Update dataset to v2.0.0: use native parquet format

README.md

@@ -1,98 +1,209 @@
 ---
-license: cc-by-nc-4.0
+license: apache-2.0
 task_categories:
-- text-classification
+  - text-classification
 tags:
-- TAAC2026
-- recommendation
+  - TAAC2026
+  - recommendation
 size_categories:
-- n<1K
+  - n<1K
 ---
 
-# TAAC2026 Demo Dataset — demo_1000_0408
+# TAAC2026 Demo Dataset — data_1000
 
-A sample dataset containing 1,016 user-item interaction records for the TAAC2026 competition. Compared to the previous `sample_data.parquet`, this version uses a **flat column layout** — all features are stored as individual top-level columns instead of nested structs/arrays.
+> ⚠️ **Warning**: This is a demo dataset for demonstration purposes only. It is not intended for model training or evaluation, and may not reflect the full complexity of the actual TAAC2026 data.
 
-## Dataset Description
+A sample dataset containing **1,000** user-item interaction records for the TAAC2026 competition. This dataset uses a **flat column layout** — all features are stored as individual top-level columns instead of nested structs/arrays. It is a 1,000-row subset (first 1,000 rows) of the `demo_1000_0408.gz.parquet` (1,016 rows).
 
-- **Rows**: 1,016
-- **Columns**: 120
-- **Format**: Gzip-compressed Parquet (`demo_1000_0408.gz.parquet`)
-- **File Size**: ~27.42 MB
-- **Unique Users**: 1,016
-- **Unique Items**: 849
-- **Timestamp Range**: `2026-03-05 23:36:40` — `2026-03-05 23:49:41`
+## Dataset Overview
 
-## Column Overview
+| Property | Value |
+|---|---|
+| **File** | `demo_1000.parquet` |
+| **Rows** | 1,000 |
+| **Columns** | 120 |
+| **Format** | Apache Parquet (uncompressed) |
+| **File Size** | ~38.41 MB |
+| **Unique Users** | 1,000 |
+| **Unique Items** | 837 |
+| **Timestamp Range** | `2026-03-05 23:36:40` — `2026-03-05 23:49:41` |
 
-The 120 columns fall into **6 categories**:
+## Label Distribution
 
-| Category | Columns | Count | Arrow Type | Description |
-|---|---|---|---|---|
-| **ID & Label** | `user_id`, `item_id`, `label_type`, `label_time`, `timestamp` | 5 | `int64` / `int32` | Core identifiers, label, and timestamp. |
-| **User Int Features** | `user_int_feats_{1,3,4,15,48–60,62–66,80,82,86,89–109}` | 46 | `int64` or `list<int64>` | Integer-valued user features (scalar or array). |
-| **User Dense Features** | `user_dense_feats_{61–66,87,89–91}` | 10 | `list<float>` | Float-array user features (e.g. embeddings). |
-| **Item Int Features** | `item_int_feats_{5–13,16,81,83–85}` | 14 | `int64` or `list<int64>` | Integer-valued item features (scalar or array). |
-| **Domain Sequence Features** | `domain_a_seq_{38–46}`, `domain_b_seq_{67–79,88}`, `domain_c_seq_{27–37,47}`, `domain_d_seq_{17–26}` | 45 | `list<int64>` | Behavioral sequence features from 4 domains. |
+| `label_type` | Count | Percentage |
+|---|---|---|
+| 1 | 876 | 87.6% |
+| 2 | 124 | 12.4% |
 
-### ID & Label Columns
+## Column Categories
 
-| Column | Type | Nulls | Description |
+The 120 columns fall into **6 categories**:
+
+| Category | Count | Arrow Type | Description |
 |---|---|---|---|
-| `user_id` | `int64` | 0 | User identifier (1,016 unique). |
-| `item_id` | `int64` | 0 | Target item identifier (849 unique). |
-| `label_type` | `int32` | 0 | Label type: **1** (890 rows, ~87.6%) or **2** (126 rows, ~12.4%). |
-| `label_time` | `int64` | 0 | Label event timestamp. |
-| `timestamp` | `int64` | 0 | Event timestamp. |
+| **ID & Label** | 5 | `int64` / `int32` | Core identifiers, label, and timestamp |
+| **User Int Features** | 46 | `int64` / `double` / `list<int64>` | Integer-valued user features (scalar or array) |
+| **User Dense Features** | 10 | `list<float>` | Float-array user features (e.g. embeddings) |
+| **Item Int Features** | 14 | `double` / `list<int64>` | Integer-valued item features (scalar or array) |
+| **Domain Sequence Features** | 45 | `list<int64>` | Behavioral sequence features from 4 domains |
 
