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Add option to control depth in PC algorithm #242

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28 changes: 18 additions & 10 deletions causallearn/search/ConstraintBased/PC.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@ def pc(
verbose: bool = False,
show_progress: bool = True,
node_names: List[str] | None = None,
max_k: int = None,
**kwargs
):
if data.shape[0] < data.shape[1]:
Expand All @@ -41,11 +42,11 @@ def pc(
return mvpc_alg(data=data, node_names=node_names, alpha=alpha, indep_test=indep_test, correction_name=correction_name, stable=stable,
uc_rule=uc_rule, uc_priority=uc_priority, background_knowledge=background_knowledge,
verbose=verbose,
show_progress=show_progress, **kwargs)
show_progress=show_progress, max_k=max_k, **kwargs)
else:
return pc_alg(data=data, node_names=node_names, alpha=alpha, indep_test=indep_test, stable=stable, uc_rule=uc_rule,
uc_priority=uc_priority, background_knowledge=background_knowledge, verbose=verbose,
show_progress=show_progress, **kwargs)
show_progress=show_progress, max_k=max_k, **kwargs)


def pc_alg(
Expand All @@ -59,6 +60,7 @@ def pc_alg(
background_knowledge: BackgroundKnowledge | None = None,
verbose: bool = False,
show_progress: bool = True,
max_k=None,
**kwargs
) -> CausalGraph:
"""
Expand Down Expand Up @@ -103,7 +105,7 @@ def pc_alg(
indep_test = CIT(data, indep_test, **kwargs)
cg_1 = SkeletonDiscovery.skeleton_discovery(data, alpha, indep_test, stable,
background_knowledge=background_knowledge, verbose=verbose,
show_progress=show_progress, node_names=node_names)
show_progress=show_progress, node_names=node_names, max_k=max_k)

if background_knowledge is not None:
orient_by_background_knowledge(cg_1, background_knowledge)
Expand Down Expand Up @@ -142,14 +144,15 @@ def mvpc_alg(
data: ndarray,
node_names: List[str] | None,
alpha: float,
indep_test: str,
indep_test: Any,
correction_name: str,
stable: bool,
uc_rule: int,
uc_priority: int,
background_knowledge: BackgroundKnowledge | None = None,
verbose: bool = False,
show_progress: bool = True,
max_k: int | None = None,
**kwargs,
) -> CausalGraph:
"""
Expand Down Expand Up @@ -197,14 +200,14 @@ def mvpc_alg(
start = time.time()
indep_test = CIT(data, indep_test, **kwargs)
## Step 1: detect the direct causes of missingness indicators
prt_m = get_parent_missingness_pairs(data, alpha, indep_test, stable)
prt_m = get_parent_missingness_pairs(data, alpha, indep_test, stable, max_k=max_k)
# print('Finish detecting the parents of missingness indicators. ')

## Step 2:
## a) Run PC algorithm with the 1st step skeleton;
cg_pre = SkeletonDiscovery.skeleton_discovery(data, alpha, indep_test, stable,
background_knowledge=background_knowledge,
verbose=verbose, show_progress=show_progress, node_names=node_names)
verbose=verbose, show_progress=show_progress, node_names=node_names, max_k=max_k)
if background_knowledge is not None:
orient_by_background_knowledge(cg_pre, background_knowledge)

Expand Down Expand Up @@ -251,7 +254,7 @@ def mvpc_alg(

#######################################################################################################################
## *********** Functions for Step 1 ***********
def get_parent_missingness_pairs(data: ndarray, alpha: float, indep_test, stable: bool = True) -> Dict[str, list]:
def get_parent_missingness_pairs(data: ndarray, alpha: float, indep_test, stable: bool = True, max_k: int | None = None) -> Dict[str, list]:
"""
Detect the parents of missingness indicators
If a missingness indicator has no parent, it will not be included in the result
Expand All @@ -272,7 +275,7 @@ def get_parent_missingness_pairs(data: ndarray, alpha: float, indep_test, stable
## Get the index of parents of missingness indicators
# If the missingness indicator has no parent, then it will not be collected in prt_m
for missingness_i in missingness_index:
parent_of_missingness_i = detect_parent(missingness_i, data, alpha, indep_test, stable)
parent_of_missingness_i = detect_parent(missingness_i, data, alpha, indep_test, stable, max_k=max_k)
if not isempty(parent_of_missingness_i):
parent_missingness_pairs['prt'].append(parent_of_missingness_i)
parent_missingness_pairs['m'].append(missingness_i)
Expand All @@ -299,7 +302,7 @@ def get_missingness_index(data: ndarray) -> List[int]:
return missingness_index


def detect_parent(r: int, data_: ndarray, alpha: float, indep_test, stable: bool = True) -> ndarray:
def detect_parent(r: int, data_: ndarray, alpha: float, indep_test: Any, stable: bool = True, max_k: int | None = None) -> ndarray:
"""Detect the parents of a missingness indicator
:param r: the missingness indicator
:param data_: data set (numpy ndarray)
Expand Down Expand Up @@ -334,15 +337,19 @@ def detect_parent(r: int, data_: ndarray, alpha: float, indep_test, stable: bool

no_of_var = data.shape[1]
cg = CausalGraph(no_of_var)
cg.set_ind_test(CIT(data, indep_test.method))
cg.set_ind_test(indep_test)


node_ids = range(no_of_var)
pair_of_variables = list(permutations(node_ids, 2))

depth = -1
while cg.max_degree() - 1 > depth:
depth += 1
if max_k is not None and depth > max_k:
break
edge_removal = []

for (x, y) in pair_of_variables:

## *********** Adaptation 2 ***********
Expand Down Expand Up @@ -495,3 +502,4 @@ def matrix_diff(cg1: CausalGraph, cg2: CausalGraph) -> (float, List[Tuple[int, i
diff_ls.append((i, j))
count += 1
return count / 2, diff_ls

4 changes: 3 additions & 1 deletion causallearn/utils/PCUtils/SkeletonDiscovery.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ def skeleton_discovery(
background_knowledge: BackgroundKnowledge | None = None,
verbose: bool = False,
show_progress: bool = True,
node_names: List[str] | None = None,
node_names: List[str] | None = None, max_k=None,
) -> CausalGraph:
"""
Perform skeleton discovery
Expand Down Expand Up @@ -63,6 +63,8 @@ def skeleton_discovery(
pbar = tqdm(total=no_of_var) if show_progress else None
while cg.max_degree() - 1 > depth:
depth += 1
if max_k is not None and depth > max_k:
break
edge_removal = []
if show_progress:
pbar.reset()
Expand Down