Returns a metric for the given Auto-Sklearn Scorer object. It's direction is determined by the metric itself. Parameters ----------

Delete runs Will not delete dummy runs Parameters ---------- runs : Sequence[Run] The runs to delete

Whether this task has been finished

Get the properties of the underlying algorithm. Find more information at :ref:`get_properties` Parameters ----------

Return validation performance of ensemble. Returns ------- float

Output is encoded with a single column encoding

Calculate the loss for a run Parameters ---------- run: Run The run to calculate the loss for metric: Sc

Generate some arbitrary x,y data Parameters ---------- kind: int = BINARY_CLASSIFICATION The task type, one of BINARY_CLASSIFICAT

Return a list of the best hyperparameters of neighboring datasets

An iterator over pairs of items in the iterator ..code:: python # Check if sorted if all(a < b for a, b in pairs(items)):

Fit, predict and get loss for eval_partial_cv (iterative and normal)

Predict regression target for X. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features]

predict. Parameters ---------- X: array-like, shape = (n_samples, n_features) batch_size: int or None, defaults to N

Reduces the precision of a np.ndarray or spmatrix containing floats Parameters ---------- X: np.ndarray | spmatrix The data to r

Parameters ---------- runs : Sequence[Run] The runs to process metrics: Sequence[Scorer] | None = None

Subsamples data returning the same type as it recieved. If `is_classification`, we split using a stratified shuffle split which preserves uni

Allow us to catch known and valid exceptions for all evaluate scripts.

If dealing with classification, this utility encodes the targets. It does so by also using the classes from the test data, to preven

When working with a logging server, clients are expected to create a logger using this method. For example, the automl object will create a m

Given a configuration in config, this procedure makes sure that the given implementation, which should be a autosklearn component, ho

Bounds a value between a low and high .. code:: python x = bound(14, low=0, high=13.1) # x == 13.1 Parameters ---------

Build the ensemble Parameters ---------- dask_client: dask.distributed.Client The dask client to use

Calculate all metafeatures.

Check For the existence of a unix pid.

Dummy implementation of the ``fit`` method. Actualy work of passing the model index is done in the constructor. This method only stor

Select the single best model. Parameters ---------- base_models_predictions: np.ndarray shape = (n_base_models, n

Fit OneHotEncoder to X. Parameters ---------- X : array-like, shape=(n_samples, n_feature) Input array of type in

Return the dependency of metafeature "name".

Return a list of all runs for a dataset.

Return if a helper function has already been executed. Necessary as get_value() can return None if the helper function hasn't been ex

Converts a list to a DataFrame. In this process, column types are inferred. If test data is provided, we proactively match it to tra

Factory to make a Dask client and a function to close it Parameters ---------- n_workers: int = 1 How many workers to have in the

Suggest the next promosing hyperparameters which were not yet evaluated