搜索时查看当前在 gridsearchcv 中的自定义评分函数中使用的参数。理想情况下，这看起来像：

编辑：为了澄清，我希望使用网格搜索中的参数，因此我需要能够在函数中访问它们。

def fit(X, y): 
    grid = {'max_features':[0.8,'sqrt'],
            'subsample':[1, 0.7],
            'min_samples_split' : [2, 3],
            'min_samples_leaf' : [1, 3],
            'learning_rate' : [0.01, 0.1],
            'max_depth' : [3, 8, 15],
            'n_estimators' : [10, 20, 50]}   
    clf = GradientBoostingClassifier()
    score_func = make_scorer(make_custom_score, needs_proba=True)


    model = GridSearchCV(estimator=clf, 
                         param_grid=grid, 
                         scoring=score_func,
                         cv=5)


def make_custom_score(y_true, y_score):
    '''
    y_true: array-like, shape = [n_samples] Ground truth (true relevance labels).
    y_score : array-like, shape = [n_samples] Predicted scores
    '''

    print(parameters_used_in_current_gridsearch)

    …

    return score

我知道我可以在执行完成后获取参数，但是我试图在代码执行时获取参数。

【问题讨论】：

标签： python scikit-learn grid-search

【解决方案1】：

不确定这是否满足您的用例，但有一个 verbose 参数可用于此类东西：

from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import SGDRegressor

estimator = SGDRegressor()
gscv = GridSearchCV(estimator, {
    'alpha': [0.001, 0.0001], 'average': [True, False],
    'shuffle': [True, False], 'max_iter': [5], 'tol': [None]
}, cv=3, verbose=2)

gscv.fit([[1,1,1],[2,2,2],[3,3,3]], [1, 2, 3])

这将打印到stdout：

Fitting 3 folds for each of 8 candidates, totalling 24 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV] alpha=0.001, average=True, max_iter=5, shuffle=True, tol=None ...
[CV]  alpha=0.001, average=True, max_iter=5, shuffle=True, tol=None, total=   0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[CV] alpha=0.001, average=True, max_iter=5, shuffle=True, tol=None ...
[CV]  alpha=0.001, average=True, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.001, average=True, max_iter=5, shuffle=True, tol=None ...
[CV]  alpha=0.001, average=True, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.001, average=True, max_iter=5, shuffle=False, tol=None ..
[CV]  alpha=0.001, average=True, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.001, average=True, max_iter=5, shuffle=False, tol=None ..
[CV]  alpha=0.001, average=True, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.001, average=True, max_iter=5, shuffle=False, tol=None ..
[CV]  alpha=0.001, average=True, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.001, average=False, max_iter=5, shuffle=True, tol=None ..
[CV]  alpha=0.001, average=False, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.001, average=False, max_iter=5, shuffle=True, tol=None ..
[CV]  alpha=0.001, average=False, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.001, average=False, max_iter=5, shuffle=True, tol=None ..
[CV]  alpha=0.001, average=False, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.001, average=False, max_iter=5, shuffle=False, tol=None .
[CV]  alpha=0.001, average=False, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.001, average=False, max_iter=5, shuffle=False, tol=None .
[CV]  alpha=0.001, average=False, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.001, average=False, max_iter=5, shuffle=False, tol=None .
[CV]  alpha=0.001, average=False, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.0001, average=True, max_iter=5, shuffle=True, tol=None ..
[CV]  alpha=0.0001, average=True, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.0001, average=True, max_iter=5, shuffle=True, tol=None ..
[CV]  alpha=0.0001, average=True, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.0001, average=True, max_iter=5, shuffle=True, tol=None ..
[CV]  alpha=0.0001, average=True, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.0001, average=True, max_iter=5, shuffle=False, tol=None .
[CV]  alpha=0.0001, average=True, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.0001, average=True, max_iter=5, shuffle=False, tol=None .
[CV]  alpha=0.0001, average=True, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.0001, average=True, max_iter=5, shuffle=False, tol=None .
[CV]  alpha=0.0001, average=True, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.0001, average=False, max_iter=5, shuffle=True, tol=None .
[CV]  alpha=0.0001, average=False, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.0001, average=False, max_iter=5, shuffle=True, tol=None .
[CV]  alpha=0.0001, average=False, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.0001, average=False, max_iter=5, shuffle=True, tol=None .
[CV]  alpha=0.0001, average=False, max_iter=5, shuffle=True, tol=None, total=   0.0s
[CV] alpha=0.0001, average=False, max_iter=5, shuffle=False, tol=None
[CV]  alpha=0.0001, average=False, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.0001, average=False, max_iter=5, shuffle=False, tol=None
[CV]  alpha=0.0001, average=False, max_iter=5, shuffle=False, tol=None, total=   0.0s
[CV] alpha=0.0001, average=False, max_iter=5, shuffle=False, tol=None
[CV]  alpha=0.0001, average=False, max_iter=5, shuffle=False, tol=None, total=   0.0s
[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s finished

