# -*- coding: utf-8 -*- """ Created on Tue Aug 09 22:38:37 2016 @author: Administrator """ import time import numpy as np from sklearn.datasets import load_digits from sklearn.ensemble import RandomForestClassifier from sklearn.grid_search import GridSearchCV from sklearn.grid_search import RandomizedSearchCV # 生成数据 digits = load_digits() X, y = digits.data, digits.target # 元分类器 meta_clf = RandomForestClassifier(n_estimators=20) # ================================================================= # 设置参数 param_dist = {"max_depth": [3, None], "max_features": [1,5,7,11], "min_samples_split": [1,5,7,11], "min_samples_leaf": [1,5,7,11], "bootstrap": [True, False], "criterion": ["gini", "entropy"]} # 运行随机搜索 RandomizedSearch n_iter_search = 20 rs_clf = RandomizedSearchCV(meta_clf, param_distributions=param_dist,n_iter=n_iter_search) start = time.time() rs_clf.fit(X, y) print("RandomizedSearchCV took %.2f seconds for %d candidates parameter settings." % ((time.time() - start), n_iter_search)) print(rs_clf.grid_scores_) # ================================================================= # 设置参数 param_grid = {"max_depth": [3, None], "max_features": [1, 3, 10], "min_samples_split": [1, 3, 10], "min_samples_leaf": [1, 3, 10], "bootstrap": [True, False], "criterion": ["gini", "entropy"]} # 运行网格搜索 GridSearch gs_clf = GridSearchCV(meta_clf, param_grid=param_grid) start = time.time() gs_clf.fit(X, y) print("GridSearchCV took %.2f seconds for %d candidate parameter settings." % (time.time() - start, len(gs_clf.grid_scores_))) print(gs_clf.grid_scores_)
RandomizedSearchCV took 8.64 seconds for 20 candidates parameter settings.
[mean: 0.78075, std: 0.00987, params: {'bootstrap': True, 'min_samples_leaf': 11, 'max_features': 11, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': 3},mean: 0.90874, std: 0.01377, params: {'bootstrap': False, 'min_samples_leaf': 7, 'max_features': 11, 'criterion': 'entropy', 'min_samples_split': 1, 'max_depth': None}, mean: 0.90929, std: 0.01682, params: {'bootstrap': True, 'min_samples_leaf': 5, 'max_features': 11, 'criterion': 'gini', 'min_samples_split': 1, 'max_depth': None}, mean: 0.91987, std: 0.01263, params: {'bootstrap': False, 'min_samples_leaf': 5, 'max_features': 5, 'criterion': 'gini', 'min_samples_split': 1, 'max_depth': None}, mean: 0.79911, std: 0.00789, params: {'bootstrap': False, 'min_samples_leaf': 11, 'max_features': 5, 'criterion': 'entropy', 'min_samples_split': 11, 'max_depth': 3}, mean: 0.91764, std: 0.01220, params: {'bootstrap': True, 'min_samples_leaf': 1, 'max_features': 11, 'criterion': 'entropy', 'min_samples_split': 7, 'max_depth': None}, mean: 0.90874, std: 0.01730, params: {'bootstrap': False, 'min_samples_leaf': 11, 'max_features': 7, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': None}, mean: 0.76628, std: 0.01378, params: {'bootstrap': True, 'min_samples_leaf': 1, 'max_features': 1, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.79076, std: 0.01176, params: {'bootstrap': False, 'min_samples_leaf': 5, 'max_features': 7, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.91152, std: 0.01354, params: {'bootstrap': False, 'min_samples_leaf': 1, 'max_features': 1, 'criterion': 'entropy', 'min_samples_split': 7, 'max_depth': None}, mean: 0.78854, std: 0.02561, params: {'bootstrap': True, 'min_samples_leaf': 5, 'max_features': 5, 'criterion': 'entropy', 'min_samples_split': 1, 'max_depth': 3}, mean: 0.80078, std: 0.00885, params: {'bootstrap': False, 'min_samples_leaf': 1, 'max_features': 5, 'criterion': 'gini', 'min_samples_split': 5, 'max_depth': 3}, mean: 0.92098, std: 0.00853, params: {'bootstrap': False, 'min_samples_leaf': 5, 'max_features': 7, 'criterion': 'gini', 'min_samples_split': 5, 'max_depth': None}, mean: 0.83751, std: 0.01976, params: {'bootstrap': True, 'min_samples_leaf': 11, 'max_features': 1, 'criterion': 'gini', 'min_samples_split': 5, 'max_depth': None}, mean: 0.82415, std: 0.01263, params: {'bootstrap': True, 'min_samples_leaf': 1, 'max_features': 5, 'criterion': 'entropy', 'min_samples_split': 11, 'max_depth': 3}, mean: 0.75348, std: 0.01295, params: {'bootstrap': True, 'min_samples_leaf': 5, 'max_features': 1, 'criterion': 'entropy', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.83472, std: 0.00987, params: {'bootstrap': True, 'min_samples_leaf': 11, 'max_features': 1, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': None}, mean: 0.80690, std: 0.01698, params: {'bootstrap': False, 'min_samples_leaf': 11, 'max_features': 5, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.75014, std: 0.01576, params: {'bootstrap': True, 'min_samples_leaf': 11, 'max_features': 1, 'criterion': 'entropy', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.73901, std: 0.03013, params: {'bootstrap': True, 'min_samples_leaf': 7, 'max_features': 1, 'criterion': 'entropy', 'min_samples_split': 5, 'max_depth': 3}]
GridSearchCV took 83.70 seconds for 216 candidate parameter settings.
