120 lines
4.1 KiB
Python
120 lines
4.1 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 19:44:06 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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#====================
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# Import ML functions
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#====================
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from ml_data_combined import *
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from MultClfs_logo_skf 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 , shuffle = True,**rs)
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#logo = LeaveOneGroupOut()
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#%%
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def CMLogoSkf(combined_df
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, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
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, bts_genes = ["embb", "katg", "rpob", "pnca", "gid"]
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, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
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, target_var = 'dst_mode'
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, gene_group = 'gene_name'
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, std_gene_omit = []
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):
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for bts_gene in bts_genes:
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print('\n BTS gene:', bts_gene)
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tr_gene_omit = std_gene_omit + [bts_gene]
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n_tr_genes = (len(bts_genes) - (len(std_gene_omit)))
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#n_total_genes = (len(bts_genes) - len(std_gene_omit))
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n_total_genes = len(all_genes)
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training_genesL = std_gene_omit + list(set(bts_genes) - set(tr_gene_omit))
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#training_genesL = [element for element in bts_genes if element not in tr_gene_omit]
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print('\nTotal genes: ', n_total_genes
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,'\nTraining on:', n_tr_genes
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,'\nTraining on genes:', training_genesL
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, '\nOmitted genes:', tr_gene_omit
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, '\nBlind test gene:', bts_gene)
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tts_split_type = "logoBT_" + bts_gene
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outFile = "/home/tanu/git/Data/ml_combined/" + str(n_tr_genes+1) + "genes_" + tts_split_type + ".csv"
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print(outFile)
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#-------
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# training
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#------
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cm_training_df = combined_df[~combined_df['gene_name'].isin(tr_gene_omit)]
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cm_X = cm_training_df.drop(cols_to_drop, axis=1, inplace=False)
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#cm_y = cm_training_df.loc[:,'dst_mode']
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cm_y = cm_training_df.loc[:, target_var]
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gene_group = cm_training_df.loc[:,'gene_name']
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print('\nTraining data dim:', cm_X.shape
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, '\nTraining Target dim:', cm_y.shape)
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if all(cm_X.columns.isin(cols_to_drop) == False):
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print('\nChecked training df does NOT have Target var')
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else:
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sys.exit('\nFAIL: training data contains Target var')
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#---------------
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# BTS: genes
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#---------------
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cm_test_df = combined_df[combined_df['gene_name'].isin([bts_gene])]
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cm_bts_X = cm_test_df.drop(cols_to_drop, axis = 1, inplace = False)
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#cm_bts_y = cm_test_df.loc[:, 'dst_mode']
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cm_bts_y = cm_test_df.loc[:, target_var]
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print('\nTraining data dim:', cm_bts_X.shape
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, '\nTraining Target dim:', cm_bts_y.shape)
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#%%:Running Multiple models on LOGO with SKF
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cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X
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, target = cm_y
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, group = 'none'
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, sel_cv = skf_cv
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, blind_test_df = cm_bts_X
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, blind_test_target = cm_bts_y
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, tts_split_type = tts_split_type
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, resampling_type = 'none' # default
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, add_cm = True
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, add_yn = True
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, var_type = 'mixed'
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, run_blind_test = True
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, return_formatted_output = True
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, random_state = 42
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, n_jobs = 10
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)
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cD3_v2.to_csv(outFile)
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#%%
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CMLogoSkf(combined_df)
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CMLogoSkf(combined_df, std_gene_omit=['alr'])
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