From 304db40f5bdfc88389908e953d56838e5d2447bc Mon Sep 17 00:00:00 2001 From: James Hensman Date: Mon, 3 Nov 2014 14:03:37 +0000 Subject: [PATCH 1/9] improved kernel plotting --- GPy/kern/_src/kern.py | 13 ++++++-- GPy/plotting/matplot_dep/kernel_plots.py | 41 +++++++++++++++++------- 2 files changed, 40 insertions(+), 14 deletions(-) diff --git a/GPy/kern/_src/kern.py b/GPy/kern/_src/kern.py index 506d32e6..57b2bff5 100644 --- a/GPy/kern/_src/kern.py +++ b/GPy/kern/_src/kern.py @@ -132,13 +132,20 @@ class Kern(Parameterized): """ raise NotImplementedError - def plot(self, *args, **kwargs): + def plot(self, x=None, fignum=None, ax=None, title=None, plot_limits=None, resolution=None, **mpl_kwargs): """ - See GPy.plotting.matplot_dep.plot + plot this kernel. + :param x: the value to use for the other kernel argument (kernels are a function of two variables!) + :param fignum: figure number of the plot + :param ax: matplotlib axis to plot on + :param title: the matplotlib title + :param plot_limits: the range over which to plot the kernel + :resolution: the resolution of the lines used in plotting + :mpl_kwargs avalid keyword arguments to pass through to matplotlib (e.g. lw=7) """ assert "matplotlib" in sys.modules, "matplotlib package has not been imported." from ...plotting.matplot_dep import kernel_plots - kernel_plots.plot(self,*args) + kernel_plots.plot(self, x, fignum, ax, title, plot_limits, resolution, **mpl_kwargs) def plot_ARD(self, *args, **kw): """ diff --git a/GPy/plotting/matplot_dep/kernel_plots.py b/GPy/plotting/matplot_dep/kernel_plots.py index c2bd7d38..1e15f224 100644 --- a/GPy/plotting/matplot_dep/kernel_plots.py +++ b/GPy/plotting/matplot_dep/kernel_plots.py @@ -99,7 +99,26 @@ def plot_ARD(kernel, fignum=None, ax=None, title='', legend=False, filtering=Non return ax -def plot(kernel, x=None, plot_limits=None, resolution=None, *args, **kwargs): + +def plot(kernel,x=None, fignum=None, ax=None, title=None, plot_limits=None, resolution=None, **mpl_kwargs): + """ + plot a kernel. + :param x: the value to use for the other kernel argument (kernels are a function of two variables!) + :param fignum: figure number of the plot + :param ax: matplotlib axis to plot on + :param title: the matplotlib title + :param plot_limits: the range over which to plot the kernel + :resolution: the resolution of the lines used in plotting + :mpl_kwargs avalid keyword arguments to pass through to matplotlib (e.g. lw=7) + """ + fig, ax = ax_default(fignum,ax) + + if title is None: + ax.set_title('%s kernel' % kernel.name) + else: + ax.set_title(title) + + if kernel.input_dim == 1: if x is None: x = np.zeros((1, 1)) @@ -117,10 +136,10 @@ def plot(kernel, x=None, plot_limits=None, resolution=None, *args, **kwargs): Xnew = np.linspace(xmin, xmax, resolution or 201)[:, None] Kx = kernel.K(Xnew, x) - pb.plot(Xnew, Kx, *args, **kwargs) - pb.xlim(xmin, xmax) - pb.xlabel("x") - pb.ylabel("k(x,%0.1f)" % x) + ax.plot(Xnew, Kx, **mpl_kwargs) + ax.set_xlim(xmin, xmax) + ax.set_xlabel("x") + ax.set_ylabel("k(x,%0.1f)" % x) elif kernel.input_dim == 2: if x is None: @@ -142,11 +161,11 @@ def plot(kernel, x=None, plot_limits=None, resolution=None, *args, **kwargs): Xnew = np.vstack((xx.flatten(), yy.flatten())).T Kx = kernel.K(Xnew, x) Kx = Kx.reshape(resolution, resolution).T - pb.contour(xx, xx, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, *args, **kwargs) # @UndefinedVariable - pb.xlim(xmin[0], xmax[0]) - pb.ylim(xmin[1], xmax[1]) - pb.xlabel("x1") - pb.ylabel("x2") - pb.title("k(x1,x2 ; %0.1f,%0.1f)" % (x[0, 0], x[0, 1])) + ax.contour(xx, xx, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, **mpl_kwargs) # @UndefinedVariable + ax.set_xlim(xmin[0], xmax[0]) + ax.set_ylim(xmin[1], xmax[1]) + ax.set_xlabel("x1") + ax.set_ylabel("x2") + ax.set_title("k(x1,x2 ; %0.1f,%0.1f)" % (x[0, 0], x[0, 1])) else: raise NotImplementedError, "Cannot plot a kernel with more than two input dimensions" From b3aa8fa1e2485291fd758d81f09e594604f332e8 Mon Sep 17 00:00:00 2001 From: James Hensman Date: Tue, 4 Nov 2014 09:44:57 +0000 Subject: [PATCH 2/9] work on kernel plotting --- GPy/plotting/matplot_dep/kernel_plots.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/GPy/plotting/matplot_dep/kernel_plots.py b/GPy/plotting/matplot_dep/kernel_plots.py index 1e15f224..cbf0fa54 100644 --- a/GPy/plotting/matplot_dep/kernel_plots.py +++ b/GPy/plotting/matplot_dep/kernel_plots.py @@ -161,7 +161,8 @@ def plot(kernel,x=None, fignum=None, ax=None, title=None, plot_limits=None, reso Xnew = np.vstack((xx.flatten(), yy.flatten())).T Kx = kernel.K(Xnew, x) Kx = Kx.reshape(resolution, resolution).T - ax.contour(xx, xx, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, **mpl_kwargs) # @UndefinedVariable + stop + ax.contour(xx, yy, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, **mpl_kwargs) # @UndefinedVariable ax.set_xlim(xmin[0], xmax[0]) ax.set_ylim(xmin[1], xmax[1]) ax.set_xlabel("x1") From 8ed9f0735ad55a769f4b0946d98b54c0720cd377 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Tue, 4 Nov 2014 16:16:16 +0000 Subject: [PATCH 3/9] Added kern _src files using api-doc -P ../doc ../ to include private modules --- doc/GPy.kern._src.psi_comp.rst | 62 ++++++++++ doc/GPy.kern._src.rst | 197 ++++++++++++++++++++++++++++++++ doc/GPy.kern.rst | 7 ++ doc/GPy.likelihoods.rst | 8 -- doc/conf.py | 26 ++++- doc/log.txt | 200 +++++++++++++++++++++++++++++++++ 6 files changed, 490 insertions(+), 10 deletions(-) create mode 100644 doc/GPy.kern._src.psi_comp.rst create mode 100644 doc/GPy.kern._src.rst create mode 100644 doc/log.txt diff --git a/doc/GPy.kern._src.psi_comp.rst b/doc/GPy.kern._src.psi_comp.rst new file mode 100644 index 00000000..d84eeaa0 --- /dev/null +++ b/doc/GPy.kern._src.psi_comp.rst @@ -0,0 +1,62 @@ +GPy.kern._src.psi_comp package +============================== + +Submodules +---------- + +GPy.kern._src.psi_comp.linear_psi_comp module +--------------------------------------------- + +.. automodule:: GPy.kern._src.psi_comp.linear_psi_comp + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.psi_comp.rbf_psi_comp module +------------------------------------------ + +.. automodule:: GPy.kern._src.psi_comp.rbf_psi_comp + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.psi_comp.rbf_psi_gpucomp module +--------------------------------------------- + +.. automodule:: GPy.kern._src.psi_comp.rbf_psi_gpucomp + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.psi_comp.sslinear_psi_comp module +----------------------------------------------- + +.. automodule:: GPy.kern._src.psi_comp.sslinear_psi_comp + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.psi_comp.ssrbf_psi_comp module +-------------------------------------------- + +.. automodule:: GPy.kern._src.psi_comp.ssrbf_psi_comp + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.psi_comp.ssrbf_psi_gpucomp module +----------------------------------------------- + +.. automodule:: GPy.kern._src.psi_comp.ssrbf_psi_gpucomp + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: GPy.kern._src.psi_comp + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/GPy.kern._src.rst b/doc/GPy.kern._src.rst new file mode 100644 index 00000000..93dc0058 --- /dev/null +++ b/doc/GPy.kern._src.rst @@ -0,0 +1,197 @@ +GPy.kern._src package +===================== + +Subpackages +----------- + +.. toctree:: + + GPy.kern._src.psi_comp + +Submodules +---------- + +GPy.kern._src.ODE_UY module +--------------------------- + +.. automodule:: GPy.kern._src.ODE_UY + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.ODE_UYC module +---------------------------- + +.. automodule:: GPy.kern._src.ODE_UYC + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.ODE_st module +--------------------------- + +.. automodule:: GPy.kern._src.ODE_st + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.ODE_t module +-------------------------- + +.. automodule:: GPy.kern._src.ODE_t + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.add module +------------------------ + +.. automodule:: GPy.kern._src.add + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.brownian module +----------------------------- + +.. automodule:: GPy.kern._src.brownian + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.coregionalize module +---------------------------------- + +.. automodule:: GPy.kern._src.coregionalize + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.hierarchical