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| import numpy as np | |
| #import matplotlib as mpl | |
| #mpl.use('Agg') | |
| import matplotlib.pyplot as plt | |
| import metrics | |
| class ConfidenceHistogram(metrics.MaxProbCELoss): | |
| def plot(self, output, labels, n_bins = 15, logits = True, title = None): | |
| super().loss(output, labels, n_bins, logits) | |
| #scale each datapoint | |
| n = len(labels) | |
| w = np.ones(n)/n | |
| plt.rcParams["font.family"] = "serif" | |
| #size and axis limits | |
| plt.figure(figsize=(3,3)) | |
| plt.xlim(0,1) | |
| plt.ylim(0,1) | |
| plt.xticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0], ['0.0', '0.2', '0.4', '0.6', '0.8', '1.0']) | |
| plt.yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0], ['0.0', '0.2', '0.4', '0.6', '0.8', '1.0']) | |
| #plot grid | |
| plt.grid(color='tab:grey', linestyle=(0, (1, 5)), linewidth=1,zorder=0) | |
| #plot histogram | |
| plt.hist(self.confidences,n_bins,weights = w,color='b',range=(0.0,1.0),edgecolor = 'k') | |
| #plot vertical dashed lines | |
| acc = np.mean(self.accuracies) | |
| conf = np.mean(self.confidences) | |
| plt.axvline(x=acc, color='tab:grey', linestyle='--', linewidth = 3) | |
| plt.axvline(x=conf, color='tab:grey', linestyle='--', linewidth = 3) | |
| if acc > conf: | |
| plt.text(acc+0.03,0.9,'Accuracy',rotation=90,fontsize=11) | |
| plt.text(conf-0.07,0.9,'Avg. Confidence',rotation=90, fontsize=11) | |
| else: | |
| plt.text(acc-0.07,0.9,'Accuracy',rotation=90,fontsize=11) | |
| plt.text(conf+0.03,0.9,'Avg. Confidence',rotation=90, fontsize=11) | |
| plt.ylabel('% of Samples',fontsize=13) | |
| plt.xlabel('Confidence',fontsize=13) | |
| plt.tight_layout() | |
| if title is not None: | |
| plt.title(title,fontsize=16) | |
| return plt | |
| class ReliabilityDiagram(metrics.MaxProbCELoss): | |
| def plot(self, output, labels, n_bins = 15, logits = True, title = None): | |
| super().loss(output, labels, n_bins, logits) | |
| #computations | |
| delta = 1.0/n_bins | |
| x = np.arange(0,1,delta) | |
| mid = np.linspace(delta/2,1-delta/2,n_bins) | |
| error = np.abs(np.subtract(mid,self.bin_acc)) | |
| plt.rcParams["font.family"] = "serif" | |
| #size and axis limits | |
| plt.figure(figsize=(3,3)) | |
| plt.xlim(0,1) | |
| plt.ylim(0,1) | |
| #plot grid | |
| plt.grid(color='tab:grey', linestyle=(0, (1, 5)), linewidth=1,zorder=0) | |
| #plot bars and identity line | |
| plt.bar(x, self.bin_acc, color = 'b', width=delta,align='edge',edgecolor = 'k',label='Outputs',zorder=5) | |
| plt.bar(x, error, bottom=np.minimum(self.bin_acc,mid), color = 'mistyrose', alpha=0.5, width=delta,align='edge',edgecolor = 'r',hatch='/',label='Gap',zorder=10) | |
| ident = [0.0, 1.0] | |
| plt.plot(ident,ident,linestyle='--',color='tab:grey',zorder=15) | |
| #labels and legend | |
| plt.ylabel('Accuracy',fontsize=13) | |
| plt.xlabel('Confidence',fontsize=13) | |
| plt.legend(loc='upper left',framealpha=1.0,fontsize='medium') | |
| if title is not None: | |
| plt.title(title,fontsize=16) | |
| plt.tight_layout() | |
| return plt | |