[Class] SupervisingTrainer 📦

Supervising Trainer

A Supervising Trainer is a Pipeline Unit. Meaning, you can instantiate one and include it in a Pipeline.

How to instantiate

Give a (1) Supervised Model and a list (1...*) of Supervised Train Conditions. You may also name the Supervising Trainer.

model = SupervisedModel(...)

conditions1 = SupervisedTrainConditions(...)
conditions2 = SupervisedTrainConditions(...)
conditions3 = SupervisedTrainConditions(...)
(...)

trainer = SupervisingTrainer(model, (conditions1, conditions2, conditions3, ...))

How to use it

In a Pipeline

Add it to a Pipeline:

pipeline.add(trainer)

Independently

Apply it directly by calling the method apply. You need to give:

• 1...* object: A collection of Timeseries that are equally equally segmented. These are to be fed as input to the model during trains and tests.

• 1 target: A Timeseries to be compared to the model output during trains and tests.

trainer.apply(object = (timeseries1, timeseries2, ...), target = target_timeseries)

The trainer will iterate through every set of conditions defined upon instantiation, and report performance metrics and plots for each.

Supervised Train Conditions

Comming soon.