[mean: 0.74124, std: 0.04157, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.76405, std: 0.01263, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.75348, std: 0.03088, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.77852, std: 0.03227, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.75960, std: 0.03054, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.77017, std: 0.00473, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.74402, std: 0.01357, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.76183, std: 0.00884, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.77017, std: 0.03727, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.78186, std: 0.06008, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.79633, std: 0.02073, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.80579, std: 0.02625, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.79911, std: 0.02291, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.81135, std: 0.02082, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.81135, std: 0.01920, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.80301, std: 0.01797, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.79243, std: 0.03765, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.80801, std: 0.01096, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.79911, std: 0.01477, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.80134, std: 0.00711, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.79744, std: 0.02293, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.79744, std: 0.03528, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.78019, std: 0.00396, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.80189, std: 0.00813, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.82359, std: 0.01467, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 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'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.91263, std: 0.01212, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.90818, std: 0.01799, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.90540, std: 0.00526, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.91708, std: 0.00769, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.90540, std: 0.00594, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.88370, std: 0.01752, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 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None}, mean: 0.89093, std: 0.00730, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 1, 'max_depth': None}, mean: 0.88481, std: 0.01372, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 1, 'max_depth': None}, mean: 0.84140, std: 0.01263, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 1, 'max_depth': None}, mean: 0.85253, std: 0.02692, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 1, 'max_depth': None}, mean: 0.84808, std: 0.02225, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 1, 'max_depth': None}, mean: 0.93434, std: 0.00135, params: {'bootstrap': False, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.91375, std: 0.00740, params: {'bootstrap': False, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.92988, std: 0.00238, params: {'bootstrap': False, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.91653, std: 0.00467, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.91597, std: 0.00770, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.92543, std: 0.01502, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.89983, std: 0.00773, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.90985, std: 0.00839, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.90428, std: 0.00662, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}, mean: 0.93267, std: 0.01880, params: {'bootstrap': False, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.92432, std: 0.01951, params: {'bootstrap': False, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.92098, std: 0.00897, params: {'bootstrap': False, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.92265, std: 0.01260, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.92654, std: 0.01771, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.92599, std: 0.01215, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.90150, std: 0.02026, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.89649, std: 0.01794, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.91208, std: 0.00546, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}]
- GridSearchCV:
- Does exhaustive search over a grid of parameters.
- RandomizedSearchCV:
- Randomized search on hyper parameters.
-
RandomizedSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.
The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings.
In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.
If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
