update doc

Author: kunwuz

docs/source/search_methods_index/Constrained-based causal discovery methods/CDNOD.rst

@@ -21,6 +21,9 @@ Usage
     # note that the last node is the c_indx
     cg.draw_pydot_graph()
 
+Visualization using pydot is recommended. If specific label names are needed, please refer to this `usage example <https://github.com/cmu-phil/causal-learn/blob/e4e73f8b58510a3cd5a9125ba50c0ac62a425ef3/tests/TestGraphVisualization.py#L106>`_ (e.g., GraphUtils.to_pydot(cg.G, labels=["A", "B", "C"]).
+
+
 Parameters
 -------------------
 **data**:  numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples

docs/source/search_methods_index/Constrained-based causal discovery methods/FCI.rst

@@ -22,6 +22,9 @@ Usage
     pdy = GraphUtils.to_pydot(G)
     pdy.write_png('simple_test.png')
 
+Visualization using pydot is recommended. If specific label names are needed, please refer to this `usage example <https://github.com/cmu-phil/causal-learn/blob/e4e73f8b58510a3cd5a9125ba50c0ac62a425ef3/tests/TestGraphVisualization.py#L106>`_ (e.g., GraphUtils.to_pydot(cg.G, labels=["A", "B", "C"]).
+
+
 Parameters
 -------------------
 **dataset**: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples

docs/source/search_methods_index/Constrained-based causal discovery methods/PC.rst

@@ -26,6 +26,8 @@ Usage
     # cg.to_nx_graph()
     # cg.draw_nx_graph(skel=False)
 
+Visualization using pydot is recommended. If specific label names are needed, please refer to this `usage example <https://github.com/cmu-phil/causal-learn/blob/e4e73f8b58510a3cd5a9125ba50c0ac62a425ef3/tests/TestGraphVisualization.py#L106>`_ (e.g., GraphUtils.to_pydot(cg.G, labels=["A", "B", "C"]).
+
 Parameters
 -------------------
 **data**: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples

docs/source/search_methods_index/Score-based causal discovery methods/GES.rst

@@ -16,6 +16,8 @@ Usage
     from causallearn.search.ScoreBased.GES import ges
     Record = ges(X, score_func, maxP, parameters)
 
+Visualization using pydot is recommended (`usage example <https://github.com/cmu-phil/causal-learn/blob/e4e73f8b58510a3cd5a9125ba50c0ac62a425ef3/tests/TestGES.py#L16>`_). If specific label names are needed, please refer to this `usage example <https://github.com/cmu-phil/causal-learn/blob/e4e73f8b58510a3cd5a9125ba50c0ac62a425ef3/tests/TestGraphVisualization.py#L106>`_ (e.g., GraphUtils.to_pydot(Record['G'], labels=["A", "B", "C"]).
+
 Parameters
 -------------------
 **X**: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples