Bugfix in progress funcs

groupselect/algorithms/algorithm_dream.py

@@ -22,7 +22,7 @@ def algorithm_dream(
     fields: dict[int, FieldMode],
     groups: list[(int, int)],
     manuals: dict[int, int],
-    progress_func: None | Callable = None,
+    progress_func: None | Callable[[float], None] = None,
     seed: None | int = None,
     pareto_prob: float = 0.5,
     swap_rounds: int = 1,
@@ -736,7 +736,7 @@ def allocate(
     cluster_tables,
     pareto_prob,
     n_swap_loops,
-    progress_bar,
+    progress_bar: None | Callable[[float], None],
     previous_meetings,
     no_cluster_agents,
     val_cluster,
@@ -759,7 +759,7 @@ def allocate(
 
     for round_no in range(n_rounds):
         if progress_bar:
-            progress_bar(round_no + 1)
+            progress_bar((round_no + 1) / n_rounds)
 
         if not (isinstance(tables, int)):
             no_tables = tables[round_no]

groupselect/algorithms/algorithm_hermes.py

@@ -25,7 +25,7 @@ def algorithm_hermes(
     fields: dict[int, FieldMode],
     groups: list[(int, int)],
     manuals: dict[int, int],
-    progress_func: None | Callable = None,
+    progress_func: None | Callable[[float], None] = None,
     seed: None | int = None,
     pareto_probs: dict[int, float] = None,
     swap_rounds: int = 1,

groupselect/algorithms/algorithm_legacy.py

@@ -19,7 +19,7 @@ def algorithm_legacy(
     fields: dict[int, FieldMode],
     groups: list[(int, int)],
     manuals: dict[int, int],
-    progress_func: None | Callable = None,
+    progress_func: None | Callable[[float], None] = None,
     n_attempts: int = 100,
     seed: None | int = None,
 ) -> AllocatorResult:
@@ -67,7 +67,7 @@ def algorithm_legacy(
     ]
     for n, ensemble in enumerate(allocation_attempts):
         if progress_func is not None:
-            progress_func(n)
+            progress_func(n / n_attempts)
         for n_gr, n_ppgr in groups:
             # Shuffle participant IDs.
             shuffle = list(range(len(participants)))

groupselect/allocate_numpy.py

@@ -18,10 +18,16 @@ def allocate_numpy(
     n_part_per_group: int | Iterable[int],
     manuals: None | dict[int, int] = None,
     algorithm: Algorithm | str = Algorithm.Legacy,
-    progress_func: None | Callable = None,
+    progress_func: None | Callable[[float], None] = None,
     settings: None | dict = None,
 ) -> AllocatorResult:
-    """Allocate participants into groups using the chosen algorithm."""
+    """Allocate participants into groups using the chosen algorithm.
+
+    `progress_func`, if given, is called repeatedly by the chosen algorithm
+    with a single float in [0.0, 1.0] indicating the fraction of work done
+    so far -- this is comparable across all algorithms, regardless of how
+    each one internally breaks its work into steps.
+    """
     # Check arguments: participants.
     if not np.issubdtype(participants.dtype, np.integer):
         raise Exception("Argument participants must only contain integers.")

groupselect/allocate_pandas.py

@@ -22,11 +22,17 @@ def allocate_pandas(
     n_part_per_group: int | Iterable[int],
     manuals: None | dict[int, int] = None,
     algorithm: Algorithm | str = Algorithm.Legacy,
-    progress_func: None | Callable = None,
+    progress_func: None | Callable[[float], None] = None,
     settings: None | dict = None,
     return_full: bool = False,
 ) -> pd.DataFrame | tuple[pd.DataFrame, pd.DataFrame, AllocatorResult]:
-    """Allocate participants of a dataframe into groups."""
+    """Allocate participants of a dataframe into groups.
+
+    `progress_func`, if given, is called repeatedly by the chosen algorithm
+    with a single float in [0.0, 1.0] indicating the fraction of work done
+    so far -- this is comparable across all algorithms, regardless of how
+    each one internally breaks its work into steps.
+    """
     # Check that the dataframe index is unique.
     if not participants.index.is_unique:
         raise Exception("Index of dataframe must be unique.")
@@ -129,7 +135,7 @@ def allocate(
         n_part_per_group: int | Iterable[int],
         manuals: None | dict[int, int] = None,
         algorithm: Algorithm | str = Algorithm.Legacy,
-        progress_func: None | Callable = None,
+        progress_func: None | Callable[[float], None] = None,
         settings: None | dict = None,
         return_full: bool = False,
     ) -> pd.DataFrame: