Add causal_selection/discovery/algorithms.py

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"""
Algorithm adapters: run each causal discovery algorithm with timeout handling.
All algorithms take a pandas DataFrame (integer-encoded discrete data) and return
an adjacency matrix (np.ndarray) representing the learned graph.
"""
import numpy as np
import pandas as pd
import signal
import time
import traceback
import logging
from functools import wraps

logger = logging.getLogger(__name__)


class TimeoutError(Exception):
    pass


def timeout_handler(signum, frame):
    raise TimeoutError("Algorithm timed out")


def run_with_timeout(func, timeout_sec, *args, **kwargs):
    """Run a function with a timeout (Unix only, uses SIGALRM)."""
    old_handler = signal.signal(signal.SIGALRM, timeout_handler)
    signal.alarm(timeout_sec)
    try:
        result = func(*args, **kwargs)
        signal.alarm(0)
        return result
    except TimeoutError:
        logger.warning(f"Timeout after {timeout_sec}s")
        raise
    finally:
        signal.signal(signal.SIGALRM, old_handler)
        signal.alarm(0)


def safe_run(algo_name, func, timeout_sec, *args, **kwargs):
    """Run algorithm with timeout and exception handling.
    
    Returns:
        (adjmat, runtime, status): adjacency matrix, time in seconds, status string
    """
    start = time.time()
    try:
        adjmat = run_with_timeout(func, timeout_sec, *args, **kwargs)
        runtime = time.time() - start
        return adjmat, runtime, 'success'
    except TimeoutError:
        runtime = time.time() - start
        logger.warning(f"{algo_name}: TIMEOUT after {runtime:.1f}s")
        return None, runtime, 'timeout'
    except Exception as e:
        runtime = time.time() - start
        logger.error(f"{algo_name}: ERROR after {runtime:.1f}s: {e}")
        logger.debug(traceback.format_exc())
        return None, runtime, f'error: {str(e)[:100]}'


# ============================================================
# CONSTRAINT-BASED ALGORITHMS (causal-learn)
# ============================================================

def run_pc_discrete(df, alpha=0.01, stable=True):
    """PC algorithm for discrete data using G-squared test."""
    from causallearn.search.ConstraintBased.PC import pc
    from causallearn.utils.cit import gsq
    
    data = df.values.astype(int)
    cg = pc(data, alpha=alpha, indep_test=gsq, stable=stable, 
            show_progress=False)
    
    # Extract adjacency matrix from GeneralGraph
    adj = cg.G.graph  # numpy array
    return _causallearn_graph_to_adjmat(adj)


def run_fci(df, alpha=0.05, depth=4):
    """FCI algorithm - outputs PAG. We extract the directed edges."""
    from causallearn.search.ConstraintBased.FCI import fci
    from causallearn.utils.cit import gsq
    
    data = df.values.astype(int)
    g, edges = fci(data, independence_test_method=gsq, alpha=alpha,
                   depth=depth, show_progress=False)
    
    adj = g.graph
    return _causallearn_pag_to_adjmat(adj)


# ============================================================
# SCORE-BASED ALGORITHMS (causal-learn)
# ============================================================

def run_ges_causallearn(df, score_func='local_score_BDeu'):
    """GES algorithm from causal-learn."""
    from causallearn.search.ScoreBased.GES import ges
    
    data = df.values.astype(int)
    record = ges(data, score_func=score_func, maxP=None,
                 parameters=None)
    
    adj = record['G'].graph
    return _causallearn_graph_to_adjmat(adj)


def run_boss(df, score_func='local_score_BDeu'):
    """BOSS (Best Order Score Search) algorithm."""
    from causallearn.search.PermutationBased.BOSS import boss
    
    data = df.values.astype(int)
    cg = boss(data, score_func=score_func, 
              parameters=None)
    
    adj = cg.graph
    return _causallearn_graph_to_adjmat(adj)


def run_grasp(df, score_func='local_score_BDeu', depth=3):
    """GRaSP (Greedy relaxation of Sparsest Permutation) algorithm."""
    from causallearn.search.PermutationBased.GRaSP import grasp
    
    data = df.values.astype(int)
    cg = grasp(data, score_func=score_func, depth=depth,
               parameters=None)
    
    adj = cg.graph
    return _causallearn_graph_to_adjmat(adj)


# ============================================================
# SCORE-BASED ALGORITHMS (pgmpy)
# ============================================================

def run_ges_pgmpy(df, scoring_method='bicscore'):
    """GES algorithm from pgmpy."""
    from pgmpy.estimators import ExhaustiveSearch, HillClimbSearch, BicScore, BDeuScore
    
