improve matching availability

README.md

@@ -153,12 +153,10 @@ Let prediction instances be $P = \{p_1, p_2, \ldots, p_n\}$ and reference instan
 - **.\*_features**: (list of float): The values computed individually for each feature.
 - **macro_.\***: (float): Averaged values computed for each feature, average of the \*\_features.
 - **distance**: (numpy.ndarray): The distance matrix between the generated instances and the reference instances.
-- **match**: (numpy.ndarray): The matching matrix between the generated instances and the reference instances.
-- **match_inv**: (numpy.ndarray): The matching matrix between the reference instances and the generated instances.
-- **g2r_index**: (numpy.ndarray): The index of the reference instance that matches the generated instance for each feature.
-- **r2g_index**: (numpy.ndarray): The index of the generated instance that matches the reference instance for each feature.
-- **mean_g2r_index**: (numpy.ndarray): The index of the reference instance that matches the generated instance.
-- **mean_r2g_index**: (numpy.ndarray): The index of the generated instance that matches the reference instance.
+- **match**: (numpy.ndarray): The matching between the generated instances and the reference instances.
+- **match_inv**: (numpy.ndarray): The matching between the reference instances and the generated instances.
+- **match_features**: (numpy.ndarray): The matching between the generated instances and the reference instances for each feature.
+- **match_inv_features**: (numpy.ndarray): The matching between the reference instances and the generated instances for each feature.
 
 <!-- #### Values from Popular Papers -->
 <!-- *Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.* -->

__main__.py

@@ -46,6 +46,7 @@ results = metric.compute(
     num_processes=args.num_processes,
     return_each_features=True,
     return_coverages=True,
+    return_matching=True,
     dtype=args.dtype,
 )
 logger.info(f"Time taken: {time.time() - s}")

matching_series.py

@@ -229,7 +229,6 @@ class matching_series(evaluate.Metric):
         return_matching: bool = False,
         return_each_features: bool = False,
         return_coverages: bool = False,
-        return_matching_indices: bool = False,
         return_all: bool = False,
         dtype=np.float32,
         eps: float = 1e-8,
@@ -270,6 +269,8 @@ class matching_series(evaluate.Metric):
                 distance (numpy.ndarray): The distance matrix.
                 match (numpy.ndarray): The matching matrix.
                 match_inv (numpy.ndarray): The inverse matching matrix.
+                match_features (list of numpy.ndarray): The matching matrix for each feature.
+                match_inv_features (list of numpy.ndarray): The inverse matching matrix for each feature.
         """
         if return_all:
             return_distance = True
@@ -328,6 +329,12 @@ class matching_series(evaluate.Metric):
             out["distance"] = distance
         if return_matching:
             out.update({k: v for k, v in metrics.items() if "match" in k})
+
+            g2r_index = distance.argmin(axis=1)
+            r2g_index = distance.argmin(axis=0)
+            out["match_features"] = g2r_index
+            out["match_inv_features"] = r2g_index
+
         if return_coverages:
             out["coverages"] = metrics["coverages"]
         if return_each_features:
@@ -344,18 +351,6 @@ class matching_series(evaluate.Metric):
                         "coverages_features": [m["coverages"] for m in metrics_feature],
                     }
                 )
-        if return_matching_indices:
-            # matching index
-            g2r_index = distance.argmin(axis=1)
-            r2g_index = distance.argmin(axis=0)
-            # mean
-            mean_g2r_index = distance_mean.argmin(axis=1)
-            mean_r2g_index = distance_mean.argmin(axis=0)
-
-            out["g2r_index"] = g2r_index
-            out["r2g_index"] = r2g_index
-            out["mean_g2r_index"] = mean_g2r_index
-            out["mean_r2g_index"] = mean_r2g_index
 
         return out