RandomizedSearchCV 使用相同的 random_state 给出不同的结果

Question

我正在使用管道来执行特征选择和使用RandomizedSearchCV 的超参数优化。以下是代码摘要：

from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest
from sklearn.grid_search import RandomizedSearchCV
from sklearn.pipeline import make_pipeline
from scipy.stats import randint as sp_randint

rng = 44

X_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)

我对@9​​87654324@、RandomForestClassifer 和RandomizedSearchCV 使用常量random_state。但是，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会导致测试单元失败。我不应该因为使用相同的random_state 而获得相同的结果吗？我的代码中是否遗漏了任何会在部分代码中产生随机性的内容？

Answer 1

我通常会回答自己的问题！我会把它留给其他有类似问题的人：

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_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest
from sklearn.grid_search import RandomizedSearchCV
from sklearn.pipeline import make_pipeline
from scipy.stats import randint as sp_randint

seed = 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)

我希望它可以帮助别人！