infer labels in LabelCountCollector (#351)

* LabelCountCollector: allow labels = "INFERRED" and add label_attribute parameter

* log a warning and remove "mean", "std", "min" from aggregation_functions if labels are inferred from data

* log a warning and remove "mean", "std", "min" from aggregation_functions if labels are inferred from data

* make pre-commit happy

src/pytorch_ie/metrics/statistics.py

@@ -1,3 +1,4 @@
+import logging
 from collections import defaultdict
 from typing import Any, Dict, List, Optional, Type, Union
 
@@ -6,6 +7,8 @@
 from pytorch_ie.core import Document, DocumentStatistic
 from pytorch_ie.documents import TextBasedDocument
 
+logger = logging.getLogger(__name__)
+
 
 class TokenCountCollector(DocumentStatistic):
     """Collects the token count of a field when tokenizing its content with a Huggingface tokenizer.
@@ -112,14 +115,38 @@ class LabelCountCollector(DocumentStatistic):
 
     DEFAULT_AGGREGATION_FUNCTIONS = ["mean", "std", "min", "max", "len"]
 
-    def __init__(self, field: str, labels: List[str], **kwargs):
+    def __init__(
+        self, field: str, labels: Union[List[str], str], label_attribute: str = "label", **kwargs
+    ):
         super().__init__(**kwargs)
         self.field = field
+        self.label_attribute = label_attribute
+        if not (isinstance(labels, list) or labels == "INFERRED"):
+            raise ValueError("labels must be a list of strings or 'INFERRED'")
+        if labels == "INFERRED":
+            logger.warning(
+                f"Inferring labels with {self.__class__.__name__} from data produces wrong results "
+                f"for certain aggregation functions (e.g. 'mean', 'std', 'min') because zero values "
+                f"are not included in the calculation. We remove these aggregation functions from "
+                f"this collector, but be aware that the results may be wrong for your own aggregation "
+                f"functions that rely on zero values."
+            )
+            self.aggregation_functions = {
+                name: func
+                for name, func in self.aggregation_functions.items()
+                if name not in ["mean", "std", "min"]
+            }
+
         self.labels = labels
 
     def _collect(self, doc: Document) -> Dict[str, int]:
         field_obj = getattr(doc, self.field)
-        counts: Dict[str, int] = {label: 0 for label in self.labels}
+        counts: Dict[str, int]
+        if self.labels == "INFERRED":
+            counts = defaultdict(int)
+        else:
+            counts = {label: 0 for label in self.labels}
         for elem in field_obj:
-            counts[elem.label] += 1
+            label = getattr(elem, self.label_attribute)
+            counts[label] += 1
         return dict(counts)