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