Please find below a piece of code to retrieve variable importance raw data for a trained model in an Analysis:
import pandas as pd
client = dataiku.api_client()
project = client.get_project(dataiku.default_project_key())
analysis = project.get_analysis("rGRd5qWg")
ml_task = analysis.get_ml_task("fm97lWNq")
trained_model_ids = ml_task.get_trained_models_ids()
trained_model_detail = ml_task.get_trained_model_details(trained_model_ids)
feature_importance = trained_model_detail.get_raw().get("iperf").get("rawImportance")
feature_importance_df = pd.DataFrame(feature_importance)
Note that you can get the analysis_id, ml_task_id and trained_model_id from the URL of the page of the model. Or you can get that using our API: https://doc.dataiku.com/dss/latest/python-api/rest-api-client/ml.html#exploration-of-results
Hope it helps,
This is really awesome.
I saw you referred to the "get_raw()" method. I didn't saw any reference for it in the docs. Is it?
How, for example, i can get regression coefficients in a similar approach?