Merge 7cbbaf4 into 64f0b2c

.travis.yml

@@ -30,7 +30,6 @@ env:
   - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py pymc3/tests/test_parallel_sampling.py pymc3/tests/test_dist_math.py pymc3/tests/test_distribution_defaults.py pymc3/tests/test_distributions_timeseries.py pymc3/tests/test_random.py"
   - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py pymc3/tests/test_gp.py"
   - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py pymc3/tests/test_shape_handling.py"
-  - PYTHON_VERSION=3.5 FLOATX='float64' ENVNAME="py35_testenv" TESTCMD="--cov-append pymc3/tests/test_sampling.py pymc3/tests/test_model_graph.py"
 
 script:
   - . ./scripts/test.sh $TESTCMD

RELEASE-NOTES.md

@@ -45,17 +45,13 @@
 - Fixed an issue in `model_graph` that caused construction of the graph of the model for rendering to hang: replaced a search over the powerset of the nodes with a breadth-first search over the nodes. Fix for #3458.
 - Removed variable annotations from `model_graph` but left type hints (Fix for #3465). This means that we support `python>=3.5.4`.
 - Default `target_accept`for `HamiltonianMC` is now 0.65, as suggested in Beskos et. al. 2010 and Neal 2001.
-- Fixed bug in `draw_values` that lead to intermittent errors in python3.5. This happened with some deterministic nodes that were drawn but not added to `givens`.
 
 ### Deprecations
 
 - `nuts_kwargs` and `step_kwargs` have been deprecated in favor of using the standard `kwargs` to pass optional step method arguments.
 - `SGFS` and `CSG` have been removed (Fix for [#3353](https://github.com/pymc-devs/pymc3/issues/3353)). They have been moved to [pymc3-experimental](https://github.com/pymc-devs/pymc3-experimental).
 - References to `live_plot` and corresponding notebooks have been removed.
-- Function `approx_hessian` was removed, due to `numdifftools` becoming incompatible with current `scipy`. The function was already optional, only available to a user who installed `numdifftools` separately, and not hit on any common codepaths. [#3485](https://github.com/pymc-devs/pymc3/pull/3485).
-- Deprecated `vars` parameter of `sample_posterior_predictive` in favor of `varnames`.
--  References to `live_plot` and corresponding notebooks have been removed.
-- Deprecated `vars` parameters of `sample_posterior_predictive` and `sample_prior_predictive` in favor of `var_names`.  At least for the latter, this is more accurate, since the `vars` parameter actually took names.
+- Function `approx_hessian` was removed, due to `numdifftools` becoming incompatible with current `scipy`. The function was already optional, only available to a user who installed `numdifftools` separately, and not hit on any common codepaths. [#3485](https://github.com/pymc-devs/pymc3/pull/3485).- Deprecated `vars` parameters of `sample_posterior_predictive` and `sample_prior_predictive` in favor of `var_names`.  At least for the latter, this is more accurate, since the `vars` parameter actually took names.
 
 
 ## PyMC3 3.6 (Dec 21 2018)

pymc3/distributions/distribution.py

@@ -401,7 +401,8 @@ def draw_values(params, point=None, size=None):
                     # nodes in the `params` list.
                     stack.extend([node for node in named_nodes_parents[next_]
                                   if node is not None and
-                                  (node, size) not in drawn])
+                                  (node, size) not in drawn and
+                                  node not in params])
 
         # the below makes sure the graph is evaluated in order
         # test_distributions_random::TestDrawValues::test_draw_order fails without it
@@ -419,20 +420,6 @@ def draw_values(params, point=None, size=None):
                     evaluated[param_idx] = drawn[(param, size)]
                 else:
                     try:  # might evaluate in a bad order,
-                        # Sometimes _draw_value recurrently calls draw_values.
-                        # This may set values for certain nodes in the drawn
-                        # dictionary, but they don't get added to the givens
-                        # dictionary. Here, we try to fix that.
-                        if param in named_nodes_children:
-                            for node in named_nodes_children[param]:
-                                if (
-                                    node.name not in givens and
-                                    (node, size) in drawn
-                                ):
-                                    givens[node.name] = (
-                                        node,
-                                        drawn[(node, size)]
-                                    )
                         value = _draw_value(param,
                                             point=point,
                                             givens=givens.values(),

