Machine Learning Pipeline Generation

A computer program product for automated generation of trained machine learning models, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• receive a space of possible automatically generated trained machine learning model pipelines, the space defined by a context-free grammar;
• generate a planning model from the context-free grammar;
• and
• automatically generate a plurality of candidate trained machine learning pipelines based upon the planning model.

A system for generating trained machine learning models, the system comprising a processor configured to execute instructions that, when executed on the processor, cause the processor to:

• receive a space of possible automatically generated trained machine learning model pipelines, the space defined by a context-free grammar;
• generate a planning model from the context-free grammar;
• and
• automatically generate a plurality of candidate trained machine learning pipelines based upon the planning model.

A computer implemented method for automated generation of trained machine learning models, comprising:

• receiving a space of possible automatically generated trained machine learning models, the space defined by a context-free grammar;
• generating, by a processor, a planning model from the context-free grammar, wherein the planning model comprises a strategy of action for training a machine learning model, and wherein generating comprises translating the context-free grammar to a hierarchical task network planning model, and wherein the hierarchical task network planning model comprises a solution to PG=(Σ,V,O,M,sI,tnI), where:
• O={(n,∅,∅)|n∈Σ}
• M={mr=(α,∅,(Tr,r,τr))|r=α→β∈R}, where β=e1· . . . ·en, Tr={t1, . . . ,tn}, tirtj if and only if i<j, and τr(ti)=ei for 1≤i≤n,
• sI=∅, and
• tnI=({tI},∅,τI), where τI(tI)=v0.
• translating the hierarchical task network planning model into a classical planning model;
• automatically generating, by the processor, a plurality of candidate trained machine learning pipelines based upon the classical planning model;
• training a plurality of candidate pipelines to generate a plurality of trained pipelines;
• generating feedback about the plurality of trained pipelines;
• presenting the feedback to a user;
• receiving a selection from a user for a preferred machine learning pipeline from the plurality of candidate trained machine learning pipelines;
• providing, by the processor, feedback from the selection of the preferred machine learning pipeline to an optimizer;
• and
• updating, by the optimizer, the planning model based upon the feedback.