Update comments a little bit

kunwuz · kunwuz · commit 8443c497f18a · 2021-12-28T00:28:28.000-05:00
diff --git a/causallearn/search/ConstraintBased/CDNOD.py b/causallearn/search/ConstraintBased/CDNOD.py
@@ -71,7 +71,7 @@ def cdnod_alg(data, alpha, indep_test, stable, uc_rule, uc_priority, background_
 
     Returns
     -------
-    cg : a CausalGraph object, where cg.G.graph[j,i]=1 and cg.G.graph[i,j]=-1 indicates  i --> j ,
+    cg : a CausalGraph object, where cg.G.graph[j,i]=1 and cg.G.graph[i,j]=-1 indicate i --> j ,
                     cg.G.graph[i,j] = cg.G.graph[j,i] = -1 indicates i --- j,
                     cg.G.graph[i,j] = cg.G.graph[j,i] = 1 indicates i <-> j.
 
diff --git a/causallearn/search/ConstraintBased/FCI.py b/causallearn/search/ConstraintBased/FCI.py
@@ -787,9 +787,9 @@ def fci(dataset, independence_test_method=fisherz, alpha=0.05, depth=-1, max_pat
                     graph.graph[j,i]=1 and graph.graph[i,j]=2 indicates  i o-> j.
     edges : list
         Contains graph's edges properties.
-        If an edge.properties have the Property dd, then it means there is no latent confounder. Otherwise,
-            there is possibly latent confounder.
-        If an edge.properties have the Property nl, then it is definitely direct. Otherwise,
+        If edge.properties have the Property 'dd', then there is no latent confounder. Otherwise,
+            there might be latent confounders.
+        If edge.properties have the Property nl, then it is definitely direct. Otherwise,
             it is possibly direct.
     '''
 
diff --git a/docs/source/search_methods_index/Constrained-based causal discovery methods/CDNOD.rst b/docs/source/search_methods_index/Constrained-based causal discovery methods/CDNOD.rst
@@ -65,6 +65,6 @@ For detailed usage, please kindly refer to its `usage example <https://github.co
 
 Returns
 -------------------
-**cg** : a CausalGraph object, where cg.G.graph[j,i]=1 and cg.G.graph[i,j]=-1 indicates  i --> j; cg.G.graph[i,j] = cg.G.graph[j,i] = -1 indicates i --- j; cg.G.graph[i,j] = cg.G.graph[j,i] = 1 indicates i <-> j.
+**cg** : a CausalGraph object, where cg.G.graph[j,i]=1 and cg.G.graph[i,j]=-1 indicate  i --> j; cg.G.graph[i,j] = cg.G.graph[j,i] = -1 indicates i --- j; cg.G.graph[i,j] = cg.G.graph[j,i] = 1 indicates i <-> j.
 
 .. [1] Huang, B., Zhang, K., Zhang, J., Ramsey, J. D., Sanchez-Romero, R., Glymour, C., & Schölkopf, B. (2020). Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res., 21(89), 1-53.
diff --git a/docs/source/search_methods_index/Constrained-based causal discovery methods/FCI.rst b/docs/source/search_methods_index/Constrained-based causal discovery methods/FCI.rst
@@ -52,7 +52,7 @@ Returns
 -------------------
 **graph**: a CausalGraph object, where graph.graph[j,i]=1 and graph.graph[i,j]=-1 indicates  i --> j; graph.graph[i,j] = graph.graph[j,i] = -1 indicates i --- j; graph.graph[i,j] = graph.graph[j,i] = 1 indicates i <-> j; graph.graph[j,i]=1 and graph.graph[i,j]=2 indicates  i o-> j.
 
-**edges**: list. Contains graph's edges properties. If an edge.properties have the Property dd, then it means there is no latent confounder. Otherwise, there is possibly latent confounder. If an edge.properties have the Property nl, then it is definitely direct. Otherwise, it is possibly direct.
+**edges**: list. Contains graph's edges properties. If edge.properties have the Property 'dd', then there is no latent confounder. Otherwise, there might be latent confounders. If edge.properties have the Property 'nl', then it is definitely direct. Otherwise, it is possibly direct.
 
 
 .. [1] Spirtes, P., Meek, C., & Richardson, T. (1995, August). Causal inference in the presence of latent variables and selection bias. In Proceedings of the Eleventh conference on Uncertainty in artificial intelligence (pp. 499-506).
diff --git a/docs/source/search_methods_index/Constrained-based causal discovery methods/PC.rst b/docs/source/search_methods_index/Constrained-based causal discovery methods/PC.rst
@@ -69,7 +69,7 @@ For detailed usage, please kindly refer to its `usage example <https://github.co
 
 Returns
 -------------------
-**cg** : a CausalGraph object, where cg.G.graph[j,i]=1 and cg.G.graph[i,j]=-1 indicates  i --> j; cg.G.graph[i,j] = cg.G.graph[j,i] = -1 indicates i --- j; cg.G.graph[i,j] = cg.G.graph[j,i] = 1 indicates i <-> j.
+**cg** : a CausalGraph object, where cg.G.graph[j,i]=1 and cg.G.graph[i,j]=-1 indicate  i --> j; cg.G.graph[i,j] = cg.G.graph[j,i] = -1 indicate i --- j; cg.G.graph[i,j] = cg.G.graph[j,i] = 1 indicates i <-> j.
 
 .. [1] Spirtes, P., Glymour, C. N., Scheines, R., & Heckerman, D. (2000). Causation, prediction, and search. MIT press.
 .. [2] Tu, R., Zhang, C., Ackermann, P., Mohan, K., Kjellström, H., & Zhang, K. (2019, April). Causal discovery in the presence of missing data. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 1762-1770). PMLR.
diff --git a/docs/source/search_methods_index/Score-based causal discovery methods/GES.rst b/docs/source/search_methods_index/Score-based causal discovery methods/GES.rst
@@ -40,7 +40,7 @@ and n_features is the number of features.
 
 Returns
 -------------------
-- **Record['G']**: learned causal graph, where Record['G'].graph[j,i]=1 and Record['G'].graph[i,j]=-1 indicates  i --> j; Record['G'].graph[i,j] = Record['G'].graph[j,i] = -1 indicates i --- j.
+- **Record['G']**: learned causal graph, where Record['G'].graph[j,i]=1 and Record['G'].graph[i,j]=-1 indicate i --> j; Record['G'].graph[i,j] = Record['G'].graph[j,i] = -1 indicates i --- j.
 
 - **Record['update1']**: each update (Insert operator) in the forward step.
 
