From d71aabceb50bd0a50f9105c569fd499adcfd2069 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Mon, 11 Mar 2013 14:58:42 +0000 Subject: [PATCH 1/9] Changed example tests --- GPy/testing/examples_tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py index 25cfad04..0e41a541 100644 --- a/GPy/testing/examples_tests.py +++ b/GPy/testing/examples_tests.py @@ -13,7 +13,7 @@ class ExamplesTests(unittest.TestCase): pass def test_all_examples(self): - pass + examples_module = __import__("GPy").examples #Load models #Loop through models From c39af496a633b44b2e42356a444dba81c6630b65 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Mon, 11 Mar 2013 17:05:18 +0000 Subject: [PATCH 2/9] Added test generator (not quite finished yet) --- GPy/examples/regression.py | 4 +-- GPy/examples/tutorials.py | 18 +++------- GPy/testing/examples_tests.py | 62 ++++++++++++++++++++++++++++------- 3 files changed, 58 insertions(+), 26 deletions(-) diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py index 6c22b68e..f5d0d3b1 100644 --- a/GPy/examples/regression.py +++ b/GPy/examples/regression.py @@ -194,7 +194,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000 # Remove the mean (no bias kernel to ensure signal/noise is in RBF/white) data['Y'] = data['Y'] - np.mean(data['Y']) - lls = GPy.examples.regression.contour_data(data, length_scales, log_SNRs, GPy.kern.rbf) + lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf) pb.contour(length_scales, log_SNRs, np.exp(lls), 20) ax = pb.gca() pb.xlabel('length scale') @@ -229,7 +229,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000 ax.set_ylim(ylim) return (models, lls) -def contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf): +def _contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf): """Evaluate the GP objective function for a given data set for a range of signal to noise ratios and a range of lengthscales. :data_set: A data set from the utils.datasets director. diff --git a/GPy/examples/tutorials.py b/GPy/examples/tutorials.py index 9d892b8e..a199aba9 100644 --- a/GPy/examples/tutorials.py +++ b/GPy/examples/tutorials.py @@ -6,14 +6,14 @@ Code of Tutorials """ +import pylab as pb +pb.ion() +import numpy as np +import GPy + def tuto_GP_regression(): """The detailed explanations of the commands used in this file can be found in the tutorial section""" - import pylab as pb - pb.ion() - import numpy as np - import GPy - X = np.random.uniform(-3.,3.,(20,1)) Y = np.sin(X) + np.random.randn(20,1)*0.05 @@ -39,11 +39,6 @@ def tuto_GP_regression(): # 2-dimensional example # ########################### - import pylab as pb - pb.ion() - import numpy as np - import GPy - # sample inputs and outputs X = np.random.uniform(-3.,3.,(50,2)) Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(50,1)*0.05 @@ -67,9 +62,6 @@ def tuto_GP_regression(): def tuto_kernel_overview(): """The detailed explanations of the commands used in this file can be found in the tutorial section""" - import pylab as pb - import numpy as np - import GPy pb.ion() ker1 = GPy.kern.rbf(1) # Equivalent to ker1 = GPy.kern.rbf(D=1, variance=1., lengthscale=1.) diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py index 0e41a541..967d7a6a 100644 --- a/GPy/testing/examples_tests.py +++ b/GPy/testing/examples_tests.py @@ -4,23 +4,63 @@ import unittest import numpy as np import GPy +import inspect +import pkgutil +import os + class ExamplesTests(unittest.TestCase): - def test_check_model_returned(self): - pass + def _checkgrad(self, model): + self.assertTrue(model.checkgrad()) - def test_model_checkgrads(self): - pass + def _model_instance(self, model): + self.assertTrue(isinstance(model, GPy.models)) - def test_all_examples(self): - examples_module = __import__("GPy").examples - #Load models +""" +def model_instance_generator(model): + def check_model_returned(self): + self._model_instance(model) + return check_model_returned - #Loop through models - #for model in models: - #self.assertTrue(m.checkgrad()) +def checkgrads_generator(model): + def model_checkgrads(self): + self._checkgrad(model) + return model_checkgrads +""" +def model_checkgrads(model): + assert model.checkgrad() is True +def model_instance(model): + assert model.checkgrad() is True + +def test_models(): + examples_path = os.path.dirname(GPy.examples.