Add TsFile (converted from TAAC2026/data_sample_1000)

.gitattributes

@@ -58,3 +58,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_domain_a_seq.tsfile filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_domain_b_seq.tsfile filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_domain_c_seq.tsfile filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_domain_d_seq_1.tsfile filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_domain_d_seq_2.tsfile filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_item_lists.tsfile filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_scalar.tsfile filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_user_dense_lists.tsfile filter=lfs diff=lfs merge=lfs -text
+data/data_sample_1000_user_int_lists.tsfile filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-nc-4.0
+tags:
+- tsfile
+- time-series
+- recommendation
+- taac2026
+modality: timeseries
+pretty_name: TAAC2026 Data Sample 1000 TsFile
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/*.tsfile
+---
+
+# TAAC2026 Data Sample 1000 TsFile
+
+This dataset is a TsFile conversion of
+[`TAAC2026/data_sample_1000`](https://huggingface.co/datasets/TAAC2026/data_sample_1000),
+the TAAC2026 demo recommendation dataset with 1,000 user-item interaction
+records.
+
+Modalities: Time-series. The source dataset is a flat Parquet file where all
+features are top-level columns. Some top-level columns are variable-length list
+features, so the conversion stores scalar interaction features and sequence
+features in separate TsFile tables instead of expanding the lists into more than
+130,000 wide columns.
+
+## Source Dataset
+
+- Original dataset: [`TAAC2026/data_sample_1000`](https://huggingface.co/datasets/TAAC2026/data_sample_1000)
+- Source file: `demo_1000.parquet`
+- Source license: `cc-by-nc-4.0`
+- Source tags: `TAAC2026`, `recommendation`
+- Source scale: 1,000 rows, 120 top-level columns, about 39 MB
+- Source schema groups: 5 ID/label columns, 46 user integer features, 10 user dense features, 14 item integer features, and 45 domain sequence features
+
+## Converted Files
+
+The upload contains 9 TsFile files under `data/`.
+
+| File | Table | Rows | Notes |
+|---|---|---:|---|
+| `data/data_sample_1000_scalar.tsfile` | `data_sample_1000_scalar` | 1,000 | Scalar interaction features |
+| `data/data_sample_1000_user_int_lists.tsfile` | `data_sample_1000_user_int_lists` | 11,560 | 11 user integer list features |
+| `data/data_sample_1000_user_dense_lists.tsfile` | `data_sample_1000_user_dense_lists` | 318,538 | 10 user dense list features |
+| `data/data_sample_1000_item_lists.tsfile` | `data_sample_1000_item_lists` | 2,086 | 1 item list feature |
+| `data/data_sample_1000_domain_a_seq.tsfile` | `data_sample_1000_domain_a_seq` | 701,086 | 9 domain A sequence features |
+| `data/data_sample_1000_domain_b_seq.tsfile` | `data_sample_1000_domain_b_seq` | 570,758 | 14 domain B sequence features |
+| `data/data_sample_1000_domain_c_seq.tsfile` | `data_sample_1000_domain_c_seq` | 449,431 | 12 domain C sequence features |
+| `data/data_sample_1000_domain_d_seq_1.tsfile` | `data_sample_1000_domain_d_seq` | 1,048,576 | Domain D sequence shard 1 |
+| `data/data_sample_1000_domain_d_seq_2.tsfile` | `data_sample_1000_domain_d_seq` | 51,283 | Domain D sequence shard 2 |
+
+The sequence tables contain 3,153,318 rows in total. `domain_d_seq` was split
+into two TsFile shards by the TsFile conversion tool.
+
+## Schema Design
+
+Scalar event table:
+
+- `Time`: epoch milliseconds derived from source `timestamp * 1000`
+- TAG columns: `event_index`, `user_id`
+- FIELD columns: `item_id`, `label_type`, `label_time`, `event_timestamp`, and all source scalar user/item features
+
+Sequence tables:
+
+- `Time`: `sequence_index` within each source interaction event
+- TAG columns: `event_index`, `user_id`, `item_id`
+- FIELD columns: `event_timestamp`, `label_time`, `label_type`, plus the list feature values for that sequence family
+- `event_timestamp` preserves the source `timestamp` value in seconds
+
+## Conversion Notes
+
+- No source columns are intentionally dropped.
+- Source `timestamp` is renamed to `event_timestamp` and also used to create
+  the scalar event table's TsFile `Time`.
+- Variable-length list columns are reshaped into per-family sequence tables.
+  This preserves list positions while avoiding an extremely wide table.
+- Missing list positions caused by unequal sequence lengths are stored as nulls.
+- The converted layout keeps one `event_index` per original source row so users
+  can join scalar and sequence tables back to the original interaction record.
+
+## Validation
+
+Local validation confirmed that all 9 TsFile files are non-empty. TsFile
+metadata row counts match the staged Parquet row counts for every table,
+including both shards of `data_sample_1000_domain_d_seq`.
+
+## Minimal Read Example
+
+```python
+from tsfile import TsFileReader
+
+path = "data/data_sample_1000_scalar.tsfile"
+with TsFileReader(path) as reader:
+    schemas = reader.get_all_table_schemas()
+    print(schemas.keys())
+```

data/data_sample_1000_domain_a_seq.tsfile

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+size 7660462

data/data_sample_1000_domain_b_seq.tsfile

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data/data_sample_1000_domain_c_seq.tsfile

