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legacy/_transformed/rel-amazon/README.md

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+# rel-amazon
+
+Amazon product reviews: customers, products, and time-stamped reviews and ratings across the Amazon catalog.
+
+## Schema
+
+![schema diagram](schema.svg)
+
+## Tasks
+
+| task | kind | type | description |
+|---|---|---|---|
+| `item-churn` | forecast | binary_classification | Churn for a product is 1 if the product recieves at least one review in the time window, else 0. |
+| `item-ltv` | forecast | regression | LTV (life-time value) for a product is the numer of times the product is purchased in the time window multiplied by price. |
+| `review-rating` | autocomplete | regression | Predict the `rating` column of the `review` table. |
+| `user-churn` | forecast | binary_classification | Churn for a customer is 1 if the customer does not review any product in the time window, else 0. |
+| `user-item-purchase` | forecast | link_prediction | Predict the list of distinct items each customer will purchase in the next two years. |
+| `user-item-rate` | forecast | link_prediction | Predict the list of distinct items each customer will purchase and give a 5 star review in the next two years. |
+| `user-item-review` | forecast | link_prediction | Predict the list of distinct items each customer will purchase and give a detailed review in the next two years. |
+| `user-ltv` | forecast | regression | LTV (life-time value) for a customer is the sum of prices of products that the customer reviews in the time window. |
+
+## Loading
+
+```python
+import relbench
+ds = relbench.load_dataset("relbench/v1/rel-amazon")
+task = relbench.load_task("relbench/v1/rel-amazon", "<task>")
+```
+
+## Citation
+
+Please cite [RelBench](https://proceedings.neurips.cc/paper_files/paper/2024/hash/25cd345233c65fac1fec0ce61d0f7836-Abstract-Datasets_and_Benchmarks_Track.html):
+
+```bibtex
+@inproceedings{robinson2024relbench,
+  title     = {{RelBench}: A Benchmark for Deep Learning on Relational Databases},
+  author    = {Robinson, Joshua and Ranjan, Rishabh and Hu, Weihua and Huang, Kexin and Han, Jiaqi and Dobles, Alejandro and Fey, Matthias and Lenssen, Jan E. and Yuan, Yiwen and Zhang, Zecheng and He, Xinwei and Leskovec, Jure},
+  booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
+  year      = {2024}
+}
+```

legacy/_transformed/rel-avito/README.md

@@ -0,0 +1,39 @@
+# rel-avito
+
+Avito online classifieds: users, ads, search queries, and impression / click / visit streams.
+
+## Schema
+
+![schema diagram](schema.svg)
+
+## Tasks
+
+| task | kind | type | description |
+|---|---|---|---|
+| `ad-ctr` | forecast | regression | Assuming the ad will be clicked in the next 4 days, predict the Click-Through- Rate (CTR) for each ad. |
+| `searchinfo-isuserloggedon` | autocomplete | binary_classification | Predict the `IsUserLoggedOn` column of the `SearchInfo` table. |
+| `searchstream-click` | autocomplete | binary_classification | Predict the `IsClick` column of the `SearchStream` table. |
+| `user-ad-visit` | forecast | link_prediction | Predict the distinct list of ads a user will visit in the next 4 days. |
+| `user-clicks` | forecast | binary_classification | Predict whether the each customer will click on more than one ads in the next 4 days. |
+| `user-visits` | forecast | binary_classification | Predict whether each customer will visit more than one ad in the next 4 days. |
+
+## Loading
+
+```python
+import relbench
+ds = relbench.load_dataset("relbench/v1/rel-avito")
+task = relbench.load_task("relbench/v1/rel-avito", "<task>")
+```
+
+## Citation
+
+Please cite [RelBench](https://proceedings.neurips.cc/paper_files/paper/2024/hash/25cd345233c65fac1fec0ce61d0f7836-Abstract-Datasets_and_Benchmarks_Track.html):
+
+```bibtex
+@inproceedings{robinson2024relbench,
+  title     = {{RelBench}: A Benchmark for Deep Learning on Relational Databases},
+  author    = {Robinson, Joshua and Ranjan, Rishabh and Hu, Weihua and Huang, Kexin and Han, Jiaqi and Dobles, Alejandro and Fey, Matthias and Lenssen, Jan E. and Yuan, Yiwen and Zhang, Zecheng and He, Xinwei and Leskovec, Jure},
+  booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
+  year      = {2024}
+}
+```

legacy/_transformed/rel-avito/manifest.yaml

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+name: rel-avito
+manifest_version: 1
+description: 'Avito online classifieds: users, ads, search queries, and impression / click / visit streams.'
+val_timestamp: '2015-05-08'
+test_timestamp: '2015-05-14'
+tables:
+  VisitStream:
+    pkey: null
+    time_col: ViewDate
+    fkeys:
+      UserID: UserInfo
+      AdID: AdsInfo
+  AdsInfo:
+    pkey: AdID
+    time_col: null
+    fkeys:
+      LocationID: Location
+      CategoryID: Category
+  SearchStream:
+    pkey: null
+    time_col: SearchDate
+    fkeys:
+      SearchID: SearchInfo
+      AdID: AdsInfo
+  SearchInfo:
+    pkey: SearchID
+    time_col: SearchDate
+    fkeys:
+      UserID: UserInfo
+      LocationID: Location
+      CategoryID: Category
+  Category:
+    pkey: CategoryID
+    time_col: null
+    fkeys: {}
+  PhoneRequestsStream:
+    pkey: null
+    time_col: PhoneRequestDate
+    fkeys:
+      UserID: UserInfo
+      AdID: AdsInfo
+  UserInfo:
+    pkey: UserID
+    time_col: null
+    fkeys: {}
+  Location:
+    pkey: LocationID
+    time_col: null
+    fkeys: {}

legacy/_transformed/rel-avito/tasks/searchinfo-isuserloggedon/manifest.yaml

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+name: searchinfo-isuserloggedon
+kind: autocomplete
+task_type: binary_classification
+description: Predict the `IsUserLoggedOn` column of the `SearchInfo` table.
+entity_table: SearchInfo
+target_col: IsUserLoggedOn
+manifest_version: 1

legacy/_transformed/rel-avito/tasks/searchstream-click/manifest.yaml

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+name: searchstream-click
+kind: autocomplete
+task_type: binary_classification
+description: Predict the `IsClick` column of the `SearchStream` table.
+entity_table: SearchStream
+target_col: IsClick
+manifest_version: 1

legacy/_transformed/rel-avito/tasks/user-ad-visit/manifest.yaml

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+name: user-ad-visit
+kind: forecast
+task_type: link_prediction
+description: Predict the distinct list of ads a user will visit in the next 4 days.
+time_col: timestamp
+src_entity_table: UserInfo
+src_entity_col: UserID
+dst_entity_table: AdsInfo
+dst_entity_col: AdID
+eval_k: 12
+timedelta: 4 days
+sql: "\n            SELECT\n                visit_ads.UserID,\n                t.timestamp,\n                LIST(DISTINCT visit_ads.AdID) AS AdID,\n            FROM\n                timestamps t\n            LEFT JOIN\n            (\n                    UserInfo\n                LEFT JOIN\n                    VisitStream\n                ON\n                    UserInfo.UserID == VisitStream.UserID\n            ) visit_ads\n            ON\n                visit_ads.ViewDate > t.timestamp AND\n                visit_ads.ViewDate <= t.timestamp + INTERVAL '{timedelta}'\n            GROUP BY\n                t.timestamp,\n                visit_ads.UserID\n            "
+manifest_version: 1