pymc3/model_graph.py

@@ -161,6 +161,8 @@ def make_graph(self):
                               '\tconda install -c conda-forge python-graphviz')
         graph = graphviz.Digraph(self.model.name)
         for shape, var_names in self.get_plates().items():
+            if isinstance(shape, SharedVariable):
+                shape = shape.eval()
             label = ' x '.join(map('{:,d}'.format, shape))
             if label:
                 # must be preceded by 'cluster' to get a box around it

pymc3/tests/test_distributions.py

@@ -3,8 +3,8 @@
 
 from .helpers import SeededTest, select_by_precision
 from ..vartypes import continuous_types
-from ..model import Model, Point, Deterministic
-from ..blocking import DictToVarBijection
+from ..model import Model, Point, Potential, Deterministic
+from ..blocking import DictToVarBijection, DictToArrayBijection, ArrayOrdering
 from ..distributions import (
     DensityDist, Categorical, Multinomial, VonMises, Dirichlet,
     MvStudentT, MvNormal, MatrixNormal, ZeroInflatedPoisson,
@@ -471,9 +471,37 @@ def check_int_to_1(self, model, value, domain, paramdomains):
             area = integrate_nd(pdfx, domain, value.dshape, value.dtype)
             assert_almost_equal(area, 1, err_msg=str(pt))
 
+    def check_dlogp(self, model, value, domain, paramdomains):
+        try:
+            from numdifftools import Gradient
+        except ImportError:
+            return
+        if not model.cont_vars:
+            return
+
+        domains = paramdomains.copy()
+        domains['value'] = domain
+        bij = DictToArrayBijection(
+            ArrayOrdering(model.cont_vars), model.test_point)
+        dlogp = bij.mapf(model.fastdlogp(model.cont_vars))
+        logp = bij.mapf(model.fastlogp)
+
+        def wrapped_logp(x):
+            try:
+                return logp(x)
+            except:
+                return np.nan
+
+        ndlogp = Gradient(wrapped_logp)
+        for pt in product(domains, n_samples=100):
+            pt = Point(pt, model=model)
+            pt = bij.map(pt)
+            decimals = select_by_precision(float64=6, float32=4)
+            assert_almost_equal(dlogp(pt), ndlogp(pt), decimal=decimals, err_msg=str(pt))
+
     def checkd(self, distfam, valuedomain, vardomains, checks=None, extra_args=None):
         if checks is None:
-            checks = (self.check_int_to_1, )
+            checks = (self.check_int_to_1, self.check_dlogp)
 
         if extra_args is None:
             extra_args = {}
@@ -912,8 +940,7 @@ def test_wishart(self, n):
         # This check compares the autodiff gradient to the numdiff gradient.
         # However, due to the strict constraints of the wishart,
         # it is impossible to numerically determine the gradient as a small
-        # pertubation breaks the symmetry. Thus disabling. Also, numdifftools was
-        # removed in June 2019, so an alternative would be needed.
+        # pertubation breaks the symmetry. Thus disabling.
         #
         # self.checkd(Wishart, PdMatrix(n), {'n': Domain([2, 3, 4, 2000]), 'V': PdMatrix(n)},
         #             checks=[self.check_dlogp])
@@ -1093,6 +1120,12 @@ def logp(x):
             return -log(2 * .5) - abs(x - .5) / .5
         self.checkd(DensityDist, R, {}, extra_args={'logp': logp})
 
