:py:mod:`causal_testing.discovery.hill_climber_discovery`
=========================================================

.. py:module:: causal_testing.discovery.hill_climber_discovery

.. autoapi-nested-parse::

   This module implements a hill climbing algorithm to optimise causal DAGs based on the tests that pass/fail.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   causal_testing.discovery.hill_climber_discovery.HillClimberDiscovery




.. py:class:: HillClimberDiscovery(df: pandas.DataFrame, random_seed: int = 0, include_edges: str = None, exclude_edges: str = None, alpha: float = 0.05, max_iterations: int = 100, max_iterations_without_improvement: int = 10)


   Bases: :py:obj:`causal_testing.discovery.abstract_discovery.Discovery`

   Simple hill climber evolution of cauasl DAGs via 1+1EA.
   Attempts to maximise the number of passing tests and minimise the number of failing tests.

   .. py:method:: sum_test_outcomes(test_results: pandas.DataFrame) -> dict

      Aggregate the number of passing, failing, and inestimable tests
      :param test_results: Dataframe containing the raw pass/fail/inestimable outcome of each test case.
      :returns: Dictionary containing the number of pass/fail/inestimable outcomes.


   .. py:method:: evaluate_fitness(individual: causal_testing.specification.causal_dag.CausalDAG) -> tuple[tuple[float, float, float], list[tuple[str, str]]]

      Evaluate the fitness of a given causal DAG by evaluating the corresponding test cases using a tier based
      fitness metric.
      lexicographical order (max pass, minimise failure, minimise unknown)
      e.g. (X pass, Y fail, Z+1 unknown) is better than (X pass, Y+1 fail, Z unknown)

      :param individual: The candidate individual to evaluate.
      :returns: Tuple of the form (X, Y), where X is a triple containing the number of passing, failing, and
                inestimable tests respectively, and Y is a list of failing edges.


   .. py:method:: discover() -> causal_testing.specification.causal_dag.CausalDAG

      Discover the causal DAG.

      :returns: The inferred causal DAG.