Params

On the back-end, if a ParamSearch object is passed when creating a ModelPipeline, then a hyperparameter search will be conducted. All Hyperparameter search types are implemented on the backend with facebook’s Nevergrad library or a sklearn GridSearch.

Specific hyper-parameters distributions in which to search over are set within their corresponding base ModelPipeline object, e.g., the params argument is Model. For any object with a params argument you can set an associated hyperparameter distribution, which specifies values to search over (again assuming that param_search != None, if param_search is None, only passed params with constant values will be applied to object of interest, and any with associated Nevergrad parameter distributions will just be ignored).

You have two different options in terms of input that params can accept, these are:

• Select a preset distribution

  To select a preset, BPt defined, distribution, the selected object must first have at least one preset distribution. These options can be found for each object specifically in the documentation under where that object is defined. Specifically, they will be listed with both an integer index, and a corresponding str name (see Models).

  For example, in creating a binary Model we could pass:

  # Option 1 - as int
  model = Model(obj = "dt classifier",
                params = 1)
  
  # Option 2 - as str
  model = Model(obj = "dt classifier",
                params = "dt classifier dist")
  

  In both cases, this selects the same preset distribution for the decision tree classifier.

• Pass a custom distributions

  If you would like to specify your own custom hyperparameter distribution to search over, you can create it using parameter objects from Parameter Distributions which are based on nevergrad parameters .

  Specifically the dictionary of params should follow the scikit_learn param dictionary format, where the each key corresponds to a parameter, but the value as an instance of a BPt Parameter

  Further, if you need to specify nested parameters, e.g., for a custom object, you separate parameters with ‘__’, so e.g., if your custom model has a base_estimator param, you can pass:

  params = {'base_estimator__some_param' : dist}
  

  Lastly, it is worth noting that you can pass either just static values or a combination of distributions and static values, e.g.,

  {'base_estimator__some_param' : 6}
  

  Note: extra params can also be used to pass static values, and extra_params takes precedence if a param is passed to both params and extra_params.

The special input wrapper Select can also be used to implicitly introduce hyper-parameters into the ModelPipeline.