RSNA abstract checkpoint
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Stefan
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@ -24,7 +24,7 @@ if args.comparison:
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colors = ['r','r','b','b','g','g','y','y']
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plot_type = ['-','--','-','--','-','--','-','--']
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else:
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colors = ['r','b','g','k','y','c']
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colors = ['r','b','k','g','c','y']
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plot_type = ['-','-','-','-','-','-']
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yaml_metric = args.yaml_metric
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@ -38,61 +38,61 @@ sensitivity = []
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fig = plt.figure(1)
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ax = fig.add_subplot(111)
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False_possitives_mean = np.linspace(0, 5, 200)
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for idx in range(len(args.experiment)):
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False_possitives_mean = np.linspace(0, 2.5, 200)
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for fold in range(5):
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print('fold:',fold)
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paufroc = []
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for fold in range(4):
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# print('fold:',fold)
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fold = fold + 1
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experiment_metrics = {}
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experiment_path = f'./../train_output/{experiments[idx]}_{fold}/froc_metrics_{yaml_metric}.yml'
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experiment_metrics = read_yaml_to_dict(experiment_path)
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pfroc = partial_auc(experiment_metrics["sensitivity"],experiment_metrics["FP_per_case"],low=0.1, high=2.5)
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pfroc = partial_auc(experiment_metrics["sensitivity"],experiment_metrics["FP_per_case"],low=0.1, high=5)
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paufroc.append(round(pfroc,2))
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False_possitives.append(experiment_metrics["FP_per_case"])
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sensitivity_ = np.interp(False_possitives_mean,experiment_metrics["FP_per_case"],experiment_metrics["sensitivity"])
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sensitivity.append(sensitivity_)
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print(f'pfROC van {experiments[idx]}: {paufroc}')
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# calculate mean and std
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sensitivity_mean = np.squeeze(np.mean(sensitivity,axis=0))
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sensitivity_std = np.multiply(np.squeeze(np.std(sensitivity,axis=0)),2)
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plt.plot(False_possitives_mean, sensitivity_mean,color=colors[idx],linestyle=plot_type[idx])
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plt.fill_between(False_possitives_mean, np.subtract(sensitivity_mean,sensitivity_std), np.add(sensitivity_mean,sensitivity_std))
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plt.fill_between(False_possitives_mean, np.subtract(sensitivity_mean,sensitivity_std), np.add(sensitivity_mean,sensitivity_std),alpha=0.15,color=colors[idx],)
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ax.set(xscale="log")
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ax.axes.xaxis.set_minor_locator(tkr.LogLocator(base=10, subs='all'))
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ax.axes.xaxis.set_minor_formatter(tkr.NullFormatter())
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ax.axes.xaxis.set_major_formatter(tkr.ScalarFormatter())
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ax.axes.grid(True, which="both", ls="--", c='#d3d3d3')
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ax.axes.set_xlim(left=0, right=2.5)
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ax.axes.xaxis.set_major_locator(tkr.FixedLocator([0,0.1,0.5,1,2.5]))
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ax.axes.set_xlim(left=0.1, right=5)
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ax.axes.xaxis.set_major_locator(tkr.FixedLocator([0.1,0.5,1,5]))
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fpr = []
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tpr = []
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fpr_mean = np.linspace(0, 1, 200)
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for idx in range(len(args.experiment)):
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experiment_path = f'./../train_output/{experiments[idx]}/froc_metrics_{yaml_metric}.yml'
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experiment_metrics = read_yaml_to_dict(experiment_path)
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auroc.append(round(experiment_metrics['auroc'],3))
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fpr_mean = np.linspace(0, 1, 200)
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for fold in range(5):
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auroc = []
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for fold in range(4):
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fold= fold + 1
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print('fold:',fold)
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experiment_metrics = {}
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experiment_path = f'./../train_output/{experiments[idx]}_{fold}/froc_metrics_{yaml_metric}.yml'
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experiment_metrics = read_yaml_to_dict(experiment_path)
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# pfroc = partial_auc(experiment_metrics["tpr"],experiment_metrics["fpr"],low=0.1, high=2.5)
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paufroc.append(round(pfroc,2))
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auroc.append(round(experiment_metrics['auroc'],3))
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fpr.append(experiment_metrics["fpr"])
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tpr_ = np.interp(fpr_mean,experiment_metrics["fpr"],experiment_metrics["tpr"])
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tpr.append(tpr_)
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print(f'auROC van {experiments[idx]}: {auroc}')
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tpr_mean = np.squeeze(np.mean(tpr,axis=0))
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tpr_std = np.multiply(np.squeeze(np.std(tpr,axis=0)),2)
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plt.figure(2)
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plt.plot(fpr_mean, tpr_mean,color=colors[idx],linestyle=plot_type[idx])
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plt.fill_between(fpr_mean, np.subtract(tpr_mean,tpr_std), np.add(tpr_mean,tpr_std))
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plt.fill_between(fpr_mean, np.subtract(tpr_mean,tpr_std), np.add(tpr_mean,tpr_std),alpha=0.15,color=colors[idx],)
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print(auroc)
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experiments = [exp.replace('train_10h_', '') for exp in experiments]
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@ -107,7 +107,9 @@ plt.figure(1)
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plt.title('fROC curve')
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plt.xlabel('False positive per case')
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plt.ylabel('Sensitivity')
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plt.legend(experiments_paufroc,loc='lower right')
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# plt.legend(experiments_paufroc,loc='lower right')
