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grid_search.cpython-35.opt-1.pyc
(�(X6� � @ sJ d Z d d l m Z d d l m Z m Z d d l m Z m Z m Z d d l m Z m Z d d l m Z d d l Z d d l Z d d l Z d d l m Z m Z m Z d d l m Z d d l m Z d d l m Z d d l m Z m Z d d l m Z d d l m! Z! d d l" m# Z# d d l$ m% Z% m& Z& d d l' m( Z( d d l) m* Z* d d l+ m, Z, d d d d d g Z- e j. d e/ � Gd d � d e0 � Z1 Gd d � d e0 � Z2 d d d � Z3 d d � Z4 Gd! d" � d" e d" d* � � Z5 Gd& d' � d' e j6 e e e � � Z7 Gd( d � d e7 � Z8 Gd) d � d e7 � Z9 d S)+z` The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters of an estimator. � )�print_function)�ABCMeta�abstractmethod)�Mapping� namedtuple�Sized)�partial�reduce)�productN� )� BaseEstimator� is_classifier�clone)�MetaEstimatorMixin)�check_cv)�_fit_and_score)�Parallel�delayed)�six)�check_random_state)�sample_without_replacement)� _num_samples� indexable)�if_delegate_has_method)� check_scoring)�ChangedBehaviorWarning� GridSearchCV� ParameterGrid�fit_grid_point�ParameterSampler�RandomizedSearchCVz�This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.c @ sF e Z d Z d Z d d � Z d d � Z d d � Z d d � Z d S) r ag Grid of parameters with a discrete number of values for each. .. deprecated:: 0.18 This module will be removed in 0.20. Use :class:`sklearn.model_selection.ParameterGrid` instead. Can be used to iterate over parameter value combinations with the Python built-in function iter. Read more in the :ref:`User Guide <grid_search>`. Parameters ---------- param_grid : dict of string to sequence, or sequence of such The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values. An empty dict signifies default parameters. A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See the examples below. Examples -------- >>> from sklearn.grid_search import ParameterGrid >>> param_grid = {'a': [1, 2], 'b': [True, False]} >>> list(ParameterGrid(param_grid)) == ( ... [{'a': 1, 'b': True}, {'a': 1, 'b': False}, ... {'a': 2, 'b': True}, {'a': 2, 'b': False}]) True >>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}] >>> list(ParameterGrid(grid)) == [{'kernel': 'linear'}, ... {'kernel': 'rbf', 'gamma': 1}, ... {'kernel': 'rbf', 'gamma': 10}] True >>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1} True See also -------- :class:`GridSearchCV`: uses ``ParameterGrid`` to perform a full parallelized parameter search. c C s% t | t � r | g } | | _ d S)N)� isinstancer � param_grid)�selfr" � r$ �/grid_search.py�__init__] s zParameterGrid.__init__c c s{ xt | j D]i } t | j � � } | s0 i Vq t | � \ } } x. t | � D] } t t | | � � } | VqO Wq Wd S)z�Iterate over the points in the grid. Returns ------- params : iterator over dict of string to any Yields dictionaries mapping each estimator parameter to one of its allowed values. N)r" �sorted�items�zipr �dict)r# �pr( �keys�values�v�paramsr$ r$ r% �__iter__d s zParameterGrid.__iter__c s2 t t t j � � t � f d d � | j D� � S)z Number of points on the grid.c 3 s: | ]0 } | r. � d d � | j � D� � n d Vq d S)c s s | ] } t | � Vq d S)N)�len)�.0r. r$ r$ r% � <genexpr>| s z2ParameterGrid.__len__.<locals>.<genexpr>.<genexpr>r N)r- )r2 r+ )r r$ r% r3 | s z(ParameterGrid.__len__.<locals>.<genexpr>)r r �operator�mul�sumr" )r# r$ )r r% �__len__x s zParameterGrid.__len__c C s x� | j D]� } | s3 | d k r& i S| d 8} q t t | j � � d d d � � \ } } d d � | D� } t j | � } | | k r� | | 8} q i } xF t | | | � D]2 \ } } } t | | � \ } } | | | | <q� W| Sq Wt d � � d S)a Get the parameters that would be ``ind``th in iteration Parameters ---------- ind : int The iteration index Returns ------- params : dict of string to any Equal to list(self)[ind] r r Nc S s g | ] } t | � � q Sr$ )r1 )r2 �v_listr$ r$ r% � <listcomp>� s z-ParameterGrid.__getitem__.<locals>.<listcomp>z ParameterGrid index out of range���)r" r) r' r( �npr �divmod� IndexError) r# ZindZsub_gridr, Z values_listsZsizes�total�out�keyr8 �n�offsetr$ r$ r% � __getitem__ s" + "zParameterGrid.__getitem__N)�__name__� __module__� __qualname__�__doc__r& r0 r7 rC r$ r$ r$ r% r . s - c @ s= e Z d Z d Z d d d � Z d d � Z d d � Z d S) r aW Generator on parameters sampled from given distributions. .. deprecated:: 0.18 This module will be removed in 0.20. Use :class:`sklearn.model_selection.ParameterSampler` instead. Non-deterministic iterable over random candidate combinations for hyper- parameter search. 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. Note that as of SciPy 0.12, the ``scipy.stats.distributions`` do not accept a custom RNG instance and always use the singleton RNG from ``numpy.random``. Hence setting ``random_state`` will not guarantee a deterministic iteration whenever ``scipy.stats`` distributions are used to define the parameter search space. Read more in the :ref:`User Guide <grid_search>`. Parameters ---------- param_distributions : dict Dictionary where the keys are parameters and values are distributions from which a parameter is to be sampled. Distributions either have to provide a ``rvs`` function to sample from them, or can be given as a list of values, where a uniform distribution is assumed. n_iter : integer Number of parameter settings that are produced. 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. Returns ------- params : dict of string to any **Yields** dictionaries mapping each estimator parameter to as sampled value. Examples -------- >>> from sklearn.grid_search import ParameterSampler >>> from scipy.stats.distributions import expon >>> import numpy as np >>> np.random.seed(0) >>> param_grid = {'a':[1, 2], 'b': expon()} >>> param_list = list(ParameterSampler(param_grid, n_iter=4)) >>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items()) ... for d in param_list] >>> rounded_list == [{'b': 0.89856, 'a': 1}, ... {'b': 0.923223, 'a': 1}, ... {'b': 1.878964, 'a': 2}, ... {'b': 1.038159, 'a': 2}] True Nc C s | | _ | | _ | | _ d S)N)�param_distributions�n_iter� random_state)r# rH rI rJ r$ r$ r% r& � s zParameterSampler.__init__c c sM t j d d � | j j � D� � } t | j � } | r� t | j � } t | � } | | j k r� t d | | j f d � � x� t | | j d | �D] } | | Vq� Wn� t | j j � � } x� t j j | j � D]j } t � } xS | D]K \ } } t | d � r | j � | | <q� | | j t | � � | | <q� W| Vq� Wd S)Nc S s g | ] } t | d � � q S)�rvs)�hasattr)r2 r. r$ r$ r% r9 � s z-ParameterSampler.__iter__.<locals>.<listcomp>z;The total space of parameters %d is smaller than n_iter=%d.z+ For exhaustive searches, use GridSearchCV.rJ rK )r; �allrH r- r rJ r r1 rI � ValueErrorr r' r( r Zmoves�ranger* rL rK �randint) r# Z all_listsZrndr" Z grid_size�ir( �_r/ �kr. r$ r$ r% r0 � s, !zParameterSampler.__iter__c C s | j S)z&Number of points that will be sampled.)rI )r# r$ r$ r% r7 s zParameterSampler.__len__)rD rE rF rG r& r0 r7 r$ r$ r$ r% r � s ; !�raisec K s= t | | | | | | | | | | � \ } } } | | | f S)a� Run fit on one set of parameters. .. deprecated:: 0.18 This module will be removed in 0.20. Use :func:`sklearn.model_selection.fit_grid_point` instead. Parameters ---------- X : array-like, sparse matrix or list Input data. y : array-like or None Targets for input data. 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. parameters : dict Parameters to be set on estimator for this grid point. train : ndarray, dtype int or bool Boolean mask or indices for training set. test : ndarray, dtype int or bool Boolean mask or indices for test set. scorer : callable or None. If provided must be a scorer callable object / function with signature ``scorer(estimator, X, y)``. verbose : int Verbosity level. **fit_params : kwargs Additional parameter passed to the fit function of the estimator. 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. Returns ------- score : float Score of this parameter setting on given training / test split. parameters : dict The parameters that have been evaluated. n_samples_test : int Number of test samples in this split. )r ) �X�y� estimator� parameters�train�testZscorer�verbose� error_score� fit_params�scoreZn_samples_testrR r$ r$ r% r s : c s� t | d � r | g } x� | D]� } x� | j � D]� \ } � t � t j � rk � j d k rk t d � � � f d d � t t t j f D� } d | k r� t d j | � � � t � � d k r2 t d j | � � � q2 Wq Wd S) Nr( r z*Parameter array should be one-dimensional.c s g | ] } t � | � � q Sr$ )r! )r2 rS )r. r$ r% r9 Y s z%_check_param_grid.