82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Sat Jun 25 11:07:30 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 MultClfs import *
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from GetMLData import *
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#from SplitTTS import *
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#%% Load all gene files #######################################################
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# param dict
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combined_model_paramD = {'data_combined_model' : True
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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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pnca_df = getmldata('pncA', 'pyrazinamide' , **combined_model_paramD)
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embb_df = getmldata('embB', 'ethambutol' , **combined_model_paramD)
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katg_df = getmldata('katG', 'isoniazid' , **combined_model_paramD)
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rpob_df = getmldata('rpoB', 'rifampicin' , **combined_model_paramD)
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gid_df = getmldata('gid' , 'streptomycin' , **combined_model_paramD)
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alr_df = getmldata('alr' , 'cycloserine' , **combined_model_paramD)
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# quick check
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foo = pd.concat([alr_df, pnca_df])
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check1 = foo.filter(regex= '.*_affinity|gene_name|ligand_distance', axis = 1)
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# So, pd.concat will join correctly but introduce NAs.
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# TODO: discuss whether to make these 0 and use it or just omit
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# For now I am omitting these i.e combining only on common columns
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expected_nrows = len(pnca_df) + len(embb_df) + len(katg_df) + len(rpob_df) + len(gid_df) + len(alr_df)
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# finding common columns
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dfs_combine = [pnca_df, embb_df, katg_df, rpob_df, gid_df, alr_df]
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common_cols = list(set.intersection(*(set(df.columns) for df in dfs_combine)))
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expected_ncols = np.min([len(pnca_df.columns)] + [len(embb_df.columns)] + [len(katg_df.columns)] + [len(rpob_df.columns)] + [len(gid_df.columns)] + [len(alr_df.columns)])
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expected_ncols
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if len(common_cols) == expected_ncols:
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print('\nProceeding to combine based on common cols (n):', len(common_cols))
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combined_df = pd.concat([df[common_cols] for df in dfs_combine], ignore_index = False)
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print('\nSuccessfully combined dfs:'
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, '\nNo. of dfs combined:', len(dfs_combine)
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, '\nDim of combined df:', combined_df.shape)
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else:
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print('\nFAIL: could not combine dfs, length mismatch'
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, '\nExpected ncols:', expected_ncols
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, '\nGot:', len(common_cols))
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colnames_combined_df = combined_df.columns
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if 'gene_name' in colnames_combined_df:
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print("\nGene name included")
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else:
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('\nGene name NOT included')
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omit_gene_alr = ['alr']
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cm_input_df5 = combined_df[~combined_df['gene_name'].isin(omit_gene_alr)]
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#%% COMPLETE data
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combined_df['dst'].isna().sum()
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combined_df['dst'].value_counts().sum()
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combined_df_actual = combined_df[~combined_df['dst'].isna()]
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##############################################################################
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