Problem with GridSearchCV when using Custom Classifier-CodePudding

I have written my own CustomClassifier which binarizes the dependent variable. This is the code

class OwnClassifier(BaseEstimator, ClassifierMixin):
    def __init__(self, estimator=None):
        self.yt = None
        
        if estimator is None:
            estimator = LogisticRegression(solver='liblinear')
        self.estimator = estimator
                    
        self.discr = KBinsDiscretizer(n_bins=4, encode='ordinal')
        
    def fit(self, X, y):
        self.yt = y.copy()
        self.yt = self.discr.fit_transform(self.yt.reshape(-1, 1)).astype(int)
        
        self.estimator.fit(X,self.yt.ravel())
        
        return self
    
    def predict(self, X):
        return self.estimator.predict(X)
    
    def predict_proba(self, X):
        return self.estimator.predict_proba(X)
    
    def score(self, X, y=None):
        return accuracy_score(self.yt, self.predict(X))

When using GridSearchCV on it, it throws an error:

grid = [{'estimator__C': [1, 10, 100, 1000]}]
myLogi = OwnClassifier()
gridCv = GridSearchCV(myLogi, grid)
gridCv.fit(X, y)

How can the classifier be compatible with GridSearchCV?

I use the Boston Housing data

boston_data = load_boston()
X = boston_data['data']
y = boston_data['target']

CodePudding user response：

The problem is in the score method, as you are forcing it to always use the training data self.yt to calculate the accuracy, which is why the traceback says that the shapes are incompatible. This has been fixed in the code below:

from sklearn.linear_model import LogisticRegression
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_boston
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.model_selection import GridSearchCV

class OwnClassifier(BaseEstimator, ClassifierMixin):

    def __init__(self, estimator=None):

        if estimator is None:
            estimator = LogisticRegression(solver='liblinear')

        self.estimator = estimator
        self.discr = KBinsDiscretizer(n_bins=4, encode='ordinal')

    def fit(self, X, y):

        # fit the discretizer
        self.discr.fit(y.reshape(-1, 1))

        # transform the target
        yt = self.discr.transform(y.reshape(-1, 1)).astype(int).ravel()

        # fit the model
        self.estimator.fit(X, yt)

    def predict(self, X):
        return self.estimator.predict(X)

    def predict_proba(self, X):
        return self.estimator.predict_proba(X)

    def score(self, X, y):

        # transform the target using the fitted discretizer
        yt = self.discr.transform(y.reshape(-1, 1)).astype(int).ravel()

        # calculate the accuracy using the fitted model
        return accuracy_score(yt, self.predict(X))

boston_data = load_boston()
X = boston_data['data']
y = boston_data['target']

grid = [{'estimator__C': [1, 10, 100, 1000]}]
myLogi = OwnClassifier()
gridCv = GridSearchCV(myLogi, grid)
gridCv.fit(X, y)