Adding dtype to all numpy arrays

Author: Simon-Rey

prefsampling/__init__.py

@@ -1,6 +1,6 @@
 __author__ = "Simon Rey and Stanisław Szufa"
 __email__ = "[email protected]"
-__version__ = "0.1.14"
+__version__ = "0.1.15"
 
 from enum import Enum
 from itertools import chain

prefsampling/approval/euclidean.py

@@ -298,7 +298,8 @@ def euclidean_constant_size(
             [
                 np.linalg.norm(voter_pos - candidates_pos[c])
                 for c in range(num_candidates)
-            ]
+            ],
+            dtype=float
         )
         arg_sort_distances = distances.argsort()
         votes.append(set(arg_sort_distances[:num_approvals]))

prefsampling/core/euclidean.py

@@ -23,7 +23,7 @@ def _sample_points(
         sampler_args["num_points"] = num_points
         positions = sampler(**sampler_args)
     else:
-        positions = np.array(positions)
+        positions = np.array(positions, dtype=float)
         if len(positions) != num_points:
             raise ValueError(
                 f"The provided number of points does not match the number of "

prefsampling/ordinal/didi.py

@@ -63,4 +63,4 @@ def didi(
         points = rng.dirichlet(alphas)
         votes[i] = np.flip(points.argsort())
 
-    return np.array(votes)
+    return votes

prefsampling/ordinal/identity.py

@@ -25,4 +25,4 @@ def identity(num_voters: int, num_candidates: int, seed: int = None) -> np.ndarr
             Ordinal votes.
     """
 
-    return np.array([np.arange(num_candidates) for _ in range(num_voters)])
+    return np.array([np.arange(num_candidates) for _ in range(num_voters)], dtype=int)

prefsampling/ordinal/plackettluce.py

@@ -48,7 +48,7 @@ def plackett_luce(
 
     rng = np.random.default_rng(seed)
 
-    alphas = np.array(alphas)
+    alphas = np.array(alphas, dtype=float)
 
     votes = np.zeros((num_voters, num_candidates), dtype=int)
 

prefsampling/ordinal/singlecrossing.py

@@ -207,7 +207,7 @@ def single_crossing_impartial(num_voters, num_candidates, seed=None):
     vote_node_map = {top_node.vote_as_tuple: top_node}
 
     def graph_builder(node):
-        vote = np.array(node.vote)
+        vote = np.array(node.vote, dtype=int)
         for j in range(num_candidates - 1):
             if vote[j] < vote[j + 1]:
                 new_vote = vote.copy()
@@ -226,4 +226,4 @@ def graph_builder(node):
     top_node.count_elections(num_voters)
 
     votes = top_node.sample_votes(num_voters)
-    return np.array(votes)
+    return np.array(votes, dtype=int)

prefsampling/point/ball.py

@@ -149,9 +149,9 @@ def ball_resampling(
     for _ in range(num_points):
         point = inner_sampler(**inner_sampler_args)
         if isinstance(point, Iterable):
-            point = np.array(point)
+            point = np.array(point, dtype=float)
         else:
-            point = np.array([point])
+            point = np.array([point], dtype=float)
         if len(point) != num_dimensions:
             raise ValueError(
                 f"The inner sampler did not return a point with the suitable number "
@@ -160,4 +160,4 @@ def ball_resampling(
         while np.linalg.norm(point - center_point) > (width / 2):
             point = inner_sampler(**inner_sampler_args)
         points.append(point)
-    return np.array(points)
+    return np.array(points, dtype=float)

prefsampling/point/gaussian.py

@@ -75,4 +75,4 @@ def gaussian(
                 point = rng.normal(loc=center_point, scale=sigmas, size=num_dimensions)
             points.append(point)
 
-        return np.array(points)
+        return np.array(points, dtype=float)

prefsampling/point/utils.py

@@ -25,7 +25,7 @@ def validate_center_point(
             The coordinates of the center point to be used by the point samplers.
     """
     if center_point is None:
-        center_point = np.array([0 for _ in range(num_dimensions)])
+        center_point = np.array([0 for _ in range(num_dimensions)], dtype=float)
     else:
         if not isinstance(center_point, Iterable):
             raise TypeError(