drop legacy/_transformed: scratch from the RT-v1 transform, not an artifact

legacy/_transformed/rel-amazon/README.md

@@ -1,41 +0,0 @@
-# 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-amazon/db/customer.parquet

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legacy/_transformed/rel-amazon/db/product.parquet

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legacy/_transformed/rel-amazon/db/review.parquet

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legacy/_transformed/rel-amazon/manifest.yaml

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-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

@@ -1,11 +0,0 @@
-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: "\n            SELECT\n                timestamp,\n                product_id,\n                CAST(\n                    NOT EXISTS (\n                        SELECT 1\n                        FROM review\n                        WHERE\n                            review.product_id = product.product_id AND\n                            review_time > timestamp AND\n                            review_time <= timestamp + INTERVAL '{timedelta}'\n                    ) AS INTEGER\n                ) AS churn\n            FROM\n                timestamps,\n                product,\n            WHERE\n                EXISTS (\n                    SELECT 1\n                    FROM review\n                    WHERE\n                        review.product_id = product.product_id AND\n                        review_time > timestamp - INTERVAL '{timedelta}' AND\n                        review_time <= timestamp\n                )\n            "
-manifest_version: 1

legacy/_transformed/rel-amazon/tasks/item-churn/test.parquet

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legacy/_transformed/rel-amazon/tasks/item-churn/train.parquet

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

@@ -1,11 +0,0 @@
-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: "\n            SELECT\n                timestamp,\n                product.product_id,\n                COALESCE(SUM(price), 0) AS ltv,\n            FROM\n                timestamps,\n                product,\n                review\n            WHERE\n                review.product_id = product.product_id AND\n                review_time > timestamp AND\n                review_time <= timestamp + INTERVAL '{timedelta}'\n            GROUP BY\n                timestamp,\n                product.product_id\n            "
-manifest_version: 1

legacy/_transformed/rel-amazon/tasks/item-ltv/test.parquet

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

@@ -1,12 +0,0 @@
-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/review-rating/test.parquet

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

@@ -1,11 +0,0 @@
-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: "\n            SELECT\n                timestamp,\n                customer_id,\n                CAST(\n                    NOT EXISTS (\n                        SELECT 1\n                        FROM review\n                        WHERE\n                            review.customer_id = customer.customer_id AND\n                            review_time > timestamp AND\n                            review_time <= timestamp + INTERVAL '{timedelta}'\n                    ) AS INTEGER\n                ) AS churn\n            FROM\n                timestamps,\n                customer,\n            WHERE\n                EXISTS (\n                    SELECT 1\n                    FROM review\n                    WHERE\n                        review.customer_id = customer.customer_id AND\n                        review_time > timestamp - INTERVAL '{timedelta}' AND\n                        review_time <= timestamp\n                )\n            "
-manifest_version: 1

legacy/_transformed/rel-amazon/tasks/user-churn/test.parquet

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

@@ -1,14 +0,0 @@
-name: user-item-purchase
-kind: forecast
-task_type: link_prediction
-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: "\n            SELECT\n                t.timestamp,\n                review.customer_id,\n                LIST(DISTINCT review.product_id) AS product_id\n            FROM\n                timestamps t\n            LEFT JOIN\n                review\n            ON\n                review.review_time > t.timestamp AND\n                review.review_time <= t.timestamp + INTERVAL '{timedelta}'\n            WHERE\n                review.customer_id is not null and review.product_id is not null\n            GROUP BY\n                t.timestamp,\n                review.customer_id\n            "
-manifest_version: 1

legacy/_transformed/rel-amazon/tasks/user-item-purchase/test.parquet

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

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legacy/_transformed/rel-amazon/tasks/user-item-purchase/val.parquet

