Add dataset loading script (KDD v2 URL map)

EventXBench.py

@@ -0,0 +1,161 @@
+"""EventXBench dataset loading script for Hugging Face `datasets` library.
+
+This script is auto-detected by HF when the repo contains a .py file with the
+same name as the repo. It defines dataset configs for each task (t1--t6) and
+for the auxiliary data (posts, markets, ohlcv).
+
+Usage:
+    from datasets import load_dataset
+
+    # Load a specific task
+    ds = load_dataset("mlsys-io/EventXBench", "t1")
+    train_df = ds["train"].to_pandas()
+
+    # Load all configs
+    ds = load_dataset("mlsys-io/EventXBench", "t4")
+"""
+from __future__ import annotations
+
+import json
+import os
+
+import datasets
+
+
+_DESCRIPTION = (
+    "EventX: A multimodal benchmark linking Twitter/X posts to "
+    "Polymarket prediction market dynamics across seven tasks."
+)
+
+_HOMEPAGE = "https://github.com/mlsys-io/EventXBench"
+_LICENSE = "cc-by-nc-4.0"
+
+_URLS = {
+    "t1_train": "data/t1/train.jsonl",
+    "t1_test": "data/t1/test.jsonl",
+    "t2_train": "data/t2/t2_train.jsonl",
+    "t2_validation": "data/t2/t2_val.jsonl",
+    "t2_test": "data/t2/t2_test.jsonl",
+    "t3_test": "data/t3/test.jsonl",
+    "t4_train": "data/t4/train.jsonl",
+    "t4_validation": "data/t4/validation.jsonl",
+    "t4_test": "data/t4/test.jsonl",
+    "t5_train": "data/t5/train.jsonl",
+    "t5_validation": "data/t5/validation.jsonl",
+    "t5_test": "data/t5/test.jsonl",
+    "t6_train": "data/t6/train.jsonl",
+    "t6_validation": "data/t6/validation.jsonl",
+    "t6_test": "data/t6/test.jsonl",
+    "t7_train": "data/t7/train.jsonl",
+    "t7_test": "data/t7/test.jsonl",
+}
+
+
+class EventXBenchConfig(datasets.BuilderConfig):
+    """BuilderConfig for EventXBench."""
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+class EventXBench(datasets.GeneratorBasedBuilder):
+    """EventXBench dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        EventXBenchConfig(
+            name="t1",
+            version=VERSION,
+            description="T1: Conditional Market Volume Prediction (3-class)",
+        ),
+        EventXBenchConfig(
+            name="t2",
+            version=VERSION,
+            description="T2: Post-to-Market Linking",
+        ),
+        EventXBenchConfig(
+            name="t3",
+            version=VERSION,
+            description="T3: Evidence Grading (ordinal 0-5)",
+        ),
+        EventXBenchConfig(
+            name="t4",
+            version=VERSION,
+            description="T4: Market Movement Prediction (direction x magnitude)",
+        ),
+        EventXBenchConfig(
+            name="t5",
+            version=VERSION,
+            description="T5: Volume & Price Impact (decay classification)",
+        ),
+        EventXBenchConfig(
+            name="t6",
+            version=VERSION,
+            description="T6: Cross-Market Propagation (3-class)",
+        ),
+        EventXBenchConfig(
+            name="t7",
+            version=VERSION,
+            description="T7: Impact Persistence / Decay classification (3-class)",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "t1"
+
+    def _info(self):
+        # Use generic features since each task has different schemas.
+        # HF will infer the schema from the first batch of examples.
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=None,  # auto-inferred from data
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        config = self.config.name
+
+        # Determine which files to download
+        files_to_dl = {}
+        for key, url in _URLS.items():
+            if key.startswith(config + "_"):
+                files_to_dl[key] = url
+
+        downloaded = dl_manager.download_and_extract(files_to_dl)
+
+        splits = []
+        train_key = f"{config}_train"
+        validation_key = f"{config}_validation"
+        test_key = f"{config}_test"
+
+        if train_key in downloaded:
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": downloaded[train_key]},
+                )
+            )
+        if validation_key in downloaded:
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"filepath": downloaded[validation_key]},
+                )
+            )
+        if test_key in downloaded:
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": downloaded[test_key]},
+                )
+            )
+
+        return splits
+
+    def _generate_examples(self, filepath):
+        with open(filepath, "r", encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                line = line.strip()
+                if line:
+                    yield idx, json.loads(line)