我刚开始接触机器学习领域，并通过参加Kaggle竞赛来获取一些实践经验。我正在参加CIFAR 10图像对象识别竞赛，需要将数千张图像分类到10个类别中，所有我使用的数据都可以在竞赛中找到。我尝试使用Gridsearch来优化我的机器学习算法的参数，但每次尝试用我的训练数据来拟合分类器时都会遇到错误。我已经找到了引发错误的函数，这似乎与我的标签类型不正确有关，但我不知道如何解决这个问题。我使用的标签是字符串，并且我对它们进行了预处理，以便将它们输入到算法中。我在这方面做错了什么吗？还是在为网格搜索分割数据集时出了问题？坦白说，我缺乏解决这个问题的经验和知识，我非常需要你们的帮助。

涉及的代码:

import globimport osfrom sklearn.svm import SVCfrom sklearn import preprocessingimport pandas as pdfrom sklearn import cross_validation from sklearn import metricsfrom sklearn.grid_search import GridSearchCVdef label_preprocessing(Labels):    Labels = np.array(Labels)[:,1]    le = preprocessing.LabelEncoder()    le.fit_transform(Labels)    return Labelsdef model_selection(train,Labels):    parameters = {"C":[0.1,1,10,100],"gamma":[0.0001,0.001,0.01,0.1]}    X_train, X_test, y_train, y_test = cross_validation.train_test_split(train, Labels, test_size = 0.2, random_state = 0)    svm = SVC()    clf  = GridSearchCV(svm,parameters)    clf  = clf.fit(X_train,y_train)    print ("20 fold cv score: ",np.mean(cross_validation.cross_val_score(clf,X_test,y_test,cv = 10,scoring = "roc_auc")))    return clfif  __name__ == "__main__":    train_images = np.array(file_open(image_dir1,"*.png"))[:100]    test_images = np.array(file_open(image_dir2,"*.png"))[:100]    Labels = label_preprocessing(pd.read_csv(image_dir3)[:100])    train_set = [matrix_image(image) for image in train_images]    test_set = [matrix_image(image) for image in test_images]    train_set = np.array(train_set)    test_set = np.array(test_set)    print("selecting best model and evaluating it")    svm = model_selection(train_set,Labels)    print("predicting stuff")    result = svm.predict(test_set)    np.savetxt("submission.csv", result, fmt = "%s", delimiter = ",")

完整的错误追踪:

Traceback (most recent call last):  File "C:\Users\Abdc\workspace\final_submission\src\SVM.py", line 49, in <module>    svm = model_selection(train_set,Labels)  File "C:\Users\Abdc\workspace\final_submission\src\SVM.py", line 35, in model_selection    clf  = clf.fit(X_train,y_train)  File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 707, in fit    return self._fit(X, y, ParameterGrid(self.param_grid))  File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 493, in _fit    for parameters in parameter_iterable  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 517, in __call__    self.dispatch(function, args, kwargs)  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 312, in dispatch    job = ImmediateApply(func, args, kwargs)  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 136, in __init__    self.results = func(*args, **kwargs)  File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 311, in fit_grid_point    this_score = clf.score(X_test, y_test)  File "C:\Python27\lib\site-packages\sklearn\base.py", line 294, in score    return accuracy_score(y, self.predict(X))  File "C:\Python27\lib\site-packages\sklearn\metrics\metrics.py", line 1064, in accuracy_score    y_type, y_true, y_pred = _check_clf_targets(y_true, y_pred)  File "C:\Python27\lib\site-packages\sklearn\metrics\metrics.py", line 123, in _check_clf_targets    raise ValueError("{0} is not supported".format(y_type))ValueError: unknown is not supported

引发错误的函数位于sklearn.metrics模块中:

def _check_clf_targets(y_true, y_pred):    """Check that y_true and y_pred belong to the same classification task    This converts multiclass or binary types to a common shape, and raises a    ValueError for a mix of multilabel and multiclass targets, a mix of    multilabel formats, for the presence of continuous-valued or multioutput    targets, or for targets of different lengths.    Column vectors are squeezed to 1d.    Parameters    ----------    y_true : array-like,    y_pred : array-like    Returns    -------    type_true : one of {'multilabel-indicator', 'multilabel-sequences', \                        'multiclass', 'binary'}        The type of the true target data, as output by        ``utils.multiclass.type_of_target``    y_true : array or indicator matrix or sequence of sequences    y_pred : array or indicator matrix or sequence of sequences    """    y_true, y_pred = check_arrays(y_true, y_pred, allow_lists=True)    type_true = type_of_target(y_true)    type_pred = type_of_target(y_pred)    y_type = set([type_true, type_pred])    if y_type == set(["binary", "multiclass"]):        y_type = set(["multiclass"])    if len(y_type) > 1:        raise ValueError("Can't handle mix of {0} and {1}"                         "".format(type_true, type_pred))    # We can't have more than one value on y_type => The set is no more needed    y_type = y_type.pop()    # No metrics support "multiclass-multioutput" format    if (y_type not in ["binary", "multiclass", "multilabel-indicator",                       "multilabel-sequences"]):        raise ValueError("{0} is not supported".format(y_type))    if y_type in ["binary", "multiclass"]:        y_true = column_or_1d(y_true)        y_pred = column_or_1d(y_pred)    return y_type, y_true, y_pred

关于标签的额外信息:

标签内容和数据类型:

In [21]:Labels = np.array(Labels)[:,1]LabelsOut[21]:array(['frog', 'truck', 'truck', ..., 'truck', 'automobile', 'automobile'], dtype=object)

预处理后的标签内容:

In [25]:Labels = np.array(Labels)[:,1]Labelsle = preprocessing.LabelEncoder()Labels = le.fit_transform(Labels)LabelsOut[25]:array([6, 9, 9, ..., 9, 1, 1])

预处理后的标签形状:

In [18]:    Labels = np.array(Labels)[:,1]    Labels.shape    le = preprocessing.LabelEncoder()    Labels = le.fit_transform(Labels)    Labels.shapeOut[18]:(50000L,)

原始数据可以在这里找到。数据包含一个数据点的ID及其类别标签，因此它是一个nx2的矩阵。

回答：

这可能是由#2374问题引起的。作为一种解决方法，你可以尝试使用Labels = Labels.astype(str)。

此外，我建议你遵循PEP8代码规范来与社区共享Python代码。特别是变量名通常应为小写。