Upgrade to Theano-PyMC 1.1.0

Many gof imports were changed to explicit imports.
This commit also changes the usage of change_flags because of pending deprecation.

Author: michaelosthege

docs/source/Advanced_usage_of_Theano_in_PyMC3.rst

@@ -186,7 +186,7 @@ Now, we use this to define a theano op, that also computes the gradient::
     import theano
     import theano.tensor as tt
     import theano.tests.unittest_tools
-    from theano.gof.op import Op
+    from theano.graph.op import Op
 
     class MuFromTheta(Op):
         itypes = [tt.dscalar]

pymc3/data.py

@@ -26,7 +26,7 @@
 import theano
 import theano.tensor as tt
 
-from theano.gof.graph import Apply
+from theano.graph.basic import Apply
 
 import pymc3 as pm
 
@@ -296,7 +296,7 @@ class Minibatch(tt.TensorVariable):
 
     RNG = collections.defaultdict(list)  # type: Dict[str, List[Any]]
 
-    @theano.configparser.change_flags(compute_test_value="raise")
+    @theano.config.change_flags(compute_test_value="raise")
     def __init__(
         self,
         data,

pymc3/distributions/dist_math.py

@@ -27,8 +27,8 @@
 
 from theano import scan
 from theano.compile.builders import OpFromGraph
-from theano.gof.graph import Apply
-from theano.gof.op import Op
+from theano.graph.basic import Apply
+from theano.graph.op import Op
 from theano.scalar import UnaryScalarOp, upgrade_to_float_no_complex
 from theano.scan import until
 from theano.tensor.slinalg import Cholesky

pymc3/distributions/distribution.py

@@ -29,6 +29,7 @@
 
 import numpy as np
 import theano
+import theano.graph.basic
 import theano.tensor as tt
 
 from theano import function
@@ -790,7 +791,7 @@ def draw_values(params, point=None, size=None):
                     value = _draw_value(next_, point=point, givens=temp_givens, size=size)
                     givens[next_.name] = (next_, value)
                     drawn[(next_, size)] = value
-                except theano.gof.fg.MissingInputError:
+                except theano.graph.fg.MissingInputError:
                     # The node failed, so we must add the node's parents to
                     # the stack of nodes to try to draw from. We exclude the
                     # nodes in the `params` list.
@@ -833,7 +834,7 @@ def draw_values(params, point=None, size=None):
                         value = _draw_value(param, point=point, givens=givens.values(), size=size)
                         evaluated[param_idx] = drawn[(param, size)] = value
                         givens[param.name] = (param, value)
-                    except theano.gof.fg.MissingInputError:
+                    except theano.graph.fg.MissingInputError:
                         missing_inputs.add(param_idx)
 
     return [evaluated[j] for j in params]  # set the order back
@@ -994,7 +995,7 @@ def _draw_value(param, point=None, givens=None, size=None):
                 variables = values = []
             # We only truly care if the ancestors of param that were given
             # value have the matching dshape and val.shape
-            param_ancestors = set(theano.gof.graph.ancestors([param], blockers=list(variables)))
+            param_ancestors = set(theano.graph.basic.ancestors([param], blockers=list(variables)))
             inputs = [(var, val) for var, val in zip(variables, values) if var in param_ancestors]
             if inputs:
                 input_vars, input_vals = list(zip(*inputs))

pymc3/distributions/mixture.py

@@ -253,7 +253,7 @@ def _comp_logp(self, value):
                 val_shape = tuple(value.shape.eval())
             except AttributeError:
                 val_shape = value.shape
-            except theano.gof.MissingInputError:
+            except theano.graph.fg.MissingInputError:
                 val_shape = None
             try:
                 self_shape = tuple(self.shape)

