Delete cuebench

cuebench/README.md

@@ -1,17 +0,0 @@
-# CUEBench: Contextual Unobserved Entity Benchmark
-
-CUEBench is a neurosymbolic benchmark that emphasizes **contextual entity prediction** in autonomous driving scenes. Unlike traditional detection tasks, CUEBench focuses on reasoning over **unobserved entities** — objects that may be occluded, out-of-frame, or affected by sensor failures.
-
-## Task
-
-**Input**: A scene ID and a set of `observed_classes` present in the scene
-**Output**: Predict the `target_classes` that were present but unobserved
-
-### Example
-```json
-{
-  "image_id": "00003.00019",
-  "observed_classes": ["Car", "Bus", "Pedestrian"],
-  "target_classes": ["PickupTruck"]
-}
-

cuebench/convert_to_jsonl.py

@@ -1,21 +0,0 @@
-import json
-
-def convert_tsv_to_jsonl(input_path, output_path):
-    with open(input_path, "r") as fin, open(output_path, "w") as fout:
-        for line in fin:
-            parts = line.strip().split("\t")
-            if len(parts) != 3:
-                continue
-            image_id = parts[0]
-            observed = sorted(eval(parts[1]))  # safely convert set to list
-            target = sorted(eval(parts[2]))
-            obj = {
-                "image_id": image_id,
-                "observed_classes": observed,
-                "target_classes": target
-            }
-            fout.write(json.dumps(obj) + "\n")
-
-# Example usage:
-# convert_tsv_to_jsonl("data/train.tsv", "data/train.jsonl")
-

cuebench/cuebench.py

@@ -1,34 +0,0 @@
-import json
-import os
-from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split, Value, Features
-
-class CUEBench(GeneratorBasedBuilder):
-    def _info(self):
-        return DatasetInfo(
-            description="CUEBench: Contextual Entity Prediction for Occluded or Unobserved Entities in Autonomous Driving.",
-            features=Features({
-                "image_id": Value("string"),
-                "observed_classes": Value("string"),  # Will be jsonified list
-                "target_classes": Value("string"),    # Will be jsonified list
-            }),
-            supervised_keys=None,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract("path_or_url_to_data_dir")  # Update this
-        return [
-            SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "train.jsonl")}),
-            SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "val.jsonl")}),
-            SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl")}),
-        ]
-
-    def _generate_examples(self, filepath):
-        with open(filepath, "r", encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                example = json.loads(line)
-                yield idx, {
-                    "image_id": example["image_id"],
-                    "observed_classes": json.dumps(example["observed_classes"]),
-                    "target_classes": json.dumps(example["target_classes"]),
-                }
-

cuebench/metric.py

@@ -1,35 +0,0 @@
-from datasets import Metric
-
-class CUEBenchMetric(Metric):
-    def _info(self):
-        return {
-            "description": "F1, Precision, and Recall for multi-label set prediction in CUEBench",
-            "inputs_description": "List of predicted and reference class sets",
-            "citation": "",
-        }
-
-    def _compute(self, predictions, references):
-        total_precision, total_recall, total_f1 = 0.0, 0.0, 0.0
-        count = len(predictions)
-
-        for pred, ref in zip(predictions, references):
-            pred_set = set(pred)
-            ref_set = set(ref)
-            tp = len(pred_set & ref_set)
-            fp = len(pred_set - ref_set)
-            fn = len(ref_set - pred_set)
-
-            precision = tp / (tp + fp) if (tp + fp) else 0.0
-            recall = tp / (tp + fn) if (tp + fn) else 0.0
-            f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0
-
-            total_precision += precision
-            total_recall += recall
-            total_f1 += f1
-
-        return {
-            "precision": total_precision / count,
-            "recall": total_recall / count,
-            "f1": total_f1 / count
-        }
-