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GDELT.py

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+# Copyright 2023 Xueyuan Lin
+# Apache 2.0 License
+"""Loading script for DiffusionDB."""
+from typing import List, Dict
+import json
+import os
+from huggingface_hub import hf_hub_url
+import datasets
+
+
+_CITATION = """\
+@inproceedings{
+  xueyuan2023tflex,
+  title={TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph},
+  author={Lin Xueyuan and Haihong E and Chengjin Xu and Gengxian Zhou and Haoran Luo and Tianyi Hu and Fenglong Su and Ningyuan Li and Mingzhi Sun},
+  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
+  year={2023},
+  url={https://openreview.net/forum?id=oaGdsgB18L}
+}\
+"""
+
+_DESCRIPTION = """\
+TL;DR: The datasets for temporal knowledge graph reasoning task.
+
+[[Github]](https://github.com/LinXueyuanStdio/TFLEX)
+[[OpenReview]](https://openreview.net/forum?id=oaGdsgB18L)
+[[arXiv]](https://arxiv.org/abs/2205.14307)
+
+- Built over ICEWS and GDELT, which are widly used benchmarks in TKGC.
+- First introduced in paper "TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph"
+- Please refer to the original paper for more details.
+"""
+
+_HOMEPAGE = "https://github.com/LinXueyuanStdio/TFLEX"
+
+_LICENSE = "[Apache License 2.0](https://github.com/LinXueyuanStdio/TFLEX/blob/main/LICENSE)"
+
+query_name_to_args: Dict[str, List[str]] = {
+    # 1. 1-hop Pe and Pt, manually
+    "Pe": ["e1", "r1", "t1"],
+    "Pt": ["e1", "r1", "e2"],
+    # 2. entity multi-hop
+    "Pe2": ["e1", "r1", "t1", "r2", "t2"],
+    "Pe3": ["e1", "r1", "t1", "r2", "t2", "r3", "t3"],
+    # 3. time multi-hop
+    "aPt": ["s", "r", "o"],
+    "bPt": ["s", "r", "o"],
+    "Pt_sPe": ["e1", "r1", "t1", "r2", "e2"],
+    "Pt_oPe": ["e1", "r1", "e2", "r2", "t1"],
+    "Pe_Pt": ["e1", "r1", "e2", "r2", "e3"],
+    "Pe_aPt": ["e1", "r1", "e2", "r2", "e3"],
+    "Pe_bPt": ["e1", "r1", "e2", "r2", "e3"],
+    "Pe_nPt": ["e1", "r1", "e2", "r2", "e3"],
+    "Pt_sPe_Pt": ["s1", "r1", "s2", "r2", "o1", "r3", "o2"],
+    "Pt_oPe_Pt": ["s1", "r1", "s2", "r2", "s3", "r3", "o1"],
+    # 4. entity and & time and
+    "e2i": ["e1", "r1", "t1", "e2", "r2", "t2"],
+    "e3i": ["e1", "r1", "t1", "e2", "r2", "t2", "e3", "r3", "t3"],
+    "t2i": ["e1", "r1", "e2", "e3", "r2", "e4"],
+    "t3i": ["e1", "r1", "e2", "e3", "r2", "e4", "e5", "r3", "e6"],
+    # 5. complex time and
+    "e2i_Pe": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
+    "Pe_e2i": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
+    "Pt_se2i": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "e3"],
+    "Pt_oe2i": ["e1", "r1", "e2", "r2", "t1", "e3", "r3", "t2"],
+    "t2i_Pe": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
+    "Pe_t2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
+    "Pe_at2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
+    "Pe_bt2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
+    "Pe_nt2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
+    "between": ["e1", "r1", "e2", "e3", "r2", "e4"],
+    # 5. entity not
+    "e2i_N": ["e1", "r1", "t1", "e2", "r2", "t2"],
+    "e3i_N": ["e1", "r1", "t1", "e2", "r2", "t2", "e3", "r3", "t3"],
+    "Pe_e2i_Pe_NPe": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
+    "e2i_NPe": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
+    "e2i_PeN": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
+    # 6. time not
+    "t2i_N": ["e1", "r1", "e2", "e3", "r2", "e4"],
+    "t3i_N": ["e1", "r1", "e2", "e3", "r2", "e4", "e5", "r3", "e6"],
