From 999661c092310b1f247f14037f795a852425e9c9 Mon Sep 17 00:00:00 2001
From: Maxim Kochurov <maxim.v.kochurov@gmail.com>
Date: Sat, 10 Mar 2018 11:28:43 +0300
Subject: [PATCH] fix PyMC3 variable is not replaced if provided in
 more_replacements (VI) (#2891)

* fixes #2890

* float32 y

* update release notes

* use floatX
---
 RELEASE-NOTES.md                          |  1 +
 pymc3/tests/test_variational_inference.py | 14 ++++++++++++++
 pymc3/variational/opvi.py                 |  1 +
 3 files changed, 16 insertions(+)

diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
index 574ca45f824..2da2c329a6f 100644
--- a/RELEASE-NOTES.md
+++ b/RELEASE-NOTES.md
@@ -23,6 +23,7 @@
 
 - `VonMises` does not overflow for large values of kappa. i0 and i1 have been removed and we now use log_i0 to compute the logp.
 - The bandwidth for KDE plots is computed using a modified version of Scott's rule. The new version uses entropy instead of standard deviation. This works better for multimodal distributions. Functions using KDE plots has a new argument `bw` controlling the bandwidth.
+- fix PyMC3 variable is not replaced if provided in more_replacements (#2890)
 
 ### Deprecations
 
diff --git a/pymc3/tests/test_variational_inference.py b/pymc3/tests/test_variational_inference.py
index e7f43ed0e07..080bf0e1ca3 100644
--- a/pymc3/tests/test_variational_inference.py
+++ b/pymc3/tests/test_variational_inference.py
@@ -835,6 +835,20 @@ def test_sample_replacements(binomial_model_inference):
     assert sampled.shape[0] == 101
 
 
+def test_var_replacement():
+    X_mean = pm.floatX(np.linspace(0, 10, 10))
+    y = pm.floatX(np.random.normal(X_mean*4, .05))
+    with pm.Model():
+        inp = pm.Normal('X', X_mean, shape=X_mean.shape)
+        coef = pm.Normal('b', 4.)
+        mean = inp * coef
+        pm.Normal('y', mean, .1, observed=y)
+        advi = pm.fit(100)
+        assert advi.sample_node(mean).eval().shape == (10, )
+        x_new = pm.floatX(np.linspace(0, 10, 11))
+        assert advi.sample_node(mean, more_replacements={inp: x_new}).eval().shape == (11, )
+
+
 def test_empirical_from_trace(another_simple_model):
     with another_simple_model:
         step = pm.Metropolis()
diff --git a/pymc3/variational/opvi.py b/pymc3/variational/opvi.py
index 3481b51420a..1d2b851cc04 100644
--- a/pymc3/variational/opvi.py
+++ b/pymc3/variational/opvi.py
@@ -1459,6 +1459,7 @@ def sample_node(self, node, size=None,
         sampled node(s) with replacements
         """
         node_in = node
+        node = theano.clone(node, more_replacements)
         if size is None:
             node_out = self.symbolic_single_sample(node)
         else: