evaluation/visualize.py

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