Refresh ER schema diagrams (1-7 of 7)

dbinfer-amazon/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: dbinfer-amazon
----
-
 # dbinfer-amazon
 
 Amazon from the 4DBInfer benchmark: a large product-review dataset linking users, products and reviews, used for rating prediction and user purchase/churn prediction.
@@ -13,10 +6,6 @@ Amazon from the 4DBInfer benchmark: a large product-review dataset linking users
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2016-01-03 00:00:00`, test `2016-01-04 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -29,8 +18,6 @@ Splits: validation `2016-01-03 00:00:00`, test `2016-01-04 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("dbinfer-amazon")
-task = relbench.load_task("dbinfer-amazon", "<task>")
+ds = relbench.load_dataset("relbench/dbinfer/dbinfer-amazon")
+task = relbench.load_task("relbench/dbinfer/dbinfer-amazon", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

dbinfer-avs/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: dbinfer-avs
----
-
 # dbinfer-avs
 
 Acquire Valued Shoppers (AVS) from the 4DBInfer benchmark: a retail dataset of customer transaction histories and promotional offers, used to predict shopper behavior such as offer repeat purchases.
@@ -13,10 +6,6 @@ Acquire Valued Shoppers (AVS) from the 4DBInfer benchmark: a retail dataset of c
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2013-07-30 00:00:00`, test `2013-07-31 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2013-07-30 00:00:00`, test `2013-07-31 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("dbinfer-avs")
-task = relbench.load_task("dbinfer-avs", "<task>")
+ds = relbench.load_dataset("relbench/dbinfer/dbinfer-avs")
+task = relbench.load_task("relbench/dbinfer/dbinfer-avs", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

dbinfer-diginetica/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: dbinfer-diginetica
----
-
 # dbinfer-diginetica
 
 Diginetica from the 4DBInfer benchmark: an e-commerce dataset of user browsing and purchasing sessions over a product catalog (CIKM Cup 2016), used for click-through-rate and purchase prediction.
@@ -13,10 +6,6 @@ Diginetica from the 4DBInfer benchmark: an e-commerce dataset of user browsing a
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2016-11-11 00:00:00`, test `2016-11-12 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2016-11-11 00:00:00`, test `2016-11-12 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("dbinfer-diginetica")
-task = relbench.load_task("dbinfer-diginetica", "<task>")
+ds = relbench.load_dataset("relbench/dbinfer/dbinfer-diginetica")
+task = relbench.load_task("relbench/dbinfer/dbinfer-diginetica", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

dbinfer-outbrain-small/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: dbinfer-outbrain-small
----
-
 # dbinfer-outbrain-small
 
 Outbrain (small) from the 4DBInfer benchmark: a content-recommendation dataset of document page views and promoted-content displays/clicks, used for click-through-rate prediction.
@@ -13,10 +6,6 @@ Outbrain (small) from the 4DBInfer benchmark: a content-recommendation dataset o
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2016-09-03 00:00:00`, test `2016-09-04 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2016-09-03 00:00:00`, test `2016-09-04 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("dbinfer-outbrain-small")
-task = relbench.load_task("dbinfer-outbrain-small", "<task>")
+ds = relbench.load_dataset("relbench/dbinfer/dbinfer-outbrain-small")
+task = relbench.load_task("relbench/dbinfer/dbinfer-outbrain-small", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

dbinfer-retailrocket/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: dbinfer-retailrocket
----
-
 # dbinfer-retailrocket
 
 RetailRocket from the 4DBInfer benchmark: an e-commerce dataset of visitor events (views, add-to-cart, transactions) over an item catalog, used to predict conversion (whether a viewed item is later purchased).
@@ -13,10 +6,6 @@ RetailRocket from the 4DBInfer benchmark: an e-commerce dataset of visitor event
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2015-09-20 00:00:00`, test `2015-09-21 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2015-09-20 00:00:00`, test `2015-09-21 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("dbinfer-retailrocket")
-task = relbench.load_task("dbinfer-retailrocket", "<task>")
+ds = relbench.load_dataset("relbench/dbinfer/dbinfer-retailrocket")
+task = relbench.load_task("relbench/dbinfer/dbinfer-retailrocket", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

dbinfer-seznam/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: dbinfer-seznam
----
-
 # dbinfer-seznam
 
 Seznam from the 4DBInfer benchmark: a digital-advertising dataset from the Seznam.cz search engine, containing client prepaid-account charges and transactions, used to predict account charging/prepayment behavior.
@@ -13,10 +6,6 @@ Seznam from the 4DBInfer benchmark: a digital-advertising dataset from the Sezna
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2015-10-03 00:00:00`, test `2015-10-04 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2015-10-03 00:00:00`, test `2015-10-04 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("dbinfer-seznam")
-task = relbench.load_task("dbinfer-seznam", "<task>")
+ds = relbench.load_dataset("relbench/dbinfer/dbinfer-seznam")
+task = relbench.load_task("relbench/dbinfer/dbinfer-seznam", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

dbinfer-stackexchange/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: dbinfer-stackexchange
----
-
 # dbinfer-stackexchange
 
 StackExchange from the 4DBInfer benchmark: a community-Q&A dataset of users, posts, votes and badges, used to predict user churn and post upvotes.
@@ -13,10 +6,6 @@ StackExchange from the 4DBInfer benchmark: a community-Q&A dataset of users, pos
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2023-09-05 00:00:00`, test `2023-09-06 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2023-09-05 00:00:00`, test `2023-09-06 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("dbinfer-stackexchange")
-task = relbench.load_task("dbinfer-stackexchange", "<task>")
+ds = relbench.load_dataset("relbench/dbinfer/dbinfer-stackexchange")
+task = relbench.load_task("relbench/dbinfer/dbinfer-stackexchange", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).