various bugs
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3 changed files with 39 additions and 24 deletions
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@ -213,32 +213,41 @@ str(a)
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###################################################
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###################################################
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#
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# source("~/git/LSHTM_analysis/config/alr.R")
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# source("~/git/LSHTM_analysis/config/embb.R")
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# source("~/git/LSHTM_analysis/config/gid.R")
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# source("~/git/LSHTM_analysis/config/katg.R")
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# source("~/git/LSHTM_analysis/config/pnca.R")
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# source("~/git/LSHTM_analysis/config/rpob.R")
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source("~/git/LSHTM_analysis/config/alr.R")
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source("~/git/LSHTM_analysis/config/embb.R")
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source("~/git/LSHTM_analysis/config/gid.R")
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source("~/git/LSHTM_analysis/config/katg.R")
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source("~/git/LSHTM_analysis/config/pnca.R")
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source("~/git/LSHTM_analysis/config/rpob.R")
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#
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# df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
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# df3 = read.csv(df3_filename)
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df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
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df3 = read.csv(df3_filename)
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#
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# # mutationinformation
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# length(unique((df3$mutationinformation)))
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# mutationinformation
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length(unique((df3$mutationinformation)))
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#
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# #dm _om
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# table(df3$mutation_info)
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# table(df3$mutation_info_labels)
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# table(df3$mutation_info_orig)
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# table(df3$mutation_info_labels_orig)
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#
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# # test_set
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# na_count <-sapply(df3, function(y) sum(length(which(is.na(y)))))
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# na_count[drug]
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table(df3$mutation_info)
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table(df3$mutation_info_labels)
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table(df3$mutation_info_orig)
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table(df3$mutation_info_labels_orig)
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# test_set
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na_count <-sapply(df3, function(y) sum(length(which(is.na(y)))))
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na_count[drug]
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#
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# # training set
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# table(df3[drug])
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table(df3[drug])
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#
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# # drtype: MDR and XDR
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# #table(df3$drtype) orig i.e. incorrect ones!
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# table(df3$drtype_mode_labels)
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df3_complete = df3
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table(df3_complete$dst_mode)
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df3_actual = df3[!is.na(df3$dst), ]
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table(df3_actual$dst_mode)
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@ -354,9 +354,10 @@ def MultModelsCl(input_df, target
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y_pred = cross_val_predict(model_pipeline
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, input_df
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, target
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, cv = sel_cv
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#, groups = group
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, **njobs)
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#, commented out thing,
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, cv=sel_cv
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, **njobs
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)
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#_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally
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tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel()
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@ -48,8 +48,13 @@ ml_gene_drugD = {'pncA' : 'pyrazinamide'
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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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'80_20',
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'sl'
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]
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split_data_types = ['actual',
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'complete'
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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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