One-hot encoder using lazy evaluation of relational statements

One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to train and implement a one-hot encoder, wherein the instructions further cause the one or more computing devices to perform a method comprising:

• during a training phase, performing computation of an encoder state by executing a set of relational statements to extract unique categories in a first training data set, associate each unique category with a unique index, and generate a one-hot encoding for each unique category, wherein:
• the set of relational statements are executed by a query optimization engine;
• execution of the set of relational statements is postponed until a result of each relational statement is needed;
• and
• the query optimization engine implements one or more optimizations when executing the set of relational statements;
• and
• during an encoding phase, encoding a set of categorical features in a second training data set based on the encoder state to form a set of encoded categorical features.