ValueError: continuous is not supported

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Your error continuous is not supported tells me you're trying to do "something" from regression domain on classification domain.

At least 1 thing captures my eye as your target is regression:

 scores = ['precision', 'recall']

To start with, both have nothing to do with regression (as @zero323 pointed out in a comment to your question): they are accuracy measures for classification. Try any regression scores that suit your tastes from this sklearn docs page, section "3.3.1.1. Common cases: predefined values"

As far as the rest of the code is concerned, I would strongly encourage you to rewrite your code from scratch: chunk for Lasso, chunk for Ridge, chunk for ElasticNet and chunk for SVM (why would you run Ridge and Lasso separately from ElasticNet as they are special cases of ElasticNet???). This will take you no more than 10-15 lines of code. Only after you made it sure all of them execute, optimal hyperparameters are found, and desirable regression metrics are calculated I would attempt optimizing the code and putting everything together in a loop.

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Toly
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Toly

Updated on July 09, 2022

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  • Toly
    Toly 6 months

    I am using GridSearchCV for cross validation of a linear regression (not a classifier nor a logistic regression).

    I also use StandardScaler for normalization of X

    My dataframe has 17 features (X) and 5 targets (y) (observations). Around 1150 rows

    I keep getting ValueError: continuous is not supported error message and ran out of options.

    here is some code (assume all imports are done properly):

    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
    

    These are the main pieces of the code

    This is the main part of Error output:

    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
    

    Here is some data after using soil.head(15). It does not show all the columns but it should behave in the same way with 8 features instead of 17. As for target: these are the last 5 columns but the code here calculated only one (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 rows × 22 columns