gridsearchcv期間在網格搜索中使用的打印參數

Question

我正在嘗試在執行網格搜索時查看gridsearchcv的自定義評分功能中當前正在使用的參數。 理想情況下，它看起來像：

編輯 ：澄清一下，我正在尋找使用網格搜索中的參數，因此我需要能夠在函數中訪問它們。

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

我知道執行完成后就可以獲取參數，但是我正在嘗試在代碼執行時獲取參數。

Answer 1

不知道這是否滿足您的用例，但是有一個verbose參數可用於此類事情：

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])

這將打印到以下內容到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

您可以參考文檔，但也可以為更高的詳細程度指定更高的值。

Answer 2

如果需要在網格搜索步驟之間實際執行某項操作，則需要使用一些較低級的Scikit學習功能編寫自己的例程。

GridSearchCV內部使用ParameterGrid類，您可以對其進行迭代以獲得參數值的組合。

基本循環看起來像這樣

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

Answer 3

而不是使用的make_scorer()在你的"custom score" ，你可以讓自己的scorer （注意之間的差異score和scorer ！），它接受三個參數與簽名(estimator, X_test, y_test) 有關更多詳細信息，請參見文檔 。

在此功能中，您可以訪問在網格搜索中已針對訓練數據進行訓練的estimator對象。 然后，您可以輕松訪問該估算器的所有參數。 但是請確保返回浮點值作為得分。

就像是：

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