-### User Int Features (46 columns)
+---
 
-Scalar `int64` columns and `list<int64>` array columns representing user-side integer features.
+## Detailed Column Schema
 
-| Sub-type | Columns | Example |
-|---|---|---|
-| Scalar `int64` | `user_int_feats_1`, `_3`, `_4`, `_48`–`_59`, `_82`, `_86`, `_92`–`_109` | `user_int_feats_1` ∈ {1, 2, 4}, mean ≈ 3.38 |
-| Array `list<int64>` | `user_int_feats_15`, `_60`, `_62`–`_66`, `_80`, `_89`–`_91` | `user_int_feats_15`: length 1–13, mean 3.8 |
+### ID & Label Columns (5 columns)
+
+| Column | Arrow Type | Nulls | Min | Max | Mean | Unique |
+|---|---|---|---|---|---|---|
+| `user_id` | `int64` | 0 | 2,727,076 | 12,728,427 | 7,835,799.34 | 1,000 |
+| `item_id` | `int64` | 0 | 6,854 | 278,202,253 | 112,417,687.39 | 837 |
+| `label_type` | `int32` | 0 | 1 | 2 | 1.124 | 2 |
+| `label_time` | `int64` | 0 | 1,772,725,027 | 1,772,725,910 | 1,772,725,503.90 | 553 |
+| `timestamp` | `int64` | 0 | 1,772,725,000 | 1,772,725,781 | 1,772,725,275.45 | 501 |
 
-> Many scalar columns are nullable (null rates vary from 0% to ~91%).
+### User Int Features (46 columns)
+
+#### Scalar Columns (`int64` / `double`)
+
+| Column | Nulls | Null% | Min | Max | Mean | Unique |
+|---|---|---|---|---|---|---|
+| `user_int_feats_1` | 0 | 0.0% | 1 | 4 | 3.381 | 3 |
+| `user_int_feats_3` | 30 | 3.0% | 9 | 1,839 | 987.557 | 341 |
+| `user_int_feats_4` | 30 | 3.0% | 1 | 986 | 498.813 | 268 |
+| `user_int_feats_48` | 2 | 0.2% | 3 | 99 | 58.006 | 52 |
+| `user_int_feats_49` | 7 | 0.7% | 1 | 2 | 1.582 | 2 |
+| `user_int_feats_50` | 4 | 0.4% | 0 | 1 | 0.998 | 2 |
+| `user_int_feats_51` | 1 | 0.1% | 40 | 150 | 56.157 | 5 |
+| `user_int_feats_52` | 1 | 0.1% | 5 | 174 | 93.856 | 36 |
+| `user_int_feats_53` | 1 | 0.1% | 3 | 557 | 288.542 | 264 |
+| `user_int_feats_54` | 368 | 36.8% | 3 | 2,843 | 1,476.783 | 462 |
+| `user_int_feats_55` | 19 | 1.9% | 8 | 41 | 29.682 | 13 |
+| `user_int_feats_56` | 19 | 1.9% | 1 | 1,434 | 752.658 | 405 |
+| `user_int_feats_57` | 31 | 3.1% | 2 | 250 | 126.588 | 105 |
+| `user_int_feats_58` | 150 | 15.0% | 1 | 2 | 1.699 | 2 |
+| `user_int_feats_59` | 150 | 15.0% | 1 | 14 | 8.371 | 8 |
+| `user_int_feats_82` | 204 | 20.4% | 1 | 23 | 9.097 | 23 |
+| `user_int_feats_86` | 692 | 69.2% | 2 | 245 | 105.474 | 61 |
+| `user_int_feats_92` | 494 | 49.4% | 1 | 2 | 1.500 | 2 |
+| `user_int_feats_93` | 171 | 17.1% | 1 | 37 | 14.667 | 36 |
+| `user_int_feats_94` | 521 | 52.1% | 1 | 6 | 3.770 | 6 |
+| `user_int_feats_95` | 318 | 31.8% | 1 | 3 | 2.758 | 3 |
+| `user_int_feats_96` | 678 | 67.8% | 1 | 3 | 1.817 | 3 |
+| `user_int_feats_97` | 292 | 29.2% | 1 | 3 | 1.599 | 3 |
+| `user_int_feats_98` | 103 | 10.3% | 1 | 3 | 2.678 | 3 |
+| `user_int_feats_99` | 812 | 81.2% | 1 | 3 | 2.936 | 2 |
+| `user_int_feats_100` | 845 | 84.5% | 1 | 2 | 1.955 | 2 |
+| `user_int_feats_101` | 910 | 91.0% | 2 | 3 | 2.956 | 2 |
+| `user_int_feats_102` | 877 | 87.7% | 1 | 3 | 1.130 | 2 |
+| `user_int_feats_103` | 862 | 86.2% | 1 | 3 | 2.717 | 3 |
+| `user_int_feats_104` | 372 | 37.2% | 1 | 3 | 2.360 | 3 |
+| `user_int_feats_105` | 309 | 30.9% | 1 | 3 | 2.287 | 3 |
+| `user_int_feats_106` | 160 | 16.0% | 1 | 3 | 1.760 | 3 |
+| `user_int_feats_107` | 300 | 30.0% | 1 | 2 | 1.094 | 2 |
+| `user_int_feats_108` | 516 | 51.6% | 2 | 7 | 5.455 | 6 |
+| `user_int_feats_109` | 854 | 85.4% | 1 | 7 | 2.993 | 7 |
+
+#### Array Columns (`list<int64>`)
+
+| Column | Nulls | Null% | Element Type |
+|---|---|---|---|
+| `user_int_feats_15` | 139 | 13.9% | `list<int64>` |
+| `user_int_feats_60` | 592 | 59.2% | `list<int64>` |
+| `user_int_feats_62` | 70 | 7.0% | `list<int64>` |
+| `user_int_feats_63` | 70 | 7.0% | `list<int64>` |
+| `user_int_feats_64` | 70 | 7.0% | `list<int64>` |
+| `user_int_feats_65` | 80 | 8.0% | `list<int64>` |
+| `user_int_feats_66` | 86 | 8.6% | `list<int64>` |
+| `user_int_feats_80` | 200 | 20.0% | `list<int64>` |
+| `user_int_feats_89` | 55 | 5.5% | `list<int64>` |
+| `user_int_feats_90` | 91 | 9.1% | `list<int64>` |
+| `user_int_feats_91` | 450 | 45.0% | `list<int64>` |
 
