Merge pull request #2250 from junpenglao/bugfix#2247

Fix issue #2247

Author: Junpeng Lao

pymc3/examples/lasso_missing.py

@@ -14,22 +14,21 @@
                                                 'mother_hs', 'early_ident']].astype(float).values.T
 
 with pm.Model() as model:
-
     # Impute missing values
-    sib_mean = pm.Exponential('sib_mean', 1)
+    sib_mean = pm.Exponential('sib_mean', 1.)
     siblings_imp = pm.Poisson('siblings_imp', sib_mean,
                               observed=masked_values(siblings, value=-999))
 
-    p_disab = pm.Beta('p_disab', 1, 1)
+    p_disab = pm.Beta('p_disab', 1., 1.)
     disability_imp = pm.Bernoulli(
         'disability_imp', p_disab, observed=masked_values(disability, value=-999))
 
-    p_mother = pm.Beta('p_mother', 1, 1)
+    p_mother = pm.Beta('p_mother', 1., 1.)
     mother_imp = pm.Bernoulli('mother_imp', p_mother,
                               observed=masked_values(mother_hs, value=-999))
 
-    s = pm.HalfCauchy('s', 5, testval=5)
-    beta = pm.Laplace('beta', 0, 100, shape=7, testval=.1)
+    s = pm.HalfCauchy('s', 5., testval=5)
+    beta = pm.Laplace('beta', 0., 100., shape=7, testval=.1)
 
     expected_score = (beta[0] + beta[1] * male + beta[2] * siblings_imp + beta[3] * disability_imp +
                       beta[4] * age + beta[5] * mother_imp + beta[6] * early_ident)
@@ -41,7 +40,6 @@
 with model:
     start = pm.find_MAP()
     step1 = pm.NUTS([beta, s, p_disab, p_mother, sib_mean], scaling=start)
-
     step2 = pm.Metropolis([mother_imp.missing_values,
                            disability_imp.missing_values,
                            siblings_imp.missing_values])
@@ -51,7 +49,7 @@ def run(n=5000):
     if n == 'short':
         n = 100
     with model:
-        pm.sample(n, [step1, step2], start)
+        pm.sample(n, step=[step1, step2], start=start)
 
 
 if __name__ == '__main__':