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超参数优化RandomizedSearchCV。下面是代码的摘要：

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)

我使用的是固定random_state的train_test_split，RandomForestClassifer和RandomizedSearchCV。但是，如果我多次运行上面的代码，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结果而获得相同的结果random_state吗？我的代码中是否遗漏了在部分代码中产生随机性的任何内容？

Answer 1

我通常会回答我自己的问题！我会把它留在这里给有类似问题的其他人：

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)

我希望它能帮助别人！