Class for parallelizing RandomizedSearchCV jobs in scikit-learn

from sklearn.model_selection import ParameterSampler
from spark_sklearn.base_search import SparkBaseSearchCV

class RandomizedSearchCV(SparkBaseSearchCV):
    """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 combinations of 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 for all parameters.
    It is highly recommended to use continuous distributions for continuous

    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
        Not used; exists for scikit-learn compatibility.

    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 the estimator is a classifier and ``y`` is
        either binary or multiclass,
        :class:`sklearn.model_selection.StratifiedKFold` is used. In all
        other cases, :class:`sklearn.model_selection.KFold` is used.

    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, 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' (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.

    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

        |  'poly'    |     --    |      2     |        0.8      |...|    2    |
        |  'poly'    |     --    |      3     |        0.7      |...|    4    |
        |  'rbf'     |     0.1   |     --     |        0.8      |...|    3    |
        |  'rbf'     |     0.2   |     --     |        0.9      |...|    1    |

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

            'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
                                         mask = [False False False False]...)
            'param_gamma': masked_array(data = [-- -- 0.1 0.2],
                                        mask = [ True  True False False]...),
            'param_degree': masked_array(data = [2.0 3.0 -- --],
                                         mask = [False False  True  True]...),
            'split0_test_score'  : [0.8, 0.7, 0.8, 0.9],
            'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],
            'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],
            'std_test_score'     : [0.02, 0.01, 0.03, 0.03],
            'rank_test_score'    : [2, 4, 3, 1],
            'split0_train_score' : [0.8, 0.9, 0.7],
            'split1_train_score' : [0.82, 0.5, 0.7],
            'mean_train_score'   : [0.81, 0.7, 0.7],
            'std_train_score'    : [0.03, 0.03, 0.04],
            'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
            'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
            'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
            'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
            'params'             : [{'kernel': 'poly', 'degree': 2}, ...],

        NOTE that the key ``'params'`` is used to store a list of parameter
        settings dict for all the parameter candidates.
        The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
        ``std_score_time`` are all in seconds.

    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.

    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_``).

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

    See Also
        Does exhaustive search over a grid of parameters.

        A generator over parameter settings, constructed from
    def __init__(self, sc, estimator, param_distributions, n_iter=10, scoring=None,
                 fit_params=None, n_jobs=1, iid=True, refit=True, cv=None,
                 verbose=0, pre_dispatch='2*n_jobs', random_state=None,

        self.param_distributions = param_distributions
        self.n_iter = n_iter
        self.random_state = random_state
        super(RandomizedSearchCV, self).__init__(
            estimator=estimator, scoring=scoring, fit_params=fit_params,
            n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose,
            pre_dispatch=pre_dispatch, error_score=error_score)

        self.fit_params = fit_params if fit_params is not None else {}
        self.sc = sc

        self.cv_results_ = None

    def fit(self, X, y=None, groups=None):
        """Run fit on the estimator with randomly drawn 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.

        groups : array-like, with shape (n_samples,), optional
            Group labels for the samples used while splitting the dataset into
            train/test set.
        sampled_params = ParameterSampler(self.param_distributions,

        return self._fit(X, y, groups, sampled_params)