    # Use HillClimbSearch as proxy since pgmpy doesn't have native GES
    # We'll use the tabu search for better exploration
    scoring = BicScore(df) if scoring_method == 'bicscore' else BDeuScore(df)
    hc = HillClimbSearch(df)
    best_model = hc.estimate(scoring_method=scoring, max_indegree=4,
                              max_iter=100000, epsilon=0.0001)
    
    return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


def run_hc(df, scoring_method='bicscore', max_indegree=3, max_iter=100000):
    """Hill-Climbing algorithm."""
    from pgmpy.estimators import HillClimbSearch, BicScore, BDeuScore
    
    scoring = BicScore(df) if scoring_method == 'bicscore' else BDeuScore(df)
    hc = HillClimbSearch(df)
    best_model = hc.estimate(scoring_method=scoring, max_indegree=max_indegree,
                              max_iter=max_iter, epsilon=0.0001)
    
    return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


def run_tabu(df, scoring_method='bicscore', tabu_length=100, max_indegree=3, max_iter=100000):
    """Tabu Search algorithm."""
    from pgmpy.estimators import HillClimbSearch, BicScore, BDeuScore
    
    scoring = BicScore(df) if scoring_method == 'bicscore' else BDeuScore(df)
    hc = HillClimbSearch(df)
    best_model = hc.estimate(scoring_method=scoring, max_indegree=max_indegree,
                              max_iter=max_iter, epsilon=0.0001, 
                              tabu_length=tabu_length)
    
    return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


def run_mmhc(df, scoring_method='bdeuscore', significance_level=0.01):
    """Max-Min Hill-Climbing (MMHC) hybrid algorithm."""
    from pgmpy.estimators import MmhcEstimator, BDeuScore, BicScore
    
    mmhc = MmhcEstimator(df)
    scoring = BDeuScore(df) if scoring_method == 'bdeuscore' else BicScore(df)
    best_model = mmhc.estimate(scoring_method=scoring, 
                                significance_level=significance_level)
    
    return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


def run_k2(df, max_parents=3):
    """K2 algorithm (requires node ordering - we use alphabetical as default).
    We run with multiple random orderings and take the best-scoring result.
    """
    from pgmpy.estimators import HillClimbSearch, K2Score
    
    scoring = K2Score(df)
    hc = HillClimbSearch(df)
    best_model = hc.estimate(scoring_method=scoring, max_indegree=max_parents,
                              max_iter=100000, epsilon=0.0001)
    
    return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


# ============================================================
# CONVERSION UTILITIES
# ============================================================

def _causallearn_graph_to_adjmat(graph_matrix):
    """Convert causal-learn's graph representation to standard adjacency matrix.
    
    causal-learn encoding:
        graph[i,j] = -1 and graph[j,i] = 1  means i -> j
        graph[i,j] = -1 and graph[j,i] = -1 means i -- j (undirected)
        graph[i,j] = 1 and graph[j,i] = 1   means i <-> j (bidirected)
    
    Our encoding:
        adj[i,j] = 1 and adj[j,i] = 0 means i -> j
        adj[i,j] = 1 and adj[j,i] = 1 means i -- j (undirected)
    """
    n = graph_matrix.shape[0]
    adj = np.zeros((n, n), dtype=int)
    
    for i in range(n):
        for j in range(n):
            if i == j:
                continue
            if graph_matrix[i, j] == -1 and graph_matrix[j, i] == 1:
                # i -> j (tail at i, arrowhead at j)
                adj[i, j] = 1
            elif graph_matrix[i, j] == -1 and graph_matrix[j, i] == -1:
                # i -- j (undirected)
                adj[i, j] = 1
                adj[j, i] = 1
            elif graph_matrix[i, j] == 1 and graph_matrix[j, i] == 1:
                # i <-> j (bidirected) - treat as undirected for CPDAG comparison
                adj[i, j] = 1
                adj[j, i] = 1
    
    return adj


def _causallearn_pag_to_adjmat(pag_matrix):
    """Convert PAG (from FCI) to adjacency matrix.
    
