ó ‡ˆ\c@ sÙdZddlmZddlmZddlmZmZddlmZm Z ddl m Z m Z ddl mZddlZddlZddlZddlZdd lmZd d lmZmZmZd d lmZd dlmZd dlmZd dlmZd dl m!Z!d dl"m#Z#m$Z$d dl%m&Z&d dl'm(Z(d dl)m*Z*d dl)m+Z+d dl)m,Z-m.Z/d dl)m0Z1d dl2m3Z3d dl4m5Z5m6Z6d dl7m8Z8d dl9m:Z:d dl;m<Z<d dl;m=Z=dd d!d"d#gZ>d e?fd$„ƒYZ@d"e?fd%„ƒYZAd&d'„ZBd(„ZCd)ed)d*d+d,fƒfd-„ƒYZDd.e&jEeeeƒfd/„ƒYZFdeFfd0„ƒYZGd#eFfd1„ƒYZHdS(2sl The :mod:`sklearn.model_selection._search` includes utilities to fine-tune the parameters of an estimator. iÿÿÿÿ(tprint_function(tdivision(tABCMetatabstractmethod(t namedtuplet defaultdict(tpartialtreduce(tproductN(trankdatai(t BaseEstimatort is_classifiertclone(tMetaEstimatorMixini(tcheck_cv(t_fit_and_score(t_aggregate_score_dicts(tNotFittedError(tParalleltdelayed(tsix(tcheck_random_state(t sp_version(t MaskedArray(t_Mappingt _Sequence(t _Iterable(tsample_without_replacement(t indexabletcheck_is_fitted(tif_delegate_has_method(tDeprecationDict(t_check_multimetric_scoring(t check_scoringt GridSearchCVt ParameterGridtfit_grid_pointtParameterSamplertRandomizedSearchCVcB s2eZdZd„Zd„Zd„Zd„ZRS(síGrid of parameters with a discrete number of values for each. Can be used to iterate over parameter value combinations with the Python built-in function iter. Read more in the :ref:`User Guide `. 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.model_selection 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 :class:`ParameterGrid` to perform a full parallelized parameter search. cC sÐt|ttfƒs-tdj|ƒƒ‚nt|tƒrH|g}nxx|D]p}t|tƒs|tdj|ƒƒ‚nx@|D]8}t||tƒsƒtdj|||ƒƒ‚qƒqƒWqOW||_dS(Ns-Parameter grid is not a dict or a list ({!r})s#Parameter grid is not a dict ({!r})s;Parameter grid value is not iterable (key={!r}, value={!r})(t isinstancetMappingtIterablet TypeErrortformattdictt param_grid(tselfR-tgridtkey((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyt__init___s      cc s{xt|jD]i}t|jƒƒ}|s0iVq t|Œ\}}x.t|ŒD] }tt||ƒƒ}|VqOWq WdS(sîIterate 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-tsortedtitemstzipRR,(R.tpR3tkeystvaluestvtparams((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyt__iter__vs c s/tttjƒ‰t‡fd†|jDƒƒS(sNumber of points on the grid.c3 s7|]-}|r+ˆd„|jƒDƒƒndVqdS(cs s|]}t|ƒVqdS(N(tlen(t.0R8((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pys ŽsiN(R7(R<R5(R(s>lib/python2.7/site-packages/sklearn/model_selection/_search.pys Žs(RRtoperatortmultsumR-(R.((Rs>lib/python2.7/site-packages/sklearn/model_selection/_search.pyt__len__Šsc C sxü|jD]ñ}|s6|dkr&iS|d8}q ntt|jƒƒddd…Œ\}}g|D]}t|ƒ^qh}tj|ƒ}||kr¨||8}q i}xFt|||ƒD]2\} }} t|| ƒ\}} || || lib/python2.7/site-packages/sklearn/model_selection/_search.pyt __getitem__‘s"  +  "(t__name__t __module__t__doc__R1R:R@RM(((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR#3s *   cB s,eZdZdd„Zd„Zd„ZRS(s= Generator on parameters sampled from given distributions. 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 before SciPy 0.16, 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. Deterministic behavior is however guaranteed from SciPy 0.16 onwards. Read more in the :ref:`User Guide `. 