SMC change initialization (#2755)

* initialize smc using sample() insted of random numbers * use metropolis * init from random and modify scaling * fix indentation * update trace reference values * revert tuning changes * revert tuning changes * update release notes
pymc-devs · Dec 18, 2017 · da3ef19 · da3ef19
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commit da3ef19
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diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
@@ -26,7 +26,7 @@
 - Add test for `model.logp_array` and `model.bijection` (#2724) 
 - Fixed `sample_ppc` and `sample_ppc_w` to iterate all chains(#2633, #2748)
 - Add Bayesian R2 score (for GLMs) `stats.r2_score` (#2696) and test (#2729).
-- SMC works with transformed variables (#2749)
+- SMC works with transformed variables (#2755)
 
 
 ## PyMC3 3.2 (October 10, 2017)

diff --git a/pymc3/step_methods/smc.py b/pymc3/step_methods/smc.py
@@ -183,15 +183,16 @@ def __init__(self, vars=None, out_vars=None, n_chains=100, scaling=1., covarianc
         # create initial population
         self.population = []
         self.array_population = np.zeros(n_chains)
+        start = model.test_point
 
         init_rnd = {}
         for v in vars:
             if pm.util.is_transformed_name(v.name):
                 trans = v.distribution.transform_used.forward
-                rnd = trans(v.distribution.dist.random(size=self.n_chains))
+                rnd = trans(v.distribution.dist.random(size=self.n_chains, point=start))
                 init_rnd[v.name] = rnd.eval()
             else:
-                init_rnd[v.name] = v.random(size=self.n_chains)
+                init_rnd[v.name] = v.random(size=self.n_chains, point=start)
 
         for i in range(self.n_chains):
             self.population.append(pm.Point({v.name: init_rnd[v.name][i] for v in vars},

diff --git a/pymc3/tests/test_smc.py b/pymc3/tests/test_smc.py
@@ -44,11 +44,7 @@ def two_gaussians(x):
             return tt.log(w1 * tt.exp(log_like1) + w2 * tt.exp(log_like2))
 
         with pm.Model() as self.ATMIP_test:
-            X = pm.Uniform('X',
-                           shape=n,
-                           lower=-2. * np.ones_like(mu1),
-                           upper=2. * np.ones_like(mu1),
-                           testval=-1. * np.ones_like(mu1))
+            X = pm.Uniform('X', lower=-2, upper=2., shape=n)
             llk = pm.Potential('muh', two_gaussians(X))
 
         self.step = smc.SMC(

diff --git a/pymc3/tests/test_step.py b/pymc3/tests/test_step.py
@@ -451,3 +451,4 @@ def test_linalg(self):
 
             with pytest.raises(SamplingError):
                 sample(20, init=None, nuts_kwargs={'on_error': 'raise'})
+