diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3ac4614..9c0fe73 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -29,6 +29,28 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
### Fixed
+* **The GES, LGES and TGES baselines were substantially understated.** Two independent
+ problems, both now fixed and covered by regression tests:
+ * `ges_discovery` read causal-learn's adjacency matrix with the endpoints transposed,
+ so every directed contemporaneous edge came back reversed and every directed lagged
+ edge was silently dropped — only undirected edges survived.
+ * The vendored GES search behind `lges_discovery` / `tges_discovery`
+ (`causalts/lges.py`) is a hand-extracted condensation of upstream `ges`, and the
+ extraction broke the CPDAG construction and the Insert, Delete and Turn operators.
+ The forward phase stopped far short of the optimum, so LGES never converged — its
+ F1 sat at 0.35–0.52 on `ex2` no matter how much data it was given.
+
+ All three now also apply temporal background knowledge (a variable can only cause
+ another at an equal or later time step), which the lag-embedded search previously
+ ignored. On the `baseline_comparison` datasets, F1 moves from 0.125/0.154/0.522/0.333
+ (GES) and 0.400/0.417/0.526/0.333 (LGES/TGES) to 0.636/0.917/0.949/0.435 for all
+ three; LGES now reaches F1 1.000 on `ex2` by T=5,000. Every correction was verified
+ against upstream `ges` 1.1.1 — no defect originated with the upstream authors — and
+ the corrected search returns CPDAGs identical to upstream's on random DAGs while
+ running faster than it. `ges_discovery` gains an `engine=` argument selecting the
+ vendored search (default) or causal-learn.
+
+ Any prior comparison against these baselines understates them.
* `corrplot` dropped the right and bottom edges of its grid border. All axes
spines are hidden, and the border was drawn with `axhline`/`axvline` at
exactly the axis limits, so half of each boundary line fell outside the clip
diff --git a/causalts/baselines.py b/causalts/baselines.py
index 8562b58..0c6cb23 100644
--- a/causalts/baselines.py
+++ b/causalts/baselines.py
@@ -221,7 +221,60 @@ def _patched(Data, i, PAi, parameters=None):
_ges_mod.local_score_BIC_from_cov = _patched
-def ges_discovery(df, max_lag=1, score_func="local_score_BIC", lambda_value=None):
+def _lag_embed(df, max_lag):
+ """Stack the present block beside one block per lag: [X_t, X_{t-1}, ...]."""
+ data = df.values if isinstance(df, pd.DataFrame) else np.asarray(df)
+ T_orig, d = data.shape
+ blocks = [data[max_lag - lag : T_orig - lag, :] for lag in range(max_lag + 1)]
+ return np.hstack(blocks), d
+
+
+def _ges_fast(embedded, d, max_lag, lambda_value, forbidden):
+ """GES via the vendored search in :mod:`causalts.lges`.
+
+ Same algorithm as causal-learn's ``ges`` -- forward Insert phase then backward
+ Delete phase, no turning -- plus temporal background knowledge forbidding
+ edges from the present into the past (see ``temporal_forbidden``). Without
+ it, the search treats every lag-embedded column as an ordinary variable and
+ happily inserts and scores backward-in-time edges -- e.g. present X2 into
+ past-lag-1 X5 -- which are never reported (extraction only reads out
+ lag-to-present cells) but still consume search budget and can change which
+ forward edges win. Verified to return the same graph as causal-learn's
+ ``ges`` when there is no lag structure to protect (``max_lag == 0``, where
+ ``forbidden`` is all zeros). It is also much faster on large samples
+ because ``GaussObsL0Pen`` caches the scatter matrix, so each local score is
+ O(1) in the sample size instead of a fresh pass over the data.
+ """
+ from .lges import GaussObsL0Pen, fit
+
+ n = embedded.shape[0]
+ # causal-learn penalises lambda_value * (|pa| + 1) * log(n); GaussObsL0Pen
+ # penalises lmbda * (|pa| + 1), so fold the log(n) in to match.
+ lmbda = None if lambda_value is None else lambda_value * np.log(n)
+ A, metrics = fit(
+ GaussObsL0Pen(embedded, lmbda=lmbda),
+ phases=["forward", "backward"],
+ score_based=False,
+ prune=False,
+ forbidden=forbidden,
+ )
+
+ G_hat = np.zeros((d, d, max_lag + 1), dtype=int)
+ for lag in range(max_lag + 1):
+ for i in range(d):
+ for j in range(d):
+ if lag == 0 and i == j:
+ continue
+ # ges-package convention: A[u, v] != 0 means u -> v (both
+ # directions set means the edge is undirected).
+ if A[lag * d + i, j] != 0:
+ G_hat[i, j, lag] = 1
+ return G_hat, {"method": "ges", "engine": "fast", "cpdag": A, "metrics": metrics}
+
+
+def ges_discovery(
+ df, max_lag=1, score_func="local_score_BIC", lambda_value=None, engine="fast"
+):
"""Run GES (Greedy Equivalence Search) on lag-embedded data.
Score-based method that searches over equivalence classes of DAGs.
@@ -235,63 +288,87 @@ def ges_discovery(df, max_lag=1, score_func="local_score_BIC", lambda_value=None
max_lag : int
Number of lags to embed.
score_func : str
- Scoring function for GES (e.g. ``"local_score_BIC"``).
+ Scoring function for GES (e.g. ``"local_score_BIC"``). Only
+ ``"local_score_BIC"`` is available under ``engine="fast"``; any other
+ value transparently selects the causal-learn engine.
lambda_value : float or None
BIC penalty hyperparameter. Larger values produce sparser graphs.
+ engine : {"fast", "causal-learn"}
+ Which implementation of GES to run. ``"fast"`` (the default) uses the
+ vendored search in :mod:`causalts.lges`, which forbids edges from the
+ present into the past during the search itself -- background knowledge
+ that a lagged time series always licenses -- and is dramatically
+ quicker on large samples, since ``GaussObsL0Pen`` caches the scatter
+ matrix (a 24-column embedding at T=20,000 takes 1.2s against
+ causal-learn's 131s). ``"causal-learn"`` runs the original
+ implementation, which has no parameter for background knowledge; it is
+ offered for parity/validation and matches the fast engine exactly at
+ ``max_lag=0``, where there is no temporal ordering to protect. At
+ ``max_lag > 0`` it raises, rather than silently return a graph from an
+ unconstrained search that can orient edges backward in time.
Returns
-------
G_hat : ndarray, shape ``(d, d, max_lag+1)``
Estimated graph.
info : dict
- ``score``, ``ges_graph`` (raw GES output).
+ ``engine`` plus, for the causal-learn engine, ``score`` and ``ges_graph``
+ (raw GES output); for the fast engine, ``cpdag`` and ``metrics``.
"""
- from causallearn.search.ScoreBased.GES import ges
+ if engine not in ("fast", "causal-learn"):
+ raise ValueError(f"engine must be 'fast' or 'causal-learn', got {engine!r}")
- _patch_ges_numpy2()
+ embedded, d = _lag_embed(df, max_lag)
- data = df.values if isinstance(df, pd.DataFrame) else np.asarray(df)
- T_orig, d = data.shape
+ from .lges import temporal_forbidden
- embedded_cols = []
- for lag in range(max_lag + 1):
- start = max_lag - lag
- end = T_orig - lag
- embedded_cols.append(data[start:end, :])
+ forbidden = temporal_forbidden(d, max_lag)
+
+ if engine == "fast" and score_func == "local_score_BIC":
+ return _ges_fast(embedded, d, max_lag, lambda_value, forbidden)
- embedded = np.hstack(embedded_cols)
- n_vars = embedded.shape[1] # noqa: F841
+ if max_lag > 0:
+ raise ValueError(
+ "the causal-learn engine cannot honor temporal background "
+ "knowledge: causal-learn's ges() has no forbidden-edges "
+ "parameter, so running it on lagged data (max_lag > 0) would "
+ "search unconstrained and could orient edges backward in time. "
+ f"This was reached because {'engine=' + repr(engine) if engine == 'causal-learn' else 'score_func=' + repr(score_func) + ' is not local_score_BIC'} " # noqa: E501
+ "-- use engine='fast' with score_func='local_score_BIC' (the "
+ "default) instead, or call with max_lag=0."
+ )
+
+ from causallearn.search.ScoreBased.GES import ges
+
+ _patch_ges_numpy2()
result = ges(embedded, score_func=score_func, lambda_value=lambda_value)
ges_graph = result["G"].graph
G_hat = np.zeros((d, d, max_lag + 1), dtype=int)
+ # causal-learn adjacency convention (verified against GeneralGraph.add_edge):
+ # u -> v is graph[u, v] == -1 (tail at u) and graph[v, u] == 1 (arrow at v)
+ # u -- v is graph[u, v] == graph[v, u] == -1
+ # Undirected edges are recorded in the temporal direction, which is the only
+ # one consistent with the lag embedding.
for lag in range(max_lag + 1):
- cause_start = lag * d
- cause_end = (lag + 1) * d
- effect_start = 0
- effect_end = d
-
- block = ges_graph[effect_start:effect_end, cause_start:cause_end]
for i in range(d):
for j in range(d):
if lag == 0 and i == j:
continue
- if (
- block[j, i] == -1
- and ges_graph[cause_start + i, effect_start + j] == 1
- ):
- G_hat[i, j, lag] = 1
- elif (
- block[j, i] == -1
- and ges_graph[cause_start + i, effect_start + j] == -1
- ):
+ cause = lag * d + i # variable i at time t - lag
+ effect = j # variable j at time t
+ tail, head = ges_graph[cause, effect], ges_graph[effect, cause]
+ directed = tail == -1 and head == 1
+ undirected = tail == -1 and head == -1
+ if directed or undirected:
G_hat[i, j, lag] = 1
info = {
"method": "ges",
+ "engine": "causal-learn",
"score": result.get("score"),
"score_func": score_func,
"ges_graph": ges_graph,
diff --git a/causalts/lges.py b/causalts/lges.py
index b988580..d3ac6c3 100644
--- a/causalts/lges.py
+++ b/causalts/lges.py
@@ -117,24 +117,46 @@ def topological_ordering(A):
return ordering
-def semi_directed_paths(fro, to, A):
- """Find all semi-directed paths from *fro* to *to* in PDAG *A*."""
- paths = []
- _sdp_dfs(fro, to, A, [fro], paths)
- return paths
-
-
-def _sdp_dfs(current, to, A, path, paths):
- if current == to:
- paths.append(list(path))
- return
- for nxt in range(len(A)):
- if nxt in path:
- continue
- if A[current, nxt] != 0:
- path.append(nxt)
- _sdp_dfs(nxt, to, A, path, paths)
- path.pop()
+def chain_component(i, G):
+ """The undirected-connected component containing *i*."""
+ U = only_undirected(G)
+ visited, to_visit = set(), {i}
+ while to_visit:
+ j = to_visit.pop()
+ visited.add(j)
+ to_visit |= neighbors(j, U) - visited
+ return visited
+
+
+def induced_subgraph(S, G):
+ """The subgraph of *G* induced by the node set *S*."""
+ mask = np.zeros_like(G, dtype=bool)
+ mask[list(S), :] = True
+ mask = np.logical_and(mask, mask.T)
+ sub = np.zeros_like(G)
+ sub[mask] = G[mask]
+ return sub
+
+
+def separates(S, A_set, B_set, G):
+ """Does *S* block every semi-directed path between *A_set* and *B_set* in *G*?
+
+ Searches for one surviving path instead of enumerating all of them.
+ """
+ if (A_set & B_set) or (A_set & S) or (B_set & S):
+ raise ValueError("S, A and B must be pairwise disjoint")
+ for a in A_set:
+ stack, seen = [a], {a}
+ while stack:
+ cur = stack.pop()
+ if cur in B_set:
+ return False
+ for nxt in np.where(G[cur, :] != 0)[0]:
+ if nxt in seen or nxt in S:
+ continue
+ seen.add(nxt)
+ stack.append(nxt)
+ return True
def cartesian(arrays):
@@ -180,54 +202,75 @@ def delete(i, j, H, A):
# --- PDAG -> DAG -> CPDAG pipeline ---
-def pdag_to_dag(P):
- """Dor-Tarsi algorithm: find a consistent extension DAG of PDAG *P*."""
+def pdag_to_dag(P, forbidden=None):
+ """Dor-Tarsi algorithm: find a consistent extension DAG of PDAG *P*.
+
+ Repeatedly pick a node ``x`` of the remaining subgraph that is a sink (no
+ directed edge leaves it) and whose undirected neighbours are adjacent to
+ everything else adjacent to ``x``; orient every undirected edge at ``x``
+ *into* ``x``, then remove it. Orienting at removal time is what keeps the
+ result acyclic — deriving the directions afterwards from the removal order
+ inverts them, because the first node removed is the last topologically.
+
+ Plain Dor-Tarsi treats every undirected edge as equally resolvable in
+ either direction, which is wrong once ``forbidden`` says otherwise. But
+ rejecting a *candidate sink* whenever any neighbour has a forced direction
+ (an earlier version of this function did that) is incomplete: it can
+ report "no consistent extension" for perfectly resolvable graphs, because
+ Dor-Tarsi's one move -- turn every undirected edge at a chosen node into an
+ incoming edge -- has no way to express "most of these neighbours are free,
+ but this one specific edge must point out."
+
+ The fix used here relies on a property specific to how ``forbidden`` is
+ built by :func:`temporal_forbidden`: for any two nodes, either neither
+ direction is forbidden (free, e.g. two variables at the same lag) or
+ *exactly* one is (never both -- that would make the pair unconnectable).
+ So every such edge already has a unique legal direction with no search
+ needed; force them all before Dor-Tarsi runs, and hand it a graph with no
+ remaining background knowledge to violate. Classical Dor-Tarsi's
+ completeness guarantee then applies exactly as in the unconstrained case.
+ """
P = P.copy()
+ if forbidden is not None:
+ p = len(P)
+ for i in range(p):
+ for j in range(p):
+ if i == j or P[i, j] == 0 or P[j, i] == 0:
+ continue # already directed, or no edge
+ if forbidden[j, i] != 0:
+ # j -> i is illegal, i -> j is the only option (and, by
+ # construction, is not itself forbidden).
+ P[j, i] = 0
+
+ remaining_P = P.copy()
p = len(P)
- ordering = []
+ G = only_directed(P).copy()
remaining = set(range(p))
while remaining:
- found = False
- for x in list(remaining):
- x_neighbors = neighbors(x, P) & remaining
- x_pa = pa(x, P) & remaining
- x_adj = x_neighbors | x_pa
- if len(x_neighbors) == 0 or (
- x_adj == x_adj and _is_sink_in_subgraph(x, P, remaining)
- ):
- ordering.append(x)
- remaining.remove(x)
- found = True
- break
- if not found:
+ for x in sorted(remaining):
+ if not _is_sink_in_subgraph(x, remaining_P):
+ continue
+ for y in neighbors(x, remaining_P):
+ G[y, x] = 1
+ G[x, y] = 0
+ remaining.discard(x)
+ remaining_P[x, :] = 0
+ remaining_P[:, x] = 0
+ break
+ else:
raise ValueError("No consistent extension exists")
- G = np.zeros((p, p))
- for i in range(p):
- for j in range(p):
- if P[i, j] != 0 and P[j, i] == 0:
- G[i, j] = 1
- elif P[i, j] != 0 and P[j, i] != 0:
- idx_i = ordering.index(i)
- idx_j = ordering.index(j)
- if idx_i < idx_j:
- G[i, j] = 1
- else:
- G[j, i] = 1
return G
-def _is_sink_in_subgraph(x, P, remaining):
- """Check if x is a valid sink in the subgraph induced by remaining."""
- for j in remaining:
- if j == x:
- continue
- if P[x, j] != 0 and P[j, x] == 0:
- return False
- neigh = neighbors(x, P) & remaining
- adj_x = (neigh | (pa(x, P) & remaining)) - {x}
- for a, b in combinations(adj_x, 2):
- if P[a, b] == 0 and P[b, a] == 0:
- return False
+def _is_sink_in_subgraph(x, P):
+ """Is *x* a Dor-Tarsi sink of the subgraph *P* (removed nodes zeroed out)?"""
+ if len(ch(x, P)) > 0: # a directed edge leaves x
+ return False
+ adj_x = adj(x, P)
+ for y in neighbors(x, P):
+ for z in adj_x - {y}:
+ if P[y, z] == 0 and P[z, y] == 0:
+ return False
return True
@@ -246,37 +289,63 @@ def dag_to_cpdag(G):
def order_edges(edges, order_map):
- """Order edges for labeling."""
+ """Order edges for labelling (Chickering's Order-Edges).
+
+ Heads are visited in topological order; among the edges sharing a head, the
+ one whose *tail* comes latest in the topological order is ordered first.
+ Sorting the tail ascending instead reverses that inner order and makes
+ ``label_edges`` mark reversible edges as compelled.
+ """
def edge_key(e):
- return (order_map[e[1]], order_map[e[0]])
+ return (order_map[e[1]], -order_map[e[0]])
return sorted(edges, key=edge_key)
def label_edges(ordered_edges, G):
- """Label each edge as compelled or reversible."""
+ """Label each edge of DAG *G* compelled or reversible (Chickering 1995).
+
+ Phase one — propagating compelledness *through* ``x`` — is what forces edges
+ that no v-structure pins down but that Meek's rules require. Omitting it
+ leaves such edges reversible, so the CPDAG comes back under-oriented.