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plt.legend(['calculated with b50-400','calculated with b50-800'],loc='lower right')
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# plt.xlim([0,50])
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plt.grid()
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plt.ylim([0,1])
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@ -119,8 +121,11 @@ experiments_auroc = list(map(concat_func,experiments,auroc)) # list the map func
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plt.figure(2)
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plt.title('ROC curve')
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plt.legend(experiments_auroc,loc='lower right')
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# plt.legend(experiments_auroc,loc='lower right')
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plt.legend(['calculated with b50-400','calculated with b50-800'],loc='lower right')
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plt.xlabel('False positive rate')
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plt.ylabel('True positive rate')
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plt.ylim([0,1])
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plt.xlim([0,1])
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plt.grid()
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plt.savefig(f"./../train_output/ROC_{args.saveas}.png", dpi=300)
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@ -48,13 +48,18 @@ MODEL_PATH = f'./../train_output/{EXPERIMENT}_{series_}_{fold}/models/{EXPERIMEN
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YAML_DIR = f'./../train_output/{EXPERIMENT}_{series_}_{fold}'
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IMAGE_DIR = f'./../train_output/{EXPERIMENT}_{series_}_{fold}'
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# MODEL_PATH = f'./../train_output/{EXPERIMENT}_{series_}/models/{EXPERIMENT}_{series_}.h5'
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# YAML_DIR = f'./../train_output/{EXPERIMENT}_{series_}'
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# IMAGE_DIR = f'./../train_output/{EXPERIMENT}_{series_}'
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DATA_DIR = "./../data/Nijmegen paths/"
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TARGET_SPACING = (0.5, 0.5, 3)
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INPUT_SHAPE = (192, 192, 24, len(SERIES))
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IMAGE_SHAPE = INPUT_SHAPE[:3]
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DATA_SPLIT_INDEX = read_yaml_to_dict(f'./../data/Nijmegen paths/train_val_test_idxs_{fold}.yml')
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TEST_INDEX = DATA_SPLIT_INDEX['val_set0']
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# DATA_SPLIT_INDEX = read_yaml_to_dict(f'./../data/Nijmegen paths/train_val_test_idxs.yml')
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TEST_INDEX = DATA_SPLIT_INDEX['test_set0']
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N_CPUS = 12
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@ -168,7 +173,7 @@ predictions = zeros
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# perform Froc
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metrics = evaluate(y_true=segmentations, y_pred=predictions)
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dump_dict_to_yaml(metrics, YAML_DIR, "froc_metrics_focal_10", verbose=True)
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dump_dict_to_yaml(metrics, YAML_DIR, "froc_metrics_focal_10_test", verbose=True)
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############## save image as example #################
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@ -1,24 +1,27 @@
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import matplotlib.pyplot as plt
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import matplotlib.ticker as tkr
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import seaborn as sns
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import matplotlib.ticker as tkr
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from p_auc import partial_auc
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x = [0,0.11,0.23,0.5,0.90,1.00,1.500,3]
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y = [0,0.02,0.09,1,2,3,4,12]
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from scipy import stats
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import numpy as np
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# siemens_froc = [1.68,1.81,1.44,1.55]
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# b400_froc = [3.4,3.93,2.82,]
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# b800_froc = [1.58,1.99,1.36,1.6]
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tick_spacing = 1
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# siemens_roc = [0.782, 0.732, 0.775, 0.854]
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b400_roc = [0.746, 0.814, 0.789, 0.763]
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b800_roc = [0.786, 0.731, 0.67, 0.782]
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fig1, ax1 = plt.subplots(1,1)
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ax1.plot(x,y)
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pauc = partial_auc(x,y)
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print(pauc)
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# stat_test = stats.wilcoxon(siemens_froc,b800_froc,alternative='less')
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# print('froc stats siemens > b400',stat_test)
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# print(' Mean and std siemens froc:', np.mean(siemens_froc),'+-',np.std(siemens_froc))
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# print(' Mean and std b400 froc:', np.mean(b400_froc),'+-',np.std(b400_froc))
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# print(' Mean and std b800 froc:', np.mean(b800_froc),'+-',np.std(b800_froc))
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# ax.set_xticks([0,100,1500])
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# ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
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# ax1.set(xscale="log")
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# ax1.xaxis.set_minor_locator(tkr.LogLocator(base=10, subs='all'))
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# ax1.xaxis.set_minor_formatter(tkr.NullFormatter())
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# ax1.xaxis.set_major_formatter(tkr.ScalarFormatter())
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# ax1.grid(True, which="both", ls="--", c='#d3d3d3')
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# ax1.set_xlim(left=0, right=150)
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# ax1.xaxis.set_major_locator(tkr.FixedLocator([0,1,3]))
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# print(' Mean and std siemens roc:', np.mean(siemens_roc),'+-',np.std(siemens_roc))
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print(' Mean and std b400 roc:', np.mean(b400_roc),'+-',np.std(b400_roc))
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print(' Mean and std b800 roc:', np.mean(b800_roc),'+-',np.std(b800_roc))
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# The test has been introduced in [4]. Given n independent samples (xi, yi) from a bivariate distribution
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# (i.e. paired samples), it computes the differences di = xi - yi. One assumption of the test is that the
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# differences are symmetric, see [2]. The two-sided test has the null hypothesis that the median of the
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# differences is zero against the alternative that it is different from zero. The one-sided test has the
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# null hypothesis that the median is positive against the alternative that it is negative
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# (alternative == 'less'), or vice versa (alternative == 'greater.').
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