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bootstrapping.py
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import argparse
import pandas as pd
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score,balanced_accuracy_score, roc_auc_score, roc_curve, auc, precision_score, recall_score
import matplotlib.pyplot as plt
import os
import numpy as np
import torch
parser = argparse.ArgumentParser(description='Model names input split by commas')
parser.add_argument('--model_names', type=str, default=None,help='models to plot')
parser.add_argument('--bootstraps', type=int, default=100000,
help='Number of bootstraps to calculate')
parser.add_argument('--run_repeats', type=int, default=10,
help='Number of model repeats')
parser.add_argument('--folds', type=int, default=10,
help='Number of cross-validation folds')
parser.add_argument('--data_csv', type=str, default='set_all_714.csv')
parser.add_argument('--model_folder', type=str, default='/mnt/results/eval_results')
parser.add_argument('--num_classes',type=int,default=2)
parser.add_argument('--plot_roc_curves', action='store_true', default=False, help="Plot an ROC curve for each run repeat")
parser.add_argument('--roc_plot_dir', type=str, default='../mount_outputs/roc_plots/',help='directory to plot ROC curves')
parser.add_argument('--ensemble', action='store_true', default=False, help="Ensemble the predictions from different folds into one prediction. Only works if all folds test sets are identical.")
args = parser.parse_args()
model_names=args.model_names.split(",")
bootstraps=args.bootstraps
model_folder = args.model_folder
for model_name in model_names:
full_model_name=model_folder+'/EVAL_'+model_name
all_auc_means=[]
all_f1_means=[]
all_accuracy_means=[]
all_balanced_accuracy_means=[]
all_auc_sds=[]
all_f1_sds=[]
all_accuracy_sds=[]
all_balanced_accuracy_sds=[]
all_auc_cis=[]
all_f1_cis=[]
all_accuracy_cis=[]
all_balanced_accuracy_cis=[]
for run_no in range(args.run_repeats):
all_Yhats=[]
all_Ys=[]
all_p1s=[]
all_probs=[]
all_losses=list(pd.read_csv(full_model_name+'/summary.csv')['loss'])
print("run: ",run_no)
for fold_no in range(args.folds):
if args.run_repeats>1:
full_df = pd.read_csv(full_model_name+'_run{}/fold_{}.csv'.format(run_no,fold_no))
else:
full_df = pd.read_csv(full_model_name+'/fold_{}.csv'.format(fold_no))
all_Yhats=all_Yhats+list(full_df['Y_hat'])
all_Ys=all_Ys+list(full_df['Y'])
if args.num_classes==2:
all_p1s=all_p1s+list(full_df['p_1'])
else:
if len(all_probs)<1:
all_probs=full_df.iloc[:,-args.num_classes:]
else:
all_probs=all_probs.append(full_df.iloc[:,-args.num_classes:])
if args.ensemble:
num_of_samples = int(len(all_Ys)/args.folds)
all_Ys=all_Ys[:num_of_samples]
ensemble_probs = all_probs.head(num_of_samples)
ensemble_Yhats = ['None'] * num_of_samples
for i in range(num_of_samples):
ensemble_probs.iloc[i] = all_probs.iloc[i::num_of_samples].mean(axis=0)
ensemble_Yhats[i] = torch.topk(torch.tensor(ensemble_probs.iloc[i]), 1, dim = 0)[1].item()
all_probs = ensemble_probs
all_Yhats = ensemble_Yhats
AUC_scores=[]
err_scores=[]
accuracies=[]
f1s=[]
balanced_accuracies=[]
print("confusion matrix (predicted x axis, true y axis): \n")
print(confusion_matrix(all_Ys,all_Yhats),"\n")
for i in range(len(np.unique(all_Ys))):
#indxs = [index for index, value in enumerate(all_Ys) if value == i]
#precision = precision_score([all_Ys[index] for index in indxs],[all_Yhats[index] for index in indxs])
print("class {} precision: {:.5f} recall: {:.5f} f1: {:.5f}".format(i,precision_score(all_Ys,all_Yhats,labels=[i],average='macro'),recall_score(all_Ys,all_Yhats,labels=[i],average='macro'),f1_score(all_Ys,all_Yhats,labels=[i],average='macro')))
