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How to use test_classnames method in unittest-xml-reporting

Best Python code snippet using unittest-xml-reporting_python

IAP_eval.py

Source:IAP_eval.py

1#!/usr/bin/env python2"""3Animals with Attributes Dataset, http://attributes.kyb.tuebingen.mpg.de4Perform Multiclass Predicition from binary attributes and evaluates it.5(C) 2009 Christoph Lampert <chl@tuebingen.mpg.de>6"""7import os,sys8import numpy as np9import matplotlib.pyplot as plt10from sklearn.metrics import roc_curve, auc11from utils import bzPickle, bzUnpickle, get_class_attributes, get_attributes, create_data, autolabel12import warnings13warnings.filterwarnings('ignore')14def nameonly(x):15    return x.split('\t')[1]16def loadstr(filename,converter=str):17    return [converter(c.strip()) for c in open(filename).readlines()]18def loaddict(filename,converter=str):19    D={}20    for line in open(filename).readlines():21        line = line.split()22        D[line[0]] = converter(line[1].strip())23    return D24# adapt these paths and filenames to match local installation25classnames = loadstr('classes.txt',nameonly)26numexamples = loaddict('numexamples.txt',int)27def evaluate(split,C, attributepattern):28    global test_classnames29    if split == 0:30        test_classnames=loadstr('testclasses.txt')31        train_classnames=loadstr('trainclasses.txt')32    else:33        startid= (split-1)*1034        stopid = split*1035        test_classnames = classnames[startid:stopid]36        train_classnames = classnames[0:startid]+classnames[stopid:]37    test_classes = [ classnames.index(c) for c in test_classnames]38    train_classes = [ classnames.index(c) for c in train_classnames]39    M = np.loadtxt('predicate-matrix-binary.txt',dtype=float)40    L=[]41    for c in test_classes:42        L.extend( [c]*numexamples[classnames[c]] )43    L=np.array(L)  # (n,)44    P_prime = np.loadtxt(attributepattern)45    P = np.dot(P_prime, M[train_classes]) # (85,n)46    prior = np.mean(M[train_classes],axis=0)47    prior[prior==0.]=0.548    prior[prior==1.]=0.5    # disallow degenerated priors49    M = M[test_classes] # (10,85)50    prob=[]51    for p in P:52        prob.append( np.prod(M*p + (1-M)*(1-p),axis=1)/np.prod(M*prior+(1-M)*(1-prior), axis=1) )53    MCpred = np.argmax( prob, axis=1 )54    d = len(test_classes)55    confusion=np.zeros([d,d])56    for pl,nl in zip(MCpred,L):57        try:58            gt = test_classes.index(nl)59            confusion[gt,pl] += 1.60        except:61            pass62    for row in confusion:63        row /= sum(row)64    return confusion,np.asarray(prob),P65def plot_confusion(confusion, clf):66    fig=plt.figure(figsize=(10,9))67    plt.imshow(confusion,interpolation='nearest',origin='upper')68    plt.clim(0,1)69    plt.xticks(np.arange(0,10),[c.replace('+',' ') for c in test_classnames],rotation='vertical',fontsize=24)70    plt.yticks(np.arange(0,10),[c.replace('+',' ') for c in test_classnames],fontsize=24)71    plt.axis([-.5,9.5,9.5,-.5])72    plt.setp(plt.gca().xaxis.get_major_ticks(), pad=18)73    plt.setp(plt.gca().yaxis.get_major_ticks(), pad=12)74    fig.subplots_adjust(left=0.30)75    