added cm run for logo_skf for actual data
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4 changed files with 56 additions and 124 deletions
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@ -98,13 +98,14 @@ skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True, random_state = 42)
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# COMPLETE data: No tts_split
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# COMPLETE data: No tts_split
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########################################################################
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########################################################################
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#%%
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#%%
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def CMLogoSkf(combined_df
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def CMLogoSkf(cm_input_df
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, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
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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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, bts_genes = ["embb", "katg", "rpob", "pnca", "gid"]
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, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
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, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
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, target_var = 'dst_mode'
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, target_var = 'dst_mode'
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, gene_group = 'gene_name'
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, gene_group = 'gene_name'
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, std_gene_omit = []
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, std_gene_omit = []
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, file_suffix = ""
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):
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):
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for bts_gene in bts_genes:
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for bts_gene in bts_genes:
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@ -127,17 +128,24 @@ def CMLogoSkf(combined_df
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,'\nTraining on genes:', training_genesL
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,'\nTraining on genes:', training_genesL
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, '\nOmitted genes:', tr_gene_omit
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, '\nOmitted genes:', tr_gene_omit
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, '\nBlind test gene:', bts_gene)
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, '\nBlind test gene:', bts_gene)
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print('\nDim of data:', cm_input_df.shape)
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tts_split_type = "logo_skf_BT_" + bts_gene
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tts_split_type = "logo_skf_BT_" + bts_gene
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outFile = outdir + str(n_tr_genes+1) + "genes_" + tts_split_type + ".csv"
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# if len(file_suffix) > 0:
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# file_suffix = '_' + file_suffix
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# else:
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# file_suffix = file_suffix
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outFile = outdir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix + ".csv"
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print(outFile)
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print(outFile)
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#-------
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#-------
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# training
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# training
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#------
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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_training_df = cm_input_df[~cm_input_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_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[:,'dst_mode']
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@ -156,7 +164,7 @@ def CMLogoSkf(combined_df
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#---------------
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#---------------
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# BTS: genes
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# BTS: genes
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#---------------
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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_test_df = cm_input_df[cm_input_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_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[:, 'dst_mode']
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@ -165,31 +173,40 @@ def CMLogoSkf(combined_df
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print('\nTEST data dim:', cm_bts_X.shape
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print('\nTEST data dim:', cm_bts_X.shape
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, '\nTEST Target dim:', cm_bts_y.shape)
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, '\nTEST Target dim:', cm_bts_y.shape)
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print("Running Multiple models on LOGO with SKF")
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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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#%%:Running Multiple models on LOGO with SKF
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# , blind_test_df = cm_bts_X
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cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X
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# , blind_test_target = cm_bts_y
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, target = cm_y
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#, group = 'none'
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# , tts_split_type = tts_split_type
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, sel_cv = skf_cv
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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 = os.cpu_count() # the number of jobs should equal the number of CPU cores
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# )
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, blind_test_df = cm_bts_X
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# cD3_v2.to_csv(outFile)
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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 = os.cpu_count() # the number of jobs should equal the number of CPU cores
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)
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cD3_v2.to_csv(outFile)
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#%%
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#%% RUN
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#CMLogoSkf(combined_df)
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#===============
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CMLogoSkf(combined_df, std_gene_omit=['alr'])
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# Complete Data
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#===============
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#CMLogoSkf(cm_input_df = combined_df,file_suffix = "complete")
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#CMLogoSkf(cm_input_df = combined_df, std_gene_omit=['alr'], file_suffix = "complete")
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#===============
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# Actual Data
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#===============
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CMLogoSkf(cm_input_df = combined_df_actual, file_suffix = "actual")
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CMLogoSkf(cm_input_df = combined_df_actual, std_gene_omit=['alr'], file_suffix = "actual")
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@ -1,89 +0,0 @@
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#!/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/combined/'
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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 MultClfs_logo_skf import *
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from MultClfs_logo_skf_split import *
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from GetMLData import *
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from SplitTTS import *
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# Input data
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from ml_data_combined import *
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###############################################################################
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print('\nUsing data with 5 genes:', len(cm_input_df5))
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###############################################################################
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split_types = ['70_30', '80_20', 'sl']
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split_data_types = ['actual', 'complete']
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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 + 'cm_' + split_type + '_' + data_type + '.csv'
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print(out_filename)
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tempD = split_tts(cm_input_df5
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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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scoresD = MultModelsCl_logo_skf(**paramD[k]
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XXXXXXXXXXXXXXXXXXXXXXX
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mmDD[k] = scoresD
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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(('/home/tanu/git/Data/ml_combined/genes/'+out_filename), index = False)
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@ -71,4 +71,12 @@ else:
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omit_gene_alr = ['alr']
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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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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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##############################################################################
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@ -48,11 +48,7 @@ for gene, drug in ml_gene_drugD.items():
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, '\nDrug:', drug)
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, '\nDrug:', drug)
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gene_low = gene.lower()
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gene_low = gene.lower()
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gene_dataD[gene_low] = getmldata(gene, drug
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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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, **combined_model_paramD)
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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 split_type in split_types:
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for data_type in split_data_types:
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for data_type in split_data_types:
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