added UQ import and ml call scripts

UQ_pnca_ml_CALL.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon May 16 05:59:12 2022
+
+@author: tanu
+"""
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 15 11:09:50 2022
+
+@author: tanu
+"""
+#%% Data
+X = all_df_wtgt[numerical_FN+categorical_FN]
+X = all_df_wtgt[numerical_FN]
+
+y = all_df_wtgt['dst_mode']
+#%% variables
+
+#%% MultClassPipeSKFCV: function call()
+mm_skf_scoresD = MultClassPipeSKFCV(input_df = X
+                                        , target = y
+                                        , var_type = 'numerical'
+                                        , skf_cv = skf_cv)
+
+
+mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
+mm_skf_scores_df_all
+mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
+mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results
+
+#%% CHECK with BLIND test
+#%%
+import plotly.express as px
+
+corr = X.corr(method = 'spearman')
+corr.head()
+
+#p = corr.style.background_gradient(cmap='coolwarm')
+p = corr.style.background_gradient(cmap='coolwarm').set_precision(2)
+p
+
+fig = px.imshow(corr)
+fig.show()
+
+
+#%%TODO:
+# Add correlation plot
+# Remove low variance features
+# Add feature selection
+# Then run your models on BLIND test WITHOUT CV
+
+
+