+ """
p = len(G)
- labels = {}
- for e in ordered_edges:
- labels[e] = "unknown"
+ labels = {e: "unknown" for e in ordered_edges}
+
+ def parents(v):
+ return set(np.where(G[:, v] != 0)[0])
+
for x, y in ordered_edges:
- if labels[(x, y)] == "unknown":
- parents_y = set(np.where(G[:, y] != 0)[0]) - {x}
- for w in parents_y:
- if G[w, x] == 0:
- labels[(x, y)] = "compelled"
- for z in set(np.where(G[:, y] != 0)[0]):
- if labels.get((z, y)) == "unknown":
- labels[(z, y)] = "compelled"
- break
- if labels[(x, y)] == "unknown":
+ if labels[(x, y)] != "unknown":
+ continue
+ parents_y = parents(y)
+
+ # Phase 1: every compelled w -> x either forces x -> y outright (when w
+ # is not also a parent of y) or makes w -> y compelled.
+ done = False
+ for w in sorted(parents(x)):
+ if labels.get((w, x)) != "compelled":
+ continue
+ if w not in parents_y:
+ labels[(x, y)] = "compelled"
for z in parents_y:
- if labels.get((z, y)) == "compelled":
- labels[(x, y)] = "compelled"
- break
- if labels[(x, y)] == "unknown":
- labels[(x, y)] = "reversible"
+ labels[(z, y)] = "compelled"
+ done = True
+ break
+ labels[(w, y)] = "compelled"
+ if done:
+ continue
+
+ # Phase 2: an unshielded parent of y other than x makes x -> y compelled;
+ # otherwise x -> y and every remaining unlabelled edge into y are reversible.
+ parents_x = parents(x)
+ compelled = any(z not in parents_x for z in parents_y - {x})
+ label = "compelled" if compelled else "reversible"
+ labels[(x, y)] = label
+ for z in parents_y:
+ if labels.get((z, y)) == "unknown":
+ labels[(z, y)] = label
cpdag = np.zeros((p, p))
for (i, j), lbl in labels.items():
if lbl == "compelled":
@@ -287,10 +356,16 @@ def label_edges(ordered_edges, G):
return cpdag
-def pdag_to_cpdag(P):
- """PDAG -> consistent DAG -> CPDAG."""
+def pdag_to_cpdag(P, forbidden=None):
+ """PDAG -> consistent DAG -> CPDAG.
+
+ Pass ``forbidden`` whenever *P* came from a search that used it -- without
+ it, ``pdag_to_dag``'s arbitrary sink choice can resolve an undirected edge
+ in the one direction background knowledge rules out. ``dag_to_cpdag`` never
+ reverses an edge it is given, so a legally-extended DAG stays legal.
+ """
try:
- G = pdag_to_dag(P)
+ G = pdag_to_dag(P, forbidden)
except ValueError:
return P.copy()
return dag_to_cpdag(G)
@@ -461,7 +536,18 @@ def fit(
)
if "turning" in phases:
+ # The turn operator scores each move as if the parents of x and y are
+ # the only thing that changes, but pdag_to_cpdag's completion step can
+ # legally reorient other, unrelated edges as a side effect (new
+ # v-structures, Meek propagation). When that happens the score gain
+ # the next candidate reports is computed against a baseline that
+ # completion already changed out from under it, and under background
+ # knowledge (forbidden) that can produce a genuine 2-cycle: turn A
+ # applied, turn B claims a gain that undoes A's effect, A is offered
+ # again, forever. Bound the loop with visited-state detection rather
+ # than assume every accepted move is real forward progress.
cont = True
+ seen = {A.tobytes()}
while cont:
A, cont, metrics = _turning_step(
A,
@@ -472,6 +558,10 @@ def fit(
metrics=metrics,
debug=debug,
)
+ state = A.tobytes()
+ if state in seen:
+ break
+ seen.add(state)
metrics["time"] = time.time() - start
try:
@@ -540,6 +630,7 @@ def _forward_step(
y,
A,
score_class,
+ forbidden,
prune=prune,
max_subset_size=max_subset_size,
debug=debug,
@@ -559,7 +650,7 @@ def _forward_step(
_, x, y, T = best_operator
new_A = _apply_insert(x, y, T, A)
- new_A = pdag_to_cpdag(new_A)
+ new_A = pdag_to_cpdag(new_A, forbidden)
metrics["inserts_actual"] += 1
if debug:
print(f" Insert {x} -> {y} | T={T}, delta={best_score:.4f}")
@@ -582,7 +673,13 @@ def _backward_step(
continue
operators = _score_valid_delete_operators(
- x, y, A, score_class, max_subset_size=max_subset_size, debug=debug
+ x,
+ y,
+ A,
+ score_class,
+ forbidden,
+ max_subset_size=max_subset_size,
+ debug=debug,
)
metrics["deletes_eval"] += len(operators)
@@ -596,7 +693,7 @@ def _backward_step(
_, x, y, H = best_operator
new_A = _apply_delete(x, y, H, A)
- new_A = pdag_to_cpdag(new_A)
+ new_A = pdag_to_cpdag(new_A, forbidden)
metrics["deletes_actual"] += 1
if debug:
print(f" Delete {x} -> {y} | H={H}, delta={best_score:.4f}")
@@ -607,38 +704,44 @@ def _turning_step(
A, score_class, required, forbidden, max_subset_size=3, metrics=None, debug=0
):
"""GES turning phase: find best turn operator and apply it."""
- p = len(A)
best_score = 0
best_operator = None
- for x in range(p):
- for y in range(p):
- if x == y:
- continue
- if not (A[x, y] != 0 and A[y, x] == 0):
- continue
- if forbidden[y, x] != 0:
- continue
+ # Candidates are the reverse of every present edge: for an edge src -> dst
+ # (or src - dst) we consider turning it so that it points dst -> src.
+ src, dst = np.where(A != 0)
+ for x, y in zip(dst, src):
+ if x == y:
+ continue
+ # The operator creates x -> y, so that is the direction to check.
+ if forbidden[x, y] != 0:
+ continue
- operators = _score_valid_turn_operators(
- x, y, A, score_class, max_subset_size=max_subset_size, debug=debug
- )
- metrics["turns_eval"] += len(operators)
+ operators = _score_valid_turn_operators(
+ x,
+ y,
+ A,
+ score_class,
+ forbidden,
+ max_subset_size=max_subset_size,
+ debug=debug,
+ )
+ metrics["turns_eval"] += len(operators)
- for score_delta, C in operators:
- if score_delta > best_score:
- best_score = score_delta
- best_operator = ("turn", x, y, C)
+ for score_delta, C in operators:
+ if score_delta > best_score:
+ best_score = score_delta
+ best_operator = ("turn", x, y, C)
if best_operator is None:
return A, False, metrics
_, x, y, C = best_operator
new_A = _apply_turn(x, y, C, A)
- new_A = pdag_to_cpdag(new_A)
+ new_A = pdag_to_cpdag(new_A, forbidden)
metrics["turns_actual"] += 1
if debug:
- print(f" Turn {x} -> {y} to {y} -> {x} | C={C}, delta={best_score:.4f}")
+ print(f" Turn to {x} -> {y} | C={C}, delta={best_score:.4f}")
return new_A, True, metrics
@@ -646,7 +749,7 @@ def _turning_step(
def _score_valid_insert_operators(
- x, y, A, score_class, prune=False, max_subset_size=3, debug=0
+ x, y, A, score_class, forbidden, prune=False, max_subset_size=3, debug=0
):
"""Score all valid insert(x, y, T) operators.
@@ -657,17 +760,29 @@ def _score_valid_insert_operators(
operators = []
found_lower = False
- for T in subsets(na_yx, max_size=max_subset_size):
+ # T ranges over subsets of Ne(y) \\ Adj(x) -- neighbours of y NOT adjacent to
+ # x. Drawing T from na_yx (= Ne(y) INTERSECT Adj(x)) instead explores the wrong
+ # operators and makes condition 1 vacuous, since T would already be inside na_yx.
+ # Every t in T is oriented t -> y by _apply_insert, so any t forbidden from
+ # causing y must never enter the candidate pool -- checking only the (x, y)
+ # pair (as the caller does) misses this, since T is a second, independent
+ # source of new edges into y.
+ candidates_T = {t for t in neighbors(y, A) - adj(x, A) if forbidden[t, y] == 0}
+
+ for T in subsets(candidates_T, max_size=max_subset_size):
# Validity condition 1: NA_yx ∪ T is a clique
if not is_clique(na_yx | T, A):
continue
- # Validity condition 2: no semi-directed path from y to x
- new_pa = pa_y | T | {x}
+ # Validity condition 2: every semi-directed path y -> x is blocked by NA_yx ∪ T
if _creates_cycle(x, y, T, A):
continue
- old_score = score_class.local_score(y, pa_y)
- new_score = score_class.local_score(y, new_pa)
+ # The undirected neighbours in NA_yx and the members of T all become
+ # parents of y under this operator, so they belong in BOTH terms of the
+ # delta; omitting them scores a different operator than the one applied.
+ base = pa_y | na_yx | T
+ old_score = score_class.local_score(y, base)
+ new_score = score_class.local_score(y, base | {x})
score_delta = new_score - old_score
if prune and score_delta < 0:
@@ -679,22 +794,36 @@ def _score_valid_insert_operators(
return operators, found_lower
-def _score_valid_delete_operators(x, y, A, score_class, max_subset_size=3, debug=0):
+def _score_valid_delete_operators(
+ x, y, A, score_class, forbidden, max_subset_size=3, debug=0
+):
"""Score all valid delete(x, y, H) operators."""
na_yx = _na(y, x, A)
pa_y = pa(y, A)
+ n_x = neighbors(x, A)
operators = []
- for H in subsets(na_yx - {x}, max_size=max_subset_size):
+ # _apply_delete orients every h in H as y -> h, and additionally x -> h
+ # when h is also an undirected neighbour of x. Either forbidden[y, h] or
+ # (h in n_x and forbidden[x, h]) makes h unusable, regardless of the (x, y)
+ # pair's own validity.
+ candidates_H = {
+ h
+ for h in na_yx - {x}
+ if forbidden[y, h] == 0 and not (h in n_x and forbidden[x, h] != 0)
+ }
+
+ for H in subsets(candidates_H, max_size=max_subset_size):
# Validity: NA_yx \\ H is a clique
if not is_clique(na_yx - H, A):
continue
- if A[y, x] != 0:
- old_parents = pa_y | na_yx | {x}
- else:
- old_parents = pa_y | na_yx
- new_parents = (pa_y | na_yx | {x}) - H - {x}
+ # Both terms share the base pa_y | (NA_yx \\ H) and differ only by x --
+ # the operator removes x, nothing else. Keeping H in the "old" set makes
+ # the delta also charge for dropping H, scoring a different move.
+ base = (na_yx - H) | pa_y
+ old_parents = base | {x}
+ new_parents = base - {x}
old_score = score_class.local_score(y, old_parents)
# Skip when old_score is -inf (inadmissible parent set, e.g. tier
@@ -710,47 +839,128 @@ def _score_valid_delete_operators(x, y, A, score_class, max_subset_size=3, debug
return operators
-def _score_valid_turn_operators(x, y, A, score_class, max_subset_size=3, debug=0):
- """Score all valid turn(x, y, C) operators."""
- na_yx = _na(y, x, A)
- pa_y = pa(y, A)
- operators = []
+def _turn_unblocked_path(x, y, C, A):
+ """Does a semi-directed path y -> x survive the blocking set ``C | ne(x)``?
+
+ The direct edge y-x is exempt. Reachability rather than path enumeration:
+ enumerating every path is exponential and made the turning phase dominate
+ runtime on dense graphs.
+ """
+ blocked = set(C) | neighbors(x, A)
+ stack, seen = [y], {y}
+ while stack:
+ cur = stack.pop()
+ for nxt in np.where(A[cur, :] != 0)[0]:
+ if nxt == x:
+ if cur != y: # a path of length > 1 got through
+ return True
+ continue
+ if nxt in seen or nxt in blocked:
+ continue
+ seen.add(nxt)
+ stack.append(nxt)
+ return False
- for C in subsets(na_yx - {x}, max_size=max_subset_size):
- if not is_clique(na_yx | C, A):
+
+def _score_valid_turn_operators_dir(x, y, A, score_class, forbidden, max_subset_size=3):
+ """Turn the directed edge y -> x into x -> y (upstream ges.main)."""
+ na_yx = _na(y, x, A)
+ pa_y, pa_x = pa(y, A), pa(x, A)
+ out = []
+ # _apply_turn orients every member of C = na_yx | T as c -> y. na_yx is fixed
+ # per (x, y), so if it already contains a node forbidden from causing y, every
+ # possible C is invalid and there is nothing to search.
+ if any(forbidden[c, y] != 0 for c in na_yx):
+ return out
+ candidates_T = {t for t in neighbors(y, A) - adj(x, A) if forbidden[t, y] == 0}
+ for T in subsets(candidates_T, max_size=max_subset_size):
+ C = na_yx | T
+ if not is_clique(C, A):
continue
- new_pa_y = pa_y | na_yx | {x} - C # noqa: F841
- if _turn_creates_cycle(x, y, C, A):
+ if _turn_unblocked_path(x, y, C, A):
continue
+ new = score_class.local_score(y, pa_y | C | {x}) + score_class.local_score(
+ x, pa_x - {y}
+ )
+ old = score_class.local_score(y, pa_y | C) + score_class.local_score(x, pa_x)
+ out.append((new - old, C))
+ return out
- old_pa = pa_y | (na_yx - C - {x})
- new_pa = pa_y | na_yx | {x} - C
- old_score = score_class.local_score(y, old_pa)
- new_score = score_class.local_score(y, new_pa)
- score_delta = new_score - old_score
+def _score_valid_turn_operators_undir(
+ x, y, A, score_class, forbidden, max_subset_size=3
+):
+ """Turn the undirected edge y - x into x -> y (upstream ges.main)."""
+ non_adjacent = neighbors(y, A) - adj(x, A) - {x}
+ if not non_adjacent:
+ return []
+ na_yx = _na(y, x, A)
+ pa_y, pa_x = pa(y, A), pa(x, A)
+ subgraph = induced_subgraph(chain_component(y, A), A)
+ out = []
+ # _apply_turn orients every member of C as c -> y, so C's candidate pool
+ # must exclude anything forbidden from causing y.
+ candidates_C = {c for c in neighbors(y, A) - {x} if forbidden[c, y] == 0}
+ for C in subsets(candidates_C, max_size=max_subset_size):
+ # C must contain at least one neighbour of y that is not adjacent to x
+ if not (C & non_adjacent):
+ continue
+ if not is_clique(C, A):
+ continue
+ if not separates({x, y}, C - {x, y}, (na_yx - C) - {x, y}, subgraph):
+ continue
+ new = score_class.local_score(y, pa_y | C | {x}) + score_class.local_score(
+ x, pa_x | (C & na_yx)
+ )
+ old = score_class.local_score(y, pa_y | C) + score_class.local_score(
+ x, pa_x | (C & na_yx) | {y}
+ )
+ out.append((new - old, C))
+ return out
- operators.append((score_delta, C))
- return operators
+def _score_valid_turn_operators(
+ x, y, A, score_class, forbidden, max_subset_size=3, debug=0
+):
+ """Score every valid turn(x, y, C), producing the edge x -> y."""
+ if A[x, y] != 0 and A[y, x] == 0:
+ return [] # x -> y already exists
+ if A[x, y] == 0 and A[y, x] == 0:
+ return [] # not connected
+ if A[x, y] != 0 and A[y, x] != 0:
+ return _score_valid_turn_operators_undir(
+ x, y, A, score_class, forbidden, max_subset_size=max_subset_size
+ )
+ return _score_valid_turn_operators_dir(
+ x, y, A, score_class, forbidden, max_subset_size=max_subset_size
+ )
def _creates_cycle(x, y, T, A):
- """Check if insert(x, y, T) would create a cycle."""
- paths = semi_directed_paths(y, x, A)
- return len(paths) > 0
+ """Is insert(x, y, T) invalid because some semi-directed path is unblocked?
+ GES requires every semi-directed path from *y* to *x* to contain a node of
+ ``NA_yx | T``. Rejecting the operator whenever *any* such path exists — as
+ opposed to any *unblocked* one — starves the forward phase: paths multiply as
+ the graph grows, so the search stalls long before it reaches the optimum.
-def _turn_creates_cycle(x, y, C, A):
- """Check if turn(x->y to y->x) with C would create a cycle."""
- test_A = A.copy()
- test_A[x, y] = 0
- test_A[y, x] = 1
- for c in C:
- test_A[y, c] = 0
- test_A[c, y] = 1
- paths = semi_directed_paths(x, y, test_A)
- return len(paths) > 0
+ Searches for one unblocked path rather than enumerating them all, which also
+ avoids the exponential blow-up of enumerating every path on dense graphs.
+ """
+ blocked = _na(y, x, A) | set(T)
+ stack, seen = [y], {y}
+ while stack:
+ cur = stack.pop()
+ if cur == x:
+ return True # reached x without passing through the blocking set
+ for nxt in np.where(A[cur, :] != 0)[0]:
+ # A[cur, nxt] != 0 admits cur -> nxt and cur - nxt, and excludes
+ # nxt -> cur, which is exactly a semi-directed step away from y.
+ if nxt in seen or nxt in blocked:
+ continue
+ seen.add(nxt)
+ stack.append(nxt)
+ return False
def _apply_insert(x, y, T, A):
@@ -764,24 +974,35 @@ def _apply_insert(x, y, T, A):
def _apply_delete(x, y, H, A):
- """Apply delete operator: remove x-y and orient H->y."""
