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https://github.com/SheffieldML/GPy.git
synced 2026-05-02 08:12:39 +02:00
changed the behaviour of checkgrad
checkgrad usd to check the passed string (for name matching) against the list of _get_param_names(). Then it would index along _get_param_names_transformed()! this led to inconsistensies when fixed or tied variables were used, which screwed up the ordering of the variable names. We now match against _get_param_names_transformed().
This commit is contained in:
James Hensman
parent
903e66486d
commit
dea4359b4e
3 changed files with 48 additions and 29 deletions
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@ -392,7 +392,11 @@ class model(parameterised):
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if target_param is None:
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param_list = range(len(x))
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else:
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param_list = self.grep_param_names(target_param)
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param_list = self.grep_param_names(target_param, transformed=True, search=True)
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if not param_list:
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print "No free parameters to check"
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return
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for i in param_list:
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xx = x.copy()
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@ -119,7 +119,7 @@ class parameterised(object):
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"""Unties all parameters by setting tied_indices to an empty list."""
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self.tied_indices = []
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def grep_param_names(self, regexp):
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def grep_param_names(self, regexp, transformed=False, search=False):
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"""
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:param regexp: regular expression to select parameter names
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:type regexp: re | str | int
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@ -129,13 +129,21 @@ class parameterised(object):
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Other objects are passed through - i.e. integers which weren't meant for grepping
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"""
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if transformed:
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names = self._get_param_names_transformed()
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else:
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names = self._get_param_names()
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if type(regexp) in [str, np.string_, np.str]:
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regexp = re.compile(regexp)
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return np.nonzero([regexp.match(name) for name in self._get_param_names()])[0]
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elif type(regexp) is re._pattern_type:
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return np.nonzero([regexp.match(name) for name in self._get_param_names()])[0]
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pass
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else:
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return regexp
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if search:
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return np.nonzero([regexp.search(name) for name in names])[0]
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else:
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return np.nonzero([regexp.match(name) for name in names])[0]
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def Nparam_transformed(self):
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removed = 0
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@ -223,7 +231,14 @@ class parameterised(object):
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To fix multiple parameters to the same value, simply pass a regular expression which matches both parameter names, or pass both of the indexes
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"""
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matches = self.grep_param_names(regexp)
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assert not np.any(matches[:, None] == self.all_constrained_indices()), "Some indices are already constrained"
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overlap = set(matches).intersection(set(self.all_constrained_indices()))
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if overlap:
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self.unconstrain(np.asarray(list(overlap)))
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print 'Warning: re-constraining these parameters'
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pn = self._get_param_names()
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for i in overlap:
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print pn[i]
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self.fixed_indices.append(matches)
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if value != None:
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self.fixed_values.append(value)
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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(max_nb_eval_optim=100):
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def toy_rbf_1d(optim_iters=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(max_nb_eval_optim=100):
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# optimize
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m.ensure_default_constraints()
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m.optimize(max_f_eval=max_nb_eval_optim)
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m.optimize(max_f_eval=optim_iters)
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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(max_nb_eval_optim=100):
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def rogers_girolami_olympics(optim_iters=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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@ -37,14 +37,14 @@ def rogers_girolami_olympics(max_nb_eval_optim=100):
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# optimize
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m.ensure_default_constraints()
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m.optimize(max_f_eval=max_nb_eval_optim)
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m.optimize(max_f_eval=optim_iters)
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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(max_nb_eval_optim=100):
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def toy_rbf_1d_50(optim_iters=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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@ -53,14 +53,14 @@ def toy_rbf_1d_50(max_nb_eval_optim=100):
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# optimize
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m.ensure_default_constraints()
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m.optimize(max_f_eval=max_nb_eval_optim)
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m.optimize(max_f_eval=optim_iters)
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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(max_nb_eval_optim=100):
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def silhouette(optim_iters=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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@ -69,12 +69,12 @@ def silhouette(max_nb_eval_optim=100):
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=True,max_f_eval=max_nb_eval_optim)
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m.optimize(messages=True,max_f_eval=optim_iters)
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print(m)
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return m
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def coregionalisation_toy2(max_nb_eval_optim=100):
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def coregionalisation_toy2(optim_iters=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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@ -93,7 +93,7 @@ def coregionalisation_toy2(max_nb_eval_optim=100):
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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',messages=1,max_f_eval=max_nb_eval_optim)
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m.optimize('sim',messages=1,max_f_eval=optim_iters)
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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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@ -106,7 +106,7 @@ def coregionalisation_toy2(max_nb_eval_optim=100):
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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(max_nb_eval_optim=100):
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def coregionalisation_toy(optim_iters=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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@ -125,7 +125,7 @@ def coregionalisation_toy(max_nb_eval_optim=100):
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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(max_f_eval=max_nb_eval_optim)
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m.optimize(max_f_eval=optim_iters)
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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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@ -139,7 +139,7 @@ def coregionalisation_toy(max_nb_eval_optim=100):
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return m
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def coregionalisation_sparse(max_nb_eval_optim=100):
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def coregionalisation_sparse(optim_iters=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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@ -162,9 +162,9 @@ def coregionalisation_sparse(max_nb_eval_optim=100):
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m.scale_factor = 10000.
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m.constrain_fixed('.*rbf_var',1.)
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#m.constrain_positive('kappa')
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m.constrain_fixed('iip')
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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, max_f_eval=max_nb_eval_optim)
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m.optimize_restarts(5, robust=True, messages=1, max_f_eval=optim_iters)
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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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@ -181,7 +181,7 @@ def coregionalisation_sparse(max_nb_eval_optim=100):
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return m
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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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def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000, optim_iters=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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@ -219,7 +219,7 @@ 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, max_f_eval=max_nb_eval_optim)
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m.optimize(xtol=1e-6, ftol=1e-6, max_f_eval=optim_iters)
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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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@ -266,7 +266,7 @@ 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, max_nb_eval_optim=100):
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def sparse_GP_regression_1D(N = 400, M = 5, optim_iters=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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@ -281,11 +281,11 @@ def sparse_GP_regression_1D(N = 400, M = 5, max_nb_eval_optim=100):
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m.ensure_default_constraints()
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m.checkgrad(verbose=1)
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m.optimize('tnc', messages = 1, max_f_eval=max_nb_eval_optim)
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m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
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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, max_nb_eval_optim=100):
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def sparse_GP_regression_2D(N = 400, M = 50, optim_iters=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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@ -306,12 +306,12 @@ def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
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# optimize and plot
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pb.figure()
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m.optimize('tnc', messages = 1, max_f_eval=max_nb_eval_optim)
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m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
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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(max_nb_eval_optim=100):
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def uncertain_inputs_sparse_regression(optim_iters=100):
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"""Run a 1D example of a sparse GP regression with uncertain inputs."""
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fig, axes = pb.subplots(1,2,figsize=(12,5))
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@ -327,7 +327,7 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
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# create simple GP model - no input uncertainty on this one
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m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z)
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=max_nb_eval_optim)
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m.optimize('scg', messages=1, max_f_eval=optim_iters)
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m.plot(ax=axes[0])
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axes[0].set_title('no input uncertainty')
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@ -335,7 +335,7 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
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#the same model with uncertainty
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m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z, X_variance=S)
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=max_nb_eval_optim)
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m.optimize('scg', messages=1, max_f_eval=optim_iters)
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m.plot(ax=axes[1])
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axes[1].set_title('with input uncertainty')
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print(m)
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