module +--------------------------------- + +.. automodule:: GPy.kern._src.hierarchical + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.independent_outputs module +---------------------------------------- + +.. automodule:: GPy.kern._src.independent_outputs + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.kern module +------------------------- + +.. automodule:: GPy.kern._src.kern + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.kernel_slice_operations module +-------------------------------------------- + +.. automodule:: GPy.kern._src.kernel_slice_operations + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.linear module +--------------------------- + +.. automodule:: GPy.kern._src.linear + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.mlp module +------------------------ + +.. automodule:: GPy.kern._src.mlp + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.periodic module +----------------------------- + +.. automodule:: GPy.kern._src.periodic + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.poly module +------------------------- + +.. automodule:: GPy.kern._src.poly + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.prod module +------------------------- + +.. automodule:: GPy.kern._src.prod + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.rbf module +------------------------ + +.. automodule:: GPy.kern._src.rbf + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.splitKern module +------------------------------ + +.. automodule:: GPy.kern._src.splitKern + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.static module +--------------------------- + +.. automodule:: GPy.kern._src.static + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.stationary module +------------------------------- + +.. automodule:: GPy.kern._src.stationary + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.symbolic module +----------------------------- + +.. automodule:: GPy.kern._src.symbolic + :members: + :undoc-members: + :show-inheritance: + +GPy.kern._src.trunclinear module +-------------------------------- + +.. automodule:: GPy.kern._src.trunclinear + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: GPy.kern._src + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/GPy.kern.rst b/doc/GPy.kern.rst index 9ee59ed6..5a0c61c2 100644 --- a/doc/GPy.kern.rst +++ b/doc/GPy.kern.rst @@ -1,6 +1,13 @@ GPy.kern package ================ +Subpackages +----------- + +.. toctree:: + + GPy.kern._src + Module contents --------------- diff --git a/doc/GPy.likelihoods.rst b/doc/GPy.likelihoods.rst index 9d74fead..323bb609 100644 --- a/doc/GPy.likelihoods.rst +++ b/doc/GPy.likelihoods.rst @@ -60,14 +60,6 @@ GPy.likelihoods.mixed_noise module :undoc-members: :show-inheritance: -GPy.likelihoods.ordinal module ------------------------------- - -.. automodule:: GPy.likelihoods.ordinal - :members: - :undoc-members: - :show-inheritance: - GPy.likelihoods.poisson module ------------------------------ diff --git a/doc/conf.py b/doc/conf.py index f051c986..1b2410f3 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -11,8 +11,29 @@ # All configuration values have a default; values that are commented out # serve to show the default. -autodoc_default_flags = ['members', 'show-inheritance', 'private-members', 'special-members'] -autodoc_member_order = "source" +#autodoc_default_flags = ['members', 'show-inheritance', 'private-members', 'special-members'] +#autodoc_default_flags = ['private-members', 'special-members'] +#autodoc_default_flags = 'private-members' +#autodoc_member_order = "source" + +#def autodoc_skip_member(app, what, name, obj, skip, options): + #exclusions = ('__weakref__', # special-members + #'__doc__', '__module__', '__dict__', # undoc-members + #) + #exclude = name in exclusions + + #inclusions = ('_src') + #include = name in inclusions + #if include: + #print app, what, name, obj, skip, options + #return False + #return skip or exclude + +#def setup(app): + ##app.connect('autodoc-process-docstring', cut_lines(2)) + ##app.connect('autodoc_default_flags', autodoc_default_flags) + ##app.connect('autodoc_member_order', autodoc_member_order) + #app.connect('autodoc-skip-member', autodoc_skip_member) import sys import os @@ -114,6 +135,7 @@ MOCK_MODULES = ['sympy', for mod_name in MOCK_MODULES: sys.modules[mod_name] = Mock() + # ----------------------- READTHEDOCS ------------------ on_rtd = os.environ.get('READTHEDOCS', None) == 'True' diff --git a/doc/log.txt b/doc/log.txt new file mode 100644 index 00000000..02532259 --- /dev/null +++ b/doc/log.txt @@ -0,0 +1,200 @@ +/home/alans/Work/GPy/GPy/__init__.py:docstring of GPy.load:1: WARNING: Inline interpreted text or phrase reference start-string without end-string. +/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP:7: WARNING: Field list ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP:10: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:5: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/core/model.py:docstring of GPy.core.model.Model.optimize_restarts:29: WARNING: Explicit markup ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/doc/GPy.core.rst:65: WARNING: autodoc: failed to import module u'GPy.core.symbolic'; the following exception was raised: +Traceback (most recent call last): + File "/home/alans/anaconda/envs/GPy/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 335, in import_object + __import__(self.modname) + File "/home/alans/Work/GPy/GPy/core/symbolic.py", line 10, in + from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace +ImportError: No module named lambdify +/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Indexable.unset_priors:1: WARNING: Inline emphasis start-string without end-string. +/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Nameable.hierarchy_name:4: WARNING: Field list ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Observable.notify_observers:5: SEVERE: Unexpected section title or transition. + +^^^^^^^^^^^^^^^^ +/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Observable.notify_observers:6: WARNING: Definition list ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline emphasis start-string without end-string. +/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline strong start-string without end-string. +/home/alans/Work/GPy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:18: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:20: WARNING: Block quote ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/core/parameterization/ties_and_remappings.py:docstring of GPy.core.parameterization.ties_and_remappings.Tie:18: SEVERE: Unexpected section title or transition. + +================================ +/home/alans/Work/GPy/doc/GPy.examples.rst:18: WARNING: autodoc: failed to import module u'GPy.examples.coreg_example'; the following exception was raised: +Traceback (most recent call last): + File "/home/alans/anaconda/envs/GPy/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 335, in import_object + __import__(self.modname) + File "/home/alans/Work/GPy/GPy/examples/coreg_example.py", line 44, in + m = GPy.models.SparseGPCoregionalizedRegression(X_list=[X1], Y_list=[Y1], Z_list = [Z1], kernel=kern) + File "/home/alans/Work/GPy/GPy/core/parameterization/parameterized.py", line 19, in __call__ + self = super(ParametersChangedMeta, self).__call__(*args, **kw) + File "/home/alans/Work/GPy/GPy/models/sparse_gp_coregionalized_regression.py", line 66, in __init__ + self['.