+    "Pe_t2i_PtPe_NPt": ["e1", "r1", "e2", "r2", "t2", "r3", "e3", "e4", "r4", "e5"],
+    "t2i_NPt": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
+    "t2i_PtN": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
+    # 7. entity union & time union
+    "e2u": ["e1", "r1", "t1", "e2", "r2", "t2"],
+    "Pe_e2u": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
+    "t2u": ["e1", "r1", "e2", "e3", "r2", "e4"],
+    "Pe_t2u": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
+}
+query_structures: Dict[str, str] = {
+    # 1. 1-hop Pe and Pt, manually
+    "Pe": "def Pe(e1, r1, t1): return Pe(e1, r1, t1)",  # 1p
+    "Pt": "def Pt(e1, r1, e2): return Pt(e1, r1, e2)",  # 1p, temporal
+    # 2. entity multi-hop
+    "Pe2": "def Pe2(e1, r1, t1, r2, t2): return Pe(Pe(e1, r1, t1), r2, t2)",  # 2p
+    "Pe3": "def Pe3(e1, r1, t1, r2, t2, r3, t3): return Pe(Pe(Pe(e1, r1, t1), r2, t2), r3, t3)",  # 3p
+    # 3. time multi-hop
+    "aPt": "def aPt(s, r, o): return after(Pt(s, r, o))",  # a for after
+    "bPt": "def bPt(s, r, o): return before(Pt(s, r, o))",  # b for before
+    "Pt_lPe": "def Pt_lPe(e1, r1, t1, r2, e2): return Pt(Pe(e1, r1, t1), r2, e2)",  # l for left (as head entity)
+    "Pt_rPe": "def Pt_rPe(e1, r1, e2, r2, t1): return Pt(e1, r1, Pe(e2, r2, t1))",  # r for right (as tail entity)
+    "Pt_sPe": "def Pt_sPe(e1, r1, t1, r2, e2): return Pt(Pe(e1, r1, t1), r2, e2)",  # l for left (as head entity)
+    "Pt_oPe": "def Pt_oPe(e1, r1, e2, r2, t1): return Pt(e1, r1, Pe(e2, r2, t1))",  # r for right (as tail entity)
+    "Pe_Pt": "def Pe_Pt(e1, r1, e2, r2, e3): return Pe(e1, r1, Pt(e2, r2, e3))",  # at
+    "Pe_aPt": "def Pe_aPt(e1, r1, e2, r2, e3): return Pe(e1, r1, after(Pt(e2, r2, e3)))",  # a for after
+    "Pe_bPt": "def Pe_bPt(e1, r1, e2, r2, e3): return Pe(e1, r1, before(Pt(e2, r2, e3)))",  # b for before
+    "Pe_nPt": "def Pe_nPt(e1, r1, e2, r2, e3): return Pe(e1, r1, next(Pt(e2, r2, e3)))",  # n for next
+    "Pt_sPe_Pt": "def Pt_sPe_Pt(s1, r1, s2, r2, o1, r3, o2): return Pt(Pe(s1, r1, Pt(s2, r2, o1)), r3, o2)",
+    "Pt_oPe_Pt": "def Pt_oPe_Pt(s1, r1, s2, r2, s3, r3, o1): return Pt(s1, r1, Pe(s2, r2, Pt(s3, r3, o1)))",
+    # 4. entity and & time and
+    "e2i": "def e2i(e1, r1, t1, e2, r2, t2): return And(Pe(e1, r1, t1), Pe(e2, r2, t2))",  # 2i
+    "e3i": "def e3i(e1, r1, t1, e2, r2, t2, e3, r3, t3): return And3(Pe(e1, r1, t1), Pe(e2, r2, t2), Pe(e3, r3, t3))",  # 3i
+    "t2i": "def t2i(e1, r1, e2, e3, r2, e4): return TimeAnd(Pt(e1, r1, e2), Pt(e3, r2, e4))",  # t-2i
+    "t3i": "def t3i(e1, r1, e2, e3, r2, e4, e5, r3, e6): return TimeAnd3(Pt(e1, r1, e2), Pt(e3, r2, e4), Pt(e5, r3, e6))",  # t-3i
+    # 5. complex time and
+    "e2i_Pe": "def e2i_Pe(e1, r1, t1, r2, t2, e2, r3, t3): return And(Pe(Pe(e1, r1, t1), r2, t2), Pe(e2, r3, t3))",  # pi
+    "Pe_e2i": "def Pe_e2i(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(e2i(e1, r1, t1, e2, r2, t2), r3, t3)",  # ip
+    "Pt_le2i": "def Pt_le2i(e1, r1, t1, e2, r2, t2, r3, e3): return Pt(e2i(e1, r1, t1, e2, r2, t2), r3, e3)",  # mix ip
+    "Pt_re2i": "def Pt_re2i(e1, r1, e2, r2, t1, e3, r3, t2): return Pt(e1, r1, e2i(e2, r2, t1, e3, r3, t2))",  # mix ip
+    "Pt_se2i": "def Pt_se2i(e1, r1, t1, e2, r2, t2, r3, e3): return Pt(e2i(e1, r1, t1, e2, r2, t2), r3, e3)",  # mix ip
+    "Pt_oe2i": "def Pt_oe2i(e1, r1, e2, r2, t1, e3, r3, t2): return Pt(e1, r1, e2i(e2, r2, t1, e3, r3, t2))",  # mix ip
+    "t2i_Pe": "def t2i_Pe(e1, r1, t1, r2, e2, e3, r3, e4): return TimeAnd(Pt(Pe(e1, r1, t1), r2, e2), Pt(e3, r3, e4))",  # t-pi