+ """Apply delete(x, y, H): drop the x-y edge, then orient y -> h and x -> h.
+
+ The orientation runs *away* from y and x, not toward them. Orienting h -> y
+ instead (and skipping the x - h edges entirely) yields a graph that does not
+ match the operator that was scored, so the search can leave the space of
+ valid PDAGs.
+ """
new_A = A.copy()
new_A[x, y] = 0
new_A[y, x] = 0
+ n_x = neighbors(x, A)
for h in H:
- new_A[y, h] = 0
- new_A[h, y] = 1
+ new_A[h, y] = 0 # leaves y -> h
+ if h in n_x:
+ new_A[h, x] = 0 # leaves x -> h
return new_A
def _apply_turn(x, y, C, A):
- """Apply turn operator: reverse x->y to y->x, orient C->y."""
+ """Apply turn(x, y, C): make the edge x -> y and orient every c in C as c -> y.
+
+ Matches upstream ``ges.main.turn``. Clearing ``A[y, c]`` leaves ``c -> y``;
+ setting ``A[c, y]`` as well would keep the edge undirected.
+ """
new_A = A.copy()
- new_A[x, y] = 0
- new_A[y, x] = 1
+ new_A[y, x] = 0
+ new_A[x, y] = 1
for c in C:
new_A[y, c] = 0
- new_A[c, y] = 1
return new_A
@@ -831,6 +1052,29 @@ def _apply_tier_orientation(cpdag, tier_of):
# ---------------------------------------------------------------------------
+def temporal_forbidden(d, max_lag):
+ """Forbidden-edges matrix enforcing "past can cause present, not vice versa".
+
+ Variable blocks in a lag embedding are ``[lag0: 0..d-1] [lag1: d..2d-1] ...
+ [lagK: Kd..(K+1)d-1]``. A variable at ``lag_a`` may cause one at ``lag_b``
+ only if ``lag_a >= lag_b``. Also forbids lag-0 self-loops (a variable
+ cannot cause itself at the same time step). Every lag-embedded search in
+ this module and in :mod:`causalts.baselines` shares this constraint.
+ """
+ n = d * (max_lag + 1)
+ forbidden = np.zeros((n, n))
+ for lag_cause in range(max_lag + 1):
+ for lag_effect in range(max_lag + 1):
+ if lag_cause < lag_effect:
+ cause_start, effect_start = lag_cause * d, lag_effect * d
+ for i in range(d):
+ for j in range(d):
+ forbidden[cause_start + i, effect_start + j] = 1
+ for i in range(d):
+ forbidden[i, i] = 1
+ return forbidden
+
+
def lges_discovery(df, max_lag=1, mode="lges", lambda_value=None):
"""Run LGES on lag-embedded time series data.
@@ -867,26 +1111,7 @@ def lges_discovery(df, max_lag=1, mode="lges", lambda_value=None):
# Set up scoring
score_class = GaussObsL0Pen(embedded, lmbda=lambda_value)
- n_embedded = d * (max_lag + 1)
-
- # Build forbidden-edges matrix enforcing temporal constraints:
- # Variable blocks: [lag0: 0..d-1] [lag1: d..2d-1] ... [lagK: Kd..(K+1)d-1]
- # Rule: a variable at lag_a can only cause a variable at lag_b if a >= b
- # (the past can cause the present, not vice versa).
- # Also forbid self-loops at lag 0.
- forbidden = np.zeros((n_embedded, n_embedded))
- for lag_cause in range(max_lag + 1):
- for lag_effect in range(max_lag + 1):
- if lag_cause < lag_effect:
- # Block: present (lag_cause) cannot cause past (lag_effect)
- cause_start = lag_cause * d
- effect_start = lag_effect * d
- for i in range(d):
- for j in range(d):
- forbidden[cause_start + i, effect_start + j] = 1
- # Forbid self-loops at lag 0
- for i in range(d):
- forbidden[i, i] = 1
+ forbidden = temporal_forbidden(d, max_lag)
# Set LGES mode
if mode == "lges":
@@ -978,24 +1203,10 @@ def tges_discovery(df, max_lag=1, mode="lges", lambda_value=None):
embedded_cols.append(data[start:end, :])
embedded = np.hstack(embedded_cols)
- n_embedded = d * (max_lag + 1)
-
# tier_of[node] = lag index of that node (0 = present, k = k steps back)
tier_of = {lag * d + i: lag for lag in range(max_lag + 1) for i in range(d)}
- # Forbidden matrix (fast-path guard; redundant with score but keeps
- # operator checks O(1) rather than hitting the score for every pair)
- forbidden = np.zeros((n_embedded, n_embedded))
- for lag_cause in range(max_lag + 1):
- for lag_effect in range(max_lag + 1):
- if lag_cause < lag_effect:
- cause_start = lag_cause * d
- effect_start = lag_effect * d
- for i in range(d):
- for j in range(d):
- forbidden[cause_start + i, effect_start + j] = 1
- for i in range(d):
- forbidden[i, i] = 1
+ forbidden = temporal_forbidden(d, max_lag)
# Standard BIC score — temporal constraints are enforced via the forbidden
# matrix (same guarantee as TieredGaussObsL0Pen's -inf scoring, without
diff --git a/docs/_static/img/thumbnails/baseline_comparison_thumb.png b/docs/_static/img/thumbnails/baseline_comparison_thumb.png
index b698d0a..8c8946e 100644
Binary files a/docs/_static/img/thumbnails/baseline_comparison_thumb.png and b/docs/_static/img/thumbnails/baseline_comparison_thumb.png differ
diff --git a/docs/_static/img/thumbnails/overrides/baseline_comparison_thumb.png b/docs/_static/img/thumbnails/overrides/baseline_comparison_thumb.png
new file mode 100644
index 0000000..9b9788a
Binary files /dev/null and b/docs/_static/img/thumbnails/overrides/baseline_comparison_thumb.png differ
diff --git a/examples/baseline_comparison.ipynb b/examples/baseline_comparison.ipynb
index 2348105..6d8f182 100644
--- a/examples/baseline_comparison.ipynb
+++ b/examples/baseline_comparison.ipynb
@@ -16,7 +16,7 @@
"| **CDNOTS+** | Constraint-based (PCMCI+) | Any CI test, handles nonstationarity | CI test p-value |\n",
"| **CEDAR (linear)** | Autoregressive | Lag selection + ParCorr CI testing | CI test p-value |\n",
"| **CEDAR (nonlinear)** | Autoregressive | Lag selection + SplitKCI CI testing | CI test p-value |\n",
- "| **GES** | Score-based (BIC) | Linear Gaussian, i.i.d. (lag-embedded) | BIC penalty (automatic) |\n",
+ "| **GES** | Score-based (BIC) | Linear Gaussian, lag-embedded with temporal background knowledge (no edge from present into past) | BIC penalty (automatic) |\n",
"| **LGES** | Score-based (BIC) | Linear Gaussian, less greedy (SafeInsert + ConservativeInsert) | BIC penalty (automatic) |\n",
"| **TGES** | Score-based (BIC) | Linear Gaussian, LGES + tier-based orientation of undirected edges | BIC penalty (automatic) |\n",
"| **VARLiNGAM** | ICA-based | Linear, non-Gaussian noise | Coefficient threshold (default 0.05) |\n",
@@ -37,12 +37,14 @@
"> variant passes `SplitKCIGPU` (kernel CI test, handles arbitrary nonlinear\n",
"> dependencies at the cost of speed).\n",
"\n",
- "> **GES vs. LGES vs. TGES:** GES is vanilla greedy equivalence search. LGES (Less Greedy ES)\n",
- "> adds SafeInsert (score check before inserting) and ConservativeInsert (early stopping), making\n",
- "> it more conservative and often more precise. TGES (Temporal GES, Larsen et al. 2025) adds a\n",
- "> post-CPDAG orientation step that uses the temporal tier structure to direct remaining undirected\n",
- "> edges — producing a \"tiered maximally oriented PDAG\" with strictly more directed edges than\n",
- "> the raw CPDAG.\n",
+ "> **GES vs. LGES vs. TGES:** all three run on the lag-embedded matrix under the same temporal\n",
+ "> background knowledge — a variable can only cause another at an equal or later time step, so the\n",
+ "> search never considers an edge from the present into the past. GES is vanilla greedy equivalence\n",
+ "> search under that constraint. LGES (Less Greedy ES) adds SafeInsert (score check before\n",
+ "> inserting) and ConservativeInsert (early stopping), making it more conservative. TGES (Temporal\n",
+ "> GES, Larsen et al. 2025) adds a post-CPDAG orientation step that uses the temporal tier structure\n",
+ "> to direct remaining undirected edges — producing a \"tiered maximally oriented PDAG\" with strictly\n",
+ "> more directed edges than the raw CPDAG.\n",
"\n",
"> **Note on evaluation fairness:** All methods are evaluated on the full ground truth including\n",
"> autoregressive self-edges (X_{t-k}→X_t). CEDAR adds AR(1) self-loops for all variables\n",
@@ -57,14 +59,14 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 1,
"id": "f1c8e99b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-16T12:00:01.566697Z",
- "iopub.status.busy": "2026-05-16T12:00:01.566570Z",
- "iopub.status.idle": "2026-05-16T12:00:05.316479Z",
- "shell.execute_reply": "2026-05-16T12:00:05.315975Z"
+ "iopub.execute_input": "2026-08-29T19:49:15.902993Z",
+ "iopub.status.busy": "2026-08-29T19:49:15.902838Z",
+ "iopub.status.idle": "2026-08-29T19:49:17.638134Z",
+ "shell.execute_reply": "2026-08-29T19:49:17.637757Z"
}
},
"outputs": [],
@@ -88,14 +90,14 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 2,
"id": "7c4edc90",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-16T12:00:05.317895Z",
- "iopub.status.busy": "2026-05-16T12:00:05.317746Z",
- "iopub.status.idle": "2026-05-16T12:00:05.321546Z",
- "shell.execute_reply": "2026-05-16T12:00:05.321100Z"
+ "iopub.execute_input": "2026-08-29T19:49:17.639727Z",
+ "iopub.status.busy": "2026-08-29T19:49:17.639602Z",
+ "iopub.status.idle": "2026-08-29T19:49:17.644473Z",
+ "shell.execute_reply": "2026-08-29T19:49:17.644119Z"
}
},
"outputs": [],
@@ -235,14 +237,14 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 3,
"id": "68931a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-16T12:00:05.322671Z",
- "iopub.status.busy": "2026-05-16T12:00:05.322610Z",
- "iopub.status.idle": "2026-05-16T12:00:24.788267Z",
- "shell.execute_reply": "2026-05-16T12:00:24.787813Z"
+ "iopub.execute_input": "2026-08-29T19:49:17.645694Z",
+ "iopub.status.busy": "2026-08-29T19:49:17.645639Z",
+ "iopub.status.idle": "2026-08-29T19:49:26.256675Z",
+ "shell.execute_reply": "2026-08-29T19:49:26.256289Z"
}
},
"outputs": [
@@ -334,26 +336,26 @@
"
\n",
" 6 \n",
" GES \n",
- " 0.125 \n",
- " 0.143 \n",
- " 0.111 \n",
- " 14 \n",
+ " 0.636 \n",
+ " 0.538 \n",
+ " 0.778 \n",
+ " 8 \n",
" \n",
" \n",
" 7 \n",
" LGES \n",
- " 0.400 \n",
- " 0.364 \n",
- " 0.444 \n",
- " 12 \n",
+ " 0.636 \n",
+ " 0.538 \n",
+ " 0.778 \n",
+ " 8 \n",
" \n",
" \n",
" 8 \n",
" TGES \n",
- " 0.400 \n",
- " 0.364 \n",
- " 0.444 \n",
- " 12 \n",
+ " 0.636 \n",
+ " 0.538 \n",
+ " 0.778 \n",
+ " 8 \n",
" \n",
" \n",
" 9 \n",
@@ -391,15 +393,15 @@
"3 CDNOTS+ (nonlinear) 0.571 0.800 0.444 6\n",
"4 CEDAR (linear) 0.588 0.625 0.556 7\n",
"5 CEDAR (nonlinear) 0.778 0.778 0.778 4\n",
- "6 GES 0.125 0.143 0.111 14\n",
- "7 LGES 0.400 0.364 0.444 12\n",
- "8 TGES 0.400 0.364 0.444 12\n",
+ "6 GES 0.636 0.538 0.778 8\n",
+ "7 LGES 0.636 0.538 0.778 8\n",
+ "8 TGES 0.636 0.538 0.778 8\n",
"9 VARLiNGAM 0.368 0.241 0.778 24\n",
"10 LASSO-VAR 0.255 0.152 0.778 41\n",
"11 Granger 0.255 0.152 0.778 41"
]
},
- "execution_count": 17,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -422,14 +424,14 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 4,
"id": "2fbcfd47",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-16T12:00:24.789704Z",
- "iopub.status.busy": "2026-05-16T12:00:24.789613Z",
- "iopub.status.idle": "2026-05-16T12:00:29.958682Z",
- "shell.execute_reply": "2026-05-16T12:00:29.958222Z"
+ "iopub.execute_input": "2026-08-29T19:49:26.258031Z",
+ "iopub.status.busy": "2026-08-29T19:49:26.257953Z",
+ "iopub.status.idle": "2026-08-29T19:49:27.533180Z",
+ "shell.execute_reply": "2026-08-29T19:49:27.532812Z"
}
},
"outputs": [
@@ -497,26 +499,26 @@
" \n",
" 3 \n",
" GES \n",
- " 0.154 \n",
- " 0.500 \n",
- " 0.091 \n",
- " 11 \n",
+ " 0.917 \n",
+ " 0.846 \n",
+ " 1.000 \n",
+ " 2 \n",
" \n",
" \n",
" 4 \n",
" LGES \n",
- " 0.417 \n",
- " 0.385 \n",
- " 0.455 \n",
- " 14 \n",
+ " 0.917 \n",
+ " 0.846 \n",
+ " 1.000 \n",
+ " 2 \n",
" \n",
" \n",
" 5 \n",
" TGES \n",
- " 0.417 \n",
- " 0.385 \n",
- " 0.455 \n",
- " 14 \n",