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, 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`. Returns ------- params : dict of string to any **Yields** dictionaries mapping each estimator parameter to as sampled value. Examples -------- >>> from sklearn.model_selection 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 cC s||_||_||_dS(N(tparam_distributionstn_itert random_state(R.RQRRRS((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR1øs  c c s‘tjg|jjƒD]}t|dƒ ^qƒ}t|jƒ}|rÏt|jƒ}t|ƒ}|j }||kr£t j d||j |ft ƒ|}nxçt ||d|ƒD]}||Vq¹Wn¾t|jjƒƒ}x¦tjj|j ƒD]} tƒ} xx|D]p\} }t|dƒrctdkrJ|jƒ| | lib/python2.7/site-packages/sklearn/model_selection/_search.pyR:ýs2 ,       !cC s|jS(s&Number of points that will be sampled.(RR(R.((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR@$sN(RNRORPtNoneR1R:R@(((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR%»s<  'sraise-deprecatingc K sSt||ƒt||||||||d| dtd|ƒ\} } | || fS(sÄRun fit on one set of parameters. 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 The scorer callable object / function must have its signature as ``scorer(estimator, X, y)``. If ``None`` the estimator's default scorer is used. verbose : int Verbosity level. **fit_params : kwargs Additional parameter passed to the fit function of the estimator. 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 'raise' but from version 0.22 it will change to np.nan. 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. t fit_paramstreturn_n_test_samplest error_score(R!RtTrue( tXtyt estimatort parametersttrainttesttscorertverboseRfRdtscorestn_samples_test((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR$)s;   cC sêt|dƒr|g}nxÈ|D]À}x·|jƒD]©\}}t|tjƒrq|jdkrqtdƒ‚nt|tjƒsœt|tjt fƒ r´tdj |ƒƒ‚nt |ƒdkr5tdj |ƒƒ‚q5q5Wq"WdS(NR3is*Parameter array should be one-dimensional.s[Parameter values for parameter ({0}) need to be a sequence(but not a string) or np.ndarray.isEParameter values for parameter ({0}) need to be a non-empty sequence.( RVR3R'RAtndarraytndimt ValueErrorRt string_typestSequenceR+R;(R-R5tnameR8((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyt_check_param_gridns  !  t _CVScoreTupleRktmean_validation_scoretcv_validation_scorescB seZdZd„ZRS(cC s%dj|jtj|jƒ|jƒS(s-Simple custom repr to summarize the main infos(mean: {0:.5f}, std: {1:.5f}, params: {2}(R+RzRAtstdR{Rk(R.((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyt__repr__‘s((RNROt __slots__R}(((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyRyƒs t BaseSearchCVc B seZdZeddddedddded„ ƒZed„ƒZdd„Z d„Z e d dƒd „ƒZ e d dƒd „ƒZ e d dƒd„ƒZe d dƒd„ƒZe d dƒd„ƒZe d dƒd„ƒZed„ƒZd„Zddd„Zd„ZRS(sJAbstract base class for hyper parameter search with cross-validation. RXis2*n_jobssraise-deprecatingc C sg||_||_||_||_||_||_||_||_| |_| |_ | |_ dS(N( tscoringRjtn_jobsRdtiidtrefittcvRot pre_dispatchRftreturn_train_score( R.RjR€RdRR‚RƒR„RoR…RfR†((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR1žs          cC s |jjS(N(Rjt_estimator_type(R.((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR‡°scC sg|jdƒ|jdkr2td|jƒ‚n|jrK|j|jn|j}||j||ƒS(sšReturns 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 tscoresNNo score function explicitly defined, and the estimator doesn't provide one %sN(t_check_is_fittedtscorer_RcRttbest_estimator_t multimetric_Rƒ(R.RhRiRˆ((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyRˆ´s  "cC s<|js+tdt|ƒj|fƒ‚n t|dƒdS(Ns¸This %s instance was initialized with refit=False. %s is available only after refitting on the best parameters. You can refit an estimator manually using the ``best_params_`` attributeR‹(RƒRttypeRNR(R.t