<locals>.<listcomp>Tz;Parameter values for parameter ({0}) need to be a sequence.r zEParameter values for parameter ({0}) need to be a non-empty sequence.) rL r( r! r; Zndarray�ndimrN �list�tuple�formatr1 )r" r+ �name�checkr$ )r. r% �_check_param_gridP s ! % re c @ s" e Z d Z f Z d d � Z d S)� _CVScoreTuplec C s% d j | j t j | j � | j � S)z-Simple custom repr to summarize the main infoz(mean: {0:.5f}, std: {1:.5f}, params: {2})rb �mean_validation_scorer; Zstd�cv_validation_scoresrX )r# r$ r$ r% �__repr__q s z_CVScoreTuple.__repr__N)rD rE rF � __slots__ri r$ r$ r$ r% rf c s rf rX rg rh c @ s e Z d Z d Z e d d d d d d d d d d d � � Z e d d � � Z d d d � Z e d d � d d � � Z e d d � d d � � Z e d d! � d d � � Z e d d" � d d � � Z e d d# � d d � � Z e d d$ � d d � � Z d d � Z d S)%� BaseSearchCVz<Base class for hyper parameter search with cross-validation.Nr Tr z2*n_jobsrT c C sp | | _ | | _ | | _ | d k r- | n i | _ | | _ | | _ | | _ | | _ | | _ | | _ d S)N) �scoringrW �n_jobsr] �iid�refit�cvr[ � pre_dispatchr\ ) r# rW rl r] rm rn ro rp r[ rq r\ r$ r$ r% r& } s zBaseSearchCV.__init__c C s | j j S)N)rW �_estimator_type)r# r$ r$ r% rr � s z BaseSearchCV._estimator_typec C sx | j d k r" t d | j � � | j d k rb t | j d � rb t j d j | j j � t � | j | j | | � S)a� 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, 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, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. Returns ------- score : float Notes ----- * The long-standing behavior of this method changed in version 0.16. * It no longer uses the metric provided by ``estimator.score`` if the ``scoring`` parameter was set when fitting. NzNNo score function explicitly defined, and the estimator doesn't provide one %sr^ z}The long-standing behavior to use the estimator's score function in {0}.score has changed. The scoring parameter is now used.) �scorer_rN �best_estimator_rl rL �warnings�warnrb � __class__rD r )r# rU rV r$ r$ r% r^ � s ! zBaseSearchCV.scoreZdelegatert rW c C s | j j | � S)aT 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. )rt �predict)r# rU r$ r$ r% rx � s zBaseSearchCV.predictc C s | j j | � S)a` 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. )rt � predict_proba)r# rU r$ r$ r% ry � s zBaseSearchCV.predict_probac C s | j j | � S)ah 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. )rt �predict_log_proba)r# rU r$ r$ r% rz � s z BaseSearchCV.predict_log_probac C s | j j | � S)ah 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. )rt �decision_function)r# rU r$ r$ r% r{ � s z BaseSearchCV.decision_functionc C s | j j | � S)aX 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. )rt � transform)r# rU r$ r$ r% r| � s zBaseSearchCV.transformc C s | j j | � S)ak Call inverse_transform on the estimator with the best found parameters. 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. )rt r| )r# ZXtr$ r$ r% �inverse_transform s z BaseSearchCV.inverse_transformc s � j } � j � t � j d � j �� _ t � � } t � � � \ � � � d k r� t � � | k r� t d t � � | f � � t � � � d t | � �� � j d k r� t | t � r� t | � } t d j t � � | | t � � � � t � j � � � j } t d � j d � j d | � � � � � � f d d � | D� � } t | � } t � � } t � } t � } x� t d | | � D]� } d } d } g } x\ | | | | � D]F \ } } } } | j | � � j r�| | 9} | | 7} | | 7} q�W� j r'| t | � } n | t | � } | j | | f � | j t | | t j | � � � q�W| � _ t | d d d � d d �d } | j � _ | j � _! � j" rt � � j# | j � } � d k r�| j$ � � � j% � n | j$ � � j% � | � _& � S)z7Actual fitting, performing the search over parameters.rl NzTTarget variable (y) has a different number of samples (%i) than data (X: %i samples)Z classifierr z@Fitting {0} folds for each of {1} candidates, totalling {2} fitsrm r[ rq c 3 sj | ]` } � D]S \ } } t t � t � � � � � j | | � j | � j d d d � j � Vq q d S)Zreturn_parametersTr\ N)r r r rs r[ r] r\ )r2 rX rY rZ )rU �base_estimatorrp r# rV r$ r% r3 9 s z$BaseSearchCV._fit.