 ### User Dense Features (10 columns)
 
-All are `list<float>` arrays. For example, `user_dense_feats_61` has a fixed length of **256** (likely an embedding vector).
+All columns are `list<float>` arrays (e.g. embedding vectors).
 
-| Column | Nulls | Array Length |
-|---|---|---|
-| `user_dense_feats_61` | 2 | 256 (fixed) |
-| `user_dense_feats_62`–`_66` | 72–89 | variable |
-| `user_dense_feats_87` | 15 | variable |
-| `user_dense_feats_89`–`_91` | 56–456 | variable |
+| Column | Nulls | Null% | Description |
+|---|---|---|---|
+| `user_dense_feats_61` | 2 | 0.2% | 256-dim embedding vector |
+| `user_dense_feats_62` | 70 | 7.0% | Variable-length float array |
+| `user_dense_feats_63` | 70 | 7.0% | Variable-length float array |
+| `user_dense_feats_64` | 70 | 7.0% | Variable-length float array |
+| `user_dense_feats_65` | 80 | 8.0% | Variable-length float array |
+| `user_dense_feats_66` | 86 | 8.6% | Variable-length float array |
+| `user_dense_feats_87` | 15 | 1.5% | 320-dim embedding vector |
+| `user_dense_feats_89` | 55 | 5.5% | Variable-length float array |
+| `user_dense_feats_90` | 91 | 9.1% | Variable-length float array |
+| `user_dense_feats_91` | 450 | 45.0% | Variable-length float array |
 
