如何嵌套LabelKFold？

Question

我有一个具有〜300点和32个不同标签的数据集，我想通过使用网格搜索和LabelKFold验证绘制其学习曲线来评估LinearSVR模型。

我的代码看起来是这样的：

import numpy as np 
from sklearn import preprocessing 
from sklearn.svm import LinearSVR 
from sklearn.pipeline import Pipeline 
from sklearn.cross_validation import LabelKFold 
from sklearn.grid_search import GridSearchCV 
from sklearn.learning_curve import learning_curve 
    ... 
#get data (x, y, labels) 
    ... 
C_space = np.logspace(-3, 3, 10) 
epsilon_space = np.logspace(-3, 3, 10) 

svr_estimator = Pipeline([ 
    ("scale", preprocessing.StandardScaler()), 
    ("svr", LinearSVR), 
]) 

search_params = dict(
    svr__C = C_space, 
    svr__epsilon = epsilon_space 
) 

kfold = LabelKFold(labels, 5) 

svr_search = GridSearchCV(svr_estimator, param_grid = search_params, cv = ???) 

train_space = np.linspace(.5, 1, 10) 
train_sizes, train_scores, valid_scores = learning_curve(svr_search, x, y, train_sizes = train_space, cv = ???, n_jobs = 4) 
    ... 
#plot learning curve 

我的问题是如何设置的CV属性网格搜索和学习曲线，这样它会打破我原来设置成训练和测试集，唐不共享用于计算学习曲线的任何标签。然后从这些训练集中，进一步将它们分成训练集和测试集，而不共享网格搜索标签？

本质上，我该如何运行一个嵌套的LabelKFold？

---

我，谁创造了赏金这一问题的用户，使用数据可从sklearn写了下面的可重复的例子。

import numpy as np 
from sklearn.datasets import load_digits 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.metrics import make_scorer, roc_auc_score 
from sklearn.grid_search import GridSearchCV 
from sklearn.cross_validation import cross_val_score, LabelKFold 

digits = load_digits() 
X = digits['data'] 
Y = digits['target'] 
Z = np.zeros_like(Y) ## this is just to make a 2-class problem, purely for the sake of an example 
Z[np.where(Y>4)]=1 

strata = [x % 13 for x in xrange(Y.size)] # define the strata for use in 

## define stuff for nested cv... 
mtry = [5, 10] 
tuned_par = {'max_features': mtry} 
toy_rf = RandomForestClassifier(n_estimators=10, max_depth=10, random_state=10, 
           class_weight="balanced") 
roc_auc_scorer = make_scorer(roc_auc_score, needs_threshold=True) 

## define outer k-fold label-aware cv 
outer_cv = LabelKFold(labels=strata, n_folds=5) 

############################################################################# 
## this works: using regular randomly-allocated 10-fold CV in the inner folds 
############################################################################# 
vanilla_clf = GridSearchCV(estimator=toy_rf, param_grid=tuned_par, scoring=roc_auc_scorer, 
         cv=5, n_jobs=1) 
vanilla_results = cross_val_score(vanilla_clf, X=X, y=Z, cv=outer_cv, n_jobs=1) 

########################################################################## 
## this does not work: attempting to use label-aware CV in the inner loop 
########################################################################## 
inner_cv = LabelKFold(labels=strata, n_folds=5) 
nested_kfold_clf = GridSearchCV(estimator=toy_rf, param_grid=tuned_par, scoring=roc_auc_scorer, 
           cv=inner_cv, n_jobs=1) 
nested_kfold_results = cross_val_score(nested_kfold_clf, X=X, y=Y, cv=outer_cv, n_jobs=1) 

Answer 1

从你的问题，你正在寻找LabelKFold得分上的数据，而网搜索每本外LabelKFold的迭代您的管道的参数，再次使用一个LabelKFold。

outer_cv = LabelKFold(labels=strata, n_folds=3) 
strata = np.array(strata) 
scores = [] 
for outer_train, outer_test in outer_cv: 
    print "Outer set. Train:", set(strata[outer_train]), "\tTest:", set(strata[outer_test]) 
    inner_cv = LabelKFold(labels=strata[outer_train], n_folds=3) 
    print "\tInner:" 
    for inner_train, inner_test in inner_cv: 
     print "\t\tTrain:", set(strata[outer_train][inner_train]), "\tTest:", set(strata[outer_train][inner_test]) 
    clf = GridSearchCV(estimator=toy_rf, param_grid=tuned_par, scoring=roc_auc_scorer, cv= inner_cv, n_jobs=1) 
    clf.fit(X[outer_train],Z[outer_train]) 
    scores.append(clf.score(X[outer_test], Z[outer_test])) 

运行代码，第一次迭代产量：尽管外的开箱只需要一个循环我没能做到这一点

Outer set. Train: set([0, 1, 4, 5, 7, 8, 10, 11]) Test: set([9, 2, 3, 12, 6]) 
Inner: 
    Train: set([0, 10, 11, 5, 7]) Test: set([8, 1, 4]) 
    Train: set([1, 4, 5, 8, 10, 11]) Test: set([0, 7]) 
    Train: set([0, 1, 4, 8, 7])  Test: set([10, 11, 5]) 

因此，很容易验证它是否按预期执行。您的交叉验证分数在列表scores中，您可以轻松处理它们。我已经使用了变量，例如您在最后一段代码中定义的strata。