*inducing'][:,-1].fix() + File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 444, in constrain_fixed + self.notify_observers(self, None if trigger_parent else -np.inf) + File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 148, in notify_observers + [callble(self, which=which) for _, _, callble in self.observers] + File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 1077, in _pass_through_notify_observers + self.notify_observers(which=which) + File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 148, in notify_observers + [callble(self, which=which) for _, _, callble in self.observers] + File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 1075, in _parameters_changed_notification + self.parameters_changed() + File "/home/alans/Work/GPy/GPy/core/sparse_gp.py", line 66, in parameters_changed + self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata) + File "/home/alans/Work/GPy/GPy/inference/latent_function_inference/var_dtc.py", line 170, in inference + import ipdb; ipdb.set_trace() +ImportError: No module named ipdb +/home/alans/Work/GPy/GPy/kern/_src/coregionalize.py:docstring of GPy.kern._src.coregionalize.Coregionalize:5: ERROR: Unexpected indentation. +/home/alans/Work/GPy/doc/GPy.kern._src.rst:73: WARNING: autodoc: failed to import module u'GPy.kern._src.hierarchical'; the following exception was raised: +Traceback (most recent call last): + File "/home/alans/anaconda/envs/GPy/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 335, in import_object + __import__(self.modname) + File "/home/alans/Work/GPy/GPy/kern/_src/hierarchical.py", line 4, in + from kernpart import Kernpart +ImportError: No module named kernpart +/home/alans/Work/GPy/GPy/kern/_src/independent_outputs.py:docstring of GPy.kern._src.independent_outputs.IndependentOutputs:9: WARNING: Field list ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:12: WARNING: Block quote ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:22: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:23: WARNING: Block quote ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/doc/GPy.kern._src.rst:177: WARNING: autodoc: failed to import module u'GPy.kern._src.symbolic'; the following exception was raised: +Traceback (most recent call last): + File "/home/alans/anaconda/envs/GPy/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 335, in import_object + __import__(self.modname) + File "/home/alans/Work/GPy/GPy/kern/_src/symbolic.py", line 5, in + from ...core.symbolic import Symbolic_core + File "/home/alans/Work/GPy/GPy/core/symbolic.py", line 10, in + from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace +ImportError: No module named lambdify +/home/alans/Work/GPy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:13: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:18: WARNING: Block quote ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/models/gp_var_gauss.py:docstring of GPy.models.gp_var_gauss.GPVariationalGaussianApproximation:9: WARNING: Definition list ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Field list ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Inline interpreted text or phrase reference start-string without end-string. +/home/alans/Work/GPy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:34: WARNING: Definition list ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/models/sparse_gp_minibatch.py:docstring of GPy.models.sparse_gp_minibatch.SparseGPMiniBatch:2: WARNING: Block quote ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imread:6: SEVERE: Unexpected section title. + +Examples +-------- +/home/alans/Work/GPy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imsave:4: SEVERE: Unexpected section title. + +Examples +-------- +/home/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:6: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:7: WARNING: Block quote ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:7: WARNING: Definition list ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:9: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/util/datasets.py:docstring of GPy.util.datasets.hapmap3:7: WARNING: Block quote ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/doc/GPy.util.rst:2: SEVERE: Duplicate ID: "module-GPy.util.diag". +/home/alans/Work/GPy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imread:6: SEVERE: Unexpected section title. + +Examples +-------- +/home/alans/Work/GPy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imsave:4: SEVERE: Unexpected section title. + +Examples +-------- +/home/alans/Work/GPy/doc/GPy.util.rst:2: SEVERE: Duplicate ID: "module-GPy.util.subarray_and_sorting". +/home/alans/Work/GPy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:8: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:11: SEVERE: Unexpected section title. + +Examples: +========= +/home/alans/Work/GPy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:19: ERROR: Unexpected indentation. +/home/alans/Work/GPy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:21: WARNING: Block quote ends without a blank line; unexpected unindent. +/home/alans/Work/GPy/doc/installation.rst:22: ERROR: Unexpected indentation. +/home/alans/Work/GPy/doc/installation.rst:27: ERROR: Unexpected indentation. +/home/alans/Work/GPy/doc/tuto_creating_new_kernels.rst:58: WARNING: Inline literal start-string without end-string. +/home/alans/Work/GPy/doc/tuto_creating_new_models.rst:24: ERROR: Unknown target name: "parameterized". +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short. + +Interacting with Parameters: +======================= +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short. + +Interacting with Parameters: +======================= +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:109: WARNING: Title underline too short. + +Regular expressions +---------------- +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:164: WARNING: Title underline too short. + +Setting and fetching parameters `parameter_array` +------------------------------------------ +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:164: WARNING: Title underline too short. + +Setting and fetching parameters `parameter_array` +------------------------------------------ +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:220: WARNING: Title underline too short. + +Getting the model parameter's gradients +============================ +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:220: WARNING: Title underline too short. + +Getting the model parameter's gradients +============================ +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:236: WARNING: Title underline too short. + +Adjusting the model's constraints +================================ +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:236: WARNING: Title underline too short. + +Adjusting the model's constraints +================================ +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:287: WARNING: Title underline too short. + +Available Constraints +============== +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:287: WARNING: Title underline too short. + +Available Constraints +============== +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:299: WARNING: Title underline too short. + +Tying Parameters +============ +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:299: WARNING: Title underline too short. + +Tying Parameters +============ +/home/alans/Work/GPy/doc/tuto_parameterized.rst:3: WARNING: Title overline too short. + +******************* +Parameterization handling +******************* +/home/alans/Work/GPy/doc/tuto_parameterized.rst:10: WARNING: Title underline too short. + +Parameter handles +============== +/home/alans/Work/GPy/doc/tuto_parameterized.rst:16: WARNING: Title underline too short. + +:py:class:`~GPy.core.parameterization.parameterized.Parameterized` +========== +/home/alans/Work/GPy/doc/tuto_parameterized.rst:16: WARNING: Title underline too short. + +:py:class:`~GPy.core.parameterization.parameterized.Parameterized` +========== +/home/alans/Work/GPy/doc/tuto_parameterized.rst:21: WARNING: Title underline too short. + +:py:class:`~GPy.core.parameterization.param.Param` +=========== +/home/alans/Work/GPy/doc/tuto_parameterized.rst:21: WARNING: Title underline too short. + +:py:class:`~GPy.core.parameterization.param.Param` +=========== +/home/alans/Work/GPy/doc/kernel_implementation.rst:: WARNING: document isn't included in any toctree +/home/alans/Work/GPy/doc/modules.rst:: WARNING: document isn't included in any toctree +/home/alans/Work/GPy/doc/tuto_GP_regression.rst:: WARNING: document isn't included in any toctree +/home/alans/Work/GPy/doc/tuto_creating_new_kernels.rst:: WARNING: document isn't included in any toctree +/home/alans/Work/GPy/doc/tuto_creating_new_models.rst:: WARNING: document isn't included in any toctree +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:: WARNING: document isn't included in any toctree +/home/alans/Work/GPy/doc/tuto_kernel_overview.rst:: WARNING: document isn't included in any toctree +/home/alans/Work/GPy/doc/tuto_parameterized.rst:: WARNING: document isn't included in any toctree +WARNING: dvipng command 'dvipng' cannot be run (needed for math display), check the pngmath_dvipng