import pandas as pd
import numpy as np
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.base import BaseEstimator, TransformerMixin


class TypeSelector(BaseEstimator, TransformerMixin):
    def __init__(self, dtype):
        self.dtype = dtype

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        assert isinstance(X, pd.DataFrame)
        return X.select_dtypes(include=[self.dtype])


class StringIndexer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        assert isinstance(X, pd.DataFrame)
        return X.apply(lambda s: s.cat.codes.replace(
            {-1: len(s.cat.categories)}))


transformer = Pipeline([
    ('features', FeatureUnion(n_jobs=1, transformer_list=[
        # Part 1
        ('boolean', Pipeline([
            ('selector', TypeSelector('bool')),
        ])),  # booleans close

        ('numericals', Pipeline([
            ('selector', TypeSelector(np.number)),
            ('scaler', StandardScaler()),
        ])),  # numericals close

        # Part 2
        ('categoricals', Pipeline([
            ('selector', TypeSelector('category')),
            ('labeler', StringIndexer()),
            ('encoder', OneHotEncoder(handle_unknown='ignore')),
        ]))  # categoricals close
    ])),  # features close
])  # pipeline close



df = pd.DataFrame({
    'boolean_column': [True,False,True,False],
    'integer_column': [1,2,3,4],
    'float_column': [1.,2.,3.,4.]
})


df_transformed = transformer.fit_transform(df)