Get any paths associated to items in this dir

Select into some data by indices

Start timing a task Parameters ---------- name : str The name of the task to measure

Shout a warning during prediction

Transform the given target or features to a numpy array Parameters ---------- X: SUPPORTED_FEAT_TYPES

A context manager to ignore warnings >>> with ignore_warnings(classifier_warnings): >>> ... Parameters ---------- to_ignore:

Validates and fit a categorical encoder (if needed) to the targets The supported data types are List, numpy arrays and pandas DataFra

Make a scorer from a performance metric or loss function. Factory inspired by scikit-learn which wraps scikit-learn scoring functions to be u

Returns a dictionary containing dictionaries of ensemble models. Each model in the ensemble can be accessed by giving its ``model_id`` as key

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

Returns a pandas table of results for all evaluated models. Gives an overview of all models trained during the search process along w

Load an item from the cache with a given name

Return the configuration space for the CASH problem. Returns ------- cs : ConfigSpace.configuration_space.Configuration

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

Validates input data to Auto-Sklearn. The supported data types are List, numpy arrays and pandas DataFrames. CSR sparse data

Return the cost of crash. Return value can be either a list (multi-objective optimization) or a raw float (single objective) because SMAC ass

Parameters ---------- name : str = "iris" Name of the dataset to get make_sparse : bool = False Wehther to make the

Returns the losses (a magnitude that allows casting the optimization problem as a minimization one) for the given Auto-Sklearn Scorer obj

Fit, predict and compute the loss for cross-validation and holdout (both iterative and non-iterative)

Path to an item for this cache

predict_proba. Parameters ---------- X : array-like, shape = (n_samples, n_features) batch_size: int or None, defaul

wrapper function for ExecuteTARun.run_wrapper() to cap the target algorithm runtime if it would run over the total allowed runtime.

Validates and fit a categorical encoder (if needed) to the features. The supported data types are List, numpy arrays and pandas DataF

Auto-sklearn follows a minimization goal. The calculate_loss internally translate a score function to a minimization problem.

Run the main loop of ensemble building The process is: * Load all available runs + previous candidates (if any) * Update the

Make a backend Parameters ---------- path: Union[str, Path] The path to place the backend at template: Optional[Path] = None

Predict the classes using the selected model. Parameters ---------- X : array-like, shape = (n_samples, n_features)

Refit the models to a new given set of data Parameters ---------- X : SUPPORTED_FEAT_TYPES The data to dit to

Run the ensemble building process Parameters ---------- iteration : int What iteration to associate with this run

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.

Validates and return a correct dataset_compression argument The returned value can be safely used with `reduce_dataset_size_if_too_large`. P

Convert data to allowed types

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

Get the path to certain predictions

Dump an item to cache with a name

Get a list of candidates from `runs`, garuanteeing at least one Applies a set of reductions in order of parameters to reach a set of final

Transfers a backend to a new path Parameters ---------- old_backend: Backend | Path | str The backend to transfer from new_p

Fit the selected algorithm to the training data. Parameters ---------- X : array-like or sparse, shape = (n_samples, n_featur

See `_create_automl`

Should close up any resources needed for the dask client

Do everything necessary after the fitting is done: * predicting * saving the files for the ensembles_statistics * generate ou

Returns the Configuration Space object, from which Auto-Sklearn will sample configurations and build pipelines. Parameters

Return identifiers of models in the ensemble. This includes models which have a weight of zero! Returns ------- list

Gives the access to a cache.

Load the predictions for this run Parameters ---------- kind : "ensemble" | "test" The kind of predictions to loa

f"""Reduces the size of the dataset if it's too close to the memory limit. Follows the order of the operations passed in and retains the type of

Start a given task with a name

Validates and fit a categorical encoder (if needed) to the features. The supported data types are List, numpy arrays and pandas DataF

Get a dictionary of all available runs on the filesystem Returns ------- dict[RunID, Run] A dictionary from RunId

If the labels `y` are sparse, it will convert it to its dense representation Parameters ---------- y: {array-like, sparse matrix} of shap