我使用管道（pipeline）来进行特征选择和超参数优化，使用的是RandomizedSearchCV。以下是代码摘要：
from sklearn.cross_validation import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.feature_selection import SelectKBestfrom sklearn.grid_search import RandomizedSearchCVfrom sklearn.pipeline import make_pipelinefrom scipy.stats import randint as sp_randintrng = 44X_train, X_test, y_train, y_test = train_test_split(data[features], data['target'], random_state=rng)clf = RandomForestClassifier(random_state=rng)kbest = SelectKBest()pipe = make_pipeline(kbest,clf)upLim = X_train.shape[1]param_dist = {'selectkbest__k':sp_randint(upLim/2,upLim+1), 'randomforestclassifier__n_estimators': sp_randint(5,150), 'randomforestclassifier__max_depth': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, None], 'randomforestclassifier__criterion': ["gini", "entropy"], 'randomforestclassifier__max_features': ['auto', 'sqrt', 'log2']}clf_opt = RandomizedSearchCV(pipe, param_distributions= param_dist, scoring='roc_auc', n_jobs=1, cv=3, random_state=rng)clf_opt.fit(X_train,y_train)y_pred = clf_opt.predict(X_test)我在train_test_split、RandomForestClassifer和RandomizedSearchCV中使用了相同的random_state。然而，如果我多次运行上述代码，结果会略有不同。更具体地说，我的代码中有几个测试单元，这些略有不同的结果导致测试单元失败。难道不是因为使用了相同的random_state，我应该得到相同的结果吗？我在代码中是否遗漏了什么，导致代码的某些部分产生了随机性？
回答：
我通常会自己回答自己的问题！我将它留在这里，以帮助有类似问题的人：
为了确保避免任何随机性，我定义了一个随机种子。代码如下：
from sklearn.cross_validation import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.feature_selection import SelectKBestfrom sklearn.grid_search import RandomizedSearchCVfrom sklearn.pipeline import make_pipelinefrom scipy.stats import randint as sp_randintseed = np.random.seed(22)X_train, X_test, y_train, y_test = train_test_split(data[features], data['target'])clf = RandomForestClassifier()kbest = SelectKBest()pipe = make_pipeline(kbest,clf)upLim = X_train.shape[1]param_dist = {'selectkbest__k':sp_randint(upLim/2,upLim+1), 'randomforestclassifier__n_estimators': sp_randint(5,150), 'randomforestclassifier__max_depth': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, None], 'randomforestclassifier__criterion': ["gini", "entropy"], 'randomforestclassifier__max_features': ['auto', 'sqrt', 'log2']}clf_opt = RandomizedSearchCV(pipe, param_distributions= param_dist, scoring='roc_auc', n_jobs=1, cv=3)clf_opt.fit(X_train,y_train)y_pred = clf_opt.predict(X_test)希望这对其他人有所帮助！
