Customer Transformers Not Working #1360

def fit(self, spectrum):
    if self.method == 'minmax':
        self.min_ = np.min(spectrum)
        self.max_ = np.max(spectrum)
    elif self.method == 'zscore':
        self.mean = np.mean(spectrum)
        self.std = np.std(spectrum)
    else:
        raise ValueError("Unsupported normalization method. Choose 'minmax' or 'zscore'.")
    return self

def transform(self, spectrum):
    if self.method == 'minmax':
        if self.min_ is None or self.max_ is None:
            raise ValueError("Normalization instance is not fitted yet.")
        range_min, range_max = self.feature_range
        return ((spectrum - self.min_) / (self.max_ - self.min_)) * (range_max - range_min) + range_min
    elif self.method == 'zscore':
        if self.mean is None or self.std is None:
            raise ValueError("Normalization instance is not fitted yet.")
        return (spectrum - self.mean) / self.std
        
 config = {
'Normalization2': {  # Wrapper for Normalization
    'method': ['minmax'],
    'feature_range': [(0, 1)],
},
'sklearn.linear_model.Ridge': {  # Replace PLSRegression with Ridge
    'alpha': [0.1, 1.0, 10.0],  # Regularization strength
    'solver': ['auto', 'svd', 'cholesky', 'lsqr', 'sag', 'saga'],  # Solvers to optimize
},