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This public repository contains the main functionality of the Hyppo System, in addition it also contrains a pipeline generator.

HYPPO’s system architecture


Hyppo Components

We implemented HYPPO on top of the sklearn.pipelines API and networkX

Dictionary

he HYPPO's dictionary involved all the physical operators that we have implemented. For extending the dictonary make sure you implement the core functions:

  • fit: This function is for training or fitting your operator to the data. It's where any initial analysis or setup specific to your operator should be done.
  • transform: This function is used for the transformation or processing of the data using your operator. Whatever unique operations your operator is designed to perform will be coded here.
  • score: If your operator involves any form of evaluation or scoring, this function should be implemented. It's used to assess the performance or output of your operator. Ensure Full HYPPO Compatibility: By implementing these functions, your custom operators will be compatible with HYPPO's features and functionalities, allowing them to seamlessly integrate with the rest of the system.

Pipeline generator

The pipeline generator is not part of the HYPPO system, it was created to for testing and evaluating purposes. The pipeline generator gets as input a dataset and a pool of operators and generates a sequence of pipelines for execution. A possible pipeline is annotated with an operator id and the operators are seperated by the pipe: |

"SI|SS|SVM()|F1" -> Example of a pipeline 

The example above is referring to a pipeline with the following steps Imputation->Scaler->SVM-> F1 Score. After a pipeline is expressed we let our generator randomly select a physical implementation for each operator.

History

The output of our pipeline generator is a history. After the execution of each pipeline the history graph is dropped into a file in the directory /graphs/iterative. By executing N pipelines N graphs will be dropped.

Plan generator

the Plan generator was created as a separate project at https://github.com/akontaxakis/Plan-Generator

Getting Started

Contact

For any questions don't hesitate to ask:

Antonios Kontaxakis, antonios.kontaxakis-ATNOSPAM-ulb.be

About

HYPPO: Using Equivalences to Optimize Pipelines in Exploratory Machine Learning

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