我喜欢使用scikit的LOGO（留一组出）作为交叉验证方法，并结合学习曲线。在我处理的大多数情况下，这种方法效果很好，但我只能（高效地）使用我认为在这些情况下最关键的两个参数（基于经验）：最大特征数和估计器数量。以下是我的代码示例：
Fscorer = make_scorer(f1_score, average = 'micro') gp = training_data["GP"].values logo = LeaveOneGroupOut() from sklearn.ensemble import RandomForestClassifier RF_clf100 = RandomForestClassifier (n_estimators=100, n_jobs=-1, random_state = 49) RF_clf200 = RandomForestClassifier (n_estimators=200, n_jobs=-1, random_state = 49) RF_clf300 = RandomForestClassifier (n_estimators=300, n_jobs=-1, random_state = 49) RF_clf400 = RandomForestClassifier (n_estimators=400, n_jobs=-1, random_state = 49) RF_clf500 = RandomForestClassifier (n_estimators=500, n_jobs=-1, random_state = 49) RF_clf600 = RandomForestClassifier (n_estimators=600, n_jobs=-1, random_state = 49) param_name = "max_features" param_range = param_range = [5, 10, 15, 20, 25, 30] plt.figure() plt.suptitle('n_estimators = 100', fontsize=14, fontweight='bold') _, test_scores = validation_curve(RF_clf100, X, y, cv=logo.split(X, y, groups=gp), param_name=param_name, param_range=param_range, scoring=Fscorer, n_jobs=-1) test_scores_mean = np.mean(test_scores, axis=1) plt.plot(param_range, test_scores_mean) plt.xlabel(param_name) plt.xlim(min(param_range), max(param_range)) plt.ylabel("F1") plt.ylim(0.47, 0.57) plt.legend(loc="best") plt.show() plt.figure() plt.suptitle('n_estimators = 200', fontsize=14, fontweight='bold') _, test_scores = validation_curve(RF_clf200, X, y, cv=logo.split(X, y, groups=gp), param_name=param_name, param_range=param_range, scoring=Fscorer, n_jobs=-1) test_scores_mean = np.mean(test_scores, axis=1) plt.plot(param_range, test_scores_mean) plt.xlabel(param_name) plt.xlim(min(param_range), max(param_range)) plt.ylabel("F1") plt.ylim(0.47, 0.57) plt.legend(loc="best") plt.show() ... ...但我真正想要的是将LOGO与网格搜索或随机搜索结合起来，以便更彻底地搜索参数空间。
目前我的代码如下所示：
param_dist = {"n_estimators": [100, 200, 300, 400, 500, 600], "max_features": sp_randint(5, 30), "max_depth": sp_randint(2, 18), "criterion": ['entropy', 'gini'], "min_samples_leaf": sp_randint(2, 17)}clf = RandomForestClassifier(random_state = 49)n_iter_search = 45random_search = RandomizedSearchCV(clf, param_distributions=param_dist, n_iter=n_iter_search, scoring=Fscorer, cv=8, n_jobs=-1)random_search.fit(X, y)当我将cv = 8替换为cv=logo.split(X, y, groups=gp)时，我得到了以下错误消息：
---------------------------------------------------------------------------TypeError Traceback (most recent call last)<ipython-input-10-0092e11ffbf4> in <module>()---> 35 random_search.fit(X, y)/Applications/anaconda/lib/python2.7/site-packages/sklearn/model_selection/_search.pyc in fit(self, X, y, groups) 1183 self.n_iter, 1184 random_state=self.random_state)-> 1185 return self._fit(X, y, groups, sampled_params)/Applications/anaconda/lib/python2.7/site-packages/sklearn/model_selection/_search.pyc in _fit(self, X, y, groups, parameter_iterable) 540 541 X, y, groups = indexable(X, y, groups)--> 542 n_splits = cv.get_n_splits(X, y, groups) 543 if self.verbose > 0 and isinstance(parameter_iterable, Sized): 544 n_candidates = len(parameter_iterable)/Applications/anaconda/lib/python2.7/site-packages/sklearn/model_selection/_split.pyc in get_n_splits(self, X, y, groups) 1489 Returns the number of splitting iterations in the cross-validator. 1490 """-> 1491 return len(self.cv) # Both iterables and old-cv objects support len 1492 1493 def split(self, X=None, y=None, groups=None):TypeError: object of type 'generator' has no len()关于（1）发生了什么，以及更重要的是，（2）如何使其工作（将RandomizedSearchCV与LeaveOneGroupOut结合使用），有什么建议吗？
* 更新 2017年2月8日*
使用cv=logo和@Vivek Kumar的建议random_search.fit(X, y, wells)成功了
回答：
您不应该将logo.split()传递给RandomizedSearchCV，只需将cv对象如logo传递给它即可。RandomizedSearchCV会在内部调用split()来生成训练测试索引。您可以将您的gp组传递给RandomizedSearchCV或GridSearchCV对象的fit()调用中。
不要这样做：
random_search.fit(X, y)而是这样做：
random_search.fit(X, y, gp)编辑：您还可以在GridSearchCV或RandomizedSearchCV的构造函数中将gp作为字典传递给fit_params参数。
