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legacy/_transformed/rel-amazon/README.md

@@ -0,0 +1,41 @@
+# 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 | recommendation | Predict the list of distinct items each customer will purchase in the next two years. |
+| `user-item-rate` | forecast | recommendation | 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 | recommendation | 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-amazon/manifest.yaml

@@ -0,0 +1,20 @@
+name: rel-amazon
+manifest_version: 1
+description: 'Amazon product reviews: customers, products, and time-stamped reviews and ratings across the Amazon catalog.'
+val_timestamp: '2015-10-01'
+test_timestamp: '2016-01-01'
+tables:
+  review:
+    pkey: null
+    time_col: review_time
+    fkeys:
+      customer_id: customer
+      product_id: product
+  product:
+    pkey: product_id
+    time_col: null
+    fkeys: {}
+  customer:
+    pkey: customer_id
+    time_col: null
+    fkeys: {}

legacy/_transformed/rel-amazon/tasks/item-churn/manifest.yaml

@@ -0,0 +1,36 @@
+name: item-churn
+kind: forecast
+task_type: binary_classification
+description: Churn for a product is 1 if the product recieves at least one review in the time window, else 0.
+entity_table: product
+entity_col: product_id
+target_col: churn
+time_col: timestamp
+timedelta: 91 days
+sql: |-
+  SELECT
+      timestamp,
+      product_id,
+      CAST(
+          NOT EXISTS (
+              SELECT 1
+              FROM review
+              WHERE
+                  review.product_id = product.product_id AND
+                  review_time > timestamp AND
+                  review_time <= timestamp + INTERVAL '{timedelta}'
+          ) AS INTEGER
+      ) AS churn
+  FROM
+      timestamps,
+      product,
+  WHERE
+      EXISTS (
+          SELECT 1
+          FROM review
+          WHERE
+              review.product_id = product.product_id AND
+              review_time > timestamp - INTERVAL '{timedelta}' AND
+              review_time <= timestamp
+      )
+manifest_version: 1

legacy/_transformed/rel-amazon/tasks/item-ltv/manifest.yaml

@@ -0,0 +1,26 @@
+name: item-ltv
+kind: forecast
+task_type: regression
+description: LTV (life-time value) for a product is the numer of times the product is purchased in the time window multiplied by price.
+entity_table: product
+entity_col: product_id
+target_col: ltv
+time_col: timestamp
+timedelta: 91 days
+sql: |-
+  SELECT
+      timestamp,
+      product.product_id,
+      COALESCE(SUM(price), 0) AS ltv,
+  FROM
+      timestamps,
+      product,
+      review
+  WHERE
+      review.product_id = product.product_id AND
+      review_time > timestamp AND
+      review_time <= timestamp + INTERVAL '{timedelta}'
+  GROUP BY
+      timestamp,
+      product.product_id
+manifest_version: 1

legacy/_transformed/rel-amazon/tasks/review-rating/manifest.yaml

@@ -0,0 +1,12 @@
+name: review-rating
+kind: autocomplete
+task_type: regression
+description: Predict the `rating` column of the `review` table.
+entity_table: review
+target_col: rating
+remove_columns:
+- - review
+  - review_text
+- - review
+  - summary
+manifest_version: 1

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

@@ -0,0 +1,36 @@
+name: user-churn
+kind: forecast
+task_type: binary_classification
+description: Churn for a customer is 1 if the customer does not review any product in the time window, else 0.
+entity_table: customer
+entity_col: customer_id
+target_col: churn
+time_col: timestamp
+timedelta: 91 days
+sql: |-
+  SELECT
+      timestamp,
+      customer_id,
+      CAST(
+          NOT EXISTS (
+              SELECT 1
+              FROM review
+              WHERE
+                  review.customer_id = customer.customer_id AND
+                  review_time > timestamp AND
+                  review_time <= timestamp + INTERVAL '{timedelta}'
+          ) AS INTEGER
+      ) AS churn
+  FROM
+      timestamps,
+      customer,
+  WHERE
+      EXISTS (
+          SELECT 1
+          FROM review
+          WHERE
+              review.customer_id = customer.customer_id AND
+              review_time > timestamp - INTERVAL '{timedelta}' AND
+              review_time <= timestamp
+      )
+manifest_version: 1