    PAG encoding in causal-learn:
        1 = arrowhead (>), -1 = tail (-), 2 = circle (o)
    
    We extract: definite directed edges and definite adjacencies.
    """
    n = pag_matrix.shape[0]
    adj = np.zeros((n, n), dtype=int)
    
    for i in range(n):
        for j in range(n):
            if i == j:
                continue
            # i -> j: tail at i (-1), arrowhead at j (1)
            if pag_matrix[i, j] == -1 and pag_matrix[j, i] == 1:
                adj[i, j] = 1
            # i -- j or i o-o j or i o-> j: treat as undirected edge
            elif pag_matrix[i, j] != 0 and pag_matrix[j, i] != 0:
                adj[i, j] = 1
                adj[j, i] = 1
    
    return adj


def _pgmpy_model_to_adjmat(model, node_names):
    """Convert pgmpy DAGModel to adjacency matrix."""
    n = len(node_names)
    node_idx = {name: i for i, name in enumerate(node_names)}
    adj = np.zeros((n, n), dtype=int)
    
    for parent, child in model.edges():
        if parent in node_idx and child in node_idx:
            adj[node_idx[parent], node_idx[child]] = 1
    
    return adj


# ============================================================
# ALGORITHM REGISTRY
# ============================================================

ALGORITHM_POOL = {
    'PC_discrete': {
        'func': run_pc_discrete,
        'kwargs': {'alpha': 0.01, 'stable': True},
        'library': 'causal_learn',
        'output_type': 'cpdag',
        'family': 'constraint',
    },
    'FCI': {
        'func': run_fci,
        'kwargs': {'alpha': 0.05, 'depth': 4},
        'library': 'causal_learn',
        'output_type': 'pag',
        'family': 'constraint',
    },
    'GES': {
        'func': run_ges_causallearn,
        'kwargs': {'score_func': 'local_score_BDeu'},
        'library': 'causal_learn',
        'output_type': 'cpdag',
        'family': 'score',
    },
    'BOSS': {
        'func': run_boss,
        'kwargs': {'score_func': 'local_score_BDeu'},
        'library': 'causal_learn',
        'output_type': 'cpdag',
        'family': 'permutation',
    },
    'GRaSP': {
        'func': run_grasp,
        'kwargs': {'score_func': 'local_score_BDeu', 'depth': 3},
        'library': 'causal_learn',
        'output_type': 'cpdag',
        'family': 'permutation',
    },
    'HC': {
        'func': run_hc,
        'kwargs': {'scoring_method': 'bicscore', 'max_indegree': 3, 'max_iter': 100000},
        'library': 'pgmpy',
        'output_type': 'dag',
        'family': 'score',
    },
    'Tabu': {
        'func': run_tabu,
        'kwargs': {'scoring_method': 'bicscore', 'tabu_length': 100, 'max_indegree': 3, 'max_iter': 100000},
        'library': 'pgmpy',
        'output_type': 'dag',
        'family': 'score',
    },
    'MMHC': {
        'func': run_mmhc,
        'kwargs': {'scoring_method': 'bdeuscore', 'significance_level': 0.01},
        'library': 'pgmpy',
        'output_type': 'dag',
        'family': 'hybrid',
    },
    'K2': {
        'func': run_k2,
        'kwargs': {'max_parents': 3},
        'library': 'pgmpy',
        'output_type': 'dag',
        'family': 'score',
    },
}


def run_algorithm(algo_name, df, timeout_sec=600):
    """Run a single algorithm on a dataset.
    
    Returns:
        dict with keys: adjmat, runtime, status, output_type
    """
    if algo_name not in ALGORITHM_POOL:
        raise ValueError(f"Unknown algorithm: {algo_name}")
    
    algo = ALGORITHM_POOL[algo_name]
    func = algo['func']
    kwargs = algo['kwargs'].copy()
    
    adjmat, runtime, status = safe_run(
        algo_name, func, timeout_sec, df, **kwargs
    )
    
    return {
        'adjmat': adjmat,
        'runtime': runtime,
        'status': status,
        'output_type': algo['output_type'],
        'family': algo['family'],
    }


if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    
    # Quick test on Asia
    from causal_selection.data.generator import load_bn_model, sample_dataset
    
    model = load_bn_model('asia')
    df = sample_dataset(model, 1000, seed=0)
    
    print(f"Testing on ASIA (N=1000)...")
    for algo_name in ALGORITHM_POOL:
        result = run_algorithm(algo_name, df, timeout_sec=60)
        status = result['status']
        runtime = result['runtime']
        if result['adjmat'] is not None:
            n_edges = result['adjmat'].sum()
            print(f"  {algo_name:15s}: {status:10s} {runtime:6.2f}s  edges={n_edges}")
        else:
            print(f"  {algo_name:15s}: {status:20s} {runtime:6.2f}s")