legacy/_transformed/rel-avito/tasks/user-clicks/manifest.yaml

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+name: user-clicks
+kind: forecast
+task_type: binary_classification
+description: Predict whether the each customer will click on more than one ads in the next 4 days.
+entity_table: UserInfo
+entity_col: UserID
+target_col: num_click
+time_col: timestamp
+timedelta: 4 days
+sql: "\n            SELECT\n                search_ads.UserID,\n                t.timestamp,\n                COALESCE(COUNT(search_ads.AdID), 0) > 1 AS num_click\n            FROM\n                timestamps t\n            LEFT JOIN\n            (\n                (\n                    UserInfo\n                        LEFT JOIN\n                    SearchInfo\n                        ON\n                    UserInfo.UserID == SearchInfo.UserID\n                ) user_search_info\n                LEFT JOIN\n                    SearchStream\n                ON\n                    user_search_info.SearchID == SearchStream.SearchID AND\n                    SearchStream.IsClick == 1.0\n            ) search_ads\n            ON\n                search_ads.SearchDate > t.timestamp AND\n                search_ads.SearchDate <= t.timestamp + INTERVAL '{timedelta}'\n            GROUP BY\n                t.timestamp,\n                search_ads.UserID\n            "
+manifest_version: 1

legacy/_transformed/rel-avito/tasks/user-visits/manifest.yaml

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+name: user-visits
+kind: forecast
+task_type: binary_classification
+description: Predict whether each customer will visit more than one ad in the next 4 days.
+entity_table: UserInfo
+entity_col: UserID
+target_col: num_click
+time_col: timestamp
+timedelta: 4 days
+sql: "\n            SELECT\n                visit_ads.UserID,\n                t.timestamp,\n                COALESCE(COUNT(DISTINCT visit_ads.AdID), 0) > 1 AS num_click\n            FROM\n                timestamps t\n            LEFT JOIN\n            (\n                    UserInfo\n                LEFT JOIN\n                    VisitStream\n                ON\n                    UserInfo.UserID == VisitStream.UserID\n            ) visit_ads\n            ON\n                visit_ads.ViewDate > t.timestamp AND\n                visit_ads.ViewDate <= t.timestamp + INTERVAL '{timedelta}'\n            GROUP BY\n                t.timestamp,\n                visit_ads.UserID\n            "
+manifest_version: 1

legacy/_transformed/rel-event/README.md

@@ -0,0 +1,39 @@
+# rel-event
+
+Event recommendation: users, events, attendance records, and social and interest signals.
+
+## Schema
+
+![schema diagram](schema.svg)
+
+## Tasks
+
+| task | kind | type | description |
+|---|---|---|---|
+| `event_interest-interested` | autocomplete | binary_classification | Predict the `interested` column of the `event_interest` table. |
+| `event_interest-not_interested` | autocomplete | binary_classification | Predict the `not_interested` column of the `event_interest` table. |
+| `user-attendance` | forecast | regression | Predict the number of events a user will go to in the next seven days 7 days. |
+| `user-ignore` | forecast | binary_classification | Predict whether a user will ignore more than 2 event invitations in the next 7 days. |
+| `user-repeat` | external | binary_classification | Predict whether a user will attend an event in the next 7 days if they have already attended an event in the last 14 days. |
+| `users-birthyear` | autocomplete | regression | Predict the `birthyear` column of the `users` table. |
+
+## Loading
+
+```python
+import relbench
+ds = relbench.load_dataset("relbench/v1/rel-event")
+task = relbench.load_task("relbench/v1/rel-event", "<task>")
+```
+
+## Citation
+
+Please cite [RelBench](https://proceedings.neurips.cc/paper_files/paper/2024/hash/25cd345233c65fac1fec0ce61d0f7836-Abstract-Datasets_and_Benchmarks_Track.html):
+
+```bibtex
+@inproceedings{robinson2024relbench,
+  title     = {{RelBench}: A Benchmark for Deep Learning on Relational Databases},
+  author    = {Robinson, Joshua and Ranjan, Rishabh and Hu, Weihua and Huang, Kexin and Han, Jiaqi and Dobles, Alejandro and Fey, Matthias and Lenssen, Jan E. and Yuan, Yiwen and Zhang, Zecheng and He, Xinwei and Leskovec, Jure},
+  booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
+  year      = {2024}
+}
+```

legacy/_transformed/rel-event/manifest.yaml

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+name: rel-event
+manifest_version: 1
+description: 'Event recommendation: users, events, attendance records, and social and interest signals.'
+val_timestamp: '2012-11-21'
+test_timestamp: '2012-11-29'
+tables:
+  user_friends:
+    pkey: null
+    time_col: null
+    fkeys:
+      user: users
+      friend: users
+  event_interest:
+    pkey: null
+    time_col: timestamp
+    fkeys:
+      event: events
+      user: users
+  event_attendees:
+    pkey: null
+    time_col: start_time
+    fkeys:
+      event: events
+      user_id: users
+  users:
+    pkey: user_id
+    time_col: joinedAt
+    fkeys: {}
+  events:
+    pkey: event_id
+    time_col: start_time
+    fkeys:
+      user_id: users

legacy/_transformed/rel-event/tasks/user-repeat/manifest.yaml

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+name: user-repeat
+kind: external
+task_type: binary_classification
+description: Predict whether a user will attend an event in the next 7 days if they have already attended an event in the last 14 days.
+entity_table: users
+entity_col: user
+target_col: target
+time_col: timestamp
+timedelta: 7 days
+sql: "\n            WITH all_timestamps AS (\n                SELECT (SELECT MIN(timestamp) FROM timestamps) - INTERVAL '{timedelta}' * 2 AS timestamp\n                UNION ALL\n                SELECT (SELECT MIN(timestamp) FROM timestamps) - INTERVAL '{timedelta}' AS timestamp\n                UNION ALL\n                SELECT timestamp FROM timestamps\n            ),\n            tb AS(\n                SELECT\n                    t.timestamp AS timestamp,\n                    event_attendees.user_id AS user,\n                    MAX(CASE WHEN event_attendees.status IN ('yes', 'maybe') THEN 1 ELSE 0 END) AS target,\n                    MAX(MAX(CASE WHEN event_attendees.status IN ('yes', 'maybe') THEN 1 ELSE 0 END)) OVER (PARTITION BY event_attendees.user_id ORDER BY t.timestamp ROWS BETWEEN 2 PRECEDING AND 1 PRECEDING) as prev_target\n                FROM\n                    all_timestamps t\n                LEFT JOIN\n                    event_attendees\n                ON\n                    event_attendees.start_time > t.timestamp AND\n                    event_attendees.start_time <= t.timestamp + INTERVAL '{timedelta}'\n                GROUP BY\n                    t.timestamp,\n                    event_attendees.user_id\n            )\n            SELECT\n                timestamp,\n                CAST(user AS BIGINT) AS user,\n                target\n            FROM\n                tb\n            WHERE\n                prev_target = 1\n                AND user IS NOT NULL\n                AND timestamp = (SELECT MAX(timestamp) FROM all_timestamps);\n            "
+manifest_version: 1

legacy/_transformed/rel-f1/README.md

@@ -0,0 +1,39 @@
+# rel-f1
+
+Formula 1 motorsport database: races, drivers, constructors, circuits, race results, qualifying, and championship standings.
+
+## Schema
+
+![schema diagram](schema.svg)
+
+## Tasks
+
+| task | kind | type | description |
+|---|---|---|---|
+| `driver-circuit-compete` | forecast | link_prediction | Predict on which circuits a driver will compete in the next 1 year. |
+| `driver-dnf` | forecast | binary_classification | Predict the if each driver will DNF (not finish) a race in the next 1 month. |
+| `driver-position` | forecast | regression | Predict the average finishing position of each driver all races in the next 2 months. |
+| `driver-top3` | forecast | binary_classification | Predict if each driver will qualify in the top-3 for a race within the next 1 month. |
+| `qualifying-position` | autocomplete | regression | Predict the `position` column of the `qualifying` table. |
+| `results-position` | autocomplete | regression | Predict the `position` column of the `results` table. |
+
+## Loading
+
+```python
+import relbench
+ds = relbench.load_dataset("relbench/v1/rel-f1")
+task = relbench.load_task("relbench/v1/rel-f1", "<task>")
+```
+
+## Citation
+
+Please cite [RelBench](https://proceedings.neurips.cc/paper_files/paper/2024/hash/25cd345233c65fac1fec0ce61d0f7836-Abstract-Datasets_and_Benchmarks_Track.html):
+
+```bibtex
+@inproceedings{robinson2024relbench,
+  title     = {{RelBench}: A Benchmark for Deep Learning on Relational Databases},
+  author    = {Robinson, Joshua and Ranjan, Rishabh and Hu, Weihua and Huang, Kexin and Han, Jiaqi and Dobles, Alejandro and Fey, Matthias and Lenssen, Jan E. and Yuan, Yiwen and Zhang, Zecheng and He, Xinwei and Leskovec, Jure},
+  booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
+  year      = {2024}
+}
+```