 ### Item Int Features (14 columns)
 
-| Sub-type | Columns | Example |
-|---|---|---|
-| Scalar `int64` | `item_int_feats_5`–`_10`, `_12`, `_13`, `_16`, `_81`, `_83`–`_85` | `item_int_feats_5`: min=4, max=325, mean≈118.5 |
-| Array `list<int64>` | `item_int_feats_11` | length 1–20, mean 3.7 |
-
-> Most item features have only 2 nulls; `item_int_feats_83`–`_85` have 844 nulls (~83%).
+| Column | Arrow Type | Nulls | Null% | Min | Max | Mean | Unique |
+|---|---|---|---|---|---|---|---|
+| `item_int_feats_5` | `double` | 2 | 0.2% | 4 | 325 | 118.452 | 82 |
+| `item_int_feats_6` | `double` | 2 | 0.2% | 0 | 977 | 419.073 | 216 |
+| `item_int_feats_7` | `double` | 2 | 0.2% | 0 | 2,806 | 1,052.866 | 349 |
+| `item_int_feats_8` | `double` | 2 | 0.2% | -1 | 2,431 | 463.712 | 226 |
+| `item_int_feats_9` | `double` | 2 | 0.2% | 3 | 37 | 21.171 | 24 |
+| `item_int_feats_10` | `double` | 2 | 0.2% | 2 | 309 | 150.007 | 110 |
+| `item_int_feats_11` | `list<int64>` | 439 | 43.9% | — | — | — | — |
+| `item_int_feats_12` | `double` | 2 | 0.2% | 0 | 2,777 | 1,039.381 | 352 |
+| `item_int_feats_13` | `double` | 2 | 0.2% | 1 | 8 | 4.457 | 8 |
+| `item_int_feats_16` | `double` | 2 | 0.2% | 2 | 35,259 | 12,356.101 | 662 |
+| `item_int_feats_81` | `double` | 2 | 0.2% | 0 | 2 | 0.508 | 3 |
+| `item_int_feats_83` | `double` | 832 | 83.2% | 1 | 31 | 17.595 | 22 |
+| `item_int_feats_84` | `double` | 832 | 83.2% | 3 | 226 | 131.131 | 66 |
+| `item_int_feats_85` | `double` | 832 | 83.2% | 4 | 1,001 | 439.816 | 103 |
 
 ### Domain Sequence Features (45 columns)
 
 Variable-length `list<int64>` sequences from 4 behavioral domains:
 
-| Domain | Columns | Count | Nulls | Seq Length (min–max, mean) |
+| Domain | Columns | Count | Nulls per Col | Max Seq Length |
 |---|---|---|---|---|
-| **domain_a** | `domain_a_seq_38`–`_46` | 9 | 5 | 1–1,888, mean ≈ 704 |
-| **domain_b** | `domain_b_seq_67`–`_79`, `_88` | 14 | 12 | 1–1,952, mean ≈ 580 |
-| **domain_c** | `domain_c_seq_27`–`_37`, `_47` | 12 | 2 | 1–3,894, mean ≈ 451 |
-| **domain_d** | `domain_d_seq_17`–`_26` | 10 | 81 | 1–3,951, mean ≈ 1,197 |
+| **domain_a** | `domain_a_seq_38` – `_46` | 9 | 5 | 1,888 |
+| **domain_b** | `domain_b_seq_67` – `_79`, `_88` | 14 | 12 | 1,952 |
+| **domain_c** | `domain_c_seq_27` – `_37`, `_47` | 12 | 2 | 3,894 |
+| **domain_d** | `domain_d_seq_17` – `_26` | 10 | 80 | 3,951 |
 
-## Label Distribution
+---
 
-| `label_type` | Count | Percentage |
+## Null Coverage Summary
+
+| Group | Columns | Zero Coverage | Low Coverage (<50%) | Notes |
+|---|---|---|---|---|
+| `user_int_feats_` | 46 | 0 | 11 | Columns 99–103, 109 have >80% nulls |
+| `user_dense_feats_` | 10 | 0 | 0 | `user_dense_feats_91` has 45% nulls |
+| `item_int_feats_` | 14 | 0 | 3 | `item_int_feats_83`–`85` have ~83% nulls |
+| `domain_a_seq_` | 9 | 0 | 0 | Very low null rate (0.5%) |
+| `domain_b_seq_` | 14 | 0 | 0 | Low null rate (1.2%) |
+| `domain_c_seq_` | 12 | 0 | 0 | Very low null rate (0.2%) |
+| `domain_d_seq_` | 10 | 0 | 0 | Moderate null rate (8.0%) |
+
+### High-Null Columns (>50% null)
+
+| Column | Null Count | Null% |
 |---|---|---|
-| 1 | 890 | 87.6% |
-| 2 | 126 | 12.4% |
+| `user_int_feats_101` | 910 | 91.0% |
+| `user_int_feats_102` | 877 | 87.7% |
+| `user_int_feats_103` | 862 | 86.2% |
+| `user_int_feats_109` | 854 | 85.4% |
+| `user_int_feats_100` | 845 | 84.5% |
+| `item_int_feats_83` | 832 | 83.2% |
+| `item_int_feats_84` | 832 | 83.2% |
+| `item_int_feats_85` | 832 | 83.2% |
+| `user_int_feats_99` | 812 | 81.2% |
+| `user_int_feats_86` | 692 | 69.2% |
+| `user_int_feats_96` | 678 | 67.8% |
+| `user_int_feats_60` | 592 | 59.2% |
+| `user_int_feats_94` | 521 | 52.1% |
+| `user_int_feats_108` | 516 | 51.6% |
+| `user_int_feats_92` | 494 | 49.4% |
+| `user_dense_feats_91` | 450 | 45.0% |
+| `user_int_feats_91` | 450 | 45.0% |
+| `item_int_feats_11` | 439 | 43.9% |
+
+---
 
