Implemented get_first_level_conditionals to try to get rid of the added conditional_on attribute of every distribution. This function does a breadth first search on the node's logpt or transformed.logpt graph, looking for named nodes which are different from the root node, or the node's transformed, and is also not a TensorConstant or SharedVariable. Each branch was searched until the first named node was found. This way, the parent conditionals of the root searched node, which were only one step away from it in the bayesian network were returned. However, this ran into a problem with Mixture classes. These add to the logpt graph, another logpt graph from the comp_dists. This leads to the problem that the logpt's first level conditionals will also be seen as if they were first level conditional of the root. Furthermore, many copies of nodes done by the added logpt ended up being inserted into the computed conditional_on. This lead to a very strange error, in which loops appeared in the DAG, and depths started to be wrong. In particular, there were no depth 0 nodes. My view is that the explicit conditional_on attribute prevents problems like this one from happening, and so I left it as is, to discuss. Other changes done in this commit are that test_exact_step for the SMC uses draw_values on a hierarchy, and given that draw_values's behavior changed in the hierarchy situations, the exact trace values must also be adjusted. Finally test_bad_init was changed to run on one core, this way the parallel exception chaining does not change the exception type.

Author: lucianopaz

pymc3/model.py

@@ -1542,6 +1542,52 @@ def not_shared_or_constant_variable(x):
             ) or (isinstance(x, (FreeRV, MultiObservedRV, TransformedRV)))
 
 
+def get_first_level_conditionals(root):
+    """Performs a breadth first search on the supplied root node's logpt or
+    transformed logpt graph searching for named input nodes, which are
+    different from the supplied root. Each explored branch will stop when
+    either when it ends or when it finds its first named node.
+
+    Parameters
+    ----------
+    root: theano.Variable (mandatory)
+        The node from which to get the transformed.logpt or logpt and perform
+        the search. If root does not have either of these attributes, the
+        function returns None.
+
+    Returns
+    -------
+    conditional_on : set, with named nodes that are not theano.Constant nor
+    SharedVariable. The input `root` is conditionally dependent on these nodes
+    and is one step away from them in the bayesian network that specifies the
+    relationships, hence the name `get_first_level_conditionals`.
+    """
+    transformed = getattr(root, 'transformed', None)
+    try:
+        cond = transformed.logpt
+    except AttributeError:
+        cond = getattr(root, 'logpt', None)
+    if cond is None:
+        return None
+    conditional_on = set()
+    queue = copy(getattr(cond.owner, 'inputs', []))
+    while queue:
+        parent = queue.pop(0)
+        if (parent is not None and getattr(parent, 'name', None) is not None
+                and not_shared_or_constant_variable(parent)):
+            # We don't include as a conditional relation either logpt depending
+            # on root or on transformed because they are both deterministic
+            # relations
+            if parent == root and parent == transformed:
+                conditional_on.add(parent)
+        else:
+            parent_owner = getattr(parent, 'owner', None)
+            queue.extend(getattr(parent_owner, 'inputs', []))
+    if not conditional_on:
+        return None
+    return conditional_on
+
+
 class DependenceDAG(object):
     """
     `DependenceDAG` instances represent the directed acyclic graph (DAG) that
@@ -1866,6 +1912,7 @@ def add(self, node, force=False, return_added_node=False,
         self.depth[node] = 0
 
         # Try to get the conditional parents of node and add them
+#        cond = get_first_level_conditionals(node)
         try:
             cond = node.distribution.conditional_on
         except AttributeError:

pymc3/tests/test_step.py

@@ -105,56 +105,46 @@ class TestStepMethods(object):  # yield test doesn't work subclassing object
         1.58740483,  1.67905741,  0.77744868,  0.15050587,  0.15050587,
         0.73979127,  0.15445515,  0.13134717,  0.85068974,  0.85068974,
         0.6974799 ,  0.16170472,  0.86405959,  0.86405959, -0.22032854]),
-       SMC: np.array([ 5.10950205e-02,  1.09811720e+00,  1.78330202e-01,  6.85938766e-01,
-        1.42354476e-01, -1.59630758e+00,  1.57176810e+00, -4.01398917e-01,
-        1.14567871e+00,  1.14954938e+00,  4.94399840e-01,  1.16253017e+00,
-        1.17432244e+00,  7.79195162e-01,  1.29017945e+00,  2.53722905e-01,
-        5.38589898e-01,  3.52121216e-01,  1.35795966e+00,  1.02086933e-01,
-        1.58845251e+00,  6.76852927e-01, -1.04716592e-02, -1.01613324e-01,
-        1.37680965e+00,  7.40036542e-01,  2.89069320e-01,  1.48153741e+00,
-        9.58156958e-01,  5.73623782e-02,  7.68850721e-01,  3.68643390e-01,
-        1.47645964e+00,  2.32596780e-01, -1.85008158e-01,  3.71335958e-01,
-        2.68600102e+00, -4.89504443e-01,  6.54265561e-02,  3.80455349e-01,
-        1.17875338e+00,  2.30233324e-01,  6.90960231e-01,  8.81668685e-01,
-       -2.19754340e-01,  1.27686862e-01,  3.28444250e-01,  1.34820635e-01,
-        5.29725257e-01,  1.43783915e+00, -1.64754264e-01,  7.41446719e-01,
-       -1.17733186e+00,  6.01215658e-02,  1.82638158e-01, -2.23232214e-02,
-       -1.79877583e-02,  8.37949150e-01,  4.41964955e-01, -8.66524743e-01,
-        4.90738093e-01,  2.42056488e-01,  4.67699626e-01,  2.91075351e-01,
-        1.49541153e+00,  8.30730845e-01,  1.03956404e+00, -5.16162910e-01,
-        2.84338859e-01,  1.72305888e+00,  9.52445566e-01,  1.48831718e+00,
-        8.03455325e-01,  1.48840970e+00,  6.98122664e-01,  3.30187139e-01,
-        7.88029712e-01,  9.31510828e-01,  1.01326878e+00,  2.26637755e-01,
-        1.70703646e-01, -8.54429841e-01,  2.97254590e-01, -2.77843274e-01,
-       -2.25544207e-01,  1.98862826e-02,  5.05953885e-01,  4.98203941e-01,
-        1.20897382e+00, -6.32958669e-05, -7.22425896e-01,  1.60930869e+00,
-       -5.02773645e-01,  2.46405678e+00,  9.16039706e-01,  1.14146060e+00,
-       -1.95781984e-01, -2.44653942e-01,  2.67851290e-01,  2.37462012e-01,
-        6.71471950e-01,  1.18319765e+00,  1.29146530e+00, -3.14177753e-01,
-       -1.31041215e-02,  1.05029405e+00,  1.31202399e+00,  7.40532839e-02,
-        9.15510041e-01,  7.71054604e-01,  9.83483263e-01,  9.03032142e-01,
-        9.14191160e-01,  9.32285366e-01,  1.13937607e+00, -4.29155928e-01,
-        3.44609229e-02, -5.46423555e-02,  1.34625982e+00, -1.28287047e-01,
-       -1.55214879e-02,  3.25294234e-01,  1.06120585e+00, -5.09891282e-01,
-        1.25789335e+00,  1.01808348e+00, -9.92590713e-01,  1.72832932e+00,
-        1.12232980e+00,  8.54801892e-01,  1.41534752e+00,  3.50798405e-01,
-        3.69381623e-01,  1.48608411e+00, -1.15506310e-02,  1.57066360e+00,
-        2.00747378e-01,  4.47219763e-01,  5.57720524e-01, -7.74295353e-02,
-        1.79192501e+00,  7.66510475e-01,  1.38852488e+00, -4.06055122e-01,
-        2.73203156e-01,  3.61014687e-01,  1.23574043e+00,  1.64565746e-01,
-       -9.89896480e-02,  9.26130265e-02,  1.06440134e+00, -1.55890408e-01,
-        4.47131846e-01, -7.59186008e-01, -1.50881256e+00, -2.13928005e-01,
-       -4.19160151e-01,  1.75815544e+00,  7.45423008e-01,  6.94781506e-01,
-        1.58596346e+00,  1.75508724e+00,  4.56070434e-01,  2.94128709e-02,
-        1.17703970e+00, -9.90230827e-02,  8.42796845e-01,  1.79154944e+00,
-        5.92779197e-01,  2.73562285e-01,  1.61597907e+00,  1.23514403e+00,
-        4.86261080e-01, -3.10434934e-01,  5.57873722e-01,  6.50365217e-01,
-       -3.41009850e-01,  9.26851109e-01,  8.28936486e-01,  9.16180689e-02,
-        1.30226405e+00,  3.73945789e-01,  6.04560122e-02,  6.00698708e-01,
-        9.68764731e-02,  1.41904148e+00,  6.94182961e-03,  3.17504138e-01,