setting +/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:336: WARNING: undefined label: creating_new_kernels (if the link has no caption the label must precede a section header) +WARNING: html_static_path entry u'/home/alans/Work/GPy/doc/_static' does not exist From a79b8006831c78f6a8692fc71e25bd9c953e4f0e Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Tue, 4 Nov 2014 16:17:09 +0000 Subject: [PATCH 4/9] Added verbose checks for likelihood --- GPy/testing/likelihood_tests.py | 37 ++++++++++++++------------------- 1 file changed, 16 insertions(+), 21 deletions(-) diff --git a/GPy/testing/likelihood_tests.py b/GPy/testing/likelihood_tests.py index 19cff9b8..9a188de5 100644 --- a/GPy/testing/likelihood_tests.py +++ b/GPy/testing/likelihood_tests.py @@ -75,7 +75,7 @@ def dparam_checkgrad(func, dfunc, params, params_names, args, constraints=None, if verbose: print grad grad.checkgrad(verbose=1) - if not grad.checkgrad(): + if not grad.checkgrad(verbose=True): gradchecking = False return gradchecking @@ -364,9 +364,8 @@ class TestNoiseModels(object): dlogpdf_df = functools.partial(model.dlogpdf_df, y=Y) grad = GradientChecker(logpdf, dlogpdf_df, f.copy(), 'g') grad.randomize() - grad.checkgrad(verbose=1) print model - assert grad.checkgrad() + assert grad.checkgrad(verbose=1) @with_setup(setUp, tearDown) def t_d2logpdf_df2(self, model, Y, f): @@ -375,9 +374,8 @@ class TestNoiseModels(object): d2logpdf_df2 = functools.partial(model.d2logpdf_df2, y=Y) grad = GradientChecker(dlogpdf_df, d2logpdf_df2, f.copy(), 'g') grad.randomize() - grad.checkgrad(verbose=1) print model - assert grad.checkgrad() + assert grad.checkgrad(verbose=1) @with_setup(setUp, tearDown) def t_d3logpdf_df3(self, model, Y, f): @@ -386,9 +384,8 @@ class TestNoiseModels(object): d3logpdf_df3 = functools.partial(model.d3logpdf_df3, y=Y) grad = GradientChecker(d2logpdf_df2, d3logpdf_df3, f.copy(), 'g') grad.randomize() - grad.checkgrad(verbose=1) print model - assert grad.checkgrad() + assert grad.checkgrad(verbose=1) ############## # df_dparams # @@ -439,8 +436,8 @@ class TestNoiseModels(object): grad.randomize() print grad - grad.checkgrad(verbose=1) - assert grad.checkgrad() + print model + assert grad.checkgrad(verbose=1) @with_setup(setUp, tearDown) def t_d2logpdf_dlink2(self, model, Y, f, link_f_constraints): @@ -454,9 +451,9 @@ class TestNoiseModels(object): constraint('g', grad) grad.randomize() - grad.checkgrad(verbose=1) print grad - assert grad.checkgrad() + print model + assert grad.checkgrad(verbose=1) @with_setup(setUp, tearDown) def t_d3logpdf_dlink3(self, model, Y, f, link_f_constraints): @@ -470,9 +467,9 @@ class TestNoiseModels(object): constraint('g', grad) grad.randomize() - grad.checkgrad(verbose=1) print grad - assert grad.checkgrad() + print model + assert grad.checkgrad(verbose=1) ################# # dlink_dparams # @@ -535,13 +532,12 @@ class TestNoiseModels(object): #m.optimize(max_iters=8) print m - m.checkgrad(verbose=1, step=step) #if not m.checkgrad(step=step): #m.checkgrad(verbose=1, step=step) #NOTE this test appears to be stochastic for some likelihoods (student t?) # appears to all be working in test mode right now... #if isinstance(model, GPy.likelihoods.StudentT): - assert m.checkgrad(step=step) + assert m.checkgrad(verbose=1, step=step) ########### # EP test # @@ -563,9 +559,8 @@ class TestNoiseModels(object): constraints[param_num](name, m) m.randomize() - m.checkgrad(verbose=1, step=step) print m - assert m.checkgrad(step=step) + assert m.checkgrad(verbose=1, step=step) class LaplaceTests(unittest.TestCase): @@ -616,8 +611,8 @@ class LaplaceTests(unittest.TestCase): d2logpdf_df2 = functools.partial(self.gauss.d2logpdf_df2, y=self.Y) grad = GradientChecker(dlogpdf_df, d2logpdf_df2, self.f.copy(), 'g') grad.randomize() - grad.checkgrad(verbose=1) - self.assertTrue(grad.checkgrad()) + + self.assertTrue(grad.checkgrad(verbose=1)) def test_laplace_log_likelihood(self): debug = False @@ -705,8 +700,8 @@ class LaplaceTests(unittest.TestCase): #Check they are checkgradding #m1.checkgrad(verbose=1) #m2.checkgrad(verbose=1) - self.assertTrue(m1.checkgrad()) - self.assertTrue(m2.checkgrad()) + self.assertTrue(m1.checkgrad(verbose=True)) + self.assertTrue(m2.checkgrad(verbose=True)) if __name__ == "__main__": print "Running unit tests" From 17fcaca054ce0e9e81ee7603e54840de7ebee90e Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Tue, 4 Nov 2014 16:26:19 +0000 Subject: [PATCH 5/9] Updated conf.py to work again rather than cause an infinite loop --- doc/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 1b2410f3..91a6c75b 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -151,8 +151,8 @@ if on_rtd: proc = subprocess.Popen("ls ../", stdout=subprocess.PIPE, shell=True) (out, err) = proc.communicate() print "program output:", out - #proc = subprocess.Popen("sphinx-apidoc -f -o . ../GPy", stdout=subprocess.PIPE, shell=True) - proc = subprocess.Popen("make html", stdout=subprocess.PIPE, shell=True) + #Lets regenerate our rst files from the source, -P adds private modules (i.e kern._src) + proc = subprocess.Popen("sphinx-apidoc -P -f -o . ../GPy", stdout=subprocess.PIPE, shell=True) (out, err) = proc.communicate() print "program output:", out #proc = subprocess.Popen("whereis numpy", stdout=subprocess.PIPE, shell=True) From 669a2f0cef182eaf1d146436c95b1182d1fe77b5 Mon Sep 17 00:00:00 2001 From: James Hensman Date: Tue, 4 Nov 2014 17:55:55 +0000 Subject: [PATCH 6/9] remove dead hierachical code --- GPy/kern/_src/hierarchical.py | 76 ------------------------ GPy/plotting/matplot_dep/kernel_plots.py | 1 - 2 files changed, 77 deletions(-) delete mode 100644 GPy/kern/_src/hierarchical.py diff --git a/GPy/kern/_src/hierarchical.py b/GPy/kern/_src/hierarchical.py deleted file mode 100644 index ac360ec7..00000000 --- a/GPy/kern/_src/hierarchical.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright (c) 2012, James Hesnsman -# Licensed under the BSD 3-clause license (see LICENSE.txt) - -from kernpart import Kernpart -import numpy as np -from independent_outputs import index_to_slices - -class Hierarchical(Kernpart): - """ - A kernel part which can reopresent a hierarchy of indepencnce: a generalisation of independent_outputs - - """ - def __init__(self,parts,name='hierarchy'): - self.levels = len(parts) - self.input_dim = parts[0].input_dim + 1 - self.num_params = np.sum([k.num_params for k in parts]) - self.name = name - self.parts = parts - - self.param_starts = np.hstack((0,np.cumsum([k.num_params for k in self.parts[:-1]]))) - self.param_stops = np.cumsum([k.num_params for k in self.parts]) - - def _get_params(self): - return np.hstack([k._get_params() for k in self.parts]) - - def _set_params(self,x): - [k._set_params(x[start:stop]) for k, start, stop in zip(self.parts, self.param_starts, self.param_stops)] - - def _get_param_names(self): - return sum([[str(i)+'_'+k.name+'_'+n for n in k._get_param_names()] for i,k in enumerate(self.parts)],[]) - - def _sort_slices(self,X,X2): - slices = [index_to_slices(x) for x in X[:,-self.levels:].T] - X = X[:,:-self.levels] - if X2 is None: - slices2 = slices - X2 = X - else: - slices2 = [index_to_slices(x) for x in X2[:,-self.levels:].T] - X2 = X2[:,:-self.levels] - return X, X2, slices, slices2 - - def K(self,X,X2,target): - X, X2, slices, slices2 = self._sort_slices(X,X2) - - [[[[k.K(X[s],X2[s2],target[s,s2]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_,slices2_)] for k, slices_, slices2_ in zip(self.parts,slices,slices2)] - - def Kdiag(self,X,target): - raise NotImplementedError - #X,slices = X[:,:-1],index_to_slices(X[:,-1]) - #[[self.k.Kdiag(X[s],target[s]) for s in slices_i] for slices_i in slices] - - def _param_grad_helper(self,dL_dK,X,X2,target): - X, X2, slices, slices2 = self._sort_slices(X,X2) - [[[[k._param_grad_helper(dL_dK[s,s2],X[s],X2[s2],target[p_start:p_stop]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_, slices2_)] for k, p_start, p_stop, slices_, slices2_ in zip(self.parts, self.param_starts, self.param_stops, slices, slices2)] - - - def gradients_X(self,dL_dK,X,X2,target): - raise NotImplementedError - #X,slices = X[:,:-1],index_to_slices(X[:,-1]) - #if X2 is None: - #X2,slices2 = X,slices - #else: - #X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1]) - #[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)] -# - def dKdiag_dX(self,dL_dKdiag,X,target): - raise