__file__) + #Load modules + for loader, module_name, is_pkg in pkgutil.iter_modules([examples_path]): + #Load examples + module_examples = loader.find_module(module_name).load_module(module_name) + functions = [ func for func in [inspect.getmembers(module_examples, predicate=inspect.isfunction)[0]] if func[0].startswith('_') is False ] + for example in functions: + print "Testing example: ", example[0] + #Generate model + model = example[1]() + print model + + #Create tests for instance check + """ + test = model_instance_generator(model) + test.__name__ = 'test_instance_%s' % example[0] + setattr(ExamplesTests, test.__name__, test) + + #Create tests for checkgrads check + test = checkgrads_generator(model) + test.__name__ = 'test_checkgrads_%s' % example[0] + setattr(ExamplesTests, test.__name__, test) + """ + model_checkgrads.description = 'test_checkgrads_%s' % example[0] + yield model_checkgrads, model + model_instance.description = 'test_checkgrads_%s' % example[0] + yield model_instance, model if __name__ == "__main__": print "Running unit tests, please be (very) patient..." - unittest.main() From 5c768231eb94c538ecbe87dc70bc124ffb9fbf1e Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Mon, 11 Mar 2013 17:35:01 +0000 Subject: [PATCH 3/9] Add example test generation --- GPy/testing/examples_tests.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py index 967d7a6a..9636b286 100644 --- a/GPy/testing/examples_tests.py +++ b/GPy/testing/examples_tests.py @@ -27,19 +27,27 @@ def checkgrads_generator(model): self._checkgrad(model) return model_checkgrads """ + def model_checkgrads(model): assert model.checkgrad() is True + def model_instance(model): assert model.checkgrad() is True + def test_models(): examples_path = os.path.dirname(GPy.examples.__file__) #Load modules for loader, module_name, is_pkg in pkgutil.iter_modules([examples_path]): #Load examples module_examples = loader.find_module(module_name).load_module(module_name) - functions = [ func for func in [inspect.getmembers(module_examples, predicate=inspect.isfunction)[0]] if func[0].startswith('_') is False ] + print "MODULE", module_examples + print "Before" + print inspect.getmembers(module_examples, predicate=inspect.isfunction) + functions = [ func for func in inspect.getmembers(module_examples, predicate=inspect.isfunction) if func[0].startswith('_') is False ] + print "After" + print functions for example in functions: print "Testing example: ", example[0] #Generate model @@ -59,7 +67,7 @@ def test_models(): """ model_checkgrads.description = 'test_checkgrads_%s' % example[0] yield model_checkgrads, model - model_instance.description = 'test_checkgrads_%s' % example[0] + model_instance.description = 'test_instance_%s' % example[0] yield model_instance, model if __name__ == "__main__": From e6acaf651e5798250c1f28265714e0b9edb850da Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Mon, 11 Mar 2013 18:05:25 +0000 Subject: [PATCH 4/9] Should now test all (although upon error it stops trying to generate any more) --- GPy/testing/examples_tests.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py index 9636b286..5ae741ee 100644 --- a/GPy/testing/examples_tests.py +++ b/GPy/testing/examples_tests.py @@ -29,11 +29,11 @@ def checkgrads_generator(model): """ def model_checkgrads(model): - assert model.checkgrad() is True + assert model.checkgrad() def model_instance(model): - assert model.checkgrad() is True + assert isinstance(model, GPy.core.model) def test_models(): @@ -45,7 +45,7 @@ def test_models(): print "MODULE", module_examples print "Before" print inspect.getmembers(module_examples, predicate=inspect.isfunction) - functions = [ func for func in inspect.getmembers(module_examples, predicate=inspect.isfunction) if func[0].startswith('_') is False ] + functions = [ func for func in inspect.getmembers(module_examples, predicate=inspect.isfunction) if func[0].startswith('_') is False ][::-1] print "After" print functions for example in functions: @@ -72,3 +72,4 @@ def test_models(): if __name__ == "__main__": print "Running unit tests, please be (very) patient..." + unittest.main() From 62615f2ca891229bc9bb883bdfacecb00f17418b Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Mon, 11 Mar 2013 18:09:47 +0000 Subject: [PATCH 5/9] Trying to shuffle --- GPy/testing/examples_tests.