Classification examples corrected (2/3)

GPy/examples/classification.py

@@ -3,16 +3,15 @@
 
 
 """
-Simple Gaussian Processes classification
+Gaussian Processes classification
 """
 import pylab as pb
 import numpy as np
 import GPy
 
 default_seed=10000
-######################################
-## 2 dimensional example
-def crescent_data(model_type='Full', inducing=10, seed=default_seed):
+
+def crescent_data(model_type='Full', inducing=10, seed=default_seed): #FIXME
     """Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
 
     :param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
@@ -30,7 +29,7 @@ def crescent_data(model_type='Full', inducing=10, seed=default_seed):
         # create sparse GP EP model
         m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
 
-    m.approximate_likelihood()
+    m.update_likelihood_approximation()
     print(m)
 
     # optimize
@@ -42,53 +41,66 @@ def crescent_data(model_type='Full', inducing=10, seed=default_seed):
     return m
 
 def oil():
-    """Run a Gaussian process classification on the oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood."""
+    """
+    Run a Gaussian process classification on the oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
+    """
     data = GPy.util.datasets.oil()
-    likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
+    # Kernel object
+    kernel = GPy.kern.rbf(12)
 
-    # create simple GP model
-    m = GPy.models.GP_EP(data['X'],likelihood)
+    # Likelihood object
+    distribution = GPy.likelihoods.likelihood_functions.probit()
+    likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1],distribution)
 
-    # contrain all parameters to be positive
+    # Create GP model
+    m = GPy.models.GP(data['X'],kernel,likelihood=likelihood)
+
+    # Contrain all parameters to be positive
     m.constrain_positive('')
     m.tie_param('lengthscale')
-    m.approximate_likelihood()
+    m.update_likelihood_approximation()
 
-    # optimize
+    # Optimize
     m.optimize()
 
-    # plot
-    #m.plot()
     print(m)
     return m
 
-def toy_linear_1d_classification(model_type='Full', inducing=4, seed=default_seed):
-    """Simple 1D classification example.
-    :param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
+def toy_linear_1d_classification(seed=default_seed):
+    """
+    Simple 1D classification example
     :param seed : seed value for data generation (default is 4).
     :type seed: int
-    :param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
     :type inducing: int
     """
+
     data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
-    likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
-    assert model_type in ('Full','DTC','FITC')
 
-    # create simple GP model
-    if model_type=='Full':
-        m = GPy.models.GP_EP(data['X'],likelihood)
-    else:
-        # create sparse GP EP model
-        m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
+    # Kernel object
+    kernel = GPy.kern.rbf(1)
 
-    m.constrain_positive('var')
-    m.constrain_positive('len')
-    m.tie_param('lengthscale')
-    m.approximate_likelihood()
+    # Likelihood object
+    distribution = GPy.likelihoods.likelihood_functions.probit()
+    likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1],distribution)
 
-    # Optimize and plot
-    m.em(plot_all=False) # EM algorithm
-    m.plot()
+    # Model definition
+    m = GPy.models.GP(data['X'],kernel,likelihood=likelihood)
 
+    # Optimize
+    """
+    EPEM runs a loop that consists of two steps:
+    1) EP likelihood approximation:
+        m.update_likelihood_approximation()
+    2) Parameters optimization:
+        m.optimize()
+    """
+    m.EPEM()
+
+    # Plot
+    pb.subplot(211)
+    m.plot_GP()
+    pb.subplot(212)
+    m.plot_output()
     print(m)
+
     return m

GPy/examples/ep_fix.py

@@ -1,53 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-"""
-Simple Gaussian Processes classification 1D
-probit likelihood
-"""
-import pylab as pb
-import numpy as np
-import GPy
-pb.ion()
-
-pb.close('all')
-
-# Inputs
-N = 30
-X1 = np.random.normal(5,2,N/2)
-X2 = np.random.normal(10,2,N/2)
-X = np.hstack([X1,X2])[:,None]
-
-# Output
-Y = np.hstack([np.ones(N/2),np.repeat(-1,N/2)])[:,None]
-
-# Kernel object
-kernel = GPy.kern.rbf(1)
-
-# Likelihood object
-distribution = GPy.likelihoods.likelihood_functions.probit()
-likelihood = GPy.likelihoods.EP(Y,distribution)
-
-# Model definition
-m = GPy.models.GP(X,kernel,likelihood=likelihood)
-
-# Model constraints
-m.ensure_default_constraints()
-
-# Optimize model
-"""
-EPEM runs a loop that consists of two steps:
-1) EP likelihood approximation:
-    m.update_likelihood_approximation()
-2) Parameters optimization:
-    m.optimize()
-"""
-m.EPEM()
-
-# Plot
-pb.subplot(211)
-m.plot_GP()
-pb.subplot(212)
-m.plot_output()
-
-print(m)

GPy/testing/unit_tests.py

@@ -154,17 +154,16 @@ class GradientTests(unittest.TestCase):
         m.constrain_positive('(linear|bias|white)')
         self.assertTrue(m.checkgrad())
 
-    def test_GP_EP(self):
-        return # Disabled TODO
+    def test_GP_EP_probit(self):
         N = 20
-        X = np.hstack([np.random.rand(N/2)+1,np.random.rand(N/2)-1])[:,None]
-        k = GPy.kern.rbf(1) + GPy.kern.white(1)
-        Y = np.hstack([np.ones(N/2),-np.ones(N/2)])[:,None]
-        likelihood = GPy.inference.likelihoods.probit(Y)
-        m = GPy.models.GP_EP(X,likelihood,k)
-        m.constrain_positive('(var|len)')
-        m.approximate_likelihood()
-        self.assertTrue(m.checkgrad())
+        X = np.hstack([np.random.normal(5,2,N/2),np.random.normal(10,2,N/2)])[:,None]
+        Y = np.hstack([np.ones(N/2),np.repeat(-1,N/2)])[:,None]
+        kernel = GPy.kern.rbf(1)
+        distribution = GPy.likelihoods.likelihood_functions.probit()
+        likelihood = GPy.likelihoods.EP(Y,distribution)
+        m = GPy.models.GP(X,kernel,likelihood=likelihood)
+        m.ensure_default_constraints()
+        self.assertTrue(m.EPEM)
 
     @unittest.skip("FITC will be broken for a while")
     def test_generalized_FITC(self):