Implemented get_first_level_conditionals to try to get rid of the a…

…dded `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.

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):