Fixup mypy errors in sampling.py

pymc3/sampling.py

@@ -14,18 +14,16 @@
 
 """Functions for MCMC sampling."""
 
+import collections.abc as abc
 import logging
 import pickle
 import sys
 import time
 import warnings
 
 from collections import defaultdict
-from collections.abc import Iterable
 from copy import copy
-from typing import Any, Dict
-from typing import Iterable as TIterable
-from typing import List, Optional, Union, cast
+from typing import Any, Dict, Iterable, List, Optional, Set, Union, cast
 
 import arviz
 import numpy as np
@@ -57,8 +55,8 @@
     HamiltonianMC,
     Metropolis,
     Slice,
-    arraystep,
 )
+from pymc3.step_methods.arraystep import BlockedStep, PopulationArrayStepShared
 from pymc3.step_methods.hmc import quadpotential
 from pymc3.util import (
     chains_and_samples,
@@ -93,15 +91,19 @@
     CategoricalGibbsMetropolis,
     PGBART,
 )
+Step = Union[BlockedStep, CompoundStep]
 
 ArrayLike = Union[np.ndarray, List[float]]
 PointType = Dict[str, np.ndarray]
 PointList = List[PointType]
+Backend = Union[BaseTrace, MultiTrace, NDArray]
 
 _log = logging.getLogger("pymc3")
 
 
-def instantiate_steppers(_model, steps, selected_steps, step_kwargs=None):
+def instantiate_steppers(
+    _model, steps: List[Step], selected_steps, step_kwargs=None
+) -> Union[Step, List[Step]]:
     """Instantiate steppers assigned to the model variables.
 
     This function is intended to be called automatically from ``sample()``, but
@@ -142,7 +144,7 @@ def instantiate_steppers(_model, steps, selected_steps, step_kwargs=None):
         raise ValueError("Unused step method arguments: %s" % unused_args)
 
     if len(steps) == 1:
-        steps = steps[0]
+        return steps[0]
 
     return steps
 
@@ -216,7 +218,7 @@ def assign_step_methods(model, step=None, methods=STEP_METHODS, step_kwargs=None
     return instantiate_steppers(model, steps, selected_steps, step_kwargs)
 
 
-def _print_step_hierarchy(s, level=0):
+def _print_step_hierarchy(s: Step, level=0) -> None:
     if isinstance(s, CompoundStep):
         _log.info(">" * level + "CompoundStep")
         for i in s.methods:
@@ -447,7 +449,7 @@ def sample(
         if random_seed is not None:
             np.random.seed(random_seed)
         random_seed = [np.random.randint(2 ** 30) for _ in range(chains)]
-    if not isinstance(random_seed, Iterable):
+    if not isinstance(random_seed, abc.Iterable):
         raise TypeError("Invalid value for `random_seed`. Must be tuple, list or int")
 
     if not discard_tuned_samples and not return_inferencedata:
@@ -542,7 +544,7 @@ def sample(
 
     has_population_samplers = np.any(
         [
-            isinstance(m, arraystep.PopulationArrayStepShared)
+            isinstance(m, PopulationArrayStepShared)
             for m in (step.methods if isinstance(step, CompoundStep) else [step])
         ]
     )
@@ -706,7 +708,7 @@ def _sample_many(
     trace: MultiTrace
         Contains samples of all chains
     """
-    traces = []
+    traces: List[Backend] = []
     for i in range(chains):
         trace = _sample(
             draws=draws,
@@ -1140,7 +1142,7 @@ def _run_secondary(c, stepper_dumps, secondary_end):
             # has to be updated, therefore we identify the substeppers first.
             population_steppers = []
             for sm in stepper.methods if isinstance(stepper, CompoundStep) else [stepper]:
-                if isinstance(sm, arraystep.PopulationArrayStepShared):
+                if isinstance(sm, PopulationArrayStepShared):
                     population_steppers.append(sm)
             while True:
                 incoming = secondary_end.recv()
@@ -1259,7 +1261,7 @@ def _prepare_iter_population(
     population = [Point(start[c], model=model) for c in range(nchains)]
 
