adjust tests to new threshold

causy-dev · Feb 8, 2025 · 2fbd7e2 · 2fbd7e2
1 parent 0c80428
commit 2fbd7e2
Show file tree

Hide file tree

Showing 2 changed files with 8 additions and 8 deletions.
diff --git a/tests/test_effect_estimation.py b/tests/test_effect_estimation.py
@@ -197,15 +197,15 @@ def test_direct_effect_estimation_partially_directed(self):
 
         model = IIDSampleGenerator(
             edges=[
-                SampleEdge(NodeReference("X"), NodeReference("Z"), 5),
-                SampleEdge(NodeReference("Y"), NodeReference("Z"), 6),
-                SampleEdge(NodeReference("W"), NodeReference("X"), 3),
-                SampleEdge(NodeReference("W"), NodeReference("Y"), 4),
+                SampleEdge(NodeReference("X"), NodeReference("Z"), 1),
+                SampleEdge(NodeReference("Y"), NodeReference("Z"), 1),
+                SampleEdge(NodeReference("W"), NodeReference("X"), 1),
+                SampleEdge(NodeReference("W"), NodeReference("Y"), 1),
             ],
         )
 
         tst = PC()
-        sample_size = 1000000
+        sample_size = 100000
         test_data, graph = model.generate(sample_size)
         tst.create_graph_from_data(test_data)
         tst.create_all_possible_edges()
@@ -217,14 +217,14 @@ def test_direct_effect_estimation_partially_directed(self):
             tst.graph.edge_value(tst.graph.nodes["X"], tst.graph.nodes["Z"])[
                 "direct_effect"
             ],
-            5.0,
+            1.0,
             0,
         )
         self.assertAlmostEqual(
             tst.graph.edge_value(tst.graph.nodes["Y"], tst.graph.nodes["Z"])[
                 "direct_effect"
             ],
-            6.0,
+            1.0,
             0,
         )
 

diff --git a/tests/test_pc_e2e.py b/tests/test_pc_e2e.py
@@ -440,7 +440,7 @@ def test_orientation_conflict_tracking(self):
                 SampleEdge(NodeReference("U4"), NodeReference("X"), 1),
             ],
         )
-        test_data, graph = causal_insufficiency_four_nodes.generate(10000)
+        test_data, graph = causal_insufficiency_four_nodes.generate(1000)
         test_data.pop("U1")
         test_data.pop("U2")
         test_data.pop("U3")