RandomizedSearchCV 抽样分布

Question

根据 RandomizedSearchCV documentation（强调我的）：

param_distributions: dict or list of dicts

Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.

如果我对上述内容的理解是正确的，则在给定 n_iter = 10 的情况下，以下示例中的两种算法（XGBClassifier 和 LogisticRegression）都应该以高概率（>99%）进行采样。

from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler
from xgboost.sklearn import XGBClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE
from sklearn.model_selection import RandomizedSearchCV
from sklearn.pipeline import Pipeline


param_grid = [
              {'scaler': [StandardScaler()],
               'feature_selection': [RFE(estimator=XGBClassifier(use_label_encoder=False, eval_metric='logloss'))],
               'feature_selection__n_features_to_select': [3],
               'classification': [XGBClassifier(use_label_encoder=False, eval_metric='logloss')],
               'classification__n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000],
               'classification__max_depth': [2, 5, 10],
               },
              {'scaler': [StandardScaler()],
               'feature_selection': [RFE(estimator=LogisticRegression())],
               'feature_selection__n_features_to_select': [3],
               'classification': [LogisticRegression()],
               'classification__C': [0.1],
               },
              ]


pipe = Pipeline(steps=[('scaler', StandardScaler()), ('feature_selection', RFE(estimator=LogisticRegression())),
                       ('classification', LogisticRegression())])

classifier = RandomizedSearchCV(estimator=pipe, param_distributions=param_grid,
                                scoring='neg_brier_score', n_jobs=-1, verbose=10)

data = load_breast_cancer()
X = data.data
y = data.target.ravel()
classifier.fit(X, y)

虽然每次我运行 XGBClassifier 都会被选中 10/10 次。我希望一名候选人来自 Logistic Regression，因为每个 dict 被采样的概率是 50-50。如果两个算法之间的搜索 space 更平衡 ('classification__n_estimators': [100])，则采样按预期工作。有人可以澄清这里发生了什么吗？

Answer 1

是的，这是不正确的行为。有一个 Issue filed: when all the entries are lists (none are scipy distributions), the current code 从 ParameterGrid 中选择点，这意味着它将不成比例地从列表中较大的字典网格中选择点。

在合并修复之前，您可以通过对您不关心的内容使用 scipy 分发来解决此问题，比如 verbose?

RandomizedSearchCV 抽样分布

RandomizedSearchCV sampling distribution

scikit-learn

python-3.8