+ " 0.917 \n",
+ " 0.846 \n",
+ " 1.000 \n",
+ " 2 \n",
" \n",
" \n",
" 6 \n",
@@ -551,15 +553,15 @@
"0 CDNOTS (linear) 0.800 0.889 0.727 4\n",
"1 CDNOTS+ (linear) 0.870 0.833 0.909 3\n",
"2 CEDAR (linear) 0.870 0.833 0.909 3\n",
- "3 GES 0.154 0.500 0.091 11\n",
- "4 LGES 0.417 0.385 0.455 14\n",
- "5 TGES 0.417 0.385 0.455 14\n",
+ "3 GES 0.917 0.846 1.000 2\n",
+ "4 LGES 0.917 0.846 1.000 2\n",
+ "5 TGES 0.917 0.846 1.000 2\n",
"6 VARLiNGAM 0.357 0.222 0.909 36\n",
"7 LASSO-VAR 0.468 0.306 1.000 25\n",
"8 Granger 0.468 0.306 1.000 25"
]
},
- "execution_count": 18,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -582,14 +584,14 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 5,
"id": "bfef67b7",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-16T12:00:29.959941Z",
- "iopub.status.busy": "2026-05-16T12:00:29.959876Z",
- "iopub.status.idle": "2026-05-16T12:00:36.993568Z",
- "shell.execute_reply": "2026-05-16T12:00:36.993065Z"
+ "iopub.execute_input": "2026-08-29T19:49:27.534485Z",
+ "iopub.status.busy": "2026-08-29T19:49:27.534422Z",
+ "iopub.status.idle": "2026-08-29T19:49:32.274114Z",
+ "shell.execute_reply": "2026-08-29T19:49:32.273575Z"
}
},
"outputs": [
@@ -657,26 +659,26 @@
" \n",
" 3 \n",
" GES \n",
- " 0.522 \n",
- " 0.667 \n",
- " 0.429 \n",
- " 22 \n",
+ " 0.949 \n",
+ " 0.903 \n",
+ " 1.000 \n",
+ " 3 \n",
" \n",
" \n",
" 4 \n",
" LGES \n",
- " 0.526 \n",
- " 0.517 \n",
- " 0.536 \n",
- " 27 \n",
+ " 0.949 \n",
+ " 0.903 \n",
+ " 1.000 \n",
+ " 3 \n",
" \n",
" \n",
" 5 \n",
" TGES \n",
- " 0.526 \n",
- " 0.517 \n",
- " 0.536 \n",
- " 27 \n",
+ " 0.949 \n",
+ " 0.903 \n",
+ " 1.000 \n",
+ " 3 \n",
" \n",
" \n",
" 6 \n",
@@ -711,15 +713,15 @@
"0 CDNOTS (linear) 0.945 0.963 0.929 3\n",
"1 CDNOTS+ (linear) 0.964 0.964 0.964 2\n",
"2 CEDAR (linear) 1.000 1.000 1.000 0\n",
- "3 GES 0.522 0.667 0.429 22\n",
- "4 LGES 0.526 0.517 0.536 27\n",
- "5 TGES 0.526 0.517 0.536 27\n",
+ "3 GES 0.949 0.903 1.000 3\n",
+ "4 LGES 0.949 0.903 1.000 3\n",
+ "5 TGES 0.949 0.903 1.000 3\n",
"6 VARLiNGAM 0.469 0.358 0.679 43\n",
"7 LASSO-VAR 0.444 0.286 1.000 70\n",
"8 Granger 0.444 0.286 1.000 70"
]
},
- "execution_count": 19,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -745,9 +747,16 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 6,
"id": "henon_cd",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T19:49:32.275295Z",
+ "iopub.status.busy": "2026-08-29T19:49:32.275218Z",
+ "iopub.status.idle": "2026-08-29T19:49:36.594691Z",
+ "shell.execute_reply": "2026-08-29T19:49:36.594253Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -837,26 +846,26 @@
" \n",
" 6 \n",
" GES \n",
+ " 0.435 \n",
" 0.333 \n",
- " 0.300 \n",
- " 0.375 \n",
- " 24 \n",
+ " 0.625 \n",
+ " 26 \n",
" \n",
" \n",
" 7 \n",
" LGES \n",
+ " 0.435 \n",
" 0.333 \n",
- " 0.250 \n",
- " 0.500 \n",
- " 32 \n",
+ " 0.625 \n",
+ " 26 \n",
" \n",
" \n",
" 8 \n",
" TGES \n",
+ " 0.435 \n",
" 0.333 \n",
- " 0.250 \n",
- " 0.500 \n",
- " 32 \n",
+ " 0.625 \n",
+ " 26 \n",
" \n",
" \n",
" 9 \n",
@@ -894,15 +903,15 @@
"3 CDNOTS+ (nonlinear) 0.621 0.692 0.562 11\n",
"4 CEDAR (linear) 0.560 0.778 0.438 11\n",
"5 CEDAR (nonlinear) 0.667 1.000 0.500 8\n",
- "6 GES 0.333 0.300 0.375 24\n",
- "7 LGES 0.333 0.250 0.500 32\n",
- "8 TGES 0.333 0.250 0.500 32\n",
+ "6 GES 0.435 0.333 0.625 26\n",
+ "7 LGES 0.435 0.333 0.625 26\n",
+ "8 TGES 0.435 0.333 0.625 26\n",
"9 VARLiNGAM 0.500 0.333 1.000 32\n",
"10 LASSO-VAR 0.566 0.405 0.938 23\n",
"11 Granger 0.566 0.405 0.938 23"
]
},
- "execution_count": 20,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -914,6 +923,96 @@
"results_henon"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "3ad3367f",
+ "metadata": {},
+ "source": [
+ "## Do GES, LGES and TGES ever actually differ?\n",
+ "\n",
+ "On the four datasets above they land on identical graphs, which raises a fair question:\n",
+ "what is LGES's extra machinery (SafeInsert, ConservativeInsert) or TGES's tier-orientation\n",
+ "pass buying you, if the output never changes?\n",
+ "\n",
+ "That is an artifact of testing on small (d <= 11), clean systems where plain GES's own\n",
+ "search already reaches the same fixed point. At larger `d`, lower `T`, or with\n",
+ "contemporaneous edges on top of the lagged structure, they diverge.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8c1db2b4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T19:49:36.596004Z",
+ "iopub.status.busy": "2026-08-29T19:49:36.595934Z",
+ "iopub.status.idle": "2026-08-29T19:49:39.237671Z",
+ "shell.execute_reply": "2026-08-29T19:49:39.237314Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GES F1=0.870 Precision=0.781 TPR=0.980 SHD=15 edges=64\n",
+ "LGES F1=0.936 Precision=0.879 TPR=1.000 SHD=7 edges=58\n",
+ "TGES F1=0.936 Precision=0.879 TPR=1.000 SHD=7 edges=58\n",
+ "\n",
+ "GES vs LGES identical? False\n",
+ "LGES vs TGES identical? True\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from causalts.baselines import ges_discovery, lges_discovery, tges_discovery\n",
+ "from causalts.utils import evaluate_graph\n",
+ "\n",
+ "\n",
+ "def stable_var(d, T, n_parents, seed, radius=0.7, contemp_frac=0.3):\n",
+ " \"\"\"A VAR(1) with some contemporaneous (lag-0) edges mixed in.\"\"\"\n",
+ " r = np.random.default_rng(seed)\n",
+ " B_lag = np.zeros((d, d))\n",
+ " for j in range(d):\n",
+ " for i in r.choice([k for k in range(d) if k != j], size=n_parents, replace=False):\n",
+ " B_lag[i, j] = r.uniform(0.3, 0.8) * r.choice([-1, 1])\n",
+ " ev = np.max(np.abs(np.linalg.eigvals(B_lag)))\n",
+ " if ev > 0:\n",
+ " B_lag *= radius / ev\n",
+ " order = r.permutation(d)\n",
+ " B_c = np.zeros((d, d))\n",
+ " for idx, j in enumerate(order):\n",
+ " if idx > 0 and r.random() < contemp_frac:\n",
+ " i = order[r.integers(0, idx)]\n",
+ " B_c[i, j] = r.uniform(0.3, 0.7) * r.choice([-1, 1])\n",
+ " X = np.zeros((T, d))\n",
+ " I_minus_Bc_inv = np.linalg.inv(np.eye(d) - B_c.T)\n",
+ " for t in range(1, T):\n",
+ " eps = r.standard_normal(d) + X[t - 1] @ B_lag\n",
+ " X[t] = eps @ I_minus_Bc_inv.T\n",
+ " gt = np.zeros((d, d, 2), dtype=int)\n",
+ " gt[:, :, 1] = (B_lag != 0).astype(int)\n",
+ " gt[:, :, 0] = (B_c != 0).astype(int)\n",
+ " return pd.DataFrame(X, columns=[f\"X{i}\" for i in range(d)]), gt\n",
+ "\n",
+ "\n",
+ "df_big, gt_big = stable_var(d=15, T=300, n_parents=3, seed=7)\n",
+ "\n",
+ "G_ges, _ = ges_discovery(df_big, max_lag=1)\n",
+ "G_lges, _ = lges_discovery(df_big, max_lag=1, mode=\"lges\")\n",
+ "G_tges, _ = tges_discovery(df_big, max_lag=1, mode=\"lges\")\n",
+ "\n",
+ "for name, G in [(\"GES\", G_ges), (\"LGES\", G_lges), (\"TGES\", G_tges)]:\n",
+ " m = evaluate_graph(G, gt_big)\n",
+ " print(f\"{name:<5} F1={m['F1']:.3f} Precision={m['Precision']:.3f} \"\n",
+ " f\"TPR={m['TPR']:.3f} SHD={m['SHD']} edges={int(G.sum())}\")\n",
+ "\n",
+ "print(f\"\\nGES vs LGES identical? {np.array_equal(G_ges, G_lges)}\")\n",
+ "print(f\"LGES vs TGES identical? {np.array_equal(G_lges, G_tges)}\")\n"
+ ]
+ },
{
"cell_type": "markdown",
"id": "f81512d4",
@@ -924,20 +1023,20 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 8,
"id": "875f197f",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-16T12:00:36.994793Z",
- "iopub.status.busy": "2026-05-16T12:00:36.994728Z",
- "iopub.status.idle": "2026-05-16T12:00:37.093619Z",
- "shell.execute_reply": "2026-05-16T12:00:37.093192Z"
+ "iopub.execute_input": "2026-08-29T19:49:39.239207Z",
+ "iopub.status.busy": "2026-08-29T19:49:39.239119Z",
+ "iopub.status.idle": "2026-08-29T19:49:39.390426Z",
+ "shell.execute_reply": "2026-08-29T19:49:39.390072Z"
}
},
"outputs": [
{
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rRoBk9u7dm+Tn119/PZI9e/bIhAkT3M979uyJ7Nu3L7Jx48bIgw8+GFm6dGlo96G20/PPP/9EChcuHGnQoEH0tR07drj/fvvtt5G+fftGwrS933//feS6666L1KlTJ7Js2bIkyw0ZMsTth+3bt0fiScdkhQoVItOnT3c/61j88ssvD1jujTfeiJx22mmRP//8MxJmY8eOjVxwwQWRSZMmuZ83bNgQWbhw4QHLvffee5GaNWtGVq5cGQmTTz/9NFKlSpXIlClT3M+rVq2K/PHHHwcsp3Na1apVI+vWrYvDWvrTnDlz3Gd91KhRkVdeeSVy5513RsqUKRMZPHiw+/3ixYtD9/n56quvIsOHD48sX748yestWrSIZMuWLbJ79+7oa7rerV27NhIGul7pfDh//nz3ebnnnnsi27Zti/z000+R1q1bRzp16hTvVQTSLMwxq9Y91qOPPhq56KKLIh999FE0/vTcdNNN7vV4C3PcHLttun4o3rr++usPiCWGDh0aueqqqyKbN2/OsvUJQsx+uNv2999/R2rXru3iVo93fR4/fnykd+/eCXffEIZ4P1Fj9iDG20GMl78NWaxL0hYpBoi6WE6bNs1dBOTtt9+OFC1aNDJ58uTo7+XZZ591J5wwig2W//33X/ff1atXR4477rjIww8/nGTZBQsWRJo1a3bAySxIkt8ciE50upgo4Et+AXn66acjv/zySyReFMTpJuWOO+5wP69fvz5yzjnnuIuK57fffosMGjQocuqpp7ptCSt9Hjdt2hTJnz9/5O67744GorpIxd7ErVmzJvLEE0+41+fNmxcJEwWkev+94F3B3/nnnx95//33kxwPAwcOjJx++umhPh7SYubMmZGRI0cmOZ9//fXXkYsvvjgyevRoF3Dp8+3d2IQxyaObWH1m9HnQjY0CaU/z5s1dUJ3SeTHo2/38889HxowZ497bs88+O5rEUoJFwWyifzYQHGGOWVPbtgEDBkTuuusud0Mae6P88ccfu9fiKcxxc0rXAiWbmjZt6q4fikk9es/69euXqYmLoMXsh7ttim3lr7/+ilxxxRXRmD82rlWCfO7cuQlx3xCWeD+RYvYgx9tBjJf3hjTWzab/iXe1L/zBa+6tYQkaTqNhCmo2rRJ3Df354IMPXD8Q9Sq57LLLon+nmVM19CJMYnsoaZiJmvurcfXtt9/u+kJpOID6uWgIgEdDHTQRgXoIBXl7NVRDPV82btzohqUsWLDA9X068cQT3bBDNbr3+ihp6JU302pW9wTSsAv1ntJ6ivrXqUeQhtTEHpuaGELDntRzJ6y8CTHUE0r91e655x77/PPPXR85fe/RDMcjR450w/fUkyhsnnvuOTeUTT3+9L0+uxqa6dGwWA230usanpSo53l9LjRUVX0RH3744egQSp27pkyZ4oYPqZ+bhtWFkYatapIYnT903tC5TTP37t692/VP69u3b7R3pIaUqg9mGK7tmiRCw6g1HFFDJ/XzuHHjrEyZMq4Hos4PGkYWqOFiSFiJELNq2xR3aRi32g2ozUCbNm3cRDWaKEn9PBWP6neKTfXZ1TksXusa1rg5dtuaNWvm4knFvurLq8m+NExfM8DXrVs3ui2ffvqp6y2qXoqJHrMf7rbp+NawcU1ApXYniuE0lF7XsRdffDH6N0uWLHG9QQ/2uQ7LfUOY4v2wx+xhiLeDFC9Hwh7rxjtrDP9p2bKle0IsJUuWjNSrVy/yzjvvuJ//7//+L9K+ffsDhq6EkbavUaNGrqrhk08+cUO8HnvssciuXbvcEA39rCdkYdkP2o7LLrss8swzz0S+++67yEknnRS58sor3ZNpPSXUcTFx4sQky8drOE21atUiI0aMiPz444+RL774wj3l0xC12OGCL7/8cuTNN9+MhJ0qPfQ0fcWKFe5nPVEsVKiQGyYXS8fwu+++65snoRnlm2++cUOKvEoCPZXX03rvPOV56623IlOnTo3TWvpzaN2ll14a6datW+T++++PXHjhha6ay6uAOvPMM935Pmw0bEvVHfo8eJU7qhoQVeBpmJeqcDRsSlVsqlYJMu88raGzGhKq84W2W+dLVR2qGmvWrFmukmXcuHHxXl0g3cIcs2oIp2IyVaFpmKeqnfSz1yrh1VdfjfhJmONmL0ZWSwdVA1auXDly2223uYrizz//3L03Os9m9fr4PWY/HFpXVc/qOFJsp+tyly5dIlu3bnXHUd26dd09QOzyiXDfEIZ4PxFj9qDG20GKl/clQKxL0hYH9APTxU0Hukrdhw0b5k6cJUqUSHKBDKvYC78uFroR0DCUSy65xJ1oFRB17NjRXeQzu1dVVm+v+gjpJKwTry4oGl6lE3HDhg3dUIIlS5ZE4k19pxSYJg+MNbTpqaeecoHAzp07Ix988IHrf6TgLMy8flAKbNXXyzNjxgx38/raa6+5nxUMaLmU+l0FmS681atXj7z44otJhsa99NJLLpBQYC4KXnU8/Pzzz5FEFnuuV3Cl4ao//PCD+1k3MOqpdeutt7r9paFaQer9mJ598OSTT7pjxBuipiSDrnWVKsC7/akAADpDSURBVFVyN+M6v3Tt2jU026+AW0NCdaNbpEgRF7hqG9XfU8Pz6tevH/nwww8Dd2OPxBTmmDV22xR7ql+lYk19dhWP6nx11llnuUSoXyRK3Kzzpq4beo903lTyrHPnzi6xrhg5K+KLoMXsh7ttSmSpnYMS0DqOFN9rn99yyy0ukaSvRLtvCEO8n0gxexji7aDFy5+EPNYlaZvgYntmzZ49O9qD6vfff3cXfo96gQQxAE6P1J5IakIBnZC8i+GJJ56Y5Ol18huIIG6vtw36r3fxFFWr6MIZO1lEPE90CuLUoyZWz549I1dffXXk9ttvj/Tv3z9yww03uAqIoPTuOly6+CsQ1VPrWN526wl82bJlXc84BfRBDn5SoptDfRaTB+LediooVN8/3VTpKXbYj4dD0XGic7gXNMoDDzzgKoVUlaMATFVbmmxAAX9sf76w0Q1P+fLlo8eKjhElebwbhrDQuVq90mrUqBGdaEU3+bpZ8HqnqU+m13cxiEEsEkuYY9bYbVMlnVdNp76Obdu2dd/rvKxrujdhjbd8vIQ5bk5t29TvVMloUTx63nnnuXgjs9+PIMbsh7Ntsd+/8MILrkevqJpZVaVKzKRn28Jw3xCGeD+RYvYwxdtBiJf3JUismyPe7RkQX+qD5fVEUj8g9cmpXr26NW/e3P7++2/Xr+nPP/90vX1atWoV2rdLDzC8fdG6dWvXY0u9kW699VYrUqSITZ482f766y/75ptv3Gux/dEC1xPlfz2jvO3Ve12qVCn3c//+/S1//vyWN29eW716tX300UfWrVs3t4xH/WLi1adm8eLFVrJkyejr7777rusJdMcdd9jo0aPt33//de+XXg9i/6P0WLp0qTVs2ND1TPOod5x6+Kg3lHqZqZ+V+q998sknvu1pdTjUH2/Xrl2uX6F6/O3du9cdvzpfqe/S9ddfb3feeacNHjzYHcPqaRj24+Fg1FvvoYcecj2cdu7cGX29d+/errfW+eef7/o8NWrUyL0+a9Ysy5kzp4WFevwtX77c6tWr535Wjzf133r55Zddb8jatWu7fXTppZcmOd8EnbZBvTCrVavmvlcvPPVNU48y7Qtdz6pWreq211se8LMwx6zetumzefbZZ9t7771nzzzzjNs+xTm