package sklearn

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in-package search v0.2.0

sklearn
- Sklearn

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type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?n_iter:int ->
  ?scoring:
    [ `S of string
    | `Callable of Py.Object.t
    | `List_tuple of Py.Object.t
    | `Dict of Dict.t ] ->
  ?n_jobs:int ->
  ?iid:bool ->
  ?refit:[ `Bool of bool | `S of string | `Callable of Py.Object.t ] ->
  ?cv:[ `I of int | `CrossValGenerator of Py.Object.t | `Arr of Arr.t ] ->
  ?verbose:int ->
  ?pre_dispatch:[ `I of int | `S of string ] ->
  ?random_state:int ->
  ?error_score:[ `Raise | `F of float ] ->
  ?return_train_score:bool ->
  estimator:Py.Object.t ->
  param_distributions:[ `Dict of Dict.t | `List_of_dicts of Py.Object.t ] ->
  unit ->
  t

Randomized search on hyper parameters.

RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used.

The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings.

In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.

If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.

Read more in the :ref:`User Guide <randomized_parameter_search>`.

.. versionadded:: 0.14

Parameters ---------- estimator : estimator object. A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed.

param_distributions : dict or list of dicts Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a ``rvs`` method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.

n_iter : int, default=10 Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.

scoring : string, callable, list/tuple, dict or None, default: None A single string (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`) to evaluate the predictions on the test set.

For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values.

NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each.

See :ref:`multimetric_grid_search` for an example.

If None, the estimator's score method is used.

n_jobs : int or None, optional (default=None) Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs

An int, giving the exact number of total jobs that are spawned

A string, giving an expression as a function of n_jobs, as in '2*n_jobs'

iid : boolean, default=False If True, return the average score across folds, weighted by the number of samples in each test set. In this case, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.

.. deprecated:: 0.22 Parameter ``iid`` is deprecated in 0.22 and will be removed in 0.24

cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are:

None, to use the default 5-fold cross validation,
integer, to specify the number of folds in a `(Stratified)KFold`,
:term:`CV splitter`,
An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here.

.. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold.

refit : boolean, string, or callable, default=True Refit an estimator using the best found parameters on the whole dataset.

For multiple metric evaluation, this needs to be a string denoting the scorer that would be used to find the best parameters for refitting the estimator at the end.

Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given the ``cv_results``. In that case, the ``best_estimator_`` and ``best_parameters_`` will be set according to the returned ``best_index_`` while the ``best_score_`` attribute will not be available.

The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``RandomizedSearchCV`` instance.

Also for multiple metric evaluation, the attributes ``best_index_``, ``best_score_`` and ``best_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this specific scorer.

See ``scoring`` parameter to know more about multiple metric evaluation.

.. versionchanged:: 0.20 Support for callable added.

verbose : integer Controls the verbosity: the higher, the more messages.

random_state : int, RandomState instance or None, optional, default=None Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.

error_score : 'raise' or numeric Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error. Default is ``np.nan``.

return_train_score : boolean, default=False If ``False``, the ``cv_results_`` attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance.

Attributes ---------- cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``.

For instance the below given table

+--------------+-------------+-------------------+---+---------------+ | param_kernel | param_gamma | split0_test_score |...|rank_test_score| +==============+=============+===================+===+===============+ | 'rbf' | 0.1 | 0.80 |...| 2 | +--------------+-------------+-------------------+---+---------------+ | 'rbf' | 0.2 | 0.90 |...| 1 | +--------------+-------------+-------------------+---+---------------+ | 'rbf' | 0.3 | 0.70 |...| 1 | +--------------+-------------+-------------------+---+---------------+

will be represented by a ``cv_results_`` dict of::

'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'], mask = False), 'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False), 'split0_test_score' : [0.80, 0.90, 0.70], 'split1_test_score' : [0.82, 0.50, 0.70], 'mean_test_score' : [0.81, 0.70, 0.70], 'std_test_score' : [0.01, 0.20, 0.00], 'rank_test_score' : [3, 1, 1], 'split0_train_score' : [0.80, 0.92, 0.70], 'split1_train_score' : [0.82, 0.55, 0.70], 'mean_train_score' : [0.81, 0.74, 0.70], 'std_train_score' : [0.01, 0.19, 0.00], 'mean_fit_time' : [0.73, 0.63, 0.43], 'std_fit_time' : [0.01, 0.02, 0.01], 'mean_score_time' : [0.01, 0.06, 0.04], 'std_score_time' : [0.00, 0.00, 0.00], 'params' : [{'kernel' : 'rbf', 'gamma' : 0.1, ...],

}

NOTE

The key ``'params'`` is used to store a list of parameter settings dicts for all the parameter candidates.

The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and ``std_score_time`` are all in seconds.