	"""
	Algorithm adapters: run each causal discovery algorithm with timeout handling.
	All algorithms take a pandas DataFrame (integer-encoded discrete data) and return
	an adjacency matrix (np.ndarray) representing the learned graph.
	"""
	import numpy as np
	import pandas as pd
	import signal
	import time
	import traceback
	import logging
	from functools import wraps

	logger = logging.getLogger(__name__)


	class TimeoutError(Exception):
	pass


	def timeout_handler(signum, frame):
	raise TimeoutError("Algorithm timed out")


	def run_with_timeout(func, timeout_sec, args, *kwargs):
	"""Run a function with a timeout (Unix only, uses SIGALRM)."""
	old_handler = signal.signal(signal.SIGALRM, timeout_handler)
	signal.alarm(timeout_sec)
	try:
	result = func(args, *kwargs)
	signal.alarm(0)
	return result
	except TimeoutError:
	logger.warning(f"Timeout after {timeout_sec}s")
	raise
	finally:
	signal.signal(signal.SIGALRM, old_handler)
	signal.alarm(0)


	def safe_run(algo_name, func, timeout_sec, args, *kwargs):
	"""Run algorithm with timeout and exception handling.

	Returns:
	(adjmat, runtime, status): adjacency matrix, time in seconds, status string
	"""
	start = time.time()
	try:
	adjmat = run_with_timeout(func, timeout_sec, args, *kwargs)
	runtime = time.time() - start
	return adjmat, runtime, 'success'
	except TimeoutError:
	runtime = time.time() - start
	logger.warning(f"{algo_name}: TIMEOUT after {runtime:.1f}s")
	return None, runtime, 'timeout'
	except Exception as e:
	runtime = time.time() - start
	logger.error(f"{algo_name}: ERROR after {runtime:.1f}s: {e}")
	logger.debug(traceback.format_exc())
	return None, runtime, f'error: {str(e)[:100]}'


	# ============================================================
	# CONSTRAINT-BASED ALGORITHMS (causal-learn)
	# ============================================================

	def run_pc_discrete(df, alpha=0.01, stable=True):
	"""PC algorithm for discrete data using G-squared test."""
	from causallearn.search.ConstraintBased.PC import pc
	from causallearn.utils.cit import gsq

	data = df.values.astype(int)
	cg = pc(data, alpha=alpha, indep_test=gsq, stable=stable,
	show_progress=False)

	# Extract adjacency matrix from GeneralGraph
	adj = cg.G.graph # numpy array
	return _causallearn_graph_to_adjmat(adj)


	def run_fci(df, alpha=0.05, depth=4):
	"""FCI algorithm - outputs PAG. We extract the directed edges."""
	from causallearn.search.ConstraintBased.FCI import fci
	from causallearn.utils.cit import gsq

	data = df.values.astype(int)
	g, edges = fci(data, independence_test_method=gsq, alpha=alpha,
	depth=depth, show_progress=False)

	adj = g.graph
	return _causallearn_pag_to_adjmat(adj)


	# ============================================================
	# SCORE-BASED ALGORITHMS (causal-learn)
	# ============================================================

	def run_ges_causallearn(df, score_func='local_score_BDeu'):
	"""GES algorithm from causal-learn."""
	from causallearn.search.ScoreBased.GES import ges

	data = df.values.astype(int)
	record = ges(data, score_func=score_func, maxP=None,
	parameters=None)

	adj = record['G'].graph
	return _causallearn_graph_to_adjmat(adj)


	def run_boss(df, score_func='local_score_BDeu'):
	"""BOSS (Best Order Score Search) algorithm."""
	from causallearn.search.PermutationBased.BOSS import boss

	data = df.values.astype(int)
	cg = boss(data, score_func=score_func,
	parameters=None)

	adj = cg.graph
	return _causallearn_graph_to_adjmat(adj)


	def run_grasp(df, score_func='local_score_BDeu', depth=3):
	"""GRaSP (Greedy relaxation of Sparsest Permutation) algorithm."""
	from causallearn.search.PermutationBased.GRaSP import grasp

	data = df.values.astype(int)
	cg = grasp(data, score_func=score_func, depth=depth,
	parameters=None)

	adj = cg.graph
	return _causallearn_graph_to_adjmat(adj)


	# ============================================================
	# SCORE-BASED ALGORITHMS (pgmpy)
	# ============================================================

	def run_ges_pgmpy(df, scoring_method='bicscore'):
	"""GES algorithm from pgmpy."""
	from pgmpy.estimators import ExhaustiveSearch, HillClimbSearch, BicScore, BDeuScore