fig.subplots_adjust(top=0.98)76    fig.subplots_adjust(right=0.98)77    fig.subplots_adjust(bottom=0.22)78    plt.gray()79    plt.colorbar(shrink=0.79)80    plt.savefig('results/AwA-ROC-confusion-IAP-%s.pdf' %clf)81    return82def plot_roc(P,GT, clf):83    AUC=[]84    CURVE=[]85    for i,c in enumerate(test_classnames):86        class_id = classnames.index(c)87        fp, tp, _ = roc_curve(GT==class_id,  P[:,i])88        roc_auc = auc(fp, tp)89        print ("AUC: %s %5.3f" % (c,roc_auc))90        AUC.append(roc_auc)91        CURVE.append(np.array([fp,tp]))92    print ("----------------------------------")93    print ("Mean classAUC %g" % (np.mean(AUC)*100))94    order = np.argsort(AUC)[::-1]95    styles=['-','-','-','-','-','-','-','--','--','--']96    plt.figure(figsize=(9,5))97    for i in order:98        c = test_classnames[i]99        plt.plot(CURVE[i][0],CURVE[i][1],label='%s (AUC: %3.2f)' % (c,AUC[i]),linewidth=3,linestyle=styles[i])100    plt.legend(loc='lower right')101    plt.xticks([0.0,0.2,0.4,0.6,0.8,1.0], [r'$0$', r'$0.2$',r'$0.4$',r'$0.6$',r'$0.8$',r'$1.0$'],fontsize=18)102    plt.yticks([0.0,0.2,0.4,0.6,0.8,1.0], [r'$0$', r'$0.2$',r'$0.4$',r'$0.6$',r'$0.8$',r'$1.0$'],fontsize=18)103    plt.xlabel('false negative rate',fontsize=18)104    plt.ylabel('true positive rate',fontsize=18)105    plt.savefig('results/AwA-ROC-IAP-%s.pdf' %clf)106def plot_attAUC(P, attributepattern, clf):107    AUC=[]108    #P = np.loadtxt(attributepattern)109    attributes = get_attributes()110    # Loading ground truth111    test_index = bzUnpickle('./CreatedData/test_features_index.txt')112    test_attributes = get_class_attributes('./', name='test')113    _, y_true = create_data('./CreatedData/test_featuresVGG19.pic.bz2',test_index, test_attributes)114    print(y_true.shape, P.shape)115    for i in range(y_true.shape[1]):116        fp, tp, _ = roc_curve(y_true[:,i], P[:,i])117        roc_auc = auc(fp, tp)118        AUC.append(roc_auc)119    print ("Mean attrAUC %g" % (np.nanmean(AUC)) )120    xs = np.arange(y_true.shape[1])121    width = 0.5122    fig = plt.figure(figsize=(15,5))123    ax = fig.add_subplot(1,1,1)124    rects = ax.bar(xs, AUC, width, align='center')125    ax.set_xticks(xs)126    ax.set_xticklabels(attributes,  rotation=90)127    ax.set_ylabel("area under ROC curve")128    autolabel(rects, ax)129    plt.savefig('results/AwA-AttAUC-IAP-%s.pdf' %clf)130def main():131    list_clf = ['SVM', 'NN']132    try:133        clf = str(sys.argv[1])134    except IndexError:135        clf = 'SVM'136    if clf not in list_clf:137        print ("Non valid choice of classifier (SVM, NN)")138        raise SystemExit139    try:140        split = int(sys.argv[2])141    except IndexError:142        split = 0143    try:144        C = float(sys.argv[3])145    except IndexError:146        C = 10.147    attributepattern = 'IAP/probabilities_' + clf148    confusion,prob,L = evaluate(split,C, attributepattern)149    #plot_confusion(confusion, clf)150    #plot_roc(prob,L, clf)151    plot_attAUC(L, attributepattern, clf)152    print ("Mean class accuracy %g" % np.mean(np.diag(confusion)*100))153if __name__ == '__main__':...

