Replaced sigma argument with noise in MarginalSparse.marginal_likelihood

pymc3/gp/gp.py

@@ -1,4 +1,5 @@
 import functools
+import warnings
 
 import numpy as np
 import theano.tensor as tt
@@ -671,7 +672,7 @@ def _build_marginal_likelihood_logp(self, y, X, Xu, sigma):
         quadratic = 0.5 * (tt.dot(r, r_l) - tt.dot(c, c))
         return -1.0 * (constant + logdet + quadratic + trace)
 
-    def marginal_likelihood(self, name, X, Xu, y, sigma, is_observed=True, **kwargs):
+    def marginal_likelihood(self, name, X, Xu, y, noise=None, is_observed=True, **kwargs):
         R"""
         Returns the approximate marginal likelihood distribution, given the input
         locations `X`, inducing point locations `Xu`, data `y`, and white noise
@@ -689,7 +690,7 @@ def marginal_likelihood(self, name, X, Xu, y, sigma, is_observed=True, **kwargs)
         y : array-like
             Data that is the sum of the function with the GP prior and Gaussian
             noise.  Must have shape `(n, )`.
-        sigma : scalar, Variable
+        noise : scalar, Variable
             Standard deviation of the Gaussian noise.
         is_observed : bool
             Whether to set `y` as an `observed` variable in the `model`.
@@ -702,9 +703,19 @@ def marginal_likelihood(self, name, X, Xu, y, sigma, is_observed=True, **kwargs)
         self.X = X
         self.Xu = Xu
         self.y = y
-        self.sigma = sigma
+        if noise is None:
+            sigma = kwargs.get('sigma')
+            if sigma is None:
+                raise ValueError('noise argument must be specified')
+            else:
+                self.sigma = sigma
+                warnings.warn(
+                    "The 'sigma' argument has been deprecated. Use 'noise' instead.",
+                DeprecationWarning)
+        else:
+            self.sigma = noise
         logp = functools.partial(self._build_marginal_likelihood_logp,
-                                 X=X, Xu=Xu, sigma=sigma)
+                                 X=X, Xu=Xu, sigma=noise)
         if is_observed:
             return pm.DensityDist(name, logp, observed=y, **kwargs)
         else:

pymc3/tests/test_gp.py

@@ -791,12 +791,12 @@ def testAdditiveMarginalSparse(self, approx):
             gp3 = pm.gp.MarginalSparse(self.means[2], self.covs[2], approx=approx)
 
             gpsum = gp1 + gp2 + gp3
-            fsum = gpsum.marginal_likelihood("f", self.X, Xu, self.y, sigma=sigma)
+            fsum = gpsum.marginal_likelihood("f", self.X, Xu, self.y, noise=sigma)
             model1_logp = model1.logp({"fsum": self.y})
 
         with pm.Model() as model2:
             gptot = pm.gp.MarginalSparse(reduce(add, self.means), reduce(add, self.covs), approx=approx)
-            fsum = gptot.marginal_likelihood("f", self.X, Xu, self.y, sigma=sigma)
+            fsum = gptot.marginal_likelihood("f", self.X, Xu, self.y, noise=sigma)
             model2_logp = model2.logp({"fsum": self.y})
         npt.assert_allclose(model1_logp, model2_logp, atol=0, rtol=1e-2)