<locals>.<genexpr>r@ c S s | j S)N)rg )�xr$ r$ r% �<lambda>` s z#BaseSearchCV._fit.<locals>.<lambda>�reverseT)'rW rp r rl rs r r r1 rN r r r[ r! r �printrb r rq r rm r` rO �appendrn �floatrf r; �arrayZ grid_scores_r' rX Z best_params_rg Z best_score_ro Z set_params�fitr] rt )r# rU rV Zparameter_iterablerW Z n_samplesZ n_candidatesrq r? Zn_fitsZn_foldsZscoresZ grid_scoresZ grid_startZn_test_samplesr^ Z all_scoresZ this_scoreZthis_n_test_samplesrR rX ZbestZbest_estimatorr$ )rU r~ rp r# rV r% �_fit sv $ zBaseSearchCV._fit)rt rW )rt rW )rt rW )rt rW )rt rW )rt rW )rD rE rF rG r r& �propertyrr r^ r rx ry rz r{ r| r} r� r$ r$ r$ r% rk y s 'rk c sR e Z d Z d Z d d d d d d d d d � f d d � Z d d d � Z � S) r a$ Exhaustive search over specified parameter values for an estimator. .. deprecated:: 0.18 This module will be removed in 0.20. Use :class:`sklearn.model_selection.GridSearchCV` instead. Important members are fit, predict. GridSearchCV 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 grid-search over a parameter grid. Read more in the :ref:`User Guide <grid_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_grid : dict or list of dictionaries Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings. 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. .. versionchanged:: 0.17 Upgraded to joblib 0.9.3. 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 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. 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 GridSearchCV instance after fitting. verbose : integer Controls the verbosity: the higher, the more messages. 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. Examples -------- >>> from sklearn import svm, grid_search, datasets >>> iris = datasets.load_iris() >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} >>> svr = svm.SVC() >>> clf = grid_search.GridSearchCV(svr, parameters) >>> clf.fit(iris.data, iris.target) ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS GridSearchCV(cv=None, error_score=..., estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., decision_function_shape=None, degree=..., gamma=..., kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=..., verbose=False), fit_params={}, iid=..., n_jobs=1, param_grid=..., pre_dispatch=..., refit=..., scoring=..., verbose=...) 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. scorer_ : function Scorer function used on the held out data to choose the best parameters for the model. Notes ------ The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead. If `n_jobs` was set to a value higher than one, the data is copied for each point in the grid (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:`ParameterGrid`: generates all the combinations of a hyperparameter grid. :func:`sklearn.cross_validation.train_test_split`: utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation. :func:`sklearn.metrics.make_scorer`: Make a scorer from a performance metric or loss function. Nr Tr z2*n_jobsrT c sH t t | � j | | | | | | | | | | � | | _ t | � d S)N)�superr r& r" re ) r# rW r" rl r] rm rn ro rp r[ rq r\ )rw r$ r% r& $ s zGridSearchCV.__init__c C s | j | | t | j � � S)a� Run fit with all sets of parameters. Parameters ---------- X : 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 : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. )r� r r" )r# rU rV r$ r$ r% r� . s zGridSearchCV.fit)rD rE rF rG r& r� r$ r$ )rw r% r r s �c sX e Z d Z d Z d d d d d d d d d d d � f d d � Z d d d � Z � S) r a� Randomized search on hyper parameters. .. deprecated:: 0.18 This module will be removed in 0.20. Use :class:`sklearn.model_selection.RandomizedSearchCV` instead. 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 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. 