Merge pull request #54 from causy-dev/simplify-requirements

fix(packaging): simplify dependencies + error handling
causy-dev · Aug 6, 2024 · ebd66d8 · ebd66d8
2 parents 480cb45 + e31e8a8
commit ebd66d8
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Showing 3 changed files with 273 additions and 773 deletions.
diff --git a/causy/causal_discovery/constraint/independence_tests/common.py b/causy/causal_discovery/constraint/independence_tests/common.py
@@ -170,9 +170,17 @@ def process(
 
         if not set(nodes[2:]).issubset(set([on for on in list(other_neighbours)])):
             return
-        inverse_cov_matrix = torch.inverse(
-            torch.cov(torch.stack([graph.nodes[node].values for node in nodes]))
+        cov_matrix = torch.cov(
+            torch.stack([graph.nodes[node].values for node in nodes])
         )
+        # check if the covariance matrix is ill-conditioned
+        if torch.det(cov_matrix) == 0:
+            logger.warning(
+                "The covariance matrix is ill-conditioned. The precision matrix is not reliable."
+            )
+            return
+
+        inverse_cov_matrix = torch.inverse(cov_matrix)
 
         n = inverse_cov_matrix.size(0)
         diagonal = torch.diag(inverse_cov_matrix)
@@ -186,15 +194,22 @@ def process(
         sample_size = len(graph.nodes[nodes[0]].values)
         nb_of_control_vars = len(nodes) - 2
 
-        t, critical_t = get_t_and_critical_t(
-            sample_size,
-            nb_of_control_vars,
-            (
-                (-1 * precision_matrix[0][1])
-                / torch.sqrt(precision_matrix[0][0] * precision_matrix[1][1])
-            ).item(),
-            self.threshold,
-        )
+        # prevent math domain error
+        try:
+            t, critical_t = get_t_and_critical_t(
+                sample_size,
+                nb_of_control_vars,
+                (
+                    (-1 * precision_matrix[0][1])
+                    / torch.sqrt(precision_matrix[0][0] * precision_matrix[1][1])
+                ).item(),
+                self.threshold,
+            )
+        except ValueError:
+            logger.warning(
+                "Math domain error. The covariance matrix is ill-conditioned. The precision matrix is not reliable."
+            )
+            return
 
         if abs(t) < critical_t:
             logger.debug(