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

@@ -0,0 +1,30 @@
+name: user-item-purchase
+kind: forecast
+task_type: recommendation
+description: Predict the list of distinct items each customer will purchase in the next two years.
+target_col: product_id
+time_col: timestamp
+src_entity_table: customer
+src_entity_col: customer_id
+dst_entity_table: product
+dst_entity_col: product_id
+eval_k: 10
+timedelta: 91 days
+sql: |-
+  SELECT
+      t.timestamp,
+      review.customer_id,
+      LIST(DISTINCT review.product_id) AS product_id
+  FROM
+      timestamps t
+  LEFT JOIN
+      review
+  ON
+      review.review_time > t.timestamp AND
+      review.review_time <= t.timestamp + INTERVAL '{timedelta}'
+  WHERE
+      review.customer_id is not null and review.product_id is not null
+  GROUP BY
+      t.timestamp,
+      review.customer_id
+manifest_version: 1

legacy/_transformed/rel-amazon/tasks/user-item-rate/manifest.yaml

@@ -0,0 +1,32 @@
+name: user-item-rate
+kind: forecast
+task_type: recommendation
+description: Predict the list of distinct items each customer will purchase and give a 5 star review in the next two years.
+target_col: product_id
+time_col: timestamp
+src_entity_table: customer
+src_entity_col: customer_id
+dst_entity_table: product
+dst_entity_col: product_id
+eval_k: 10
+timedelta: 91 days
+sql: |-
+  SELECT
+      t.timestamp,
+      review.customer_id,
+      LIST(DISTINCT review.product_id) AS product_id
+  FROM
+      timestamps t
+  LEFT JOIN
+      review
+  ON
+      review.review_time > t.timestamp AND
+      review.review_time <= t.timestamp + INTERVAL '{timedelta}'
+  WHERE
+      review.customer_id IS NOT NULL
+      AND review.product_id IS NOT NULL
+      AND review.rating = 5.0
+  GROUP BY
+      t.timestamp,
+      review.customer_id
+manifest_version: 1

legacy/_transformed/rel-amazon/tasks/user-item-review/manifest.yaml

@@ -0,0 +1,32 @@
+name: user-item-review
+kind: forecast
+task_type: recommendation
+description: Predict the list of distinct items each customer will purchase and give a detailed review in the next two years.
+target_col: product_id
+time_col: timestamp
+src_entity_table: customer
+src_entity_col: customer_id
+dst_entity_table: product
+dst_entity_col: product_id
+eval_k: 10
+timedelta: 91 days
+sql: |-
+  SELECT
+      t.timestamp,
+      review.customer_id,
+      LIST(DISTINCT review.product_id) AS product_id
+  FROM
+      timestamps t
+  LEFT JOIN
+      review
+  ON
+      review.review_time > t.timestamp AND
+      review.review_time <= t.timestamp + INTERVAL '{timedelta}'
+  WHERE
+      review.customer_id IS NOT NULL
+      AND review.product_id IS NOT NULL
+      AND (LENGTH(review.review_text) > 300 AND review.review_text IS NOT NULL)
+  GROUP BY
+      t.timestamp,
+      review.customer_id
+manifest_version: 1

legacy/_transformed/rel-amazon/tasks/user-ltv/manifest.yaml

@@ -0,0 +1,39 @@
+name: user-ltv
+kind: forecast
+task_type: regression
+description: LTV (life-time value) for a customer is the sum of prices of products that the customer reviews in the time window.
+entity_table: customer
+entity_col: customer_id
+target_col: ltv
+time_col: timestamp
+timedelta: 91 days
+sql: |-
+  SELECT
+      timestamp,
+      customer_id,
+      ltv,
+  FROM
+      timestamps,
+      customer,
+      (
+          SELECT
+              COALESCE(SUM(price), 0) as ltv,
+          FROM
+              review,
+              product
+          WHERE
+              review.customer_id = customer.customer_id AND
+              review.product_id = product.product_id AND
+              review_time > timestamp AND
+              review_time <= timestamp + INTERVAL '{timedelta}'
+      )
+  WHERE
+      EXISTS (
+          SELECT 1
+          FROM review
+          WHERE
+              review.customer_id = customer.customer_id AND
+              review_time > timestamp - INTERVAL '{timedelta}' AND
+              review_time <= timestamp
+      )
+manifest_version: 1

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 | recommendation | 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/db/Category.parquet

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+oid sha256:e0a1cb03abae73a2b2bd3d26dd1906413b4df943131f17158df65a01b7aca69f
+size 2044

legacy/_transformed/rel-avito/db/PhoneRequestsStream.parquet

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legacy/_transformed/rel-avito/db/SearchInfo.parquet

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+size 28728539

legacy/_transformed/rel-avito/db/VisitStream.parquet

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+size 62197827

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/ad-ctr/val.parquet

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+size 11108

legacy/_transformed/rel-avito/tasks/searchinfo-isuserloggedon/train.parquet

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+size 6312728

legacy/_transformed/rel-avito/tasks/searchstream-click/test.parquet

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+size 5879378

legacy/_transformed/rel-avito/tasks/user-clicks/train.parquet

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+size 168265

legacy/_transformed/rel-avito/tasks/user-visits/train.parquet

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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 | recommendation | 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/db/customer.parquet

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+oid sha256:ca2224c83eefea62147c97037833741cfb5e5c10da656534e34be03b3ef3e820
+size 45399774

legacy/_transformed/rel-hm/manifest.yaml

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+name: rel-hm
+manifest_version: 1
+description: 'H&M e-commerce: customers, articles, and time-stamped purchase transactions.'
+val_timestamp: '2020-09-07'
+test_timestamp: '2020-09-14'
+tables:
+  transactions:
+    pkey: null
+    time_col: t_dat
+    fkeys:
+      customer_id: customer
+      article_id: article
+  article:
+    pkey: article_id
+    time_col: null
+    fkeys: {}
+  customer:
+    pkey: customer_id
+    time_col: null
+    fkeys: {}

legacy/_transformed/rel-hm/tasks/user-item-purchase/train.parquet

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+size 40810052

legacy/_transformed/rel-stack/README.md

@@ -0,0 +1,38 @@
+# rel-stack
+
+Stack Exchange Q&A: users, posts, comments, votes, badges, and post links.
+
+## Schema
+
+![schema diagram](schema.svg)
+
+## Tasks
+
+| task | kind | type | description |
+|---|---|---|---|
+| `post-post-related` | forecast | recommendation | Predict a list of existing posts that users will link a given post to in the next two years. |
+| `post-votes` | forecast | regression | Predict the number of upvotes that an existing question will receive in the next 2 years. |
+| `user-badge` | forecast | binary_classification | Predict if each user will receive in a new badge the next 2 years. |
+| `user-engagement` | forecast | binary_classification | Predict if a user will make any votes/posts/comments in the next 2 years. |
+| `user-post-comment` | forecast | recommendation | Predict a list of existing posts that a user will comment in the next two years. |
+
+## Loading
+
+```python
+import relbench
+ds = relbench.load_dataset("relbench/v1/rel-stack")
+task = relbench.load_task("relbench/v1/rel-stack", "<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}
+}
+```

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legacy/_transformed/rel-stack/manifest.yaml

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+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

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legacy/_transformed/rel-stack/tasks/user-badge/val.parquet

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legacy/_transformed/rel-stack/tasks/user-post-comment/val.parquet

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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 | recommendation | 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 | recommendation | 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/db/outcome_analyses.parquet

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legacy/_transformed/rel-trial/manifest.yaml

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+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/eligibilities-child/manifest.yaml

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+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: recommendation
+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/train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3b7613be052e3b0585871275dc3a3e800e021a3c9c05d1badab0d137fd540867
+size 556198

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/rel-amazon/column_index.json

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legacy/rel-amazon/meta.json

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   "name": "rel-amazon",
   "num_db_tables": 3,
-  "num_nodes": 73583121,
-  "num_task_tables": 24,
-  "num_text_strings": 31580670,
+  "num_nodes": 60938584,
+  "num_task_tables": 15,
+  "num_text_strings": 31580658,
   "source": "/dfs/user/ranjanr/share/stanford-star/relbench/rel-amazon",
   "tasks": [
     {
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       "task_type": "binary_classification",
       "time_col": "timestamp"
     },
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-      "time_col": "timestamp"
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-      "time_col": "timestamp"
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     {
       "entity_table": "customer",
       "kind": "forecast",

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