DAP_eval.py

Source:DAP_eval.py

1#!/usr/bin/env python2"""3Animals with Attributes Dataset, http://attributes.kyb.tuebingen.mpg.de4Perform Multiclass Predicition from binary attributes and evaluates it.5(C) 2009 Christoph Lampert <chl@tuebingen.mpg.de>6"""7import os,sys8sys.path.append('/agbs/cluster/chl/libs/python2.5/site-packages/')9from numpy import *10def nameonly(x):11    return x.split('\t')[1]12def loadstr(filename,converter=str):13    return [converter(c.strip()) for c in file(filename).readlines()]14def loaddict(filename,converter=str):15    D={}16    for line in file(filename).readlines():17        line = line.split()18        D[line[0]] = converter(line[1].strip())19    20    return D21# adapt these paths and filenames to match local installation22classnames = loadstr('../classes.txt',nameonly)23numexamples = loaddict('numexamples.txt',int)24def evaluate(split,C):25    global test_classnames26    attributepattern = './DAP/cvfold%d_C%g_%%02d.prob' % (split,C)27    28    if split == 0:29        test_classnames=loadstr('/agbs/share/datasets/Animals_with_Attributes/testclasses.txt')30        train_classnames=loadstr('/agbs/share/datasets/Animals_with_Attributes/trainclasses.txt')31    else:32        startid= (split-1)*1033        stopid = split*1034        test_classnames = classnames[startid:stopid]35        train_classnames = classnames[0:startid]+classnames[stopid:]36    37    test_classes = [ classnames.index(c) for c in test_classnames]38    train_classes = [ classnames.index(c) for c in train_classnames]39    M = loadtxt('/agbs/share/datasets/Animals_with_Attributes/predicate-matrix-binary.txt',dtype=float)40    L=[]41    for c in test_classes:42        L.extend( [c]*numexamples[classnames[c]] )43    L=array(L)  # (n,)44    P = []45    for i in range(85):46        P.append(loadtxt(attributepattern % i,float))47    P = array(P).T   # (85,n)48    prior = mean(M[train_classes],axis=0)49    prior[prior==0.]=0.550    prior[prior==1.]=0.5    # disallow degenerated priors51    M = M[test_classes] # (10,85)52    prob=[]53    for p in P:54        prob.append( prod(M*p + (1-M)*(1-p),axis=1)/prod(M*prior+(1-M)*(1-prior), axis=1) )55    MCpred = argmax( prob, axis=1 )56    57    d = len(test_classes)58    confusion=zeros([d,d])59    for pl,nl in zip(MCpred,L):60        try:61            gt = test_classes.index(nl)62            confusion[gt,pl] += 1.63        except:64            pass65    for row in confusion:66        row /= sum(row)67    68    return confusion,asarray(prob),L69def plot_confusion(confusion):70    from pylab import figure,imshow,clim,xticks,yticks,axis,setp,gray,colorbar,savefig,gca71    fig=figure(figsize=(10,9))72    imshow(confusion,interpolation='nearest',origin='upper')73    clim(0,1)74    xticks(arange(0,10),[c.replace('+',' ') for c in test_classnames],rotation='vertical',fontsize=24)75    yticks(arange(0,10),[c.replace('+',' ') for c in test_classnames],fontsize=24)76    axis([-.5,9.5,9.5,-.5])77    setp(gca().xaxis.get_major_ticks(), pad=18)78    setp(gca().yaxis.get_major_ticks(), pad=12)79    fig.subplots_adjust(left=0.30)80    fig.subplots_adjust(top=0.98)81    fig.subplots_adjust(right=0.98)82    fig.subplots_adjust(bottom=0.22)83    gray()84    colorbar(shrink=0.79)85    savefig('AwA-ROC-confusion-DAP.pdf')86    return 87def plot_roc(P,GT):88    from pylab import figure,xticks,yticks,axis,setp,gray,colorbar,savefig,gca,clf,plot,legend,xlabel,ylabel89    from roc import roc90    AUC=[]91    CURVE=[]92    for i,c in enumerate(test_classnames):93        class_id = classnames.index(c)94        tp,fp,auc=roc(None,GT==class_id,  P[:,i] ) # larger is better95        print "AUC: %s %5.3f" % (c,auc)96        AUC.append(auc)97        CURVE.append(array([fp,tp]))98    order = argsort(AUC)[::-1]99    styles=['-','-','-','-','-','-','-','--','--','--']100    figure(figsize=(9,5))101    for i in order:102        c = test_classnames[i]103        plot(CURVE[i][0],CURVE[i][1],label='%s (AUC: %3.2f)' % (c,AUC[i]),linewidth=3,linestyle=styles[i])104    105    legend(loc='lower right')106    xticks([0.0,0.2,0.4,0.6,0.8,1.0], [r'$0$', r'$0.2$',r'$0.4$',r'$0.6$',r'$0.8$',r'$1.0$'],fontsize=18)107    yticks([0.0,0.2,0.4,0.6,0.8,1.0], [r'$0$', r'$0.2$',r'$0.4$',r'$0.6$',r'$0.8$',r'$1.0$'],fontsize=18)108    xlabel('false negative rate',fontsize=18)109    ylabel('true positive rate',fontsize=18)110    savefig('AwA-ROC-DAP.pdf')111def main():112    try:113        split = int(sys.argv[1])114    except IndexError:115        split = 0116    try:117        C = float(sys.argv[2])118    except IndexError:119        C = 10.120    confusion,prob,L = evaluate(split,C)121    print "Mean class accuracy %g" % mean(diag(confusion)*100)122    plot_confusion(confusion) 123    plot_roc(prob,L)124    125if __name__ == '__main__':...

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