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fs: cut down the number of iterations

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Tanushree Tunstall 2022-07-02 11:12:39 +01:00
parent 7ba838b493
commit 9071a87056
1 changed files with 74 additions and 70 deletions
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68
scripts/ml/feature_selection_iterator.py
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@ -42,6 +42,7 @@ ml_gene_drugD = {'pncA' : 'pyrazinamide'
# , 'gid' : 'streptomycin'
}
gene_dataD={}
# NOTE: for gid, run 'actual' on 80/20 and sl only
split_types = ['70_30', '80_20', 'sl']
split_data_types = ['actual', 'complete']
#split_types = ['70_30']
@ -49,24 +50,25 @@ split_data_types = ['actual', 'complete']
#fs_models = [('Logistic Regression' , LogisticRegression(**rs) )]
fs_models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
, ('Decision Tree' , DecisionTreeClassifier(**rs) )
fs_models = [
('Logistic Regression' , LogisticRegression(**rs) )
, ('Ridge Classifier' , RidgeClassifier(**rs) )
#, ('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) )
]
for gene, drug in ml_gene_drugD.items():
print ('\nGene:', gene
, '\nDrug:', drug)
#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.
@ -92,26 +94,22 @@ for gene, drug in ml_gene_drugD.items():
, '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'}
#, '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_fsD = {}
@ -124,13 +122,16 @@ for gene, drug in ml_gene_drugD.items():
index = index+1
#out_fsD[model_name] = {}
current_model = {}
for k, v in paramD.items():
out_filename = (gene.lower() + '_' + split_type + '_' + data_type + '_' + model_name + '_' + k + '.json')
out_filename = gene.lower() + '_' + split_type + '_' + data_type + '_' + model_name + '_' + k + '.json'
fsD_params=paramD[k]
#out_fsD[model_name][k] = fsgs_rfecv(
thingg = foo(
)
#current_model[k] = v
# NOTE: this will silently fail with a syntax error if you don't have all the necessary libraries installed.
# Python will NOT warn you of the missing lib!
current_model[k] = fsgs_rfecv(
**fsD_params
, param_gridLd = [{'fs__min_features_to_select': [1]}]
@ -138,9 +139,12 @@ for gene, drug in ml_gene_drugD.items():
, 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
)
with open(out_filename, 'w') as f:
f.write(json.dumps(current_model)
# write current model to disk
#print(current_model)
out_json = json.dumps(current_model)
with open(out_filename, 'w', encoding="utf-8") as file:
file.write(out_json)