Change aggregate stats type and added unique method. (#603)

* added the `unique()` method to stats

* fix broken test

* Update test_core_stats_functions.py

tests/stats/test_core_stats_functions.py

@@ -13,6 +13,7 @@
 
 ### General functionality
 
+import numpy as np
 import pandas as pd
 import pytest
 
@@ -422,13 +423,15 @@ def test_hypergraph_aggregates(edgelist1, edgelist2, edgelist8):
     assert round(H.nodes.degree.mean(), 3) == 1.125
     assert round(H.nodes.degree.std(), 3) == 0.331
     assert round(H.nodes.degree.var(), 3) == 0.109
+    assert np.allclose(H.nodes.degree.unique(), np.array([1, 2]))
 
     assert H.edges.order.max() == 2
     assert H.edges.order.min() == 0
     assert H.edges.order.sum() == 5
     assert round(H.edges.order.mean(), 3) == 1.25
     assert round(H.edges.order.std(), 3) == 0.829
     assert round(H.edges.order.var(), 3) == 0.688
+    assert np.allclose(H.edges.order.unique(), np.array([0, 1, 2]))
 
     H = xgi.Hypergraph(edgelist2)
     assert H.nodes.degree.max() == 2
@@ -442,13 +445,17 @@ def test_hypergraph_aggregates(edgelist1, edgelist2, edgelist8):
     assert round(H.nodes.degree.mean(), 3) == 1.167
     assert round(H.nodes.degree.std(), 3) == 0.373
     assert round(H.nodes.degree.var(), 3) == 0.139
+    assert np.allclose(H.nodes.degree.unique(), np.array([1, 2]))
 
     assert H.edges.order.max() == 2
     assert H.edges.order.min() == 1
     assert H.edges.order.sum() == 4
     assert round(H.edges.order.mean(), 3) == 1.333
     assert round(H.edges.order.std(), 3) == 0.471
     assert round(H.edges.order.var(), 3) == 0.222
+    assert np.allclose(H.edges.order.unique(), np.array([1, 2]))
+    assert len(H.edges.order.unique(return_counts=True)) == 2
+    assert np.allclose(H.edges.order.unique(return_counts=True)[1], np.array([2, 1]))
 
     H = xgi.Hypergraph(edgelist8)
     assert H.nodes.degree.max() == 6
@@ -973,3 +980,15 @@ def test_multi_with_attrs(hyperwithattrs):
         5: [2, "blue"],
     }
     assert multi.asdict(list) == d
+
+
+def test_aggregate_stats_types(edgelist1):
+    H = xgi.Hypergraph(edgelist1)
+    assert isinstance(H.nodes.degree.max(), int)
+    assert isinstance(H.nodes.degree.min(), int)
+    assert isinstance(H.nodes.degree.median(), float)
+    assert isinstance(H.nodes.degree.mean(), float)
+    assert isinstance(H.nodes.degree.sum(), int)
+    assert isinstance(H.nodes.degree.std(), float)
+    assert isinstance(H.nodes.degree.var(), float)
+    assert isinstance(H.nodes.degree.moment(), float)

xgi/stats/__init__.py

@@ -203,23 +203,23 @@ def ashist(self, bins=10, bin_edges=False, density=False, log_binning=False):
 
     def max(self):
         """The maximum value of this stat."""
-        return self.asnumpy().max(axis=0)
+        return self.asnumpy().max(axis=0).item()
 
     def min(self):
         """The minimum value of this stat."""
-        return self.asnumpy().min(axis=0)
+        return self.asnumpy().min(axis=0).item()
 
     def sum(self):
         """The sum of this stat."""
-        return self.asnumpy().sum(axis=0)
+        return self.asnumpy().sum(axis=0).item()
 
     def mean(self):
         """The arithmetic mean of this stat."""
-        return self.asnumpy().mean(axis=0)
+        return self.asnumpy().mean(axis=0).item()
 
     def median(self):
         """The median of this stat."""
-        return np.median(self.asnumpy(), axis=0)
+        return np.median(self.asnumpy(), axis=0).item()
 
     def std(self):
         """The standard deviation of this stat.
@@ -231,7 +231,7 @@ def std(self):
         See https://www.allendowney.com/blog/2024/06/08/which-standard-deviation/
         for more details.
         """
-        return self.asnumpy().std(axis=0)
+        return self.asnumpy().std(axis=0).item()
 
     def var(self):
         """The variance of this stat.
@@ -243,7 +243,7 @@ def var(self):
         See https://www.allendowney.com/blog/2024/06/08/which-standard-deviation/
         for more details.
         """
-        return self.asnumpy().var(axis=0)
+        return self.asnumpy().var(axis=0).item()
 
     def moment(self, order=2, center=False):
         """The statistical moments of this stat.
@@ -257,7 +257,9 @@ def moment(self, order=2, center=False):
 
         """
         arr = self.asnumpy()
-        return spmoment(arr, moment=order) if center else np.mean(arr**order)
+        return (
+            spmoment(arr, moment=order) if center else np.mean(arr**order).item()
+        )
 
     def argmin(self):
         """The ID corresponding to the minimum of the stat
@@ -306,6 +308,9 @@ def argsort(self, reverse=False):
         d = self.asdict()
         return sorted(d, key=d.get, reverse=reverse)
 
+    def unique(self, return_counts=False):
+        return np.unique(self.asnumpy(), return_counts=return_counts)
+
 
 class NodeStat(IDStat):
     """An arbitrary node-quantity mapping.