multiple changes
This commit is contained in:
parent
2fda32901b
commit
dccd3c8eb2
7 changed files with 82 additions and 122 deletions
|
|
@ -80,7 +80,7 @@ homedir = os.path.expanduser("~")
|
|||
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
|
||||
sys.path
|
||||
###############################################################################
|
||||
outdir = homedir + '/git/LSHTM_ML/output/combined/
|
||||
outdir = homedir + '/git/LSHTM_ML/output/combined/'
|
||||
|
||||
#====================
|
||||
# Import ML functions
|
||||
|
|
|
|||
|
|
@ -15,13 +15,15 @@ homedir = os.path.expanduser("~")
|
|||
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
|
||||
sys.path
|
||||
###############################################################################
|
||||
outdir = homedir + '/git/LSHTM_ML/output/combined/
|
||||
outdir = homedir + '/git/LSHTM_ML/output/combined/'
|
||||
|
||||
#====================
|
||||
# Import ML functions
|
||||
#====================
|
||||
#from MultClfs import *
|
||||
from MultClfs_logo_skf import *
|
||||
#from MultClfs_logo_skf import *
|
||||
from MultClfs_logo_skf_split import *
|
||||
|
||||
from GetMLData import *
|
||||
from SplitTTS import *
|
||||
|
||||
|
|
@ -29,73 +31,59 @@ from SplitTTS import *
|
|||
from ml_data_combined import *
|
||||
|
||||
###############################################################################
|
||||
#ml_genes = ["pncA", "embB", "katG", "rpoB", "gid"]
|
||||
print('\nUsing data with 5 genes:', len(cm_input_df5))
|
||||
|
||||
###############################################################################
|
||||
|
||||
ml_gene_drugD = {'pncA' : 'pyrazinamide'
|
||||
, 'embB' : 'ethambutol'
|
||||
, 'katG' : 'isoniazid'
|
||||
, 'rpoB' : 'rifampicin'
|
||||
, 'gid' : 'streptomycin'
|
||||
}
|
||||
gene_dataD={}
|
||||
split_types = ['70_30', '80_20', 'sl']
|
||||
split_data_types = ['actual', 'complete']
|
||||
|
||||
for gene, drug in ml_gene_drugD.items():
|
||||
print ('\nGene:', gene
|
||||
, '\nDrug:', drug)
|
||||
gene_low = gene.lower()
|
||||
gene_dataD[gene_low] = getmldata(gene, drug
|
||||
, data_combined_model = False # this means it doesn't include 'gene_name' as a feauture as a single gene-target shouldn't have it.
|
||||
, use_or = False
|
||||
, omit_all_genomic_features = False
|
||||
, write_maskfile = False
|
||||
, write_outfile = False)
|
||||
for split_type in split_types:
|
||||
for data_type in split_data_types:
|
||||
|
||||
for split_type in split_types:
|
||||
for data_type in split_data_types:
|
||||
out_filename = outdir + gene.lower()+ '_' + split_type + '_' + data_type + '.csv'
|
||||
tempD=split_tts(gene_dataD[gene_low]
|
||||
, 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'}
|
||||
}
|
||||
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
|
||||
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)
|
||||
# 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)
|
||||
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)
|
||||
|
||||
|
|
|
|||
|
|
@ -345,11 +345,6 @@ def MultModelsCl(input_df, target, skf_cv
|
|||
mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
|
||||
#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
|
||||
|
||||
#ADD: target numbers for bts
|
||||
yc2 = Counter(blind_test_target)
|
||||
yc2_ratio = yc2[0]/yc2[1]
|
||||
mm_skf_scoresD[model_name]['n_test_size'] = len(blind_test_df)
|
||||
mm_skf_scoresD[model_name]['n_testY_ratio']= round(yc2_ratio,2)
|
||||
|
||||
#return(mm_skf_scoresD)
|
||||
#============================
|
||||
|
|
|
|||
|
|
@ -358,12 +358,6 @@ def MultModelsCl_logo(input_df
|
|||
mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
|
||||
#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
|
||||
|
||||
#ADD: target numbers for bts
|
||||
yc2 = Counter(blind_test_target)
|
||||
yc2_ratio = yc2[0]/yc2[1]
|
||||
mm_skf_scoresD[model_name]['n_test_size'] = len(blind_test_df)
|
||||
mm_skf_scoresD[model_name]['n_testY_ratio']= round(yc2_ratio,2)
|
||||
|
||||
#return(mm_skf_scoresD)
|
||||
#============================
|
||||
# Process the dict to have WF
|
||||
|
|
|
|||
|
|
@ -370,11 +370,6 @@ def MultModelsCl_logo_skf(input_df
|
|||
mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
|
||||
#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
|
||||
|
||||
#ADD: target numbers for bts
|
||||
yc2 = Counter(blind_test_target)
|
||||
yc2_ratio = yc2[0]/yc2[1]
|
||||
mm_skf_scoresD[model_name]['n_test_size'] = len(blind_test_df)
|
||||
mm_skf_scoresD[model_name]['n_testY_ratio']= round(yc2_ratio,2)
