added the cm_ml_iterator_TODO.py for later

scripts/ml/combined_model/cm_ml_iterator_TODO.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jun 29 20:29:36 2022
+
+@author: tanu
+"""
+import sys, os
+import pandas as pd
+import numpy as np
+import re
+    
+###############################################################################
+homedir = os.path.expanduser("~")
+sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
+sys.path
+###############################################################################
+outdir = homedir + '/git/LSHTM_ML/output/combined/'
+
+#====================
+# Import ML functions 
+#====================
+#from MultClfs import *
+#from MultClfs_logo_skf import *
+from MultClfs_logo_skf_split import *
+
+from GetMLData import *
+from SplitTTS import *
+
+# Input data
+from ml_data_combined import *
+
+###############################################################################
+print('\nUsing data with 5 genes:', len(cm_input_df5))
+
+###############################################################################
+
+split_types = ['70_30', '80_20', 'sl']
+split_data_types = ['actual', 'complete']
+
+for split_type in split_types:
+    for data_type in split_data_types:
+
+        out_filename = outdir + 'cm_' + split_type + '_' + data_type + '.csv'
+        print(out_filename)
+        tempD = split_tts(cm_input_df5
+                  , data_type = data_type
+                  , split_type = split_type
+                  , oversampling = True
+                  , dst_colname = 'dst'
+                  , target_colname = 'dst_mode'
+                  , include_gene_name = True
+              )
+        paramD = {
+                'baseline_paramD': { 'input_df'         : tempD['X']
+                                    , 'target'          : tempD['y']
+                                    , 'var_type'        : 'mixed'
+                                    , 'resampling_type' : 'none'}
+                , 'smnc_paramD'  : { 'input_df'         : tempD['X_smnc']
+                                    , 'target'          : tempD['y_smnc']
+                                    , 'var_type'        : 'mixed'
+                                    , 'resampling_type' : 'smnc'}
+                , 'ros_paramD'   : { 'input_df'         : tempD['X_ros']
+                                    , 'target'          : tempD['y_ros']
+                                    , 'var_type'        : 'mixed'
+                                    , 'resampling_type' : 'ros'}
+                , 'rus_paramD'   : { 'input_df'         : tempD['X_rus']
+                                    , 'target'          : tempD['y_rus']
+                                    , 'var_type'        : 'mixed'
+                                    , 'resampling_type' : 'rus'}
+                , 'rouC_paramD'  : { 'input_df'         : tempD['X_rouC']
+                                    , 'target'          : tempD['y_rouC']
+                                    , 'var_type'        : 'mixed'
+                                    , 'resampling_type' : 'rouC'}
+                }
+        
+        mmDD = {}
+        for k, v in paramD.items():
+            scoresD = MultModelsCl_logo_skf(**paramD[k]
+            XXXXXXXXXXXXXXXXXXXXXXX
+            mmDD[k] = scoresD
+
+        # Extracting the dfs from within the dict and concatenating to output as one df
+        for k, v in mmDD.items():
+            out_wf= pd.concat(mmDD, ignore_index = True)
+        
+        out_wf_f = out_wf.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
+        out_wf_f.to_csv(('/home/tanu/git/Data/ml_combined/genes/'+out_filename), index = False)
+