Bryan Chen
Bryan Chen
Some data checks, like the ID data check, has messages like [this](https://github.com/alteryx/evalml/blob/main/evalml/tests/data_checks_tests/test_id_columns_data_check.py#L72). We should change this so that if the message is `100%`, we say `is 100% likely` rather than...
In conjunction with work being done with Innovation Days
testing min_dependencies of min dependencies
This issue was brought up [here](https://alteryx.atlassian.net/wiki/spaces/PS/pages/643268717/Handle+Unknown+types+from+Woodwork+in+EvalML?focusedCommentId=653230126#comment-653230126) as we integrate the new WW update into EvalML. Primarily, we want to raise a datacheck warning/error when the dataset a user passed in...
Preview of the error: ![image](https://user-images.githubusercontent.com/22552445/132767824-2af2d1c6-46ef-49f7-ab06-0e3392615ce0.png) We've noticed this for several of our perf testing datasets. One in particular was `regress.csv` Cloudwatch link [here](https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#logsV2:log-groups/log-group/$252Fecs$252Fevalml-test/log-events/ecs$252Fprod-evalml$252F9da7d5e7ccc44ca6a2fdd9c3943d1997) The warning being printed: ``` 2021-09-09T17:33:44.934-04:00 |...
Related to [this discussion](https://github.com/alteryx/evalml/pull/3373#discussion_r832473688). With [this PR](https://github.com/alteryx/evalml/pull/3373) we have consolidated most of the pipeline parameter-creating logic into the `AutoMLAlgorithm` class. However, there are still cases, like with the sampler parameters,...
Choose a different pivot point for 2-digit year rather than the default that pandas datetime uses. Chose 2030 as the pivot date, but can easily be changed to another
Implement the Chatterjee correlation on WW. This does not include work to add it to the dependence dictionaries that [this PR](https://github.com/alteryx/woodwork/pull/1265) has, but does include testing to ensure it works...
Add Chatterjee to the [dependence dictionaries](https://github.com/alteryx/woodwork/pull/1265) and update our docs to include example usage and explanation
Extension of issue [470](https://github.com/alteryx/evalml/issues/470). PR [1454](https://github.com/alteryx/evalml/pull/1454) addresses adding the FeatureTools component, but only handles single dataframes/datatables. In order to use FeatureTools fully, we want to be able to use it...