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data/data_sample_1000_domain_d_seq_1.tsfile

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data/data_sample_1000_domain_d_seq_2.tsfile

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data/data_sample_1000_item_lists.tsfile

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+size 388320

data/data_sample_1000_scalar.tsfile

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+size 6759267

data/data_sample_1000_user_dense_lists.tsfile

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+size 3587213

data/data_sample_1000_user_int_lists.tsfile

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+size 2043332

meta/conversion_summary.json

@@ -0,0 +1,153 @@
+{
+  "source_dataset": "TAAC2026/data_sample_1000",
+  "source_file": "demo_1000.parquet",
+  "source_rows": 1000,
+  "source_columns": 120,
+  "converted_layout": "scalar event table plus per-family sequence tables",
+  "time_mapping": {
+    "scalar_table": "Time = timestamp * 1000 (epoch milliseconds)",
+    "sequence_tables": "Time = sequence_index within each source interaction event"
+  },
+  "tag_columns": {
+    "scalar_table": [
+      "event_index",
+      "user_id"
+    ],
+    "sequence_tables": [
+      "event_index",
+      "user_id",
+      "item_id"
+    ]
+  },
+  "scalar_table": {
+    "table_name": "data_sample_1000_scalar",
+    "rows": 1000,
+    "parquet": "data_sample_1000_scalar.parquet",
+    "tsfile": "data/data_sample_1000_scalar.tsfile"
+  },
+  "sequence_tables": {
+    "user_int_lists": {
+      "columns": [
+        "user_int_feats_15",
+        "user_int_feats_60",
+        "user_int_feats_62",
+        "user_int_feats_63",
+        "user_int_feats_64",
+        "user_int_feats_65",
+        "user_int_feats_66",
+        "user_int_feats_80",
+        "user_int_feats_89",
+        "user_int_feats_90",
+        "user_int_feats_91"
+      ],
+      "rows": 11560,
+      "parquet": "data_sample_1000_user_int_lists.parquet",
+      "tsfile": "data/data_sample_1000_user_int_lists.tsfile"
+    },
+    "user_dense_lists": {
+      "columns": [
+        "user_dense_feats_61",
+        "user_dense_feats_62",
+        "user_dense_feats_63",
+        "user_dense_feats_64",
+        "user_dense_feats_65",
+        "user_dense_feats_66",
+        "user_dense_feats_87",
+        "user_dense_feats_89",
+        "user_dense_feats_90",
+        "user_dense_feats_91"
+      ],
+      "rows": 318538,
+      "parquet": "data_sample_1000_user_dense_lists.parquet",
+      "tsfile": "data/data_sample_1000_user_dense_lists.tsfile"
+    },
+    "item_lists": {
+      "columns": [
+        "item_int_feats_11"
+      ],
+      "rows": 2086,
+      "parquet": "data_sample_1000_item_lists.parquet",
+      "tsfile": "data/data_sample_1000_item_lists.tsfile"
+    },
+    "domain_a_seq": {
+      "columns": [
+        "domain_a_seq_38",
+        "domain_a_seq_39",
+        "domain_a_seq_40",
+        "domain_a_seq_41",
+        "domain_a_seq_42",
+        "domain_a_seq_43",
+        "domain_a_seq_44",
+        "domain_a_seq_45",
+        "domain_a_seq_46"
+      ],
+      "rows": 701086,
+      "parquet": "data_sample_1000_domain_a_seq.parquet",
+      "tsfile": "data/data_sample_1000_domain_a_seq.tsfile"
+    },
+    "domain_b_seq": {
+      "columns": [
+        "domain_b_seq_67",
+        "domain_b_seq_68",
+        "domain_b_seq_69",
+        "domain_b_seq_70",
+        "domain_b_seq_71",
+        "domain_b_seq_72",
+        "domain_b_seq_73",
+        "domain_b_seq_74",
+        "domain_b_seq_75",
+        "domain_b_seq_76",
+        "domain_b_seq_77",
+        "domain_b_seq_78",
+        "domain_b_seq_79",
+        "domain_b_seq_88"
+      ],
+      "rows": 570758,
+      "parquet": "data_sample_1000_domain_b_seq.parquet",
+      "tsfile": "data/data_sample_1000_domain_b_seq.tsfile"
+    },
+    "domain_c_seq": {
+      "columns": [
+        "domain_c_seq_27",
+        "domain_c_seq_28",
+        "domain_c_seq_29",
+        "domain_c_seq_30",
+        "domain_c_seq_31",
+        "domain_c_seq_32",
+        "domain_c_seq_33",
+        "domain_c_seq_34",
+        "domain_c_seq_35",
+        "domain_c_seq_36",
+        "domain_c_seq_37",
+        "domain_c_seq_47"
+      ],
+      "rows": 449431,
+      "parquet": "data_sample_1000_domain_c_seq.parquet",
+      "tsfile": "data/data_sample_1000_domain_c_seq.tsfile"
+    },
+    "domain_d_seq": {
+      "columns": [
+        "domain_d_seq_17",
+        "domain_d_seq_18",
+        "domain_d_seq_19",
+        "domain_d_seq_20",
+        "domain_d_seq_21",
+        "domain_d_seq_22",
+        "domain_d_seq_23",
+        "domain_d_seq_24",
+        "domain_d_seq_25",
+        "domain_d_seq_26"
+      ],
+      "rows": 1099859,
+      "parquet": "data_sample_1000_domain_d_seq.parquet",
+      "tsfiles": [
+        "data/data_sample_1000_domain_d_seq_1.tsfile",
+        "data/data_sample_1000_domain_d_seq_2.tsfile"
+      ]
+    }
+  },
+  "renamed_columns": {
+    "timestamp": "event_timestamp"
+  },
+  "dropped_columns": []
+}

