ValueError：不支持连续[重复]

我正在使用 GridSearchCV 进行线性回归的交叉验证（不是分类器也不是逻辑回归）。

我还使用 StandardScaler 对 X 进行标准化

我的数据框有 17 个特征 (X) 和 5 个目标 (y)（观察）。约1150行

我不断收到 ValueError: Continuous is notsupported 错误消息，并且没有选项。

这是一些代码（假设所有导入均正确完成）：

soilM = pd.read_csv('C:/training.csv', index_col=0)
soilM = getDummiedSoilDepth(soilM) #transform text values in 0 and 1

soilM = soilM.drop('Depth', 1) 

soil = soilM.iloc[:,-22:]

X_train, X_test, Ca_train, Ca_test, P_train, P_test, pH_train, pH_test, SOC_train, SOC_test, Sand_train, Sand_test = splitTrainTestAdv(soil)

scores = ['precision', 'recall']


for score in scores:

    for model in MODELS.keys():

        print model, score

        performParameterSelection(model, score, X_test, Ca_test, X_train, Ca_train)

def performParameterSelection(model_name, criteria, X_test, y_test, X_train, y_train):

    model, param_grid = MODELS[model_name]
    gs = GridSearchCV(model, param_grid, n_jobs= 1, cv=5, verbose=1, scoring='%s_weighted' % criteria)

    gs.fit(X_train, y_train) 

    print(gs.best_params_)

    for params, mean_score, scores in gs.grid_scores_:
        print("%0.3f (+/-%0.03f) for %r"
          % (mean_score, scores.std() * 2, params))


    y_true, y_pred = y_test, gs.predict(X_test)
    print(classification_report(y_true, y_pred))


MODELS = {
    'lasso': (
        linear_model.Lasso(),
        {'alpha': [0.95]}
    ),
    'ridge': (
        linear_model.Ridge(),
        {'alpha': [0.01]}
    ),
    'elasticnet': (
        linear_model.ElasticNet(),
        {
            'alpha': [0.6],
            'l1_ratio': [0.4]
        }
    ),
    'svr': (
        svm.SVR(),
        {
            'C': [5.0],
            'epsilon': [0.1],
            'kernel': ['linear']
        }
    )
 }


def performLasso(X_train, y_train, X_test, parameter):

     alpha = parameter[0]

    model = linear_model.Lasso(alpha=alpha, normalize=True) #pass alpha to Lasso
    model.fit(X_train, y_train)



    return model.predict(X_test)

def splitTrainTestAdv(df):


    y = df.iloc[:,-5:].copy()  # last 5 columns
    X1 = df.iloc[:,:-5].copy()  # Except for last 5 columns

    Ca = y['Ca'].copy()
    P = y['P'].copy()
    pH = y['pH'].copy()
    SOC = y['SOC'].copy()
    Sand = y['Sand'].copy()


    #Scaling and Sampling

    X = StandardScaler(copy=False).fit_transform(X1)

    X_train, X_test, Ca_train, Ca_test = train_test_split(X, Ca, test_size=0.2, random_state=0)


    return X_train, X_test, Ca_train, Ca_test, P_train, P_test, pH_train, pH_test, SOC_train, SOC_test, Sand_train, Sand_test

这些是代码的主要部分

这是错误输出的主要部分：

ValueError                                Traceback (most recent call last)
<ipython-input-90-1315d47e2551> in <module>()
     20         print '####################'
     21         print featuresV[1]
---> 22         performParameterSelection(model, score, X_test, Ca_test,  X_train, Ca_train)
     23         print featuresV[2]
     24         performParameterSelection(model, score, X_test, P_test, X_train, P_train)

<ipython-input-41-7075e1a49412> in performParameterSelection(model_name, criteria, X_test, y_test, X_train, y_train)
     12     # cv=5 - constant; verbose - keep writing
     13 
---> 14     gs.fit(X_train, y_train) # Will get grid scores with outputs from ALL models described above
     15 
     16         #pprint(sorted(gs.grid_scores_, key=lambda x: -x.mean_validation_score))

C:\Users\Tony\Anaconda\lib\site-packages\sklearn\grid_search.pyc in fit(self, X, y)
    730 
    731         """
--> 732         return self._fit(X, y, ParameterGrid(self.param_grid))



     90     if (y_type not in ["binary", "multiclass", "multilabel-indicator",
     91                        "multilabel-sequences"]):
---> 92         raise ValueError("{0} is not supported".format(y_type))
     93 
     94     if y_type in ["binary", "multiclass"]:

 ValueError: continuous is not supported

这是使用soil.head(15)后的一些数据。它没有显示所有列，但它应该以相同的方式运行，有 8 个特征，而不是 17 个。至于目标：这些是最后 5 列，但这里的代码只计算了一个 (Ca)

    BSAN    BSAS    BSAV    CTI ELEV    EVI LSTD    LSTN    REF1    REF2    ... RELI    Subsoil Topsoil TMAP    TMFI    Ca  P   pH  SOC Sand
PIDN                                                                                    
92RkYor6    -0.405797   -0.563636   -0.806271   -0.228241   -0.691982     1.653790  -0.605889   0.627488    -0.856727   0.056586    ... -0.062181   0     1 0.896228    1.651807    -0.394962   0.031291    0.488676    -0.389042   0.630347
nPv9P04t    -0.688406   -0.709091   -0.739082   -0.189180   1.185523    0.395773    -0.381748   -0.338928   -0.774545   -0.818182   ... 2.995923    1   0   1.539208    1.618022    -0.460044   -0.366432   -0.549490   0.204798    -1.162260
oCASbXEx    -0.623188   -0.654545   -0.727884   -0.155835   0.711136    0.517493    -0.035002   -0.092554   -0.725818   -0.651206   ... -0.300034   1   0   0.286952    0.657765    0.259613    -0.407934   0.591558    -0.529688   -0.793082
xq94dGBz    -0.746377   -0.781818   -0.862262   -0.340427   0.791314    0.672741    -0.665032   -0.128613   -0.853091   -0.741187   ... -0.418960   0     1 0.276740    0.678724    -0.467854   -0.245386   -0.577548   -0.428111   -0.130845
GYSYA8Yf    -0.862319   -0.836364   -0.783875   -0.020427   4.715590    0.473032    -1.321194   -2.560069   -0.791273   -0.827458   ... 2.299354    1   0   0.583042    1.825040    1.442361    -0.328389   0.797320    -0.443738   -0.892037
G4e9Ahvi    -0.710145   -0.736364   -0.727884   -0.175122   -1.003786   0.744898    -0.678329   0.851702    -0.661818   -0.474954   ... -0.300034   1   0   1.544703    1.641861    -0.355335   -0.079380   -0.287610   -0.256209   0.287810
SHU443XO    -0.579710   -0.736364   -0.963046   -0.536744   -0.179733   1.793003    -0.914052   0.291898    -0.966545   -0.086271   ... 0.260618    0   1   1.840689    2.223996    -0.499961   0.155796    -0.886192   -0.107749   0.942435
oAeygDKu    -0.152174   -0.154545   -0.134378   1.252267    -0.796659   -0.155977   1.309391    0.642680    -0.205818   -0.341373   ... -0.537887   1   0   -0.320335   0.429981    -0.441821   -0.352598   0.339031    -0.826609   1.650344
agBvYkUI    -0.724638   -0.790909   -0.839866   0.114245    1.363697    0.726676    -1.687885   0.060034    -0.706909   -0.523191   ... 1.127081    1   0   1.254782    0.972442    -0.505456   -0.345681   -1.774712   0.071966    -1.207931
8ujcZd8d    -0.427536   -0.600000   -0.806271   -0.667808   -1.208686   2.008018    -1.276453   1.203854    -0.698182   0.224490    ... 0.107713    0   1   0.288463    0.013744    -0.362277   -0.338764   0.039740    -0.232768   0.451467
hqO5LhmQ    -0.644928   -0.690909   -0.772676   -0.195877   1.138753    0.390671    0.145537    -0.544813   -0.722909   -0.729128   ... -0.537887   0   1   0.153926    0.422784    -0.460333   -0.300721   -0.063142   -0.607825   1.208852
QsfH8CWp    -0.449275   -0.618182   -0.862262   -0.512923   -0.712027   1.537901    -0.665190   0.595265    -0.884364   -0.103896   ... -0.028203   1   0   0.896228    1.651807    -0.475953   -0.252303   -0.128612   -0.670335   0.786391
5hhEGbrX    -0.260870   -0.290909   -0.335946   -0.175122   -0.749889   0.400146    0.299908    0.567983    -0.423273   -0.244898   ... -0.520897   1   0   0.249117    0.907095    -0.142446   -0.397558   0.423206    -0.412483   -0.678903
XlJWsmdz    -0.768116   -0.800000   -0.873460   -0.737115   0.682183    1.013848    -1.013065   -0.376346   -0.837818   -0.544527   ... 1.619776    1   0   0.942437    1.482143    -0.358517   1.283256    -0.072494   -0.490620   -0.899649
FY3riRgw    -0.818841   -0.863636   -0.873460   -0.739177   1.715590    1.434402    -1.669818   -0.090647   -0.874909   -0.388683   ... 3.182807    0   1   1.254782    0.972442    -0.333063   0.020916    -0.942309   1.314342    -0.690321

15行×22列

你的错误continuous is not supported告诉我你正在尝试从回归域到分类域做“某事”。

当你的目标是回归时，至少有一件事吸引了我的注意：

 scores = ['precision', 'recall']

首先，两者都与回归无关（正如 @zero323 在对您的问题的评论中指出的那样）：它们是分类的准确性度量。尝试任何适合您口味的回归分数this http://scikit-learn.org/stable/modules/model_evaluation.htmlsklearn 文档页面，“3.3.1.1. 常见情况：预定义值”部分

就其余代码而言，我强烈鼓励您从头开始重写代码：Lasso 的 chunk、Ridge 的 chunk、ElasticNet 的 chunk 和 SVM 的 chunk（为什么要与 ElasticNet 分开运行 Ridge 和 Lasso，因为它们是 ElasticNet 的特例？？？）。这将需要您不超过 10-15 行代码。只有在确保所有这些都执行、找到最佳超参数并计算出所需的回归指标之后，我才会尝试优化代码并将所有内容放在一个循环中。