legacy/_transformed/rel-f1/manifest.yaml

@@ -0,0 +1,55 @@
+name: rel-f1
+manifest_version: 1
+description: 'Formula 1 motorsport database: races, drivers, constructors, circuits, race results, qualifying, and championship standings.'
+val_timestamp: '2005-01-01'
+test_timestamp: '2010-01-01'
+tables:
+  races:
+    pkey: raceId
+    time_col: date
+    fkeys:
+      circuitId: circuits
+  qualifying:
+    pkey: qualifyId
+    time_col: date
+    fkeys:
+      raceId: races
+      driverId: drivers
+      constructorId: constructors
+  constructor_standings:
+    pkey: constructorStandingsId
+    time_col: date
+    fkeys:
+      raceId: races
+      constructorId: constructors
+  standings:
+    pkey: driverStandingsId
+    time_col: date
+    fkeys:
+      raceId: races
+      driverId: drivers
+  constructors:
+    pkey: constructorId
+    time_col: null
+    fkeys: {}
+  drivers:
+    pkey: driverId
+    time_col: null
+    fkeys: {}
+  constructor_results:
+    pkey: constructorResultsId
+    time_col: date
+    fkeys:
+      raceId: races
+      constructorId: constructors
+  circuits:
+    pkey: circuitId
+    time_col: null
+    fkeys: {}
+  results:
+    pkey: resultId
+    time_col: date
+    fkeys:
+      raceId: races
+      driverId: drivers
+      constructorId: constructors

legacy/_transformed/rel-hm/README.md

@@ -0,0 +1,37 @@
+# rel-hm
+
+H&M e-commerce: customers, articles, and time-stamped purchase transactions.
+
+## Schema
+
+![schema diagram](schema.svg)
+
+## Tasks
+
+| task | kind | type | description |
+|---|---|---|---|
+| `item-sales` | forecast | regression | Predict the total sales for an article (the sum of prices of the associated transactions) in the next week. |
+| `transactions-price` | autocomplete | regression | Predict the `price` column of the `transactions` table. |
+| `user-churn` | forecast | binary_classification | Predict the churn for a customer (no transactions) in the next week. |
+| `user-item-purchase` | forecast | link_prediction | Predict the list of articles each customer will purchase in the next seven days. |
+
+## Loading
+
+```python
+import relbench
+ds = relbench.load_dataset("relbench/v1/rel-hm")
+task = relbench.load_task("relbench/v1/rel-hm", "<task>")
+```
+
+## Citation
+
+Please cite [RelBench](https://proceedings.neurips.cc/paper_files/paper/2024/hash/25cd345233c65fac1fec0ce61d0f7836-Abstract-Datasets_and_Benchmarks_Track.html):
+
+```bibtex
+@inproceedings{robinson2024relbench,
+  title     = {{RelBench}: A Benchmark for Deep Learning on Relational Databases},
+  author    = {Robinson, Joshua and Ranjan, Rishabh and Hu, Weihua and Huang, Kexin and Han, Jiaqi and Dobles, Alejandro and Fey, Matthias and Lenssen, Jan E. and Yuan, Yiwen and Zhang, Zecheng and He, Xinwei and Leskovec, Jure},
+  booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
+  year      = {2024}
+}
+```

legacy/_transformed/rel-hm/tasks/item-sales/manifest.yaml

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+name: item-sales
+kind: forecast
+task_type: regression
+description: Predict the total sales for an article (the sum of prices of the associated transactions) in the next week.
+entity_table: article
+entity_col: article_id
+target_col: sales
+time_col: timestamp
+timedelta: 7 days
+sql: "\n            SELECT\n                timestamp,\n                article_id,\n                sales\n            FROM\n                timestamps,\n                article,\n                (\n                    SELECT\n                        COALESCE(SUM(price), 0) as sales\n                    FROM\n                        transactions,\n                    WHERE\n                        transactions.article_id = article.article_id AND\n                        t_dat > timestamp AND\n                        t_dat <= timestamp + INTERVAL '{timedelta}'\n                )\n            "
+manifest_version: 1

legacy/_transformed/rel-hm/tasks/transactions-price/manifest.yaml

@@ -0,0 +1,7 @@
+name: transactions-price
+kind: autocomplete
+task_type: regression
+description: Predict the `price` column of the `transactions` table.
+entity_table: transactions
+target_col: price
+manifest_version: 1

legacy/_transformed/rel-hm/tasks/user-churn/manifest.yaml

@@ -0,0 +1,11 @@
+name: user-churn
+kind: forecast
+task_type: binary_classification
+description: Predict the churn for a customer (no transactions) in the next week.
+entity_table: customer
+entity_col: customer_id
+target_col: churn
+time_col: timestamp
+timedelta: 7 days
+sql: "\n            SELECT\n                timestamp,\n                customer_id,\n                CAST(\n                    NOT EXISTS (\n                        SELECT 1\n                        FROM transactions\n                        WHERE\n                            transactions.customer_id = customer.customer_id AND\n                            t_dat > timestamp AND\n                            t_dat <= timestamp + INTERVAL '{timedelta}'\n                    ) AS INTEGER\n                ) AS churn\n            FROM\n                timestamps,\n                customer,\n            WHERE\n                EXISTS (\n                    SELECT 1\n                    FROM transactions\n                    WHERE\n                        transactions.customer_id = customer.customer_id AND\n                        t_dat > timestamp - INTERVAL '{timedelta}' AND\n                        t_dat <= timestamp\n                )\n            "
+manifest_version: 1

legacy/_transformed/rel-hm/tasks/user-item-purchase/manifest.yaml

@@ -0,0 +1,14 @@
+name: user-item-purchase
+kind: forecast
+task_type: link_prediction
+description: Predict the list of articles each customer will purchase in the next seven days.
+target_col: article_id
+time_col: timestamp
+src_entity_table: customer
+src_entity_col: customer_id
+dst_entity_table: article
+dst_entity_col: article_id
+eval_k: 12
+timedelta: 7 days
+sql: "\n            SELECT\n                t.timestamp,\n                transactions.customer_id,\n                LIST(DISTINCT transactions.article_id) AS article_id\n            FROM\n                timestamps t\n            LEFT JOIN\n                transactions\n            ON\n                transactions.t_dat > t.timestamp AND\n                transactions.t_dat <= t.timestamp + INTERVAL '{timedelta}'\n            GROUP BY\n                t.timestamp,\n                transactions.customer_id\n            "
+manifest_version: 1

legacy/_transformed/rel-stack/manifest.yaml

@@ -0,0 +1,45 @@
+name: rel-stack
+manifest_version: 1
+description: 'Stack Exchange Q&A: users, posts, comments, votes, badges, and post links.'
+val_timestamp: '2020-10-01'
+test_timestamp: '2021-01-01'
+tables:
+  badges:
+    pkey: Id
+    time_col: Date
+    fkeys:
+      UserId: users
+  votes:
+    pkey: Id
+    time_col: CreationDate
+    fkeys:
+      PostId: posts
+      UserId: users
+  users:
+    pkey: Id
+    time_col: CreationDate
+    fkeys: {}
+  comments:
+    pkey: Id
+    time_col: CreationDate
+    fkeys:
+      UserId: users
+      PostId: posts
+  posts:
+    pkey: Id
+    time_col: CreationDate
+    fkeys:
+      OwnerUserId: users
+      ParentId: posts
+  postLinks:
+    pkey: Id
+    time_col: CreationDate
+    fkeys:
+      PostId: posts
+      RelatedPostId: posts
+  postHistory:
+    pkey: Id
+    time_col: CreationDate
+    fkeys:
+      PostId: posts
+      UserId: users

