partitioning for ppsfp

tests/test_safsim.py

@@ -13,6 +13,7 @@ The three simulators are exercised side-by-side and must agree exactly:
 """
 
 import itertools
+from collections import defaultdict
 
 import numpy as np
 import pytest
@@ -41,9 +42,8 @@ def make_patterns(*specs):
     return pat if pat.ndim == 2 else pat.reshape(-1, 1)
 
 
-def classify(alg, circuit, faults, patterns):
-    # PPSFP requires the batch size to match the pattern count (as main.py does).
-    sim = alg(circuit, patterns.shape[1])
+def classify(alg, circuit, faults, patterns, partitioning = None):
+    sim = alg(circuit, batch_size=patterns.shape[1], partitioning=partitioning)
     return sim.classify_faults(list(faults), patterns)
 
 
@@ -167,24 +167,36 @@ def test_s27_exact_simulators_agree(s27_bench, s27_resolved):
     assert len(faults) == 32  # collapsed fault count, see test_fault_set.test_s27
 
     simple = classify(SAFSimSimple, s27_resolved, faults, pat)
-    incr = classify(SAFSimIncremental, s27_resolved, faults, pat)
 
-    # The two exact simulators must classify every fault identically.
-    assert simple['DS'] == incr['DS']
-    assert simple['NO'] == incr['NO']
     # This deterministic pattern set leaves exactly 6 faults unobserved.
     assert len(simple['DS']) == 26
     assert len(simple['NO']) == 6
 
-
-def test_s27_ppsfp_matches_exact(s27_bench, s27_resolved):
-    faults, pat = _s27_setup(s27_bench, s27_resolved)
-
-    exact = classify(SAFSimSimple, s27_resolved, faults, pat)
+    incr = classify(SAFSimIncremental, s27_resolved, faults, pat)
     ppsfp = classify(SAFSimPPSFP, s27_resolved, faults, pat)
 
-    # With exact FFR-stem observability, PPSFP classifies identically to the
-    # brute-force simulator -- no longer an over-approximation.
+    assert simple['DS'] == incr['DS']
+    assert simple['NO'] == incr['NO']
+    assert simple['DS'] == ppsfp['DS']
+    assert simple['NO'] == ppsfp['NO']
+
+
+def test_s27_ppsfp_partitioning(s27_bench, s27_resolved):
+    faults, pat = _s27_setup(s27_bench, s27_resolved)
+
+    stems = [stem.index for stem, _ in s27_resolved.fanout_free_regions(KYUPY)]
+    k = 4
+    rng = np.random.default_rng(1)
+    parts = rng.integers(0, k, size=len(stems))
+    partitioning = defaultdict(set)
+    for part, stem in zip(parts, stems):
+        partitioning[int(part)].add(stem)
+
+    print(partitioning)
+
+    exact = classify(SAFSimSimple, s27_resolved, faults, pat)
+    ppsfp = classify(SAFSimPPSFP, s27_resolved, faults, pat, partitioning)
+
     assert ppsfp['DS'] == exact['DS']
     assert ppsfp['NO'] == exact['NO']