+    def test_addpotential(self):
+        with Model() as model:
+            value = Normal('value', 1, 1)
+            Potential('value_squared', -value ** 2)
+            self.check_dlogp(model, value, R, {})
+
     def test_get_tau_sigma(self):
         sigma = np.array([2])
         assert_almost_equal(continuous.get_tau_sigma(sigma=sigma), [1. / sigma**2, sigma])

pymc3/tests/test_model_graph.py

@@ -1,5 +1,6 @@
 import numpy as np
 import pymc3 as pm
+import theano as th
 from pymc3.model_graph import ModelGraph, model_to_graphviz
 
 from .helpers import SeededTest
@@ -19,6 +20,9 @@ def radon_model():
         np.tile(np.arange(counties, dtype=int), d),
         np.arange(r)
         ))
+
+    floor_measure = th.shared(floor_measure)
+
     with pm.Model() as model:
         sigma_a = pm.HalfCauchy('sigma_a', 5)
         gamma = pm.Normal('gamma', mu=0., sigma=1e5, shape=3)

pymc3/tuning/__init__.py

@@ -1,2 +1,2 @@
 from .starting import find_MAP
-from .scaling import find_hessian, trace_cov, guess_scaling
+from .scaling import approx_hessian, find_hessian, trace_cov, guess_scaling

pymc3/tuning/scaling.py

@@ -4,7 +4,41 @@
 from ..theanof import hessian_diag, inputvars
 from ..blocking import DictToArrayBijection, ArrayOrdering
 
-__all__ = ['find_hessian', 'trace_cov', 'guess_scaling']
+__all__ = ['approx_hessian', 'find_hessian', 'trace_cov', 'guess_scaling']
+
+
+def approx_hessian(point, vars=None, model=None):
+    """
+    Returns an approximation of the Hessian at the current chain location.
+
+    Parameters
+    ----------
+    model : Model (optional if in `with` context)
+    point : dict
+    vars : list
+        Variables for which Hessian is to be calculated.
+    """
+    from numdifftools import Jacobian
+
+    model = modelcontext(model)
+    if vars is None:
+        vars = model.cont_vars
+    vars = inputvars(vars)
+
+    point = Point(point, model=model)
+
+    bij = DictToArrayBijection(ArrayOrdering(vars), point)
+    dlogp = bij.mapf(model.fastdlogp(vars))
+
+    def grad_logp(point):
+        return np.nan_to_num(dlogp(point))
+
+    '''
+    Find the jacobian of the gradient function at the current position
+    this should be the Hessian; invert it to find the approximate
+    covariance matrix.
+    '''
+    return -Jacobian(grad_logp)(bij.map(point))
 
 
 def fixed_hessian(point, vars=None, model=None):

requirements-dev.txt

@@ -7,6 +7,7 @@ Keras>=2.0.8
 nbsphinx>=0.2.13
 nose>=1.3.7
 nose-parameterized==0.6.0
+numdifftools>=0.9.20
 numpy>=1.13.0
 numpydoc==0.7.0
 pycodestyle>=2.3.1

scripts/create_testenv.sh

@@ -20,7 +20,7 @@ command -v conda >/dev/null 2>&1 || {
   exit 1;
 }
 
-ENVNAME="${ENVNAME:-testenv}" # if no ENVNAME is specified, use testenv
+ENVNAME="testenv"
 PYTHON_VERSION=${PYTHON_VERSION:-3.6} # if no python specified, use 3.6
 
 if [ -z ${GLOBAL} ]
@@ -33,11 +33,10 @@ then
     fi
     source activate ${ENVNAME}
 fi
-pip install --upgrade pip
-
-conda install --yes mkl-service
+conda install --yes numpy scipy mkl-service
 conda install --yes -c conda-forge python-graphviz
 
+pip install --upgrade pip
 
 #  Install editable using the setup.py
 

scripts/install_miniconda.sh

@@ -46,5 +46,3 @@ fi
 export PATH="$INSTALL_FOLDER/bin:$PATH"
 echo "Adding $INSTALL_FOLDER to PATH.  Consider adding it in your .rc file as well."
 conda update -q -y conda
-# Uninstalling miniconda's numpy to avoid conflicting versions when creating the test env
-pip uninstall -y numpy

setup.py

@@ -59,7 +59,6 @@ def get_version():
           package_data={'docs': ['*']},
           include_package_data=True,
           classifiers=classifiers,
-          python_requires=">=3.5.4",
           install_requires=install_reqs,
           extras_require={
               "plots": ["arviz"],