legacy/_transformed/rel-trial/README.md

@@ -0,0 +1,42 @@
+# rel-trial
+
+ClinicalTrials.gov clinical trials: studies, outcomes, adverse events, eligibilities, sponsors, conditions, and facilities.
+
+## Schema
+
+![schema diagram](schema.svg)
+
+## Tasks
+
+| task | kind | type | description |
+|---|---|---|---|
+| `condition-sponsor-run` | forecast | link_prediction | Predict whether this condition will have which sponsors. |
+| `eligibilities-adult` | autocomplete | binary_classification | Predict the `adult` column of the `eligibilities` table. |
+| `eligibilities-child` | autocomplete | binary_classification | Predict the `child` column of the `eligibilities` table. |
+| `site-sponsor-run` | forecast | link_prediction | Predict whether this sponsor will have a trial in a facility. |
+| `site-success` | forecast | regression | Predict the success rate of a trial site in the next 1 year. |
+| `studies-enrollment` | autocomplete | regression | Predict the `enrollment` column of the `studies` table. |
+| `studies-has_dmc` | autocomplete | binary_classification | Predict the `has_dmc` column of the `studies` table. |
+| `study-adverse` | forecast | regression | Predict the number of affected patients with severe advsere events/death for the trial in the next 1 year. |
+| `study-outcome` | forecast | binary_classification | Predict if the trials in the next 1 year will achieve its primary outcome. |
+
+## Loading
+
+```python
+import relbench
+ds = relbench.load_dataset("relbench/v1/rel-trial")
+task = relbench.load_task("relbench/v1/rel-trial", "<task>")
+```
+
+## Citation
+
+Please cite [RelBench](https://proceedings.neurips.cc/paper_files/paper/2024/hash/25cd345233c65fac1fec0ce61d0f7836-Abstract-Datasets_and_Benchmarks_Track.html):
+
+```bibtex
+@inproceedings{robinson2024relbench,
+  title     = {{RelBench}: A Benchmark for Deep Learning on Relational Databases},
+  author    = {Robinson, Joshua and Ranjan, Rishabh and Hu, Weihua and Huang, Kexin and Han, Jiaqi and Dobles, Alejandro and Fey, Matthias and Lenssen, Jan E. and Yuan, Yiwen and Zhang, Zecheng and He, Xinwei and Leskovec, Jure},
+  booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
+  year      = {2024}
+}
+```

legacy/_transformed/rel-trial/manifest.yaml

@@ -0,0 +1,81 @@
+name: rel-trial
+manifest_version: 1
+description: 'ClinicalTrials.gov clinical trials: studies, outcomes, adverse events, eligibilities, sponsors, conditions, and facilities.'
+val_timestamp: '2020-01-01'
+test_timestamp: '2021-01-01'
+tables:
+  conditions_studies:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+      condition_id: conditions
+  interventions:
+    pkey: intervention_id
+    time_col: null
+    fkeys: {}
+  drop_withdrawals:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+  outcome_analyses:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+      outcome_id: outcomes
+  sponsors_studies:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+      sponsor_id: sponsors
+  facilities_studies:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+      facility_id: facilities
+  eligibilities:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+  interventions_studies:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+      intervention_id: interventions
+  outcomes:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+  facilities:
+    pkey: facility_id
+    time_col: null
+    fkeys: {}
+  reported_event_totals:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies
+  sponsors:
+    pkey: sponsor_id
+    time_col: null
+    fkeys: {}
+  studies:
+    pkey: nct_id
+    time_col: start_date
+    fkeys: {}
+  conditions:
+    pkey: condition_id
+    time_col: null
+    fkeys: {}
+  designs:
+    pkey: id
+    time_col: date
+    fkeys:
+      nct_id: studies

legacy/_transformed/rel-trial/tasks/condition-sponsor-run/manifest.yaml

@@ -0,0 +1,24 @@
+name: condition-sponsor-run
+kind: forecast
+task_type: link_prediction
+description: Predict whether this condition will have which sponsors.
+target_col: sponsor_id
+time_col: timestamp
+src_entity_table: conditions
+src_entity_col: condition_id
+dst_entity_table: sponsors
+dst_entity_col: sponsor_id
+eval_k: 10
+timedelta: 365 days
+sql: |-
+  SELECT
+      t.timestamp,
+      cs.condition_id,
+      LIST(DISTINCT ss.sponsor_id) AS sponsor_id
+  FROM timestamps t
+  LEFT JOIN conditions_studies cs
+  LEFT JOIN sponsors_studies ss ON ss.nct_id = cs.nct_id
+  ON cs.date > t.timestamp
+      and cs.date <= t.timestamp + INTERVAL '{timedelta}'
+  GROUP BY t.timestamp, cs.condition_id;
+manifest_version: 1

legacy/_transformed/rel-trial/tasks/eligibilities-adult/manifest.yaml

@@ -0,0 +1,22 @@
+name: eligibilities-adult
+kind: autocomplete
+task_type: binary_classification
+description: Predict the `adult` column of the `eligibilities` table.
+entity_table: eligibilities
+target_col: adult
+remove_columns:
+- - eligibilities
+  - child
+- - eligibilities
+  - older_adult
+- - eligibilities
+  - minimum_age
+- - eligibilities
+  - maximum_age
+- - eligibilities
+  - population
+- - eligibilities
+  - criteria
+- - eligibilities
+  - gender_description
+manifest_version: 1

legacy/_transformed/rel-trial/tasks/eligibilities-child/manifest.yaml

@@ -0,0 +1,22 @@
+name: eligibilities-child
+kind: autocomplete
+task_type: binary_classification
+description: Predict the `child` column of the `eligibilities` table.
+entity_table: eligibilities
+target_col: child
+remove_columns:
+- - eligibilities
+  - adult
+- - eligibilities
+  - older_adult
+- - eligibilities
+  - minimum_age
+- - eligibilities
+  - maximum_age
+- - eligibilities
+  - population
+- - eligibilities
+  - criteria
+- - eligibilities
+  - gender_description
+manifest_version: 1

legacy/_transformed/rel-trial/tasks/site-sponsor-run/manifest.yaml

@@ -0,0 +1,24 @@
+name: site-sponsor-run
+kind: forecast
+task_type: link_prediction
+description: Predict whether this sponsor will have a trial in a facility.
+target_col: sponsor_id
+time_col: timestamp
+src_entity_table: facilities
+src_entity_col: facility_id
+dst_entity_table: sponsors
+dst_entity_col: sponsor_id
+eval_k: 10
+timedelta: 365 days
+sql: |-
+  SELECT
+      t.timestamp,
+      fs.facility_id,
+      LIST(DISTINCT ss.sponsor_id) AS sponsor_id
+  FROM timestamps t
+  LEFT JOIN facilities_studies fs
+  LEFT JOIN sponsors_studies ss ON ss.nct_id = fs.nct_id
+  ON fs.date > t.timestamp
+      and fs.date <= t.timestamp + INTERVAL '{timedelta}'
+  GROUP BY t.timestamp, fs.facility_id;
+manifest_version: 1

legacy/_transformed/rel-trial/tasks/site-success/manifest.yaml

@@ -0,0 +1,37 @@
+name: site-success
+kind: forecast
+task_type: regression
+description: Predict the success rate of a trial site in the next 1 year.
+entity_table: facilities
+entity_col: facility_id
+target_col: success_rate
+time_col: timestamp
+timedelta: 365 days
+sql: |-
+  WITH TRIAL_INFO AS (
+      SELECT
+          oa.nct_id,
+          MIN(CASE WHEN oa.p_value < 0.05 THEN 1 ELSE 0 END) AS is_successful, -- Determine if the trial is successful
+          oa.date,
+      FROM outcome_analyses oa
+      LEFT JOIN outcomes o
+      ON oa.outcome_id = o.id
+      WHERE (oa.p_value_modifier is null or oa.p_value_modifier != '>')
+      and oa.p_value >=0
+      and oa.p_value <=1
+      and o.outcome_type = 'Primary'
+      GROUP BY oa.nct_id, oa.date
+  )
+
+  SELECT
+      t.timestamp,
+      fs.facility_id,
+      SUM(tr.is_successful)/COUNT(tr.is_successful) AS success_rate
+  FROM timestamps t
+  LEFT JOIN TRIAL_INFO tr
+  LEFT JOIN facilities_studies fs ON fs.nct_id = tr.nct_id
+  ON tr.date > t.timestamp
+      and tr.date <= t.timestamp + INTERVAL '{timedelta}'
+  WHERE fs.facility_id is not null
+  GROUP BY t.timestamp, fs.facility_id;
+manifest_version: 1