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py index 5ae741ee..07ef4177 100644 --- a/GPy/testing/examples_tests.py +++ b/GPy/testing/examples_tests.py @@ -7,6 +7,7 @@ import GPy import inspect import pkgutil import os +import random class ExamplesTests(unittest.TestCase): @@ -45,7 +46,7 @@ def test_models(): print "MODULE", module_examples print "Before" print inspect.getmembers(module_examples, predicate=inspect.isfunction) - functions = [ func for func in inspect.getmembers(module_examples, predicate=inspect.isfunction) if func[0].startswith('_') is False ][::-1] + functions = [ func for func in inspect.getmembers(module_examples, predicate=inspect.isfunction) if func[0].startswith('_') is False ] print "After" print functions for example in functions: From 2a1b5f94c8a0f682ee23d42b9953b1ca8a783b2a Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Mon, 11 Mar 2013 18:14:23 +0000 Subject: [PATCH 6/9] Got rid of foo --- GPy/kern/rbf.py | 1 - GPy/testing/examples_tests.py | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/GPy/kern/rbf.py b/GPy/kern/rbf.py index 133895ff..3c3d59e6 100644 --- a/GPy/kern/rbf.py +++ b/GPy/kern/rbf.py @@ -55,7 +55,6 @@ class rbf(kernpart): self._X, self._X2, self._params = np.empty(shape=(3,1)) def _get_params(self): - foo return np.hstack((self.variance,self.lengthscale)) def _set_params(self,x): diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py index 07ef4177..141d3999 100644 --- a/GPy/testing/examples_tests.py +++ b/GPy/testing/examples_tests.py @@ -46,7 +46,7 @@ def test_models(): print "MODULE", module_examples print "Before" print inspect.getmembers(module_examples, predicate=inspect.isfunction) - functions = [ func for func in inspect.getmembers(module_examples, predicate=inspect.isfunction) if func[0].startswith('_') is False ] + functions = [ func for func in inspect.getmembers(module_examples, predicate=inspect.isfunction) if func[0].startswith('_') is False ][::-1] print "After" print functions for example in functions: From b06b4ea8f1c8793540bec7d68340310489afdadd Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Mon, 11 Mar 2013 18:25:11 +0000 Subject: [PATCH 7/9] Fixed checkgrad test to randomize before checking --- GPy/testing/examples_tests.py | 1 + 1 file changed, 1 insertion(+) diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py index 141d3999..feba2b50 100644 --- a/GPy/testing/examples_tests.py +++ b/GPy/testing/examples_tests.py @@ -30,6 +30,7 @@ def checkgrads_generator(model): """ def model_checkgrads(model): + model.randomize() assert model.checkgrad() From b336d914739f8965aeec322691d0b1638313e400 Mon Sep 17 00:00:00 2001 From: Nicolo Fusi Date: Mon, 11 Mar 2013 18:43:59 +0000 Subject: [PATCH 8/9] fixed bug in RBF, added inducing inputs to BGPLVM plots --- GPy/kern/rbf.py | 1 - GPy/models/Bayesian_GPLVM.py | 4 ++++ GPy/models/GPLVM.py | 4 +--- GPy/models/sparse_GPLVM.py | 4 ++++ 4 files changed, 9 insertions(+), 4 deletions(-) diff --git a/GPy/kern/rbf.py b/GPy/kern/rbf.py index 133895ff..3c3d59e6 100644 --- a/GPy/kern/rbf.py +++ b/GPy/kern/rbf.py @@ -55,7 +55,6 @@ class rbf(kernpart): self._X, self._X2, self._params = np.empty(shape=(3,1)) def _get_params(self): - foo return np.hstack((self.variance,self.lengthscale)) def _set_params(self,x): diff --git a/GPy/models/Bayesian_GPLVM.py b/GPy/models/Bayesian_GPLVM.py index 0eb957a9..430c2718 100644 --- a/GPy/models/Bayesian_GPLVM.py +++ b/GPy/models/Bayesian_GPLVM.py @@ -83,3 +83,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM): def _log_likelihood_gradients(self): return np.hstack((self.dL_dmuS().flatten(), sparse_GP._log_likelihood_gradients(self))) + + def plot_latent(self, *args, **kwargs): + input_1, input_2 = GPLVM.plot_latent(*args, **kwargs) + pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w') diff --git a/GPy/models/GPLVM.py b/GPy/models/GPLVM.py index d0dc766f..b44801fc 100644 --- a/GPy/models/GPLVM.py +++ b/GPy/models/GPLVM.py @@ -117,6 +117,4 @@ class GPLVM(GP): pb.xlim(xmin[0],xmax[0]) pb.ylim(xmin[1],xmax[1]) - - - + return input_1, input_2 diff --git a/GPy/models/sparse_GPLVM.py b/GPy/models/sparse_GPLVM.py index 542fbe0e..591c49b2 100644 --- a/GPy/models/sparse_GPLVM.py +++ b/GPy/models/sparse_GPLVM.py @@ -55,3 +55,7 @@ class sparse_GPLVM(sparse_GP_regression, GPLVM): #passing Z without a small amout of jitter will induce the white kernel where we don;t want it! mu, var, upper, lower = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001) pb.plot(mu[:, 0] , mu[:, 1], 