sklearn transformers that can account for categorical variables

cat_transformers.py

import numpy as np
from sklearn.base import TransformerMixin, BaseEstimator


class StandardTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, variables=[], ignore=[]):
        self.variables = variables
        self.ignore = ignore
        self.transform_idx = np.asarray([True if i not in self.ignore
                                         else False for i in self.variables])

    def fit(self, X, y=None):
        self.mean_ = X[:, self.transform_idx].mean(axis=0)
        self.std_ = X[:, self.transform_idx].std(axis=0, ddof=1)
        return self

    def transform(self, X, y=None):
        X = X.copy()
        X[:, self.transform_idx] -= self.mean_
        X[:, self.transform_idx] /= self.std_
        return X

    def inverse_transform(self, X, y=None):
        X = X.copy()
        X[:, self.transform_idx] *= self.std_
        X[:, self.transform_idx] += self.mean_
        return X

    def get_params(self, deep=True):
        return dict(variables=self.variables, ignore=self.ignore)


class MinMaxTransformer(StandardTransformer):
    def __init__(self, variables=[], ignore=[], feature_range=(0, 1)):
        self.min, self.max = feature_range
        super(MinMaxTransformer, self).__init__(variables, ignore)

    def transform(self, X, y=None):
        X = X.copy()
        X_min = self.X_min
        X_max = self.X_max
        X_std = (X[:, self.transform_idx] - X_min)/(X_max - X_min)
        X[:, self.transform_idx] = X_std * (self.max - self.min) + self.min
        return X

    def fit(self, X, y=None):
        X = X.copy()
        self.X_min = X[:, self.transform_idx].min(0)
        self.X_max = X[:, self.transform_idx].max(0)
        return self

    def inverse_transform(self, X, y=None):
        X = X.copy()
        X_std = (X[:, self.transform_idx] - self.min) / (self.max - self.min)
        X[:, self.transform_idx] = (X_std *
                                    (self.X_max - self.X_min) + self.X_min)
        return X

    def get_params(self, deep=True):
        return dict(variables=self.variables, ignore=self.ignore,
                    feature_range=(self.min, self.max))