various minor

pycid/core/cpd.py

@@ -4,7 +4,7 @@
 import itertools
 import types
 from inspect import getsourcelines
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Union, Iterator
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterator, List, Optional, Sequence, Union
 
 import numpy as np
 from pgmpy.factors.discrete import TabularCPD  # type: ignore
@@ -217,7 +217,7 @@ def __repr__(self) -> str:
             mapping = "\n".join([str(key) + "  ->  " + str(dictionary[key]) for key in sorted(list(dictionary.keys()))])
         else:
             mapping = ""
-        return f"<FunctionCPD {self.variable}:{self.stochastic_function}> \n{mapping}"
+        return f"{type(self).__name__}<{self.variable}:{self.stochastic_function}> \n{mapping}"
 
     def __str__(self) -> str:
         return self.__repr__()

pycid/random/random_cpd.py

@@ -1,5 +1,7 @@
+from __future__ import annotations
+
 import contextlib
-from typing import Dict, Iterator, List
+from typing import Dict, Iterator, Sequence
 
 import numpy as np
 
@@ -22,7 +24,9 @@ class RandomCPD(StochasticFunctionCPD):
     Sample a random CPD, with outcomes in the given domain
     """
 
-    def __init__(self, variable: str, domain: List = [0, 1], smoothness: float = 1.0, seed: int = None) -> None:
+    def __init__(
+        self, variable: str, domain: Sequence[Outcome] = [0, 1], smoothness: float = 1.0, seed: int = None
+    ) -> None:
         """
         Parameters
         ----------
@@ -50,3 +54,6 @@ def random_stochastic_function(**pv: Outcome) -> Dict[Outcome, float]:
             domain=domain if domain else [0, 1],
             label=f"RandomCPD({self.smoothness}, {self.seed})",
         )
+
+    def copy(self) -> RandomCPD:
+        return RandomCPD(self.variable, self.domain, self.smoothness, self.seed)  # type: ignore

tests/test_random.py

@@ -23,14 +23,15 @@ def test_random_dag_create_one() -> None:
     assert nx.is_directed_acyclic_graph(dag)
 
 
-def test_random_cpd() -> None:
-    cbn = CausalBayesianNetwork([("Y", "A"), ("Y", "D")])
+def test_random_cpd_copy() -> None:
+    """check that a copy of a random cpd yields the same distribution"""
+    cbn = CausalBayesianNetwork([("A", "B")])
     cbn.add_cpds(
-        RandomCPD("Y"),
-        FunctionCPD("A", lambda y: y),
-        FunctionCPD("D", lambda y: y),
+        RandomCPD("A"),
+        FunctionCPD("B", lambda a: a),
     )
-    assert cbn.expected_value(["D"], {}, intervention={"A": 0}) == cbn.expected_value(["D"], {}, intervention={"A": 1})
+    cbn2 = cbn.copy()
+    assert cbn.expected_value(["B"], {}) == cbn2.expected_value(["B"], {})
 
 
 if __name__ == "__main__":