Merge pull request #45 from pymc-devs/master

Sync Fork from Upstream Repo

.gitignore

@@ -36,3 +36,7 @@ benchmarks/results/
 # VSCode
 .vscode/
 .mypy_cache
+
+pytestdebug.log
+.dir-locals.el
+.pycheckers

RELEASE-NOTES.md

@@ -7,6 +7,8 @@
 - `DEMetropolis` can now tune both `lambda` and `scaling` parameters, but by default neither of them are tuned. See [#3743](https://github.com/pymc-devs/pymc3/pull/3743) for more info.
 - `DEMetropolisZ`, an improved variant of `DEMetropolis` brings better parallelization and higher efficiency with fewer chains with a slower initial convergence. This implementation is experimental. See [#3784](https://github.com/pymc-devs/pymc3/pull/3784) for more info.
 - Notebooks that give insight into `DEMetropolis`, `DEMetropolisZ` and the `DifferentialEquation` interface are now located in the [Tutorials/Deep Dive](https://docs.pymc.io/nb_tutorials/index.html) section.
+- Add `fast_sample_posterior_predictive`, a vectorized alternative to `sample_posterior_predictive`.  This alternative is substantially faster for large models.
+- `SamplerReport` (`MultiTrace.report`) now has properties `n_tune`, `n_draws`, `t_sampling` for increased convenience (see [#3827](https://github.com/pymc-devs/pymc3/pull/3827))
 
 ### Maintenance
 - Remove `sample_ppc` and `sample_ppc_w` that were deprecated in 3.6.

pymc3/__init__.py

@@ -44,7 +44,7 @@
 from . import sampling
 
 from .backends.tracetab import *
-from .backends import save_trace, load_trace
+from .backends import save_trace, load_trace, point_list_to_multitrace
 
 from .plots import *
 from .tests import test

pymc3/backends/__init__.py

@@ -131,7 +131,7 @@
 
 For specific examples, see pymc3.backends.{ndarray,text,sqlite}.py.
 """
-from ..backends.ndarray import NDArray, save_trace, load_trace
+from ..backends.ndarray import NDArray, save_trace, load_trace, point_list_to_multitrace
 from ..backends.text import Text
 from ..backends.sqlite import SQLite
 from ..backends.hdf5 import HDF5

pymc3/backends/base.py

@@ -19,13 +19,14 @@
 """
 import itertools as itl
 import logging
-from typing import List
+from typing import Dict, List, Optional
+from abc import ABC
 
 import numpy as np
 import warnings
 import theano.tensor as tt
 
-from ..model import modelcontext
+from ..model import modelcontext, Model
 from .report import SamplerReport, merge_reports
 
 logger = logging.getLogger('pymc3')
@@ -35,7 +36,7 @@ class BackendError(Exception):
     pass
 
 
-class BaseTrace:
+class BaseTrace(ABC):
     """Base trace object
 
     Parameters

pymc3/backends/ndarray.py

@@ -20,12 +20,12 @@
 import json
 import os
 import shutil
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, List
 
 import numpy as np
 from pymc3.backends import base
 from pymc3.backends.base import MultiTrace
-from pymc3.model import Model
+from pymc3.model import Model, modelcontext
 from pymc3.exceptions import TraceDirectoryError
 
 
@@ -366,3 +366,19 @@ def _slice_as_ndarray(strace, idx):
         sliced.draw_idx = (stop - start) // step
 
     return sliced
+
+def point_list_to_multitrace(point_list: List[Dict[str, np.ndarray]], model: Optional[Model]=None) -> MultiTrace:
+    '''transform point list into MultiTrace'''
+    _model = modelcontext(model)
+    varnames = list(point_list[0].keys())
+    with _model:
+        chain = NDArray(model=_model, vars=[_model[vn] for vn in varnames])
+        chain.setup(draws=len(point_list), chain=0)
+        # since we are simply loading a trace by hand, we need only a vacuous function for
+        # chain.record() to use. This crushes the default.
+        def point_fun(point):
+            return [point[vn] for vn in varnames]
+        chain.fn = point_fun
+        for point in point_list:
+            chain.record(point)
+    return MultiTrace([chain])

pymc3/backends/report.py

@@ -15,6 +15,7 @@
 from collections import namedtuple
 import logging
 import enum
+import typing
 from ..util import is_transformed_name, get_untransformed_name
 
