Updates for miniKanren package v1

Author: brandonwillard

setup.py

@@ -36,12 +36,14 @@ def get_long_description():
         "Theano>=1.0.4",
         "tf-nightly-2.0-preview==2.0.0.dev20191002",
         "tf-nightly==2.1.0.dev20191003",
-        "tensorflow-estimator-2.0-preview>=1.14.0.dev2019090801",
+        "tf-estimator-nightly==2.0.0.dev2019100301",
+        "tensorflow-estimator-2.0-preview==1.14.0.dev2019090801",
         "tfp-nightly==0.9.0.dev20191003",
         "multipledispatch>=0.6.0",
-        "logical-unification>=0.2.2",
-        "miniKanren>=0.4.0",
-        "cons>=0.1.3",
+        "logical-unification>=0.4.3",
+        "miniKanren>=1.0.1",
+        "etuples>=0.3.1",
+        "cons>=0.4.0",
         "toolz>=0.9.0",
         "sympy>=1.3",
         "cachetools",

symbolic_pymc/relations/__init__.py

@@ -1,9 +1,3 @@
-from itertools import tee, chain
-from functools import reduce
-
-from toolz import interleave
-
-from kanren.core import goaleval
 from kanren.facts import Relation
 
 from unification import unify, reify, Var
@@ -34,58 +28,3 @@ def concat_goal(S):
             yield S
 
     return concat_goal
-
-
-def ldisj_seq(goals):
-    """Produce a goal that returns the appended state stream from all successful goal arguments.
-
-    In other words, it behaves like logical disjunction/OR for goals.
-    """
-
-    def ldisj_seq_goal(S):
-        nonlocal goals
-
-        goals, _goals = tee(goals)
-
-        yield from interleave(goaleval(g)(S) for g in _goals)
-
-    return ldisj_seq_goal
-
-
-def lconj_seq(goals):
-    """Produce a goal that returns the appended state stream in which all goals are necessarily successful.
-
-    In other words, it behaves like logical conjunction/AND for goals.
-    """
-
-    def lconj_seq_goal(S):
-        nonlocal goals
-
-        goals, _goals = tee(goals)
-
-        g0 = next(iter(_goals), None)
-
-        if g0 is None:
-            return
-
-        z0 = goaleval(g0)(S)
-
-        yield from reduce(lambda z, g: chain.from_iterable(map(goaleval(g), z)), _goals, z0)
-
-    return lconj_seq_goal
-
-
-def ldisj(*goals):
-    return ldisj_seq(goals)
-
-
-def lconj(*goals):
-    return lconj_seq(goals)
-
-
-def conde(*goals):
-    return ldisj_seq(lconj_seq(g) for g in goals)
-
-
-lall = lconj
-lany = ldisj

symbolic_pymc/unify.py

@@ -5,9 +5,8 @@
 
 from kanren.term import arguments, operator
 
-from unification.more import unify
 from unification.variable import Var
-from unification.core import _reify, _unify, reify
+from unification.core import _reify, _unify, reify, unify
 
 from etuples import etuple
 

tests/test_relations.py

@@ -1,8 +1,8 @@
 from unification import var
 
-from kanren import eq, run
+from kanren import run
 
-from symbolic_pymc.relations import lconj, lconj_seq, ldisj, ldisj_seq, conde, concat
+from symbolic_pymc.relations import concat
 