6nr/++uvuM6s+nPE+V4U5bo6NkdU3tGLFiq4P6sCBA93rij9E/WzVY1jb58mM9yNoMfvhHkfqLVu4cGHXS1bxSLly5VwfzXnz5tn7779vDz74oDVo0CBN2xam+4agx/uJFLMHPd4OYrycLVFi3XhnjRF/Gk7z3HPPuZkcK1as6PpNiWbV6969u/vyBPXpRFqpP5SGdukJmPqe6ImgeiipYkB9XVq1ahWafaH119Pl2J5R+q9m8tXQDfVIatKkSZLl401P2tWvTkMeRH3SvPXScaq+Xtu3b48kAj391Puk7deXPq/6LGvW7HPPPdc9wZXYfl1hoCoLVZKo0kXDYT06JvT51dCXe+65xw0lE68SK1GpukEVGhpCF8sbmqYhde3atYtW7ejLD5/1jLJlyxZXSaMKj0ceecSd073Py4033ujO76LznSpygr7t3vprxnavqkBDqTU0T30xRedJVeOo2kPXfSBIwhyzaqi9qiQV25xyyimuWsgbVvzQQw+5vp5+27awxs1ef94+ffq4mdIVI6v6T8fdRRdd5Co1Y3uJZva2BTFmTw/1sFV/U1Vmn3zyye44UrW5rsvqeaqh5IdTqR2G+4Ygx/uJFLMHPd4OUry8LwFjXZK2CSj2Q6ZeSOpX8vHHH7uhZd4EB+q/krwfUhCGMx0JffD1YRddSBQYiQIi7bPYfi1+OskeLp3kNJHF4sWL3QQKOlGLejt5J2+/vffqh6ZhTbr4xw6xUU8gBXV6r8Iq+TGnYTcaCvLTTz+5n3Ux9Za577773PBJL8ALiwkTJriLr4bFiiba+Oijjw44PnV8aEIJRCJvvPFGNInh7Sfd+Cso09BDBfkaQuRNLhBWOjcoWNYw0qZNm7qeeOp7paFe3o2drgFhoOu5Ej66ge/Ro4cbbqpheW3atHFDTU899VQXhGvbg9b3EIknzDFr8uuz2gpo+KwmrVGyUDQsNfkNp1+2Lcxxs2Jk9TxVv2T1QtRkNqJrprZFw22z8v0IYsyeVkrOKsmv5Kl6IHsTC2ofK8nqJWUO5zgK4n1DWOL9RIvZwxJvByVe/jjBYl2StgkmpZ5Iminz6KOPdk/xPQoIYmel9duFIDP6VenCrkC5VKlS7uTq6dChg+uXFvR9kXx79TRTFRCaUVVNxT16ghYb2Phte3XzosBVkxMoyNPTQJ20/djDKTPeP920ejQRRunSpd3ECV7g/vrrr0dq1arlAvsw+eyzz1wfJVUb6EZQ26ub97vvvjtJAKRAXA3nFy1aFNf19QvdaCqI8ajfkwJnfcZ1E6CJBVTFoUlNwsY7d3mfGd3orF+/3vWH1D7RpCYKpsP0WdG1SkG2KgwUVOumVL3U5Ntvv3VJoV9//dVV5mg2+jBUHyC8whyzptQzVROm5M+f31XQea699lrfJDTCHDcn3zbNNq7Z0lUlqGSMR9eO2EmUsqKHbZBj9rRsm9ZZ12MdR15yXMlaVdfGxvaHu21Bu28IQ7yfiDF7kOPtoMXLvydgrJtN/xPvFg3IGl4PGfUNUi+crVu3up4r6gOi/jHqK9OkSRPXa6VYsWI2fPjw0L412gfqqaX/vvLKK1a+fHmrW7eu652kfXHLLbe4nmnqs6NeUW+88YaFZXvVM+ikk06yiy66yCZMmGA9evRwvdPUU+eRRx5x/bG0T/xs27ZtNnPmTLf+pUuXdr3S1HMsrDZs2OD6IE2bNs2OPvpo1xsqV65c7ndPPfWU62Glz6w+y+rd88EHH1jlypUtLCZNmuR6YT366KO2du1a17vwpptucj30dDz/8ssvtnDhQtfvS59h9T47/fTTLVFNnz7dVqxY4c5f6k913XXX2RNPPOHOabJ79273edexo3NB06ZNLWyS99nyPjP//POP+wxNnTrV5syZ43pGan+pR2DQt1d94zp06GCbNm1y5/AyZcrYli1b7KqrrrIKFSq4nmQyd+5cu+GGG9xnJZE/J/C3MMessTGZPouVKlWyM844w32v/pSKw7w+leoT64cYNMxxs3e90LYNGzbMzjzzTNdLWD1PdU799ddf3fGo40/vzYgRIzJ1fcIWs6e2ba+++qrly5fP9eDVvh43bpzVr1/fHUOK8fS71157LaHuG8IQ7ydSzB6GeDtI8XIkgWNdkrYJRge7DnQ1XdeXmrHrxKoL14wZM+y3335zF8muXbsmubiGiRpUa/IK7YvGjRu7E5UCMk3soAulJrFQUKR9on3hBUN+aLZ9OGLXW9u7fft21+Rf26lJLUaNGmVff/21uyE6/vjjXaPx5H+H+FOgpkkY9F7ps6sG97lz53a/U6CgQE9BqY5bBUZhos+ngodzzz3Xbevbb7/tjk9NEKKb3c2bN7vjWJNWKCg68cQTLZFpkgDdXD722GMukNRNjyas0OdfP4v2lyaw+L//+79Q7S9t+xdffGFLlixxNwk6JnTzLV999ZU9//zzNmjQICtbtqx7TedDJRiCKvl5+ssvv7ShQ4e6GwbdlOrGTp+Pyy+/3CUjqlSp4pZbs2ZNkslZAD8Ke8yqyWl0rtLkLkrS6PN59913W9++fd22KF7VpDbx3rYwx82x66iHAEoIaCInba+SFNoWXVcUbx133HHuupn87zJrfcIWs8ceR3ooUahQIfc5VlyiY0nJISX7dQxpQrIXX3wxMNuWkYIe7ydSzB7keDtI8XKEWJeJyBKBmnzHlu5Xrlw5+rOaYjdr1sz1mEk+7CRoPZHSS32ANExDNNxETbbVF0jDvTSMY9u2bYHfF14/Mb23atDdrVs397O2sVevXpHGjRtH+3KpZ03Qtzfs1B9KPXr++eefJJMOaOiN13g9zLzjUsNiNLxNX8kb/iey2EkNNKmDhmONGTMmsm7dOje8U8fOzTff7Ho9aWigej2FydixY13/NG2zJklQbzENLdZwNNEkOe+//7773ttPQRhKmhpv3dVfTD37NMxQ53YNB7vpppvcEGv1+oz97HBuh9+FOWZVv1pvMpfrr7/enZ+8WFNDOtVH0OsL68dtC1vc7MW/OpbUV9PrkaxhtY8//njkrrvuisZbsRMkZda2hTlm9/ajqFewN7HYpk2bIpdffrm7hqklQhC3LTOEId4Pc8we9Hg7SPEyse5+9LQNOQUZOkl6FBSqP5MXCHg9cdTnx2sUHlbqt+U1t9dFsEWLFpEKFSpEm2lrdljNzKq+R7EBQ1Bv6j/44AN3UvaOA/UMUn8uNfTXxcYLAtUPSX1r4n1SRvoCub///tv9rBsmzVYahn496aEgUJ9V3WjpRjjRTZw40fUWVM8w9eKLnbzCOw8sXLjQ9X1UP7Ew9A+LpUSIJiZSzzCPPhPq/aeef/o+9gY9LPTennXWWe46rtnMNQGDzudTpkxxAbhm4o6dLRvwszDHrPPmzYu88sorSSZRKV++fGTUqFHRpIw+t0qEKn7zgzDHzboWqu+mlzisWLGim3DMo8StEqdKrnsPvzJz28Ics+shizdxmtZdyfAqVaq440cUz2rfq39mbJI2CNuWmcIU74cpZg96vB3EeHkssS5J2zBTtY1HN3KdOnVy3+splxpj6ylybGAcZt7TW2+iB++kde+990Zat24dDUB10tVFMehiZ+zVDc5vv/3mZsC95ppr3EVGwZ8XqCqYQvACubPPPttV5OhJqRf4JppffvnFVWzoJiDR6Ym4ZqU95phjXCWabq712VYz/nPOOcclCMJMCQNtpyYlSD4rdceOHaPVA2Gi65ZugHRDoPdaE9RokhVVMCnBMnny5IQ9NyB4whyzqnrYq5hTNaeXvB03bpxL3OqaLtu3b3fxmh+EOW7WuntVwUqa6/qh90dxVewEd0q0Z0WMHOaY3ZtUS8lY7VtViXoPZ/Qw5qeffnK/V5LIq3RGOOP9sMTsQY+3gxYvE+vuR6VtSC1fvtw9xRwxYkT0Zz0d11NjLwjWTIBdunRJ8nd+eaKSkV566SV3QvUuEhdeeGGkbt267nsFRRoSoBlZkz+5DOq+0I36RRdd5G6AVLnRvn37yMMPPxxZsmSJe2KvaiwdBwr+wrC9ieqTTz6JZMuWLcksxoko9sYyESl5MX36dHdjpCFaGoqlz7sC+0aNGrnZVPPkyeOOlZEjR0bCRglL71ymG0Cvai1Wjx49XJIhTDQr8aBBg1w1x4IFC1yFh/aF3ncNa9OMzYn+2UBwhDlm1UzhisOUpFLyTZVXGjbrnY+9itvYthDx3rYwx82qrtWxpISFkoeKiXv37u0S0kocKkHmzUKeFdsW5phdx5FGgnhD+9Va47LLLnNDyJW81Xap5casWbMCt21ZKUzxfpDjkqDH20GMl4l1/xOcbv1IM01EoqbRQ4YMsbfeess1ctfPamiuBtma1fDss8+2O+64wzUAjxW2Ru+rVq1yzbV37NhhTz75pGvm7jV2v/TSS61o0aLWpUsXy5Mnj2v8HvR9oQkUNMNsixYt3PuvGTrVHF00m6KaiOu/aj6u4yTo25vIGjRo4GbLrFq1qiUyzcqayHRu08ypGzdudJNNXXnllfbzzz+7GXs1q7A+/5qopHXr1tFJqMJCM01ru3744Qf3sybk0GRbmjFWD6U9xYsXd7NFB/191pdo8qVevXrZFVdc4a7hmp24WrVqboILneM1+cfDDz+c8J8NBEPYY9aTTz7Zmjdvbnv37rX+/fu7c7Ti0rFjx9q7777rZrzu06ePmwneD9sW9rhZ14mzzjrLTQ60bt06d9z98ccfNnDgQDfJjuJjHYd6bzJ728Ies7dt29bN2t6oUSM3I70mYzrnnHOsWbNmbkIy/V7boWta0LYtK4Up3g9yzB7keDso8TKx7kHEJHARAuqtoqfjXpNy9ceqU6dO5MUXX3Q/63X1w9GQyrA/1dQTMTXXli1btkSaNm0a6dChQ3RIlPpwqS+aeBNDBJme/l111VVu+zTMS8Pv1NNQT7BVPaAqDw1NUmVW8if2AIJL1U4nn3xydIitej9pwhJN2pN8iGJYqFrqzDPPTNKTSzT0TlVL6iU2adIkN9RUlad+mwQiPTRUVpV5OqerOqxJkyau16HXQ1Lne+0LVb6VLFnSDbkGgiDMMWvyc67aCGg71NdRbRB0vlb/Wp2r/CLMcXPs+6GKM/XqVVWrRipoWLCqy3T90HbrK7OFOWZPfuzr+qVKbU1W51XzqSeoegXHTvAG+F0Q4+2gxMvEugdH0jaE+vbt64IP74ZOQZiCYA1TEQUnGo4SdosXL45Ur1498s0336QagCr41EkrSDcCBxtyokBIw6lE/00eBGpo0ujRo0OxvUCiSml4mYY5qUfV559/7n5W4q5evXqRt956KxJGSn54QbJuaBVkah/o5lez4epmV9uvSWQ0y3lQ/fzzz671wZAhQ6ITXmg7lTzRcGoNVRZtt671Qe53h8QUxpjVm0xJ/1UCWp9VxVvq6aht0YRSStxq8in1QfSLsMbNsX1V1XpDbSq0bTr2NCmWl7hVvOxNJOQtn1nCGrN7+0zrOnXq1Ojr+owrUeslbtU7U20pgrRtSDxhiLeDEC8T6x5aNv3PwSpxERx6KzWkREObNMymVq1arnw/V65c9t1339kjjzxiDRs2tE6dOh3wN2Hjbdfbb7/thj1pSMBxxx1nW7dudcNxihUr5oaj5cuXz8Lk3nvvtQ0bNtibb74ZHXqlIRH60nA2DdfInz9/vFcTwGHSMMIvvvjCrrvuOitZsmSS373//vv29NNPu3PbJZdcYp999plVrlzZnfvCYvPmzVawYEHr3r27FSpUyBo3bmxPPfWUO9f9+eef9u+//7ohuzrXaVjvUUcdFdjheBqCp2t2y5Yt3TXMu65pGLWGzOr9v/jii6127dpuyDIQJGGNWfft2+fOO1pXrX/27NndOevvv/92sdjvv/9ur7zyivu9ztcaJu4HYY2bvfdD/73wwgvddui9eP75561SpUru2FuyZIndeeedbljw0UcfnWXrFraYXS1AdLzrWFL7Hg0b15B+tTbRdapdu3a2cOFCt31qrwH4WdDj7aDEy8S6aUNP2xDxAlmdWEqVKmWTJ0+26dOnu/4g5513nj300EPRnnjJ/yZsvO069dRT3Ulp6tSp7iSl4Ec3uzoR64TrCfqzC2/91ZNL29m+fXv38zHHHOP6penGfsaMGdHgL+jbCySqX3/91fXQ+uijj9wNkUc3pAosu3Xr5m6Q1P+ufv36vgogj9SECRNcwPnPP/9YmzZtXDB98803uySPEjs//fST2/bevXu7m8fcuXMHNmErWv9y5cq5fpjedW3w4MHuRl/n8Hnz5rmbXyW4tL1AkIQ1ZvUStkoK6vOrc7WSctpO9aY85ZRTXA/TGjVq+CZhG+a4We+HqJ+qkrZ6P2699VZr0qSJu54qXlYid9asWdGEbWZvW1hjdi9he//997uk7Zw5c6xChQquP7AeNqqHph4wars9Qdk2JJ4gx9tBipeJddOGStuA04dSTzEV8Mbas2eP+yDq6YUmblDT99inx0GoVsgommxAJys95dWXAlI9UdJJIky8J9x6an/bbbe5IPTVV19NuPcbCCNNNuUFVOPHj3fnNE1wo8CxRIkS0c+4KoZWr17trgnly5e3sNBELPfdd5+b5EEJnQIFCrgqgm3btrnt96qpXnjhBTeZi27Qg37OU7CtbdXkRboB1nusSZl0U6/qNyVQVBmmm4Uzzjgj3qsLJHTM6p2DRJWqOl9puwYMGOAqOkWxmSZ+UYLQ2x4/bltY4mZv32pyu9dff92uvfZaV8Eq3iR3mnSsevXqWZ5AD1PMHru+33//vdWpU8d69OhhDzzwgLtW6bqsyj49tKhXr168VxcIdbwdtHiZWDdtqLQNMA1dGj58uDu5KDCMDQQUfKgc/vzzz3dPh2644Qb3xFPBgQQpGDhc3tPbpk2b2tVXX+0CBs24rRvdIFdfeSfkBQsWJHnyp+DPm/lRT7XXrFljt99+u73zzjtu+KHwRBsIHg27UgCm6hVVQOnGRzNOL1q0yN577z33Wdc5/aWXXnLJvdNOO81XAWRGBHSqGNAwViUoVSmg695ff/3lAlBRQkHVbBp2rOqBMFzjlLS655573Hs8e/Zst00PPvignXnmme5ari9VaJGwRRCEOWbVNngVtqqcPP74410C7thjj7WJEye6m3sZMWKEG4Yfuz1+2rawxM3eyANv36qy9pprrnHJdB1XSlrotc6dO9vo0aOjCdvMipHDHLNrX3v7WftVbU6UIH/jjTds7Nixrlr47rvvtsKFC9u6deuifxeEbUPiCXq8HcR4mVg3bai0DSjdyCmYGjNmzCGfEomCAH0ozj33XNfXJKySb3ds5YMqOBSs6Yl7q1at3M1B0HpziYI6VWnopkAnYD3NVrVVSk+8FYzqRK0n3R07dvTVUDwAabvZU7VK165dbejQoa5iS4GWaMjW119/7W6Sli5d6hIiCipVyRYmCkLV11U3gJs2bXJVARq6u3z5cjfcVftFw3d1k6jv1Z8rLHSTO2jQIHceVyJFFUzffvutSzaoSoyqJQRBIsSsijfVw1ZVTStXrnRDUHWzr8pOPVjR71St5fFLNWXY4ubYHraKl1UFp/dEQ5mVgFYi5qabbnIPv7zEaWYKc8zurbv2ta5J2rd169Z11dl6UPHwww+7hzFKmOtBjd+3B4ktDPF2UONlYt00SMNkZfAZzRat2U7Xr1/vfp4xY4abFbBr166RN954I+Fm4pw7d27k8ccfj/68bt26g85+qFljvZlkg0az+LZt29Z9P2XKlEj79u0j33///QHv+e7du5P8nWYpBhAsv//+e6RUqVKRYcOGuZ937NgROe+88yITJ06MLvPZZ59FbrjhhkiZMmXcuTCsNPN05cqVIyeccEKkZcuWkeeeey6ycOHCSPny5SMTJkxwM897s3CHzZo1ayKDBg2KVKxYMXLTTTdFzjrrLDfrPBAEiRKz3n333W47RXGZzt3vv/9+ZMWKFZFrrrnGnaf8Iuxxs46lxo0bRzp06BD5+uuvIxdddJG7bkiPHj0ibdq0iSxevDjT1yNRYvarr746ct9990XefvvtSKdOnSKPPPJIZMmSJZGxY8e6a/bs2bND8zlHOIUp3g5qvEyse3BU2gaMntirn53K2jXU5sorr3Qz/6kiQX3A1AxbTzv1lCVRqMxffWY067CGO1166aXuSbb2TZhoQo4TTjjBVQioR5rovVaPMb3fGnqUvE8cgODSLMt6Gi46x+mJvoasqvJMn3tNLqDZalesWOFeK1u2rIWF+uKpiuecc85JUgWhyihVm6qSSt/rHKifVcUWduqdpqqmXbt2uaotv1TqAYkYsyb//GkWcZ2Db7zxRvezJgh87LHH3HBbVWZpUjK/CHvcrGqztm3buiHMmvhKdJ3Q5GPqH6uqOfVNzkyJErOrHYiqiT/44AP3syZu0iR8l112mTvGZs6c6SbdA/wsyPF22OJlYt2UkbQNkLvuusv191LvI1EJv4JgBSAa5qMTiGaW1jAn9ShJBF4jf9HJdf78+a7nn06uYbyh1dCjCy64wJ1w1bNGs7IWKVLEChYs6IY+KBBUAO6niwmAw6dzmiZC+Pvvv12fu8WLF7shh5pERcMNNQx3ypQpoRp2qOGhSh5oOLWSHnny5IkmQmLpWqjJizSkNHbYMYD4C3PMGht7et9ruKzO1V4LCA1xb9GihetjqwSd+CEuDWPcHLtNogl3tO87dOhgF198sXtNDwuUUNTQZ09mb28YY/bk+1quv/56N5T80Ucfdb9TP2dNuKfWPim13QD8KIjxNvFy4vDPUYeDateunXtiPGPGjOhrTz/99AEXT51svAAlEWYf9gIAzYqomwNVb+jptigQC2oAGiu2QkPB3jfffONmg1QwpGNCT/HVd0wXEi0bpOAPQFKqylJvJ/WF0znu9NNPd+cx9c6aPn26m9VbCQ9VSImW9VMAmRE0i7aq85Ts2L59u+vNpYo8TQ5x3HHHuZ6KDz30kLsJHzlyJAlbwGfCHLN626AklCapUXxWsmRJl7DShIHXXnutm6BG53LFpF7CVuIVj4Y5btb6eu+H+nyfcsoprpewEqL33nuvq4BWQlHXE/03VmZsZ5hjdu1jb18//vjjrlewJsPUftb12OsbrM+14pRYJGzhN2GIt4mXEweVtgGgC6JOFAoO1RD7mWeecZMzeIGVhjmpekEXSn141WA6zLMPK9jRUCM18tfJU0+yNdOwnvRqOJSGPClg0g1C0Gn2Sk3I0ahRI1dxljdvXpew0DbrGNBQL72eXNCCbgD7n5j37NnTJSJVJaTPcP/+/d2u0Yy1Ov+rgkhVqPr8h/Gz7lXjKKGgG3BVKGnyG92E6zUNN33kkUds2bJlbgIFKmwBf0mUmFUxps7LSsKpylbJLE3soht9va7zlaq04n2eTpS4WaMxtmzZ4iZ9++GHH1yS9scff3QxtKqelVjX+5OZwhyzew8rtK6KTzQCRklnJbqaN2/ullFbBE1spwcVmrwO8KswxNvEy4mFpK3PKbB966233BNNUXCr4FAXxpo1a7oTiC6QCk5++eUXN0tgWIehpDb7sII0zcyqm3pRcKahDLpxCDpVqXjvvYZmqF+NLh7qFaUgUBUqClC9PjwAgun333+3Dz/80MqXL+8qtnTjrxt+3Wieeuqpdsstt7ibpmHDhrmkhxKX6rMVBikFwqr8Uo9F9W5V5Zpmve3bt68bUvrxxx+76tv8+fPHbZ0BJFbMOnnyZFetqn6Gqvr/9ddfXUJar6mvpxIA2rYhQ4ZYrly5on8Xz20Lc9ysHpR6aKf9q32vBIuSLoqVddypx6qStFpGCXS1JMjs9yOsMbuSWIpF9PnVdVnDyL0etqqyVYsTJf9V5axjybs2B+FzjcQT5HibeDmBHWKiMsTRqlWr3Ky7yWdsHT58eKRmzZpJZiCNtXfv3kgizD786quvRu6///7IyJEjo8tptsdYQZ6lVOv+559/utlmf/rpJzcDsbY1b968kQEDBkTmzZvnZpjVTLgAgk0z1FapUiXyzz//uNmX9RkXzbBeokQJ97v/+7//i7z33nuRv//+OxIm3jUu+Qzay5cvd9ueL1++yMcffxx9XTPfAvCXMMesisVuvfVWN7u1TJkyJXLppZe687S3vevWrYvcdNNNkaFDh0b8IMxx87fffhvp37+/+15x8NVXXx255JJLIosXL3brv2vXrsgLL7wQOeOMM9ys6VmxbWGN2adPnx658sorI5s2bXLX3oEDB0ZKliwZGT9+fPT4ueeeeyK33HJLZOXKlYE6jpCYghxvEy8nLiptfUql+mqE/ccff7gnsxp+cvfdd0d/ryoFDT3RU2UNHfX4rXQ/s2cfPvHEE131Q6dOnQI5+3BaaKIObbv662giD/Xg0eQR6vHYsWNHN9wqrO89kEj0+VbFlj7fqkJTZZqqV/T512zY6oGnCq9jjjnGwkI9FXUNmzZtmjune0Mwvf++8MILrqJK1zpVPKjagXMd4C+JELP269fPxaBqM6BqW52z1Fv75ptvdtuvtgOxFZ3xFPa4ecmSJa61w8CBA93xpN7nmkhNlXLdu3e34sWL265du+y7775zs6VnpbDF7Koef/LJJ+3OO++0EiVK2KZNm9zkgp9//rmLT9QPVNdmTQiqzwIQBEGMt4mXExtJWx9S4KEPpkr3NdOphoNquM0111zjmrx7NNxMPVlU2h9WYZ59OCWaXXb16tUuSKpcuXL0pkcz+6qxvyY1ULAt2i8KTAEEk4ZLqi+cvuSnn35yfbQ0m7SCRX3WJ06c6G6KvJYB3rJhomSCznFff/21O8cpYashlbqh1XBT9WRUSwRvchcA/hH2mNVLrmk2cbU+0JD3+vXruxYISgpq6KxauKi1gDcUPJ4JubDHzd6+ffvtt91DAl0fNEGlhuUr6aLEohK3xx577AF/k9ESJWZXCwStvxJaop7V2r5x48ZZq1at3EMBT1CS0UgsYYm3iZcTl7+mwIM7SSjwUDAiCgL0pabu6tmkJ0HeU+PYKoYwCvPswym5/fbbbeXKle5p9ty5c10CQ0/m1Tfq7LPPtvfff9+9LgoQveCPAAkIHj3JV8WNZpVWEkCT9qg6be3ate5mW30gO3fubNWqVYv+jR8DyIygGz5Vqan339SpU92Nrs5xSiroxluv62cA/pIIMauXgFIVp2YY/+qrr9z2adt0/la1reLU2N6d8UpaJULc7O1b9Z7UhGO6ZugYU0Wcetkqga4qUE0Ql/xvMlIixOzeuqqiWQ8q9GBCDy50XdZ1W5/9RYsWJfmboGwbEkeY4m3i5cRFd3Cf0YVdF/kvvvgiyevVq1d3J5gVK1ZYIlBgoIoMPQXTE14FZV4AocBTQ3E0K2zjxo2tTJky1rVrVwsyzdyrSRT0BE1D2tQQfdCgQa6Jv6omFITqhsGb2VfJDA8BEhA8Gqaq4cKaJEU3fapM07lfN50a+qlJYjTztIbgJgLdED777LMuQasAW+c4TVJUr149e+KJJ6xChQrxXkUACRKzTpgwwVVQxlJSVklRnbs1EeKrr77qzlWXXnqpq+z0YtR4SbS4WVXDSsAoQauRGJoYThNg6fvYhG1mSJSYXeuqhL9ogkF9nlVZK0qSa1s12R3gZ2GLt4mXExNJW5/QDLR6SqteWJq1UDMbaiiK6IKpPn462WgoUNhpGFfRokVdfyQFB+q5pd5bqhxQAOE9+dXJVUOidNIVBUtBpB5oelpfoECB6GvaFgXV3tArBeIKjPzUWwfAkdE5Xb0Q1fNON9Tq96hqgHz58rnZvXXuU9VaovACUe+/6merWX057wH+EuaYVcPtVXmlhPSePXuir2u7NCJACVr1UVWV1g033GBz5syJbnu8EnKJFjd7yfGmTZu6npR//vmn9erVy1W3xiZIM0OYY/ZPP/3UHUs6tj1ehbYqhVWVuGbNGldl/M4777iWIfF+WAEkYrxNvJx46GnrA2qWr5vTjz76yD2pFQWFjRo1clVGxx9/vHtNgclFF13keq+ElYIdBfsaehY7lEuVAxrypZ5oGpKWnAKm2KFpQaEhhXoiryBQExQoCPSCaQVB6sGjm4PXXnvNVa3oSSCAcF4HpEePHq6Ka+PGja4CQH0TE42G7qp6ScmQqlWrxnt1ACRIzKpEmxKAY8aMSfH3XtsWjxJXquLSCAFNThYPiRI3J9/3seuv66WuF6qmU9JacbOSMRktzDG7ErGqONTnV32BFYuULl06+vvYtg56OKDEtNojaD/oYQYQFGGKt4mXEwdJWx8ZNmyYe4qpoEOz8KqHkJ7y66Kp2bPVQ0jDscIqEWYfjqUJIJYtW+aG4YlmsdRQI1VLaDs91113nasg0P4AEE6x5zEFj+qnpf6JiUpDTzPjphtAxghbzKpt0KRiirdUVah+qZpVXF+Kv1RR7LeYM+xxs9o5qN2B2g94cbKS1GeccUaKy6tiTonS2OR1RglzzK52D3rooCT/t99+ayNHjnTHlpfs944Xfb5jE7R+nawJSKR4m3g5MQTnEWsI6Ym+equoF5g+cOqVpdldFRhoIhZdPFWhULFiRdefyQt+gzqc6VCzD2/atMnNRKoKB1VsqIKhT58+0WW0b26++eYDeqcFIfBMTpUAqhrwgr/p06e7YVRDhw51T+r15Fq0vRp+GKTgD0D6eUNYpXz58oEOIDMCCVvAX8Ics3bp0sXmzZvnKrBUSalepJqYRsPt9Vq/fv3s9ddf91XMmQhxc6VKlVx8PHjwYJeQ1QiMg/VJVuVrZiRswxyzK/GqyfW8dg9K7qviUBOqqVWCkuTe8ZK8opaELYIobPE28XJioNI2TtQPSUHAzz//7CZs0JNy9WZSZYKCYTV8V9VC8gt/0IYzpTVg0L7QUK7YE48mUdDsww8++GB09uEwUM+o66+/3qZMmeImUlDvRg2vUq8xDW/T0/sOHTq4IFxBuYYhBqkyAgAAhEeYY1ZVS6onraqG5YEHHnDJKiU8FaOVLVvWJQ01NF8Jaj9IhLhZPYS9BKxabcyfP99tl+LirIyHEyFmV+L5ggsusIYNG7q+vJr4Uw9ilIBevny56/d56623us8CACDr0YQmTtSwXhd+TeagmWe3b9/uLo4awqSgQJMHqFJBT9BjL5J+D36PdPZhBWZhmX04JeoBddZZZ9mAAQPcTYDec90EqSeNgj/R03vd/NSqVctVegTlxgcAAIRPWGNWVQurQljr71GVbWzCUBSjqf2AX4Q1blYlq/qYq/LNO3bUP1ZJdfUOVrJaYidXy0xhjtmXLl1q5cqVc98rQfvNN9/Yeeed5/pU6zOhz7N6fSpZrWVJ2AJA/Pj7ihJCn3/+uRsy5s3gqqfGkyZNcsHHNddc436v4Wd6gn7ZZZeF+iIZ5tmHU6Jhg48//ritW7fOrrzySteTS9Up6j/mBU7y3HPPuZuFIAV/AAAgXMIcs7Zv394l5VSteu+997oqVdG2KQbTDOPqqdq4cWOXtO7atWu8VznUcXPr1q1t+PDhLiGt3qlKyKoCVD1idXxpkiz1W1UltGR2wjbMMfv999/vErTeZ0DV8kqK6zNwwgkn2G+//eaWU2JaLSlUSSzekHIAQNaiPUIW0lArDaPRECsFJeoZpEkPFHw9+uijruG7huCULFkyyd8FaYhNWoV59uGUaFiRtrlv377uqbze+3/++cfNRKsgSTdClStXthtuuMH1iHrjjTfivcoAACBBhTlmVfsDJeCUlBMNd1fC8Pnnn3fbpW3QNmsiL01EpsRcvBNyYY6b1S/5zz//dC0GYqmPrZK12h7ZunWrvfbaay7ZmJnCHrOrstw79pUgP+ecc+zcc891D16UKFdVuZLUQdsuAAgrkrZZ+ARZwZ43McOiRYts7NixboiNgkRdNBUcKAhQoKyZa4MS/B6JsM0+nJIvv/zSDbebOHHiAb9ThYQmutAyGs6mYVgagpUI7z0AAPCfMMesmlhJFamqIoxtgaCKWw0L9xK3yfmlgjJscbO2QQ8DevXq5Y6jH374wbUg0FeNGjVc72RR5bPeN09mHWthj9m1nqtWrbLevXu7Hs2qsP3uu+9cNfOTTz7pEreq1tbv9Z4AAOKPnrZZ1MReAdbkyZOjQ5hOOukka9asmevVpN/16NHDXTA11MkLfiUIAUB6n6arekHDnfRUV/3ENGOpAgdVEChgVqA2e/Zs1x/qxhtv9FWwfDgUTHszU+r72NlXVbFyySWXuEAwf/780eAvyNsLAACCKcwxa4sWLVySUO0DVE2o/q8a7u4lqrX++lm9es8///wkia54xWRhjpu7dOniht+rqvXaa691bQg++eQTl0g88cQTXeJQfWw1EVZswjYzj7Wwx+zab2r3UalSJWvbtq1Nnz7d5syZYxUrVnRVtmp30rFjx2jCNijJaAAIMyptM5l6Bemir+FMavKuYFBBlefNN990QYmGqigA8ITxIhnm2YcPNQxP26nA2nvvFaBquJWGWSkIVLCkn8OwvQAAIHjCHLO2adPGJds+/PBDF4cuX77cDRFXb95u3bpFl1OlrfaDWkDEW5jj5rvuusu9H3oIIOpVq3hZldua3E79kZUUVXsOJaizShhj9qeeespVmCv5r+p470GF2jyoel4TkXkPafSexD6IAQDEn7+vMgEX28ReT5Jr165t/fr1s5kzZ0aX0ZN+9RLKqifIfph9uG7dutakSRM3iUKxYsVcRYOe9HqzDyef9dbvwdChnHLKKda8eXMbOXKke5otXrCnSSW+//776M/xrOYAAACJKcwxq6o11Q9VPUlFiasrrrjCbd8XX3xhX331VXRZJbT8kLANc9ysamG1eYid3E0tCdR6QD15vQntlFDcuHFjlq5b2GL222+/3bU/qF+/vptMUBPZqapZzj77bNu+fXs0YaukbmyrEwCAP/j7ShNgamKvp5oa+qMnsuo1pYb1derUcYGJnuSKZuTUU9vY4TdhE+bZh9NCAZ2GdqkqRRUqmthi3rx5bvII9VRT9UdQbnwAAEC4hD1mVTJq7ty5LkEbS9WrqlJNnvSMtzDHzZpETJXM6iF87733uupV0bapx7B61/7444/WuHFjl7SOTexmhTDF7GppoopstZzQg5iHH37YTWKn6mBVOit5q4kE9RkXVTUHZdsAIJHQHiETHKyJvQKV9957zy2jmVIVLGoygaAML0uvMM8+nF4a1vbZZ5+5gEmTF6hSRRUUQRleBQAAwiXMMasqIzXE/aKLLnJDxBWDKkGtakL16lWScMiQIbZmzRpXZewHYY6blfxXSwdvX7/88ss2fPjw6ORv2gZtsxKlv/zyiz333HNxi5GDHrOr/3SDBg1cVbmqmmPXW+02lMDVvleVrXrZqqczAMCfSNpmAgW+Ckpef/31A5rY6wmy+miph5Nm41VgEpQAIL3CPPvwkfB6Y3nC+N4DAAD/C2vMqlhLE3WpP68Sb9K9e3dXMVmvXj07/vjj3Wsajq+krtpDxFuY42ZVcivxqWpVJcs9qrhV/1gvcZtcvI+1IMbsagOiKlolbjWpmCauU4LcO1aUpNVEe6+99pp7EJM3b954rzIA4CBI2maCMDaxP5zZhzW0Tk9wNfzMq2hQ0OYFDpp9WL/X7MPquZQoYoPrIATaAAAgnMIes6rKUElnVQirelITdyn5qRYQCxcudJN6eUnSeApz3NyiRQu37//44w/XF1mJQm8yLNHDAL0/6tWrZKLHDzFy0GJ2Tdq2bNkymzBhgvtZVdtqhVC0aNFolbxcd9111qtXrwMmsQMA+A9J20yggFZDstauXesCFc2C6tETfvUV8noiBSEASK8wzz4MAAAQFmGLWe+55x43xF5JqXPPPdfy5cvn4k4lBlV5q2SthofPnj3bihQpYjfeeGPck9Fhjpt17GzYsME+/PBDt99Vua32COrN261bt+hyqrTVfvDLJHBBpB7HOh68inhNVqeJA5W4VT9qtdRQ1fnNN9/sKupVaQsA8D+StplET8bVJ0tPwy+//HI3Q6d6UmlYkIaghZWGl6k6QEO8NMPtBx984KoCFJjVqFHDLaO+aD179nRPfIM2mQUAAECYhCVmXblypUtcKTmoalVVd6qKWNW0mpBJbSBUaZu8ujCeCdswx807duxw74daICh57lEFt7ZHE6xpsjtkTKW2eh1PmTLFPXhR/11VaGsyO32OvcTtmDFjXCJdDzCCkvgHgERH0jYTBb2JfXppcgcNqVO/rWOOOcYNpVOQMHr0aDeJxRNPPGGVKlVylRvlypVzgTMAAADiKywxqxK2qhy+9NJLbfv27a69g+JNJbL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",
"text/plain": [
""
]
@@ -1003,8 +1102,8 @@
"\n",
"1. **CEDAR leads on ex1 (nonlinear) and ex3; CDNOTS+ ties CEDAR on ex2; CDNOTS leads on Henon.**\n",
" - **ex1** (nonlinear, d=5): CEDAR (nonlinear) achieves the highest F1 at 0.778 vs CDNOTS+ (nonlinear) 0.571. Partial distance correlation lag selection captures nonlinear associations that ParCorr misses, giving CEDAR an edge before the CI test even runs.\n",
- " - **ex2** (linear VAR, d=6): CDNOTS+ and CEDAR tie at 0.870, both ahead of CDNOTS (0.800).\n",
- " - **ex3** (cascade, d=11): CEDAR achieves perfect F1=1.000 (CDNOTS 0.945, CDNOTS+ 0.964). The chain structure and AR self-loops in the ground truth align exactly with CEDAR's pairwise design and `assume_ar1=True`.\n",
+ " - **ex2** (linear VAR, d=6): GES, LGES and TGES tie for the lead at 0.917, ahead of CDNOTS+ and CEDAR (both 0.870) and CDNOTS (0.800).\n",
+ " - **ex3** (cascade, d=11): CEDAR achieves perfect F1=1.000 (GES/LGES/TGES 0.949, CDNOTS 0.945, CDNOTS+ 0.964). The chain structure and AR self-loops in the ground truth align exactly with CEDAR's pairwise design and `assume_ar1=True`.\n",
" - **Henon** (nonlinear, dense, d=5): CDNOTS (nonlinear) dominates at 0.828. Coupled-map dynamics violate the AR(1) assumption; lag selection misidentifies the dominant lag and Cond2 is unreliable (CEDAR 0.560 linear, 0.667 nonlinear).\n",
"\n",
"2. **CDNOTS+ advantage is dataset-dependent.**\n",
@@ -1018,7 +1117,7 @@
"\n",
"5. **CEDAR self-loops are assumed, not discovered.** `assume_ar1=True` unconditionally adds one self-loop per variable. When the ground truth has self-loops (ex3), these are true positives. When it lacks them (ex2, Henon), they become false positives. All methods here are evaluated on the full ground truth including self-loops.\n",
"\n",
- "6. **LGES consistently beats GES; TGES adds no benefit here.** LGES's SafeInsert and ConservativeInsert improve precision over vanilla GES on all datasets. TGES's tier-based orientation finds no undirected cross-lag edges left by LGES on these instances, so both tie.\n",
+ "6. **GES, LGES and TGES are identical on all four datasets here — but not in general.** All three run on the lag-embedded matrix under the same temporal background knowledge (a variable can only cause another at an equal or later time step). LGES adds SafeInsert and ConservativeInsert on top; TGES adds a tier-based orientation pass on top of LGES. On these four small, clean systems all three land on the same F1/Precision/TPR/SHD, because plain GES's own search already reaches the fixed point the extra machinery is there to reach for it. That is a property of these datasets, not of the methods: at `d=15`, `T=300`, with contemporaneous edges mixed into the lagged structure (see the demonstration above), GES adds 7 spurious edges LGES's ConservativeInsert withholds (F1 0.870 → 0.936, SHD 15 → 7).\n",
"\n",
"7. **VARLiNGAM, LASSO-VAR, and Granger lag far behind.** VARLiNGAM achieves high recall but poor precision; non-Gaussian identifiability rarely holds. LASSO-VAR and Granger produce nearly identical results and are dominated by constraint-based and CEDAR methods on every dataset.\n",
"\n",
@@ -1026,8 +1125,7 @@
"\n",
"---\n",
"\n",
- "All baselines are available via `causalts.baselines`.\n",
- ""
+ "All baselines are available via `causalts.baselines`.\n"
]
}
],
diff --git a/examples/beginers_guide.ipynb b/examples/beginers_guide.ipynb
index e90a3b4..681c149 100644
--- a/examples/beginers_guide.ipynb
+++ b/examples/beginers_guide.ipynb
@@ -23,8 +23,15 @@
},
{
"cell_type": "code",
- "execution_count": 50,
- "metadata": {},
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:30:59.187802Z",
+ "iopub.status.busy": "2026-08-29T20:30:59.187618Z",
+ "iopub.status.idle": "2026-08-29T20:31:02.868519Z",
+ "shell.execute_reply": "2026-08-29T20:31:02.868024Z"
+ }
+ },
"outputs": [],
"source": [
"import warnings\n",
@@ -62,8 +69,15 @@
},
{
"cell_type": "code",
- "execution_count": 51,
- "metadata": {},
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:02.870045Z",
+ "iopub.status.busy": "2026-08-29T20:31:02.869907Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.076012Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.075530Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -157,8 +171,15 @@
},
{
"cell_type": "code",
- "execution_count": 52,
- "metadata": {},
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.091316Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.091216Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.185440Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.184969Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -249,8 +270,15 @@
},
{
"cell_type": "code",
- "execution_count": 53,
- "metadata": {},
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.186631Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.186550Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.247634Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.247208Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -309,8 +337,15 @@
},
{
"cell_type": "code",
- "execution_count": 54,
- "metadata": {},
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.248912Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.248827Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.312973Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.312578Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -381,8 +416,15 @@
},
{
"cell_type": "code",
- "execution_count": 55,
- "metadata": {},
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.314326Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.314237Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.375586Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.375161Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -563,8 +605,15 @@
},
{
"cell_type": "code",
- "execution_count": 56,
- "metadata": {},
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.377167Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.377068Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.458404Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.458006Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -648,8 +697,15 @@
},
{
"cell_type": "code",
- "execution_count": 57,
- "metadata": {},
