def evaluate_random_numbers(model, split, n_marginals=5):
"""
Evaluates the model by looking at the distribution of the random numbers for some data split. The more gaussian it
look, the better the model fits the data.
:param model: the model
:param split: the data split, must be 'trn', 'val', or 'tst'
:param n_marginals: number of marginal histograms of random numbers to plot
"""
assert is_data_loaded(), 'Dataset hasn\'t been loaded'
# choose which data split to use
data_split = getattr(data, split, None)
if data_split is None:
raise ValueError('Invalid data split')
# determine whether model is conditional
if is_conditional(model):
x = [data_split.y, data_split.x]
else:
x = data_split.x
# calculate random numbers
u = model.calc_random_numbers(x)
# estimate kl to unit gaussian
q = pdfs.fit_gaussian(u)
p = pdfs.Gaussian(m=np.zeros(data.n_dims), S=np.eye(data.n_dims))
print('KL(q||p) = {0:.2f}'.format(q.kl(p)))
# plot some marginals
util.plot_hist_marginals(u[:, :n_marginals])
plt.show()
def show_histograms(self, split):
data_split = getattr(self, split, None)
if data_split is None:
raise ValueError('Invalid data split')
util.plot_hist_marginals(data_split.x)
plt.show()