     # 3. Set up the steppers
-    steppers = [None] * nchains
+    steppers: List[Step] = []
     for c in range(nchains):
         # need indepenent samplers for each chain
         # it is important to copy the actual steppers (but not the delta_logp)
@@ -1269,9 +1271,9 @@ def _prepare_iter_population(
             chainstep = copy(step)
         # link population samplers to the shared population state
         for sm in chainstep.methods if isinstance(step, CompoundStep) else [chainstep]:
-            if isinstance(sm, arraystep.PopulationArrayStepShared):
+            if isinstance(sm, PopulationArrayStepShared):
                 sm.link_population(population, c)
-        steppers[c] = chainstep
+        steppers.append(chainstep)
 
     # 4. configure tracking of sampler stats
     for c in range(nchains):
@@ -1349,7 +1351,7 @@ def _iter_population(draws, tune, popstep, steppers, traces, points):
                 steppers[c].report._finalize(strace)
 
 
-def _choose_backend(trace, chain, **kwds):
+def _choose_backend(trace, chain, **kwds) -> Backend:
     """Selects or creates a NDArray trace backend for a particular chain.
 
     Parameters
@@ -1562,8 +1564,8 @@ class _DefaultTrace:
         `insert()` method
     """
 
-    trace_dict = {}  # type: Dict[str, np.ndarray]
-    _len = None  # type: int
+    trace_dict: Dict[str, np.ndarray] = {}
+    _len: Optional[int] = None
 
     def __init__(self, samples: int):
         self._len = samples
@@ -1600,7 +1602,7 @@ def sample_posterior_predictive(
     trace,
     samples: Optional[int] = None,
     model: Optional[Model] = None,
-    vars: Optional[TIterable[Tensor]] = None,
+    vars: Optional[Iterable[Tensor]] = None,
     var_names: Optional[List[str]] = None,
     size: Optional[int] = None,
     keep_size: Optional[bool] = False,
@@ -1885,8 +1887,7 @@ def sample_posterior_predictive_w(
 def sample_prior_predictive(
     samples=500,
     model: Optional[Model] = None,
-    vars: Optional[TIterable[str]] = None,
-    var_names: Optional[TIterable[str]] = None,
+    var_names: Optional[Iterable[str]] = None,
     random_seed=None,
 ) -> Dict[str, np.ndarray]:
     """Generate samples from the prior predictive distribution.
@@ -1896,9 +1897,6 @@ def sample_prior_predictive(
     samples : int
         Number of samples from the prior predictive to generate. Defaults to 500.
     model : Model (optional if in ``with`` context)
-    vars : Iterable[str]
-        A list of names of variables for which to compute the posterior predictive
-        samples.  *DEPRECATED* - Use ``var_names`` argument instead.
     var_names : Iterable[str]
         A list of names of variables for which to compute the posterior predictive
         samples. Defaults to both observed and unobserved RVs.
@@ -1913,22 +1911,14 @@ def sample_prior_predictive(
     """
     model = modelcontext(model)
 
-    if vars is None and var_names is None:
+    if var_names is None:
         prior_pred_vars = model.observed_RVs
         prior_vars = (
             get_default_varnames(model.unobserved_RVs, include_transformed=True) + model.potentials
         )
-        vars_ = [var.name for var in prior_vars + prior_pred_vars]
-        vars = set(vars_)
-    elif vars is None:
-        vars = var_names
-        vars_ = vars
-    elif vars is not None:
-        warnings.warn("vars argument is deprecated in favor of var_names.", DeprecationWarning)
-        vars_ = vars
+        vars_: Set[str] = {var.name for var in prior_vars + prior_pred_vars}
     else:
-        raise ValueError("Cannot supply both vars and var_names arguments.")
-    vars = cast(TIterable[str], vars)  # tell mypy that vars cannot be None here.
+        vars_ = set(var_names)
 
     if random_seed is not None:
         np.random.seed(random_seed)
@@ -1940,8 +1930,8 @@ def sample_prior_predictive(
     if data is None:
         raise AssertionError("No variables sampled: attempting to sample %s" % names)
 