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

@@ -1,14 +0,0 @@
-name: user-item-rate
-kind: forecast
-task_type: link_prediction
-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: "\n                SELECT\n                    t.timestamp,\n                    review.customer_id,\n                    LIST(DISTINCT review.product_id) AS product_id\n                FROM\n                    timestamps t\n                LEFT JOIN\n                    review\n                ON\n                    review.review_time > t.timestamp AND\n                    review.review_time <= t.timestamp + INTERVAL '{timedelta}'\n                WHERE\n                    review.customer_id IS NOT NULL\n                    AND review.product_id IS NOT NULL\n                    AND review.rating = 5.0\n                GROUP BY\n                    t.timestamp,\n                    review.customer_id\n            "
-manifest_version: 1

legacy/_transformed/rel-amazon/tasks/user-item-rate/test.parquet

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-size 2077389

legacy/_transformed/rel-amazon/tasks/user-item-rate/train.parquet

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legacy/_transformed/rel-amazon/tasks/user-item-rate/val.parquet

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-size 1809279

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

@@ -1,14 +0,0 @@
-name: user-item-review
-kind: forecast
-task_type: link_prediction
-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: "\n                SELECT\n                    t.timestamp,\n                    review.customer_id,\n                    LIST(DISTINCT review.product_id) AS product_id\n                FROM\n                    timestamps t\n                LEFT JOIN\n                    review\n                ON\n                    review.review_time > t.timestamp AND\n                    review.review_time <= t.timestamp + INTERVAL '{timedelta}'\n                WHERE\n                    review.customer_id IS NOT NULL\n                    AND review.product_id IS NOT NULL\n                    AND (LENGTH(review.review_text) > 300 AND review.review_text IS NOT NULL)\n                GROUP BY\n                    t.timestamp,\n                    review.customer_id\n            "
-manifest_version: 1

legacy/_transformed/rel-amazon/tasks/user-item-review/test.parquet

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legacy/_transformed/rel-amazon/tasks/user-item-review/train.parquet

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legacy/_transformed/rel-amazon/tasks/user-item-review/val.parquet

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

@@ -1,11 +0,0 @@
-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: "\n            SELECT\n                timestamp,\n                customer_id,\n                ltv,\n            FROM\n                timestamps,\n                customer,\n                (\n                    SELECT\n                        COALESCE(SUM(price), 0) as ltv,\n                    FROM\n                        review,\n                        product\n                    WHERE\n                        review.customer_id = customer.customer_id AND\n                        review.product_id = product.product_id AND\n                        review_time > timestamp AND\n                        review_time <= timestamp + INTERVAL '{timedelta}'\n                )\n            WHERE\n                EXISTS (\n                    SELECT 1\n                    FROM review\n                    WHERE\n                        review.customer_id = customer.customer_id AND\n                        review_time > timestamp - INTERVAL '{timedelta}' AND\n                        review_time <= timestamp\n                )\n            "
-manifest_version: 1

legacy/_transformed/rel-amazon/tasks/user-ltv/test.parquet

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legacy/_transformed/rel-amazon/tasks/user-ltv/train.parquet

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legacy/_transformed/rel-avito/README.md

@@ -1,39 +0,0 @@
-# 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}
-}
-```

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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/manifest.yaml

@@ -1,11 +0,0 @@
-name: ad-ctr
-kind: forecast
-task_type: regression
-description: Assuming the ad will be clicked in the next 4 days, predict the Click-Through- Rate (CTR) for each ad.
-entity_table: AdsInfo
-entity_col: AdID
-target_col: num_click
-time_col: timestamp
-timedelta: 4 days
-sql: "\n            SELECT\n                search_ads.AdID,\n                t.timestamp,\n                COALESCE(SUM(search_ads.isClick), 0) / COALESCE(COUNT(search_ads.SearchID), 1) AS num_click\n            FROM\n                timestamps t\n            LEFT JOIN (\n                AdsInfo\n                    LEFT JOIN\n                SearchStream\n                    ON\n                AdsInfo.AdID == SearchStream.AdID\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.AdID\n            HAVING\n                SUM(search_ads.isClick) > 0\n            "
-manifest_version: 1