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fixed masking condition for ML training data for genes and wrote revised mask files out

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This commit is contained in:
Tanushree Tunstall 2022-07-27 13:36:16 +01:00
parent 0adf69f75a
commit f4cab1fdfb
3 changed files with 46 additions and 26 deletions
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42
scripts/ml/ml_functions/GetMLData.py
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@ -101,14 +101,15 @@ def getmldata(gene, drug
datadir = homedir + '/git/Data/' datadir = homedir + '/git/Data/'
indir = datadir + drug + '/input/' indir = datadir + drug + '/input/'
outdir = datadir + drug + '/output/' outdir = datadir + drug + '/output/'
outdir_ml = outdir + 'ml/' #outdir_ml = outdir + 'ml/'
outdir_ml = homedir + '/git/LSHTM_ML/output/'
#========================== #==========================
# outfile for ML training: # outfile for ML training:
#========================== #==========================
outFile_ml = outdir_ml + gene.lower() + '_training_data.csv' outFile_ml = outdir_ml + gene.lower() + '_training_data.csv'
outFile_mask_ml = outdir_ml + gene.lower() + '_mask_check.csv' outFile_mask_ml = outdir_ml + 'genes/mask_check/' + gene.lower() + '_mask_check.csv'
#======= #=======
# input # input
@ -436,41 +437,58 @@ def getmldata(gene, drug
#X_stabilityN = common_cols_stabiltyN #X_stabilityN = common_cols_stabiltyN
gene_affinity_colnames = []# not needed as its the common ones gene_affinity_colnames = []# not needed as its the common ones
cols_to_mask = ['ligand_affinity_change'] cols_to_mask = ['ligand_affinity_change']
cols_to_mask_ppi2 = []
if gene.lower() in geneL_ppi2: if gene.lower() in geneL_ppi2:
gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist'] gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist']
#X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols #X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity'] #cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
cols_to_mask = ['ligand_affinity_change']
cols_to_mask_ppi2 = ['mcsm_ppi2_affinity']
if gene.lower() in geneL_na: if gene.lower() in geneL_na:
gene_affinity_colnames = ['mcsm_na_affinity'] gene_affinity_colnames = ['mcsm_na_affinity']
#X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols #X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity'] cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
cols_to_mask_ppi2 = []
if gene.lower() in geneL_na_ppi2: if gene.lower() in geneL_na_ppi2:
gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist'] gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
#X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols #X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity'] #cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
cols_to_mask_ppi2 = ['mcsm_ppi2_affinity']
#======================= #=======================
# Masking columns: # Masking columns:
# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10 # lig_dist >10 ==> mCSM-lig AND mCSM-NA col values == 0
# interface_dist >10 ==> mCSM-ppi2 col values == 0
#======================= #=======================
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts() my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts() my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts() my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
# mask the mcsm affinity related columns where ligand distance > 10 # mask the mcsm ligand affinity AND mcsm_na affinity columns where ligand distance > 10
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask] = 0 my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask] = 0
(my_df_ml['ligand_affinity_change'] == 0).sum()
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask] # mask the mcsm_ppi2_affinity column where interface_dist > 10
if len(cols_to_mask_ppi2) > 0:
my_df_ml.loc[(my_df_ml['interface_dist'] > 10), cols_to_mask_ppi2] = 0
add_cols_mask = ['interface_dist'] + cols_to_mask_ppi2
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask + add_cols_mask]
else:
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask ]
#=================================================== # sanity check: check script SANITY_CHECK_mask.py
# write file for check
if write_maskfile:
# write mask file for sanity check
#mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) #mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
#mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
#=================================================== mask_check.to_csv(outdir_ml + gene.lower() + '_mask_check.csv')
############################################################################### ###############################################################################
#%% Feature groups (FG): Build X for Input ML #%% Feature groups (FG): Build X for Input ML
############################################################################ ############################################################################

7
scripts/ml/ml_functions/MultClfs.py
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@ -77,6 +77,7 @@ import re
import itertools import itertools
from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeaveOneGroupOut
from sklearn.decomposition import PCA from sklearn.decomposition import PCA
from sklearn.naive_bayes import ComplementNB
#%% GLOBALS #%% GLOBALS
#rs = {'random_state': 42} #rs = {'random_state': 42}
@ -260,6 +261,8 @@ def MultModelsCl(input_df, target
#====================================================== #======================================================
models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) ) models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) ) , ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
#, ('Bernoulli NB' , BernoulliNB() ) # pks Naive Bayes, CAUTION
, ('Complement NB' , ComplementNB() )
, ('Decision Tree' , DecisionTreeClassifier(**rs) ) , ('Decision Tree' , DecisionTreeClassifier(**rs) )
, ('Extra Tree' , ExtraTreeClassifier(**rs) ) , ('Extra Tree' , ExtraTreeClassifier(**rs) )
, ('Extra Trees' , ExtraTreesClassifier(**rs) ) , ('Extra Trees' , ExtraTreesClassifier(**rs) )
@ -271,8 +274,8 @@ def MultModelsCl(input_df, target
, ('Logistic Regression' , LogisticRegression(**rs) ) , ('Logistic Regression' , LogisticRegression(**rs) )
, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs)) , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
, ('MLP' , MLPClassifier(max_iter = 500, **rs) ) , ('MLP' , MLPClassifier(max_iter = 500, **rs) )
, ('Multinomial' , MultinomialNB() ) , ('Multinomial NB' , MultinomialNB() )
, ('Naive Bayes' , BernoulliNB() )
, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) ) , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
, ('QDA' , QuadraticDiscriminantAnalysis() ) , ('QDA' , QuadraticDiscriminantAnalysis() )
, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000, **njobs ) ) , ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000, **njobs ) )

13
scripts/ml/ml_functions/test_func_singlegene.py
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@ -18,7 +18,6 @@ from SplitTTS import *
from MultClfs_SIMPLE import * from MultClfs_SIMPLE import *
#%% #%%
skf_cv = StratifiedKFold(n_splits = 10 skf_cv = StratifiedKFold(n_splits = 10
, shuffle = True,**rs) , shuffle = True,**rs)
#sel_cv = logo #sel_cv = logo
@ -29,16 +28,16 @@ skf_cv = StratifiedKFold(n_splits = 10
gene_model_paramD = {'data_combined_model' : False gene_model_paramD = {'data_combined_model' : False
, 'use_or' : False , 'use_or' : False
, 'omit_all_genomic_features': False , 'omit_all_genomic_features': False
, 'write_maskfile' : False , 'write_maskfile' : True
, 'write_outfile' : False } , 'write_outfile' : False }
#df = getmldata(gene, drug, **gene_model_paramD) #df = getmldata(gene, drug, **gene_model_paramD)
df = getmldata('pncA', 'pyrazinamide', **gene_model_paramD) df = getmldata('pncA', 'pyrazinamide', **gene_model_paramD)
df = getmldata('embB', 'ethambutol' , **gene_model_paramD) #df = getmldata('embB', 'ethambutol' , **gene_model_paramD)
df = getmldata('katG', 'isoniazid' , **gene_model_paramD) #df = getmldata('katG', 'isoniazid' , **gene_model_paramD)
df = getmldata('rpoB', 'rifampicin' , **gene_model_paramD) #df = getmldata('rpoB', 'rifampicin' , **gene_model_paramD)
df = getmldata('gid' , 'streptomycin' , **gene_model_paramD) #df = getmldata('gid' , 'streptomycin' , **gene_model_paramD)
#df = getmldata('alr' , 'cycloserine' , **combined_model_paramD) #df = getmldata('alr' , 'cycloserine' , **gene_model_paramD)
all(df.columns.isin(['gene_name'])) # should be False all(df.columns.isin(['gene_name'])) # should be False
spl_type = '70_30' spl_type = '70_30'