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new ml scripts
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@greenkidneybean
greenkidneybean committed Aug 16, 2019
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59 changes: 59 additions & 0 deletions data/ml_actual_predictions.txt
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0 0.5818254422232262
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1 0.863447800534888
0 0.5815120407361399
0 0.6681179147169743
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1 0.7318846412771074
190 changes: 190 additions & 0 deletions data/ml_std_out
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precision recall f1-score support

0 1.00 0.11 0.20 18
1 0.72 1.00 0.84 41

accuracy 0.73 59
macro avg 0.86 0.56 0.52 59
weighted avg 0.80 0.73 0.64 59

AUROC
0.8631436314363145
40 changes: 40 additions & 0 deletions scripts/ml_roc-plot.R
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#command: Rscript rocplot.R roc_output.pdf
#edit path
#the input file names should be df1.tsv df2.tsv ...... df9.tsv or change line 9 below according to file names

args = commandArgs(trailingOnly=TRUE)
library(tidyverse);library(data.table);library(pROC);library(ROCR);library(viridis);library("optparse");library("textreadr")
#==#
#path="/Users/kumardeep/Downloads/softwares/github/EnviroGenes_playground" #CAUTION: change path here
#--#

files <- list.files(path = '.', pattern = "ml_actual_predictions.txt", full.names = T) #file from the svm-sklearn output



color=viridis(3)
legendwa<-c()

#pdf('~/Downloads/zz.pdf',width=5,height = 5)
#pdf('/Users/kumardeep/Downloads/softwares/github/EnviroGenes_playground',width=5,height = 5)
pdf('roc.pdf',width=5,height = 5)
for (i in 1:length(files)){
#print(dim(list[[1]]))
#dfx=list[[i]]
#pred.nn <- prediction(dfx$predictions,dfx$labels)
dfx=read_tsv(files[i],col_names = F)
pred.nn <- prediction(dfx$X2,dfx$X1)
perf.nn <- performance(pred.nn,'tpr','fpr')
perf.nn <- performance(pred.nn,'tpr','fpr',spread.scale=2)
#plot(perf.nn, col="blue")
if(i>1){
plot(perf.nn, col=color[i],add=T,colorize=TRUE)
}else{
plot(perf.nn, col=color[i],colorize=TRUE)
}
lv=paste0('AUC: ', signif(ci.auc(dfx$X1,dfx$X2)[3],2),' (',signif(ci.auc(dfx$X1,dfx$X2)[3],1),'-',signif(ci.auc(dfx$X1,dfx$X2)[3],2),')')
legendwa<-c(legendwa,lv)
}
legend("bottomright", legend=legendwa, col=color, lwd=2)
abline(a=0, b=1)
dev.off()
12 changes: 9 additions & 3 deletions scripts/ml_script.py
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import argparse
import pandas as pd
from sklearn.model_selection import LeaveOneOut
from sklearn.svm import LinearSVC
Expand All @@ -7,7 +8,12 @@
from sklearn.metrics import roc_auc_score
from sklearn.feature_selection import SelectFromModel
from sklearn.ensemble import ExtraTreesClassifier
humann2=pd.read_csv("sample11.csv",index_col=0)

parser = argparse.ArgumentParser()
parser.add_argument("input", help='input sample .tsv file for ML script', type=str)
args = parser.parse_args()

humann2=pd.read_csv(args.input, index_col=0, sep='\t')
X = humann2[humann2.columns[2:]]
y=humann2['flag']
yy=y
Expand All @@ -22,6 +28,7 @@
idxs_selected = model.get_support(indices=True)
f = X.columns[idxs_selected]
features_dataframe_new=X[f]
print(features_dataframe_new.shape)
f = open("actual_pred",'w')
#############################
from sklearn.model_selection import LeaveOneOut
Expand All @@ -38,7 +45,7 @@
#clf=SVC(kernel='linear',probability=True,class_weight="balanced")
clf = RandomForestClassifier(n_estimators=100, max_depth=2,random_state=0)
clf.fit(X_train, y_train.ravel())
y_pred11 = clf.predict(X_test)
y_pred11 = clf.predict(X_test)
y_p_d=y_pred11.tolist()
final_pred_discrete.extend(y_p_d)
y_p_score=clf.predict_proba(X_test) ##### getting prob scores (coressponding to SVM score)
Expand All @@ -52,4 +59,3 @@
print(classification_report(final_actual, final_pred_discrete))
print("AUROC")
print(roc_auc_score(final_actual, final_pred))

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