#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep  2 16:10:44 2022

@author: tanu
"""
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, ExtraTreesClassifier
from boruta import BorutaPy

fooD = combined_DF_OS(combined_df)

numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
numerical_ix
print("\nNo. of numerical indices:", len(numerical_ix))

categorical_ix = fooD['X'].select_dtypes(include=['object', 'bool']).columns
categorical_ix  
print("\nNo. of categorical indices:", len(categorical_ix))


var_type = "mixed"

if var_type == 'mixed':
   
    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_train = col_transform.fit_transform(fooD['X'])
fooD['X'].shape
Xm_train.shape

Xm_test = col_transform.fit_transform(fooD['X_bts'])
fooD['X_bts'].shape
Xm_test.shape

X_train = Xm_train.copy()
X_test = Xm_test.copy()
X_train.shape
X_test.shape

y_train = fooD['y']
y_test = fooD['y_bts']
y_train.shape
y_test.shape

# perhaps
#col_transform.fit(fooD['X'])
#encoded_colnames = pd.Index(col_transform.get_feature_names_out())
#======================
#  1 model
n_jobs = os.cpu_count() 
njobs = {'n_jobs': n_jobs }
rs    = {'random_state': 42}

rf_all_features = RandomForestClassifier(n_estimators=1000, max_depth=5
                                         , **rs, **njobs)

#rf_all_features = VotingClassifier(estimators=1000)
rf_all_features = BaggingClassifier(random_state=1, n_estimators=100, verbose = 3, **njobs)
rf_all_features = AdaBoostClassifier(random_state=1, n_estimators=1000)
rf_all_features = ExtraTreesClassifier(random_state=1, n_estimators=1000, max_depth=5, verbose = 3)
rf_all_features = DecisionTreeClassifier(random_state=1, max_depth=5)


rf_all_features.fit(X_train, np.array(y_train))
accuracy_score(y_test, rf_all_features.predict(X_test))
matthews_corrcoef(y_test, rf_all_features.predict(X_test))

# BORUTA
boruta_selector = BorutaPy(rf_all_features,**rs, verbose = 3)
boruta_selector.fit(np.array(X_train), np.array(y_train))  

# Tells you how many features: GOOD
print("Ranking: ", boruta_selector.ranking_)          
print("No. of significant features: ", boruta_selector.n_features_) 


cm_df = combined_df.drop(['gene_name', 'dst', 'dst_mode'], axis = 1)
col_transform.fit(cm_df)
col_transform.get_feature_names_out()
    
var_type_colnames = col_transform.get_feature_names_out()
var_type_colnames = pd.Index(var_type_colnames)
    
if var_type == 'mixed':
        print('\nVariable type is:', var_type
              , '\nNo. of columns in input_df:', len(cm_df.columns)
              , '\nNo. of columns post one hot encoder:', len(var_type_colnames))
else:
        print('\nNo. of columns in input_df:', len(input_df.columns))


selected_rf_features = pd.DataFrame({'Feature':list(var_type_colnames),
                                       'Ranking':boruta_selector.ranking_})
sel_rf_features_sorted = selected_rf_features.sort_values(by='Ranking')


sel_features = var_type_colnames[boruta_selector.support_]


# tells you the ranking: GOOD
#foo2 = selected_rf_features.sort_values(by='Ranking')

X_important_train = boruta_selector.transform(np.array(X_train))
X_important_test = boruta_selector.transform(np.array(X_test)) 

rf_all_features.fit(X_important_train, y_train) 
accuracy_score(y_test, rf_all_features.predict(X_important_test))
matthews_corrcoef(y_test, rf_all_features.predict(X_important_test))