# -*- coding: utf-8 -*-

"""Test getting conditional independencies (and related)."""

import unittest
from typing import Iterable, Set, Union

from ananke.graphs import ADMG, SG

from y0.algorithm.conditional_independencies import (
    are_d_separated,
    get_conditional_independencies,
    get_moral_links,
)
from y0.examples import Example, d_separation_example, examples
from y0.graph import NxMixedGraph
from y0.struct import DSeparationJudgement


class TestDSeparation(unittest.TestCase):
    """Test the d-separation utility."""

    def test_mit_example(self):
        """Test checking D-separation on the MIT example."""
        graph = d_separation_example.graph.to_admg()

        self.assertFalse(are_d_separated(graph, "AA", "B", conditions=["D", "F"]))
        self.assertTrue(are_d_separated(graph, "AA", "B"))
        self.assertTrue(are_d_separated(graph, "D", "E", conditions=["C"]))
        self.assertFalse(are_d_separated(graph, "AA", "B", conditions=["C"]))
        self.assertFalse(are_d_separated(graph, "D", "E"))
        self.assertFalse(are_d_separated(graph, "D", "E", conditions=["AA", "B"]))
        self.assertFalse(are_d_separated(graph, "G", "G", conditions=["C"]))

    def test_examples(self):
        """Check that example conditional independencies are d-separations and that conditions (if present) are required.

        This test is using convenient examples to ensure that the d-separation algorithm
        isn't just always returning true or false.
        """
        testable = (
            example for example in examples if example.conditional_independencies is not None
        )

        for example in testable:
            with self.subTest(name=example.name):
                graph = example.graph.to_admg()
                for ci in example.conditional_independencies:
                    self.assertTrue(
                        are_d_separated(graph, ci.left, ci.right, conditions=ci.conditions),
                        msg="Expected d-separation not found",
                    )
                    if ci.conditions:
                        self.assertFalse(
                            are_d_separated(graph, ci.left, ci.right),
                            msg="Unexpected d-separation",
                        )

    def test_moral_links(self):
        """Test adding 'moral links' (part of the d-separation algorithm).

        This test covers several cases around moral links to ensure that they are added when needed.
        """
        graph = ADMG(
            vertices=("a", "b", "c"),
            di_edges=[("a", "b"), ("b", "c")],
        )
        links = set(tuple(sorted(e)) for e in get_moral_links(graph))
        self.assertEqual(set(), links, msg="Unexpected moral links added.")

        graph = ADMG(
            vertices=("a", "b", "c"),
            di_edges=[("a", "c"), ("b", "c")],
        )
        links = set(tuple(sorted(e)) for e in get_moral_links(graph))
        self.assertEqual(
            {("a", "b")}, links, msg="Moral links not as expected in single-link case."
        )

        graph = ADMG(
            vertices=("a", "b", "aa", "bb", "c"),
            di_edges=[("a", "c"), ("b", "c"), ("aa", "c"), ("bb", "c")],
        )
        links = set(tuple(sorted(e)) for e in get_moral_links(graph))
        self.assertEqual(
            {
                ("a", "b"),
                ("a", "aa"),
                ("a", "bb"),
                ("aa", "b"),
                ("aa", "bb"),
                ("b", "bb"),
            },
            links,
            msg="Moral links not as expected in multi-link case.",
        )

        graph = ADMG(
            vertices=("a", "b", "c", "d", "e"),
            di_edges=[("a", "c"), ("b", "c"), ("c", "e"), ("d", "e")],
        )
        links = set(tuple(sorted(e)) for e in get_moral_links(graph))
        self.assertEqual(
            {("a", "b"), ("c", "d")},
            links,
            msg="Moral links not as expected in multi-site case.",
        )


class TestGetConditionalIndependencies(unittest.TestCase):
    """Test getting conditional independencies."""

    def assert_example_has_judgements(self, example: Example) -> None:
        """Assert that the example is consistent w.r.t. D-separations."""
        self.assertIsNotNone(example.conditional_independencies)
        self.assert_has_judgements(
            graph=example.graph,
            judgements=example.conditional_independencies,
        )

    def assert_has_judgements(
        self, graph: Union[NxMixedGraph, SG], judgements: Iterable[DSeparationJudgement]
    ) -> None:
        """Assert that the graph has the correct conditional independencies.

