Overview

Hypernets is a general automated search framework, based on which it can implement automatic optimization tools for various machine learning frameworks and libraries, including deep learning frameworks such as tensorflow, keras, pytorch, and machine learning libraries like sklearn, lightgbm, xgboost, etc. We introduced an abstract search space representation, taking into account the requirements of hyperparameter optimization and neural architecture search(NAS), making Hypernets a general framework that can adapt to various automated machine learning needs.

The figure below shows conceptual model of Hypernets.

Key Components

HyperSpace

The space of all feasible solutions for a model is called Search Space. HyperSpace is an abstract representation of the search space composed of Parameter Space, Connection Space, and Module Space. The general form of HyperSpace is a DAG, so it can represent ML pipeline and neural network architecture very flexibly.

Seacher

Search algorithms that looking for a optimal solution in HyperSpace and generating samples for HyperModel.

HyperModel

High-level interface for users to perform model search and training, as long as the defined search space and training data are passed in to get the best model. HyperModel is an abstract class that needs to implement a dedicated HyperModel for different frameworks or domains. For example, HyperKeras is used to automatically search for neural networks built with keras, and HyperML is used to automatically optimize ML pipeline composed of sklearn, xgboost, and lightgbm….

Estimator

A specific HyperModle needs to be paired with a dedicated Estimator to fit and evaluate the sample given by the HyperModel. This sample may be a set of hyperparameters, a network architecture, or a mixture of them.