 
@@ -51,11 +52,15 @@ class WarningType(enum.Enum):
 
 
 class SamplerReport:
+    """This object bundles warnings, convergence statistics and metadata of a sampling run."""
     def __init__(self):
         self._chain_warnings = {}
         self._global_warnings = []
         self._ess = None
         self._rhat = None
+        self._n_tune = None
+        self._n_draws = None
+        self._t_sampling = None
 
     @property
     def _warnings(self):
@@ -68,6 +73,25 @@ def ok(self):
         return all(_LEVELS[warn.level] < _LEVELS['warn']
                    for warn in self._warnings)
 
+    @property
+    def n_tune(self) -> typing.Optional[int]:
+        """Number of tune iterations - not necessarily kept in trace!"""
+        return self._n_tune
+
+    @property
+    def n_draws(self) -> typing.Optional[int]:
+        """Number of draw iterations."""
+        return self._n_draws
+
+    @property
+    def t_sampling(self) -> typing.Optional[float]:
+        """
+        Number of seconds that the sampling procedure took.
+        
+        (Includes parallelization overhead.)
+        """
+        return self._t_sampling
+
     def raise_ok(self, level='error'):
         errors = [warn for warn in self._warnings
                   if _LEVELS[warn.level] >= _LEVELS[level]]
@@ -151,7 +175,6 @@ def _add_warnings(self, warnings, chain=None):
         warn_list.extend(warnings)
 
     def _log_summary(self):
-
         def log_warning(warn):
             level = _LEVELS[warn.level]
             logger.log(level, warn.message)

pymc3/distributions/__init__.py

@@ -16,6 +16,8 @@
 from . import transforms
 from . import shape_utils
 
+from .posterior_predictive import fast_sample_posterior_predictive
+
 from .continuous import Uniform
 from .continuous import Flat
 from .continuous import HalfFlat
@@ -168,5 +170,6 @@
            'Interpolated',
            'Bound',
            'Rice',
-           'Simulator'
+           'Simulator',
+           'fast_sample_posterior_predictive'
            ]

pymc3/distributions/distribution.py

@@ -13,6 +13,10 @@
 #   limitations under the License.
 
 import numbers
+import contextvars
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from typing import Optional, Callable
 
 import numpy as np
 import theano.tensor as tt
@@ -33,6 +37,7 @@
 __all__ = ['DensityDist', 'Distribution', 'Continuous', 'Discrete',
            'NoDistribution', 'TensorType', 'draw_values', 'generate_samples']
 
+vectorized_ppc = contextvars.ContextVar('vectorized_ppc', default=None) # type: contextvars.ContextVar[Optional[Callable]]
 
 class _Unpickling:
     pass
@@ -530,11 +535,18 @@ def draw_values(params, point=None, size=None):
             a) are named parameters in the point
             b) are RVs with a random method
     """
+    # The following check intercepts and redirects calls to
+    # draw_values in the context of sample_posterior_predictive
+    ppc_sampler = vectorized_ppc.get(None)
+    if ppc_sampler is not None:
+        # this is being done inside new, vectorized sample_posterior_predictive
+        return ppc_sampler(params, trace=point, samples=size)
+
+    if point is None:
+        point = {}
     # Get fast drawable values (i.e. things in point or numbers, arrays,
     # constants or shares, or things that were already drawn in related
     # contexts)
-    if point is None:
-        point = {}
     with _DrawValuesContext() as context:
         params = dict(enumerate(params))
         drawn = context.drawn_vars