#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 29 12:21:34 2022

@author: tanu
"""
from sklearn.svm import SVC
from sklearn.datasets import make_classification

from yellowbrick.model_selection import RFECV

# Instantiate RFECV visualizer with a linear SVM classifier
visualizer = RFECV(SVC(kernel='linear', C=1))

visualizer.fit(X[numerical_FN], y)        # Fit the data to the visualizer
visualizer.show()       


numerical_ix = X.select_dtypes(include=['int64', 'float64']).columns
numerical_ix
categorical_ix = X.select_dtypes(include=['object', 'bool']).columns
categorical_ix    

# Determine preprocessing steps ~ var_type
var_type = 'mixed'
var_type = 'numerical'

if var_type == 'numerical':
    t = [('num', MinMaxScaler(), numerical_ix)]

if var_type == 'categorical':
    t = [('cat', OneHotEncoder(), categorical_ix)]

if var_type == 'mixed':
    t = [('cat', OneHotEncoder(), categorical_ix)
         , ('num', MinMaxScaler(), numerical_ix)]
    
    t = [('num', MinMaxScaler(), numerical_ix)
         , ('cat', OneHotEncoder(), categorical_ix)]
    
col_transform = ColumnTransformer(transformers = t
                                   , remainder='passthrough')
#--------------ALEX help
# col_transform
# col_transform.fit(X)
# test = col_transform.transform(X)
# print(col_transform.get_feature_names_out())

# foo = col_transform.fit_transform(X)
Xm = col_transform.fit_transform(X)
# (foo == test).all()
#-----------------------

visualizer.fit(Xm, y)        # Fit the data to the visualizer
visualizer.show()


visualizer.fit(X[numerical_FN], y)        # Fit the data to the visualizer
visualizer.show()