tune_basesearch.md

module tune_basesearch

Parent class for a cross-validation interface built with a Ray Tune back-end.

Implementation derived from referencing the equivalent GridSearchCV interfaces from Dask and Optuna.

https://ray.readthedocs.io/en/latest/tune.html https://dask.org https://optuna.org -- Anthony Yu and Michael Chau

Global Variables

• DEFAULT_MODE

---

function resolve_early_stopping

resolve_early_stopping(early_stopping, max_iters, metric_name)

---

class TuneBaseSearchCV

Abstract base class for TuneGridSearchCV and TuneSearchCV

method __init__

__init__(
    estimator,
    early_stopping=None,
    scoring=None,
    n_jobs=None,
    cv=5,
    refit=True,
    verbose=0,
    error_score='raise',
    return_train_score=False,
    local_dir=None,
    name=None,
    max_iters=1,
    use_gpu=False,
    loggers=None,
    pipeline_auto_early_stop=True,
    stopper=None,
    time_budget_s=None,
    mode=None
)

---

property best_estimator_

estimator: Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.

See refit parameter for more information on allowed values.

---

property best_index_

int: The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting.

The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).

For multi-metric evaluation, this is present only if refit is specified.

---

property best_params_

dict: Parameter setting that gave the best results on the hold out data.

For multi-metric evaluation, this is present only if refit is specified.

---

property best_score_

float: Mean cross-validated score of the best_estimator

For multi-metric evaluation, this is present only if refit is specified.

---

property classes_

list: Get the list of unique classes found in the target y.

---

property decision_function

function: Get decision_function on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports decision_function.

---

property inverse_transform

function: Get inverse_transform on the estimator with the best found parameters.

Only available if the underlying estimator implements inverse_transform and refit=True.

---

property multimetric_

bool: Whether evaluation performed was multi-metric.

---

property n_features_in_

Number of features seen during :term:fit.

Only available when refit=True.

---

property n_splits_

int: The number of cross-validation splits (folds/iterations).

---

property predict

function: Get predict on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict.

---

property predict_log_proba

function: Get predict_log_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_log_proba.

---

property predict_proba

function: Get predict_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_proba.

---

property refit_time_

float: Seconds used for refitting the best model on the whole dataset.

This is present only if refit is not False.

---

property scorer_

function or a dict: Scorer function used on the held out data to choose the best parameters for the model.

For multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.

---

property transform

function: Get transform on the estimator with the best found parameters.

Only available if the underlying estimator supports transform and refit=True.

---

method fit

fit(X, y=None, groups=None, tune_params=None, **fit_params)

Run fit with all sets of parameters.

tune.run is used to perform the fit procedure.

Args:

• X (: obj:array-like (shape = [n_samples, n_features])): Training vector, where n_samples is the number of samples and n_features is the number of features.
• y (:obj:array-like): Shape of array expected to be [n_samples] or [n_samples, n_output]). Target relative to X for classification or regression; None for unsupervised learning.
• groups (: obj:array-like (shape (n_samples,)), optional): Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" cv instance (e.g., GroupKFold).
• tune_params (: obj:dict, optional): Parameters passed to tune.run used for parameter search.
• **fit_params (: obj:dict of str): Parameters passed to the fit method of the estimator.

Returns:

• :obj: TuneBaseSearchCV child instance, after fitting.

---

method score

score(X, y=None)

Compute the score(s) of an estimator on a given test set.

Args:

• X (:obj:array-like (shape = [n_samples, n_features])): Input data, where n_samples is the number of samples and n_features is the number of features.
• y (:obj:array-like): Shape of array is expected to be [n_samples] or [n_samples, n_output]). Target relative to X for classification or regression. You can also pass in None for unsupervised learning.

Returns:

• float: computed score

---

This file was automatically generated via lazydocs.