finalised categorical and lineage col classifications
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2 changed files with 94 additions and 61 deletions
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@ -6,64 +6,104 @@ Created on Wed May 25 02:01:19 2022
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@author: tanu
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@author: tanu
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"""
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"""
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# TODO
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# TODO
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categorical_cols = ['ss_class', 'wt_prop_water', 'mut_prop_water', 'wt_prop_polarity',
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# categorical_cols = ['ss_class'
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'mut_prop_polarity', 'wt_calcprop', 'mut_calcprop']
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# , 'wt_prop_water'
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# , 'mut_prop_water'
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# , 'wt_prop_polarity'
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# , 'mut_prop_polarity'
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# , 'wt_calcprop'
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# , 'mut_calcprop']
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foo['water_prop_change'] = foo['wt_prop_water'] + str('_to_') + foo['mut_prop_water']
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my_df['water_change'] = my_df['wt_prop_water'] + str('_to_') + my_df['mut_prop_water']
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foo['water_prop_change'].value_counts()
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my_df['water_change'].value_counts()
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water_prop_changeD = {
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water_prop_changeD = {
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'hydrophobic_to_neutral' : ''
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'hydrophobic_to_neutral' : 'change'
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, 'hydrophobic_to_hydrophobic' : 'no_change'
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, 'hydrophobic_to_hydrophobic' : 'no_change'
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, 'neutral_to_neutral' : 'no_change'
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, 'neutral_to_neutral' : 'no_change'
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, 'neutral_to_hydrophobic' : ''
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, 'neutral_to_hydrophobic' : 'change'
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, 'hydrophobic_to_hydrophilic' : ''
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, 'hydrophobic_to_hydrophilic' : 'change'
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, 'neutral_to_hydrophilic' : ''
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, 'neutral_to_hydrophilic' : 'change'
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, 'hydrophilic_to_neutral' : ''
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, 'hydrophilic_to_neutral' : 'change'
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, 'hydrophilic_to_hydrophobic' : ''
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, 'hydrophilic_to_hydrophobic' : 'change'
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, 'hydrophilic_to_hydrophilic' : 'no_change'
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, 'hydrophilic_to_hydrophilic' : 'no_change'
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}
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}
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foo['polarity_prop_change'] = foo['wt_prop_polarity'] + str('_to_') + foo['mut_prop_polarity']
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my_df['water_change'] = my_df['water_change'].map(water_prop_changeD)
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foo['polarity_prop_change'].value_counts()
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my_df['water_change'].value_counts()
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#%%
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my_df['polarity_change'] = my_df['wt_prop_polarity'] + str('_to_') + my_df['mut_prop_polarity']
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my_df['polarity_change'].value_counts()
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# add a no change category
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# add a no change category
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polarity_prop_changeD = {
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polarity_prop_changeD = {
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'non-polar_to_non-polar' : 'no_change'
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'non-polar_to_non-polar' : 'no_change'
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, 'non-polar_to_neutral' : ''
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, 'non-polar_to_neutral' : 'change'
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, 'neutral_to_non-polar' : ''
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, 'neutral_to_non-polar' : 'change'
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, 'neutral_to_neutral' : ''
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, 'neutral_to_neutral' : 'no_change'
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, 'non-polar_to_basic' : ''
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, 'non-polar_to_basic' : 'change'
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, 'acidic_to_neutral' : ''
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, 'acidic_to_neutral' : 'change'
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, 'basic_to_neutral' : ''
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, 'basic_to_neutral' : 'change'
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, 'non-polar_to_acidic' : ''
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, 'non-polar_to_acidic' : 'change'
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, 'neutral_to_basic' : ''
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, 'neutral_to_basic' : 'change'
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, 'acidic_to_non-polar' : ''
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, 'acidic_to_non-polar' : 'change'
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, 'basic_to_non-polar' : ''
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, 'basic_to_non-polar' : 'change'
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, 'neutral_to_acidic' : ''
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, 'neutral_to_acidic' : 'change'
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, 'acidic_to_acidic' : 'no_change'
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, 'acidic_to_acidic' : 'no_change'
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, 'basic_to_acidic' : ''
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, 'basic_to_acidic' : 'change'
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, 'basic_to_basic' : 'no_change'
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, 'basic_to_basic' : 'no_change'
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, 'acidic_to_basic' : ''}
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, 'acidic_to_basic' : 'change'}
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my_df['polarity_change'] = my_df['polarity_change'].map(polarity_prop_changeD)
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my_df['polarity_change'].value_counts()
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foo['calc_prop_change'] = foo['wt_calcprop'] + str('_to_') + foo['mut_calcprop']
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#%%
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foo['calc_prop_change'].value_counts()
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my_df['electrostatics_change'] = my_df['wt_calcprop'] + str('_to_') + my_df['mut_calcprop']
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my_df['electrostatics_change'].value_counts()
