Python get_eff_faker_vs_feat示例

示例#1

plt.ylabel('TPR', fontsize=14)
plt.title('AUC = ' + str(auc), fontsize=14)
plt.savefig('ROC_curve.png')
plt.close()

# Plot TPR and FPR vs decision threshold (dt)
plt.plot(dt[1:], fpr[1:], label='FPR')
plt.plot(dt[1:], tpr[1:], label='TPR')
plt.xlabel('decision thresh.', fontsize=14)
plt.ylabel('metric', fontsize=14)
plt.legend(loc='best')
plt.savefig('TPR_FPR_vs_dt.png')
plt.close()

# Create TPR/FPR vs. pt
pt, eff, faker, err_eff, err_faker = get_eff_faker_vs_feat(
    'pt', pt_test, X_test_bin, y_test, clf_GBDT)

plt.errorbar(pt, eff, yerr=err_eff, linestyle='None', fmt='.')
plt.xlabel('p$_{T}$ (GeV/c)', fontsize=14)
plt.ylabel('TPR', fontsize=14)
plt.savefig('TPR_vs_pt.png')
plt.close()

plt.errorbar(pt, faker, yerr=err_faker, linestyle='None', fmt='.')
plt.xlabel('p$_{T}$ (GeV/c)', fontsize=14)
plt.ylabel('FPR', fontsize=14)
plt.savefig('FPR_vs_pt.png')
plt.close()

# Create TPR/FPR vs. eta
pt, eff, faker, err_eff, err_faker = get_eff_faker_vs_feat(

示例#2

文件： example_scikitlearn_gbdt.py 项目： cms-L1TK/TrainTrackQuality

plt.plot(fpr, tpr)
plt.xlabel('FPR', fontsize=14)
plt.ylabel('TPR', fontsize=14)
plt.title('AUC = ' + str(auc), fontsize=14)
plt.savefig('ROC_curve.png')

# Plot TPR and FPR vs decision threshold (dt)
plt.plot(dt[1:], fpr[1:], label='FPR')
plt.plot(dt[1:], tpr[1:], label='TPR')
plt.xlabel('decision thresh.', fontsize=14)
plt.ylabel('metric', fontsize=14)
plt.legend(loc='best')
plt.savefig('TPR_FPR_vs_dt.png')

# Create TPR/FPR vs. pt
pt, eff, faker, err_eff, err_faker = get_eff_faker_vs_feat(
    'pt', features, X_test, y_test, clf_GBDT)

plt.errorbar(pt, eff, yerr=err_eff, linestyle='None', fmt='.')
plt.xlabel('p$_{T}$ (GeV/c)', fontsize=14)
plt.ylabel('TPR', fontsize=14)
plt.savefig('TPR_vs_pt.png')

plt.errorbar(pt, faker, yerr=err_faker, linestyle='None', fmt='.')
plt.xlabel('p$_{T}$ (GeV/c)', fontsize=14)
plt.ylabel('FPR', fontsize=14)
plt.savefig('FPR_vs_pt.png')

# Create TPR/FPR vs. eta
pt, eff, faker, err_eff, err_faker = get_eff_faker_vs_feat(
    'eta', features, X_test, y_test, clf_GBDT)