Special handling of comb for python 3.7

prefsampling/approval/noise.py

@@ -6,6 +6,7 @@
 import numpy as np
 
 from prefsampling.inputvalidators import validate_num_voters_candidates
+from prefsampling.utils import comb
 
 
 class NoiseType(Enum):
@@ -91,9 +92,9 @@ def noise(
 
     # Prepare buckets
     for x in range(len(A) + 1):
-        num_options_in = math.comb(len(A), x)
+        num_options_in = comb(len(A), x)
         for y in range(len(B) + 1):
-            num_options_out = math.comb(len(B), y)
+            num_options_out = comb(len(B), y)
 
             if noise_type == NoiseType.HAMMING:
                 factor = phi ** (len(A) - x + y)

prefsampling/ordinal/groupseparable.py

@@ -14,6 +14,7 @@
 from prefsampling.tree.caterpillar import caterpillar_tree
 from prefsampling.tree.balanced import balanced_tree
 from prefsampling.inputvalidators import validate_num_voters_candidates
+from prefsampling.utils import comb
 
 
 class TreeSampler(Enum):
@@ -161,7 +162,7 @@ def _number_group_separable_profiles(m: int, r: int, n: int) -> float:
     of internal nodes `r` based on the formula from `Karpov (2019)
     <https://link.springer.com/article/10.1007/s10726-019-09621-w>`_
     """
-    return math.comb(m - 1, r) * math.comb(m - 1 + r, m) * (2 ** (n - 1) - 1) ** (r - 1)
+    return comb(m - 1, r) * comb(m - 1 + r, m) * (2 ** (n - 1) - 1) ** (r - 1)
 
 
 def _sample_a_vote(node, reverse=False):

prefsampling/tree/schroeder.py

@@ -7,6 +7,7 @@
 
 from prefsampling.inputvalidators import validate_int
 from prefsampling.tree.node import Node
+from prefsampling.utils import comb
 
 
 def validate_num_leaves_nodes(num_leaves: int, num_internal_nodes: int | None):
@@ -52,8 +53,8 @@ def _random_num_internal_nodes(num_leaves: int, rng: np.random.Generator) -> int
 
 def _num_schroeder_tree(num_internal_nodes, num_leaves):
     return (
-        math.comb(num_leaves - 1, num_internal_nodes)
-        * math.comb(num_leaves - 1 + num_internal_nodes, num_leaves)
+        comb(num_leaves - 1, num_internal_nodes)
+        * comb(num_leaves - 1 + num_internal_nodes, num_leaves)
         / (num_leaves - 1)
     )
 

prefsampling/utils.py

@@ -0,0 +1,27 @@
+import math
+
+
+def comb(n: int, k: int):
+    """
+    Function to compute the binomial coefficient. It uses math.comb if available (i.e., if Python >=
+    3.8), otherwise computes it by hand.
+
+    Parameters
+    ----------
+    n
+    k
+
+    Returns
+    -------
+
+    """
+    if hasattr(math, "comb"):
+        return math.comb(n, k)
+    return _comb(n, k)
+
+
+def _comb(n, k):
+    try:
+        return math.factorial(n) // math.factorial(k) // math.factorial(n - k)
+    except ValueError:
+        return 0