Merge branch 'master' into add_isort_as_a_pre_commit_hook

fuse/data/__init__.py

@@ -7,8 +7,7 @@
     OpApplyPatterns,
     OpFunc,
     OpKeepKeypaths,
-    OpLambda,
-    OpRepeat,
+    OpSet,
 )
 from fuse.data.ops.ops_read import OpReadDataframe
 from fuse.data.pipelines.pipeline_default import PipelineDefault

fuse/eval/metrics/metrics_common.py

@@ -409,7 +409,6 @@ def get(self, ids: Optional[Sequence[Hashable]] = None) -> Tuple[Dict[str, Any]]
 
             permutation = [original_ids_pos[sample_id] for sample_id in required_ids]
 
-            # create the permuted dictionary
             data = {}
             for name, values in self._collected_data.items():
                 data[name] = [values[i] for i in permutation]
@@ -688,19 +687,27 @@ def eval(
 
 class GroupAnalysis(MetricWithCollectorBase):
     """
-    Evaluate a metric per group and compute basic statistics about the different per group results.
+    Evaluate a metric per group and compute basic statistics over the different per group results.
     eval() method returns a dictionary of the following format:
     {'mean': <>, 'std': <>, 'median': <>, <group 0>: <>, <group 1>: <>, ...}
     """
 
-    def __init__(self, metric: MetricBase, group: str, **super_kwargs: Any) -> None:
+    def __init__(
+        self,
+        metric: MetricBase,
+        group: str,
+        compute_group_stats: bool = True,
+        **super_kwargs: Any,
+    ) -> None:
         """
         :param metric: metric to analyze
         :param group: key to extract the group from
+        :compute_group_stats: wether to compute stats such as mean, std, median over the per group results
         :param super_kwargs: additional arguments for super class (MetricWithCollectorBase) constructor
         """
         super().__init__(group=group, **super_kwargs)
         self._metric = metric
+        self._compute_group_stats = compute_group_stats
 
     def collect(self, batch: Dict) -> None:
         "See super class"
@@ -718,7 +725,9 @@ def reset(self) -> None:
         return super().reset()
 
     def eval(
-        self, results: Dict[str, Any] = None, ids: Optional[Sequence[Hashable]] = None
+        self,
+        results: Dict[str, Any] = None,
+        ids: Optional[Sequence[Hashable]] = None,
     ) -> Dict[str, Any]:
         """
         See super class
@@ -731,45 +740,48 @@ def eval(
             raise Exception(
                 "Error: group analysis is supported only when a unique identifier is specified. Add key 'id' to your data"
             )
-        ids = np.array(ids)
+        # don't convert to array, this converts the tuple ids to arrays
+        # ids = np.array(ids)
 
         groups = np.array(data["group"])
         unique_groups = set(groups)
 
         group_analysis_results = {}
         for group_value in unique_groups:
-            group_ids = ids[groups == group_value]
-
+            # group_ids = ids[groups == group_value]
+            # use List comprehension instead of boolean filtering to support advance id types such as tuples
+            group_ids = [ids[i] for i in range(len(ids)) if groups[i] == group_value]
             group_analysis_results[str(group_value)] = self._metric.eval(
                 results, group_ids
             )
 
         # compute stats
-        group_results_list = list(group_analysis_results.values())
-        if isinstance(group_results_list[0], dict):  # multiple values
-            # get all keys
-            all_keys = set()
-            for group_result in group_results_list:
-                all_keys |= set(group_result.keys())
-
-            for key in all_keys:
-                values = [group_result[key] for group_result in group_results_list]
+        if self._compute_group_stats:
+            group_results_list = list(group_analysis_results.values())
+            if isinstance(group_results_list[0], dict):  # multiple values
+                # get all keys
+                all_keys = set()
+                for group_result in group_results_list:
+                    all_keys |= set(group_result.keys())
+
+                for key in all_keys:
+                    values = [group_result[key] for group_result in group_results_list]
+                    try:
+                        group_analysis_results[f"{key}.mean"] = np.mean(values)
+                        group_analysis_results[f"{key}.std"] = np.std(values)
+                        group_analysis_results[f"{key}.median"] = np.median(values)
+                    except:
+                        # do nothing
+                        pass
+            else:  # single value
+                values = [group_result for group_result in group_results_list]
                 try:
-                    group_analysis_results[f"{key}.mean"] = np.mean(values)
-                    group_analysis_results[f"{key}.std"] = np.std(values)
-                    group_analysis_results[f"{key}.median"] = np.median(values)
+                    group_analysis_results["mean"] = np.mean(values)
+                    group_analysis_results["std"] = np.std(values)
+                    group_analysis_results["median"] = np.median(values)
                 except:
                     # do nothing
                     pass
-        else:  # single value
-            values = [group_result for group_result in group_results_list]
-            try:
-                group_analysis_results["mean"] = np.mean(values)
-                group_analysis_results["std"] = np.std(values)
-                group_analysis_results["median"] = np.median(values)
-            except:
-                # do nothing
-                pass
 
         return group_analysis_results