def plot_confidence_network(A_true,
                            svi_models=None):
    A_flat = A_true.ravel()
    # Compute ROC based on E[A] under variational posterior
    # W_svi = svi_models[-1].weight_model.expected_W()
    mean_A = svi_models[-1].weight_model.expected_A()
    std_A = np.sqrt(svi_models[-1].weight_model.mf_p
                    * (1-svi_models[-1].weight_model.mf_p))

    score = mean_A / std_A

    plt.figure()
    plt.imshow(score, interpolation="none", cmap="Reds")
    plt.colorbar()
    plt.show()


# seed = 2650533028
Compute the AUC of the precision recall curve
dataset/ETHPy150Open slinderman/pyhawkes/experiments/chalearn_small_comparison.py/plot_confidence_network