legacy/_transformed/rel-trial/tasks/studies-enrollment/manifest.yaml

@@ -0,0 +1,7 @@
+name: studies-enrollment
+kind: autocomplete
+task_type: regression
+description: Predict the `enrollment` column of the `studies` table.
+entity_table: studies
+target_col: enrollment
+manifest_version: 1

legacy/_transformed/rel-trial/tasks/studies-has_dmc/manifest.yaml

@@ -0,0 +1,7 @@
+name: studies-has_dmc
+kind: autocomplete
+task_type: binary_classification
+description: Predict the `has_dmc` column of the `studies` table.
+entity_table: studies
+target_col: has_dmc
+manifest_version: 1

legacy/_transformed/rel-trial/tasks/study-adverse/manifest.yaml

@@ -0,0 +1,36 @@
+name: study-adverse
+kind: forecast
+task_type: regression
+description: Predict the number of affected patients with severe advsere events/death for the trial in the next 1 year.
+entity_table: studies
+entity_col: nct_id
+target_col: num_of_adverse_events
+time_col: timestamp
+timedelta: 365 days
+sql: |-
+  WITH TRIAL_INFO AS (
+      SELECT
+          r.nct_id,
+          r.event_type,
+          r.subjects_affected,
+          r.date,
+          s.start_date
+      FROM reported_event_totals r
+      LEFT JOIN studies s
+      ON r.nct_id = s.nct_id
+      WHERE r.event_type = 'serious' or r.event_type = 'deaths'
+      and r.subjects_affected is not null
+  )
+
+  SELECT
+      t.timestamp,
+      tr.nct_id,
+      sum(tr.subjects_affected) AS num_of_adverse_events
+  FROM timestamps t
+  LEFT JOIN TRIAL_INFO tr
+  ON tr.start_date <= t.timestamp
+      and tr.date > t.timestamp
+      and tr.date <= t.timestamp + INTERVAL '{timedelta}'
+  WHERE tr.nct_id is not null and tr.subjects_affected is not null
+  GROUP BY t.timestamp, tr.nct_id;
+manifest_version: 1

legacy/_transformed/rel-trial/tasks/study-outcome/manifest.yaml

@@ -0,0 +1,42 @@
+name: study-outcome
+kind: forecast
+task_type: binary_classification
+description: Predict if the trials in the next 1 year will achieve its primary outcome.
+entity_table: studies
+entity_col: nct_id
+target_col: outcome
+time_col: timestamp
+timedelta: 365 days
+sql: |-
+  WITH TRIAL_INFO AS (
+      SELECT
+          oa.nct_id,
+          oa.p_value,
+          s.start_date,
+          oa.date
+      FROM outcome_analyses oa
+      LEFT JOIN outcomes o
+      ON oa.outcome_id = o.id
+      LEFT JOIN studies s
+      ON s.nct_id = o.nct_id
+      where (oa.p_value_modifier is null or oa.p_value_modifier != '>')
+      and oa.p_value >=0
+      and oa.p_value <=1
+      and o.outcome_type = 'Primary'
+  )
+
+  SELECT
+      t.timestamp,
+      tr.nct_id,
+      CASE
+          WHEN MIN(tr.p_value) <= 0.05 THEN 1
+          ELSE 0
+      END AS outcome
+  FROM timestamps t
+  LEFT JOIN TRIAL_INFO tr
+  ON tr.start_date <= t.timestamp
+      and tr.date > t.timestamp
+      and tr.date <= t.timestamp + INTERVAL '{timedelta}'
+  WHERE tr.nct_id is not null
+  GROUP BY t.timestamp, tr.nct_id;
+manifest_version: 1

legacy/rel-amazon/meta.json

@@ -13,10 +13,11 @@
   "num_nodes": 73583121,
   "num_task_tables": 24,
   "num_text_strings": 31580670,
-  "source": "stanford-star/relbench/rel-amazon",
+  "source": "/dfs/user/ranjanr/share/stanford-star/relbench/rel-amazon",
   "tasks": [
     {
       "entity_table": "product",
+      "kind": "forecast",
       "name": "item-churn",
       "splits": [
         "train",
@@ -29,6 +30,7 @@
     },
     {
       "entity_table": "product",
+      "kind": "forecast",
       "name": "item-ltv",
       "splits": [
         "train",
@@ -41,7 +43,18 @@
     },
     {
       "entity_table": "review",
+      "kind": "autocomplete",
       "name": "review-rating",
+      "remove_columns": [
+        [
+          "review",
+          "review_text"
+        ],
+        [
+          "review",
+          "summary"
+        ]
+      ],
       "splits": [
         "train",
         "val",
@@ -53,6 +66,7 @@
     },
     {
       "entity_table": "customer",
+      "kind": "forecast",
       "name": "user-churn",
       "splits": [
         "train",
@@ -65,6 +79,7 @@
     },
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "user-item-purchase",
       "splits": [
         "train",
@@ -77,6 +92,7 @@
     },
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "user-item-rate",
       "splits": [
         "train",
@@ -89,6 +105,7 @@
     },
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "user-item-review",
       "splits": [
         "train",
@@ -101,6 +118,7 @@
     },
     {
       "entity_table": "customer",
+      "kind": "forecast",
       "name": "user-ltv",
       "splits": [
         "train",

