added practice and base_estimator for all the confusion in my head

MultClassPipe3_CALL.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 15 11:09:50 2022
+
+@author: tanu
+"""
+# stratified shuffle split
+X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
+                                                    , num_df_wtgt['mutation_class']
+                                                    , test_size = 0.33
+                                                    , **rs
+                                                    , shuffle = True
+                                                    , stratify = num_df_wtgt['mutation_class'])
+
+y_train.to_frame().value_counts().plot(kind = 'bar')
+y_test.to_frame().value_counts().plot(kind = 'bar')
+
+MultClassPipelineCV(X_train, X_test, y_train, y_test
+         , input_df = num_df_wtgt[numerical_FN]
+         , var_type = 'numerical')
+
+
+skf_cv_scores = MultClassPipelineCV(X_train, X_test, y_train, y_test
+         , input_df = num_df_wtgt[numerical_FN]
+         , var_type = 'numerical')
+
+pp.pprint(skf_cv_scores)
+# construct a df
+skf_cv_scores_df = pd.DataFrame(skf_cv_scores)
+skf_cv_scores_df
+skf_cv_scores_df_test = skf_cv_scores_df.filter(like='test_', axis=0)
+skf_cv_scores_df_train = skf_cv_scores_df.filter(like='train_', axis=0)