Add two variants of the KCI test

CODE_OF_CONDUCT.md

@@ -5,7 +5,7 @@
 We as members, contributors, and leaders pledge to make participation in our
 community a harassment-free experience for everyone, regardless of age, body
 size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
+identity and expression, level of experience, education, socioeconomic status,
 nationality, personal appearance, race, religion, or sexual identity
 and orientation.
 

README.md

@@ -1,6 +1,6 @@
 # causal-learn: Causal Discovery in Python
 
-Causal-learn is a python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and extension of [Tetrad](https://github.com/cmu-phil/tetrad).
+Causal-learn ([documentation](https://causal-learn.readthedocs.io/en/latest/), [paper](https://jmlr.org/papers/volume25/23-0970/23-0970.pdf)) is a python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and extension of [Tetrad](https://github.com/cmu-phil/tetrad).
 
 The package is actively being developed. Feedbacks (issues, suggestions, etc.) are highly encouraged.
 
@@ -76,10 +76,13 @@ Although causal-learn provides python implementations for some causal discovery
 Please cite as:
 
 ```
-@article{causallearn,
-  title={Causal-learn: Causal Discovery in Python},
-  author={Yujia Zheng and Biwei Huang and Wei Chen and Joseph Ramsey and Mingming Gong and Ruichu Cai and Shohei Shimizu and Peter Spirtes and Kun Zhang},
-  journal={arXiv preprint arXiv:2307.16405},
-  year={2023}
+@article{zheng2024causal,
+  title={Causal-learn: Causal discovery in python},
+  author={Zheng, Yujia and Huang, Biwei and Chen, Wei and Ramsey, Joseph and Gong, Mingming and Cai, Ruichu and Shimizu, Shohei and Spirtes, Peter and Zhang, Kun},
+  journal={Journal of Machine Learning Research},
+  volume={25},
+  number={60},
+  pages={1--8},
+  year={2024}
 }
 ```

causallearn/graph/Endpoint.py

@@ -20,4 +20,4 @@ def __str__(self):
         return self.name
 
     def __eq__(self, other):
-        return self.value == other.value
+        return isinstance(other, Endpoint) and self.value == other.value

causallearn/graph/GeneralGraph.py

@@ -508,13 +508,15 @@ def is_ancestor_of(self, node1: Node, node2: Node) -> bool:
     def is_child_of(self, node1: Node, node2: Node) -> bool:
         i = self.node_map[node1]
         j = self.node_map[node2]
-        return self.graph[i, j] == Endpoint.TAIL.value or self.graph[i, j] == Endpoint.ARROW_AND_ARROW.value
+        return (self.graph[j, i] == Endpoint.TAIL.value and self.graph[i, j] == Endpoint.ARROW.value) \
+               or self.graph[j, i] == Endpoint.TAIL_AND_ARROW.value
 
     # Returns true iff node1 is a parent of node2.
     def is_parent_of(self, node1: Node, node2: Node) -> bool:
         i = self.node_map[node1]
         j = self.node_map[node2]
-        return self.graph[j, i] == Endpoint.ARROW.value and self.graph[i, j] == Endpoint.TAIL.value
+        return (self.graph[j, i] == Endpoint.ARROW.value and self.graph[i, j] == Endpoint.TAIL.value) \
+               or self.graph[i, j] == Endpoint.TAIL_AND_ARROW.value
 
     # Returns true iff node1 is a proper ancestor of node2.
     def is_proper_ancestor_of(self, node1: Node, node2: Node) -> bool:

causallearn/score/LocalScoreFunction.py

@@ -34,9 +34,9 @@ def local_score_BIC(Data: ndarray, i: int, PAi: List[int], parameters=None) -> f
     if len(PAi) == 0:
         return n * np.log(cov[i, i])
 
-    yX = np.mat(cov[np.ix_([i], PAi)])
-    XX = np.mat(cov[np.ix_(PAi, PAi)])
-    H = np.log(cov[i, i] - yX * np.linalg.inv(XX) * yX.T)
+    yX = cov[np.ix_([i], PAi)]
+    XX = cov[np.ix_(PAi, PAi)]
+    H = np.log(cov[i, i] - yX @ np.linalg.inv(XX) @ yX.T)
 
     return n * H + np.log(n) * len(PAi) * lambda_value
 
@@ -68,9 +68,9 @@ def local_score_BIC_from_cov(
     if len(PAi) == 0:
         return n * np.log(cov[i, i])
 
-    yX = np.mat(cov[np.ix_([i], PAi)])
-    XX = np.mat(cov[np.ix_(PAi, PAi)])
-    H = np.log(cov[i, i] - yX * np.linalg.inv(XX) * yX.T)
+    yX = cov[np.ix_([i], PAi)]
+    XX = cov[np.ix_(PAi, PAi)]
+    H = np.log(cov[i, i] - yX @ np.linalg.inv(XX) @ yX.T)
 
     return n * H + np.log(n) * len(PAi) * lambda_value
 
@@ -198,7 +198,7 @@ def local_score_cv_general(
     PAi = list(PAi)
 
     T = Data.shape[0]
-    X = Data[:, Xi]
+    X = Data[:, [Xi]]
     var_lambda = parameters["lambda"]  # regularization parameter
     k = parameters["kfold"]  # k-fold cross validation
     n0 = math.floor(T / k)
@@ -715,7 +715,7 @@ def local_score_marginal_general(
     """
 
     T = Data.shape[0]
-    X = Data[:, Xi]
+    X = Data[:, [Xi]]
     dX = X.shape[1]
 
     # set the kernel for X

causallearn/score/LocalScoreFunctionClass.py

@@ -46,3 +46,9 @@ def score(self, i: int, PAi: List[int]) -> float:
                 self.score_cache[i][hash_key] = self.local_score_fun(self.data, i, PAi, self.parameters)
 
         return self.score_cache[i][hash_key]
+
+    def score_nocache(self, i: int, PAi: List[int]) -> float:
+        if self.local_score_fun == local_score_BIC_from_cov:
+            return self.local_score_fun((self.cov, self.n), i, PAi, self.parameters)
+        else:
+            return self.local_score_fun(self.data, i, PAi, self.parameters)