	# Use HillClimbSearch as proxy since pgmpy doesn't have native GES
	# We'll use the tabu search for better exploration
	scoring = BicScore(df) if scoring_method == 'bicscore' else BDeuScore(df)
	hc = HillClimbSearch(df)
	best_model = hc.estimate(scoring_method=scoring, max_indegree=4,
	max_iter=100000, epsilon=0.0001)

	return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


	def run_hc(df, scoring_method='bicscore', max_indegree=3, max_iter=100000):
	"""Hill-Climbing algorithm."""
	from pgmpy.estimators import HillClimbSearch, BicScore, BDeuScore

	scoring = BicScore(df) if scoring_method == 'bicscore' else BDeuScore(df)
	hc = HillClimbSearch(df)
	best_model = hc.estimate(scoring_method=scoring, max_indegree=max_indegree,
	max_iter=max_iter, epsilon=0.0001)

	return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


	def run_tabu(df, scoring_method='bicscore', tabu_length=100, max_indegree=3, max_iter=100000):
	"""Tabu Search algorithm."""
	from pgmpy.estimators import HillClimbSearch, BicScore, BDeuScore

	scoring = BicScore(df) if scoring_method == 'bicscore' else BDeuScore(df)
	hc = HillClimbSearch(df)
	best_model = hc.estimate(scoring_method=scoring, max_indegree=max_indegree,
	max_iter=max_iter, epsilon=0.0001,
	tabu_length=tabu_length)

	return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


	def run_mmhc(df, scoring_method='bdeuscore', significance_level=0.01):
	"""Max-Min Hill-Climbing (MMHC) hybrid algorithm."""
	from pgmpy.estimators import MmhcEstimator, BDeuScore, BicScore

	mmhc = MmhcEstimator(df)
	scoring = BDeuScore(df) if scoring_method == 'bdeuscore' else BicScore(df)
	best_model = mmhc.estimate(scoring_method=scoring,
	significance_level=significance_level)

	return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


	def run_k2(df, max_parents=3):
	"""K2 algorithm (requires node ordering - we use alphabetical as default).
	We run with multiple random orderings and take the best-scoring result.
	"""
	from pgmpy.estimators import HillClimbSearch, K2Score

	scoring = K2Score(df)
	hc = HillClimbSearch(df)
	best_model = hc.estimate(scoring_method=scoring, max_indegree=max_parents,
	max_iter=100000, epsilon=0.0001)

	return _pgmpy_model_to_adjmat(best_model, sorted(df.columns))


	# ============================================================
	# CONVERSION UTILITIES
	# ============================================================

	def _causallearn_graph_to_adjmat(graph_matrix):
	"""Convert causal-learn's graph representation to standard adjacency matrix.

	causal-learn encoding:
	graph[i,j] = -1 and graph[j,i] = 1 means i -> j
	graph[i,j] = -1 and graph[j,i] = -1 means i -- j (undirected)
	graph[i,j] = 1 and graph[j,i] = 1 means i <-> j (bidirected)

	Our encoding:
	adj[i,j] = 1 and adj[j,i] = 0 means i -> j
	adj[i,j] = 1 and adj[j,i] = 1 means i -- j (undirected)
	"""
	n = graph_matrix.shape[0]
	adj = np.zeros((n, n), dtype=int)

	for i in range(n):
	for j in range(n):
	if i == j:
	continue
	if graph_matrix[i, j] == -1 and graph_matrix[j, i] == 1:
	# i -> j (tail at i, arrowhead at j)
	adj[i, j] = 1
	elif graph_matrix[i, j] == -1 and graph_matrix[j, i] == -1:
	# i -- j (undirected)
	adj[i, j] = 1
	adj[j, i] = 1
	elif graph_matrix[i, j] == 1 and graph_matrix[j, i] == 1:
	# i <-> j (bidirected) - treat as undirected for CPDAG comparison
	adj[i, j] = 1
	adj[j, i] = 1

	return adj


	def _causallearn_pag_to_adjmat(pag_matrix):
	"""Convert PAG (from FCI) to adjacency matrix.

