Class RandomizedSearchCVScikitsLearnNode

Randomized search on hyper parameters.

This node has been automatically generated by wrapping the ``sklearn.grid_search.RandomizedSearchCV`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.


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>`.

**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
    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.

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 or None, default=None
    A string (see model evaluation documentation) or
    a scorer callable object / function with signature
    ``scorer(estimator, X, y)``.
    If ``None``, the ``score`` method of the estimator is used.

fit_params : dict, optional
    Parameters to pass to the fit method.

n_jobs : int, default=1
    Number of jobs to run in parallel.

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=True
    If True, 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.

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 3-fold cross-validation,
    - integer, to specify the number of folds.
    - An object to be used as a cross-validation generator.
    - An iterable yielding train/test splits.

    For integer/None inputs, if ``y`` is binary or multiclass,
    :class:`StratifiedKFold` used. If the estimator is a classifier
    or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

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

refit : boolean, default=True
    Refit the best estimator with the entire dataset.
    If "False", it is impossible to make predictions using
    this RandomizedSearchCV instance after fitting.

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

random_state : int or RandomState
    Pseudo random number generator state used for random uniform sampling
    from lists of possible values instead of scipy.stats distributions.

error_score : 'raise' (default) 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.


**Attributes**

``grid_scores_`` : list of named tuples
    Contains scores for all parameter combinations in param_grid.
    Each entry corresponds to one parameter setting.
    Each named tuple has the attributes:


        * ``parameters``, a dict of parameter settings
        * ``mean_validation_score``, the mean score over the
          cross-validation folds
        * ``cv_validation_scores``, the list of scores for each fold

``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.

``best_score_`` : float
    Score of best_estimator on the left out data.

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

**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 settins, constructed from
    - param_distributions.

Overrides: object.__init__

Process the data contained in x.

If the object is still in the training phase, the function stop_training will be called. x is a matrix having different variables on different columns and observations on the rows.

By default, subclasses should overwrite _execute to implement their execution phase. The docstring of the _execute method overwrites this docstring.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Run fit on the estimator with randomly drawn parameters.

This node has been automatically generated by wrapping the sklearn.grid_search.RandomizedSearchCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape = [n_samples, n_features]

Training vector, where n_samples in the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_output], optional

Target relative to X for classification or regression; None for unsupervised learning.

Overrides: Node.stop_training