More fixes to initialization and link functions.

Author: twiecki

pymc3/examples/glm_linear.py

@@ -1,13 +1,7 @@
-from __future__ import print_function
-
-import numpy as np
 import sys
 
-try:
-    import statsmodels.api as sm
-except ImportError:
-    print("Example requires statsmodels")
-    sys.exit(0)
+import numpy as np
+import scipy.optimize as opt
 
 from pymc3 import *
 
@@ -31,13 +25,12 @@ def run(n=2000):
     import matplotlib.pyplot as plt
 
     with model:
-        trace = sample(n, Slice())
+        start = find_MAP(fmin=opt.fmin_powell)
+        trace = sample(n, Slice(), start=start)
 
     plt.plot(x, y, 'x')
     glm.plot_posterior_predictive(trace)
     # plt.show()
 
 if __name__ == '__main__':
     run()
-
-

pymc3/examples/glm_robust.py

@@ -1,5 +1,3 @@
-from __future__ import print_function
-
 import numpy as np
 import sys
 
@@ -20,7 +18,7 @@
 data_outlier = dict(x=x, y=y)
 
 with Model() as model:
-    family = glm.families.T(link=glm.families.identity,
+    family = glm.families.T(#link=glm.families.identity,
                             priors={'nu': 1.5,
                                     'lam': Uniform.dist(0, 20)})
     glm.glm('y ~ x', data_outlier, family=family)

pymc3/glm/families.py

@@ -8,10 +8,13 @@
 __all__ = ['Normal', 'T', 'Binomial', 'Poisson']
 
 # Define link functions
-@staticmethod
-def identity(x):
-    return x
 
+# Hack as assigning a function in the class definition automatically binds it as a method.
+class Identity():
+    def __call__(self, x):
+        return x
+
+identity = Identity()
 logit = theano.tensor.nnet.sigmoid
 inverse = theano.tensor.inv
 log = theano.tensor.log
@@ -20,7 +23,7 @@ class Family(object):
     """Base class for Family of likelihood distribution and link functions.
     """
     priors = {}
-    link = identity
+    link = None
 
     def __init__(self, **kwargs):
         # Overwrite defaults
@@ -70,20 +73,18 @@ def __repr__(self):
     Link function: {link}.""".format(klass=self.__class__, likelihood=self.likelihood.__name__, parent=self.parent, priors=self.priors, link=self.link)
 
 
-class Normal(Family):
-    link = identity
-    likelihood = pm_dists.Normal
-    parent = 'mu'
-    priors = {'sd': pm_dists.HalfCauchy.dist(beta=10)}
-
-
 class T(Family):
     link = identity
     likelihood = pm_dists.T
     parent = 'mu'
     priors = {'lam': pm_dists.HalfCauchy.dist(beta=10),
               'nu': 1}
 
+class Normal(Family):
+    link = identity
+    likelihood = pm_dists.Normal
+    parent = 'mu'
+    priors = {'sd': pm_dists.HalfCauchy.dist(beta=10)}
 
 class Binomial(Family):
     link = logit

pymc3/glm/glm.py

@@ -124,7 +124,7 @@ def glm(*args, **kwargs):
     # Logistic regression
     vars = glm('male ~ height + weight',
                data,
-               family=glm.families.Binomial(link=glm.links.Logit))
+               family=glm.families.Binomial(link=glm.families.logit))
     """
 
     model = modelcontext(kwargs.get('model'))