Merge f10c820 into 35c6c44

pymc3/__init__.py

@@ -3,6 +3,7 @@
 from .blocking import *
 from .distributions import *
 from .external import *
+from .linear_models import *
 from .math import logsumexp, logit, invlogit
 from .model import *
 from .stats import *
@@ -23,7 +24,6 @@
 
 from .tests import test
 
-from . import glm
 from .data import *
 
 import logging

pymc3/linear_models/__init__.py

@@ -0,0 +1,2 @@
+from .linear import LinearComponent, GLM
+from . import families

pymc3/models/linear.py → pymc3/linear_models/linear.py

@@ -2,13 +2,13 @@
 import pandas as pd
 import numpy as np
 from ..distributions import Normal, Flat
-from ..glm import families
+from . import families
 from ..model import Model, Deterministic
 from .utils import any_to_tensor_and_labels
 
 __all__ = [
     'LinearComponent',
-    'Glm'
+    'GLM'
 ]
 
 
@@ -87,7 +87,7 @@ def from_formula(cls, formula, data, priors=None, vars=None, name='', model=None
                    priors=priors, vars=vars, name=name, model=model)
 
 
-class Glm(LinearComponent):
+class GLM(LinearComponent):
     """Creates glm model, y_est is accessible via attribute
     Parameters
     ----------
@@ -103,11 +103,11 @@ class Glm(LinearComponent):
             defaults to Normal.dist(mu=0, tau=1.0E-6)
     init : dict - test_vals for coefficients
     vars : dict - random variables instead of creating new ones
-    family : pymc3.glm.families object
+    family : pymc3.linear_models.families object
     """
     def __init__(self, x, y, intercept=True, labels=None,
                  priors=None, vars=None, family='normal', name='', model=None):
-        super(Glm, self).__init__(
+        super(GLM, self).__init__(
             x, y, intercept=intercept, labels=labels,
             priors=priors, vars=vars, name=name, model=model
         )

pymc3/tests/test_examples.py

@@ -192,16 +192,14 @@ def build_model(self):
         self.y = true_intercept + self.x * true_slope + np.random.normal(scale=.5, size=size)
         data = dict(x=self.x, y=self.y)
         with pm.Model() as model:
-            pm.glm.glm('y ~ x', data)
+            pm.GLM.from_formula('y ~ x', data)
         return model
 
     def test_run(self):
         with self.build_model():
             start = pm.find_MAP(fmin=opt.fmin_powell)
             trace = pm.sample(50, pm.Slice(), start=start)
 
-        pm.glm.plot_posterior_predictive(trace)
-
 
 class TestLatentOccupancy(SeededTest):
     """