Memoizing machine-learning pre-processing and feature engineering

A computer program product for memoizing data transformation patterns in machine-learning pre-processing, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• in a training phase:
• creating, by a processor, a memo table of keys and values, where each of the keys is an element of an input array which is an input of a machine-learning pre-processing pipeline, and each of the values is an output of the machine-learning pre-processing pipeline;
• and
• measuring, by the processor, (1) a hit rate H to the memo table, (2) an average time Ttable to look up the memo table, (3) an average time Tpipeline to execute a machine-learning pre-processing pipeline, and (4) a threshold Telements on a number of elements of the input array to determine whether to perform or bypass a memo table lookup;
• in an inferencing phase:
• looking up, by the processor, a value in the memo table by using an element of the input array as a key when Tpipeline×H>Ttable and the number of elements in the input array is less than Telements;
• and
• calling, by the processor, the machine learning pre-processing pipeline in place of the memo table lookup for all of the remaining elements in the input array when the value is not found in the memo table.

A computer processing system for memoizing data transformation patterns in machine-learning pre-processing, comprising:

• a memory for storing program code;
• and
• a processor operatively coupled to the memory for running the program code to:
• in a training phase:
• create a memo table of keys and values, where each of the keys is an element of an input array which is an input of a machine-learning pre-processing pipeline, and each of the values is an output of the machine-learning pre-processing pipeline;
• and
• measure (1) a hit rate H to the memo table, (2) an average time Ttable to look up the memo table, (3) an average time Tpipeline to execute a machine-learning pre-processing pipeline, and (4) a threshold Telements on a number of elements of the input array to determine whether to perform or bypass a memo table lookup;
• in an inferencing phase:
• look up a value in the memo table by using an element of the input array as a key when Tpipeline×H>Ttable and the number of elements in the input array is less than Telements;
• and
• call the machine learning pre-processing pipeline in place of the memo table lookup for all of the remaining elements in the input array when the value is not found in the memo table.