python - Scikit-learn TypeError: If no scoring is specified, the estimator passed should have a 'score' method

Question

I have created a custom model in python using scikit-learn, and I want to use cross validation.

The class for the model is defined as follows:

class MultiLabelEnsemble:
''' MultiLabelEnsemble(predictorInstance, balance=False)
    Like OneVsRestClassifier: Wrapping class to train multiple models when 
    several objectives are given as target values. Its predictor may be an ensemble.
    This class can be used to create a one-vs-rest classifier from multiple 0/1 labels
    to treat a multi-label problem or to create a one-vs-rest classifier from
    a categorical target variable.
    Arguments:
        predictorInstance -- A predictor instance is passed as argument (be careful, you must instantiate
    the predictor class before passing the argument, i.e. end with (), 
    e.g. LogisticRegression().
        balance -- True/False. If True, attempts to re-balance classes in training data
        by including a random sample (without replacement) s.t. the largest class has at most 2 times
    the number of elements of the smallest one.
    Example Usage: mymodel =  MultiLabelEnsemble (GradientBoostingClassifier(), True)'''

def __init__(self, predictorInstance, balance=False):
    self.predictors = [predictorInstance]
    self.n_label = 1
    self.n_target = 1
    self.n_estimators =  1 # for predictors that are ensembles of estimators
    self.balance=balance

def __repr__(self):
    return "MultiLabelEnsemble"

def __str__(self):
    return "MultiLabelEnsemble : \n" + "\tn_label={}\n".format(self.n_label) + "\tn_target={}\n".format(self.n_target) + "\tn_estimators={}\n".format(self.n_estimators) + str(self.predictors[0])

def fit(self, Xtrain, Ytrain):
    if len(Ytrain.shape)==1: 
        Ytrain = np.array([Ytrain]).transpose() # Transform vector into column matrix
        # This is NOT what we want: Y = Y.reshape( -1, 1 ), because Y.shape[1] out of range
    self.n_target = Ytrain.shape[1]                # Num target values = num col of Y
    self.n_label = len(set(Ytrain.ravel()))        # Num labels = num classes (categories of categorical var if n_target=1 or n_target if labels are binary )
    # Create the right number of copies of the predictor instance
    if len(self.predictors)!=self.n_target:
        predictorInstance = self.predictors[0]
        self.predictors = [predictorInstance]
        for i in range(1,self.n_target):
            self.predictors.append(copy.copy(predictorInstance))
    # Fit all predictors
    for i in range(self.n_target):
        # Update the number of desired prodictos
        if hasattr(self.predictors[i], 'n_estimators'):
            self.predictors[i].n_estimators=self.n_estimators
        # Subsample if desired
        if self.balance:
            pos = Ytrain[:,i]>0
            neg = Ytrain[:,i]<=0
            if sum(pos)<sum(neg): 
                chosen = pos
                not_chosen = neg
            else: 
                chosen = neg
                not_chosen = pos
            num = sum(chosen)
            idx=filter(lambda(x): x[1]==True, enumerate(not_chosen))
            idx=np.array(zip(*idx)[0])
            np.random.shuffle(idx)
            chosen[idx[0:min(num, len(idx))]]=True
            # Train with chosen samples            
            self.predictors[i].fit(Xtrain[chosen,:],Ytrain[chosen,i])
        else:
            self.predictors[i].fit(Xtrain,Ytrain[:,i])
    return

def predict_proba(self, Xtrain):
    if len(Xtrain.shape)==1: # IG modif Feb3 2015
        X = np.reshape(Xtrain,(-1,1))   
    prediction = self.predictors[0].predict_proba(Xtrain)
    if self.n_label==2:                 # Keep only 1 prediction, 1st column = (1 - 2nd column)
        prediction = prediction[:,1]
    for i in range(1,self.n_target): # More than 1 target, we assume that labels are binary
        new_prediction = self.predictors[i].predict_proba(Xtrain)[:,1]
        prediction = np.column_stack((prediction, new_prediction))
    return prediction

When I call this class for cross validation like this:

kf = cross_validation.KFold(len(Xtrain), n_folds=10)
score = cross_val_score(self.model, Xtrain, Ytrain, cv=kf, n_jobs=-1).mean()

I get the following error:

TypeError: If no scoring is specified, the estimator passed should have a 'score' method. The estimator MultiLabelEnsemble does not.

How do I create a score method?

Andreas Mueller Andreas Mueller · Accepted Answer · 2015-03-05T06:07:15

The easiest way to make the error go away is to pass scoring="accuracy" or scoring="hamming" to cross_val_score. The cross_val_score function itself doesn't know what kind of problem you are trying to solve, so it doesn't know what an appropriate metric is. It looks like you are trying to do multi-label classification, so maybe you want to use the hamming loss?

You can also implement a score method as explained in the "Roll your own estimator" docs, which has as signature def score(self, X, y_true). See http://scikit-learn.org/stable/developers/#different-objects

By the way, you do know about the OneVsRestClassifier, right? It looks a bit like you are reimplementing it.

python - Scikit-learn TypeError: If no scoring is specified, the estimator passed should have a 'score' method

1 Answers