'ko') + + def plot_latent(self, *args, **kwargs): + input_1, input_2 = GPLVM.plot_latent(*args, **kwargs) + pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w') From 3dd62c8251740440c037aead3b4bd2f855f9805b Mon Sep 17 00:00:00 2001 From: Nicolas Date: Mon, 11 Mar 2013 18:45:04 +0000 Subject: [PATCH 9/9] Few bugs fixed in the documentation --- GPy/kern/rbf.py | 3 +-- doc/tuto_creating_new_kernels.rst | 24 ++++++++++++------------ 2 files changed, 13 insertions(+), 14 deletions(-) diff --git a/GPy/kern/rbf.py b/GPy/kern/rbf.py index 133895ff..ae587202 100644 --- a/GPy/kern/rbf.py +++ b/GPy/kern/rbf.py @@ -12,7 +12,7 @@ class rbf(kernpart): .. math:: - k(r) = \sigma^2 \exp(- \frac{1}{2}r^2) \ \ \ \ \ \\text{ where } r^2 = \sum_{i=1}^d \frac{ (x_i-x^\prime_i)^2}{\ell_i^2}} + k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg) \ \ \ \ \ \\text{ where } r^2 = \sum_{i=1}^d \\frac{ (x_i-x^\prime_i)^2}{\ell_i^2} where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input. @@ -55,7 +55,6 @@ class rbf(kernpart): self._X, self._X2, self._params = np.empty(shape=(3,1)) def _get_params(self): - foo return np.hstack((self.variance,self.lengthscale)) def _set_params(self,x): diff --git a/doc/tuto_creating_new_kernels.rst b/doc/tuto_creating_new_kernels.rst index 672bc1e7..8ebf8b8f 100644 --- a/doc/tuto_creating_new_kernels.rst +++ b/doc/tuto_creating_new_kernels.rst @@ -22,7 +22,7 @@ We advise the reader to start with copy-pasting an existing kernel and to modify **Header** -The header is similar to all kernels:: +The header is similar to all kernels: :: from kernpart import kernpart import numpy as np @@ -35,7 +35,7 @@ The implementation of this function in mandatory. For all kernparts the first parameter ``D`` corresponds to the dimension of the input space, and the following parameters stand for the parameterization of the kernel. -The following attributes are compulsory: ``self.D`` (the dimension, integer), ``self.name`` (name of the kernel, string), ``self.Nparam`` (number of parameters, integer).:: +The following attributes are compulsory: ``self.D`` (the dimension, integer), ``self.name`` (name of the kernel, string), ``self.Nparam`` (number of parameters, integer). :: def __init__(self,D,variance=1.,lengthscale=1.,power=1.): assert D == 1, "For this kernel we assume D=1" @@ -50,7 +50,7 @@ The following attributes are compulsory: ``self.D`` (the dimension, integer), `` The implementation of this function in mandatory. -This function returns a one dimensional array of length ``self.Nparam`` containing the value of the parameters.:: +This function returns a one dimensional array of length ``self.Nparam`` containing the value of the parameters. :: def _get_params(self): return np.hstack((self.variance,self.lengthscale,self.power)) @@ -59,7 +59,7 @@ This function returns a one dimensional array of length ``self.Nparam`` containi The implementation of this function in mandatory. -The input is a one dimensional array of length ``self.Nparam`` containing the value of the parameters. The function has no output but it updates the values of the attribute associated to the parameters (such as ``self.variance``, ``self.lengthscale``, ...).:: +The input is a one dimensional array of length ``self.Nparam`` containing the value of the parameters. The function has no output but it updates the values of the attribute associated to the parameters (such as ``self.variance``, ``self.lengthscale``, ...). :: def _set_params(self,x): self.variance = x[0] @@ -70,7 +70,7 @@ The input is a one dimensional array of length ``self.Nparam`` containing the va The implementation of this function in mandatory. -It returns a list of strings of length ``self.Nparam`` corresponding to the parameter names.:: +It returns a list of strings of length ``self.Nparam`` corresponding to the parameter names. :: def _get_param_names(self): return ['variance','lengthscale','power'] @@ -79,7 +79,7 @@ It returns a list of strings of length ``self.Nparam`` corresponding to the para The implementation of this function in mandatory. -This function is used to compute the covariance matrix associated with the inputs X, X2 (np.arrays with arbitrary number of line (say :math:`n_1`, :math:`n_2`) and ``self.D`` columns). This function does not returns anything but it adds the :math:`n_1 \times n_2` covariance matrix to the kernpart to the object ``target`` (a :math:`n_1 \times n_2` np.array). This trick allows to compute the covariance matrix of a kernel containing many kernparts with a limited memory use.:: +This function is used to compute the covariance matrix associated with the inputs X, X2 (np.arrays with arbitrary number of line (say :math:`n_1`, :math:`n_2`) and ``self.D`` columns). This function does not returns anything but it adds the :math:`n_1 \times n_2` covariance matrix to the kernpart to the object ``target`` (a :math:`n_1 \times n_2` np.array). This trick allows to compute the covariance matrix of a kernel containing many kernparts with a limited memory use. :: def K(self,X,X2,target): if X2 is None: X2 = X @@ -90,7 +90,7 @@ This function is used to compute the covariance matrix associated with the input The implementation of this function in mandatory. -This function is similar to ``K`` but it computes only the values of the kernel on the diagonal. Thus, ``target`` is a 1-dimensional np.array of length :math:`n_1`.:: +This function is similar to ``K`` but it computes only the values of the kernel on the diagonal. Thus, ``target`` is a 1-dimensional np.array of length :math:`n_1`. :: def Kdiag(self,X,target): target += self.variance @@ -100,7 +100,7 @@ This function is similar to ``K`` but it computes only the values of the kernel This function is required for the optimization of the parameters. -Computes the derivative of the likelihood. As previously, the values are added to the object target which is a 1-dimensional np.array of length ``self.Nparam``. For example, if the kernel is parameterized by :math:`\sigma^2,\ \theta`, then :math:`\frac{dL}{d\sigma^2} = \frac{dL}{d K} \frac{dK}{d\sigma^2}` is added to the first element of target and :math:`\frac{dL}{d\theta} = \frac{dL}{d K} \frac{dK}{d\theta}` to the second.:: +Computes the derivative of the likelihood. As previously, the values are added to the object target which is a 1-dimensional np.array of length ``self.Nparam``. For example, if the kernel is parameterized by :math:`\sigma^2,\ \theta`, then :math:`\frac{dL}{d\sigma^2} = \frac{dL}{d K} \frac{dK}{d\sigma^2}` is added to the first element of target and :math:`\frac{dL}{d\theta} = \frac{dL}{d K} \frac{dK}{d\theta}` to the second. :: def dK_dtheta(self,dL_dK,X,X2,target): if X2 is None: X2 = X @@ -119,7 +119,7 @@ Computes the derivative of the likelihood. As previously, the values are added t This function is required for BGPLVM, sparse models and uncertain inputs. -As previously, target is an ``self.Nparam`` array and :math:`\frac{dL}{d Kdiag} \frac{dKdiag}{dparam}` is added to each element.:: +As previously, target is an ``self.Nparam`` array and :math:`\frac{dL}{d Kdiag} \frac{dKdiag}{dparam}` is added to each element. :: def dKdiag_dtheta(self,dL_dKdiag,X,target): target[0] += np.sum(dL_dKdiag) @@ -129,7 +129,7 @@ As previously, target is an ``self.Nparam`` array and :math:`\frac{dL}{d Kdiag} This function is required for GPLVM, BGPLVM, sparse models and uncertain inputs. -Computes the derivative of the likelihood with respect to the inputs ``X`` (a :math:`n \times D` np.array). The result is added to target which is a :math:`n \times D` np.array.:: +Computes the derivative of the likelihood with respect to the inputs ``X`` (a :math:`n \times D` np.array). The result is added to target which is a :math:`n \times D` np.array. :: def dK_dX(self,dL_dK,X,X2,target): """derivative of the covariance matrix with respect to X.""" @@ -141,7 +141,7 @@ Computes the derivative of the likelihood with respect to the inputs ``X`` (a :m **dKdiag_dX(self,dL_dKdiag,X,target)** -This function is required for BGPLVM, sparse models and uncertain inputs. As for ``dKdiag_dtheta``, :math:`\frac{dL}{d Kdiag} \frac{dKdiag}{dX}` is added to each element of target.:: +This function is required for BGPLVM, sparse models and uncertain inputs. As for ``dKdiag_dtheta``, :math:`\frac{dL}{d Kdiag} \frac{dKdiag}{dX}` is added to each element of target. :: def dKdiag_dX(self,dL_dKdiag,X,target): pass @@ -167,7 +167,7 @@ The following line should be added in the preamble of the file:: from rational_quadratic import rational_quadratic as rational_quadratic_part -as well as the following block:: +as well as the following block :: def rational_quadratic(D,variance=1., lengthscale=1., power=1.): part = rational_quadraticpart(D,variance, lengthscale, power)