#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat May 21 02:52:36 2022

@author: tanu
"""
# https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.datasets import make_classification
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, mutual_info_classif
#pd.options.plotting.backend = "plotly"
X_eg, y_eg = make_classification(n_samples=1000,
                           n_features=30,
                           n_informative=5,
                           n_redundant=5,
                           n_classes=2,
                           random_state=123)

pipe = Pipeline([('scaler', StandardScaler()),
                 ('selector', SelectKBest(mutual_info_classif, k=9)),
                 
                 ('classifier', LogisticRegression())])

search_space = [{'selector__k': [5, 6, 7, 10]},
                {'classifier': [LogisticRegression()],
                 'classifier__C': [0.01,1.0],
                 'classifier__solver': ['saga', 'lbfgs']},
                {'classifier': [RandomForestClassifier(n_estimators=100)],
                 'classifier__max_depth': [5, 10, None]},
                {'classifier': [KNeighborsClassifier()],
                 'classifier__n_neighbors': [3, 7, 11],
                 'classifier__weights': ['uniform', 'distance']}]



clf = GridSearchCV(pipe, search_space, cv=10, verbose=0)

clf2 = clf.fit(X_eg, y_eg)
clf2._check_feature_names
clf2.best_estimator_.named_steps['selector'].n_features_in_

clf2.best_estimator_ #n of best features
clf2.best_params_ 
clf2.best_estimator_.get_params
clf2.get_feature_names()



clf3 = clf2.best_estimator_ # 
clf3._final_estimator
clf3._final_estimator.C
clf3._final_estimator.solver


fs_bmod = clf2.best_estimator_
print('\nbest model with feature selection:', fs_bmod)