vastorbit.machine_learning.vast.pipeline.Pipeline

class vastorbit.machine_learning.vast.pipeline.Pipeline(steps: list, overwrite_model: bool = False)

Creates a Pipeline object, which sequentially applies a list of transforms and a final estimator. The intermediate steps must implement a transform method.

Parameters:

• steps (list) – List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, where the last object is an estimator.

• overwrite_model (bool, optional) – If set to True, training a model in the pipeline with the same name as an existing model overwrites the existing model.

__init__(steps: list, overwrite_model: bool = False) → None

Methods

__init__(steps[, overwrite_model])
drop()	Drops the model from the VAST DataBase.
fit(input_relation, X[, y, test_relation, ...])	Trains the model.
get_params()	Returns the model's Parameters.
inverse_transform([vdf, X])	Applies the inverse model transformation on a VastFrame.
predict([vdf, X, name])	Applies the model on a VastFrame.
report()	Computes a regression/classification report using multiple metrics to evaluate the model depending on its type.
score([metric])	Computes the model score.
set_params([parameters])	Sets the parameters of the model.
transform([vdf, X])	Applies the model on a VastFrame.