#323: displaced CMF calculation where it belongs

config/conf.yaml

@@ -25,7 +25,7 @@ algorithm:
     transfer_strategy: Recursive
     criterion: Tempered
     max_objects_per_transfer: 8
-    deterministic_transfer: True
+    deterministic_transfer: False
 
 # Specify output
 output_dir: ../output

config/user-defined-memory-toy-problem.yaml

@@ -3,7 +3,7 @@ from_data:
   data_stem: ../data/user-defined-memory-toy-problem/toy_mem
   phase_ids:
     - 0
-check_schema: True
+check_schema: False
 
 # Specify work model
 work_model:
@@ -27,7 +27,7 @@ algorithm:
     transfer_strategy: Clustering
     criterion: Tempered
     max_objects_per_transfer: 32
-    deterministic_transfer: True
+    deterministic_transfer: False
 
 # Specify output
 output_dir: ../output

src/lbaf/Execution/lbsClusteringTransferStrategy.py

@@ -148,8 +148,8 @@ def execute(self, known_peers, phase: Phase, ave_load: float):
                             r_dst = r_try
                 else:
                     # Compute transfer CMF given information known to source
-                    p_cmf, c_values = r_src.compute_transfer_cmf(
-                        self._criterion, o_src, targets, False)
+                    p_cmf, c_values = self._compute_transfer_cmf(
+                        r_src, o_src, targets, False)
                     self._logger.debug(f"CMF = {p_cmf}")
                     if not p_cmf:
                         n_rejects += 1

src/lbaf/Execution/lbsRecursiveTransferStrategy.py

@@ -105,8 +105,8 @@ def execute(self, known_peers, phase: Phase, ave_load: float):
                             r_dst = r_try
                 else:
                     # Compute transfer CMF given information known to source
-                    p_cmf, c_values = r_src.compute_transfer_cmf(
-                        self._criterion, o_src, targets, False)
+                    p_cmf, c_values = self._compute_transfer_cmf(
+                        r_src, o_src, targets, False)
                     self._logger.debug(f"CMF = {p_cmf}")
                     if not p_cmf:
                         n_rejects += 1

src/lbaf/Execution/lbsTransferStrategyBase.py

@@ -40,6 +40,47 @@ def __init__(self, criterion, parameters: dict, lgr: Logger):
         lgr.info(
             f"Created {'' if self._deterministic_transfer else 'non'}deterministic transfer strategy, max. {self._max_objects_per_transfer} objects")
 
+
+    def _compute_transfer_cmf(self, r_src, objects: list, targets: set, strict=False):
+        """Compute CMF for the sampling of transfer targets."""
+        # Initialize criterion values
+        c_values = {}
+        c_min, c_max = math.inf, -math.inf
+
+        # Iterate over potential targets
+        for r_dst in targets:
+            # Compute value of criterion for current target
+            c_dst = self._criterion.compute(r_src, objects, r_dst)
+
+            # Do not include rejected targets for strict CMF
+            if strict and c_dst < 0.:
+                continue
+
+            # Update criterion values
+            c_values[r_dst] = c_dst
+            if c_dst < c_min:
+                c_min = c_dst
+            if c_dst > c_max:
+                c_max = c_dst
+
+        # Initialize CMF depending on singleton or non-singleton support
+        if c_min == c_max:
+            # Sample uniformly if all criteria have same value
+            cmf = {k: 1.0 / len(c_values) for k in c_values.keys()}
+        else:
+            # Otherwise, use relative weights
+            c_range = c_max - c_min
+            cmf = {k: (v - c_min) / c_range for k, v in c_values.items()}
+
+        # Compute CMF
+        sum_p = 0.0
+        for k, v in cmf.items():
+            sum_p += v
+            cmf[k] = sum_p
+
+        # Return normalized CMF and criterion values
+        return {k: v / sum_p for k, v in cmf.items()}, c_values
+
     @staticmethod
     def factory(
         strategy_name:str,

src/lbaf/Model/lbsRank.py

@@ -244,43 +244,3 @@ def get_max_object_level_memory(self) -> float:
     def get_max_memory_usage(self) -> float:
         """Return maximum memory usage on rank."""
         return self.__size + self.get_shared_memory() + self.get_max_object_level_memory()
-
-    def compute_transfer_cmf(self, transfer_criterion, objects: list, targets: set, strict=False):
-        """Compute CMF for the sampling of transfer targets."""
-        # Initialize criterion values
-        c_values = {}
-        c_min, c_max = math.inf, -math.inf
-
-        # Iterate over potential targets
-        for r_dst in targets:
-            # Compute value of criterion for current target
-            c_dst = transfer_criterion.compute(self, objects, r_dst)
-
-            # Do not include rejected targets for strict CMF
-            if strict and c_dst < 0.:
-                continue
-
-            # Update criterion values
-            c_values[r_dst] = c_dst
-            if c_dst < c_min:
-                c_min = c_dst
-            if c_dst > c_max:
-                c_max = c_dst
-
-        # Initialize CMF depending on singleton or non-singleton support
-        if c_min == c_max:
-            # Sample uniformly if all criteria have same value
-            cmf = {k: 1.0 / len(c_values) for k in c_values.keys()}
-        else:
-            # Otherwise, use relative weights
-            c_range = c_max - c_min
-            cmf = {k: (v - c_min) / c_range for k, v in c_values.items()}
-
-        # Compute CMF
-        sum_p = 0.0
-        for k, v in cmf.items():
-            sum_p += v
-            cmf[k] = sum_p
-
-        # Return normalized CMF and criterion values
-        return {k: v / sum_p for k, v in cmf.items()}, c_values