meta/source_README.md

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+---
+license: cc-by-nc-4.0
+tags:
+  - TAAC2026
+  - recommendation
+---
+
+# TAAC2026 Demo Dataset (1000 Samples) 
+
+
+> [!WARNING] ⚠️**Update[2026.04.10]:**
+> This demo dataset has been updated to newest version with the following changes:
+> - The parquet file is now a **flat column layout**, with all features as top-level columns.
+> - Add a sequence feature, rename feature names and update some features.
+> Participants should refer to the updated `demo_1000.parquet` and this `README.md` for the latest schema and data details.
+
+
+A sample dataset containing 1000 user-item interaction records for the [TAAC2026 competition](https://algo.qq.com/). This dataset uses a **flat column layout** — all features are stored as individual top-level columns instead of nested structs/arrays.
+
+## Dataset Overview
+
+| Property | Value |
+|---|---|
+| **File** | `demo_1000.parquet` |
+| **Rows** | 1,000 |
+| **Columns** | 120 |
+| **File Size** | ~39 MB |
+
+## Columns
+
+The 120 columns fall into **6 categories**:
+
+| Category | Count | Data Type | Description |
+|---|---|---|---|
+| **ID & Label** | 5 | `int64` / `int32` | Core identifiers, label, and timestamp |
+| **User Int Features** | 46 | `int64` / `list<int64>` | Integer-valued user features (scalar or array) |
+| **User Dense Features** | 10 | `list<float>` | Float-array user features |
+| **Item Int Features** | 14 | `int64` / `list<int64>` | Integer-valued item features (scalar or array) |
+| **Domain Sequence Features** | 45 | `list<int64>` | Behavioral sequence features from 4 domains |
+
+---
+
+## Detailed Column Schema
+
+### ID & Label Columns (5 columns)
+
+All these 5 columns have no `null` value.
+
+| Column | Data Type | 
+|---|---|
+| `user_id` | `int64` |
+| `item_id` | `int64` |
+| `label_type` | `int32` | 
+| `label_time` | `int64` | 
+| `timestamp` | `int64` |
+
+> [!NOTE] **Note:**
+> When `user_int_feats_{fid}` and `user_dense_feats_{fid}` share the same `{fid}`, they are aligned and jointly describe the same entity or signal.
+
+### User Int Features (46 columns)
+
+- `user_int_feats_{1,3,4,48-59,82,86,92-109}`: Scalar `int64`, total 35 columns.
+- `user_int_feats_{15, 60, 62-66, 80, 89-91}`: Array `list<int64>`, total 11 columns.
+
+
+### User Dense Features (10 columns)
+
+- `user_dense_feats_{61-66, 87, 89-91}`: Array `list<float>`, total 10 columns.
+
+
+### Item Int Features (14 columns)
+
+- `item_int_feats_{5-10, 12-13, 16, 81, 83-85}`: Scalar `int64`, total 13 columns.
+- `item_int_feats_{11}`: Array `list<int64>`, total 1 column.
+
+
+### Domain Sequence Features (45 columns)
+
+`list<int64>` sequences from 4 behavioral domains:
+
+- `domain_a_seq_{38-46}`: 9 columns
+- `domain_b_seq_{67-79, 88}`: 14 columns
+- `domain_c_seq_{27-37, 47}`: 12 columns
+- `domain_d_seq_{17-26}`: 10 columns
+
+---
+
+## Usage
+
+```python
+import pyarrow.parquet as pq
+import pandas as pd
+
+# Read the parquet file
+df = pd.read_parquet("demo_1000.parquet")
+
+print(df.shape)       # (1000, 120)
+print(df.columns)     # ['user_id', 'item_id', 'label_type', ...]
+```
+
+With Hugging Face `datasets`:
+```python
+from datasets import load_dataset
+
+ds = load_dataset("TAAC2026/data_sample_1000")
+print(ds)
+```