  Automated machine learning pipeline (ML) composition and optimisation aim at
automating the process of finding the most promising ML pipelines within
allocated resources (i.e., time, CPU and memory). Existing methods, such as
Bayesian-based and genetic-based optimisation, which are implemented in
Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them.
Therefore, the pipeline composition and optimisation of these methods
frequently require a tremendous amount of time that prevents them from
exploring complex pipelines to find better predictive models. To further
explore this research challenge, we have conducted experiments showing that
many of the generated pipelines are invalid in the first place, and attempting
to execute them is a waste of time and resources. To address this issue, we
propose a novel method to evaluate the validity of ML pipelines, without their
execution, using a surrogate model (AVATAR). The AVATAR generates a knowledge
base by automatically learning the capabilities and effects of ML algorithms on
datasets' characteristics. This knowledge base is used for a simplified mapping
from an original ML pipeline to a surrogate model which is a Petri net based
pipeline. Instead of executing the original ML pipeline to evaluate its
validity, the AVATAR evaluates its surrogate model constructed by capabilities
and effects of the ML pipeline components and input/output simplified mappings.
Evaluating this surrogate model is less resource-intensive than the execution
of the original pipeline. As a result, the AVATAR enables the pipeline
composition and optimisation methods to evaluate more pipelines by quickly
rejecting invalid pipelines. We integrate the AVATAR into the sequential
model-based algorithm configuration (SMAC). Our experiments show that when SMAC
employs AVATAR, it finds better solutions than on its own.
