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ensure_default_constraints is on by default

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James Hensman 2013-06-17 15:57:19 +01:00
parent 4cc26902e4
commit 8fd8288fb8
22 changed files with 16 additions and 71 deletions
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14
GPy/examples/regression.py
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@ -18,7 +18,6 @@ def toy_rbf_1d(optimizer='tnc', max_nb_eval_optim=100):
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
m.optimize(optimizer, max_f_eval=max_nb_eval_optim)
# plot
m.plot()
@ -36,7 +35,6 @@ def rogers_girolami_olympics(optim_iters=100):
m['rbf_lengthscale'] = 10
# optimize
m.ensure_default_constraints()
m.optimize(max_f_eval=optim_iters)
# plot
@ -52,7 +50,6 @@ def toy_rbf_1d_50(optim_iters=100):
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
m.optimize(max_f_eval=optim_iters)
# plot
@ -68,7 +65,6 @@ def silhouette(optim_iters=100):
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
m.optimize(messages=True,max_f_eval=optim_iters)
print(m)
@ -92,7 +88,6 @@ def coregionalisation_toy2(optim_iters=100):
m = GPy.models.GPRegression(X,Y,kernel=k)
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('.*kappa')
m.ensure_default_constraints()
m.optimize('sim',messages=1,max_f_eval=optim_iters)
pb.figure()
@ -124,7 +119,6 @@ def coregionalisation_toy(optim_iters=100):
m = GPy.models.GPRegression(X,Y,kernel=k)
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('kappa')
m.ensure_default_constraints()
m.optimize(max_f_eval=optim_iters)
pb.figure()
@ -162,7 +156,6 @@ def coregionalisation_sparse(optim_iters=100):
m.constrain_fixed('.*rbf_var',1.)
m.constrain_fixed('iip')
m.constrain_bounded('noise_variance',1e-3,1e-1)
m.ensure_default_constraints()
m.optimize_restarts(5, robust=True, messages=1, max_f_eval=optim_iters)
#plotting:
@ -189,11 +182,9 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
log_SNRs = np.linspace(-3., 4., resolution)
data = GPy.util.datasets.della_gatta_TRP63_gene_expression(gene_number)
# Sub sample the data to ensure multiple optima
#data['Y'] = data['Y'][0::2, :]
#data['X'] = data['X'][0::2, :]
# Remove the mean (no bias kernel to ensure signal/noise is in RBF/white)
data['Y'] = data['Y'] - np.mean(data['Y'])
lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
@ -220,7 +211,6 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);
# optimize
m.ensure_default_constraints()
m.optimize('scg', xtol=1e-6, ftol=1e-6, max_f_eval=optim_iters)
optim_point_x[1] = m['rbf_lengthscale']
@ -273,7 +263,6 @@ def sparse_GP_regression_1D(N = 400, num_inducing = 5, optim_iters=100):
# create simple GP Model
m = GPy.models.SparseGPRegression(X, Y, kernel, num_inducing=num_inducing)
m.ensure_default_constraints()
m.checkgrad(verbose=1)
m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
@ -294,7 +283,6 @@ def sparse_GP_regression_2D(N = 400, num_inducing = 50, optim_iters=100):
m = GPy.models.SparseGPRegression(X,Y,kernel, num_inducing = num_inducing)
# contrain all parameters to be positive (but not inducing inputs)
m.ensure_default_constraints()
m.set('.*len',2.)
m.checkgrad()
@ -320,7 +308,6 @@ def uncertain_inputs_sparse_regression(optim_iters=100):
# create simple GP Model - no input uncertainty on this one
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=optim_iters)
m.plot(ax=axes[0])
axes[0].set_title('no input uncertainty')
@ -328,7 +315,6 @@ def uncertain_inputs_sparse_regression(optim_iters=100):
#the same Model with uncertainty
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z, X_variance=S)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=optim_iters)
m.plot(ax=axes[1])
axes[1].set_title('with input uncertainty')