legacy/rel-amazon/table_info.json

@@ -1 +1 @@
-{"user-item-review:Val":{"node_idx_offset":67996091,"num_nodes":116970},"user-ltv:Train":{"node_idx_offset":68464946,"num_nodes":4708383},"user-ltv:Val":{"node_idx_offset":73173329,"num_nodes":409792},"user-item-purchase:Test":{"node_idx_offset":55468524,"num_nodes":393985},"user-item-purchase:Val":{"node_idx_offset":60975312,"num_nodes":351876},"review-rating:Test":{"node_idx_offset":29151781,"num_nodes":8217532},"user-item-rate:Val":{"node_idx_offset":65286954,"num_nodes":257939},"user-item-review:Test":{"node_idx_offset":65544893,"num_nodes":127021},"item-ltv:Val":{"node_idx_offset":28984803,"num_nodes":166978},"item-churn:Train":{"node_idx_offset":23385087,"num_nodes":2536014},"item-churn:Val":{"node_idx_offset":25921101,"num_nodes":177689},"product:Db":{"node_idx_offset":1850193,"num_nodes":506012},"item-churn:Test":{"node_idx_offset":23218245,"num_nodes":166842},"user-churn:Test":{"node_idx_offset":49998464,"num_nodes":351885},"user-churn:Val":{"node_idx_offset":55058732,"num_nodes":409792},"customer:Db":{"node_idx_offset":0,"num_nodes":1850193},"user-item-rate:Test":{"node_idx_offset":61327188,"num_nodes":292609},"item-ltv:Train":{"node_idx_offset":26277124,"num_nodes":2707679},"review-rating:Train":{"node_idx_offset":37369313,"num_nodes":11822796},"user-ltv:Test":{"node_idx_offset":68113061,"num_nodes":351885},"user-item-purchase:Train":{"node_idx_offset":55862509,"num_nodes":5112803},"item-ltv:Test":{"node_idx_offset":26098790,"num_nodes":178334},"user-churn:Train":{"node_idx_offset":50350349,"num_nodes":4708383},"review:Db":{"node_idx_offset":2356205,"num_nodes":20862040},"review-rating:Val":{"node_idx_offset":49192109,"num_nodes":806355},"user-item-review:Train":{"node_idx_offset":65671914,"num_nodes":2324177},"user-item-rate:Train":{"node_idx_offset":61619797,"num_nodes":3667157}}
+{"user-item-rate:Train":{"node_idx_offset":61619797,"num_nodes":3667157},"item-churn:Train":{"node_idx_offset":23385087,"num_nodes":2536014},"item-churn:Val":{"node_idx_offset":25921101,"num_nodes":177689},"user-ltv:Train":{"node_idx_offset":68464946,"num_nodes":4708383},"user-ltv:Val":{"node_idx_offset":73173329,"num_nodes":409792},"item-ltv:Test":{"node_idx_offset":26098790,"num_nodes":178334},"user-item-purchase:Val":{"node_idx_offset":60975312,"num_nodes":351876},"review-rating:Test":{"node_idx_offset":29151781,"num_nodes":8217532},"item-churn:Test":{"node_idx_offset":23218245,"num_nodes":166842},"item-ltv:Train":{"node_idx_offset":26277124,"num_nodes":2707679},"review:Db":{"node_idx_offset":2356205,"num_nodes":20862040},"user-item-purchase:Train":{"node_idx_offset":55862509,"num_nodes":5112803},"user-item-rate:Test":{"node_idx_offset":61327188,"num_nodes":292609},"user-item-purchase:Test":{"node_idx_offset":55468524,"num_nodes":393985},"product:Db":{"node_idx_offset":1850193,"num_nodes":506012},"user-item-review:Val":{"node_idx_offset":67996091,"num_nodes":116970},"user-churn:Val":{"node_idx_offset":55058732,"num_nodes":409792},"item-ltv:Val":{"node_idx_offset":28984803,"num_nodes":166978},"review-rating:Train":{"node_idx_offset":37369313,"num_nodes":11822796},"user-ltv:Test":{"node_idx_offset":68113061,"num_nodes":351885},"user-item-rate:Val":{"node_idx_offset":65286954,"num_nodes":257939},"user-churn:Test":{"node_idx_offset":49998464,"num_nodes":351885},"user-item-review:Train":{"node_idx_offset":65671914,"num_nodes":2324177},"review-rating:Val":{"node_idx_offset":49192109,"num_nodes":806355},"customer:Db":{"node_idx_offset":0,"num_nodes":1850193},"user-churn:Train":{"node_idx_offset":50350349,"num_nodes":4708383},"user-item-review:Test":{"node_idx_offset":65544893,"num_nodes":127021}}

legacy/rel-avito/column_index.json

@@ -1 +1 @@
-{"timestamp of user-visits":12,"IsClick of SearchStream":3263646,"SearchQuery of SearchInfo":3246434,"SearchID of SearchStream":3263641,"UserDeviceID of UserInfo":3263638,"primary_key of searchstream-click":3246422,"SearchDate of SearchInfo":3246431,"num_click of user-clicks":9,"SearchID of searchinfo-isuserloggedon":3246418,"IsUserLoggedOn of searchinfo-isuserloggedon":3246419,"UserID of UserInfo":3263635,"UserID of user-visits":11,"SearchDate of searchstream-click":3246421,"Position of SearchStream":3263643,"UserID of SearchInfo":3246429,"SearchDate of SearchStream":3263647,"Title of AdsInfo":25,"PhoneRequestDate of PhoneRequestsStream":18,"SearchDate of searchinfo-isuserloggedon":3246417,"IPID of SearchInfo":3246432,"UserAgentID of UserInfo":3263636,"IPID of PhoneRequestsStream":16,"IsClick of searchstream-click":3246423,"AdID of user-ad-visit":3263634,"LocationID of AdsInfo":20,"LocationID of SearchInfo":3263629,"UserID of user-clicks":6,"Level of Location":3263654,"AdID of PhoneRequestsStream":17,"SearchID of SearchInfo":3246430,"LocationID of Location":3263653,"num_click of user-visits":13,"IPID of VisitStream":3263650,"AdID of AdsInfo":19,"Price of AdsInfo":24,"IsUserLoggedOn of SearchInfo":3246433,"UserAgentOSID of UserInfo":3263637,"AdID of VisitStream":3263651,"CategoryID of AdsInfo":22,"ViewDate of VisitStream":3263652,"CategoryID of Category":3246424,"UserID of user-ad-visit":3263632,"UserAgentFamilyID of UserInfo":3263639,"RegionID of Location":3263655,"timestamp of ad-ctr":3,"IsContext of AdsInfo":3246415,"AdID of SearchStream":3263642,"HistCTR of SearchStream":3263645,"UserID of PhoneRequestsStream":15,"ParentCategoryID of Category":3246426,"UserID of VisitStream":3263649,"SubcategoryID of Category":3246427,"ObjectType of SearchStream":3263644,"CategoryID of SearchInfo":3263630,"timestamp of user-ad-visit":3263633,"CityID of Location":3263656,"Level of Category":3246425,"num_click of ad-ctr":4,"AdID of ad-ctr":1,"timestamp of user-clicks":8}
+{"num_click of ad-ctr":4,"PhoneRequestDate of PhoneRequestsStream":18,"CityID of Location":3263657,"timestamp of user-clicks":8,"UserID of VisitStream":3263650,"HistCTR of SearchStream":3263646,"timestamp of user-ad-visit":3263633,"IsUserLoggedOn of SearchInfo":3246433,"UserID of SearchInfo":3246429,"SearchID of SearchInfo":3246430,"UserID of UserInfo":3263635,"SearchID of SearchStream":3263642,"LocationID of Location":3263654,"IPID of PhoneRequestsStream":16,"SearchDate of SearchInfo":3246431,"UserAgentFamilyID of UserInfo":3263639,"SubcategoryID of Category":3246427,"num_click of user-clicks":9,"CategoryID of Category":3246424,"IPID of SearchInfo":3246432,"UserAgentID of UserInfo":3263636,"SearchDate of SearchStream":3263648,"IsContext of AdsInfo":3246415,"primary_key of searchstream-click":3246422,"IsUserLoggedOn of searchinfo-isuserloggedon":3246419,"num_click of user-visits":13,"CategoryID of SearchInfo":3263630,"timestamp of ad-ctr":3,"SearchDate of searchinfo-isuserloggedon":3246417,"UserDeviceID of UserInfo":3263638,"LocationID of AdsInfo":20,"UserID of PhoneRequestsStream":15,"SearchQuery of SearchInfo":3246434,"AdID of SearchStream":3263643,"IPID of VisitStream":3263651,"AdID of VisitStream":3263652,"ParentCategoryID of Category":3246426,"Level of Location":3263655,"UserID of user-visits":11,"CategoryID of AdsInfo":22,"ObjectType of SearchStream":3263645,"SearchDate of searchstream-click":3246421,"Title of AdsInfo":25,"LocationID of SearchInfo":3263629,"AdID of user-ad-visit":3263634,"AdID of ad-ctr":1,"AdID of PhoneRequestsStream":17,"identifier of UserInfo":3263640,"IsClick of SearchStream":3263647,"UserID of user-clicks":6,"UserID of user-ad-visit":3263632,"Price of AdsInfo":24,"SearchID of searchinfo-isuserloggedon":3246418,"UserAgentOSID of UserInfo":3263637,"Position of SearchStream":3263644,"IsClick of searchstream-click":3246423,"Level of Category":3246425,"ViewDate of VisitStream":3263653,"RegionID of Location":3263656,"AdID of AdsInfo":19,"timestamp of user-visits":12}

