# -*- coding: utf-8 -*- 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# 生成數據 digits = load_digits() X, y = digits.data, digits.target# 元分類器 meta_clf = RandomForestClassifier(n_estimators=20)# ================================================================= # 設置參數 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"]}# 運行隨機搜索 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_)# ================================================================= # 設置參數 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"]}# 運行網格搜索 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_)

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