Fit an ensemble from the provided runs. Note ---- Expects all runs to have the "ensemble" predictions present Parame

Get all fixture modules

Return a dictionary with a list of all runs

Parses the run history, to identify the best performing model Populates the identifiers attribute, which is used by the backend to access

Return a (identifier, weight)-pairs for all models that were passed to the ensemble builder based on the ensemble that is best for the 1st met

Return a list of (weight, model) pairs for the ensemble that is best for the 1st metric. Parameters ---------- models

Return identifiers of models in the ensemble that is best for the 1st metric. This includes models which have a weight of zero! Retu

Validation performance of the ensemble that is best for the 1st metric. Returns ------- float

Handle multiple requests - each expected to be a 4-byte length, followed by the LogRecord in pickle format. Logs the record a

Function used for loading data.

Use `make_run` to create runs for this manager .. code:: python def test_x(make_run, make_ensemble_builder_manager): manager

Parameters ---------- X: Optional[SUPPORTED_FEAT_TYPES] = None The X data to fit models on, if None, no fitting occurs y: Op

Parameters ---------- X: Optional[SUPPORTED_FEAT_TYPES] = None The X data to fit models on, if None, no fitting occurs y: Op

Calculate the pareto front source from: https://stackoverflow.com/a/40239615 Note ---- Works on the assumption that every value is e

Predict using the ensemble which is best for the 1st metric. Parameters ---------- base_models_predictions : np.ndarray

Create ensemble predictions from the base model predictions. Parameters ---------- base_models_predictions : np.ndarray

Predict probabilities of classes for all samples X. Parameters ---------- X : array-like or sparse matrix of shape = [n_sampl

Load any previous candidates that were saved from previous runs Returns ------- dict[RunID, Run] A dictionary fro

Function which reads the csv files containing trajectories of the auto-sklearn runs.

Delete a file or directory Parameters ---------- path: Path | str The path to delete atomic: bool = False Whether to

Get the size of some path object Parameters ---------- path : Path | str The path of the file or directory to get the size of

Stop the task

Yeilds all files, iterating over directory Parameters ---------- path: Path The root path to walk from include: Optional[str

Evaluate predicted target values for X relative to y_true. Parameters ---------- y_true : array-like Gold standar

Evaluate predicted probabilities for X relative to y_true. Parameters ---------- y_true : array-like Gold standard

Evaluate decision function output for X relative to y_true. Parameters ---------- y_true : array-like Gold standar