Merge branch 'master' of github.com:SheffieldML/GPy

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.

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.)

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):

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')

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
-
-        

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')

GPy/testing/examples_tests.py

@@ -4,22 +4,73 @@
 import unittest
 import numpy as np
 import GPy
+import inspect
+import pkgutil
+import os
+import random
+
 
 class ExamplesTests(unittest.TestCase):
-    def test_check_model_returned(self):
+    def _checkgrad(self, model):
-        pass
+        self.assertTrue(model.checkgrad())
 
-    def test_model_checkgrads(self):
+    def _model_instance(self, model):
-        pass
+        self.assertTrue(isinstance(model, GPy.models))
 
-    def test_all_examples(self):
+"""
-        pass
+def model_instance_generator(model):
-        #Load models
+    def check_model_returned(self):
+        self._model_instance(model)
+    return check_model_returned
 
-        #Loop through models
+def checkgrads_generator(model):
-        #for model in models:
+    def model_checkgrads(self):
-            #self.assertTrue(m.checkgrad())
+        self._checkgrad(model)
+    return model_checkgrads
+"""
 
+def model_checkgrads(model):
+    model.randomize()
+    assert model.checkgrad()
+
+
+def model_instance(model):
+    assert isinstance(model, GPy.core.model)
+
+
+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)
+        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]
+        print "After"
+        print functions
+        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_instance_%s' % example[0]
+            yield model_instance, model
 
 if __name__ == "__main__":
     print "Running unit tests, please be (very) patient..."

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)