        :param graph: the graph to test
        :param judgements: the set of expected conditional independencies
        """
        asserted_judgements = set(judgements)
        observed_judgements = get_conditional_independencies(graph)

        self.assertIsNotNone(asserted_judgements, "Expected independencies is empty.")
        self.assertIsNotNone(observed_judgements, "Observed independencies is empty.")
        self.assertTrue(
            all(judgement.is_canonical for judgement in observed_judgements),
            msg="one or more of the returned DSeparationJudgement instances are not canonical",
        )

        self.assert_valid_judgements(graph, asserted_judgements)
        self.assert_valid_judgements(graph, observed_judgements)

        expected_pairs = {(judgement.left, judgement.right) for judgement in asserted_judgements}
        observed_pairs = {(judgement.left, judgement.right) for judgement in observed_judgements}
        self.assertEqual(
            expected_pairs,
            observed_pairs,
            "Judgements do not find same separable pairs",
        )

        def _get_match(ref, options):
            """Find a judgement that has the same left/right pair as the reference judgement."""
            for alt in options:
                if ref.left == alt.left and ref.right == alt.right:
                    return alt
            return None

        for judgement in asserted_judgements:
            with self.subTest(name=judgement):
                matching = _get_match(judgement, observed_judgements)
                self.assertIsNotNone(matching, "No matching judgement found.")
                self.assertGreaterEqual(
                    len(judgement.conditions),
                    len(matching.conditions),
                    msg="Observed conditional independence more complicated than reference.",
                )

    def assert_valid_judgements(
        self, graph: Union[NxMixedGraph, SG], judgements: Set[DSeparationJudgement]
    ) -> None:
        """Check that a set of judgments are valid with respect to a graph."""
        if isinstance(graph, NxMixedGraph):
            graph = graph.to_admg()
        for judgement in judgements:
            self.assertTrue(
                are_d_separated(
                    graph,
                    judgement.left,
                    judgement.right,
                    conditions=judgement.conditions,
                ),
                msg=f"Conditional independency is not a d-separation in {graph}",
            )

        pairs = [(judgement.left, judgement.right) for judgement in judgements]
        self.assertEqual(len(pairs), len(set(pairs)), "Duplicate left/right pair observed")

    def test_examples(self):
        """Test getting the conditional independencies from the example graphs."""
        testable = (
            example for example in examples if example.conditional_independencies is not None
        )

        for example in testable:
            with self.subTest(name=example.name):
                self.maxDiff = None
                self.assert_example_has_judgements(example)

# -*- coding: utf-8 -*-

"""Test getting conditional independencies (and related)."""

import unittest
from typing import Iterable, Set, Union

from ananke.graphs import ADMG, SG

from y0.algorithm.conditional_independencies import (
    are_d_separated,
    get_conditional_independencies,
    get_moral_links,
)
from y0.examples import Example, d_separation_example, examples
from y0.graph import NxMixedGraph
from y0.struct import DSeparationJudgement


class TestDSeparation(unittest.TestCase):
    """Test the d-separation utility."""

    def test_mit_example(self):
        """Test checking D-separation on the MIT example."""
        graph = d_separation_example.graph.to_admg()

        self.assertFalse(are_d_separated(graph, "AA", "B", conditions=["D", "F"]))
        self.assertTrue(are_d_separated(graph, "AA", "B"))
        self.assertTrue(are_d_separated(graph, "D", "E", conditions=["C"]))
        self.assertFalse(are_d_separated(graph, "AA", "B", conditions=["C"]))
        self.assertFalse(are_d_separated(graph, "D", "E"))
        self.assertFalse(are_d_separated(graph, "D", "E", conditions=["AA", "B"]))
        self.assertFalse(are_d_separated(graph, "G", "G", conditions=["C"]))

    def test_examples(self):
        """Check that example conditional independencies are d-separations and that conditions (if present) are required.

        This test is using convenient examples to ensure that the d-separation algorithm
        isn't just always returning true or false.
        """
        testable = (
            example for example in examples if example.conditional_independencies is not None
        )

        for example in testable:
            with self.subTest(name=example.name):
                graph = example.graph.to_admg()
                for ci in example.conditional_independencies:
                    self.assertTrue(
                        are_d_separated(graph, ci.left, ci.right, conditions=ci.conditions),
                        msg="Expected d-separation not found",
                    )
                    if ci.conditions:
                        self.assertFalse(
                            are_d_separated(graph, ci.left, ci.right),
                            msg="Unexpected d-separation",
                        )

    def test_moral_links(self):
        """Test adding 'moral links' (part of the d-separation algorithm).