NotImplementedError - #X,slices = X[:,:-1],index_to_slices(X[:,-1]) - #[[self.k.dKdiag_dX(dL_dKdiag[s],X[s],target[s,:-1]) for s in slices_i] for slices_i in slices] - - - def dKdiag_dtheta(self,dL_dKdiag,X,target): - raise NotImplementedError - #X,slices = X[:,:-1],index_to_slices(X[:,-1]) - #[[self.k.dKdiag_dX(dL_dKdiag[s],X[s],target) for s in slices_i] for slices_i in slices] diff --git a/GPy/plotting/matplot_dep/kernel_plots.py b/GPy/plotting/matplot_dep/kernel_plots.py index cbf0fa54..dd0f1cf5 100644 --- a/GPy/plotting/matplot_dep/kernel_plots.py +++ b/GPy/plotting/matplot_dep/kernel_plots.py @@ -161,7 +161,6 @@ def plot(kernel,x=None, fignum=None, ax=None, title=None, plot_limits=None, reso Xnew = np.vstack((xx.flatten(), yy.flatten())).T Kx = kernel.K(Xnew, x) Kx = Kx.reshape(resolution, resolution).T - stop ax.contour(xx, yy, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, **mpl_kwargs) # @UndefinedVariable ax.set_xlim(xmin[0], xmax[0]) ax.set_ylim(xmin[1], xmax[1]) From 9febc73be386096b01f17ec862f468aec1e9158e Mon Sep 17 00:00:00 2001 From: Zhenwen Dai Date: Wed, 5 Nov 2014 11:58:05 +0000 Subject: [PATCH 7/9] model checkgrad enhancement --- GPy/core/model.py | 11 ++++++++--- GPy/examples/dimensionality_reduction.py | 2 +- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/GPy/core/model.py b/GPy/core/model.py index 90294a73..15cb3f79 100644 --- a/GPy/core/model.py +++ b/GPy/core/model.py @@ -263,7 +263,7 @@ class Model(Parameterized): sgd.run() self.optimization_runs.append(sgd) - def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3): + def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3, df_tolerance=1e-12): """ Check the gradient of the ,odel by comparing to a numerical estimate. If the verbose flag is passed, individual @@ -322,7 +322,7 @@ class Model(Parameterized): except NotImplementedError: names = ['Variable %i' % i for i in range(len(x))] # Prepare for pretty-printing - header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical'] + header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical', 'dF_ratio'] max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])]) float_len = 10 cols = [max_names] @@ -359,6 +359,8 @@ class Model(Parameterized): f1 = self._objective(xx) xx[xind] -= 2.*step f2 = self._objective(xx) + df_ratio = np.abs((f1-f2)/f1) + df_unstable = df_ratio Date: Wed, 5 Nov 2014 14:09:52 +0000 Subject: [PATCH 8/9] better handling of missing pods in examples --- GPy/examples/regression.py | 32 ++++++++++++++++++++++++-------- 1 file changed, 24 insertions(+), 8 deletions(-) diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py index 6923b20d..14cf0602 100644 --- a/GPy/examples/regression.py +++ b/GPy/examples/regression.py @@ -14,7 +14,9 @@ import GPy def olympic_marathon_men(optimize=True, plot=True): """Run a standard Gaussian process regression on the Olympic marathon data.""" try:import pods - except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' + except ImportError: + print 'pods unavailable, see https://github.com/sods/ods for example datasets' + return data = pods.datasets.olympic_marathon_men() # create simple GP Model @@ -85,7 +87,9 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True): to load in the data. """ try:import pods - except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' + except ImportError: + print 'pods unavailable, see https://github.com/sods/ods for example datasets' + return data = pods.datasets.epomeo_gpx() num_data_list = [] for Xpart in data['X']: @@ -130,7 +134,9 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000 log_SNRs = np.linspace(-3., 4., resolution) try:import pods - except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' + except ImportError: + print 'pods unavailable, see https://github.com/sods/ods for example datasets' + return data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number) # data['Y'] = data['Y'][0::2, :] # data['X'] = data['X'][0::2, :] @@ -212,7 +218,9 @@ def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.RBF): def olympic_100m_men(optimize=True, plot=True): """Run a standard Gaussian process regression on the Rogers and Girolami olympics data.""" try:import pods - except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' + except ImportError: + print 'pods unavailable, see https://github.com/sods/ods for example datasets' + return data = pods.datasets.olympic_100m_men() # create simple GP Model @@ -231,7 +239,9 @@ def olympic_100m_men(optimize=True, plot=True): def toy_rbf_1d(optimize=True, plot=True): """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.""" try:import pods - except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' + except ImportError: + print 'pods unavailable, see https://github.com/sods/ods for example datasets' + return data = pods.datasets.toy_rbf_1d() # create simple GP Model @@ -247,7 +257,9 @@ def toy_rbf_1d(optimize=True, plot=True): def toy_rbf_1d_50(optimize=True, plot=True): """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.""" try:import pods - except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' + except ImportError: + print 'pods unavailable, see https://github.com/sods/ods for example datasets' + return data = pods.datasets.toy_rbf_1d_50() # create simple GP Model @@ -364,7 +376,9 @@ def toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, o def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True): """Predict the location of a robot given wirelss signal strength readings.""" try:import pods - except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' + except ImportError: + print 'pods unavailable, see https://github.com/sods/ods for example datasets' + return data = pods.datasets.robot_wireless() # create simple GP Model @@ -390,7 +404,9 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True): def silhouette(max_iters=100, optimize=True, plot=True): """Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper.""" try:import pods - except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' + except ImportError: + print 'pods unavailable, see https://github.com/sods/ods for example datasets' + return data = pods.datasets.silhouette() # create simple GP Model From 902f8f138b796e45bfea06ff7939c41bbc1ee7ce Mon Sep 17 00:00:00 2001 From: Zhenwen Dai Date: Wed, 5 Nov 2014 14:34:03 +0000 Subject: [PATCH 9/9] clean up parallel framework --- GPy/core/model.py | 2 +- GPy/core/sparse_gp_mpi.py | 7 +- .../latent_function_inference/__init__.py | 1 - .../latent_function_inference/var_dtc_gpu.py | 482 ------------------ .../var_dtc_parallel.py | 108 +++- GPy/models/bayesian_gplvm.py | 5 +- GPy/models/bayesian_gplvm_minibatch.py | 1 - GPy/models/sparse_gp_regression.py | 12 +- GPy/models/ss_gplvm.py | 2 +- GPy/util/mpi.py | 35 -- GPy/util/parallel.py | 30 +- 11 files changed, 135 insertions(+), 550 deletions(-) delete mode 100644 GPy/inference/latent_function_inference/var_dtc_gpu.py delete mode 100644 GPy/util/mpi.py diff --git a/GPy/core/model.py b/GPy/core/model.py index 15cb3f79..28223429 100644 --- a/GPy/core/model.py +++ b/GPy/core/model.py @@ -359,7 +359,7 @@ class Model(Parameterized): f1 = self._objective(xx) xx[xind] -= 2.