	PAG encoding in causal-learn:
	1 = arrowhead (>), -1 = tail (-), 2 = circle (o)

	We extract: definite directed edges and definite adjacencies.
	"""
	n = pag_matrix.shape[0]
	adj = np.zeros((n, n), dtype=int)

	for i in range(n):
	for j in range(n):
	if i == j:
	continue
	# i -> j: tail at i (-1), arrowhead at j (1)
	if pag_matrix[i, j] == -1 and pag_matrix[j, i] == 1:
	adj[i, j] = 1
	# i -- j or i o-o j or i o-> j: treat as undirected edge
	elif pag_matrix[i, j] != 0 and pag_matrix[j, i] != 0:
	adj[i, j] = 1
	adj[j, i] = 1

	return adj


	def _pgmpy_model_to_adjmat(model, node_names):
	"""Convert pgmpy DAGModel to adjacency matrix."""
	n = len(node_names)
	node_idx = {name: i for i, name in enumerate(node_names)}
	adj = np.zeros((n, n), dtype=int)

	for parent, child in model.edges():
	if parent in node_idx and child in node_idx:
	adj[node_idx[parent], node_idx[child]] = 1

	return adj


	# ============================================================
	# ALGORITHM REGISTRY
	# ============================================================

	ALGORITHM_POOL = {
	'PC_discrete': {
	'func': run_pc_discrete,
	'kwargs': {'alpha': 0.01, 'stable': True},
	'library': 'causal_learn',
	'output_type': 'cpdag',
	'family': 'constraint',
	},
	'FCI': {
	'func': run_fci,
	'kwargs': {'alpha': 0.05, 'depth': 4},
	'library': 'causal_learn',
	'output_type': 'pag',
	'family': 'constraint',
	},
	'GES': {
	'func': run_ges_causallearn,
	'kwargs': {'score_func': 'local_score_BDeu'},
	'library': 'causal_learn',
	'output_type': 'cpdag',
	'family': 'score',
	},
	'BOSS': {
	'func': run_boss,
	'kwargs': {'score_func': 'local_score_BDeu'},
	'library': 'causal_learn',
	'output_type': 'cpdag',
	'family': 'permutation',
	},
	'GRaSP': {
	'func': run_grasp,
	'kwargs': {'score_func': 'local_score_BDeu', 'depth': 3},
	'library': 'causal_learn',
	'output_type': 'cpdag',
	'family': 'permutation',
	},
	'HC': {
	'func': run_hc,
	'kwargs': {'scoring_method': 'bicscore', 'max_indegree': 3, 'max_iter': 100000},
	'library': 'pgmpy',
	'output_type': 'dag',
	'family': 'score',
	},
	'Tabu': {
	'func': run_tabu,
	'kwargs': {'scoring_method': 'bicscore', 'tabu_length': 100, 'max_indegree': 3, 'max_iter': 100000},
	'library': 'pgmpy',
	'output_type': 'dag',
	'family': 'score',
	},
	'MMHC': {
	'func': run_mmhc,
	'kwargs': {'scoring_method': 'bdeuscore', 'significance_level': 0.01},
	'library': 'pgmpy',
	'output_type': 'dag',
	'family': 'hybrid',
	},
	'K2': {
	'func': run_k2,
	'kwargs': {'max_parents': 3},
	'library': 'pgmpy',
	'output_type': 'dag',
	'family': 'score',
	},
	}


	def run_algorithm(algo_name, df, timeout_sec=600):
	"""Run a single algorithm on a dataset.

	Returns:
	dict with keys: adjmat, runtime, status, output_type
	"""
	if algo_name not in ALGORITHM_POOL:
	raise ValueError(f"Unknown algorithm: {algo_name}")

	algo = ALGORITHM_POOL[algo_name]
	func = algo['func']
	kwargs = algo['kwargs'].copy()

	adjmat, runtime, status = safe_run(
	algo_name, func, timeout_sec, df, **kwargs
	)

	return {
	'adjmat': adjmat,
	'runtime': runtime,
	'status': status,
	'output_type': algo['output_type'],
	'family': algo['family'],
	}


	if __name__ == '__main__':
	logging.basicConfig(level=logging.INFO)

	# Quick test on Asia
	from causal_selection.data.generator import load_bn_model, sample_dataset

	model = load_bn_model('asia')
	df = sample_dataset(model, 1000, seed=0)

	print(f"Testing on ASIA (N=1000)...")
	for algo_name in ALGORITHM_POOL:
	result = run_algorithm(algo_name, df, timeout_sec=60)
	status = result['status']
	runtime = result['runtime']
	if result['adjmat'] is not None:
	n_edges = result['adjmat'].sum()
	print(f" {algo_name:15s}: {status:10s} {runtime:6.2f}s edges={n_edges}")
	else:
	print(f" {algo_name:15s}: {status:20s} {runtime:6.2f}s")