visualization for GIN

Author: kunwuz

docs/source/search_methods_index/Hidden causal representation learning/gin.rst

@@ -15,6 +15,22 @@ Usage
     from causallearn.search.FCMBased.GIN.GIN import GIN
     G, K = GIN(data)
 
+    # Visualization using pydot
+    from causallearn.utils.GraphUtils import GraphUtils
+    import matplotlib.image as mpimg
+    import matplotlib.pyplot as plt
+    import io
+
+    pyd = GraphUtils.to_pydot(G)
+    tmp_png = pyd.create_png(f="png")
+    fp = io.BytesIO(tmp_png)
+    img = mpimg.imread(fp, format='png')
+    plt.axis('off')
+    plt.imshow(img)
+    plt.show()
+
+Visualization using pydot is recommended (`usage example <https://github.com/cmu-phil/causal-learn/blob/main/tests/TestGIN.py>`_). 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(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

@@ -18,6 +18,10 @@ Usage
 
     # Visualization using pydot
     from causallearn.utils.GraphUtils import GraphUtils
+    import matplotlib.image as mpimg
+    import matplotlib.pyplot as plt
+    import io
+
     pyd = GraphUtils.to_pydot(Record['G'])
     tmp_png = pyd.create_png(f="png")
     fp = io.BytesIO(tmp_png)

tests/TestGIN.py

@@ -1,10 +1,13 @@
 import random
 import sys
+import io
 
 sys.path.append("")
 import unittest
 
 import numpy as np
+import matplotlib.image as mpimg
+import matplotlib.pyplot as plt
 
 from causallearn.search.HiddenCausal.GIN.GIN import GIN
 
@@ -25,6 +28,16 @@ def test_case1(self):
         g, k = GIN(data)
         print(g, k)
 
+        # Visualization using pydot
+        from causallearn.utils.GraphUtils import GraphUtils
+        pyd = GraphUtils.to_pydot(g)
+        tmp_png = pyd.create_png(f="png")
+        fp = io.BytesIO(tmp_png)
+        img = mpimg.imread(fp, format='png')
+        plt.axis('off')
+        plt.imshow(img)
+        plt.show()
+
     def test_case2(self):
         sample_size = 1000
         np.random.seed(0)
@@ -47,6 +60,16 @@ def test_case2(self):
         g, k = GIN(data)
         print(g, k)
 
+        # Visualization using pydot
+        from causallearn.utils.GraphUtils import GraphUtils
+        pyd = GraphUtils.to_pydot(g)
+        tmp_png = pyd.create_png(f="png")
+        fp = io.BytesIO(tmp_png)
+        img = mpimg.imread(fp, format='png')
+        plt.axis('off')
+        plt.imshow(img)
+        plt.show()
+
     def test_case3(self):
         sample_size = 1000
         random.seed(42)
@@ -68,4 +91,14 @@ def test_case3(self):
         data = np.array([X1, X2, X3, X4, X5, X6, X7, X8]).T
         data = (data - np.mean(data, axis=0)) / np.std(data, axis=0)
         g, k = GIN(data)
-        print(g, k)
+        print(g, k)
+
+        # Visualization using pydot
+        from causallearn.utils.GraphUtils import GraphUtils
+        pyd = GraphUtils.to_pydot(g)
+        tmp_png = pyd.create_png(f="png")
+        fp = io.BytesIO(tmp_png)
+        img = mpimg.imread(fp, format='png')
+        plt.axis('off')
+        plt.imshow(img)
+        plt.show()