A rating table is an exportable csv representation of a Generalized Additive Model. They contain information about the features and coefficients used to make predictions. Users can influence predictions by downloading and editing values in a rating table, then reuploading the table and using it to create a new model.
See the page about interpreting Generalized Additive Models’ output in the Datarobot user guide for more details on how to interpret and edit rating tables.
Download A Rating Table¶
You can retrieve a rating table from the list of rating tables in a project:
import datarobot as dr project_id = '5506fcd38bd88f5953219da0' project = dr.Project.get(project_id) rating_tables = project.get_rating_tables() rating_table = rating_tables
Or you can retrieve a rating table from a specific model. The model must already exist:
import datarobot as dr from datarobot.models import RatingTableModel, RatingTable project_id = '5506fcd38bd88f5953219da0' project = dr.Project.get(project_id) # Get model from list of models with a rating table rating_table_models = project.get_rating_table_models() rating_table_model = rating_table_models # Or retrieve model by id. The model must have a rating table. model_id = '5506fcd98bd88f1641a720a3' rating_table_model = dr.RatingTableModel.get(project=project_id, model_id=model_id) # Then retrieve the rating table from the model rating_table_id = rating_table_model.rating_table_id rating_table = dr.RatingTable.get(projcet_id, rating_table_id)
Then you can download the contents of the rating table:
Uploading A Rating Table¶
After you’ve retrieved the rating table CSV and made the necessary edits, you can re-upload the CSV so you can create a new model from it:
job = dr.RatingTable.create(project_id, model_id, './my_rating_table.csv') new_rating_table = job.get_result_when_complete() job = new_rating_table.create_model() model = job.get_result_when_complete()