Save memory in posterior predictive (#3556)

Build the numpy ndarrays for the posterior predictive array lazily, but skip the intermediate step of building a very big python array.

RELEASE-NOTES.md

@@ -15,6 +15,7 @@
 - Parallelization of population steppers (`DEMetropolis`) is now set via the `cores` argument. ([#3559](https://github.com/pymc-devs/pymc3/pull/3559))
 - SMC: stabilize covariance matrix [3573](https://github.com/pymc-devs/pymc3/pull/3573)
 - SMC is no longer a step method of `pm.sample` now it should be called using `pm.sample_smc` [3579](https://github.com/pymc-devs/pymc3/pull/3579)
+- `sample_posterior_predictive` now preallocates the memory required for its output to improve memory usage. Addresses problems raised in this [discourse thread](https://discourse.pymc.io/t/memory-error-with-posterior-predictive-sample/2891/4).
 
 ## PyMC3 3.7 (May 29 2019)
 

pymc3/sampling.py

@@ -1,6 +1,6 @@
 from typing import Dict, List, Optional, TYPE_CHECKING, cast
 if TYPE_CHECKING:
-    from typing import Any
+    from typing import Any, Tuple
 from typing import Iterable as TIterable
 from collections.abc import Iterable
 from collections import defaultdict
@@ -1010,6 +1010,60 @@ def stop_tuning(step):
     step.stop_tuning()
     return step
 
+class _DefaultTrace():
+    '''
+    This class is a utility for collecting a number of samples
+    into a dictionary. Name comes from its similarity to `defaultdict` --
+    entries are lazily created.
+
+    Parameters
+    ----------
+    samples : int
+        The number of samples that will be collected, per variable,
+        into the trace.
+
+    Attributes
+    ----------
+    trace_dict : Dict[str, np.ndarray]
+        A dictionary constituting a trace.  Should be extracted
+        after a procedure has filled the `_DefaultTrace` using the
+        `insert()` method
+    '''
+    trace_dict = {} # type: Dict[str, np.ndarray]
+    _len = None # type: int
+    def __init__(self, samples):
+        self._len = samples
+        self.trace_dict = {}
+
+    def insert(self, k: str, v, idx: int):
+        '''
+        Insert `v` as the value of the `idx`th sample for the variable `k`.
+
+        Parameters
+        ----------
+        k : str
+            Name of the variable.
+        v : anything that can go into a numpy array (including a numpy array)
+            The value of the `idx`th sample from variable `k`
+        ids : int
+            The index of the sample we are inserting into the trace.
+        '''
+        if hasattr(v, 'shape'):
+            value_shape = tuple(v.shape) # type: Tuple[int, ...]
+        else:
+            value_shape = ()
+
+        # initialize if necessary
+        if k not in self.trace_dict:
+            array_shape = (self._len,) + value_shape
+            self.trace_dict[k] = np.full(array_shape, np.nan)
+
+        # do the actual insertion
+        if value_shape == ():
+            self.trace_dict[k][idx] = v
+        else:
+            self.trace_dict[k][idx,:] = v
+
 
 def sample_posterior_predictive(trace,
                                 samples: Optional[int]=None,
@@ -1097,10 +1151,11 @@ def sample_posterior_predictive(trace,
 
     indices = np.arange(samples)
 
+
     if progressbar:
         indices = tqdm(indices, total=samples)
 
-    ppc_trace = defaultdict(list) # type: Dict[str, List[Any]]
+    ppc_trace_t = _DefaultTrace(samples)
     try:
         for idx in indices:
             if nchain > 1:
@@ -1111,7 +1166,7 @@ def sample_posterior_predictive(trace,
 
             values = draw_values(vars, point=param, size=size)
             for k, v in zip(vars, values):
-                ppc_trace[k.name].append(v)
+                ppc_trace_t.insert(k.name, v, idx)
 
     except KeyboardInterrupt:
         pass
@@ -1120,13 +1175,12 @@ def sample_posterior_predictive(trace,
         if progressbar:
             indices.close()
 
+    ppc_trace = ppc_trace_t.trace_dict
     if keep_size:
         for k, ary in ppc_trace.items():
-            ary = np.asarray(ary)
             ppc_trace[k] = ary.reshape((nchain, len_trace, *ary.shape[1:]))
-        return ppc_trace
-    else:
-        return {k: np.asarray(v) for k, v in ppc_trace.items()}
+
+    return ppc_trace
 
 
 def sample_ppc(*args, **kwargs):