error_pinball_DGP

[findoctorlin]

Hi everone!

I am tring to implement a tutorial using GPy: https://mlatcl.github.io/gpss/lectures/11-deep-gps-I.html

In the first example of marathon dataset, using DGP and plot the mapping between layer works for me in my notebook, but when I try to plot the pinball diagram, it output error like this:

And the code is here:

import pandas as pd
import numpy as np
import torch
import tqdm
from matplotlib import pyplot as plt

import mlai
from mlai.deepgp_tutorial import initialize
from mlai.deepgp_tutorial import staged_optimize
from mlai.deepgp_tutorial import posterior_sample
from mlai.deepgp_tutorial import visualize
from mlai.deepgp_tutorial import visualize_pinball

import deepgp
deepgp.DeepGP.initialize=initialize
deepgp.DeepGP.staged_optimize=staged_optimize
deepgp.DeepGP.posterior_sample=posterior_sample
deepgp.DeepGP.visualize=visualize
deepgp.DeepGP.visualize_pinball=visualize_pinball

import pods
data = pods.datasets.olympic_marathon_men()
x = data['X']
y = data['Y']

offset = y.mean()
scale = np.sqrt(y.var())
yhat = (y-offset)/scale

import GPy
import deepgp
hidden = 1
m = deepgp.DeepGP([y.shape[1],hidden,x.shape[1]],Y=yhat, X=x, inits=['PCA','PCA'], 
                  kernels=[GPy.kern.RBF(hidden,ARD=True),
                           GPy.kern.RBF(x.shape[1],ARD=True)], # the kernels for each layer
                  num_inducing=50, back_constraint=False)
# Call the initalization
m.initialize()

# optimization
for layer in m.layers:
    layer.likelihood.variance.constrain_positive(warning=False)
m.optimize(messages=True,max_iters=10000)

# test data
xt = np.linspace(1870,2030,200)[:,np.newaxis]
yt_mean, yt_var = m.predict(xt)
yt_sd=np.sqrt(yt_var)

# plot
m.visualize(diagrams='./diagrams') # plot the layer-layer mapping
m.visualize_pinball()