Revert any encoding transformation done on a target array. Parameters ---------- y: Union[List, np.ndarray, pd.DataFrame, pd.

Return the k most similar datasets with respect to self.metric Parameters ---------- x : pandas.Series A pandas S

See `_create_automl`

The ensemble targets used for training the ensemble It will attempt to load and cache them in memory but return None if it can't.

Fit the Nearest Neighbor model. Parameters ---------- metafeatures : pandas.DataFrame A pandas dataframe. Each ro

Validates and fit a categorical encoder (if needed) to the features, and a encoder for targets in the case of classification. Specifi

Fits and individual pipeline configuration and returns the result to the user. The Estimator constraints are honored, for example the

Get the configuration of a pipeline given some dataset info Parameters ---------- datamanager: AbstractDataManager AbstractDataMa

Return a (identifier, weight)-pairs for all models that were passed to the ensemble builder. Parameters ---------- mo

Cut a list of runs into those to keep and those to delete If neither params are specified, this method should do nothing. In the cas

Query for when the ens file was last modified

Tests a configuration space by taking multiple samples and fiting each before calling predict and predict_proba. Parameters -

Returns a dictionary that maps pandas dataframe columns to a feature type. This feature type can be categorical, numerical or string

Whether this run is a dummy run or not

Split a list in two based on a predicate. Note ---- First element can not contain None Parameters ---------- lst : Iterable[

Generates the predictions from a model. This is seperated out into a seperate function to allow for multiprocessing and perform parallel pred

Converts the model predicted probabilities to useable format. In some cases, models predicted_proba can output an array of shape (2, n_sample

Cut a list in two at a given index or predicate Parameters ---------- lst : Iterable[T] An iterable of items at : int | Call

Fit AutoML to given training set (X, y). Fit both optimizes the machine learning models and builds an ensemble out of them.

Get the three components of it's id

See `_create_automl`

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

Records the last time each prediction file type was modified, if it exists

The wall clock time since a task either began or ended Parameters ---------- name : str The name of the task

Perform dimensionality and data type checks on the targets Parameters ---------- y: Union[np.ndarray, pd.DataFrame,

Feature dimensionality and data type checks Parameters ---------- X: SUPPORTED_FEAT_TYPES A set of featu

Returns a score (a magnitude that allows casting the optimization problem as a maximization one) for the given Auto-Sklearn Scorer object

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

Given a configuration in config, this procedure makes sure that the given implementation, which should be a Choice component, honors the type

Validate metrics given to Auto-sklearn. Raises an Exception in case of a problem. metrics: Sequence[Scorer] A list of objects that h