pymc3/distributions/multivariate.py

@@ -23,9 +23,9 @@
 import theano.tensor as tt
 
 from scipy import linalg, stats
-from theano.gof.graph import Apply
-from theano.gof.op import Op, get_test_value
-from theano.gof.utils import TestValueError
+from theano.graph.basic import Apply
+from theano.graph.op import Op, get_test_value
+from theano.graph.utils import TestValueError
 from theano.tensor.nlinalg import det, eigh, matrix_inverse, trace
 from theano.tensor.slinalg import Cholesky
 

pymc3/distributions/posterior_predictive.py

@@ -22,7 +22,8 @@
 )
 
 import numpy as np
-import theano
+import theano.graph.basic
+import theano.graph.fg
 import theano.tensor as tt
 
 from arviz import InferenceData
@@ -422,7 +423,7 @@ def draw_values(self) -> List[np.ndarray]:
                         assert isinstance(value, np.ndarray)
                         givens[next_.name] = (next_, value)
                         drawn[(next_, samples)] = value
-                    except theano.gof.fg.MissingInputError:
+                    except theano.graph.fg.MissingInputError:
                         # The node failed, so we must add the node's parents to
                         # the stack of nodes to try to draw from. We exclude the
                         # nodes in the `params` list.
@@ -467,7 +468,7 @@ def draw_values(self) -> List[np.ndarray]:
                             assert isinstance(value, np.ndarray)
                             self.evaluated[param_idx] = drawn[(param, samples)] = value
                             givens[param.name] = (param, value)
-                        except theano.gof.fg.MissingInputError:
+                        except theano.graph.fg.MissingInputError:
                             missing_inputs.add(param_idx)
         return [self.evaluated[j] for j in params]
 
@@ -661,7 +662,9 @@ def random_sample(
                     variables = values = []
                 # We only truly care if the ancestors of param that were given
                 # value have the matching dshape and val.shape
-                param_ancestors = set(theano.gof.graph.ancestors([param], blockers=list(variables)))
+                param_ancestors = set(
+                    theano.graph.basic.ancestors([param], blockers=list(variables))
+                )
                 inputs = [
                     (var, val) for var, val in zip(variables, values) if var in param_ancestors
                 ]

pymc3/math.py

@@ -25,8 +25,8 @@
 import theano.tensor.slinalg  # pylint: disable=unused-import
 
 from scipy.linalg import block_diag as scipy_block_diag
-from theano.gof.graph import Apply
-from theano.gof.op import Op
+from theano.graph.basic import Apply
+from theano.graph.op import Op
 
 # pylint: disable=unused-import
 from theano.tensor import (

pymc3/model.py

@@ -23,12 +23,13 @@
 import numpy as np
 import scipy.sparse as sps
 import theano
+import theano.graph.basic
 import theano.sparse as sparse
 import theano.tensor as tt
 
 from pandas import Series
 from theano.compile import SharedVariable
-from theano.gof.graph import Apply
+from theano.graph.basic import Apply
 from theano.tensor.var import TensorVariable
 
 import pymc3 as pm
@@ -284,7 +285,7 @@ def __enter__(self):
             # self._theano_config is set in Model.__new__
             self._config_context = None
             if hasattr(self, "_theano_config"):
-                self._config_context = theano.change_flags(**self._theano_config)
+                self._config_context = theano.config.change_flags(**self._theano_config)
                 self._config_context.__enter__()
             return self
 
@@ -1704,7 +1705,7 @@ def pandas_to_array(data):
             ret = data
         else:  # empty mask
             ret = data.filled()
-    elif isinstance(data, theano.gof.graph.Variable):
+    elif isinstance(data, theano.graph.basic.Variable):
         ret = data
     elif sps.issparse(data):
         ret = data
@@ -1795,7 +1796,7 @@ def __init__(
 
         if type is None:
             data = pandas_to_array(data)
-            if isinstance(data, theano.gof.graph.Variable):
+            if isinstance(data, theano.graph.basic.Variable):
                 type = data.type
             else:
                 type = TensorType(distribution.dtype, data.shape)

pymc3/model_graph.py

@@ -18,7 +18,7 @@
 VarName = str
 
 from theano.compile import SharedVariable
-from theano.gof.graph import stack_search
+from theano.graph.basic import walk
 from theano.tensor import Tensor
 
 import pymc3 as pm
@@ -69,7 +69,7 @@ def _expand(node) -> Optional[Iterator[Tensor]]:
             else:
                 return None
 