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calc_prop_changeD = {
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calc_prop_changeD = {
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'non-polar_to_non-polar' : 'no_change'
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'non-polar_to_non-polar' : 'no_change'
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, 'non-polar_to_polar' : ''
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, 'non-polar_to_polar' : 'change'
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, 'polar_to_non-polar' : ''
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, 'polar_to_non-polar' : 'change'
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, 'non-polar_to_pos' : ''
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, 'non-polar_to_pos' : 'change'
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, 'neg_to_non-polar' : ''
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, 'neg_to_non-polar' : 'change'
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, 'non-polar_to_neg' : ''
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, 'non-polar_to_neg' : 'change'
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, 'pos_to_polar' : ''
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, 'pos_to_polar' : 'change'
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, 'pos_to_non-polar' : ''
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, 'pos_to_non-polar' : 'change'
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, 'polar_to_polar' : 'no_change'
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, 'polar_to_polar' : 'no_change'
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, 'neg_to_neg' : 'no_change'
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, 'neg_to_neg' : 'no_change'
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, 'polar_to_neg' : ''
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, 'polar_to_neg' : 'change'
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, 'pos_to_neg' : ''
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, 'pos_to_neg' : 'change'
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, 'pos_to_pos' : ''
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, 'pos_to_pos' : 'no_change'
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, 'polar_to_pos' : ''
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, 'polar_to_pos' : 'change'
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, 'neg_to_polar' : ''
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, 'neg_to_polar' : 'change'
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, 'neg_to_pos' : ''
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, 'neg_to_pos' : 'change'
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}
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}
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my_df['electrostatics_change'] = my_df['electrostatics_change'].map(calc_prop_changeD)
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my_df['electrostatics_change'].value_counts()
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#%%
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#https://stackoverflow.com/questions/47181187/finding-string-over-multiple-columns-in-pandas
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detect_change = 'change'
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# if detect_change in my_df['water_change'] | my_df['polarity_change'] | my_df['electrostatics_change']:
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# print('\nChange detected')
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check = ['mutationinformation', 'wild_type', 'water_change', 'polarity_change', 'electrostatics_change']
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check_prop_cols = ['water_change', 'polarity_change', 'electrostatics_change']
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foo = my_df[check]
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foo['new'] = (foo.values == detect_change).any(1).astype(int)
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#foo['new2'] = foo[check_prop_cols].applymap(lambda x: detect_change in x).any(1).astype(int) # lose match so alwasys 1
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foo['new3'] = (foo[check_prop_cols].values == detect_change).any(1).astype(int)
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all(foo['new'] == foo['new3'])
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#%%lineage
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lineage_colnames = ['lineage', 'lineage_list_all', 'lineage_count_all', 'lineage_count_unique', 'lineage_list_unique', 'lineage_multimode']
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bar = my_df[lineage_colnames]
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tot_lineage_u = 8
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bar['lineage_proportion'] = bar['lineage_count_unique']/bar['lineage_count_all']
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bar['dist_lineage_proportion'] = bar['lineage_count_unique']/tot_lineage_u
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35
pnca_config.py
Normal file → Executable file
35
pnca_config.py
Normal file → Executable file
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@ -5,29 +5,22 @@ Created on Sat May 28 05:25:30 2022
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@author: tanu
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@author: tanu
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"""
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"""
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import os, sys
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def MyGlobalVars():
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import os
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global gene
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global drug
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global homedir
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gene = 'pncA'
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drug = 'pyrazinamide'
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homedir = os.path.expanduser("~")
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MyGlobalVars()
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os.chdir(homedir + "/git/ML_AI_training/")
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gene = 'pncA'
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drug = 'pyrazinamide'
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total_mtblineage_u = 8
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# my function
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homedir = os.path.expanduser("~")
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os.chdir( homedir + '/git/ML_AI_training/')
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from UQ_ML_data import *
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setvars(gene,drug)
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from UQ_ML_data import *
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# from YC run_all_ML: run locally
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from UQ_MultModelsCl import MultModelsCl
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from UQ_MultModelsCl import MultModelsCl
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from UQ_pnca_ML.py import *
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# from YC run_all_ML
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# YC_resD2 = run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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# CVResultsDF = YC_resD2['CrossValResultsDF']
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# CVResultsDF.sort_values(by=['matthew'], ascending=False, inplace=True)
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# BTSResultsDF = YC_resD2['BlindTestResultsDF']
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# BTSResultsDF.sort_values(by=['matthew'], ascending=False, inplace=True)
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print('TESTING cmd:', Counter(y))
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