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 settings, constructed from param_distributions. � Nr Tr z2*n_jobsrT c sn | | _ | | _ | | _ t t | � j d | d | d | d | d | d | d | d | d | d | � d S) NrW rl r] rm rn ro rp r[ rq r\ )rH rI rJ r� r r& )r# rW rH rI rl r] rm rn ro rp r[ rq rJ r\ )rw r$ r% r& � s zRandomizedSearchCV.__init__c C s1 t | j | j d | j �} | j | | | � S)a� Run fit on the estimator with randomly drawn parameters. 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. rJ )r rH rI rJ r� )r# rU rV Zsampled_paramsr$ r$ r% r� � s zRandomizedSearchCV.fit)rD rE rF rG r& r� r$ r$ )rw r% r @ s � )rX rg rh ):rG Z __future__r �abcr r � collectionsr r r � functoolsr r � itertoolsr r4 ru Znumpyr; �baser r r r Zcross_validationr r Zexternals.joblibr r Z externalsr Zutilsr Z utils.randomr Zutils.validationr r Zutils.metaestimatorsr Zmetrics.scorerr � exceptionsr �__all__rv �DeprecationWarning�objectr r r re rf Zwith_metaclassrk r r r$ r$ r$ r% �<module> sH {h? ��
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
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base.cpython-35.opt-1.pyc
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base.cpython-35.pyc
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calibration.cpython-35.opt-1.pyc
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calibration.cpython-35.pyc
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cross_validation.cpython-35.opt-1.pyc
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cross_validation.cpython-35.pyc
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discriminant_analysis.cpython-35.opt-1.pyc
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discriminant_analysis.cpython-35.pyc
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dummy.cpython-35.opt-1.pyc
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dummy.cpython-35.pyc
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exceptions.cpython-35.opt-1.pyc
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exceptions.cpython-35.pyc
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grid_search.cpython-35.opt-1.pyc
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grid_search.cpython-35.pyc
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isotonic.cpython-35.opt-1.pyc
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isotonic.cpython-35.pyc
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kernel_approximation.cpython-35.opt-1.pyc
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kernel_ridge.cpython-35.opt-1.pyc
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kernel_ridge.cpython-35.pyc
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lda.cpython-35.opt-1.pyc
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lda.cpython-35.pyc
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learning_curve.cpython-35.opt-1.pyc
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learning_curve.cpython-35.pyc
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multiclass.cpython-35.opt-1.pyc
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multiclass.cpython-35.pyc
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multioutput.cpython-35.opt-1.pyc
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multioutput.cpython-35.pyc
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naive_bayes.cpython-35.opt-1.pyc
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naive_bayes.cpython-35.pyc
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pipeline.cpython-35.opt-1.pyc
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pipeline.cpython-35.pyc
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qda.cpython-35.opt-1.pyc
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qda.cpython-35.pyc
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random_projection.cpython-35.opt-1.pyc
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random_projection.cpython-35.pyc
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setup.cpython-35.opt-1.pyc
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