-        list(stack_search(deque([func]), _expand, bfs=True))
+        list(walk(deque([func]), _expand, bfs=True))
         return retval
 
     def _filter_parents(self, var, parents) -> Set[VarName]:

pymc3/ode/ode.py

@@ -19,8 +19,8 @@
 import theano
 import theano.tensor as tt
 
-from theano.gof.graph import Apply
-from theano.gof.op import Op, get_test_value
+from theano.graph.basic import Apply
+from theano.graph.op import Op, get_test_value
 
 from pymc3.exceptions import DtypeError, ShapeError
 from pymc3.ode import utils

pymc3/sampling_jax.py

@@ -11,7 +11,7 @@
 import jax
 import numpy as np
 import pandas as pd
-import theano
+import theano.graph.fg
 
 from theano.link.jax.jax_dispatch import jax_funcify
 
@@ -45,7 +45,7 @@ def sample_tfp_nuts(
 
     seed = jax.random.PRNGKey(random_seed)
 
-    fgraph = theano.gof.FunctionGraph(model.free_RVs, [model.logpt])
+    fgraph = theano.graph.fg.FunctionGraph(model.free_RVs, [model.logpt])
     fns = jax_funcify(fgraph)
     logp_fn_jax = fns[0]
 
@@ -130,7 +130,7 @@ def sample_numpyro_nuts(
 
     seed = jax.random.PRNGKey(random_seed)
 
-    fgraph = theano.gof.FunctionGraph(model.free_RVs, [model.logpt])
+    fgraph = theano.graph.fg.FunctionGraph(model.free_RVs, [model.logpt])
     fns = jax_funcify(fgraph)
     logp_fn_jax = fns[0]
 

pymc3/step_methods/gibbs.py

@@ -31,7 +31,7 @@
     searchsorted,
 )
 from numpy.random import uniform
-from theano.gof.graph import inputs
+from theano.graph.basic import graph_inputs
 from theano.tensor import add
 
 from pymc3.distributions.discrete import Categorical
@@ -80,7 +80,7 @@ def competence(var, has_grad):
 
 
 def elemwise_logp(model, var):
-    terms = [v.logp_elemwiset for v in model.basic_RVs if var in inputs([v.logpt])]
+    terms = [v.logp_elemwiset for v in model.basic_RVs if var in graph_inputs([v.logpt])]
     return model.fn(add(*terms))
 
 

pymc3/tests/conftest.py

@@ -21,22 +21,22 @@
 
 @pytest.fixture(scope="function", autouse=True)
 def theano_config():
-    config = theano.configparser.change_flags(compute_test_value="raise")
+    config = theano.config.change_flags(compute_test_value="raise")
     with config:
         yield
 
 
 @pytest.fixture(scope="function", autouse=True)
 def exception_verbosity():
-    config = theano.configparser.change_flags(exception_verbosity="high")
+    config = theano.config.change_flags(exception_verbosity="high")
     with config:
         yield
 
 
 @pytest.fixture(scope="function", autouse=False)
 def strict_float32():
     if theano.config.floatX == "float32":
-        config = theano.configparser.change_flags(warn_float64="raise")
+        config = theano.config.change_flags(warn_float64="raise")
         with config:
             yield
     else:

pymc3/tests/test_dist_math.py

@@ -167,7 +167,7 @@ def test_logp(self):
         logp = logp_f(cov_val, delta_val)
         npt.assert_allclose(logp, expect)
 
-    @theano.configparser.change_flags(compute_test_value="ignore")
+    @theano.config.change_flags(compute_test_value="ignore")
     def test_grad(self):
         np.random.seed(42)
 
