146 lines
6.8 KiB
Python
Executable file
146 lines
6.8 KiB
Python
Executable file
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Jun 29 20:29:36 2022
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@author: tanu
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"""
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import sys, os
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import pandas as pd
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import numpy as np
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import re
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#import prettyprint as pp
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
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sys.path
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###############################################################################
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outdir = homedir + '/git/LSHTM_ML/output/fs/'
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#====================
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# Import ML functions
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#====================
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from MultClfs import *
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from GetMLData import *
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from SplitTTS import *
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from FS import *
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# param dict for getmldata()
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combined_model_paramD = {'data_combined_model' : False
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, 'use_or' : False
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, 'omit_all_genomic_features': False
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, 'write_maskfile' : False
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, 'write_outfile' : False }
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###############################################################################
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#ml_genes = ["pncA", "embB", "katG", "rpoB", "gid"]
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# outdir = homedir + '/git/Data/ml_combined/fs/'
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ml_gene_drugD = {'pncA' : 'pyrazinamide'
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# , 'embB' : 'ethambutol'
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# , 'katG' : 'isoniazid'
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# , 'rpoB' : 'rifampicin'
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# , 'gid' : 'streptomycin'
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}
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gene_dataD={}
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split_types = ['70_30', '80_20', 'sl']
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split_data_types = ['actual', 'complete']
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#split_types = ['70_30']
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#split_data_types = ['actual', 'complete']
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#fs_models = [('Logistic Regression' , LogisticRegression(**rs) )]
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fs_models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
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, ('Decision Tree' , DecisionTreeClassifier(**rs) )
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#, ('Extra Tree' , ExtraTreeClassifier(**rs) )
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#, ('Extra Trees' , ExtraTreesClassifier(**rs) )
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#, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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#, ('LDA' , LinearDiscriminantAnalysis() )
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#, ('Logistic Regression' , LogisticRegression(**rs) )
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#, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
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#, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
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#, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
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#, ('Ridge Classifier' , RidgeClassifier(**rs) )
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#, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
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#, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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]
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for gene, drug in ml_gene_drugD.items():
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print ('\nGene:', gene
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, '\nDrug:', drug)
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gene_low = gene.lower()
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gene_dataD[gene_low] = getmldata(gene, drug
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, data_combined_model = False # this means it doesn't include 'gene_name' as a feauture as a single gene-target shouldn't have it.
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, use_or = False
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, omit_all_genomic_features = False
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, write_maskfile = False
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, write_outfile = False)
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for split_type in split_types:
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for data_type in split_data_types:
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# unused per-split outfile
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#out_filename = outdir + gene.lower() + '_'+split_type+'_' + data_type + '.json'
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tempD=split_tts(gene_dataD[gene_low]
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, data_type = data_type
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, split_type = split_type
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, oversampling = True # TURN IT ON TO RUN THE OTHERS BIS
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, dst_colname = 'dst'
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, target_colname = 'dst_mode'
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, include_gene_name = True
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)
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paramD = {
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'baseline_paramD': { 'input_df' : tempD['X']
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, 'target' : tempD['y']
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, 'var_type' : 'mixed'
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, 'resampling_type': 'none'}
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, 'smnc_paramD' : { 'input_df' : tempD['X_smnc']
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, 'target' : tempD['y_smnc']
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'smnc'}
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, 'ros_paramD' : { 'input_df' : tempD['X_ros']
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, 'target' : tempD['y_ros']
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'ros'}
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, 'rus_paramD' : { 'input_df' : tempD['X_rus']
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, 'target' : tempD['y_rus']
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'rus'}
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, 'rouC_paramD' : { 'input_df' : tempD['X_rouC']
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, 'target' : tempD['y_rouC']
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, 'var_type' : 'mixed'
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, 'resampling_type': 'rouC'}
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}
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out_fsD = {}
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index = 1
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for model_name, model_fn in fs_models:
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print('\nRunning classifier with FS:', index
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, '\nModel_name:' , model_name
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, '\nModel func:' , model_fn)
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#, '\nList of models:', models)
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index = index+1
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#out_fsD[model_name] = {}
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current_model = {}
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for k, v in paramD.items():
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out_filename = (gene.lower() + '_' + split_type + '_' + data_type + '_' + model_name + '_' + k + '.json')
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fsD_params=paramD[k]
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#out_fsD[model_name][k] = fsgs_rfecv(
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thingg = foo(
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)
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current_model[k] = fsgs_rfecv(
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**fsD_params
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, param_gridLd = [{'fs__min_features_to_select': [1]}]
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, blind_test_df = tempD['X_bts']
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, blind_test_target = tempD['y_bts']
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, estimator = model_fn
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, use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
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# NOTE: IS THIS CORRECT?!?
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, custom_fs = RFECV(DecisionTreeClassifier(**rs), cv = skf_cv, scoring = 'matthews_corrcoef')
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, cv_method = skf_cv
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)
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with open(out_filename, 'w') as f:
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f.write(json.dumps(current_model)
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