|
||||
|
||||
#return(mm_skf_scoresD)
|
||||
#============================
|
||||
|
|
|
|||
|
|
@ -67,4 +67,8 @@ if 'gene_name' in colnames_combined_df:
|
|||
print("\nGene name included")
|
||||
else:
|
||||
('\nGene name NOT included')
|
||||
|
||||
|
||||
omit_gene_alr = ['alr']
|
||||
cm_input_df5 = combined_df[~combined_df['gene_name'].isin(omit_gene_alr)]
|
||||
##############################################################################
|
||||
|
|
|
|||
|
|
@ -51,17 +51,17 @@ split_data_types = ['actual', 'complete']
|
|||
|
||||
fs_models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
|
||||
, ('Decision Tree' , DecisionTreeClassifier(**rs) )
|
||||
, ('Extra Tree' , ExtraTreeClassifier(**rs) )
|
||||
, ('Extra Trees' , ExtraTreesClassifier(**rs) )
|
||||
, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
|
||||
, ('LDA' , LinearDiscriminantAnalysis() )
|
||||
, ('Logistic Regression' , LogisticRegression(**rs) )
|
||||
, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
|
||||
, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
|
||||
, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
|
||||
, ('Ridge Classifier' , RidgeClassifier(**rs) )
|
||||
, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
|
||||
, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
|
||||
#, ('Extra Tree' , ExtraTreeClassifier(**rs) )
|
||||
#, ('Extra Trees' , ExtraTreesClassifier(**rs) )
|
||||
#, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
|
||||
#, ('LDA' , LinearDiscriminantAnalysis() )
|
||||
#, ('Logistic Regression' , LogisticRegression(**rs) )
|
||||
#, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
|
||||
#, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
|
||||
#, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
|
||||
#, ('Ridge Classifier' , RidgeClassifier(**rs) )
|
||||
#, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
|
||||
#, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
|
||||
]
|
||||
|
||||
for gene, drug in ml_gene_drugD.items():
|
||||
|
|
@ -78,7 +78,7 @@ for gene, drug in ml_gene_drugD.items():
|
|||
for split_type in split_types:
|
||||
for data_type in split_data_types:
|
||||
# unused per-split outfile
|
||||
out_filename = outdir + gene.lower() + '_'+split_type+'_' + data_type + '.json'
|
||||
#out_filename = outdir + gene.lower() + '_'+split_type+'_' + data_type + '.json'
|
||||
tempD=split_tts(gene_dataD[gene_low]
|
||||
, data_type = data_type
|
||||
, split_type = split_type
|
||||
|
|
@ -122,41 +122,25 @@ for gene, drug in ml_gene_drugD.items():
|
|||
, '\nModel func:' , model_fn)
|
||||
#, '\nList of models:', models)
|
||||
index = index+1
|
||||
|
||||
out_fsD[model_name] = {}
|
||||
# current_model = {}
|
||||
#out_fsD[model_name] = {}
|
||||
current_model = {}
|
||||
for k, v in paramD.items():
|
||||
# out_filename = (gene.lower() + '_' + split_type + '_' + data_type + '_' + k + '.json')
|
||||
out_filename = (gene.lower() + '_' + split_type + '_' + data_type + '_' + model_name + '_' + k + '.json')
|
||||
fsD_params=paramD[k]
|
||||
# print("XXXXXX THIS: ", len(fsD_params['input_df']) )
|
||||
# print("XXXXXX THIS: ", out_filename )
|
||||
|
||||
# current_model[k] = fsgs_rfecv(
|
||||
out_fsD[model_name][k] = fsgs_rfecv(
|
||||
#out_fsD[model_name][k] = fsgs_rfecv(
|
||||
thingg = foo(
|
||||
)
|
||||
current_model[k] = fsgs_rfecv(
|
||||
**fsD_params
|
||||
, param_gridLd = [{'fs__min_features_to_select': [1]}]
|
||||
, blind_test_df = tempD['X_bts']
|
||||
, blind_test_target = tempD['y_bts']
|
||||
, estimator = model_fn
|
||||
, use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
|
||||
# NOTE: IS THIS CORRECT?!?
|
||||
, custom_fs = RFECV(DecisionTreeClassifier(**rs), cv = skf_cv, scoring = 'matthews_corrcoef')
|
||||
, cv_method = skf_cv
|
||||
)
|
||||
# write per-resampler outfile here
|
||||
# with open(out_filename, 'w') as f:
|
||||
# f.write(json.dumps(current_model
|
||||
# , cls = NpEncoder )
|
||||
# )
|
||||
|
||||
# write per-split outfile here
|
||||
with open(out_filename, 'w') as f:
|
||||
f.write(json.dumps(out_fsD
|
||||
#, cls = NpEncoder
|
||||
))
|
||||
#%%############################################################################
|
||||
# # Read output json
|
||||
# testF = outdir + 'pnca_70_30_actual.json'
|
||||
# testF = outdir + 'pnca_70_30_complete.json'
|
||||
|
||||
# with open(testF, 'r') as f:
|
||||
# data = json.load(f)
|
||||
with open(out_filename, 'w') as f:
|
||||
f.write(json.dumps(current_model)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue