Move stabilise_node_ordering into eval function

Fixes #709. I guess we don't need to add this to the changelog as it was never documented in the first place?

tsinfer/eval_util.py

@@ -710,8 +710,6 @@ def run_perfect_inference(
             extended_checks=extended_checks,
             progress_monitor=progress_monitor,
         )
-    # If time_chunking is turned on we need to stabilise the node ordering in the output
-    # to ensure that the node IDs are comparable.
     inferred_ts = inference.match_samples(
         sample_data,
         ancestors_ts,
@@ -720,13 +718,34 @@ def run_perfect_inference(
         num_threads=num_threads,
         extended_checks=extended_checks,
         progress_monitor=progress_monitor,
-        stabilise_node_ordering=time_chunking and not path_compression,
+        simplify=False,  # Don't simplify until we have stabilised the node order below
     )
+    # If time_chunking is turned on we need to stabilise the node ordering in the output
+    # to ensure that the node IDs are comparable.
+    if time_chunking and not path_compression:
+        inferred_ts = stabilise_node_ordering(inferred_ts)
+
     # to compare against the original, we need to remove unary nodes from the inferred TS
     inferred_ts = inferred_ts.simplify(keep_unary=False, filter_sites=False)
     return ts, inferred_ts
 
 
+def stabilise_node_ordering(ts):
+    # Ensure all the node times are distinct so that they will have
+    # stable IDs after simplifying. This could possibly also be done
+    # by reversing the IDs within a time slice. This is used for comparing
+    # tree sequences produced by perfect inference.
+    tables = ts.dump_tables()
+    times = tables.nodes.time
+    for t in range(1, int(times[0])):
+        index = np.where(times == t)[0]
+        k = index.shape[0]
+        times[index] += np.arange(k)[::-1] / k
+    tables.nodes.time = times
+    tables.sort()
+    return tables.tree_sequence()
+
+
 def count_sample_child_edges(ts):
     """
     Returns an array counting the number of edges where each sample is a child.

tsinfer/inference.py

@@ -550,7 +550,6 @@ def match_samples(
     recombination=None,  # See :class:`Matcher`
     mismatch=None,  # See :class:`Matcher`
     precision=None,
-    stabilise_node_ordering=False,
     extended_checks=False,
     engine=constants.C_ENGINE,
     progress_monitor=None,
@@ -636,9 +635,7 @@ def match_samples(
         # we sometimes assume they are in the same order as in the file
 
     manager.match_samples(sample_indexes, sample_times)
-    ts = manager.finalise(
-        simplify=simplify, stabilise_node_ordering=stabilise_node_ordering
-    )
+    ts = manager.finalise(simplify=simplify)
     return ts
 
 
@@ -1641,7 +1638,7 @@ def match_samples(self, sample_indexes, sample_times):
                 progress_monitor.update()
             progress_monitor.close()
 
-    def finalise(self, simplify, stabilise_node_ordering):
+    def finalise(self, simplify):
         logger.info("Finalising tree sequence")
         ts = self.get_samples_tree_sequence()
         if simplify:
@@ -1650,20 +1647,6 @@ def finalise(self, simplify, stabilise_node_ordering):
                 "filter_individuals=False, keep_unary=True) on "
                 f"{ts.num_nodes} nodes and {ts.num_edges} edges"
             )
-            if stabilise_node_ordering:
-                # Ensure all the node times are distinct so that they will have
-                # stable IDs after simplifying. This could possibly also be done
-                # by reversing the IDs within a time slice. This is used for comparing
-                # tree sequences produced by perfect inference.
-                tables = ts.dump_tables()
-                times = tables.nodes.time
-                for t in range(1, int(times[0])):
-                    index = np.where(times == t)[0]
-                    k = index.shape[0]
-                    times[index] += np.arange(k)[::-1] / k
-                tables.nodes.time = times
-                tables.sort()
-                ts = tables.tree_sequence()
             ts = ts.simplify(
                 samples=list(self.sample_id_map.values()),
                 filter_sites=False,