146 lines
5.9 KiB
Python
Executable File
146 lines
5.9 KiB
Python
Executable File
from sfransen.utils_quintin import *
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import matplotlib.pyplot as plt
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import argparse
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import matplotlib.ticker as tkr
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from umcglib.froc.p_auc import partial_auc
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import numpy as np
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parser = argparse.ArgumentParser(
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description='Visualise froc results')
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parser.add_argument('-saveas',
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help='')
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parser.add_argument('-comparison',
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help='')
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parser.add_argument('--experiment', '-s',
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metavar='[series_name]', required=True, nargs='+',
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help='List of series to include, must correspond with' +
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"path files in ./data/")
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parser.add_argument('-yaml_metric',
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help='List of series to include, must correspond with' +
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"path files in ./data/")
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args = parser.parse_args()
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if args.comparison:
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colors = ['b','c','r','m','g','g','y','y']
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plot_type = ['-','-','-','--','-','--','-','--']
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else:
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colors = ['b','r','g','k','c','y']
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plot_type = ['-','-','-','-','-','-']
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yaml_metric = args.yaml_metric
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experiments = args.experiment
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print(experiments)
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experiment_path = []
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auroc = []
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paufroc = []
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False_possitives = []
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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(0.1, 1, 200)
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for idx in range(len(args.experiment)):
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paufroc = []
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experiment_path = []
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auroc = []
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paufroc = []
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False_possitives = []
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sensitivity = []
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for fold in range(5):
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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["sensitivity"],experiment_metrics["FP_per_case"],low=0.0, high=1)
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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),alpha=0.10,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.get_xaxis()
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ax.axes.get_yaxis()
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ax.axes.set_xlim(left=0.1, right=1)
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ax.axes.xaxis.set_major_locator(tkr.FixedLocator([0.1,0.5,1]))
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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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aufroc = []
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experiment_path = []
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auroc = []
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fpr = []
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tpr = []
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for fold in range(5):
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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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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),alpha=0.10,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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experiments = [exp.replace('train_n0.001_', '') for exp in experiments]
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experiments = [exp.replace('_', ' ') for exp in experiments]
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# experiments = ['10% noise','1% noise','0.1% noise','0.05% noise']
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concat_func = lambda x,y: x + " (" + str(y) + ")"
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experiments_paufroc = list(map(concat_func,experiments,paufroc)) # list the map function
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plt.figure(1)
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plt.title('fROC curve', fontsize=20)
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plt.xlabel('False positive lesions', fontsize=18)
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plt.ylabel('Sensitivity', fontsize=18)
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# plt.legend(experiments_paufroc,loc='lower right')
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# plt.legend(['$T2_{tra}$ $ADC_{b50-b400}$ $b1400_{b50-b400}$','$T2_{tra}$ $ADC_{b50-b800}$ $b1400_{b50-b800}$','$T2_{tra}$ $ADC_{b50-b400-b800}$ $b1400_{b50-b400-b800}$'],loc='lower right')
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plt.legend(['$T2_{tra}$ $ADC_{b50-b400-b800}$ $b1400_{b50-b400-b800}$','$T2_{tra}$ b50 b400 b800'],loc='lower right')
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# plt.legend(["All b-values","Omitting b800","Omitting b400"],loc='lower right',fontsize=16)
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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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plt.yticks([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])
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plt.savefig(f"./../train_output/fROC_{args.saveas}.png", dpi=300)
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concat_func = lambda x,y: x + " (" + str(y) + ")"
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experiments_auroc = list(map(concat_func,experiments,auroc)) # list the map function
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plt.figure(2)
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plt.title('ROC curve',fontsize=20)
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# plt.legend(experiments_auroc,loc='lower right')
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plt.legend(['$T2_{tra}$ $ADC_{b50-b400-b800}$ $b1400_{b50-b400-b800}$','$T2_{tra}$ b50 b400 b800'],loc='lower right')
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# plt.legend(['$T2_{tra}$ $ADC_{b50-b400}$ $b1400_{b50-b400}$','$T2_{tra}$ $ADC_{b50-b800}$ $b1400_{b50-b800}$','$T2_{tra}$ $ADC_{b50-b400-b800}$ $b1400_{b50-b400-b800}$'],loc='lower right')
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# plt.legend(["All b-values","Omitting b800","Omitting b400"],loc='lower right',fontsize=16)
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plt.xlabel('False positive rate',fontsize=18)
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plt.ylabel('True positive rate',fontsize=18)
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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) |