-    prior = {}  # type: Dict[str, np.ndarray]
-    for var_name in vars:
+    prior: Dict[str, np.ndarray] = {}
+    for var_name in vars_:
         if var_name in data:
             prior[var_name] = data[var_name]
         elif is_transformed_name(var_name):
@@ -2093,15 +2083,15 @@ def init_nuts(
         var = np.ones_like(mean)
         potential = quadpotential.QuadPotentialDiagAdapt(model.ndim, mean, var, 10)
     elif init == "advi+adapt_diag_grad":
-        approx = pm.fit(
+        approx: pm.MeanField = pm.fit(
             random_seed=random_seed,
             n=n_init,
             method="advi",
             model=model,
             callbacks=cb,
             progressbar=progressbar,
             obj_optimizer=pm.adagrad_window,
-        )  # type: pm.MeanField
+        )
         start = approx.sample(draws=chains)
         start = list(start)
         stds = approx.bij.rmap(approx.std.eval())
@@ -2119,7 +2109,7 @@ def init_nuts(
             callbacks=cb,
             progressbar=progressbar,
             obj_optimizer=pm.adagrad_window,
-        )  # type: pm.MeanField
+        )
         start = approx.sample(draws=chains)
         start = list(start)
         stds = approx.bij.rmap(approx.std.eval())
@@ -2137,7 +2127,7 @@ def init_nuts(
             callbacks=cb,
             progressbar=progressbar,
             obj_optimizer=pm.adagrad_window,
-        )  # type: pm.MeanField
+        )
         start = approx.sample(draws=chains)
         start = list(start)
         stds = approx.bij.rmap(approx.std.eval())

pymc3/step_methods/arraystep.py

@@ -13,13 +13,14 @@
 #   limitations under the License.
 
 from enum import IntEnum, unique
+from typing import Dict, List
 
 import numpy as np
 
 from numpy.random import uniform
 
 from pymc3.blocking import ArrayOrdering, DictToArrayBijection
-from pymc3.model import modelcontext
+from pymc3.model import PyMC3Variable, modelcontext
 from pymc3.step_methods.compound import CompoundStep
 from pymc3.theanof import inputvars
 from pymc3.util import get_var_name
@@ -46,6 +47,8 @@ class Competence(IntEnum):
 class BlockedStep:
 
     generates_stats = False
+    stats_dtypes: List[Dict[str, np.dtype]] = []
+    vars: List[PyMC3Variable] = []
 
     def __new__(cls, *args, **kwargs):
         blocked = kwargs.get("blocked")

pymc3/step_methods/mlda.py

@@ -356,10 +356,6 @@ class MLDA(ArrayStepShared):
     default_blocked = True
     generates_stats = True
 
-    # stat data types are different, depending on the base sampler.
-    # these are assigned in the init method.
-    stats_dtypes = None
-
     def __init__(
         self,
         coarse_models: List[Model],

pymc3/tests/test_sampling.py

@@ -903,9 +903,8 @@ def test_respects_shape(self):
             with pm.Model():
                 mu = pm.Gamma("mu", 3, 1, shape=1)
                 goals = pm.Poisson("goals", mu, shape=shape)
-                with pytest.warns(DeprecationWarning):
-                    trace1 = pm.sample_prior_predictive(10, vars=["mu", "goals"])
-                    trace2 = pm.sample_prior_predictive(10, var_names=["mu", "goals"])
+                trace1 = pm.sample_prior_predictive(10, var_names=["mu", "mu", "goals"])
+                trace2 = pm.sample_prior_predictive(10, var_names=["mu", "goals"])
             if shape == 2:  # want to test shape as an int
                 shape = (2,)
             assert trace1["goals"].shape == (10,) + shape

setup.cfg

@@ -11,3 +11,6 @@ convention = numpy
 [isort]
 lines_between_types = 1
 profile = black
+
+[mypy]
+ignore_missing_imports = True