-        5.90956041e-01, -5.78113887e-01,  5.26615565e-01, -4.19715252e-01,
-        8.92891364e-01,  1.30207363e-01,  4.19899637e-01,  7.10275704e-01,
-        9.27418179e-02,  1.85758044e+00,  4.76988907e-01, -1.36341398e-01]),
+       SMC: np.array([ 0.40152748, -0.1440789 ,  1.87105436,  1.65027354,  0.78140894,
+       -0.33437271,  0.55987446,  1.05976848,  0.52126327,  0.5295624 ,
+       -0.7120724 ,  0.39250673,  0.92590897,  0.776836  ,  0.30528805,
+        1.32178809,  1.30972392,  0.77107019,  1.11885364,  0.59633151,
+        0.63584096, -0.29117982,  0.97372731,  1.06270256,  0.87424729,
+        0.49249202, -0.55942483, -0.17608982,  0.47118016,  1.0026767 ,
+        1.42476886,  1.16505966,  0.71572226,  1.14267914, -0.27628211,
+        0.66712824,  0.58322462,  0.28193361,  0.30175522, -0.11615552,
+       -0.02127047,  0.01085484,  1.21229396,  0.50109798,  0.2046552 ,
+        0.95648093,  0.26673391, -0.703456  ,  1.23223409, -0.87686456,
+        1.45480993,  1.04172093,  1.73512969,  1.00835375,  0.56551883,
+        0.43457948,  1.85267864,  0.51961398,  0.20641743,  0.70484816,
+        1.04491792, -0.70236338,  1.47248532,  0.57438209, -0.15590465,
+        0.51528505,  1.49158593,  0.02418851, -0.04563402,  1.50712686,
+        1.01211014, -0.1058956 ,  1.91153929,  1.09281243,  0.78028316,
+        0.08148316,  0.3989925 ,  0.30230531,  1.59469562, -0.53948736,
+       -0.35653048,  0.44440402,  1.02983002,  0.05184227,  0.78152799,
+        0.99204159,  0.44148902, -0.12657838,  0.97114256,  0.67963455,
+        1.33757129,  0.71977859,  0.09706076, -0.13609892, -0.39969385,
+        0.04687582,  0.053386  ,  0.33382962, -0.36082645,  0.86597207,
+        0.09824643, -0.85212079,  0.54518473, -0.26622955,  0.71836765,
+        0.81359943,  1.39550066,  0.25118273,  1.03965837, -0.65995684,
+       -0.25522586,  2.12497766,  0.69534904,  0.74613619, -0.10312994,
+        1.3244944 , -0.036056  ,  0.90976629,  0.49647046,  0.80779428,
+        0.18921903, -0.18365952,  0.56968353, -0.8232526 , -0.88612154,
+       -0.47326386,  0.18939692,  0.2298177 ,  0.65693251,  1.08908496,
+        1.04748985,  0.53615771, -0.4611776 ,  1.12076823, -0.79971572,
+        1.78908277,  1.32673932,  1.43691077,  0.2564599 ,  0.08480867,
+        0.26340606, -0.86864626,  1.05716355,  0.18611255,  0.44701292,
+       -0.06966819,  0.3325726 ,  0.94594745, -0.0904025 ,  0.14349182,
+        0.83638941,  0.57657934,  0.9549692 , -0.18496471,  0.87838048,
+        0.66938294,  0.54401984,  0.47804147,  0.32545637, -0.82626784,
+        0.93390148,  0.39170683, -0.22244643,  0.36576256,  0.62426937,
+       -0.16594267,  1.55050592,  0.60508809, -1.28925325,  1.1470063 ,
+        0.71030941,  1.20896922,  1.23267962,  0.67278808,  0.5846423 ,
+       -0.09343583, -0.28323718,  0.87891542,  0.54779014,  0.17131075,
+        1.02287448,  0.61819842,  1.28724788,  0.641085  ,  1.48324063,
+       -1.68770188,  0.03750369,  0.47352403,  0.22929128,  0.637757  ,
+        0.61735636,  0.17260147,  1.10929764, -0.33766643,  0.27064342,
+       -0.54594464, -1.23229206, -0.18328842, -0.78636148,  1.38189874]),
     }
 
     def setup_class(self):
@@ -202,7 +192,6 @@ def check_trace(self, step_method):
                 trace = sample(0, tune=n_steps,
                                discard_tuned_samples=False,
                                step=step_method(), random_seed=1, chains=1)
-
         assert_array_almost_equal(
             trace['x'],
             self.master_samples[step_method],
@@ -428,7 +417,7 @@ def test_bad_init(self):
         with Model():
             HalfNormal('a', sd=1, testval=-1, transform=None)
             with pytest.raises(ValueError) as error:
-                sample(init=None)
+                sample(init=None, cores=1)
             error.match('Bad initial')
 
     def test_linalg(self, caplog):