*step f2 = self._objective(xx) - df_ratio = np.abs((f1-f2)/f1) + df_ratio = np.abs((f1-f2)/min(f1,f2)) df_unstable = df_ratio=D): - return Y.view(np.ndarray) - else: - return jitchol(tdot(Y)) - - def gatherPsiStat(self, kern, X, Z, Y, beta, uncertain_inputs, het_noise): - num_inducing, input_dim = Z.shape[0], Z.shape[1] - num_data, output_dim = Y.shape - trYYT = self._trYYT - psi1Y_gpu = self.gpuCache['psi1Y_gpu'] - psi2_gpu = self.gpuCache['psi2_gpu'] - beta_gpu = self.gpuCache['beta_gpu'] - YT_gpu = self.gpuCache['YT_gpu'] - betaYT_gpu = self.gpuCache['betaYT_gpu'] - - beta_gpu.fill(beta) - betaYT_gpu.fill(0.) - cublas.cublasDaxpy(self.cublas_handle, betaYT_gpu.size, beta, YT_gpu.gpudata, 1, betaYT_gpu.gpudata, 1) - YRY_full = trYYT*beta - - if kern.useGPU: - psi1Y_gpu.fill(0.) - psi2_gpu.fill(0.) - psi0_full = 0 - - for n_start in xrange(0,num_data,self.batchsize): - n_end = min(self.batchsize+n_start, num_data) - ndata = n_end - n_start - X_slice = X[n_start:n_end] - betaYT_gpu_slice = betaYT_gpu[:,n_start:n_end] - - if uncertain_inputs: - psi0 = kern.psi0(Z, X_slice) - psi1p_gpu = kern.psi1(Z, X_slice) - psi2p_gpu = kern.psi2(Z, X_slice) - else: - psi0 = kern.Kdiag(X_slice) - psi1p_gpu = kern.K(X_slice, Z) - - cublas.cublasDgemm(self.cublas_handle, 'T', 'T', num_inducing, output_dim, ndata, 1.0, psi1p_gpu.gpudata, ndata, betaYT_gpu_slice.gpudata, output_dim, 1.0, psi1Y_gpu.gpudata, num_inducing) - - psi0_full += psi0.sum() - - if uncertain_inputs: - sum_axis(psi2_gpu,psi2p_gpu,1,1) - else: - cublas.cublasDgemm(self.cublas_handle, 'T', 'N', num_inducing, num_inducing, ndata, beta, psi1p_gpu.gpudata, ndata, psi1p_gpu.gpudata, ndata, 1.0, psi2_gpu.gpudata, num_inducing) - - psi0_full *= beta - if uncertain_inputs: - cublas.cublasDscal(self.cublas_handle, psi2_gpu.size, beta, psi2_gpu.gpudata, 1) - - else: - psi2_full = np.zeros((num_inducing,num_inducing)) - psi1Y_full = np.zeros((output_dim,num_inducing)) # DxM - psi0_full = 0. - YRY_full = 0. - - for n_start in xrange(0,num_data,self.batchsize): - n_end = min(self.batchsize+n_start, num_data) - Y_slice = Y[n_start:n_end] - X_slice = X[n_start:n_end] - - if het_noise: - b = beta[n_start] - YRY_full += np.inner(Y_slice, Y_slice)*b - else: - b = beta - - if uncertain_inputs: - psi0 = kern.psi0(Z, X_slice) - psi1 = kern.psi1(Z, X_slice) - psi2_full += kern.psi2(Z, X_slice)*b - else: - psi0 = kern.Kdiag(X_slice) - psi1 = kern.K(X_slice, Z) - psi2_full += np.dot(psi1.T,psi1)*b - - psi0_full += psi0.sum()*b - psi1Y_full += np.dot(Y_slice.T,psi1)*b # DxM - - if not het_noise: - YRY_full = trYYT*beta - psi1Y_gpu.set(psi1Y_full) - psi2_gpu.set(psi2_full) - - return psi0_full, YRY_full - - def inference_likelihood(self, kern, X, Z, likelihood, Y): - """ - The first phase of inference: - Compute: log-likelihood, dL_dKmm - - Cached intermediate results: Kmm, KmmInv, - """ - - num_inducing, input_dim = Z.shape[0], Z.shape[1] - num_data, output_dim = Y.shape - - #see whether we've got a different noise variance for each datum - beta = 1./np.fmax(likelihood.variance, 1e-6) - het_noise = beta.size > 1 - if het_noise: - self.batchsize=0 - - self._initGPUCache(kern, num_inducing, input_dim, output_dim, Y) - - if isinstance(X, VariationalPosterior): - uncertain_inputs = True - else: - uncertain_inputs = False - - psi1Y_gpu = self.gpuCache['psi1Y_gpu'] - psi2_gpu = self.gpuCache['psi2_gpu'] - - psi0_full, YRY_full = self.gatherPsiStat(kern, X, Z, Y, beta, uncertain_inputs, het_noise) - - #====================================================================== - # Compute Common Components - #====================================================================== - - Kmm = kern.K(Z).copy() - Kmm_gpu = self.gpuCache['Kmm_gpu'] - Kmm_gpu.set(np.asfortranarray(Kmm)) - diag.add(Kmm, self.const_jitter) - ones_gpu = self.gpuCache['ones_gpu'] - cublas.cublasDaxpy(self.cublas_handle, num_inducing, self.const_jitter, ones_gpu.gpudata, 1, Kmm_gpu.gpudata, num_inducing+1) -# assert np.allclose(Kmm, Kmm_gpu.get()) - -# Lm = jitchol(Kmm) - # - Lm_gpu = self.gpuCache['Lm_gpu'] - cublas.cublasDcopy(self.cublas_handle, Kmm_gpu.size, Kmm_gpu.gpudata, 1, Lm_gpu.gpudata, 1) - culinalg.cho_factor(Lm_gpu,'L') -# print np.abs(np.tril(Lm)-np.tril(Lm_gpu.get())).max() - -# Lambda = Kmm+psi2_full -# LL = jitchol(Lambda) - # - Lambda_gpu = self.gpuCache['LL_gpu'] - cublas.cublasDcopy(self.cublas_handle, Kmm_gpu.size, Kmm_gpu.gpudata, 1, Lambda_gpu.gpudata, 1) - cublas.cublasDaxpy(self.cublas_handle, psi2_gpu.size, np.float64(1.0), psi2_gpu.gpudata, 1, Lambda_gpu.gpudata, 1) - LL_gpu = Lambda_gpu - culinalg.cho_factor(LL_gpu,'L') -# print np.abs(np.tril(LL)-np.tril(LL_gpu.get())).max() - -# b,_ = dtrtrs(LL, psi1Y_full) -# bbt_cpu = np.square(b).sum() - # - b_gpu = self.gpuCache['b_gpu'] - cublas.cublasDcopy(self.cublas_handle, b_gpu.size, psi1Y_gpu.gpudata, 1, b_gpu.gpudata, 1) - cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'N', 'N', num_inducing, output_dim, np.float64(1.0), LL_gpu.gpudata, num_inducing, b_gpu.gpudata, num_inducing) - bbt = cublas.cublasDdot(self.cublas_handle, b_gpu.size, b_gpu.gpudata, 1, b_gpu.gpudata, 1) -# print np.abs(bbt-bbt_cpu) - -# v,_ = dtrtrs(LL.T,b,lower=False) -# vvt = np.einsum('md,od->mo',v,v) -# LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right') - # - v_gpu = self.gpuCache['v_gpu'] - cublas.cublasDcopy(self.cublas_handle, v_gpu.size, b_gpu.gpudata, 1, v_gpu.gpudata, 1) - cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'T', 'N', num_inducing, output_dim, np.float64(1.0), LL_gpu.gpudata, num_inducing, v_gpu.gpudata, num_inducing) - vvt_gpu = self.gpuCache['vvt_gpu'] - cublas.cublasDgemm(self.cublas_handle, 'N', 'T', num_inducing, num_inducing, output_dim, np.float64(1.0), v_gpu.gpudata, num_inducing, v_gpu.gpudata, num_inducing, np.float64(0.), vvt_gpu.gpudata, num_inducing) - LmInvPsi2LmInvT_gpu = self.gpuCache['KmmInvPsi2LLInvT_gpu'] - cublas.cublasDcopy(self.cublas_handle, psi2_gpu.size, psi2_gpu.gpudata, 1, LmInvPsi2LmInvT_gpu.gpudata, 1) - cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'N', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing) - cublas.cublasDtrsm(self.cublas_handle , 'r', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing) - #tr_LmInvPsi2LmInvT = cublas.cublasDasum(self.cublas_handle, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing+1) - tr_LmInvPsi2LmInvT = float(strideSum(LmInvPsi2LmInvT_gpu, num_inducing+1).get()) -# print np.abs(vvt-vvt_gpu.get()).max() -# print np.abs(np.trace(LmInvPsi2LmInvT)-tr_LmInvPsi2LmInvT) - -# Psi2LLInvT = dtrtrs(LL,psi2_full)[0].T -# LmInvPsi2LLInvT= dtrtrs(Lm,Psi2LLInvT)[0] -# KmmInvPsi2LLInvT = dtrtrs(Lm,LmInvPsi2LLInvT,trans=True)[0] -# KmmInvPsi2P = dtrtrs(LL,KmmInvPsi2LLInvT.T, trans=True)[0].T - # - KmmInvPsi2LLInvT_gpu = LmInvPsi2LmInvT_gpu # Reuse GPU memory (size:MxM) - cublas.cublasDcopy(self.cublas_handle, psi2_gpu.size, psi2_gpu.gpudata, 1, KmmInvPsi2LLInvT_gpu.gpudata, 1) - cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'N', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing) - cublas.cublasDtrsm(self.cublas_handle , 'r', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), LL_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing) - cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing) - KmmInvPsi2P_gpu = self.gpuCache['KmmInvPsi2P_gpu'] - cublas.cublasDcopy(self.cublas_handle, KmmInvPsi2LLInvT_gpu.size, KmmInvPsi2LLInvT_gpu.gpudata, 1, KmmInvPsi2P_gpu.gpudata, 1) - cublas.cublasDtrsm(self.cublas_handle , 'r', 'L', 'N', 'N', num_inducing, num_inducing, np.float64(1.0), LL_gpu.gpudata, num_inducing, KmmInvPsi2P_gpu.gpudata, num_inducing) -# print np.abs(KmmInvPsi2P-KmmInvPsi2P_gpu.get()).max() - -# dL_dpsi2R = (output_dim*KmmInvPsi2P - vvt)/2. # dL_dpsi2 with R inside psi2 - # - dL_dpsi2R_gpu = self.gpuCache['dL_dpsi2R_gpu'] - cublas.cublasDcopy(self.cublas_handle, vvt_gpu.size, vvt_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1) - cublas.cublasDaxpy(self.cublas_handle, KmmInvPsi2P_gpu.size, np.float64(-output_dim), KmmInvPsi2P_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1) - cublas.cublasDscal(self.cublas_handle, dL_dpsi2R_gpu.size, np.float64(-0.5), dL_dpsi2R_gpu.gpudata, 1) -# print np.abs(dL_dpsi2R_gpu.get()-dL_dpsi2R).max() - - #====================================================================== - # Compute log-likelihood - #====================================================================== - if het_noise: - logL_R = -np.log(beta).sum() - else: - logL_R = -num_data*np.log(beta) -# logL_old = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-np.trace(LmInvPsi2LmInvT))+YRY_full-bbt)/2.-output_dim*(-np.log(np.diag(Lm)).sum()+np.log(np.diag(LL)).sum()) - - logdetKmm = float(logDiagSum(Lm_gpu,num_inducing+1).get()) - logdetLambda = float(logDiagSum(LL_gpu,num_inducing+1).get()) - logL = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-tr_LmInvPsi2LmInvT)+YRY_full-bbt)/2.