Add a Null backend

mcbackend/__init__.py

@@ -2,6 +2,7 @@
 A framework agnostic implementation for storage of MCMC draws.
 """
 
+from .backends.null import NullBackend
 from .backends.numpy import NumPyBackend
 from .core import Backend, Chain, Run
 from .meta import ChainMeta, Coordinate, DataVariable, ExtendedValue, RunMeta, Variable
@@ -16,6 +17,7 @@
 __version__ = "0.5.2"
 __all__ = [
     "NumPyBackend",
+    "NullBackend",
     "Backend",
     "Chain",
     "Run",

mcbackend/backends/null.py

@@ -0,0 +1,117 @@
+"""
+This backend simply discards draws. There are not stored in memory.
+This can be used in situations where we want to run an MCMC but not permanently
+store its output.
+"""
+
+# Code-wise, a NullChain is essentially just a NumpyChain without the underlying data array.
+
+from typing import Dict, List, Mapping, Optional, Sequence, Tuple
+
+import numpy
+
+from ..core import Backend, Chain, Run
+from ..meta import ChainMeta, RunMeta
+from .numpy import grow_append, prepare_storage
+
+
+class NullChain(Chain):
+    """A null storage: discards values immediately and allocates no memory.
+
+    Use cases are
+
+    - Online computations: Draws are used and discarded immediately, allowing for much larger sample spaces.
+    - Profiling: To use as a baseline, to measure compute time & memory before allocating memory for draws.
+      Comparing with another backend would then show how much overhead it adds.
+
+    Since draws are not stored, only a subset of the `Chain` interface is supported:
+
+    - Supported: `__len__`, `append`, `get_stats`, `get_stats_at`
+    - Not supported: `get_draws`, `get_draws_at`
+
+    .. Todo:: Option to also sampling stats?
+    .. Todo:: Allow retrieving the most recent draw?
+
+    """
+
+    def __init__(self, cmeta: ChainMeta, rmeta: RunMeta, *, preallocate: int) -> None:
+        """Creates a null storage for draws from a chain: will gobble outputs without storing them
+
+        Parameters
+        ----------
+        cmeta : ChainMeta
+            Metadata of the chain.
+        rmeta : RunMeta
+            Metadata of the MCMC run.
+        preallocate : int
+            Influences the memory pre-allocation behavior.
+            (Draws are not saved, but stats may still be.)
+            The default is to reserve memory for ``preallocate`` draws
+            and grow the allocated memory by 10 % when needed.
+            Exceptions are variables with non-rigid shapes (indicated by 0 in the shape tuple)
+            where the correct amount of memory cannot be pre-allocated.
+            In these cases object arrays are used.
+        """
+        self._draw_idx = 0
+
+        # Create storage ndarrays only for sampler stats.
+        self._stats, self._stat_is_rigid = prepare_storage(rmeta.sample_stats, preallocate)
+
+        super().__init__(cmeta, rmeta)
+
+    def append(  # pylint: disable=duplicate-code
+        self, draw: Mapping[str, numpy.ndarray], stats: Optional[Mapping[str, numpy.ndarray]] = None
+    ):
+        if stats:
+            grow_append(self._stats, stats, self._stat_is_rigid, self._draw_idx)
+        self._draw_idx += 1
+        return
+
+    def __len__(self) -> int:
+        return self._draw_idx
+
+    def get_draws(self, var_name: str, slc: slice = slice(None)) -> numpy.ndarray:
+        raise RuntimeError("NullChain does not save draws.")
+
+    def get_draws_at(self, idx: int, var_names: Sequence[str]) -> Dict[str, numpy.ndarray]:
+        raise RuntimeError("NullChain does not save draws.")
+
+    def get_stats(  # pylint: disable=duplicate-code
+        self, stat_name: str, slc: slice = slice(None)
+    ) -> numpy.ndarray:
+        data = self._stats[stat_name][: self._draw_idx][slc]
+        if self.sample_stats[stat_name].dtype == "str":
+            return numpy.array(data.tolist(), dtype=str)
+        return data
+
+    def get_stats_at(self, idx: int, stat_names: Sequence[str]) -> Dict[str, numpy.ndarray]:
+        return {sn: numpy.asarray(self._stats[sn][idx]) for sn in stat_names}
+
+
+class NullRun(Run):
+    """An MCMC run where samples are immediately discarded."""
+
+    def __init__(self, meta: RunMeta, *, preallocate: int) -> None:
+        self._settings = {"preallocate": preallocate}
+        self._chains: List[NullChain] = []
+        super().__init__(meta)
+
+    def init_chain(self, chain_number: int) -> NullChain:
+        cmeta = ChainMeta(self.meta.rid, chain_number)
+        chain = NullChain(cmeta, self.meta, **self._settings)
+        self._chains.append(chain)
+        return chain
+
+    def get_chains(self) -> Tuple[NullChain, ...]:
+        return tuple(self._chains)
+
+
+class NullBackend(Backend):
+    """A backend which discards samples immediately."""
+
+    def __init__(self, preallocate: int = 1_000) -> None:
+        self._settings = {"preallocate": preallocate}
+        super().__init__()
+
+    def init_run(self, meta: RunMeta) -> NullRun:
+        return NullRun(meta, **self._settings)

mcbackend/backends/numpy.py

@@ -3,12 +3,12 @@
 """
 
 import math
-from typing import Dict, List, Mapping, Optional, Sequence, Tuple
+from typing import Dict, Iterable, List, Mapping, Optional, Sequence, Tuple
 
 import numpy
 
 from ..core import Backend, Chain, Run, is_rigid
-from ..meta import ChainMeta, RunMeta
+from ..meta import ChainMeta, RunMeta, Variable
 
 
 def grow_append(
@@ -34,6 +34,22 @@ def grow_append(
     return
 
 
+def prepare_storage(
+    variables: Iterable[Variable], preallocate: int
+) -> Tuple[Dict[str, numpy.ndarray], Dict[str, bool]]:
+    storage: Dict[str, numpy.ndarray] = {}
+    rigid_dict: Dict[str, bool] = {}
+    for var in variables:
+        rigid = is_rigid(var.shape) and not var.undefined_ndim and var.dtype != "str"
+        rigid_dict[var.name] = rigid
+        if rigid:
+            reserve = (preallocate, *var.shape)
+            storage[var.name] = numpy.empty(reserve, var.dtype)
+        else:
+            storage[var.name] = numpy.array([None] * preallocate, dtype=object)
+    return storage, rigid_dict
+
+
 class NumPyChain(Chain):
     """Stores value draws in NumPy arrays and can pre-allocate memory."""
 
@@ -54,25 +70,11 @@ def __init__(self, cmeta: ChainMeta, rmeta: RunMeta, *, preallocate: int) -> Non
             where the correct amount of memory cannot be pre-allocated.
             In these cases object arrays are used.
         """
-        self._var_is_rigid: Dict[str, bool] = {}
-        self._samples: Dict[str, numpy.ndarray] = {}
-        self._stat_is_rigid: Dict[str, bool] = {}
-        self._stats: Dict[str, numpy.ndarray] = {}
         self._draw_idx = 0
 
         # Create storage ndarrays for each model variable and sampler stat.
-        for target_dict, rigid_dict, variables in [
-            (self._samples, self._var_is_rigid, rmeta.variables),
-            (self._stats, self._stat_is_rigid, rmeta.sample_stats),
-        ]:
-            for var in variables:
-                rigid = is_rigid(var.shape) and not var.undefined_ndim and var.dtype != "str"
-                rigid_dict[var.name] = rigid
-                if rigid:
-                    reserve = (preallocate, *var.shape)
-                    target_dict[var.name] = numpy.empty(reserve, var.dtype)
-                else:
-                    target_dict[var.name] = numpy.array([None] * preallocate, dtype=object)
+        self._samples, self._var_is_rigid = prepare_storage(rmeta.variables, preallocate)
+        self._stats, self._stat_is_rigid = prepare_storage(rmeta.sample_stats, preallocate)
 
         super().__init__(cmeta, rmeta)