more massive and destructive changes everywhere

GPy/likelihoods/bernoulli.py

@@ -2,10 +2,10 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from scipy import stats,special
+from scipy import stats, special
 import scipy as sp
-from GPy.util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
-import gp_transformations
+from GPy.util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
+import link_functions
 from likelihood import Likelihood
 
 class Bernoulli(Likelihood):
@@ -16,29 +16,29 @@ class Bernoulli(Likelihood):
         p(y_{i}|\\lambda(f_{i})) = \\lambda(f_{i})^{y_{i}}(1-f_{i})^{1-y_{i}}
 
     .. Note::
-        Y is expected to take values in {-1,1}
+        Y is expected to take values in {-1, 1}
         Probit likelihood usually used
     """
-    def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False):
-        super(Bernoulli, self).__init__(gp_link,analytical_mean,analytical_variance)
-        if isinstance(gp_link , (gp_transformations.Heaviside, gp_transformations.Probit)):
+    def __init__(self, gp_link=None, analytical_mean=False, analytical_variance=False):
+        super(Bernoulli, self).__init__(gp_link, analytical_mean, analytical_variance)
+        if isinstance(gp_link , (link_functions.Heaviside, link_functions.Probit)):
             self.log_concave = True
 
-    def _preprocess_values(self,Y):
+    def _preprocess_values(self, Y):
         """
         Check if the values of the observations correspond to the values
         assumed by the likelihood function.
 
-        ..Note:: Binary classification algorithm works better with classes {-1,1}
+        ..Note:: Binary classification algorithm works better with classes {-1, 1}
         """
         Y_prep = Y.copy()
         Y1 = Y[Y.flatten()==1].size
         Y2 = Y[Y.flatten()==0].size
-        assert Y1 + Y2 == Y.size, 'Bernoulli likelihood is meant to be used only with outputs in {0,1}.'
+        assert Y1 + Y2 == Y.size, 'Bernoulli likelihood is meant to be used only with outputs in {0, 1}.'
         Y_prep[Y.flatten() == 0] = -1
         return Y_prep
 
-    def _moments_match_analytical(self,data_i,tau_i,v_i):
+    def _moments_match_analytical(self, data_i, tau_i, v_i):
         """
         Moments match of the marginal approximation in EP algorithm
 
@@ -51,15 +51,15 @@ class Bernoulli(Likelihood):
         elif data_i == 0:
             sign = -1
         else:
-            raise ValueError("bad value for Bernouilli observation (0,1)")
-        if isinstance(self.gp_link,gp_transformations.Probit):
+            raise ValueError("bad value for Bernouilli observation (0, 1)")
+        if isinstance(self.gp_link, link_functions.Probit):
             z = sign*v_i/np.sqrt(tau_i**2 + tau_i)
             Z_hat = std_norm_cdf(z)
             phi = std_norm_pdf(z)
             mu_hat = v_i/tau_i + sign*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i))
             sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat)
 
-        elif isinstance(self.gp_link,gp_transformations.Heaviside):
+        elif isinstance(self.gp_link, link_functions.Heaviside):
             a = sign*v_i/np.sqrt(tau_i)
             Z_hat = std_norm_cdf(a)
             N = std_norm_pdf(a)
@@ -68,24 +68,24 @@ class Bernoulli(Likelihood):
             if np.any(np.isnan([Z_hat, mu_hat, sigma2_hat])):
                 stop
         else:
-            raise ValueError("Exact moment matching not available for link {}".format(self.gp_link.gp_transformations.__name__))
+            raise ValueError("Exact moment matching not available for link {}".format(self.gp_link.__name__))
 
         return Z_hat, mu_hat, sigma2_hat
 
-    def _predictive_mean_analytical(self,mu,variance):
+    def _predictive_mean_analytical(self, mu, variance):
 
-        if isinstance(self.gp_link,gp_transformations.Probit):
+        if isinstance(self.gp_link, link_functions.Probit):
             return stats.norm.cdf(mu/np.sqrt(1+variance))
 
-        elif isinstance(self.gp_link,gp_transformations.Heaviside):
+        elif isinstance(self.gp_link, link_functions.Heaviside):
             return stats.norm.cdf(mu/np.sqrt(variance))
 
         else:
             raise NotImplementedError
 
-    def _predictive_variance_analytical(self,mu,variance, pred_mean):
+    def _predictive_variance_analytical(self, mu, variance, pred_mean):
 
-        if isinstance(self.gp_link,gp_transformations.Heaviside):
+        if isinstance(self.gp_link, link_functions.Heaviside):
             return 0.
         else:
             raise NotImplementedError
@@ -106,7 +106,7 @@ class Bernoulli(Likelihood):
         :rtype: float
 
         .. Note:
-            Each y_i must be in {0,1}
+            Each y_i must be in {0, 1}
         """
         assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
         objective = (link_f**y) * ((1.-link_f)**(1.-y))
@@ -195,13 +195,13 @@ class Bernoulli(Likelihood):
         d3logpdf_dlink3 = 2*(y/(link_f**3) - (1-y)/((1-link_f)**3))
         return d3logpdf_dlink3
 
-    def _mean(self,gp):
+    def _mean(self, gp):
         """
         Mass (or density) function
         """
         return self.gp_link.transf(gp)
 
-    def _variance(self,gp):
+    def _variance(self, gp):
         """
         Mass (or density) function
         """