misc: Add image clustering descriptive stats implementation

mteb/abstasks/Image/AbsTaskImageClustering.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import logging
+from collections import Counter
 from typing import Any
 
 from datasets import Dataset
@@ -10,10 +11,43 @@
 from ...encoder_interface import Encoder
 from ...evaluation.evaluators import ImageClusteringEvaluator
 from ..AbsTask import AbsTask, ScoresDict
+from ..TaskMetadata import DescriptiveStatistics
 
 logger = logging.getLogger(__name__)
 
 
+class ImageClusteringDescriptiveStatistics(DescriptiveStatistics):
+    """Descriptive statistics for ImageClustering
+
+    Attributes:
+        num_samples: number of samples in the dataset.
+
+        min_image_width: Minimum width of images
+        average_image_width: Average width of images
+        max_image_width: Maximum width of images
+
+        min_image_height: Minimum height of images
+        average_image_height: Average height of images
+        max_image_height: Maximum height of images
+
+        unique_labels: Number of unique labels
+        labels: dict of label frequencies
+    """
+
+    num_samples: int
+
+    min_image_width: float
+    average_image_width: float
+    max_image_width: float
+
+    min_image_height: float
+    average_image_height: float
+    max_image_height: float
+
+    unique_num_labels: int
+    labels: dict[str, dict[str, int]]
+
+
 class AbsTaskImageClustering(AbsTask):
     """Abstract class for Clustering tasks
     The similarity is computed between pairs and the results are ranked.
@@ -34,8 +68,43 @@ def _add_main_score(self, scores: dict[HFSubset, ScoresDict]) -> None:
 
     def _calculate_metrics_from_split(
         self, split: str, hf_subset: str | None = None, compute_overall: bool = False
-    ):
-        pass
+    ) -> ImageClusteringDescriptiveStatistics:
+        if hf_subset:
+            imgs = self.dataset[hf_subset][split][self.image_column_name]
+            labels = self.dataset[hf_subset][split][self.label_column_name]
+        elif compute_overall:
+            imgs = []
+            labels = []
+            for hf_subset in self.metadata.eval_langs:
+                imgs.extend(self.dataset[hf_subset][split][self.image_column_name])
+                labels.extend(self.dataset[hf_subset][split][self.label_column_name])
+        else:
+            imgs = self.dataset[split][self.image_column_name]
+            labels = self.dataset[split][self.label_column_name]
+
+        num_samples = len(labels)
+        unique_num_labels = len(set(labels))
+        label_count = Counter(labels)
+
+        img_widths, img_heights = [], []
+        for img in imgs:
+            width, height = img.size
+            img_heights.append(height)
+            img_widths.append(width)
+
+        return ImageClusteringDescriptiveStatistics(
+            num_samples=num_samples,
+            unique_num_labels=unique_num_labels,
+            min_image_width=min(img_widths),
+            average_image_width=sum(img_widths) / len(img_widths),
+            max_image_width=max(img_widths),
+            min_image_height=min(img_heights),
+            average_image_height=sum(img_heights) / len(img_heights),
+            max_image_height=max(img_heights),
+            labels={
+                str(label): {"count": count} for label, count in label_count.items()
+            },
+        )
 
     def _evaluate_subset(
         self,