我有一个数据集，有大约300个点和32个不同的标签，我想通过使用网格搜索和labelkfold验证绘制学习曲线来评估LinearSvr模型。
我的代码如下：
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网格搜索和学习曲线的cv属性，以便它将我的原始集分解为不共享任何标签来计算学习曲线的培训和测试集。然后，从这些训练集中，进一步将它们分为训练集和测试集，而不共享网格搜索的标签？
基本上，如何运行嵌套labelkfold？
我，为这个问题创建了奖金的用户，使用
sklearn中提供的数据编写了以下可复制的示例。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)
最佳答案
从您的问题来看，您正在寻找您的数据上的labelkfold分数，而网格则在这个外部labelkfold的每个迭代中搜索管道的参数，再次使用labelkfold。虽然我无法实现开箱即用，但只需要一个循环：
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]))
运行代码时，第一次迭代将生成：
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中，您可以轻松地处理它们。我使用了变量，例如，
scores您在最后一段代码中定义的变量。