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.460043Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.459968Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.486466Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.486066Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -735,8 +791,15 @@
},
{
"cell_type": "code",
- "execution_count": 58,
- "metadata": {},
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.487703Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.487631Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.500511Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.500157Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -814,7 +877,7 @@
"2 2.301345 0.056156 -0.538655 0.250631 0.296661 -0.382015"
]
},
- "execution_count": 58,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -834,8 +897,15 @@
},
{
"cell_type": "code",
- "execution_count": 59,
- "metadata": {},
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.501628Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.501549Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.605987Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.605622Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -862,12 +932,19 @@
},
{
"cell_type": "code",
- "execution_count": 60,
- "metadata": {},
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.607563Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.607475Z",
+ "iopub.status.idle": "2026-08-29T20:31:03.760701Z",
+ "shell.execute_reply": "2026-08-29T20:31:03.760371Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -886,15 +963,28 @@
},
{
"cell_type": "code",
- "execution_count": 61,
- "metadata": {},
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:03.761875Z",
+ "iopub.status.busy": "2026-08-29T20:31:03.761796Z",
+ "iopub.status.idle": "2026-08-29T20:31:04.663153Z",
+ "shell.execute_reply": "2026-08-29T20:31:04.662720Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CDNOTS (constraint-based): F1=0.762 TPR=0.727 Precision=0.800 SHD=5\n",
- "GES (score-based): F1=0.353 TPR=0.273 Precision=0.500 SHD=11\n"
+ "CDNOTS (constraint-based): F1=0.762 TPR=0.727 Precision=0.800 SHD=5\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GES (score-based): F1=0.917 TPR=1.000 Precision=0.846 SHD=2\n"
]
}
],
@@ -918,14 +1008,21 @@
},
{
"cell_type": "code",
- "execution_count": 62,
- "metadata": {},
+ "execution_count": 13,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:04.664341Z",
+ "iopub.status.busy": "2026-08-29T20:31:04.664270Z",
+ "iopub.status.idle": "2026-08-29T20:31:05.165614Z",
+ "shell.execute_reply": "2026-08-29T20:31:05.165121Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -934,11 +1031,17 @@
],
"source": [
"# Colour-coded comparison: green=TP, orange=FP, red-dashed=FN\n",
- "fig, axes = plt.subplots(1, 2, figsize=(13, 5.5))\n",
- "compare_graphs(gt, G_cb, var_names=names, fig_ax=(fig, axes[0]),\n",
- " title=f\"CDNOTS (constraint-based)\\nF1={m_cb['F1']:.2f} SHD={m_cb['SHD']}\")\n",
+ "# Reuse the first panel's node layout for the second so the two graphs are\n",
+ "# directly comparable -- otherwise each panel lays its nodes out independently\n",
+ "# and the same variable sits in a different place on each side.\n",
+ "fig, axes = plt.subplots(1, 2, figsize=(8, 4.0))\n",
+ "_, _, node_pos = compare_graphs(\n",
+ " gt, G_cb, var_names=names, fig_ax=(fig, axes[0]),\n",
+ " title=f\"CDNOTS (constraint-based)\\nF1={m_cb['F1']:.2f} SHD={m_cb['SHD']}\",\n",
+ " legend_loc=\"best\", return_pos=True)\n",
"compare_graphs(gt, G_ges, var_names=names, fig_ax=(fig, axes[1]),\n",
- " title=f\"GES (score-based)\\nF1={m_ges['F1']:.2f} SHD={m_ges['SHD']}\")\n",
+ " title=f\"GES (score-based)\\nF1={m_ges['F1']:.2f} SHD={m_ges['SHD']}\",\n",
+ " legend_loc=\"best\", node_pos=node_pos)\n",
"plt.suptitle(\"Constraint-based vs Score-based | green=correct, orange=extra, red-dashed=missed\",\n",
" fontsize=10, y=1.02)\n",
"plt.tight_layout()\n",
@@ -949,6 +1052,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
+ "**What just happened?** On this dataset the score-based method comes out ahead:\n",
+ "GES recovers every true edge (TPR 1.00, SHD 2) while CDNOTS misses a few (TPR 0.73,\n",
+ "SHD 5). Don't over-read that — `ex2` is linear, Gaussian, stationary and small, which\n",
+ "is exactly where a BIC score is at its strongest. The table below says when each family\n",
+ "is the safer default; Part 5 shows a case where the constraint-based approach is the\n",
+ "one that holds up.\n",
+ "\n",
"### Reading the Metrics\n",
"\n",
"| Metric | Meaning | Perfect |\n",
@@ -984,8 +1094,15 @@
},
{
"cell_type": "code",
- "execution_count": 63,
- "metadata": {},
+ "execution_count": 14,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:05.166895Z",
+ "iopub.status.busy": "2026-08-29T20:31:05.166808Z",
+ "iopub.status.idle": "2026-08-29T20:31:05.282072Z",
+ "shell.execute_reply": "2026-08-29T20:31:05.281767Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -1015,12 +1132,19 @@
},
{
"cell_type": "code",
- "execution_count": 64,
- "metadata": {},
+ "execution_count": 15,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:05.283652Z",
+ "iopub.status.busy": "2026-08-29T20:31:05.283565Z",
+ "iopub.status.idle": "2026-08-29T20:31:05.560506Z",
+ "shell.execute_reply": "2026-08-29T20:31:05.560218Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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",
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",
"text/plain": [
""
]
@@ -1085,8 +1209,15 @@
},
{
"cell_type": "code",
- "execution_count": 65,
- "metadata": {},
+ "execution_count": 16,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:05.561933Z",
+ "iopub.status.busy": "2026-08-29T20:31:05.561850Z",
+ "iopub.status.idle": "2026-08-29T20:31:05.649102Z",
+ "shell.execute_reply": "2026-08-29T20:31:05.648750Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -1119,8 +1250,15 @@
},
{
"cell_type": "code",
- "execution_count": 66,
- "metadata": {},
+ "execution_count": 17,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:05.650569Z",
+ "iopub.status.busy": "2026-08-29T20:31:05.650490Z",
+ "iopub.status.idle": "2026-08-29T20:31:05.933154Z",
+ "shell.execute_reply": "2026-08-29T20:31:05.932866Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -1145,12 +1283,19 @@
},
{
"cell_type": "code",
- "execution_count": 67,
- "metadata": {},
+ "execution_count": 18,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-29T20:31:05.934512Z",
+ "iopub.status.busy": "2026-08-29T20:31:05.934444Z",
+ "iopub.status.idle": "2026-08-29T20:31:06.279096Z",
+ "shell.execute_reply": "2026-08-29T20:31:06.278624Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -1161,9 +1306,9 @@
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(13, 5.5))\n",
- "plot_graph(gt_ns, var_names=names_ns, fig_ax=(fig, axes[0]),\n",
+ "plot_graph(gt_ns, var_names=names_ns, fig_ax=(fig, axes[0]), target_node='C',\n",
" title=\"Ground Truth (with C node)\", show_colorbar=False)\n",
- "compare_graphs(gt_ns, G_cdnots, var_names=names_ns, fig_ax=(fig, axes[1]),\n",
+ "compare_graphs(gt_ns, G_cdnots, var_names=names_ns, fig_ax=(fig, axes[1]),legend_loc=None,\n",
" title=f\"CDNOTS Discovered\\nF1={m_cdnots['F1']:.2f} SHD={m_cdnots['SHD']}\")\n",
"plt.suptitle(\"C→X0, C→X1, C→X2 reveal which variables are trend-driven\",\n",
" fontsize=11, y=1.02)\n",
@@ -1243,13 +1388,21 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
"name": "python",
- "version": "3.10.0"
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.12"
}
},
"nbformat": 4,
diff --git a/tests/test_baselines_ges.py b/tests/test_baselines_ges.py
new file mode 100644
index 0000000..7defedf
--- /dev/null
+++ b/tests/test_baselines_ges.py
@@ -0,0 +1,175 @@
+# Copyright 2026 Bloomberg Finance L.P.
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Tests for the GES baseline wrapper (causalts.baselines.ges_discovery).
+
+Regression cover for two bugs:
+
+* An edge-orientation bug in the extraction loop, which read causal-learn's
+ adjacency matrix with the endpoints transposed -- reversing every directed
+ lag-0 edge and silently dropping every directed lagged edge.
+* A missing-background-knowledge bug: with no temporal constraint, the search
+ could orient edges backward in time between lag blocks. The fast engine now
+ builds a ``forbidden`` matrix (``causalts.lges.temporal_forbidden``) and
+ passes it all the way through the search, including into PDAG completion
+ (``pdag_to_dag``/``pdag_to_cpdag``), which previously had no notion of it and
+ could resolve an ambiguous edge in the illegal direction after the search had
+ already respected the constraint everywhere else.
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from causalts.baselines import _ges_fast, _lag_embed, ges_discovery
+from causalts.lges import lges_discovery, temporal_forbidden
+
+T = 3000
+
+
+def _edges(G_hat, names):
+ """{(cause, lag, effect)} from a (d, d, max_lag+1) graph."""
+ return {
+ (names[i], lag, names[j])
+ for lag in range(G_hat.shape[2])
+ for i in range(G_hat.shape[0])
+ for j in range(G_hat.shape[1])
+ if G_hat[i, j, lag]
+ }
+
+
+@pytest.fixture
+def lagged_df():
+ """A(t-1) -> C(t) <- B(t-1), C(t-1) -> D(t)."""
+ r = np.random.default_rng(7)
+ a = r.standard_normal(T)
+ b = r.standard_normal(T)
+ c = np.zeros(T)
+ d = np.zeros(T)
+ for t in range(1, T):
+ c[t] = a[t - 1] + b[t - 1] + r.standard_normal()
+ d[t] = 1.2 * c[t - 1] + r.standard_normal()
+ return pd.DataFrame({"A": a, "B": b, "C": c, "D": d})
+
+
+@pytest.fixture
+def collider_df():
+ """Contemporaneous collider X -> Z <- Y (direction is identifiable)."""
+ r = np.random.default_rng(3)
+ x = r.standard_normal(T)
+ y = r.standard_normal(T)
+ return pd.DataFrame({"X": x, "Y": y, "Z": x + y + r.standard_normal(T)})
+
+
+def test_recovers_lagged_edges(lagged_df):
+ G_hat, _ = ges_discovery(lagged_df, max_lag=1)
+ assert _edges(G_hat, list("ABCD")) == {
+ ("A", 1, "C"),
+ ("B", 1, "C"),
+ ("C", 1, "D"),
+ }
+
+
+def test_agrees_with_lges_ges_mode(lagged_df):
+ """Same algorithm, independent implementation and adjacency convention."""
+ names = list("ABCD")
+ ges_edges = _edges(ges_discovery(lagged_df, max_lag=1)[0], names)
+ lges_edges = _edges(lges_discovery(lagged_df, max_lag=1, mode="ges")[0], names)
+ assert ges_edges == lges_edges
+
+
+def test_lag0_collider_not_reversed(collider_df):
+ """The bug returned Z -> X and Z -> Y here — every arrow flipped."""
+ G_hat, _ = ges_discovery(collider_df, max_lag=0)
+ assert _edges(G_hat, list("XYZ")) == {("X", 0, "Z"), ("Y", 0, "Z")}
+
+
+@pytest.mark.parametrize("lam", [None, 0.5, 2.0])
+def test_engines_agree_at_max_lag_zero(collider_df, lam):
+ """At max_lag=0 there is no temporal ordering to protect, so the two
+ engines run the literal same algorithm and must return the same graph.
+ """
+ fast, info_f = ges_discovery(
+ collider_df, max_lag=0, lambda_value=lam, engine="fast"
+ )
+ slow, info_s = ges_discovery(
+ collider_df, max_lag=0, lambda_value=lam, engine="causal-learn"
+ )
+ assert info_f["engine"] == "fast"
+ assert info_s["engine"] == "causal-learn"
+ assert np.array_equal(fast, slow)
+
+
+@pytest.mark.parametrize("max_lag", [1, 2])
+def test_causal_learn_engine_rejects_lagged_data(lagged_df, max_lag):
+ """causal-learn's ges() has no forbidden-edges parameter, so running it on
+ lagged data would search unconstrained and could orient edges backward in
+ time -- refuse rather than silently return a weaker/different graph.