+output_dim*(logdetKmm-logdetLambda) -# print np.abs(logL_old - logL) - - #====================================================================== - # Compute dL_dKmm - #====================================================================== - -# dL_dKmm = -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2. - # - dL_dKmm_gpu = self.gpuCache['dL_dKmm_gpu'] - cublas.cublasDgemm(self.cublas_handle, 'N', 'T', num_inducing, num_inducing, num_inducing, np.float64(1.0), KmmInvPsi2LLInvT_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing, np.float64(0.), dL_dKmm_gpu.gpudata, num_inducing) - cublas.cublasDaxpy(self.cublas_handle, dL_dKmm_gpu.size, np.float64(1./output_dim), vvt_gpu.gpudata, 1, dL_dKmm_gpu.gpudata, 1) - cublas.cublasDscal(self.cublas_handle, dL_dKmm_gpu.size, np.float64(-output_dim/2.), dL_dKmm_gpu.gpudata, 1) -# print np.abs(dL_dKmm - dL_dKmm_gpu.get()).max() - - #====================================================================== - # Compute the Posterior distribution of inducing points p(u|Y) - #====================================================================== - - post = Posterior(woodbury_inv=KmmInvPsi2P_gpu.get(), woodbury_vector=v_gpu.get(), K=Kmm_gpu.get(), mean=None, cov=None, K_chol=Lm_gpu.get()) - - #====================================================================== - # Compute dL_dthetaL for uncertian input and non-heter noise - #====================================================================== - - if not het_noise: - dL_dthetaL = (YRY_full + output_dim*psi0_full - num_data*output_dim)/-2. - dL_dthetaL += cublas.cublasDdot(self.cublas_handle,dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata,1,psi2_gpu.gpudata,1) - dL_dthetaL += cublas.cublasDdot(self.cublas_handle,v_gpu.size, v_gpu.gpudata,1,psi1Y_gpu.gpudata,1) - self.midRes['dL_dthetaL'] = -beta*dL_dthetaL - - return logL, dL_dKmm_gpu.get(), post - - def inference_minibatch(self, kern, X, Z, likelihood, Y): - """ - The second phase of inference: Computing the derivatives over a minibatch of Y - Compute: dL_dpsi0, dL_dpsi1, dL_dpsi2, dL_dthetaL - return a flag showing whether it reached the end of Y (isEnd) - """ - - num_data, output_dim = Y.shape - num_inducing = Z.shape[0] - - if isinstance(X, VariationalPosterior): - uncertain_inputs = True - else: - uncertain_inputs = False - - beta = 1./np.fmax(likelihood.variance, 1e-6) - het_noise = beta.size > 1 - - n_start = self.batch_pos - n_end = min(self.batchsize+n_start, num_data) - if n_end==num_data: - isEnd = True - self.batch_pos = 0 - else: - isEnd = False - self.batch_pos = n_end - - nSlice = n_end-n_start - X_slice = X[n_start:n_end] - if het_noise: - beta = beta[n_start] # nSlice==1 - - if kern.useGPU: - if not uncertain_inputs: - psi0p_gpu = kern.Kdiag(X_slice) - psi1p_gpu = kern.K(X_slice, Z) - psi2p_gpu = self.gpuCache['psi2p_gpu'] - elif het_noise: - psi0p_gpu = kern.psi0(Z, X_slice) - psi1p_gpu = kern.psi1(Z, X_slice) - psi2p_gpu = kern.psi2(Z, X_slice) - elif not uncertain_inputs or het_noise: - if not uncertain_inputs: - psi0 = kern.Kdiag(X_slice) - psi1 = kern.K(X_slice, Z) - elif het_noise: - psi0 = kern.psi0(Z, X_slice) - psi1 = kern.psi1(Z, X_slice) - psi2 = kern.psi2(Z, X_slice) - - psi0p_gpu = self.gpuCache['psi0p_gpu'] - psi1p_gpu = self.gpuCache['psi1p_gpu'] - psi2p_gpu = self.gpuCache['psi2p_gpu'] - if psi0p_gpu.shape[0] > nSlice: - psi0p_gpu = psi0p_gpu[:nSlice] - psi1p_gpu = psi1p_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing) - psi0p_gpu.set(np.asfortranarray(psi0)) - psi1p_gpu.set(np.asfortranarray(psi1)) - if uncertain_inputs: - psi2p_gpu.set(np.asfortranarray(psi2)) - - #====================================================================== - # Compute dL_dpsi - #====================================================================== - - dL_dpsi2R_gpu = self.gpuCache['dL_dpsi2R_gpu'] - v_gpu = self.gpuCache['v_gpu'] - dL_dpsi0_gpu = self.gpuCache['dL_dpsi0_gpu'] - dL_dpsi1_gpu = self.gpuCache['dL_dpsi1_gpu'] - dL_dpsi2_gpu = self.gpuCache['dL_dpsi2_gpu'] - betaYT_gpu = self.gpuCache['betaYT_gpu'] - betaYT_gpu_slice = betaYT_gpu[:,n_start:n_end] - - # Adjust to the batch size - if dL_dpsi0_gpu.shape[0] > nSlice: - dL_dpsi0_gpu = dL_dpsi0_gpu.ravel()[:nSlice] - dL_dpsi1_gpu = dL_dpsi1_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing) - - dL_dpsi0_gpu.fill(-output_dim *beta/2.) - - cublas.cublasDgemm(self.cublas_handle, 'T', 'T', nSlice, num_inducing, output_dim, 1.0, betaYT_gpu_slice.gpudata, output_dim, v_gpu.gpudata, num_inducing, 0., dL_dpsi1_gpu.gpudata, nSlice) - - if uncertain_inputs: - cublas.cublasDcopy(self.cublas_handle, dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata, 1, dL_dpsi2_gpu.gpudata, 1) - cublas.cublasDscal(self.cublas_handle, dL_dpsi2_gpu.size, beta, dL_dpsi2_gpu.gpudata, 1) - else: - cublas.cublasDgemm(self.cublas_handle, 'N', 'N', nSlice, num_inducing, output_dim, beta, psi1p_gpu.gpudata, nSlice, dL_dpsi2R_gpu.gpudata, num_inducing, 1.0, dL_dpsi1_gpu.gpudata, nSlice) - - #====================================================================== - # Compute dL_dthetaL - #====================================================================== - if het_noise: - betaY = betaYT_gpu_slice.get() - dL_dthetaL = ((np.square(betaY)).sum(axis=-1) + np.square(beta)*(output_dim*psi0p_gpu.get())-output_dim*beta)/2. - dL_dthetaL += -beta*beta*cublas.cublasDdot(self.cublas_handle,dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata,1,psi2p_gpu.gpudata,1) - dL_dthetaL += -beta*(betaY*np.dot(psi1p_gpu.get(),v_gpu.get())).sum(axis=-1) - - if kern.useGPU: - dL_dpsi0 = dL_dpsi0_gpu - dL_dpsi1 = dL_dpsi1_gpu - else: - dL_dpsi0 = dL_dpsi0_gpu.get() - dL_dpsi1 = dL_dpsi1_gpu.get() - if uncertain_inputs: - if kern.useGPU: - dL_dpsi2 = dL_dpsi2_gpu - else: - dL_dpsi2 = dL_dpsi2_gpu.get() - if not het_noise: - if isEnd: - dL_dthetaL = self.midRes['dL_dthetaL'] - else: - dL_dthetaL = 0. - if uncertain_inputs: - grad_dict = {'dL_dpsi0':dL_dpsi0, - 'dL_dpsi1':dL_dpsi1, - 'dL_dpsi2':dL_dpsi2, - 'dL_dthetaL':dL_dthetaL} - else: - grad_dict = {'dL_dKdiag':dL_dpsi0, - 'dL_dKnm':dL_dpsi1, - 'dL_dthetaL':dL_dthetaL} - - return isEnd, (n_start,n_end), grad_dict - diff --git a/GPy/inference/latent_function_inference/var_dtc_parallel.py b/GPy/inference/latent_function_inference/var_dtc_parallel.py index ae25f3e3..86842687 100644 --- a/GPy/inference/latent_function_inference/var_dtc_parallel.py +++ b/GPy/inference/latent_function_inference/var_dtc_parallel.py @@ -38,7 +38,7 @@ class VarDTC_minibatch(LatentFunctionInference): self.midRes = {} self.batch_pos = 0 # the starting position of the current mini-batch - self.Y_speedup = False # Replace Y with the cholesky factor of YY.T, but the posterior inference will be wrong + self.Y_speedup = False # Replace Y with the cholesky factor of YY.T, but the computation of posterior object will be skipped. def __getstate__(self): # has to be overridden, as Cacher objects cannot be pickled. @@ -76,6 +76,8 @@ class VarDTC_minibatch(LatentFunctionInference): def gatherPsiStat(self, kern, X, Z, Y, beta, uncertain_inputs): het_noise = beta.size > 1 + + assert beta.size == 1 trYYT = self.get_trYYT(Y) if self.Y_speedup and not het_noise: @@ -83,17 +85,16 @@ class VarDTC_minibatch(LatentFunctionInference): num_inducing = Z.shape[0] num_data, output_dim = Y.shape - if self.batchsize == None: - self.batchsize = num_data + batchsize = num_data if self.batchsize is None else self.batchsize - psi2_full = np.zeros((num_inducing,num_inducing)) + psi2_full = np.zeros((num_inducing,num_inducing)) # MxM psi1Y_full = np.zeros((output_dim,num_inducing)) # DxM psi0_full = 0. YRY_full = 0. - for n_start in xrange(0,num_data,self.batchsize): - n_end = min(self.batchsize+n_start, num_data) - if (n_end-n_start)==num_data: + for n_start in xrange(0,num_data,batchsize): + n_end = min(batchsize+n_start, num_data) + if batchsize==num_data: Y_slice = Y X_slice = X else: @@ -168,16 +169,18 @@ class VarDTC_minibatch(LatentFunctionInference): Kmm = kern.K(Z).copy() diag.add(Kmm, self.const_jitter) - KmmInv,Lm,LmInv,_ = pdinv(Kmm) + Lm = jitchol(Kmm, maxtries=100) - LmInvPsi2LmInvT = LmInv.dot(psi2_full).dot(LmInv.T) + LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right') Lambda = np.eye(Kmm.shape[0])+LmInvPsi2LmInvT - LInv,LL,LLInv,logdet_L = pdinv(Lambda) - b = LLInv.dot(LmInv.dot(psi1Y_full.T)) + LL = jitchol(Lambda, maxtries=100) + logdet_L = 2.