@@ -190,7 +190,7 @@ def func(chol_vec, delta):
         verify_grad(func, [chol_vec_val, delta_val])
 
     @pytest.mark.skip(reason="Fix in theano not released yet: Theano#5908")
-    @theano.configparser.change_flags(compute_test_value="ignore")
+    @theano.config.change_flags(compute_test_value="ignore")
     def test_hessian(self):
         chol_vec = tt.vector("chol_vec")
         chol_vec.tag.test_value = np.array([0.1, 2, 3])
@@ -209,14 +209,14 @@ def test_hessian(self):
 
 
 class TestSplineWrapper:
-    @theano.configparser.change_flags(compute_test_value="ignore")
+    @theano.config.change_flags(compute_test_value="ignore")
     def test_grad(self):
         x = np.linspace(0, 1, 100)
         y = x * x
         spline = SplineWrapper(interpolate.InterpolatedUnivariateSpline(x, y, k=1))
         verify_grad(spline, [0.5])
 
-    @theano.configparser.change_flags(compute_test_value="ignore")
+    @theano.config.change_flags(compute_test_value="ignore")
     def test_hessian(self):
         x = np.linspace(0, 1, 100)
         y = x * x
@@ -228,7 +228,7 @@ def test_hessian(self):
 
 
 class TestI0e:
-    @theano.configparser.change_flags(compute_test_value="ignore")
+    @theano.config.change_flags(compute_test_value="ignore")
     def test_grad(self):
         verify_grad(i0e, [0.5])
         verify_grad(i0e, [-2.0])

pymc3/tests/test_math.py

@@ -160,7 +160,7 @@ def setup_method(self):
         self.op_class = LogDet
         self.op = logdet
 
-    @theano.configparser.change_flags(compute_test_value="ignore")
+    @theano.config.change_flags(compute_test_value="ignore")
     def validate(self, input_mat):
         x = theano.tensor.matrix()
         f = theano.function([x], self.op(x))

pymc3/tests/test_model.py

@@ -167,13 +167,13 @@ def test_observed_type(self):
 
 class TestTheanoConfig:
     def test_set_testval_raise(self):
-        with theano.configparser.change_flags(compute_test_value="off"):
+        with theano.config.change_flags(compute_test_value="off"):
             with pm.Model():
                 assert theano.config.compute_test_value == "raise"
             assert theano.config.compute_test_value == "off"
 
     def test_nested(self):
-        with theano.configparser.change_flags(compute_test_value="off"):
+        with theano.config.change_flags(compute_test_value="off"):
             with pm.Model(theano_config={"compute_test_value": "ignore"}):
                 assert theano.config.compute_test_value == "ignore"
                 with pm.Model(theano_config={"compute_test_value": "warn"}):

pymc3/tests/test_model_helpers.py

@@ -79,7 +79,8 @@ def test_pandas_to_array(self):
 
         # Check function behavior with Theano graph variable
         theano_output = func(theano_graph_input)
-        assert isinstance(theano_output, theano.gof.graph.Variable)
+        assert isinstance(theano_output, theano.graph.basic.Variable)
+        assert theano_output == theano_graph_input
         assert theano_output.owner.inputs[0].name == input_name
 
         # Check function behavior with generator data

pymc3/tests/test_random.py

@@ -114,9 +114,9 @@ def test_dep_vars(self):
             ]
         )
 
-    def test_gof_constant(self):
+    def test_graph_constant(self):
         # Issue 3595 pointed out that slice(None) can introduce
-        # theano.gof.graph.Constant into the compute graph, which wasn't
+        # theano.graph.basic.Constant into the compute graph, which wasn't
         # handled correctly by draw_values
         n_d = 500
         n_x = 2

pymc3/tests/test_step.py

@@ -27,7 +27,7 @@
 
 from numpy.testing import assert_array_almost_equal
 from theano.compile.ops import as_op
-from theano.gof.op import Op
+from theano.graph.op import Op
 
 from pymc3.data import Data
 from pymc3.distributions import (