+    "Pe_t2i": "def Pe_t2i(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, t2i(e2, r2, e3, e4, r3, e5))",  # t-ip
+    "Pe_at2i": "def Pe_at2i(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, after(t2i(e2, r2, e3, e4, r3, e5)))",
+    "Pe_bt2i": "def Pe_bt2i(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, before(t2i(e2, r2, e3, e4, r3, e5)))",
+    "Pe_nt2i": "def Pe_nt2i(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, next(t2i(e2, r2, e3, e4, r3, e5)))",
+    "between": "def between(e1, r1, e2, e3, r2, e4): return TimeAnd(after(Pt(e1, r1, e2)), before(Pt(e3, r2, e4)))",  # between(t1, t2) == after t1 and before t2
+    # 5. entity not
+    "e2i_N": "def e2i_N(e1, r1, t1, e2, r2, t2): return And(Pe(e1, r1, t1), Not(Pe(e2, r2, t2)))",  # 2in
+    "e3i_N": "def e3i_N(e1, r1, t1, e2, r2, t2, e3, r3, t3): return And3(Pe(e1, r1, t1), Pe(e2, r2, t2), Not(Pe(e3, r3, t3)))",  # 3in
+    "Pe_e2i_Pe_NPe": "def Pe_e2i_Pe_NPe(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(And(Pe(e1, r1, t1), Not(Pe(e2, r2, t2))), r3, t3)",  # inp
+    "e2i_PeN": "def e2i_PeN(e1, r1, t1, r2, t2, e2, r3, t3): return And(Pe(Pe(e1, r1, t1), r2, t2), Not(Pe(e2, r3, t3)))",  # pin
+    "e2i_NPe": "def e2i_NPe(e1, r1, t1, r2, t2, e2, r3, t3): return And(Not(Pe(Pe(e1, r1, t1), r2, t2)), Pe(e2, r3, t3))",  # pni = e2i_N(Pe(e1, r1, t1), r2, t2, e2, r3, t3)
+    # 6. time not
+    "t2i_N": "def t2i_N(e1, r1, e2, e3, r2, e4): return TimeAnd(Pt(e1, r1, e2), TimeNot(Pt(e3, r2, e4)))",  # t-2in
+    "t3i_N": "def t3i_N(e1, r1, e2, e3, r2, e4, e5, r3, e6): return TimeAnd3(Pt(e1, r1, e2), Pt(e3, r2, e4), TimeNot(Pt(e5, r3, e6)))",  # t-3in
+    "Pe_t2i_PtPe_NPt": "def Pe_t2i_PtPe_NPt(e1, r1, e2, r2, t2, r3, e3, e4, r4, e5): return Pe(e1, r1, TimeAnd(Pt(Pe(e2, r2, t2), r3, e3), TimeNot(Pt(e4, r4, e5))))",  # t-inp
+    "t2i_PtN": "def t2i_PtN(e1, r1, t1, r2, e2, e3, r3, e4): return TimeAnd(Pt(Pe(e1, r1, t1), r2, e2), TimeNot(Pt(e3, r3, e4)))",  # t-pin
+    "t2i_NPt": "def t2i_NPt(e1, r1, t1, r2, e2, e3, r3, e4): return TimeAnd(TimeNot(Pt(Pe(e1, r1, t1), r2, e2)), Pt(e3, r3, e4))",  # t-pni
+    # 7. entity union & time union
+    "e2u": "def e2u(e1, r1, t1, e2, r2, t2): return Or(Pe(e1, r1, t1), Pe(e2, r2, t2))",  # 2u
+    "Pe_e2u": "def Pe_e2u(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(Or(Pe(e1, r1, t1), Pe(e2, r2, t2)), r3, t3)",  # up
+    "t2u": "def t2u(e1, r1, e2, e3, r2, e4): return TimeOr(Pt(e1, r1, e2), Pt(e3, r2, e4))",  # t-2u
+    "Pe_t2u": "def Pe_t2u(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, TimeOr(Pt(e2, r2, e3), Pt(e4, r3, e5)))",  # t-up
+    # 8. union-DM
+    "e2u_DM": "def e2u_DM(e1, r1, t1, e2, r2, t2): return Not(And(Not(Pe(e1, r1, t1)), Not(Pe(e2, r2, t2))))",  # 2u-DM
+    "Pe_e2u_DM": "def Pe_e2u_DM(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(Not(And(Not(Pe(e1, r1, t1)), Not(Pe(e2, r2, t2)))), r3, t3)",  # up-DM
+    "t2u_DM": "def t2u_DM(e1, r1, e2, e3, r2, e4): return TimeNot(TimeAnd(TimeNot(Pt(e1, r1, e2)), TimeNot(Pt(e3, r2, e4))))",  # t-2u-DM
+    "Pe_t2u_DM": "def Pe_t2u_DM(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, TimeNot(TimeAnd(TimeNot(Pt(e2, r2, e3)), TimeNot(Pt(e4, r3, e5)))))",  # t-up-DM
+    # 9. union-DNF
+    "e2u_DNF": "def e2u_DNF(e1, r1, t1, e2, r2, t2): return Pe(e1, r1, t1), Pe(e2, r2, t2)",  # 2u_DNF
+    "Pe_e2u_DNF": "def Pe_e2u_DNF(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(Pe(e1, r1, t1), r3, t3), Pe(Pe(e2, r2, t2), r3, t3)",  # up_DNF
+    "t2u_DNF": "def t2u_DNF(e1, r1, e2, e3, r2, e4): return Pt(e1, r1, e2), Pt(e3, r2, e4)",  # t-2u_DNF
