I have created a feature table (Databricks runtime ML 10.2) that includes a timestamp column as a primary key, that is not used as a feature but as a column to join on.

I have then created a model that trains from this feature table and some additional data, which excludes the primary keys. I tried excluding them, both using the feature store api, and from the sklearn api. The model is being trained fine, but when use the score_batch() method, I get an error claiming that 'TypeError: float() argument must be a string or a number, not 'Timestamp''.

This error is coming from sklearn, so is there some incompatibility there, or is this a bug in feature store?

Steps to reproduce :

• create feature table with one column as timestamp type
• train a model using sklearn that does not use that timestamp column
• use score_batch() method and visualize results