日本語
Hi @redenton,
I can maybe get you started on how to do things in python, it's not very complex (but a little complex). We also have the same functionality in R, if you are more familiar with that.
I will stick to python for now, since I know it well.
The first thing you need is python and the datarobot library installed (pip install datarobot)
Then you will need an API token. You can create one via the menu top right corner -> Dev tools
And here create a new key
Next, let's assume you have a project created already, with an existing eureqa model.
If we navigate to that model, we can find a project_id and a model_id in the URL (the long strings)
Now we're ready to start with our python script.
import datarobot as dr
# Connect to datarobot with your token
client = dr.Client(endpoint='https://app2.datarobot.com/api/v2', token='XYZ')
# Find the right project
project = dr.Project.get('6156fdd3000984791f39660c')
# and then the Eureqa model
eureqa_model = dr.Model.get(project.id, '6157042bbdcc650eb28a82d8')
# Now we can start the tuning session
tuning_session = eureqa_model.start_advanced_tuning_session()
A tuning session allows us to modify any parameter from any task (where 'task' is something like a preprocessing step, or a model, and parameter is then one of the hyperparameters we are interested in)
For the model I selected I see these tasks:
And when I ask for the parameters specific to the Eureqa model, I get a long list of parameters, exactly what we're after.
So, let's say we want to enable a few of the building blocks, we can do something like this
# get all the parameters for all the taskes
params = tuning_session.get_parameters()
# Predefine a few tuning settings we are interested in changing
settings = {
'building_block__less-than':4,
'building_block__round':3,
'building_block__sine':5,
'building_block__square_root':2
}
# now select those tasks we are interested in
params_to_tune = [p for p in params['tuning_parameters'] if
p['task_name'] == 'Eureqa Classifier (Quick Search: 250 Generations)'
and p['parameter_name'] in settings]
These params to tune now have this form, but we can ignore most of this noise, all we need is the names and the ID
So, to tune the model, all that's left is this:
for p in params_to_tune:
task_name = p['task_name']
parameter_name = p['parameter_name']
parameter_id = p['parameter_id']
value = settings[parameter_name]
tuning_session.set_parameter(value, task_name, parameter_name, parameter_id)
tuning_session.run()
You should now see a new eureqa model in the web interface with your tuning settings.
For convenience, here's the full code again to copy/adjust/paste:
import datarobot as dr
client = dr.Client(endpoint='https://app2.datarobot.com/api/v2', token='XYZ')
project = dr.Project.get('6156fdd3000984791f39660c')
eureqa_model = dr.Model.get(project.id, '6157042bbdcc650eb28a82d8')
tuning_session = eureqa_model.start_advanced_tuning_session()
# Run, and note down the actual task name for your model
tuning_session.get_task_names()
tuning_session.get_parameter_names('Eureqa Classifier (Quick Search: 250 Generations)')
params = tuning_session.get_parameters()
settings = {
'building_block__less-than':4,
'building_block__round':3,
'building_block__sine':5,
'building_block__square_root':2
}
params_to_tune = [p for p in params['tuning_parameters'] if
p['task_name'] == 'Eureqa Classifier (Quick Search: 250 Generations)'
and p['parameter_name'] in settings]
for p in params_to_tune:
task_name = p['task_name']
parameter_name = p['parameter_name']
parameter_id = p['parameter_id']
value = settings[parameter_name]
tuning_session.set_parameter(value, task_name, parameter_name, parameter_id)
tuning_session.run()
I hope that helps!
Kind regards,
Lukas