legacy/rel-avito/meta.json

@@ -12,11 +12,12 @@
   "num_db_tables": 8,
   "num_nodes": 31991090,
   "num_task_tables": 18,
-  "num_text_strings": 3263657,
-  "source": "stanford-star/relbench/rel-avito",
+  "num_text_strings": 3263658,
+  "source": "/dfs/user/ranjanr/share/stanford-star/relbench/rel-avito",
   "tasks": [
     {
       "entity_table": "AdsInfo",
+      "kind": "forecast",
       "name": "ad-ctr",
       "splits": [
         "train",
@@ -29,6 +30,7 @@
     },
     {
       "entity_table": "SearchInfo",
+      "kind": "autocomplete",
       "name": "searchinfo-isuserloggedon",
       "splits": [
         "train",
@@ -41,6 +43,7 @@
     },
     {
       "entity_table": "SearchStream",
+      "kind": "autocomplete",
       "name": "searchstream-click",
       "splits": [
         "train",
@@ -53,6 +56,7 @@
     },
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "user-ad-visit",
       "splits": [
         "train",
@@ -65,6 +69,7 @@
     },
     {
       "entity_table": "UserInfo",
+      "kind": "forecast",
       "name": "user-clicks",
       "splits": [
         "train",
@@ -77,6 +82,7 @@
     },
     {
       "entity_table": "UserInfo",
+      "kind": "forecast",
       "name": "user-visits",
       "splits": [
         "train",

legacy/rel-event/meta.json

@@ -12,12 +12,19 @@
   "num_db_tables": 5,
   "num_nodes": 44941234,
   "num_task_tables": 18,
-  "num_text_strings": 112196,
-  "source": "stanford-star/relbench/rel-event",
+  "num_text_strings": 112199,
+  "source": "/dfs/user/ranjanr/share/stanford-star/relbench/rel-event",
   "tasks": [
     {
       "entity_table": "event_interest",
+      "kind": "autocomplete",
       "name": "event_interest-interested",
+      "remove_columns": [
+        [
+          "event_interest",
+          "not_interested"
+        ]
+      ],
       "splits": [
         "train",
         "val",
@@ -29,7 +36,14 @@
     },
     {
       "entity_table": "event_interest",
+      "kind": "autocomplete",
       "name": "event_interest-not_interested",
+      "remove_columns": [
+        [
+          "event_interest",
+          "interested"
+        ]
+      ],
       "splits": [
         "train",
         "val",
@@ -41,6 +55,7 @@
     },
     {
       "entity_table": "users",
+      "kind": "forecast",
       "name": "user-attendance",
       "splits": [
         "train",
@@ -53,6 +68,7 @@
     },
     {
       "entity_table": "users",
+      "kind": "forecast",
       "name": "user-ignore",
       "splits": [
         "train",
@@ -65,6 +81,7 @@
     },
     {
       "entity_table": "users",
+      "kind": "external",
       "name": "user-repeat",
       "splits": [
         "train",
@@ -77,6 +94,7 @@
     },
     {
       "entity_table": "users",
+      "kind": "autocomplete",
       "name": "users-birthyear",
       "splits": [
         "train",

legacy/rel-event/table_info.json

@@ -1 +1 @@
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+{"event_interest-interested:Val":{"node_idx_offset":44837854,"num_nodes":536},"user-repeat:Test":{"node_idx_offset":44900208,"num_nodes":246},"events:Db":{"node_idx_offset":11260408,"num_nodes":3137972},"user-repeat:Val":{"node_idx_offset":44904296,"num_nodes":268},"event_interest-not_interested:Test":{"node_idx_offset":44838390,"num_nodes":420},"user-ignore:Val":{"node_idx_offset":44898195,"num_nodes":2013},"users-birthyear:Val":{"node_idx_offset":44939503,"num_nodes":1731},"user-ignore:Test":{"node_idx_offset":44876998,"num_nodes":1958},"event_interest:Db":{"node_idx_offset":11245010,"num_nodes":15398},"user-attendance:Val":{"node_idx_offset":44874985,"num_nodes":2013},"user-attendance:Test":{"node_idx_offset":44853788,"num_nodes":1958},"event_interest-interested:Test":{"node_idx_offset":44822992,"num_nodes":420},"event_interest-not_interested:Val":{"node_idx_offset":44853252,"num_nodes":536},"user-repeat:Train":{"node_idx_offset":44900454,"num_nodes":3842},"user_friends:Db":{"node_idx_offset":14398380,"num_nodes":30386403},"users-birthyear:Test":{"node_idx_offset":44904564,"num_nodes":1002},"users:Db":{"node_idx_offset":44784783,"num_nodes":38209},"users-birthyear:Train":{"node_idx_offset":44905566,"num_nodes":33937},"user-attendance:Train":{"node_idx_offset":44855746,"num_nodes":19239},"user-ignore:Train":{"node_idx_offset":44878956,"num_nodes":19239},"event_interest-not_interested:Train":{"node_idx_offset":44838810,"num_nodes":14442},"event_interest-interested:Train":{"node_idx_offset":44823412,"num_nodes":14442},"event_attendees:Db":{"node_idx_offset":0,"num_nodes":11245010}}

legacy/rel-f1/column_index.json

@@ -1 +1 @@
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+{"constructorRef of constructors":2325,"constructorId of qualifying":2972,"points of standings":2314,"lng of circuits":2966,"time of races":3036,"points of results":12,"position of qualifying-position":26,"constructorStandingsId of constructor_standings":28,"name of races":2980,"driverStandingsId of standings":2311,"qualifyId of qualifying":2969,"location of circuits":2854,"positionOrder of results":11,"name of constructors":2510,"surname of drivers":1480,"grid of results":9,"raceId of constructor_standings":29,"driverId of standings":2313,"points of constructor_standings":31,"circuitId of races":2979,"constructorResultsId of constructor_results":2319,"position of qualifying":2974,"circuitId of circuits":2701,"date of driver-dnf":2698,"date of qualifying":2975,"driverId of driver-circuit-compete":37,"driverRef of drivers":49,"date of constructor_results":2323,"date of standings":2317,"resultId of results":1,"date of driver-position":41,"raceId of results":2,"lat of circuits":2965,"driverId of driver-position":42,"position of results-position":47,"number of results":8,"raceId of standings":2312,"did_not_finish of driver-dnf":2700,"date of qualifying-position":24,"nationality of drivers":2267,"statusId of results":17,"driverId of driver-top3":21,"position of standings":2315,"raceId of constructor_results":2320,"circuitId of driver-circuit-compete":38,"nationality of constructors":2695,"driverId of driver-dnf":2699,"circuitRef of circuits":2702,"name of circuits":2776,"date of races":3035,"position of results":10,"laps of results":13,"resultId of results-position":46,"position of constructor_standings":32,"constructorId of constructors":2324,"qualifying of driver-top3":22,"position of driver-position":43,"date of constructor_standings":34,"dob of drivers":2266,"constructorId of constructor_results":2321,"points of constructor_results":2322,"alt of circuits":2967,"driverId of results":4,"date of driver-top3":20,"raceId of qualifying":2970,"rank of results":16,"date of results":18,"wins of standings":2316,"driverId of qualifying":2971,"forename of drivers":1003,"constructorId of constructor_standings":30,"country of circuits":2929,"year of races":2977,"round of races":2978,"wins of constructor_standings":33,"driverId of drivers":48,"milliseconds of results":14,"constructorId of results":6,"raceId of races":2976,"qualifyId of qualifying-position":25,"fastestLap of results":15,"date of results-position":45,"date of driver-circuit-compete":36,"code of drivers":907,"number of qualifying":2973}

legacy/rel-hm/meta.json

@@ -12,11 +12,12 @@
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   "num_nodes": 45980751,
   "num_task_tables": 12,
-  "num_text_strings": 442784,
-  "source": "stanford-star/relbench/rel-hm",
+  "num_text_strings": 442785,
+  "source": "/dfs/user/ranjanr/share/stanford-star/relbench/rel-hm",
   "tasks": [
     {
       "entity_table": "article",
+      "kind": "forecast",
       "name": "item-sales",
       "splits": [
         "train",
@@ -29,6 +30,7 @@
     },
     {
       "entity_table": "transactions",
+      "kind": "autocomplete",
       "name": "transactions-price",
       "splits": [
         "train",
@@ -41,6 +43,7 @@
     },
     {
       "entity_table": "customer",
+      "kind": "forecast",
       "name": "user-churn",
       "splits": [
         "train",
@@ -53,6 +56,7 @@
     },
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "user-item-purchase",
       "splits": [
         "train",

