132 lines
5.6 KiB
Python
132 lines
5.6 KiB
Python
#!/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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###############################################################################
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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/genes/'
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#====================
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# Import ML functions
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#====================
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from MultClfs_CVs import *
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from GetMLData import *
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from SplitTTS import *
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# skf_cv = StratifiedKFold(n_splits = 10
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# #, shuffle = False, random_state= None)
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# , shuffle = True, random_state = 42)
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# #rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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# # , n_repeats = 3
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# # , **rs)
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# param dict for getmldata()
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gene_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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ml_gene_drugD = {
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'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 = ['none']
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split_data_types = ['complete']
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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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, **gene_model_paramD)
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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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out_filename = outdir + gene.lower() + '_' + split_type + '_' + data_type + '.csv'
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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
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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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mmDD = {}
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for k, v in paramD.items():
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print(k)
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all_scoresDF = pd.DataFrame()
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# iterate over different cv thresholds
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for skf_cv_threshold in [3,5,10]:
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print('\nRunning CV threhhold:', skf_cv_threshold)
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current_scoreDF = MultModelsCl_CVs(**paramD[k]
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, skf_cv_threshold = skf_cv_threshold
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, tts_split_type = split_type
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, add_cm = True
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, add_yn = True
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, scale_numeric = ['min_max']
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, random_state = 42
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, n_jobs = os.cpu_count()
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, return_formatted_output = True
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)
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all_scoresDF = pd.concat([all_scoresDF, current_scoreDF])
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mmDD[k] = all_scoresDF
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# Extracting the dfs from within the dict and concatenating to output as one df
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for k, v in mmDD.items():
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out_wf = pd.concat(mmDD, ignore_index = True)
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#out_wf_f = out_wf.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
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#out_wf_f.to_csv(out_filename, index = False)
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out_wf.to_csv(out_filename, index = False)
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