def test_load_save(self):
cov_main = GPCov(wfn_params=[1.0,], dfn_params=[ 2.5,], wfn_str="compact0", dfn_str="euclidean")
cov_fic = GPCov(wfn_params=[1.0,], dfn_params=[ 1.5,], wfn_str="se", dfn_str="euclidean", Xu = self.u)
noise_var = 1.0
gp1 = GP(X=self.X,
y=self.y1,
noise_var = noise_var,
cov_main = cov_main,
cov_fic = cov_fic,
compute_ll=True,
sparse_threshold=0,
build_tree=False)
gp1.save_trained_model("test_csfic.npz")
gp2 = GP(fname="test_csfic.npz", build_tree=False)
pts = np.reshape(np.linspace(-5, 5, 20), (-1, 1))
p1 = gp1.predict(pts)
v1 = gp1.variance(pts)
p2 = gp2.predict(pts)
v2 = gp2.variance(pts)
self.assertTrue((p1 == p2).all())
self.assertTrue((v1 == v2).all())
def trained_gp(dataset_name, model_name, n=None, tag=None, **kwargs):
fname = gp_fname(dataset_name, model_name, n=n, tag=tag)
if os.path.exists(fname):
return GP(fname=fname, **kwargs)
X_train, y_train = training_data(dataset_name, n=n)
cov_main, cov_fic, noise_var = load_hparams(dataset_name, model_name, tag=tag)
if model_name=="se":
kwargs['sparse_invert'] = False
kwargs['build_tree'] = False
"print training GP for", dataset_name, model_name
sgp = GP(X=X_train, y=y_train,
noise_var=noise_var,
cov_main=cov_main,
cov_fic=cov_fic,
sparse_threshold=1e-8,
**kwargs)
sgp.save_trained_model(fname)
print "saved trained GP to", fname
stats_fname = fname[:-3] + "_stats.txt"
with open(stats_fname, 'w') as f:
f.write("main hyperparams: %s\n" % cov_main)
f.write("fic hyperparams: %s\n" % cov_fic)
if scipy.sparse.issparse(sgp.K):
nzr, nzc = sgp.K.nonzero()
f.write("K nonzero: %d/%d = %.3f%%\n" % (len(nzr), sgp.K.shape[0]**2, float(len(nzr))/sgp.K.shape[0]**2))
if scipy.sparse.issparse(sgp.Kinv):
nzr, nzc = sgp.Kinv.nonzero()
f.write("Kinv nonzero: %d/%d = %.3f%%\n" % (len(nzr), sgp.Kinv.shape[0]**2, float(len(nzr))/sgp.Kinv.shape[0]**2))
for k in sgp.timings:
f.write("timing %s: %.3f\n" % (k, sgp.timings[k]))
print "saved stats to", stats_fname
return sgp