Problem with Gaussian processes in v5

Questions v5
,
7

I change my codes like this:

ax.plot(X_new, pred_samples.posterior['f_pred'], 'C1-', alpha=0.3)

but still get error

AttributeError: ‘InferenceData’ object has no attribute ‘posterior’
My purpose is to plot the fitted function on the original data to visually inspect its fit to the data and the uncertainty in the predictions,like this:

截屏2023-12-21 01.26.02
截屏2023-12-21 01.26.021824×1396 83.5 KB

Here is my entire code

X = x[:, None]

with pm.Model() as model_reg:
    # hyperprior for lengthscale kernel parameter
    ℓ = pm.Gamma('ℓ', 2, 0.5)
    # instanciate a covariance function
    cov = pm.gp.cov.ExpQuad(1, ls=ℓ)
    # instanciate a GP prior
    gp = pm.gp.Marginal(cov_func=cov)
    # prior
    ϵ = pm.HalfNormal('ϵ', 25)
    # likelihood
    y_pred = gp.marginal_likelihood('y_pred', X=X, y=y, sigma=ϵ)
    trace_reg = pm.sample(2000)
az.plot_trace(trace_reg)
#%%
X_new = np.linspace(np.floor(x.min()), np.ceil(x.max()), 100)[:,None]
#%%
with model_reg:
    f_pred = gp.conditional('f_pred', X_new)
#%%
with model_reg:
#    pred_samples = pm.sample_posterior_predictive(f_pred)
    pred_samples = pm.sample_posterior_predictive(trace_reg, var_names=['f_pred'])
#%%
_, ax = plt.subplots(figsize=(12,5))
#ax.plot(X_new, pred_samples.posterior['f_pred'].T, 'C1-', alpha=0.3)
ax.plot(X_new, pred_samples.posterior['f_pred'], 'C1-', alpha=0.3)


ax.plot(X, y, 'ko')
ax.set_xlabel('X')```