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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 crossvalidated 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 scikitlearn 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 fastrunning jobs, to avoid delays due to ondemand 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, crossvalidation generator or an iterable, optional Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,  integer, to specify the number of folds.  An object to be used as a crossvalidation 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 crossvalidation 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 crossvalidation 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 heldout 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.














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

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 crossvalidated 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 scikitlearn 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 fastrunning jobs, to avoid delays due to ondemand 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, crossvalidation generator or an iterable, optional Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,  integer, to specify the number of folds.  An object to be used as a crossvalidation 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 crossvalidation 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 crossvalidation 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 heldout 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.




Process the data contained in If the object is still in the training phase, the function
stop_training will be called.
By default, subclasses should overwrite _execute to implement their execution phase. The docstring of the _execute method overwrites this docstring.



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

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