fix ETT m1/m2 test/val dataset (#4499)

* fix ETT m1/m2 test/val dataset

https://huggingface.co/datasets/ett/discussions/1

* added dataset_infos

Commit from https://github.com/huggingface/datasets/commit/ab4640ffd34e6f5389c5b50e9245613251df5c38

dataset_infos.json

@@ -1 +1 @@
-{"h1": {"description": "The data of Electricity Transformers from two separated counties\nin China collected for two years at hourly and 15-min frequencies.\nEach data point consists of the target value \"oil temperature\" and\n6 power load features. 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The train/val/test is 12/4/4 months.\n", "citation": "@inproceedings{haoyietal-informer-2021,\n  author    = {Haoyi Zhou and\n               Shanghang Zhang and\n               Jieqi Peng and\n               Shuai Zhang and\n               Jianxin Li and\n               Hui Xiong and\n               Wancai Zhang},\n  title     = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},\n  booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},\n  volume    = {35},\n  number    = {12},\n  pages     = {11106--11115},\n  publisher = {{AAAI} Press},\n  year      = {2021},\n}\n", "homepage": "https://github.com/zhouhaoyi/ETDataset", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"start": {"dtype": "timestamp[s]", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_static_cat": {"feature": {"dtype": "uint64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_dynamic_real": {"feature": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "item_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ett", "config_name": "h2", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 249540, "num_examples": 1, "dataset_name": "ett"}, "test": {"name": "test", "num_bytes": 79930740, "num_examples": 240, "dataset_name": "ett"}, "validation": {"name": "validation", "num_bytes": 34975770, "num_examples": 120, "dataset_name": "ett"}}, "download_checksums": {"https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh2.csv": {"num_bytes": 2417960, "checksum": "a3dc2c597b9218c7ce1cd55eb77b283fd459a1d09d753063f944967dd6b9218b"}}, "download_size": 2417960, "post_processing_size": null, "dataset_size": 115156050, "size_in_bytes": 117574010}, "m1": {"description": "The data of Electricity Transformers from two separated counties\nin China collected for two years at hourly and 15-min frequencies.\nEach data point consists of the target value \"oil temperature\" and\n6 power load features. 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+{"h1": {"description": "The data of Electricity Transformers from two separated counties\nin China collected for two years at hourly and 15-min frequencies.\nEach data point consists of the target value \"oil temperature\" and\n6 power load features. The train/val/test is 12/4/4 months.\n", "citation": "@inproceedings{haoyietal-informer-2021,\n  author    = {Haoyi Zhou and\n               Shanghang Zhang and\n               Jieqi Peng and\n               Shuai Zhang and\n               Jianxin Li and\n               Hui Xiong and\n               Wancai Zhang},\n  title     = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},\n  booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},\n  volume    = {35},\n  number    = {12},\n  pages     = {11106--11115},\n  publisher = {{AAAI} Press},\n  year      = {2021},\n}\n", "homepage": "https://github.com/zhouhaoyi/ETDataset", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"start": {"dtype": "timestamp[s]", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_static_cat": {"feature": {"dtype": "uint64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_dynamic_real": {"feature": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "item_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ett", "config_name": "h1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 241978, "num_examples": 1, "dataset_name": "ett"}, "test": {"name": "test", "num_bytes": 77508960, "num_examples": 240, "dataset_name": "ett"}, "validation": {"name": "validation", "num_bytes": 33916080, "num_examples": 120, "dataset_name": "ett"}}, "download_checksums": {"https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv": {"num_bytes": 2589657, "checksum": "f18de3ad269cef59bb07b5438d79bb3042d3be49bdeecf01c1cd6d29695ee066"}}, "download_size": 2589657, "post_processing_size": null, "dataset_size": 111667018, "size_in_bytes": 114256675}, "h2": {"description": "The data of Electricity Transformers from two separated counties\nin China collected for two years at hourly and 15-min frequencies.\nEach data point consists of the target value \"oil temperature\" and\n6 power load features. The train/val/test is 12/4/4 months.\n", "citation": "@inproceedings{haoyietal-informer-2021,\n  author    = {Haoyi Zhou and\n               Shanghang Zhang and\n               Jieqi Peng and\n               Shuai Zhang and\n               Jianxin Li and\n               Hui Xiong and\n               Wancai Zhang},\n  title     = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},\n  booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},\n  volume    = {35},\n  number    = {12},\n  pages     = {11106--11115},\n  publisher = {{AAAI} Press},\n  year      = {2021},\n}\n", "homepage": "https://github.com/zhouhaoyi/ETDataset", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"start": {"dtype": "timestamp[s]", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_static_cat": {"feature": {"dtype": "uint64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_dynamic_real": {"feature": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "item_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ett", "config_name": "h2", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 241978, "num_examples": 1, "dataset_name": "ett"}, "test": {"name": "test", "num_bytes": 77508960, "num_examples": 240, "dataset_name": "ett"}, "validation": {"name": "validation", "num_bytes": 33916080, "num_examples": 120, "dataset_name": "ett"}}, "download_checksums": {"https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh2.csv": {"num_bytes": 2417960, "checksum": "a3dc2c597b9218c7ce1cd55eb77b283fd459a1d09d753063f944967dd6b9218b"}}, "download_size": 2417960, "post_processing_size": null, "dataset_size": 111667018, "size_in_bytes": 114084978}, "m1": {"description": "The data of Electricity Transformers from two separated counties\nin China collected for two years at hourly and 15-min frequencies.\nEach data point consists of the target value \"oil temperature\" and\n6 power load features. 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ett.py

@@ -180,9 +180,9 @@ class ETT(datasets.GeneratorBasedBuilder):
         train_end_date_index = 12 * 30 * 24 * factor  # 1 year
 
         if split == "dev":
-            end_date_index = 12 * 30 * 24 + 4 * 30 * 24 * factor  # 1 year + 4 months
+            end_date_index = train_end_date_index + 4 * 30 * 24 * factor  # 1 year + 4 months
         else:
-            end_date_index = 12 * 30 * 24 + 8 * 30 * 24 * factor  # 1 year + 8 months
+            end_date_index = train_end_date_index + 8 * 30 * 24 * factor  # 1 year + 8 months
 
         if self.config.multivariate:
             if split in ["test", "dev"]: