gridSearch

systemds.operator.algorithm.gridSearch(X: Matrix, y: Matrix, train: str, predict: str, params: List, paramValues: List, **kwargs: Dict[str, DAGNode | str | int | float | bool])

The gridSearch-function is used to find the optimal hyper-parameters of a model which results in the most accurate predictions. This function takes train and eval functions by name.

Parameters:

• X – Input feature matrix

• y – Input Matrix of vectors.

• train – Name ft of the train function to call via ft(trainArgs)

• predict – Name fp of the loss function to call via fp((predictArgs,B))

• numB – Maximum number of parameters in model B (pass the max because the size may vary with parameters like icpt or multi-class classification)

• params – List of varied hyper-parameter names

• dataArgs – List of data parameters (to identify data parameters by name i.e. list(“X”, “Y”))

• paramValues – List of matrices providing the parameter values as columnvectors for position-aligned hyper-parameters in ‘params’

• trainArgs – named List of arguments to pass to the ‘train’ function, where gridSearch replaces enumerated hyper-parameter by name, if not provided or an empty list, the lm parameters are used

• predictArgs – List of arguments to pass to the ‘predict’ function, where gridSearch appends the trained models at the end, if not provided or an empty list, list(X, y) is used instead

• cv – flag enabling k-fold cross validation, otherwise training loss

• cvk – if cv=TRUE, specifies the the number of folds, otherwise ignored

• verbose – flag for verbose debug output

Returns:

Matrix[Double]the trained model with minimal loss (by the ‘predict’ function) Multi-column models are returned as a column-major linearized column vector

Returns:

one-row frame w/ optimal hyper-parameters (by ‘params’ position)