Change of deprecated np.infty

odeformer/metrics.py

@@ -219,7 +219,7 @@ def compute_metrics(predicted, true, predicted_tree=None, tree=None, metrics="r2
                 else:
                     try:
                         l1_error = np.mean(np.abs((true[i] - predicted[i])))
-                        if np.isnan(l1_error): results[metric].append(np.infty)
+                        if np.isnan(l1_error): results[metric].append(np.inf)
                         else: results[metric].append(l1_error)
                     except Exception as e:
                         results[metric].append(np.nan)

odeformer/model/mixins.py

@@ -117,7 +117,7 @@ def sort_candidates(
                 metrics = sorting_metric,
             )[sorting_metric][0]
             if math.isnan(_score): 
-                _score = -np.infty if descending else np.infty
+                _score = -np.inf if descending else np.inf
             _scores.append(_score)
         sorted_idx = np.argsort(_scores)  
         if descending: sorted_idx = list(reversed(sorted_idx))

odeformer/model/sklearn_wrapper.py

@@ -211,7 +211,7 @@ def sort_candidates(self, times, trajectory, candidates, metric="snmse", verbose
         for candidate in candidates:
             score = self.evaluate_tree(candidate, times, trajectory, metric)
             if math.isnan(score): 
-                score = -np.infty if descending else np.infty
+                score = -np.inf if descending else np.inf
             scores.append(score)
         sorted_idx = np.argsort(scores)  
 

odeformer/regressors.py

@@ -21,7 +21,7 @@ def order_data(X, y):
 
 def get_infinite_relative_error(prediction, truth):
     abs_relative_error = np.abs((prediction - truth) / (truth + 1e-100))
-    abs_relative_error = np.nan_to_num(abs_relative_error, nan=np.infty)
+    abs_relative_error = np.nan_to_num(abs_relative_error, nan=np.inf)
     return np.max(abs_relative_error)
 
 

odeformer/trainer.py

@@ -170,7 +170,7 @@ def __init__(self, modules, env, params, path=None, root=None):
             m = (m, True) if 'r2' in m else (m, False)
             self.metrics.append(m)
         self.best_metrics = {
-            metric: (-np.infty if biggest else np.infty)
+            metric: (-np.inf if biggest else np.inf)
             for (metric, biggest) in self.metrics
         }