This module implements the StructureMCMC class, which is a specialized Markov Chain Monte Carlo (MCMC) sampler for structure learning in graphical models.
 
The StructureMCMC class extends the base MCMC class and provides functionality for sampling graph structures based on a given dataset, scoring function, and proposal mechanism. It supports initialization with a predefined graph or a graph generated using the PC algorithm, and allows for the inclusion of edge constraints through blacklists and whitelists.
 
Classes:
    StructureMCMC: A class for performing Structure MCMC simulations.
 
Dependencies:
    - numpy
    - pandas
    - structure_learning.proposals
    - structure_learning.data_structures
    - structure_learning.scores
    - structure_learning.samplers

Modules
		numpy pandas time

Classes
		structure_learning.approximators.mcmc.MCMC(structure_learning.approximators.approximator.Approximator) StructureMCMC   class StructureMCMC(structure_learning.approximators.mcmc.MCMC)     StructureMCMC(data: pandas.core.frame.DataFrame = None, initial_state: numpy.ndarray = None, max_iter: int = 30000, score_object: Union[str, structure_learning.scores.score.Score] = None, proposal_object: structure_learning.proposals.proposal.StructureLearningProposal = None, prior: structure_learning.priors.prior.Prior = None, pc_init=True, pc_significance_level=0.01, pc_ci_test='pearsonr', blacklist: numpy.ndarray = None, whitelist: numpy.ndarray = None, seed: int = None, sparse=True, result_type: str = 'distribution', graph_type='dag', burn_in: float = 0.1)   Implementation of Structure MCMC.     Method resolution order: StructureMCMC structure_learning.approximators.mcmc.MCMC structure_learning.approximators.approximator.Approximator abc.ABC builtins.object Methods defined here: __init__(self, data: pandas.core.frame.DataFrame = None, initial_state: numpy.ndarray = None, max_iter: int = 30000, score_object: Union[str, structure_learning.scores.score.Score] = None, proposal_object: structure_learning.proposals.proposal.StructureLearningProposal = None, prior: structure_learning.priors.prior.Prior = None, pc_init=True, pc_significance_level=0.01, pc_ci_test='pearsonr', blacklist: numpy.ndarray = None, whitelist: numpy.ndarray = None, seed: int = None, sparse=True, result_type: str = 'distribution', graph_type='dag', burn_in: float = 0.1) Initilialise Structure MCMC instance.   Parameters:     data (pd.DataFrame):                            Dataset. Optional if score_object is given.     initial_state (numpy.ndarray | DAG | None):           Initial graph for the MCMC simulation.                                                     If None, simulation starts with a random graph or a graph                                                     constructed from PC algorithm.     max_iter (int):                                 The number of MCMC iterations to run. Default: 30000.     score_object (Score):                           A score object implementing compute(). If None, BGeScore is used                                                     (data must be provided). Default: None.     proposal_object (StructureLearningProposal):    A proposal object. If None, a GraphProposal instance is used.                                                     Default: None.     pc_init (bool):                                 If True and initial_graph is not given, PC algorithm will be used                                                     to generate initial graph.     blacklist (numpy.ndarray):                      Mask for edges to ignore in the proposal     whitelist (numpy.ndarray):                      Mask for edges to include in the proposal step(self) Perform one MCMC iteration   Returns:     (dict): information on one MCMC iteration Data and other attributes defined here: __abstractmethods__ = frozenset() Methods inherited from structure_learning.approximators.mcmc.MCMC: __str__(self) Return str(self). config(self) Get the configuration of the MCMC instance.   Returns:     dict: Configuration dictionary. get_chain_info(self, results, key='graph') Extract chain information from the MCMC results.   Parameters:     results (dict): Results of the MCMC simulation.     key (str): Key to extract information for. Default is 'graph'.   Returns:     list: Chain information. get_graphs(self, results) Retrieve a list of sampled graphs from the MCMC results.   Parameters:     results (dict): Results of the MCMC simulation.   Returns:     list: Sampled graphs. run(self, intervals=-1) -> Tuple[dict, float] Execute the MCMC simulation.   Returns:     Tuple[dict, float]: Results of the simulation and acceptance ratio. to_cpdag_distribution(self) to_distribution(self) Convert the MCMC results to a distribution.   Returns:     MCMCDistribution: Distribution object. to_opad(self, plus=False) Convert the MCMC results to an OPAD object.   Parameters:     plus (bool): Whether to use the plus1 neighborhood. Default is False.   Returns:     OPAD: OPAD object. traceplot(self, ax=None) Generate a trace plot of the MCMC simulation.   Parameters:     ax (matplotlib.axes.Axes): Matplotlib axis to plot on. Default is None.   Returns:     list: Plot object. update_results(self, iteration, info) Update the results of the MCMC simulation with information from the current iteration.   Parameters:     iteration (int): Current iteration number.     info (dict): Information about the current iteration. Readonly properties inherited from structure_learning.approximators.mcmc.MCMC: trace Data and other attributes inherited from structure_learning.approximators.mcmc.MCMC: RESULT_TYPE_DIST = 'distribution' RESULT_TYPE_ITER = 'iterates' RESULT_TYPE_OPAD = 'opad' RESULT_TYPE_OPAD_PLUS = 'opad+' Methods inherited from structure_learning.approximators.approximator.Approximator: save(self, filename: str, compression='gzip') Saves the Graph object to a file.   Parameters:     filename (str): Path to the output file. Class methods inherited from structure_learning.approximators.approximator.Approximator: load(filename: str, compression='gzip') Loads a Graph object from a file.   Parameters:     filename (str): Path to the input file.   Returns:     Graph: Loaded Graph object. Data descriptors inherited from structure_learning.approximators.approximator.Approximator: __dict__ dictionary for instance variables __weakref__ list of weak references to the object

Data
		Union = typing.Union