For multi-metric evaluation, the scores for all the scorers are available in the ``cv_results_`` dict at the keys ending with that scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.)

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``.

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

See ``refit`` parameter for more information on allowed values.

best_score_ : float Mean cross-validated score of the best_estimator.

For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information.

This attribute is not available if ``refit`` is a function.

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

For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information.

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 not available if ``refit`` is ``False``. See ``refit`` parameter for more information.

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.

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

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

This is present only if ``refit`` is not False.

Notes ----- The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter.

If `n_jobs` was set to a value higher than one, the data is copied for each parameter setting(and not `n_jobs` times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set `pre_dispatch`. Then, the memory is copied only `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 * n_jobs`.

See Also -------- :class:`GridSearchCV`: Does exhaustive search over a grid of parameters.

:class:`ParameterSampler`: A generator over parameter settings, constructed from param_distributions.

Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.model_selection import RandomizedSearchCV >>> from scipy.stats import uniform >>> iris = load_iris() >>> logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200, ... random_state=0) >>> distributions = dict(C=uniform(loc=0, scale=4), ... penalty='l2', 'l1') >>> clf = RandomizedSearchCV(logistic, distributions, random_state=0) >>> search = clf.fit(iris.data, iris.target) >>> search.best_params_ 'C': 2..., 'penalty': 'l1'

val decision_function : x:Arr.t -> t -> Arr.t

Call decision_function on the estimator with the best found parameters.

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

Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

val fit : 
  ?y:Arr.t ->
  ?groups:[ `Arr of Arr.t | `With of Py.Object.t ] ->
  ?fit_params:(string * Py.Object.t) list ->
  x:Arr.t ->
  t ->
  t

Run fit with all sets of parameters.

Parameters ----------

X : array-like of shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array-like of shape (n_samples, n_output) or (n_samples,), optional Target relative to X for classification or regression; None for unsupervised learning.

groups : array-like, with 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" :term:`cv` instance (e.g., :class:`~sklearn.model_selection.GroupKFold`).

**fit_params : dict of string -> object Parameters passed to the ``fit`` method of the estimator

val get_params : ?deep:bool -> t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val inverse_transform : xt:Arr.t -> t -> Py.Object.t

Call inverse_transform on the estimator with the best found params.

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

Parameters ---------- Xt : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

val predict : x:Arr.t -> t -> Arr.t

Call predict on the estimator with the best found parameters.

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

Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

val predict_log_proba : x:Arr.t -> t -> Py.Object.t

Call predict_log_proba on the estimator with the best found parameters.

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

Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

val predict_proba : x:Arr.t -> t -> Arr.t

Call predict_proba on the estimator with the best found parameters.

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

Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

val score : ?y:Arr.t -> x:Arr.t -> t -> float

Returns the score on the given data, if the estimator has been refit.

This uses the score defined by ``scoring`` where provided, and the ``best_estimator_.score`` method otherwise.

Parameters ---------- X : array-like of shape (n_samples, n_features) Input data, where n_samples is the number of samples and n_features is the number of features.

y : array-like of shape (n_samples, n_output) or (n_samples,), optional Target relative to X for classification or regression; None for unsupervised learning.

Returns ------- score : float

val set_params : ?params:(string * Py.Object.t) list -> t -> t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val transform : x:Arr.t -> t -> Arr.t

Call transform on the estimator with the best found parameters.

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

Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

val cv_results_ : t -> Dict.t

Attribute cv_results_: get value or raise Not_found if None.

val cv_results_opt : t -> Dict.t option

Attribute cv_results_: get value as an option.

val best_estimator_ : t -> Py.Object.t

Attribute best_estimator_: get value or raise Not_found if None.

val best_estimator_opt : t -> Py.Object.t option

Attribute best_estimator_: get value as an option.

val best_score_ : t -> float

Attribute best_score_: get value or raise Not_found if None.

val best_score_opt : t -> float option

Attribute best_score_: get value as an option.

val best_params_ : t -> Dict.t

Attribute best_params_: get value or raise Not_found if None.

val best_params_opt : t -> Dict.t option

Attribute best_params_: get value as an option.

val best_index_ : t -> int

Attribute best_index_: get value or raise Not_found if None.

val best_index_opt : t -> int option

Attribute best_index_: get value as an option.

val scorer_ : t -> Py.Object.t

Attribute scorer_: get value or raise Not_found if None.

val scorer_opt : t -> Py.Object.t option

Attribute scorer_: get value as an option.

val n_splits_ : t -> int

Attribute n_splits_: get value or raise Not_found if None.

val n_splits_opt : t -> int option

Attribute n_splits_: get value as an option.

val refit_time_ : t -> float

Attribute refit_time_: get value or raise Not_found if None.

val refit_time_opt : t -> float option

Attribute refit_time_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.