您可以参考文档，但也可以为更高的详细程度指定更高的值。

【讨论】：

我见过详细的参数，但我希望能够专门使用网格中的参数。因此，我需要能够访问函数中的实际参数。

【解决方案2】：

如果您需要在网格搜索步骤之间实际执行某些操作，则需要使用一些较低级别的 Scikit-learn 功能编写自己的例程。

GridSearchCV 内部使用ParameterGrid 类，您可以对其进行迭代以获得参数值的组合。

基本循环看起来像这样

import sklearn
from sklearn.model_selection import ParameterGrid, KFold

clf = GradientBoostingClassifier()

grid = {
    'max_features': [0.8,'sqrt'],
    'subsample': [1, 0.7],
    'min_samples_split': [2, 3],
    'min_samples_leaf': [1, 3],
    'learning_rate': [0.01, 0.1],
    'max_depth': [3, 8, 15],
    'n_estimators': [10, 20, 50]
}

scorer = make_scorer(make_custom_score, needs_proba=True)
sampler = ParameterGrid(grid)
cv = KFold(5)

for params in sampler:
    for ix_train, ix_test in cv.split(X, y):
        clf_fitted = clone(clf).fit(X[ix_train], y[ix_train])
        score = scorer(clf_fitted, X[ix_test], y[ix_test])
        # do something with the results

【讨论】：

我最终使用这种方法来做我特别需要做的事情，所以我接受了这个答案。其他的回答不一定是错的，他们只是没有给我我需要的东西。
@RyanL 很高兴它有所帮助。请注意，我在代码中犯了一个错误，使用ParameterSampler（随机采样）而不是ParameterGrid（确定性网格）

【解决方案3】：

除了在"custom score" 上使用make_scorer()，您还可以创建自己的scorer（注意score 和scorer 之间的区别！！）它接受三个带有签名(estimator, X_test, y_test) 的参数。见the documentation for more details。

在此函数中，您可以访问已在网格搜索中的训练数据上训练的estimator 对象。然后，您可以轻松访问该估算器的所有参数。但请确保返回一个浮点值作为分数。

类似：

def make_custom_scorer(estimator, X_test, y_test):
    '''
    estimator: scikit-learn estimator, fitted on train data
    X_test: array-like, shape = [n_samples, n_features] Data for prediction
    y_test: array-like, shape = [n_samples] Ground truth (true relevance labels).
    y_score : array-like, shape = [n_samples] Predicted scores
    '''

    # Here all_params is a dict of all the parameters in use
    all_params = estimator.get_params()

    # You need to do some filtering to get the parameters you want, 
    # but that should be easy I guess (just specify the key you want)
    parameters_used_in_current_gridsearch = {k:v for k,v in all_params.items() 
                                            if k in ['max_features', 'subsample', ..., 'n_estimators']}
    print(parameters_used_in_current_gridsearch)

    y_score = estimator.predict(X_test)

    # Use whichever metric you want here
    score = scoring_function(y_test, y_score)
    return score

【讨论】：