        This test covers several cases around moral links to ensure that they are added when needed.
        """
        graph = ADMG(
            vertices=("a", "b", "c"),
            di_edges=[("a", "b"), ("b", "c")],
        )
        links = set(tuple(sorted(e)) for e in get_moral_links(graph))
        self.assertEqual(set(), links, msg="Unexpected moral links added.")

        graph = ADMG(
            vertices=("a", "b", "c"),
            di_edges=[("a", "c"), ("b", "c")],
        )
        links = set(tuple(sorted(e)) for e in get_moral_links(graph))
        self.assertEqual(
            {("a", "b")}, links, msg="Moral links not as expected in single-link case."
        )

        graph = ADMG(
            vertices=("a", "b", "aa", "bb", "c"),
            di_edges=[("a", "c"), ("b", "c"), ("aa", "c"), ("bb", "c")],
        )
        links = set(tuple(sorted(e)) for e in get_moral_links(graph))
        self.assertEqual(
            {
                ("a", "b"),
                ("a", "aa"),
                ("a", "bb"),
                ("aa", "b"),
                ("aa", "bb"),
                ("b", "bb"),
            },
            links,
            msg="Moral links not as expected in multi-link case.",
        )

        graph = ADMG(
            vertices=("a", "b", "c", "d", "e"),
            di_edges=[("a", "c"), ("b", "c"), ("c", "e"), ("d", "e")],
        )
        links = set(tuple(sorted(e)) for e in get_moral_links(graph))
        self.assertEqual(
            {("a", "b"), ("c", "d")},
            links,
            msg="Moral links not as expected in multi-site case.",
        )


class TestGetConditionalIndependencies(unittest.TestCase):
    """Test getting conditional independencies."""

    def assert_example_has_judgements(self, example: Example) -> None:
        """Assert that the example is consistent w.r.t. D-separations."""
        self.assertIsNotNone(example.conditional_independencies)
        self.assert_has_judgements(
            graph=example.graph,
            judgements=example.conditional_independencies,
        )

    def assert_has_judgements(
        self, graph: Union[NxMixedGraph, SG], judgements: Iterable[DSeparationJudgement]
    ) -> None:
        """Assert that the graph has the correct conditional independencies.

        :param graph: the graph to test
        :param judgements: the set of expected conditional independencies
        """
        asserted_judgements = set(judgements)
        observed_judgements = get_conditional_independencies(graph)

        self.assertIsNotNone(asserted_judgements, "Expected independencies is empty.")
        self.assertIsNotNone(observed_judgements, "Observed independencies is empty.")
        self.assertTrue(
            all(judgement.is_canonical for judgement in observed_judgements),
            msg="one or more of the returned DSeparationJudgement instances are not canonical",
        )

        self.assert_valid_judgements(graph, asserted_judgements)
        self.assert_valid_judgements(graph, observed_judgements)

        expected_pairs = {(judgement.left, judgement.right) for judgement in asserted_judgements}
        observed_pairs = {(judgement.left, judgement.right) for judgement in observed_judgements}
        self.assertEqual(
            expected_pairs,
            observed_pairs,
            "Judgements do not find same separable pairs",
        )

        def _get_match(ref, options):
            """Find a judgement that has the same left/right pair as the reference judgement."""
            for alt in options:
                if ref.left == alt.left and ref.right == alt.right:
                    return alt
            return None

        for judgement in asserted_judgements:
            with self.subTest(name=judgement):
                matching = _get_match(judgement, observed_judgements)
                self.assertIsNotNone(matching, "No matching judgement found.")
                self.assertGreaterEqual(
                    len(judgement.conditions),
                    len(matching.conditions),
                    msg="Observed conditional independence more complicated than reference.",
                )

    def assert_valid_judgements(
        self, graph: Union[NxMixedGraph, SG], judgements: Set[DSeparationJudgement]
    ) -> None:
        """Check that a set of judgments are valid with respect to a graph."""
        if isinstance(graph, NxMixedGraph):
            graph = graph.to_admg()
        for judgement in judgements:
            self.assertTrue(
                are_d_separated(
                    graph,
                    judgement.left,
                    judgement.right,
                    conditions=judgement.conditions,
                ),
                msg=f"Conditional independency is not a d-separation in {graph}",
            )

        pairs = [(judgement.left, judgement.right) for judgement in judgements]
        self.assertEqual(len(pairs), len(set(pairs)), "Duplicate left/right pair observed")

    def test_examples(self):
        """Test getting the conditional independencies from the example graphs."""
        testable = (
            example for example in examples if example.conditional_independencies is not None
        )

        for example in testable:
            with self.subTest(name=example.name):
                self.maxDiff = None
                self.assert_example_has_judgements(example)

# -*- coding: utf-8 -*- Test getting conditional independencies (and related). Test the d-separation utility. Test checking D-separation on the MIT example. Check that example conditional independencies are d-separations and that conditions (if present) are required.          This test is using convenient examples to ensure that the d-separation algorithm         isn't just always returning true or false. Test adding 'moral links' (part of the d-separation algorithm).          This test covers several cases around moral links to ensure that they are added when needed. Test getting conditional independencies. Assert that the example is consistent w.r.t. D-separations. Assert that the graph has the correct conditional independencies.          :param graph: the graph to test         :param judgements: the set of expected conditional independencies Find a judgement that has the same left/right pair as the reference judgement. Check that a set of judgments are valid with respect to a graph. Test getting the conditional independencies from the example graphs.