Interaction with saved models

This is the main class that you will use in Python recipes and the iPython notebook.

For starting code samples, please see Python recipes.

As an example, see the following example of code to retrieve the associated scikit model with your DSS:

import dataiku

m = dataiku.Model(my_model_id)
my_predictor = m.get_predictor()

my_clf = my_predictor._clf
class dataiku.Model(lookup, project_key=None, ignore_flow=False)

This is a handle to interact with a saved model

static list_models(project_key=None)

Retrieve the list of saved models

Parameters

project_key – key of the project from which to list models

get_info()
get_id()

Get the unique identifier of the model

get_name()

Get the name of the model

get_type()

Get the type of the model, prediction or clustering

get_definition()
list_versions()

List the versions this saved model contains

activate_version(version_id)

Activate a version in the model

Parameters

version_id – the unique identifier of the version to activate

get_version_metrics(version_id)

Get the training metrics of a version of this model, as a SavedModelVersionMetrics

Parameters

version_id – the unique identifier of the version for which to retrieve metrics

save_external_check_values(values_dict, version_id)

Save checks on this model. The checks are saved with the type “external”

Parameters

values_dict – the values to save, as a dict. The keys of the dict are used as check names

get_predictor(version_id=None)

Returns a Predictor for the given version of this Saved Model. If no version is specified, the current active version will be used.