 ## Usage
 
@@ -100,34 +211,48 @@ Variable-length `list<int64>` sequences from 4 behavioral domains:
 import pyarrow.parquet as pq
 import pandas as pd
 
-# Read with pyarrow
-pf = pq.ParquetFile("demo_1000_0408.gz.parquet")
+# Read the parquet file
+pf = pq.ParquetFile("data_1000/demo_1000.parquet")
 table = pf.read()
 df = table.to_pandas()
 
-print(df.shape)       # (1016, 120)
-print(df.columns)     # ['user_id', 'item_id', 'label_type', 'label_time', 'timestamp', ...]
+print(df.shape)       # (1000, 120)
+print(df.columns)     # ['user_id', 'item_id', 'label_type', ...]
 ```
 
 ```python
-# Quick label check
+# Check label distribution
 print(df['label_type'].value_counts())
-# 1    890
-# 2    126
+# 1    876
+# 2    124
 
 # Access a sequence feature
 seq = df['domain_a_seq_38'].dropna().iloc[0]
 print(type(seq), len(seq))  # <class 'numpy.ndarray'> variable length
+
+# Access an embedding feature
+emb = df['user_dense_feats_61'].dropna().iloc[0]
+print(type(emb), len(emb))  # <class 'numpy.ndarray'> 256
 ```
 
-## Key Differences from Previous `sample_data.parquet`
+```python
+# Null analysis
+null_pct = df.isnull().mean().sort_values(ascending=False)
+print(null_pct[null_pct > 0.5])  # Columns with >50% nulls
+```
 
-| Aspect | `sample_data.parquet` | `demo_1000_0408.gz.parquet` |
-|---|---|---|
-| Schema style | Nested structs (`item_feature`, `user_feature`, `seq_feature`, `label`) | Flat columns (120 top-level columns) |
-| Rows | 1,000 | 1,016 |
-| Columns | 7 | 120 |
-| File size | ~68 MB | ~27 MB |
-| Compression | Uncompressed | Gzip |
-| `user_id` type | `string` | `int64` |
-| Label format | `array[struct{action_time, action_type}]` | Separate `label_type` (int32) + `label_time` (int64) |
+## Relationship to Other Files
+
+| File | Rows | Size | Compression | Description |
+|---|---|---|---|---|
+| `data_1000/demo_1000.parquet` | 1,000 | ~38 MB | None | **This dataset** — first 1,000 rows |
+| `demo_data/demo_1000_0408.gz.parquet` | 1,016 | ~27 MB | Gzip | Full 1,016-row source dataset |
+| `test_demo_data/sample_10.parquet` | 10 | ~548 KB | — | 10-row test sample |
+
+## Key Notes
+
+1. **Schema type difference**: Scalar integer features are stored as `double` (not `int64`) in this parquet file due to the presence of null values (pandas converts nullable int to float).
+2. **No nested structs**: Unlike the older `sample_data.parquet`, all features are flat top-level columns.
+3. **Sparse features**: 18 columns have >40% null values — handle missing data carefully during feature engineering.
+4. **Sequence lengths vary widely**: Domain sequences range from length 1 to ~3,951, which may require truncation or padding for model input.
+5. **Imbalanced labels**: ~87.6% label_type=1 vs ~12.4% label_type=2 — consider class balancing strategies.

sample_10.parquet → demo_1000.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6680490460c0530c713dc38f9825b6eb76e4af6af59056a12322a26bbb195e84
-size 561205
+oid sha256:3ada35e38e8728009b8579182b9e53f4ae3ff135f73eca7eecd62c259d7ea45e
+size 40279261