+    "Pe_t2u_DNF": "def Pe_t2u_DNF(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, Pt(e2, r2, e3)), Pe(e1, r1, Pt(e4, r3, e5))",  # t-up_DNF
+}
+union_query_structures: List[str] = [
+    "e2u",
+    "Pe_e2u",  # 2u, up
+    "t2u",
+    "Pe_t2u",  # t-2u, t-up
+]
+train_query_structures: List[str] = [
+    # entity
+    "Pe",
+    "Pe2",
+    "Pe3",
+    "e2i",
+    "e3i",  # 1p, 2p, 3p, 2i, 3i
+    "e2i_NPe",
+    "e2i_PeN",
+    "Pe_e2i_Pe_NPe",
+    "e2i_N",
+    "e3i_N",  # npi, pni, inp, 2in, 3in
+    # time
+    "Pt",
+    "Pt_lPe",
+    "Pt_rPe",
+    "Pe_Pt",
+    "Pe_aPt",
+    "Pe_bPt",
+    "Pe_nPt",  # t-1p, t-2p
+    "t2i",
+    "t3i",
+    "Pt_le2i",
+    "Pt_re2i",
+    "Pe_t2i",
+    "Pe_at2i",
+    "Pe_bt2i",
+    "Pe_nt2i",
+    "between",  # t-2i, t-3i
+    "t2i_NPt",
+    "t2i_PtN",
+    "Pe_t2i_PtPe_NPt",
+    "t2i_N",
+    "t3i_N",  # t-npi, t-pni, t-inp, t-2in, t-3in
+]
+test_query_structures: List[str] = train_query_structures + [
+    # entity
+    "e2i_Pe",
+    "Pe_e2i",  # pi, ip
+    "e2u",
+    "Pe_e2u",  # 2u, up
+    # time
+    "t2i_Pe",
+    "Pe_t2i",  # t-pi, t-ip
+    "t2u",
+    "Pe_t2u",  # t-2u, t-up
+    # union-DM
+    "e2u_DM",
+    "Pe_e2u_DM",  # 2u-DM, up-DM
+    "t2u_DM",
+    "Pe_t2u_DM",  # t-2u-DM, t-up-DM
+]
+
+
+_AUTHOR = "linxy"
+_DATASET = "GDELT"
+_URLS = {
+    name: hf_hub_url(f"{_AUTHOR}/{_DATASET}", filename=f"zips/{name}.zip", repo_type="dataset")
+    for name in ["all"] + list(query_name_to_args.keys())
+} | {
+    "meta": hf_hub_url(f"{_AUTHOR}/{_DATASET}", filename="meta.json", repo_type="dataset")
+}
+
+
+class GDELTDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    STANDARD_BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=query_name,
+            version=datasets.Version("1.0.0"),
+            description=query_structures[query_name],
+        )
+        for query_name in list(query_name_to_args.keys())
+    ]
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="meta",
+            version=VERSION,
+            description=f"The meta of data, including entity/relation/timestamp count, entity2idx, relation2idx, timestamp2idx, etc.",
+        ),
+        datasets.BuilderConfig(
+            name="all",
+            version=VERSION,
+            description=f"All types of queries. Train: {train_query_structures}, Valid | Test: {test_query_structures}",
+        ),
+    ] + STANDARD_BUILDER_CONFIGS
+
+    DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        if self.config.name == "meta":
+            features = datasets.Features(
+                {
+                    "dataset": datasets.Value("string"),
+                    "entity_count": datasets.Value("int32"),
+                    "relation_count": datasets.Value("int32"),
+                    "timestamp_count": datasets.Value("int32"),
+                    "valid_triples_count": datasets.Value("int32"),
+                    "test_triples_count": datasets.Value("int32"),
+                    "train_triples_count": datasets.Value("int32"),
+                    "triple_count": datasets.Value("int32"),
+                    "query_meta": datasets.Sequence(
+                        feature={
+                            "query_name": datasets.Value("string"),
+                            "queries_count": datasets.Value("int32"),
+                            "avg_answers_count": datasets.Value("float"),
+                            "train": {
+                                "queries_count": datasets.Value("int32"),
