Adapted to changes in scikit-learn v1.8

Author: ikeuchi-screen

lingam/causal_based_simulator.py

@@ -668,7 +668,7 @@ class CBSIUnobsCommonCauseLiNGAM(CBSILiNGAM):
     def _check_causal_graph(self, causal_graph, X):
         try:
             # fill nan with zeros
-            causal_graph = check_array(causal_graph, force_all_finite="allow-nan", copy=True)
+            causal_graph = check_array(causal_graph, ensure_all_finite="allow-nan", copy=True)
 
             n_features = X.shape[1]
             if causal_graph.shape != (n_features, n_features):

lingam/tools/__init__.py

@@ -84,7 +84,7 @@ def bootstrap_with_imputation(
         Elements which are not NaN are the imputation values.
     """
     # check args
-    X = check_array(X, force_all_finite="allow-nan")
+    X = check_array(X, ensure_all_finite="allow-nan")
 
     n_sampling = check_scalar(n_sampling, "n_sampling", (numbers.Integral, np.integer), min_val=1)
 

lingam/utils/__init__.py

@@ -957,7 +957,7 @@ def evaluate_model_fit(adjacency_matrix, X, is_ordinal=None):
     """
 
     # check inputs
-    adj = check_array(adjacency_matrix, force_all_finite="allow-nan")
+    adj = check_array(adjacency_matrix, ensure_all_finite="allow-nan")
     if adj.ndim != 2 or (adj.shape[0] != adj.shape[1]):
         raise ValueError("adj must be an square matrix.")
 
@@ -1037,7 +1037,7 @@ def calculate_distance_from_root_nodes(adjacency_matrix, max_distance=None):
         adjacency_matrix,
         ensure_min_samples=2,
         ensure_min_features=2,
-        force_all_finite="allow-nan",
+        ensure_all_finite="allow-nan",
     )
     if adjacency_matrix.shape[0] != adjacency_matrix.shape[1]:
         raise ValueError("adjacency_matrix must be an square matrix.")
@@ -1103,7 +1103,7 @@ def calculate_total_effect(adjacency_matrix, from_index, to_index, is_continuous
         adjacency_matrix,
         ensure_min_samples=2,
         ensure_min_features=2,
-        force_all_finite="allow-nan",
+        ensure_all_finite="allow-nan",
     )
     if adjacency_matrix.shape[0] != adjacency_matrix.shape[1]:
         raise ValueError(