#print("class {} recall: {}".format(i,recall_score(all_Ys,all_Yhats,labels=[i],average='macro')))
#print("class {} F1: {}".format(i,f1_score(all_Ys,all_Yhats,labels=[i],average='macro')))
print("\naverage loss: ",np.mean(all_losses), "(not bootstrapped)")
if args.plot_roc_curves:
fpr, tpr, threshold = roc_curve(all_Ys, all_p1s)
roc_auc = auc(fpr, tpr)
if args.run_repeats>1:
plt.plot(fpr, tpr, label = 'Repeat '+str(run_no+1))
else:
plt.plot(fpr, tpr)
bootstrap_failure_resamples = 0
for _ in range(bootstraps):
idxs=np.random.choice(range(len(all_Ys)),len(all_Ys))
classes_sampled = len(np.unique([all_Ys[idx] for idx in idxs]))
while classes_sampled < args.num_classes:
bootstrap_failure_resamples += 1
print("resampling because of failed sample",bootstrap_failure_resamples)
idxs=np.random.choice(range(len(all_Ys)),len(all_Ys))
classes_sampled = len(np.unique([all_Ys[idx] for idx in idxs]))
if args.num_classes==2:
f1s=f1s+[f1_score([all_Ys[idx] for idx in idxs],[all_Yhats[idx] for idx in idxs])]
AUC_scores=AUC_scores+[roc_auc_score([all_Ys[idx] for idx in idxs],[all_p1s[idx] for idx in idxs])]
else:
f1s=f1s+[f1_score([all_Ys[idx] for idx in idxs],[all_Yhats[idx] for idx in idxs],average='macro')]
AUC_scores=AUC_scores+[roc_auc_score([all_Ys[idx] for idx in idxs],[all_probs.iloc[idx,:] for idx in idxs],multi_class='ovr')]
accuracies=accuracies+[accuracy_score([all_Ys[idx] for idx in idxs],[all_Yhats[idx] for idx in idxs])]
balanced_accuracies=balanced_accuracies+[balanced_accuracy_score([all_Ys[idx] for idx in idxs],[all_Yhats[idx] for idx in idxs])]
if args.plot_roc_curves:
os.makedirs(args.roc_plot_dir, exist_ok=True)
print("saving ROC curves to {}{}.png \n".format(args.roc_plot_dir,model_name))
plt.savefig("{}{}.png".format(args.roc_plot_dir,model_name),dpi=300)
all_auc_means=all_auc_means+[np.mean(AUC_scores)]
all_auc_sds=all_auc_sds+[np.std(AUC_scores)]
all_auc_cis=all_auc_cis+[list(np.percentile(AUC_scores, [2.5,97.5]))]
all_f1_means=all_f1_means+[np.mean(f1s)]
all_f1_sds=all_f1_sds+[np.std(f1s)]
all_f1_cis=all_f1_cis+[list(np.percentile(f1s, [2.5,97.5]))]
all_accuracy_means=all_accuracy_means+[np.mean(accuracies)]
all_accuracy_sds=all_accuracy_sds+[np.std(accuracies)]
all_accuracy_cis=all_accuracy_cis+[list(np.percentile(accuracies, [2.5,97.5]))]
all_balanced_accuracy_means=all_balanced_accuracy_means+[np.mean(balanced_accuracies)]
all_balanced_accuracy_sds=all_balanced_accuracy_sds+[np.std(balanced_accuracies)]
all_balanced_accuracy_cis=all_balanced_accuracy_cis+[list(np.percentile(balanced_accuracies, [2.5,97.5]))]
print("AUC mean: ", all_auc_means," AUC std: ",all_auc_sds, "AUC 95%CI: ",all_auc_cis)
if args.num_classes==2:
print("F1 mean: ",all_f1_means," F1 std: ",all_f1_sds, "F1 95%CI: ",all_f1_cis)
else:
print("Macro F1 mean: ",all_f1_means," F1 std: ",all_f1_sds, "F1 95%CI: ",all_f1_cis)
print("accuracy mean: ",all_accuracy_means," accuracy std: ",all_accuracy_sds, "accuracy 95%CI: ",all_accuracy_cis)
print("balanced accuracy mean: ",all_balanced_accuracy_means," balanced accuracy std: ",all_balanced_accuracy_sds, "balanced accuracy 95%CI: ",all_balanced_accuracy_cis)
plot_CIs=False
if plot_CIs:
plot_to = "/mnt/results/CIs/"
plt.hist(AUC_scores, bins=50)
plt.axvline(np.percentile(AUC_scores, [2.5]), color="red")
plt.axvline(np.percentile(AUC_scores, [97.5]), color="red")
plt.savefig(plot_to+"AUC.png")
df=pd.DataFrame([[all_auc_means],[all_accuracy_means],[all_balanced_accuracy_means],[all_f1_means],[all_auc_sds],[all_accuracy_sds],[all_balanced_accuracy_sds],[all_f1_sds]])
df.to_csv("metric_results/"+model_name+".csv",index=False)