legacy/rel-hm/table_info.json

@@ -1 +1 @@
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+{"transactions-price:Test":{"node_idx_offset":22630441,"num_nodes":266364},"transactions:Db":{"node_idx_offset":1477522,"num_nodes":15453651},"item-sales:Val":{"node_idx_offset":22524899,"num_nodes":105542},"user-item-purchase:Test":{"node_idx_offset":41960581,"num_nodes":67144},"article:Db":{"node_idx_offset":0,"num_nodes":105542},"transactions-price:Val":{"node_idx_offset":37741096,"num_nodes":235662},"user-churn:Val":{"node_idx_offset":41884025,"num_nodes":76556},"transactions-price:Train":{"node_idx_offset":22896805,"num_nodes":14844291},"user-churn:Train":{"node_idx_offset":38051333,"num_nodes":3832692},"user-churn:Test":{"node_idx_offset":37976758,"num_nodes":74575},"customer:Db":{"node_idx_offset":105542,"num_nodes":1371980},"item-sales:Train":{"node_idx_offset":17036715,"num_nodes":5488184},"item-sales:Test":{"node_idx_offset":16931173,"num_nodes":105542},"user-item-purchase:Val":{"node_idx_offset":45906176,"num_nodes":74575},"user-item-purchase:Train":{"node_idx_offset":42027725,"num_nodes":3878451}}

legacy/rel-stack/column_index.json

@@ -1 +1 @@
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+{"AccountId of users":2,"CreationDate of users":386543,"PostHistoryTypeId of postHistory":1149619,"Comment of postHistory":2992205,"CreationDate of comments":3932258,"UserId of postHistory":1149618,"Class of badges":3141451,"UserId of comments":3141786,"OwnerDisplayName of posts":386549,"WillGetBadge of user-badge":3932262,"CreationDate of postHistory":3141447,"timestamp of user-badge":3932260,"Id of posts":386545,"PostTypeId of posts":386547,"Text of postHistory":2135552,"ContentLicense of comments":3141787,"PostId of postLinks":3932266,"Body of posts":734288,"CreationDate of posts":1149602,"postLinksIdList of post-post-related":1149606,"Id of postHistory":1149616,"timestamp of post-votes":1149612,"PostId of votes":3932272,"VoteTypeId of votes":3932273,"timestamp of user-engagement":1149608,"PostId of post-post-related":1149605,"PostId of post-votes":1149613,"PostId of comments":3141785,"AboutMe of users":329004,"UserId of votes":3932271,"CreationDate of postLinks":3932268,"identifier of votes":3932275,"DisplayName of users":3,"Id of users":1,"WebsiteUrl of users":296702,"Text of comments":3142763,"timestamp of post-post-related":1149604,"ContentLicense of posts":734284,"PostId of user-post-comment":3141783,"Location of users":282677,"Title of posts":388927,"UserId of user-badge":3932261,"CreationDate of votes":3932274,"Id of votes":3932270,"timestamp of user-post-comment":3141781,"PostId of postHistory":1149617,"RelatedPostId of postLinks":3932265,"Id of comments":3141784,"LinkTypeId of postLinks":3932267,"Tags of posts":598501,"OwnerUserId of posts":386546,"RevisionGUID of postHistory":1149655,"UserId of user-post-comment":3141782,"ParentId of posts":386548,"contribution of user-engagement":1149610,"popularity of post-votes":1149614,"Name of badges":3141452,"UserDisplayName of comments":3141788,"UserDisplayName of postHistory":1149620,"UserId of badges":3141450,"Date of badges":3141779,"OwnerUserId of user-engagement":1149609,"Id of badges":3141449,"Id of postLinks":3932264,"TagBased of badges":3141778,"ContentLicense of postHistory":1149654}

legacy/rel-stack/meta.json

@@ -12,11 +12,12 @@
   "num_db_tables": 7,
   "num_nodes": 13623878,
   "num_task_tables": 15,
-  "num_text_strings": 3932275,
-  "source": "stanford-star/relbench/rel-stack",
+  "num_text_strings": 3932276,
+  "source": "/dfs/user/ranjanr/share/stanford-star/relbench/rel-stack",
   "tasks": [
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "post-post-related",
       "splits": [
         "train",
@@ -29,6 +30,7 @@
     },
     {
       "entity_table": "posts",
+      "kind": "forecast",
       "name": "post-votes",
       "splits": [
         "train",
@@ -41,6 +43,7 @@
     },
     {
       "entity_table": "users",
+      "kind": "forecast",
       "name": "user-badge",
       "splits": [
         "train",
@@ -53,6 +56,7 @@
     },
     {
       "entity_table": "users",
+      "kind": "forecast",
       "name": "user-engagement",
       "splits": [
         "train",
@@ -65,6 +69,7 @@
     },
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "user-post-comment",
       "splits": [
         "train",

legacy/rel-trial/meta.json

@@ -12,11 +12,12 @@
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   "num_nodes": 7933813,
   "num_task_tables": 27,
-  "num_text_strings": 3194381,
-  "source": "stanford-star/relbench/rel-trial",
+  "num_text_strings": 3194386,
+  "source": "/dfs/user/ranjanr/share/stanford-star/relbench/rel-trial",
   "tasks": [
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "condition-sponsor-run",
       "splits": [
         "train",
@@ -29,7 +30,38 @@
     },
     {
       "entity_table": "eligibilities",
+      "kind": "autocomplete",
       "name": "eligibilities-adult",
+      "remove_columns": [
+        [
+          "eligibilities",
+          "child"
+        ],
+        [
+          "eligibilities",
+          "older_adult"
+        ],
+        [
+          "eligibilities",
+          "minimum_age"
+        ],
+        [
+          "eligibilities",
+          "maximum_age"
+        ],
+        [
+          "eligibilities",
+          "population"
+        ],
+        [
+          "eligibilities",
+          "criteria"
+        ],
+        [
+          "eligibilities",
+          "gender_description"
+        ]
+      ],
       "splits": [
         "train",
         "val",
@@ -41,7 +73,38 @@
     },
     {
       "entity_table": "eligibilities",
+      "kind": "autocomplete",
       "name": "eligibilities-child",
+      "remove_columns": [
+        [
+          "eligibilities",
+          "adult"
+        ],
+        [
+          "eligibilities",
+          "older_adult"
+        ],
+        [
+          "eligibilities",
+          "minimum_age"
+        ],
+        [
+          "eligibilities",
+          "maximum_age"
+        ],
+        [
+          "eligibilities",
+          "population"
+        ],
+        [
+          "eligibilities",
+          "criteria"
+        ],
+        [
+          "eligibilities",
+          "gender_description"
+        ]
+      ],
       "splits": [
         "train",
         "val",
@@ -53,6 +116,7 @@
     },
     {
       "entity_table": null,
+      "kind": "forecast",
       "name": "site-sponsor-run",
       "splits": [
         "train",
@@ -65,6 +129,7 @@
     },
     {
       "entity_table": "facilities",
+      "kind": "forecast",
       "name": "site-success",
       "splits": [
         "train",
@@ -77,6 +142,7 @@
     },
     {
       "entity_table": "studies",
+      "kind": "autocomplete",
       "name": "studies-enrollment",
       "splits": [
         "train",
@@ -89,6 +155,7 @@
     },
     {
       "entity_table": "studies",
+      "kind": "autocomplete",
       "name": "studies-has_dmc",
       "splits": [
         "train",
@@ -101,6 +168,7 @@
     },
     {
       "entity_table": "studies",
+      "kind": "forecast",
       "name": "study-adverse",
       "splits": [
         "train",
@@ -113,6 +181,7 @@
     },
     {
       "entity_table": "studies",
+      "kind": "forecast",
       "name": "study-outcome",
       "splits": [
         "train",

legacy/rel-trial/table_info.json

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