Back to the Machine Learning fundamentals: How to write code for Model deployment (Part 3/3)

encoder_transformer.py

class Encoder(BaseEstimator, TransformerMixin):

    """ A transformer that returns DataFrame 
    with variable encoded.

    Parameters
    ----------
    encoding_meta : list, default=None

    """
    
    def __init__(self, encoding_meta=None):
        if not isinstance(encoding_meta, dict):
            logging.error('The config file is corrupted in encoding_meta key!')
            sys.exit(1)
        else:
            self.encoding_meta = encoding_meta


    # We have fit method cause Sklearn Pipeline
    def fit(self, X, y=None):
        return self

    def transform(self, X):
        X = X.copy()
        for var, meta in self.encoding_meta.items():
            if var not in X.columns.values.tolist():
                pass
            X[var] = X[var].map(meta)
        return X