added count for targets for all genes and ran multiple classification models for all of the genes and target as a start

my_data_target_counts.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  3 17:08:18 2022
+
+@author: tanu
+"""
+#%% load packages
+import sys, os
+import pandas as pd
+from pandas import DataFrame
+import numpy as np
+import argparse
+from functools import reduce
+#%%
+homedir = os.path.expanduser("~")
+os.chdir(homedir + "/git/ML_AI_training/test_data")
+
+#gene = ''
+#drug = ''
+
+#==============
+# directories
+#==============
+datadir = homedir + '/git/Data/'
+indir = datadir + drug + '/input/'
+outdir = datadir + drug + '/output/'
+
+# gene_baiscL = ['pnca']
+# geneL_naL   = ['gid', 'rpob']
+# geneL_ppi2L = ['alr', 'embb', 'katg', 'rpob']
+
+#=======
+# input
+#=======
+infile_ml1 = outdir + gene.lower() + '_merged_df3.csv' 
+#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'
+ 
+my_df = pd.read_csv(infile_ml1)
+
+my_df.dtypes
+my_df_cols = my_df.columns
+
+#%%============================================================================
+# GET Y
+drug_labels = drug + '_labels'
+drug_labels
+my_df[drug_labels]  = my_df[drug] 
+my_df[drug_labels].value_counts()
+my_df[drug_labels] = my_df[drug].map({1: 'resistant', 0: 'sensitive'})
+my_df[drug_labels].value_counts()
+my_df[drug_labels] = my_df[drug_labels].fillna('unknown')
+my_df[drug_labels].value_counts()
+
+mutC = my_df[[ 'mutationinformation']].count()
+
+target1C = my_df['mutation_info_labels'].value_counts()
+
+target2C = my_df[drug_labels].value_counts()
+#target2C.index = target2C.index.to_series().map({1: 'resistant', 0: 'sensitive'})
+
+target3C = my_df['drtype'].value_counts()
+
+targetsC = pd.concat([mutC, target1C, target2C, target3C])
+targetsC
+
+# targetsC2 = pd.concat([mutC, target1C, target2C
+#                        #, target3C
+#                        ], axis = 1)
+# targetsC2
+
+#%% try combinations
+# X_vars = X_stability
+# X_vars = X_evol
+# X_vars = X_str
+
+# X_vars = pd.concat([X_stability, X_evol, X_str], axis = 1)
+# X_vars = pd.concat([X_stability, X_evol], axis = 1)
+# X_vars = pd.concat([X_stability, X_str], axis = 1)
+# X_vars = pd.concat([X_evol, X_str], axis = 1)
+