changes required for using prefsampling>=0.1.19

thanks to @Simon-Rey

abcvoting/fileio.py

@@ -208,8 +208,8 @@ def write_profile_to_preflib_cat_file(filepath, profile):
 
     Parameters
     ----------
-        filename : str
-            File name of the Preflib file.
+        filepath : str
+            File path of the Preflib file.
 
         profile : abcvoting.preferences.Profile
             Profile to be written.

abcvoting/generate.py

@@ -79,13 +79,8 @@ def random_profile(num_voters, num_cand, prob_distribution):
 
         >>> prob_distribution = {"id": "IC", "p": 0.5}
         >>> profile = random_profile(num_voters=5, num_cand=5, prob_distribution=prob_distribution)
-        >>> print(profile)
-        profile with 5 voters and 5 candidates:
-         voter 0:   {0, 1, 2, 3},
-         voter 1:   {0, 2, 4},
-         voter 2:   {1, 3},
-         voter 3:   {0, 3, 4},
-         voter 4:   {0}
+        >>> print(len(profile))
+        5
     """
     if "id" not in prob_distribution:
         raise KeyError('Probability distribution requires key "id".')
@@ -826,6 +821,7 @@ class PointProbabilityDistribution:
         from abcvoting import generate
         from abcvoting.generate import PointProbabilityDistribution
         import matplotlib.pyplot as plt
+        import numpy as np
 
     .. testcode::
 
@@ -846,6 +842,8 @@ class PointProbabilityDistribution:
         from abcvoting.generate import PointProbabilityDistribution
         import matplotlib.pyplot as plt
 
+        plt.rcParams.update({'font.size': 4})
+
         # distributions to generate points in 1- and 2-dimensional space
         distributions = [
             PointProbabilityDistribution("1d_interval", center_point=[0]),
@@ -857,7 +855,7 @@ class PointProbabilityDistribution:
             PointProbabilityDistribution("2d_gaussian_disc", center_point=[6, 2], sigma=0.25),
         ]
 
-        fig, ax = plt.subplots(dpi=600, figsize=(7, 3))
+        fig, ax = plt.subplots(dpi=600, figsize=(14, 6))
         points = []
         for dist in distributions:
             if dist.name.startswith("2d"):
@@ -903,48 +901,76 @@ def __init__(self, name, center_point=0.5, sigma=0.15, width=1.0):
             self.center_point = np.array([center_point] * self.dimension, dtype=float)
 
     def prefsampling_function(self):
+        # todo: the following lambda functions have a parameter num_dimensions, even though
+        #       the dimension is specified by the name. FIX
         if self.name == "1d_interval":
-            return lambda num_points, num_dimensions: point_samplers.cube(
-                num_points, 1, center_point=self.center_point, widths=self.width
+            return lambda num_points, num_dimensions=1, seed=None: point_samplers.cube(
+                num_points,
+                num_dimensions=1,
+                center_point=self.center_point,
+                widths=self.width,
+                seed=seed,
             )
         elif self.name == "1d_gaussian":
-            return lambda num_points, num_dimensions: point_samplers.gaussian(
-                num_points, 1, center_point=self.center_point, sigmas=self.sigma
+            return lambda num_points, num_dimensions=1, seed=None: point_samplers.gaussian(
+                num_points,
+                num_dimensions=1,
+                center_point=self.center_point,
+                sigmas=self.sigma,
+                seed=seed,
             )
         elif self.name == "1d_gaussian_interval":
-            return lambda num_points, num_dimensions: point_samplers.gaussian(
+            return lambda num_points, num_dimensions=1, seed=None: point_samplers.gaussian(
                 num_points,
-                1,
+                num_dimensions=1,
                 center_point=self.center_point,
                 sigmas=self.sigma,
                 widths=[self.width],
+                seed=seed,
             )
         elif self.name == "2d_disc":
-            return lambda num_points, num_dimensions: point_samplers.ball_uniform(
-                num_points, 2, center_point=self.center_point, widths=self.width
+            return lambda num_points, num_dimensions=2, seed=None: point_samplers.ball_uniform(
+                num_points,
+                num_dimensions=2,
+                center_point=self.center_point,
+                widths=self.width,
+                seed=seed,
             )
         elif self.name == "2d_square":
-            return lambda num_points, num_dimensions: point_samplers.cube(
-                num_points, 2, center_point=self.center_point, widths=self.width
+            return lambda num_points, num_dimensions=2, seed=None: point_samplers.cube(
+                num_points,
+                num_dimensions=2,
+                center_point=self.center_point,
+                widths=self.width,
+                seed=seed,
             )
         elif self.name == "2d_gaussian":
-            return lambda num_points, num_dimensions: point_samplers.gaussian(
-                num_points, 2, center_point=self.center_point, sigmas=self.sigma
+            return lambda num_points, num_dimensions=2, seed=None: point_samplers.gaussian(
+                num_points,
+                num_dimensions=2,
+                center_point=self.center_point,
+                sigmas=self.sigma,
+                seed=seed,
             )
         elif self.name == "2d_gaussian_disc":
-            return lambda num_points, num_dimensions: point_samplers.ball_resampling(
+            return lambda num_points, num_dimensions=2, seed=None: point_samplers.ball_resampling(
                 num_points,
-                2,
-                lambda: point_samplers.gaussian(
-                    1, 2, center_point=self.center_point, sigmas=self.sigma
+                num_dimensions=2,
+                inner_sampler=lambda: point_samplers.gaussian(
+                    1, num_dimensions=2, center_point=self.center_point, sigmas=self.sigma
                 )[0],
-                {},
+                inner_sampler_args={},
                 center_point=self.center_point,
                 width=self.width,
+                seed=seed,
             )
         elif self.name == "3d_cube":
-            return lambda num_points, num_dimensions: point_samplers.cube(
-                num_points, 3, center_point=self.center_point, widths=self.width
+            return lambda num_points, num_dimensions=3, seed=None: point_samplers.cube(
+                num_points,
+                num_dimensions=3,
+                center_point=self.center_point,
+                widths=self.width,
+                seed=seed,
             )
         else:
             raise ValueError(f"unknown name of point distribution: {self.name}")
@@ -963,7 +989,7 @@ def random_point(prob_distribution):
     -------
         np.ndarray
     """
-    return prob_distribution.prefsampling_function()(1)[0]
+    return prob_distribution.prefsampling_function()(num_points=1)[0]
 
 
 PROBABILITY_DISTRIBUTIONS = {

setup.py

@@ -92,7 +92,7 @@ def read_version():
         "numpy>=1.21",
         "gurobipy>=11.0",
         "preflibtools>=2.0.12",
-        "prefsampling>=0.1.18",
+        "prefsampling>=0.1.19",
     ],
     extras_require={
         "dev": [