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added parameters max_nb_eval_optim in regression examples

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Nicolas 2013-06-04 15:57:37 +01:00
parent b4a3ac7809
commit 96c7810cf1
1 changed files with 23 additions and 24 deletions
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47
GPy/examples/regression.py
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@ -10,7 +10,7 @@ import numpy as np
import GPy import GPy
def toy_rbf_1d(): def toy_rbf_1d(max_nb_eval_optim=100):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.""" """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
data = GPy.util.datasets.toy_rbf_1d() data = GPy.util.datasets.toy_rbf_1d()
@ -19,13 +19,13 @@ def toy_rbf_1d():
# optimize # optimize
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize() m.optimize(max_f_eval=max_nb_eval_optim)
# plot # plot
m.plot() m.plot()
print(m) print(m)
return m return m
def rogers_girolami_olympics(): def rogers_girolami_olympics(max_nb_eval_optim=100):
"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data.""" """Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
data = GPy.util.datasets.rogers_girolami_olympics() data = GPy.util.datasets.rogers_girolami_olympics()
@ -34,14 +34,14 @@ def rogers_girolami_olympics():
# optimize # optimize
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize() m.optimize(max_f_eval=max_nb_eval_optim)
# plot # plot
m.plot(plot_limits = (1850, 2050)) m.plot(plot_limits = (1850, 2050))
print(m) print(m)
return m return m
def toy_rbf_1d_50(): def toy_rbf_1d_50(max_nb_eval_optim=100):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.""" """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
data = GPy.util.datasets.toy_rbf_1d_50() data = GPy.util.datasets.toy_rbf_1d_50()
@ -50,14 +50,14 @@ def toy_rbf_1d_50():
# optimize # optimize
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize() m.optimize(max_f_eval=max_nb_eval_optim)
# plot # plot
m.plot() m.plot()
print(m) print(m)
return m return m
def silhouette(): def silhouette(max_nb_eval_optim=100):
"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper.""" """Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
data = GPy.util.datasets.silhouette() data = GPy.util.datasets.silhouette()
@ -66,12 +66,12 @@ def silhouette():
# optimize # optimize
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize(messages=True) m.optimize(messages=True,max_f_eval=max_nb_eval_optim)
print(m) print(m)
return m return m
def coregionalisation_toy2(): def coregionalisation_toy2(max_nb_eval_optim=100):
""" """
A simple demonstration of coregionalisation on two sinusoidal functions. A simple demonstration of coregionalisation on two sinusoidal functions.
""" """
@ -90,8 +90,7 @@ def coregionalisation_toy2():
m.constrain_fixed('rbf_var',1.) m.constrain_fixed('rbf_var',1.)
m.constrain_positive('kappa') m.constrain_positive('kappa')
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize('sim',max_f_eval=5000,messages=1) m.optimize('sim',messages=1,max_f_eval=max_nb_eval_optim)
#m.optimize()
pb.figure() pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1)))) Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
@ -104,7 +103,7 @@ def coregionalisation_toy2():
pb.plot(X2[:,0],Y2[:,0],'gx',mew=2) pb.plot(X2[:,0],Y2[:,0],'gx',mew=2)
return m return m
def coregionalisation_toy(): def coregionalisation_toy(max_nb_eval_optim=100):
""" """
A simple demonstration of coregionalisation on two sinusoidal functions. A simple demonstration of coregionalisation on two sinusoidal functions.
""" """
@ -123,7 +122,7 @@ def coregionalisation_toy():
m.constrain_fixed('rbf_var',1.) m.constrain_fixed('rbf_var',1.)
m.constrain_positive('kappa') m.constrain_positive('kappa')
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize() m.optimize(max_f_eval=max_nb_eval_optim)
pb.figure() pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1)))) Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
@ -137,7 +136,7 @@ def coregionalisation_toy():
return m return m
def coregionalisation_sparse(): def coregionalisation_sparse(max_nb_eval_optim=100):
""" """
A simple demonstration of coregionalisation on two sinusoidal functions using sparse approximations. A simple demonstration of coregionalisation on two sinusoidal functions using sparse approximations.
""" """
@ -162,7 +161,7 @@ def coregionalisation_sparse():
m.constrain_positive('kappa') m.constrain_positive('kappa')
m.constrain_fixed('iip') m.constrain_fixed('iip')
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize_restarts(5,robust=True,messages=1) m.optimize_restarts(5, robust=True, messages=1, max_f_eval=max_nb_eval_optim)
pb.figure() pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1)))) Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
@ -179,7 +178,7 @@ def coregionalisation_sparse():
return m return m
def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000): def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000, max_nb_eval_optim=100):
"""Show an example of a multimodal error surface for Gaussian process regression. Gene 939 has bimodal behaviour where the noisey mode is higher.""" """Show an example of a multimodal error surface for Gaussian process regression. Gene 939 has bimodal behaviour where the noisey mode is higher."""
# Contour over a range of length scales and signal/noise ratios. # Contour over a range of length scales and signal/noise ratios.
@ -217,7 +216,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
# optimize # optimize
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize(xtol=1e-6,ftol=1e-6) m.optimize(xtol=1e-6, ftol=1e-6, max_f_eval=max_nb_eval_optim)
optim_point_x[1] = m.get('rbf_lengthscale') optim_point_x[1] = m.get('rbf_lengthscale')
optim_point_y[1] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance')); optim_point_y[1] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
@ -264,7 +263,7 @@ def _contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf
lls.append(length_scale_lls) lls.append(length_scale_lls)
return np.array(lls) return np.array(lls)
def sparse_GP_regression_1D(N = 400, M = 5): def sparse_GP_regression_1D(N = 400, M = 5, max_nb_eval_optim=100):
"""Run a 1D example of a sparse GP regression.""" """Run a 1D example of a sparse GP regression."""
# sample inputs and outputs # sample inputs and outputs
X = np.random.uniform(-3.,3.,(N,1)) X = np.random.uniform(-3.,3.,(N,1))
@ -279,11 +278,11 @@ def sparse_GP_regression_1D(N = 400, M = 5):
m.constrain_positive('(variance|lengthscale|precision)') m.constrain_positive('(variance|lengthscale|precision)')
m.checkgrad(verbose=1) m.checkgrad(verbose=1)
m.optimize('tnc', messages = 1) m.optimize('tnc', messages = 1, max_f_eval=max_nb_eval_optim)
m.plot() m.plot()
return m return m
def sparse_GP_regression_2D(N = 400, M = 50): def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
"""Run a 2D example of a sparse GP regression.""" """Run a 2D example of a sparse GP regression."""
X = np.random.uniform(-3.,3.,(N,2)) X = np.random.uniform(-3.,3.,(N,2))
Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(N,1)*0.05 Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(N,1)*0.05
@ -294,7 +293,7 @@ def sparse_GP_regression_2D(N = 400, M = 50):
kernel = rbf + noise kernel = rbf + noise
# create simple GP model # create simple GP model
m = GPy.models.sparse_GP_regression(X,Y,kernel, M = M) m = GPy.models.sparse_GP_regression(X,Y,kernel, M = M, max_nb_eval_optim=100)
# contrain all parameters to be positive (but not inducing inputs) # contrain all parameters to be positive (but not inducing inputs)
m.constrain_positive('(variance|lengthscale|precision)') m.constrain_positive('(variance|lengthscale|precision)')
@ -304,12 +303,12 @@ def sparse_GP_regression_2D(N = 400, M = 50):
# optimize and plot # optimize and plot
pb.figure() pb.figure()
m.optimize('tnc', messages = 1) m.optimize('tnc', messages = 1, max_f_eval=max_nb_eval_optim)
m.plot() m.plot()
print(m) print(m)
return m return m
def uncertain_inputs_sparse_regression(): def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
"""Run a 1D example of a sparse GP regression with uncertain inputs.""" """Run a 1D example of a sparse GP regression with uncertain inputs."""
# sample inputs and outputs # sample inputs and outputs
S = np.ones((20,1)) S = np.ones((20,1))
@ -327,7 +326,7 @@ def uncertain_inputs_sparse_regression():
m.constrain_positive('(variance|prec)') m.constrain_positive('(variance|prec)')
# optimize and plot # optimize and plot
m.optimize('tnc', max_f_eval = 1000, messages=1) m.optimize('tnc', messages=1, max_f_eval=max_nb_eval_optim)
m.plot() m.plot()
print(m) print(m)
return m return m