Fix #2258

Author: Junpeng Lao

pymc3/distributions/transforms.py

@@ -4,6 +4,7 @@
 from ..theanof import gradient
 from . import distribution
 from ..math import logit, invlogit
+from .distribution import draw_values
 import numpy as np
 
 __all__ = ['transform', 'stick_breaking', 'logodds', 'interval',
@@ -22,6 +23,9 @@ class Transform(object):
     def forward(self, x):
         raise NotImplementedError
 
+    def forward_val(self, x, point):
+        raise NotImplementedError
+
     def backward(self, z):
         raise NotImplementedError
 
@@ -55,6 +59,7 @@ def __init__(self, dist, transform, *args, **kwargs):
             arguments to Distribution"""
         forward = transform.forward
         testval = forward(dist.default())
+        forward_val = transform.forward_val
 
         self.dist = dist
         self.transform_used = transform
@@ -85,6 +90,9 @@ def backward(self, x):
 
     def forward(self, x):
         return tt.log(x)
+    
+    def forward_val(self, x, point=None):
+        return self.forward(x)
 
     def jacobian_det(self, x):
         return x
@@ -103,6 +111,9 @@ def backward(self, x):
 
     def forward(self, x):
         return logit(x)
+    
+    def forward_val(self, x, point=None):
+        return self.forward(x)
 
 logodds = LogOdds()
 
@@ -125,6 +136,11 @@ def forward(self, x):
         a, b = self.a, self.b
         return tt.log(x - a) - tt.log(b - x)
 
+    def forward_val(self, x, point=None):
+        a, b = draw_values([self.a, self.b],
+                            point=point)
+        return tt.log(x - a) - tt.log(b - x)
+
     def jacobian_det(self, x):
         s = tt.nnet.softplus(-x)
         return tt.log(self.b - self.a) - 2 * s - x
@@ -147,8 +163,12 @@ def backward(self, x):
 
     def forward(self, x):
         a = self.a
-        r = tt.log(x - a)
-        return r
+        return tt.log(x - a)
+
+    def forward_val(self, x, point=None):
+        a = draw_values([self.a],
+                        point=point)[0]
+        return tt.log(x - a)
 
     def jacobian_det(self, x):
         return x
@@ -171,8 +191,12 @@ def backward(self, x):
 
     def forward(self, x):
         b = self.b
-        r = tt.log(b - x)
-        return r
+        return tt.log(b - x)
+
+    def forward_val(self, x, point=None):
+        b = draw_values([self.b],
+                        point=point)[0]
+        return tt.log(b - x)
 
     def jacobian_det(self, x):
         return x
@@ -191,6 +215,9 @@ def backward(self, y):
     def forward(self, x):
         return x[:-1]
 
+    def forward_val(self, x, point=None):
+        return self.forward(x)
+
     def jacobian_det(self, x):
         return 0
 
@@ -224,6 +251,9 @@ def forward(self, x_):
         y = logit(z) - eq_share
         return y.T
 
+    def forward_val(self, x, point=None):
+        return self.forward(x)
+
     def backward(self, y_):
         y = y_.T
         Km1 = y.shape[0]
@@ -262,6 +292,9 @@ def backward(self, y):
     def forward(self, x):
         return tt.as_tensor_variable(x)
 
+    def forward_val(self, x, point=None):
+        return self.forward(x)
+
     def jacobian_det(self, x):
         return 0
 
@@ -280,5 +313,8 @@ def backward(self, x):
     def forward(self, y):
         return tt.advanced_set_subtensor1(y, tt.log(y[self.diag_idxs]), self.diag_idxs)
 
+    def forward_val(self, x, point=None):
+        return self.forward(x)
+        
     def jacobian_det(self, y):
         return tt.sum(y[self.diag_idxs])

pymc3/sampling.py

@@ -478,12 +478,14 @@ def _update_start_vals(a, b, model):
     """Update a with b, without overwriting existing keys. Values specified for
     transformed variables on the original scale are also transformed and inserted.
     """
-    for name in a:
-        for tname in b:
-            if is_transformed_name(tname) and get_untransformed_name(tname) == name:
-                transform_func = [d.transformation for d in model.deterministics if d.name == name]
-                if transform_func:
-                    b[tname] = transform_func[0].forward(a[name]).eval()
+    if model is not None:
+        for free_RV in model.free_RVs:
+            tname = free_RV.name
+            for name in a:
+                if is_transformed_name(tname) and get_untransformed_name(tname) == name:
+                    transform_func = [d.transformation for d in model.deterministics if d.name == name]
+                    if transform_func:
+                        b[tname] = transform_func[0].forward_val(a[name], point=b).eval()
 
     a.update({k: v for k, v in b.items() if k not in a})
 

pymc3/tests/test_sampling.py

@@ -150,6 +150,19 @@ def test_soft_update_transformed(self):
         pm.sampling._update_start_vals(start, test_point, model)
         assert_almost_equal(np.exp(start['a_log__']), start['a'])
 
+    def test_soft_update_transformed2(self):
+        with pm.Model() as model:
+            a = pm.Uniform('a', lower=0., upper=1.)
+            pm.Uniform('b', lower=0., upper=1.-a)
+        start = {'a': .3, 'b': .5}
+        test_point = {'a_interval__': -0.8472978603872037,
+                      'b_interval__': 0.9162907318741552}
+        pm.sampling._update_start_vals(start, model.test_point, model)
+        assert_almost_equal(start['a_interval__'], 
+                            test_point['a_interval__'])
+        assert_almost_equal(start['b_interval__'], 
+                            test_point['b_interval__'])
+
 
 @pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
 class TestNamedSampling(SeededTest):