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from basic_metrics import metricor
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
def plotFig(data, label, score, slidingWindow, fileName, modelName, plotRange=None):
grader = metricor()
R_AUC, R_AP, R_fpr, R_tpr, R_prec = grader.RangeAUC(labels=label, score=score, window=slidingWindow, plot_ROC=True) #
L, fpr, tpr= grader.metric_new(label, score, plot_ROC=True)
precision, recall, AP = grader.metric_PR(label, score)
range_anomaly = grader.range_convers_new(label)
# print(range_anomaly)
# max_length = min(len(score),len(data), 20000)
max_length = len(score)
if plotRange==None:
plotRange = [0,max_length]
fig3 = plt.figure(figsize=(12, 10), constrained_layout=True)
gs = fig3.add_gridspec(3, 4)
f3_ax1 = fig3.add_subplot(gs[0, :-1])
plt.tick_params(labelbottom=False)
plt.plot(data[:max_length],'k')
for r in range_anomaly:
if r[0]==r[1]:
plt.plot(r[0],data[r[0]],'r.')
else:
plt.plot(range(r[0],r[1]+1),data[range(r[0],r[1]+1)],'r')
# plt.xlim([0,max_length])
plt.xlim(plotRange)
# L = [auc, precision, recall, f, Rrecall, ExistenceReward,
# OverlapReward, Rprecision, Rf, precision_at_k]
f3_ax2 = fig3.add_subplot(gs[1, :-1])
# plt.tick_params(labelbottom=False)
L1 = [ '%.2f' % elem for elem in L]
plt.plot(score[:max_length])
plt.hlines(np.mean(score)+3*np.std(score),0,max_length,linestyles='--',color='red')
plt.ylabel('score')
# plt.xlim([0,max_length])
plt.xlim(plotRange)
#plot the data
f3_ax3 = fig3.add_subplot(gs[2, :-1])
index = ( label + 2*(score > (np.mean(score)+3*np.std(score))))
cf = lambda x: 'k' if x==0 else ('r' if x == 1 else ('g' if x == 2 else 'b') )
cf = np.vectorize(cf)
color = cf(index[:max_length])
black_patch = mpatches.Patch(color = 'black', label = 'TN')
red_patch = mpatches.Patch(color = 'red', label = 'FN')
green_patch = mpatches.Patch(color = 'green', label = 'FP')
blue_patch = mpatches.Patch(color = 'blue', label = 'TP')
plt.scatter(np.arange(max_length), data[:max_length], c=color, marker='.')
plt.legend(handles = [black_patch, red_patch, green_patch, blue_patch], loc= 'best')
# plt.xlim([0,max_length])
plt.xlim(plotRange)
f3_ax4 = fig3.add_subplot(gs[0, -1])
plt.plot(fpr, tpr)
# plt.plot(R_fpr,R_tpr)
# plt.title('R_AUC='+str(round(R_AUC,3)))
plt.xlabel('FPR')
plt.ylabel('TPR')
# plt.legend(['ROC','Range-ROC'])
# f3_ax5 = fig3.add_subplot(gs[1, -1])
# plt.plot(recall, precision)
# plt.plot(R_tpr[:-1],R_prec) # I add (1,1) to (TPR, FPR) at the end !!!
# plt.xlabel('Recall')
# plt.ylabel('Precision')
# plt.legend(['PR','Range-PR'])
# print('AUC=', L1[0])
# print('F=', L1[3])
plt.suptitle(fileName + ' window='+str(slidingWindow) +' '+ modelName
+'\nAUC='+L1[0]+' R_AUC='+str(round(R_AUC,2))+' Precision='+L1[1]+ ' Recall='+L1[2]+' F='+L1[3]
+ ' ExistenceReward='+L1[5]+' OverlapReward='+L1[6]
+'\nAP='+str(round(AP,2))+' R_AP='+str(round(R_AP,2))+' Precision@k='+L1[9]+' Rprecision='+L1[7] + ' Rrecall='+L1[4] +' Rf='+L1[8]
)
def printResult(data, label, score, slidingWindow, fileName, modelName):
grader = metricor()
R_AUC = grader.RangeAUC(labels=label, score=score, window=slidingWindow, plot_ROC=False) #
L= grader.metric_new(label, score, plot_ROC=False)
L.append(R_AUC)
return L
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