+                                "avg_answers_count": datasets.Value("float"),
+                            },
+                            "valid": {
+                                "queries_count": datasets.Value("int32"),
+                                "avg_answers_count": datasets.Value("float"),
+                            },
+                            "test": {
+                                "queries_count": datasets.Value("int32"),
+                                "avg_answers_count": datasets.Value("float"),
+                            },
+                        }
+                    ),
+                    "entity2idx": datasets.Sequence(
+                        feature={
+                            "name": datasets.Value("string"),
+                            "id": datasets.Value("int32"),
+                        }
+                    ),
+                    "relation2idx": datasets.Sequence(
+                        feature={
+                            "name": datasets.Value("string"),
+                            "id": datasets.Value("int32"),
+                        }
+                    ),
+                    "timestamp2idx": datasets.Sequence(
+                        feature={
+                            "name": datasets.Value("string"),
+                            "id": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "query_name": datasets.Value("string"),
+                    "definition": datasets.Value("string"),
+                    "query": datasets.Sequence(feature=datasets.Value("int32")),
+                    "answer": datasets.Sequence(feature=datasets.Value("int32")),
+                    "easy_answer": datasets.Sequence(feature=datasets.Value("int32")),
+                    "args": datasets.Sequence(feature=datasets.Value("string")),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.download.DownloadManager):
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        url = _URLS[self.config.name]
+        if self.config.name == "meta":
+            data_file = dl_manager.download(_URLS["meta"])
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": data_file,
+                        "split": "meta",
+                    },
+                )
+            ]
+        data_dir = dl_manager.download_and_extract(url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "valid.jsonl"),
+                    "split": "valid",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.jsonl"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+        # This method yields (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        if not os.path.exists(filepath):
+            return
+        if split == "meta":
+            with open(filepath, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                yield 0, data
+            return
+        with open(filepath, "r", encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                query_name = data["query_name"]
+                easy_answer = data["easy_answer"] if "easy_answer" in data else []
+                yield key, {
+                    "query_name": query_name,
+                    "query": data["query"],
+                    "answer": data["answer"],
+                    "easy_answer": easy_answer,
+                    "args": query_name_to_args[query_name],
+                    "definition": query_structures[query_name],
+                }

meta.json

@@ -0,0 +1,4242 @@
+{
+  "dataset": "GDELT",
+  "entity_count": 500,
+  "relation_count": 20,
+  "timestamp_count": 366,
+  "valid_triples_count": 330906,