 
 def test_concat():
@@ -11,81 +11,3 @@ def test_concat():
     assert not run(0, q, concat("a", "b", "bc"))
     assert not run(0, q, concat(1, "b", "bc"))
     assert run(0, q, concat(q, "b", "bc")) == (q,)
-
-
-def test_lconj_basics():
-
-    res = list(lconj(eq(1, var("a")), eq(2, var("b")))({}))
-    assert res == [{var("a"): 1, var("b"): 2}]
-
-    res = list(lconj(eq(1, var("a")))({}))
-    assert res == [{var("a"): 1}]
-
-    res = list(lconj_seq([])({}))
-    assert res == []
-
-    res = list(lconj(eq(1, var("a")), eq(2, var("a")))({}))
-    assert res == []
-
-    res = list(lconj(eq(1, 2))({}))
-    assert res == []
-
-    res = list(lconj(eq(1, 1))({}))
-    assert res == [{}]
-
-
-def test_ldisj_basics():
-
-    res = list(ldisj(eq(1, var("a")))({}))
-    assert res == [{var("a"): 1}]
-
-    res = list(ldisj(eq(1, 2))({}))
-    assert res == []
-
-    res = list(ldisj(eq(1, 1))({}))
-    assert res == [{}]
-
-    res = list(ldisj(eq(1, var("a")), eq(1, var("a")))({}))
-    assert res == [{var("a"): 1}, {var("a"): 1}]
-
-    res = list(ldisj(eq(1, var("a")), eq(2, var("a")))({}))
-    assert res == [{var("a"): 1}, {var("a"): 2}]
-
-    res = list(ldisj_seq([])({}))
-    assert res == []
-
-
-def test_conde_basics():
-
-    res = list(conde([eq(1, var("a")), eq(2, var("b"))], [eq(1, var("b")), eq(2, var("a"))])({}))
-    assert res == [{var("a"): 1, var("b"): 2}, {var("b"): 1, var("a"): 2}]
-
-    res = list(conde([eq(1, var("a")), eq(2, 1)], [eq(1, var("b")), eq(2, var("a"))])({}))
-    assert res == [{var("b"): 1, var("a"): 2}]
-
-    res = list(
-        conde(
-            [eq(1, var("a")), conde([eq(11, var("aa"))], [eq(12, var("ab"))])],
-            [
-                eq(1, var("b")),
-                conde([eq(111, var("ba")), eq(112, var("bb"))], [eq(121, var("bc"))]),
-            ],
-        )({})
-    )
-    assert res == [
-        {var("a"): 1, var("aa"): 11},
-        {var("b"): 1, var("ba"): 111, var("bb"): 112},
-        {var("a"): 1, var("ab"): 12},
-        {var("b"): 1, var("bc"): 121},
-    ]
-
-    res = list(conde([eq(1, 2)], [eq(1, 1)])({}))
-    assert res == [{}]
-
-    assert list(lconj(eq(1, 1))({})) == [{}]
-
-    res = list(lconj(conde([eq(1, 2)], [eq(1, 1)]))({}))
-    assert res == [{}]
-
-    res = list(lconj(conde([eq(1, 2)], [eq(1, 1)]), conde([eq(1, 2)], [eq(1, 1)]))({}))
-    assert res == [{}]

tests/theano/test_kanren.py

@@ -74,7 +74,7 @@ def test_kanren():
     Y_mt = mt.MvNormalRV(E_y_mt, V_lv, y_size_lv, y_rng_lv, name=y_name_lv)
 
     with variables(Y_mt):
-        (res,) = run(0, Y_mt, (eq, Y_rv, Y_mt))
+        (res,) = run(0, Y_mt, eq(Y_rv, Y_mt))
     assert res.reify() == Y_rv
 
 

tests/theano/test_opt.py

@@ -4,7 +4,7 @@
 from unification import var
 
 from kanren import eq
-from kanren.core import lallgreedy
+from kanren.core import lall
 
 from etuples import etuple, etuplize
 
@@ -36,13 +36,11 @@ def test_kanren_opt():
     assert isinstance(fgraph.outputs[0].owner.op, tt.Dot)
 
     def distributes(in_lv, out_lv):
-        return (
-            lallgreedy,
+        return lall(
             # lhs == A * (x + b)
-            (eq, etuple(mt.dot, var("A"), etuple(mt.add, var("x"), var("b"))), etuplize(in_lv)),
+            eq(etuple(mt.dot, var("A"), etuple(mt.add, var("x"), var("b"))), etuplize(in_lv)),
             # rhs == A * x + A * b
-            (
-                eq,
+            eq(
                 etuple(
                     mt.add, etuple(mt.dot, var("A"), var("x")), etuple(mt.dot, var("A"), var("b"))
                 ),

tests/theano/test_unify.py

@@ -2,7 +2,7 @@
 
 import theano.tensor as tt
 
-from unification import unify, reify, var, variables
+from unification import reify, unify, var
 
 from cons.core import ConsError
 
@@ -37,25 +37,6 @@ def test_unification():
     assert res[x_l].reify() == b
     assert res[y_l].reify() == a
 
-    # TODO: This produces a `DimShuffle` so that the scalar constant `1`
-    # will match the dimensions of the vector `b`.  That `DimShuffle` isn't
-    # handled by the logic variable form.
-    # assert unify(mt.add(x_l, 1), mt.add(b_l, 1))[x] == b
-
-    with variables(x):
-        assert unify(x + 1, b + 1)[x].reify() == b
-
-    assert unify(mt.add(x_l, a), mt.add(b, a))[x_l].reify() == b
-    with variables(x):
-        assert unify(x, b)[x] == b
-        assert unify([x], [b])[x] == b
-        assert unify((x,), (b,))[x] == b
-        assert unify(x + 1, b + 1)[x].reify() == b
-        assert unify(x + a, b + a)[x].reify() == b
-
-    with variables(x):
-        assert unify(a + b, a + x)[x].reify() == b
-
     mt_expr_add = mt.add(x_l, y_l)
 
     # The parameters are vectors