*np.sum(np.log(np.diag(LL))) + b = dtrtrs(LL,dtrtrs(Lm,psi1Y_full.T)[0])[0] bbt = np.square(b).sum() - v = LmInv.T.dot(LLInv.T.dot(b)) + v = dtrtrs(Lm,dtrtrs(LL,b,trans=1)[0],trans=1)[0] - dL_dpsi2R = LmInv.T.dot(-LLInv.T.dot(tdot(b)+output_dim*np.eye(input_dim)).dot(LLInv)+output_dim*np.eye(input_dim)).dot(LmInv)/2. + tmp = -backsub_both_sides(LL, tdot(b)+output_dim*np.eye(input_dim), transpose='left') + dL_dpsi2R = backsub_both_sides(Lm, tmp+output_dim*np.eye(input_dim), transpose='left')/2. # Cache intermediate results self.midRes['dL_dpsi2R'] = dL_dpsi2R @@ -196,14 +199,15 @@ class VarDTC_minibatch(LatentFunctionInference): # Compute dL_dKmm #====================================================================== - dL_dKmm = dL_dpsi2R - output_dim*KmmInv.dot(psi2_full).dot(KmmInv)/2. + dL_dKmm = dL_dpsi2R - output_dim*backsub_both_sides(Lm, LmInvPsi2LmInvT, transpose='left')/2. #====================================================================== # Compute the Posterior distribution of inducing points p(u|Y) #====================================================================== if not self.Y_speedup or het_noise: - post = Posterior(woodbury_inv=LmInv.T.dot(np.eye(input_dim)-LInv).dot(LmInv), woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=Lm) + wd_inv = backsub_both_sides(Lm, np.eye(input_dim)- backsub_both_sides(LL, np.identity(input_dim), transpose='left'), transpose='left') + post = Posterior(woodbury_inv=wd_inv, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=Lm) else: post = None @@ -242,7 +246,8 @@ class VarDTC_minibatch(LatentFunctionInference): YYT_factor = Y n_start = self.batch_pos - n_end = min(self.batchsize+n_start, num_data) + batchsize = num_data if self.batchsize is None else self.batchsize + n_end = min(batchsize+n_start, num_data) if n_end==num_data: isEnd = True self.batch_pos = 0 @@ -250,8 +255,12 @@ class VarDTC_minibatch(LatentFunctionInference): isEnd = False self.batch_pos = n_end - Y_slice = YYT_factor[n_start:n_end] - X_slice = X[n_start:n_end] + if batchsize==num_data: + Y_slice = YYT_factor + X_slice =X + else: + Y_slice = YYT_factor[n_start:n_end] + X_slice = X[n_start:n_end] if not uncertain_inputs: psi0 = kern.Kdiag(X_slice) @@ -405,3 +414,66 @@ def update_gradients(model, mpi_comm=None): # dL_dthetaL model.likelihood.update_gradients(dL_dthetaL) + +def update_gradients_sparsegp(model, mpi_comm=None): + if mpi_comm == None: + Y = model.Y + X = model.X + else: + Y = model.Y_local + X = model.X[model.N_range[0]:model.N_range[1]] + + model._log_marginal_likelihood, dL_dKmm, model.posterior = model.inference_method.inference_likelihood(model.kern, X, model.Z, model.likelihood, Y) + + het_noise = model.likelihood.variance.size > 1 + + if het_noise: + dL_dthetaL = np.empty((model.Y.shape[0],)) + else: + dL_dthetaL = np.float64(0.) + + kern_grad = model.kern.gradient.copy() + kern_grad[:] = 0. + model.Z.gradient = 0. + + isEnd = False + while not isEnd: + isEnd, n_range, grad_dict = model.inference_method.inference_minibatch(model.kern, X, model.Z, model.likelihood, Y) + + if (n_range[1]-n_range[0])==X.shape[0]: + X_slice = X + elif mpi_comm ==None: + X_slice = model.X[n_range[0]:n_range[1]] + else: + X_slice = model.X[model.N_range[0]+n_range[0]:model.N_range[0]+n_range[1]] + + model.kern.update_gradients_diag(grad_dict['dL_dKdiag'], X_slice) + kern_grad += model.kern.gradient + model.kern.update_gradients_full(grad_dict['dL_dKnm'], X_slice, model.Z) + kern_grad += model.kern.gradient + + model.Z.gradient += model.kern.gradients_X(grad_dict['dL_dKnm'].T, model.Z, X_slice) + + if het_noise: + dL_dthetaL[n_range[0]:n_range[1]] = grad_dict['dL_dthetaL'] + else: + dL_dthetaL += grad_dict['dL_dthetaL'] + + # Gather the gradients from multiple MPI nodes + if mpi_comm != None: + if het_noise: + raise "het_noise not implemented!" + kern_grad_all = kern_grad.copy() + Z_grad_all = model.Z.gradient.copy() + mpi_comm.Allreduce([kern_grad, MPI.DOUBLE], [kern_grad_all, MPI.DOUBLE]) + mpi_comm.Allreduce([model.Z.gradient, MPI.DOUBLE], [Z_grad_all, MPI.DOUBLE]) + kern_grad = kern_grad_all + model.Z.gradient = Z_grad_all + + model.kern.update_gradients_full(dL_dKmm, model.Z, None) + model.kern.gradient += kern_grad + + model.Z.gradient += model.kern.gradients_X(dL_dKmm, model.Z) + + # dL_dthetaL + model.likelihood.update_gradients(dL_dthetaL) diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py index 73fd6f8f..0838b684 100644 --- a/GPy/models/bayesian_gplvm.py +++ b/GPy/models/bayesian_gplvm.py @@ -7,7 +7,6 @@ from ..core.sparse_gp_mpi import SparseGP_MPI from ..likelihoods import Gaussian from ..core.parameterization.variational import NormalPosterior, NormalPrior from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch -from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU import logging class BayesianGPLVM(SparseGP_MPI): @@ -68,14 +67,12 @@ class BayesianGPLVM(SparseGP_MPI): if isinstance(inference_method,VarDTC_minibatch): inference_method.mpi_comm = mpi_comm - if kernel.useGPU and isinstance(inference_method, VarDTC_GPU): - kernel.psicomp.GPU_direct = True - super(BayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood, name=name, inference_method=inference_method, normalizer=normalizer, mpi_comm=mpi_comm, variational_prior=self.variational_prior, ) + self.link_parameter(self.X, index=0) def set_X_gradients(self, X, X_grad): """Set the gradients of the posterior distribution of X in its specific form.""" diff --git a/GPy/models/bayesian_gplvm_minibatch.py b/GPy/models/bayesian_gplvm_minibatch.py index 7100fa72..a2e44bbe 100644 --- a/GPy/models/bayesian_gplvm_minibatch.py +++ b/GPy/models/bayesian_gplvm_minibatch.py @@ -6,7 +6,6 @@ from .. import kern from ..likelihoods import Gaussian from ..core.parameterization.variational import NormalPosterior, NormalPrior from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch -from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU import logging from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch diff --git a/GPy/models/sparse_gp_regression.py b/GPy/models/sparse_gp_regression.py index 3be2aed2..5a56bb7d 100644 --- a/GPy/models/sparse_gp_regression.py +++ b/GPy/models/sparse_gp_regression.py @@ -4,12 +4,13 @@ import numpy as np from ..core import SparseGP +from ..core.sparse_gp_mpi import SparseGP_MPI from .. import likelihoods from .. import kern from ..inference.latent_function_inference import VarDTC from ..core.parameterization.variational import NormalPosterior -class SparseGPRegression(SparseGP): +class SparseGPRegression(SparseGP_MPI): """ Gaussian Process model for regression @@ -48,7 +49,14 @@ class SparseGPRegression(SparseGP): if not (X_variance is None): X = NormalPosterior(X,X_variance) - SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC(), normalizer=normalizer) + SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC(), normalizer=normalizer) + + def parameters_changed(self): + from ..inference.latent_function_inference.var_dtc_parallel import update_gradients_sparsegp,VarDTC_minibatch + if isinstance(self.inference_method,VarDTC_minibatch): + update_gradients_sparsegp(self, mpi_comm=self.mpi_comm) + else: + super(SparseGPRegression, self).parameters_changed() class SparseGPRegressionUncertainInput(SparseGP): """ diff --git a/GPy/models/ss_gplvm.py b/GPy/models/ss_gplvm.py index 4ea4f297..04006d84 100644 --- a/GPy/models/ss_gplvm.py +++ b/GPy/models/ss_gplvm.py @@ -8,7 +8,6 @@ from .. import kern from ..likelihoods import Gaussian from ..core.parameterization.variational import SpikeAndSlabPrior, SpikeAndSlabPosterior from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch -from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU from ..kern._src.psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF_GPU class SSGPLVM(SparseGP_MPI): @@ -72,6 +71,7 @@ class SSGPLVM(SparseGP_MPI): super(SSGPLVM,self).__init__(X, Y, Z, kernel, likelihood, variational_prior=self.variational_prior, inference_method=inference_method, name=name, mpi_comm=mpi_comm, normalizer=normalizer, **kwargs) # self.X.unfix() # self.X.variance.constrain_positive() + self.link_parameter(self.X, index=0) if self.group_spike: [self.X.gamma[:,i].tie('tieGamma'+str(i)) for i in xrange(self.X.gamma.shape[1])] # Tie columns together diff --git a/GPy/util/mpi.py b/GPy/util/mpi.py deleted file mode 100644 index e3d7d928..00000000 --- a/GPy/util/mpi.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -The tools for mpi -""" - -try: - import numpy as np - from mpi4py import MPI - numpy_to_MPI_typemap = { - np.dtype(np.float64) : MPI.DOUBLE, - np.dtype(np.float32) : MPI.FLOAT, - np.dtype(np.int) : MPI.INT, - np.dtype(np.int8) : MPI.CHAR, - np.dtype(np.uint8) : MPI.UNSIGNED_CHAR, - np.dtype(np.int32) : MPI.INT, - np.dtype(np.uint32) : MPI.UNSIGNED_INT, - } -except: - pass - -def divide_data(datanum, comm): - - residue = (datanum)%comm.size - datanum_list = np.empty((comm.size),dtype=np.int32) - for i in xrange(comm.size): - if i0 + + residue = (datanum)%size + datanum_list = np.empty((size),dtype=np.int32) + for i in xrange(size): + if i