+  "test_triples_count": 330845,
+  "train_triples_count": 2308165,
+  "triple_count": 2969916,
+  "query_meta": [
+    {
+      "query_name": "Pe2",
+      "queries_count": 2235309,
+      "avg_answers_count": 2.6480848956453,
+      "train": {
+        "queries_count": 2215309,
+        "avg_answers_count": 2.6149336277693087
+      },
+      "valid": {
+        "queries_count": 10000,
+        "avg_answers_count": 6.5067
+      },
+      "test": {
+        "queries_count": 10000,
+        "avg_answers_count": 6.1335
+      }
+    },
+    {
+      "query_name": "Pe3",
+      "queries_count": 2235309,
+      "avg_answers_count": 5.163002520009538,
+      "train": {
+        "queries_count": 2215309,
+        "avg_answers_count": 5.112257477399315
+      },
+      "valid": {
+        "queries_count": 10000,
+        "avg_answers_count": 10.8633
+      },
+      "test": {
+        "queries_count": 10000,
+        "avg_answers_count": 10.7043
+      }
+    },
+    {
+      "query_name": "e2i",
+      "queries_count": 2235309,
+      "avg_answers_count": 1.0568404636674393,
+      "train": {
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+        "avg_answers_count": 1.0455159979939592
+      },
+      "valid": {
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+        "avg_answers_count": 2.3026
+      },
+      "test": {
+        "queries_count": 10000,
+        "avg_answers_count": 2.3198
+      }
+    },
+    {
+      "query_name": "e3i",
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+      },
+      "valid": {
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+        "avg_answers_count": 1.1967
+      },
+      "test": {
+        "queries_count": 10000,
+        "avg_answers_count": 1.35
+      }
+    },
+    {
+      "query_name": "e2i_NPe",
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+      "train": {
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+        "avg_answers_count": 8.1459
+      },
+      "test": {
+        "queries_count": 10000,
+        "avg_answers_count": 7.8074
+      }
+    },
+    {
+      "query_name": "e2i_PeN",
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+      "train": {
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+      },
+      "test": {
+        "queries_count": 10000,
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+    {
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+      },
+      "test": {
+        "queries_count": 10000,
+        "avg_answers_count": 8.1206
+      }
+    },
+    {
+      "query_name": "e2i_N",
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+      },
+      "test": {
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+        "avg_answers_count": 4.5768
+      }
+    },
+    {
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+      "test": {
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+      }
+    },
+    {
+      "query_name": "Pe_Pt",
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+      "train": {
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+      },
+      "valid": {
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+      },
+      "test": {
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+      }
+    },
+    {
+      "query_name": "Pe_bPt",
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+      "train": {
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+      },
+      "test": {
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+      }
+    },
+    {
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