+ """
+ with pytest.raises(ValueError, match="temporal background knowledge"):
+ ges_discovery(lagged_df, max_lag=max_lag, engine="causal-learn")
+
+
+def test_fast_is_the_default(lagged_df):
+ assert ges_discovery(lagged_df, max_lag=1)[1]["engine"] == "fast"
+
+
+def test_unknown_engine_rejected(lagged_df):
+ with pytest.raises(ValueError, match="engine must be"):
+ ges_discovery(lagged_df, max_lag=1, engine="nope")
+
+
+def test_non_bic_score_falls_back_to_causal_learn(collider_df):
+ """The fast engine only implements BIC, so other scores must still work
+ (at max_lag=0, where the causal-learn engine is actually usable).
+ """
+ _, info = ges_discovery(
+ collider_df, max_lag=0, score_func="local_score_BDeu", engine="fast"
+ )
+ assert info["engine"] == "causal-learn"
+
+
+def test_non_bic_score_rejected_with_lags(lagged_df):
+ with pytest.raises(ValueError, match="temporal background knowledge"):
+ ges_discovery(lagged_df, max_lag=1, score_func="local_score_BDeu")
+
+
+def test_no_backward_in_time_directed_edges(lagged_df):
+ """The search must never leave a directed edge pointing from a more recent
+ lag block into an older one -- this is what 'forbidden' exists to prevent,
+ and PDAG completion (pdag_to_dag) used to ignore it after the fact.
+ """
+ embedded, d = _lag_embed(lagged_df, max_lag=2)
+ forbidden = temporal_forbidden(d, 2)
+ _, info = _ges_fast(embedded, d, 2, None, forbidden)
+ A = info["cpdag"]
+ p = A.shape[0]
+ for i in range(p):
+ for j in range(p):
+ if i == j:
+ continue
+ if i // d < j // d: # i is more recent than j
+ assert not (
+ A[i, j] != 0 and A[j, i] == 0
+ ), f"directed backward-in-time edge: col{i} -> col{j}"
+
+
+def test_lag0_chain_stays_undirected():
+ """X -> Y -> Z is Markov equivalent to two other DAGs, so GES cannot orient it.
+
+ An unoriented edge must be reported in both directions, not dropped.
+ """
+ r = np.random.default_rng(11)
+ x = r.standard_normal(T)
+ y = 1.5 * x + r.standard_normal(T)
+ z = 1.5 * y + r.standard_normal(T)
+ G_hat, _ = ges_discovery(pd.DataFrame({"X": x, "Y": y, "Z": z}), max_lag=0)
+ assert _edges(G_hat, list("XYZ")) == {
+ ("X", 0, "Y"),
+ ("Y", 0, "X"),
+ ("Y", 0, "Z"),
+ ("Z", 0, "Y"),
+ }
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-v"])
diff --git a/tests/test_lges_cpdag.py b/tests/test_lges_cpdag.py
new file mode 100644
index 0000000..fdd6fd5
--- /dev/null
+++ b/tests/test_lges_cpdag.py
@@ -0,0 +1,137 @@
+# Copyright 2026 Bloomberg Finance L.P.
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Correctness tests for the PDAG/DAG/CPDAG machinery in causalts.lges.
+
+Three bugs lived here and each one silently under- or mis-oriented CPDAGs, which
+LGES then emitted as edges in both directions:
+
+* ``order_edges`` sorted the tail ascending, reversing Chickering's inner ordering
+* ``label_edges`` omitted Chickering's phase one (propagation through ``x``), so
+ edges that Meek's rules force came back reversible
+* ``pdag_to_dag`` accepted non-sinks and derived directions from the removal
+ order backwards, which could return a cyclic "DAG"
+
+The exhaustive tests below pin the whole pipeline against the definition of
+Markov equivalence (same skeleton + same v-structures) rather than against a
+reference implementation.
+"""
+
+import itertools
+
+import networkx as nx
+import numpy as np
+import pytest
+
+from causalts.lges import dag_to_cpdag, pdag_to_cpdag, pdag_to_dag
+
+
+def _all_dags(d):
+ pairs = list(itertools.permutations(range(d), 2))
+ for mask in range(1 << len(pairs)):
+ M = np.zeros((d, d))
+ for b, (i, j) in enumerate(pairs):
+ if mask >> b & 1:
+ M[i, j] = 1
+ if any(M[i, j] and M[j, i] for i, j in pairs):
+ continue
+ g = nx.DiGraph()
+ g.add_nodes_from(range(d))
+ g.add_edges_from([(i, j) for i in range(d) for j in range(d) if M[i, j]])
+ if nx.is_directed_acyclic_graph(g):
+ yield M
+
+
+def _skeleton(M):
+ d = len(M)
+ return frozenset(frozenset((i, j)) for i in range(d) for j in range(d) if M[i, j])
+
+
+def _vstructures(M):
+ """Unshielded colliders i -> c <- j."""
+ d = len(M)
+ return frozenset(
+ (min(a, b), c, max(a, b))
+ for c in range(d)
+ for a, b in itertools.combinations([p for p in range(d) if M[p, c]], 2)
+ if not M[a, b] and not M[b, a]
+ )
+
+
+def _classes(d):
+ """Group every DAG on d nodes into true Markov equivalence classes."""
+ out = {}
+ for M in _all_dags(d):
+ out.setdefault((_skeleton(M), _vstructures(M)), []).append(M)
+ return out
+
+
+def _is_acyclic(M):
+ d = len(M)
+ g = nx.DiGraph()
+ g.add_nodes_from(range(d))
+ g.add_edges_from([(i, j) for i in range(d) for j in range(d) if M[i, j]])
+ return nx.is_directed_acyclic_graph(g)
+
+
+def _check_all(d):
+ for key, members in _classes(d).items():
+ cpdags = [pdag_to_cpdag(M) for M in members]
+
+ # every member of a class must map to the SAME cpdag
+ assert len({c.tobytes() for c in cpdags}) == 1, f"class {key} not collapsed"
+
+ # compelled edges are exactly the edges shared by every member
+ C = cpdags[0]
+ compelled = {
+ (i, j) for i in range(d) for j in range(d) if C[i, j] and not C[j, i]
+ }
+ shared = {
+ (i, j) for i in range(d) for j in range(d) if all(M[i, j] for M in members)
+ }
+ assert compelled == shared, f"class {key}: {compelled} != {shared}"
+
+ # a consistent extension must exist, be acyclic, and be in the same class
+ G = pdag_to_dag(C)
+ assert _is_acyclic(G), f"class {key}: pdag_to_dag returned a cyclic graph"
+ assert (_skeleton(G), _vstructures(G)) == key, f"class {key}: wrong extension"
+ assert np.array_equal(dag_to_cpdag(G), C), f"class {key}: round trip failed"
+
+
+def test_meek_forced_edge_is_oriented():
+ """A -> C <- B with C - D: Meek R1 forces C -> D (this used to stay undirected)."""
+ M = np.zeros((4, 4))
+ M[0, 2] = M[1, 2] = M[2, 3] = 1 # A->C, B->C, C->D
+ C = pdag_to_cpdag(M)
+ directed = {(i, j) for i in range(4) for j in range(4) if C[i, j] and not C[j, i]}
+ assert directed == {(0, 2), (1, 2), (2, 3)}
+
+
+def test_chain_comes_back_undirected():
+ """A -> B -> C is Markov equivalent to two other DAGs, so nothing is compelled."""
+ M = np.zeros((3, 3))
+ M[0, 1] = M[1, 2] = 1
+ C = pdag_to_cpdag(M)
+ assert not [(i, j) for i in range(3) for j in range(3) if C[i, j] and not C[j, i]]
+
+
+def test_triangle_comes_back_undirected():
+ """A fully connected triple has no v-structure, so all 6 orientations tie."""
+ M = np.zeros((3, 3))
+ M[0, 1] = M[0, 2] = M[1, 2] = 1
+ C = pdag_to_cpdag(M)
+ assert not [(i, j) for i in range(3) for j in range(3) if C[i, j] and not C[j, i]]
+
+
+def test_exhaustive_4_nodes():
+ """All 543 DAGs / 185 equivalence classes on 4 nodes."""
+ _check_all(4)
+
+
+@pytest.mark.slow
+def test_exhaustive_5_nodes():
+ """All 29281 DAGs / 8782 equivalence classes on 5 nodes."""
+ _check_all(5)
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-v"])
diff --git a/tests/test_lges_search.py b/tests/test_lges_search.py
new file mode 100644
index 0000000..bbb7f30
--- /dev/null
+++ b/tests/test_lges_search.py
@@ -0,0 +1,207 @@
+# Copyright 2026 Bloomberg Finance L.P.
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Search-quality tests for the vendored GES/LGES implementation.
+
+Two bugs in the Insert operator made the forward phase stall far short of the
+optimum, so LGES returned a much lower-scoring graph than GES and never improved
+with more data:
+
+* ``_creates_cycle`` rejected an insert whenever *any* semi-directed path y -> x
+ existed, instead of only when one is *unblocked* by ``NA_yx | T``
+* ``_score_valid_insert_operators`` drew ``T`` from ``NA_yx`` rather than
+ ``Ne(y) \\ Adj(x)``, and omitted ``NA_yx`` from both sides of the score delta
+
+GES is a consistent estimator, so the sharpest regression test is convergence:
+given enough data from a linear-Gaussian model, the search must recover the true
+equivalence class exactly.
+"""
+
+import numpy as np
+import pytest
+
+from causalts.baselines import ges_discovery, lges_discovery, tges_discovery
+from causalts.lges import GaussObsL0Pen, fit, pdag_to_dag
+from causalts.synthetic_data.synthetic_datasets import load_dataset
+from causalts.utils import evaluate_graph
+
+
+def _collider_data(n=20000, seed=7):
+ """A -> C <- B, C -> D. Fully identifiable: the collider forces C -> D."""
+ r = np.random.default_rng(seed)
+ a = r.standard_normal(n)
+ b = r.standard_normal(n)
+ c = a + b + r.standard_normal(n)
+ d = 1.2 * c + r.standard_normal(n)
+ return np.column_stack([a, b, c, d])
+
+
+def test_recovers_fully_identifiable_cpdag():
+ """The search used to stall here and return every edge undirected."""
+ X = _collider_data()
+ A, _ = fit(GaussObsL0Pen(X), score_based=False, prune=False, forbidden=None)
+ directed = {(i, j) for i in range(4) for j in range(4) if A[i, j] and not A[j, i]}
+ undirected = {
+ (i, j) for i in range(4) for j in range(i + 1, 4) if A[i, j] and A[j, i]
+ }
+ assert directed == {(0, 2), (1, 2), (2, 3)}
+ assert undirected == set()
+
+
+def test_search_is_not_beaten_by_causal_learn():
+ """LGES must not return a lower-scoring graph than causal-learn's GES."""
+ causallearn_ges = pytest.importorskip("causallearn.search.ScoreBased.GES").ges
+ from causallearn.graph.Endpoint import Endpoint
+
+ from causalts.baselines import _patch_ges_numpy2
+
+ _patch_ges_numpy2()
+
+ X = _collider_data()
+ score = GaussObsL0Pen(X)
+
+ A_lges, _ = fit(GaussObsL0Pen(X), score_based=False, prune=False, forbidden=None)
+
+ G = causallearn_ges(X, score_func="local_score_BIC")["G"]
+ nodes = G.get_nodes()
+ A_cl = np.zeros((4, 4))
+ for e in G.get_graph_edges():
+ u, v = nodes.index(e.get_node1()), nodes.index(e.get_node2())
+ e1, e2 = e.get_endpoint1(), e.get_endpoint2()
+ if e1 == Endpoint.TAIL and e2 == Endpoint.ARROW:
+ A_cl[u, v] = 1
+ elif e1 == Endpoint.ARROW and e2 == Endpoint.TAIL:
+ A_cl[v, u] = 1
+ else:
+ A_cl[u, v] = A_cl[v, u] = 1
+
+ s_lges = score.score_dag(pdag_to_dag(A_lges))
+ s_cl = score.score_dag(pdag_to_dag(A_cl))
+ assert s_lges >= s_cl - 1e-6, f"LGES {s_lges:.2f} < causal-learn {s_cl:.2f}"
+
+
+@pytest.mark.parametrize("mode", ["ges", "lges"])
+def test_converges_on_large_sample(mode):
+ """With 5000 samples of ex2 the search must recover the graph exactly.
+
+ Before the fix this plateaued around F1 0.35-0.52 at every sample size.
+ """
+ r = load_dataset("ex2", seed=42, T=5000)
+ df, gt = r["df"], r["ground_truth"]
+ G_hat, _ = lges_discovery(df, max_lag=gt.shape[2] - 1, mode=mode)
+ assert evaluate_graph(G_hat, gt)["F1"] == pytest.approx(1.0)
+
+
+def test_tges_matches_lges_on_large_sample():
+ r = load_dataset("ex2", seed=42, T=5000)
+ df, gt = r["df"], r["ground_truth"]
+ ml = gt.shape[2] - 1
+ f_tges = evaluate_graph(tges_discovery(df, max_lag=ml, mode="lges")[0], gt)["F1"]
+ f_lges = evaluate_graph(lges_discovery(df, max_lag=ml, mode="lges")[0], gt)["F1"]
+ assert f_tges == pytest.approx(f_lges)
+
+
+def test_ges_wrapper_also_converges():
+ """Sanity anchor: the causal-learn-backed wrapper converges on the same data."""
+ r = load_dataset("ex2", seed=42, T=5000)
+ df, gt = r["df"], r["ground_truth"]
+ G_hat, _ = ges_discovery(df, max_lag=gt.shape[2] - 1)
+ assert evaluate_graph(G_hat, gt)["F1"] == pytest.approx(1.0)
+
+
+def _random_lagged_search(seed, d, max_lag, n_parents, T=600):
+ """Simulate a random legal SEM under temporal_forbidden and run fit()."""
+ from causalts.lges import temporal_forbidden
+
+ r = np.random.default_rng(seed)
+ n = d * (max_lag + 1)
+ forbidden = temporal_forbidden(d, max_lag)
+ B = np.zeros((n, n))
+ for j in range(n):
+ legal = [i for i in range(n) if forbidden[i, j] == 0 and i != j]
+ if not legal:
+ continue
+ k = min(n_parents, len(legal))
+ for i in r.choice(legal, size=k, replace=False):
+ B[i, j] = r.uniform(0.3, 0.9) * r.choice([-1, 1])
+ X = r.standard_normal((T, n))
+ for j in sorted(range(n), key=lambda x: -(x // d)):
+ parents = np.where(B[:, j] != 0)[0]
+ if len(parents):
+ X[:, j] = X[:, parents] @ B[parents, j] + r.standard_normal(T)
+ A, metrics = fit(
+ GaussObsL0Pen(X), score_based=False, prune=False, forbidden=forbidden
+ )
+ return X, forbidden, A, d
+
+
+@pytest.mark.parametrize("seed", range(30))
+def test_forbidden_search_terminates_without_backward_edges(seed):
+ """Regression cover for two defects found together:
+
+ * ``pdag_to_dag``'s completion step ignored ``forbidden`` entirely, so it
+ could resolve an edge the search legally created into the direction
+ background knowledge rules out.
+ * An early, overly-strict fix for that (reject any sink with a forbidden
+ neighbour) was *incomplete*: 39 of 44 "No consistent extension exists"
+ cases on a random sweep were false negatives -- a valid extension
+ existed, the rule just couldn't find it. Fixed by forcing every edge
+ with a uniquely-legal direction before running plain Dor-Tarsi, which
+ preserves its classical completeness guarantee.
+ * With the turning phase included, one specific case (d=5, max_lag=2,
+ 2 parents/node, seed=10) revealed a genuine 2-cycle: turn A applied,
+ then turn B claims a gain that undoes A, forever. `fit()`'s turning
+ loop now stops on a repeated graph state instead of looping forever.
+ """
+ r = np.random.default_rng(seed)
+ d = int(r.integers(3, 8))
+ max_lag = int(r.integers(0, 4))
+ n_parents = int(r.integers(1, 4))
+ X, forbidden, A, d = _random_lagged_search(seed, d, max_lag, n_parents)
+ n = A.shape[0]
+ for i in range(n):
+ for j in range(n):
+ if i == j:
+ continue
+ if i // d < j // d: # i more recent than j
+ assert not (
+ A[i, j] != 0 and A[j, i] == 0
+ ), f"seed={seed}: directed backward-in-time edge col{i}->col{j}"
+
+
+def test_turning_phase_terminates_on_known_cycle():
+ """The specific (d, max_lag, seed) that used to hang forever must finish.
+
+ No timeout plugin is installed, so without the SIGALRM guard a regression
+ here hangs the whole suite instead of failing this one test.
+ """
+ import signal
+
+ class _Hung(Exception):
+ pass
+
+ def _alarm(signum, frame):
+ raise _Hung()
+
+ previous = signal.signal(signal.SIGALRM, _alarm)
+ signal.alarm(20)
+ try:
+ X, forbidden, A, d = _random_lagged_search(seed=10, d=5, max_lag=2, n_parents=2)
+ A2, metrics = fit(
+ GaussObsL0Pen(X),
+ phases=["forward", "backward", "turning"],
+ score_based=False,
+ prune=False,
+ forbidden=forbidden,
+ )
+ except _Hung:
+ pytest.fail(
+ "turning phase did not terminate within 20s -- cycle guard regressed"
+ )
+ finally:
+ signal.alarm(0)
+ signal.signal(signal.SIGALRM, previous)
+ assert A2 is not None
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-v"])