如何嵌套LabelKFold？

Question

我有一个包含约 300 个点和 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。

我的代码如下所示：

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

我的问题是如何为网格搜索和学习曲线设置 cv 属性，以便它将我的原始集分解为不共享任何用于计算学习曲线的标签的训练集和测试集。然后从这些训练集中，进一步将它们分成训练集和测试集，而不共享网格搜索的标签？

基本上，我如何运行嵌套的 LabelKFold？

---

我，为这个问题创建赏金的用户，使用来自sklearn 的数据编写了以下可重现的示例。

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

根据您的问题，您正在寻找数据上的 LabelKFold 分数，同时再次使用 LabelKFold 在此外部 LabelKFold 的每次迭代中对管道的参数进行网格搜索。虽然我无法实现开箱即用，但它只需要一个循环：

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，第一次迭代产生：

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，您在上一段代码中定义的 strata。