method_name((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR‰Ðs tdelegateR‹RjcC s|jdƒ|jj|ƒS(sTCall 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. tpredict(R‰R‹R(R.Rh((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyRÜs cC s|jdƒ|jj|ƒS(s`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. t predict_proba(R‰R‹R‘(R.Rh((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR‘ís cC s|jdƒ|jj|ƒS(shCall 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. tpredict_log_proba(R‰R‹R’(R.Rh((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR’þs cC s|jdƒ|jj|ƒS(shCall 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. tdecision_function(R‰R‹R“(R.Rh((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR“s cC s|jdƒ|jj|ƒS(sXCall 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. t transform(R‰R‹R”(R.Rh((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR” s cC s|jdƒ|jj|ƒS(sgCall 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. tinverse_transform(R‰R‹R•(R.tXt((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR•1s cC s|jdƒ|jjS(Ntclasses_(R‰R‹R—(R.((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR—Bs cC stdƒ‚dS(s{Repeatedly calls `evaluate_candidates` to conduct a search. This method, implemented in sub-classes, makes it possible to customize the the scheduling of evaluations: GridSearchCV and RandomizedSearchCV schedule evaluations for their whole parameter search space at once but other more sequential approaches are also possible: for instance is possible to iteratively schedule evaluations for new regions of the parameter search space based on previously collected evaluation results. This makes it possible to implement Bayesian optimization or more generally sequential model-based optimization by deriving from the BaseSearchCV abstract base class. Parameters ---------- evaluate_candidates : callable This callback accepts a list of candidates, where each candidate is a dict of parameter settings. It returns a dict of all results so far, formatted like ``cv_results_``. Examples -------- :: def _run_search(self, evaluate_candidates): 'Try C=0.1 only if C=1 is better than C=10' all_results = evaluate_candidates([{'C': 1}, {'C': 10}]) score = all_results['mean_test_score'] if score[0] < score[1]: evaluate_candidates([{'C': 0.1}]) s_run_search not implemented.N(tNotImplementedError(R.tevaluate_candidates((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyt _run_searchGs c  sˆ jdk rDtjdtƒ|r8tjdtƒqDˆ j}nˆ j}tˆ jˆ dt |ƒƒ‰t ˆ jdˆ j ƒ\‰ ˆ _ ˆ j rëˆ j tk rßtˆ j tjƒ sɈ j ˆ krßtdˆ j ƒ‚qñˆ j }nd}tˆˆ ˆƒ\‰‰ ‰ˆjˆˆ ˆƒ‰tˆ jƒ‰tdˆ jdˆ jd ˆ jƒ‰td ˆ d |d ˆ jd tdtdtdˆ jdˆ jƒ‰ig‰ ˆQg‰g‰‡‡‡‡‡‡‡‡‡‡ ‡ ‡ ‡ f d†}ˆ j|ƒWdQXˆ d}ˆ j sˆ j r[|d|jƒˆ _ |dˆ j ˆ _!|d|ˆ j ˆ _"nˆ j rßtˆƒj#ˆ j!ˆ _$t%j%ƒ} ˆ dk r°ˆ j$j&ˆˆ |nˆ j$j&ˆ|t%j%ƒ} | | ˆ _'nˆ j rîˆ nˆ dˆ _(|ˆ _)ˆˆ _*ˆ S(séRun 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. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of the estimator s›"fit_params" as a constructor argument was deprecated in version 0.19 and will be removed in version 0.21. Pass fit parameters to the "fit" method instead.sgIgnoring fit_params passed as a constructor argument in favor of keyword arguments to the "fit" method.t classifierR€sFor multi-metric scoring, the parameter refit must be set to a scorer key to refit an estimator with the best parameter setting on the whole data and make the best_* attributes available for that metric. If this is not needed, refit should be set to False explicitly. %r was passed.RˆRRoR…RnRdR†Ret return_timestreturn_parametersRfc sÂt|ƒ}t|ƒ}ˆ jdkrGtdjˆ||ˆƒƒnˆ‡‡‡‡ fd†t|ˆjˆˆ ˆƒƒDƒƒ}ˆj|ƒˆj|ƒˆ jˆˆ ˆˆƒˆ d<ˆ dS(Nis@Fitting {0} folds for each of {1} candidates, totalling {2} fitsc 3 sN|]D\}\}}ttƒtˆƒˆˆd|d|d|ˆVqdS(RlRmRkN(RRR (R<RkRlRm(Rhtbase_estimatortfit_and_score_kwargsRi(s>lib/python2.7/site-packages/sklearn/model_selection/_search.pys Às( tlistR;RotprintR+Rtsplittextendt_format_results(tcandidate_paramst n_candidatesRJ( Rhtall_candidate_paramstall_outRžR„RŸtgroupstn_splitstparalleltresults_containertscorersR.Ri(s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR™·s     Nis rank_test_%sR9s mean_test_%s(+RdRcRWRXtDeprecationWarningtRuntimeWarningRjRR„R R R€RŒRƒtFalseR'RRuRtRt get_n_splitsR RRRoR…R,R†RgRfRštargmint best_index_t best_params_t best_score_t set_paramsR‹ttimetfitt refit_time_RŠt cv_results_t n_splits_( R.RhRiR©RdRjt refit_metricR™tresultstrefit_start_timetrefit_end_time(( RhR§R¨RžR„RŸR©RªR«R¬R­R.Ris>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR¸isr             3         c  st|ƒ‰|jr3t|Œ\}}}}} nt|Œ\}}}} t|ƒ} |jrot|ƒ} n|jdkr‡tƒni‰dtt‡‡‡fd†} | d|ƒ| d| ƒttt t j ˆƒdt dt ƒƒ} xIt|ƒD];\}}x,|jƒD]\}}|| d||lib/python2.7/site-packages/sklearn/model_selection/_search.pyt_stores +tfit_timet score_timetmaskRÀsparam_%sR9RÁiRÂtrtolg-Cëâ6?tatols°The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.stest_%sRÎRÏstrain_%ss£You are accessing a training score ({!r}), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True(R;R†R4RRRcR°RRRRAtemptyRgtobjectt enumerateR3tupdateRÅtintR‚R6RÇRÈtallcloseRWRXR®tsetR+t add_warningt FutureWarning(R.R¥R­RªRJttrain_score_dictsttest_score_dictsttest_sample_countsRÔRÕt test_scorest train_scoresRÓt param_resultstcand_iR9RwtvalueR‚RXt scorer_nameRptmeans_weightedtmeans_unweightedt prev_keysR0tmessage((R¦RªR½s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR¤òsl                     !N(R‹Rj(R‹Rj(R‹Rj(R‹Rj(R‹Rj(R‹Rj(RNRORPRRcRgR1tpropertyR‡RˆR‰RRR‘R’R“R”R•R—RšR¸R¤(((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR™s&     "‰c B s>eZdZeeededddddd„ Zd„ZRS(s³6Exhaustive search over specified parameter values for an estimator. 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 `. Parameters ---------- estimator : estimator object. 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, 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 default scorer (if available) is used. fit_params : dict, optional Parameters to pass to the fit method. .. deprecated:: 0.19 ``fit_params`` as a constructor argument was deprecated in version 0.19 and will be removed in version 0.21. Pass fit parameters to the ``fit`` method instead. 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 ` 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='warn' 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. If False, return the average score across folds. Default is True, but will change to False in version 0.21, to correspond to the standard definition of cross-validation. .. versionchanged:: 0.20 Parameter ``iid`` will change from True to False by default in version 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 3-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 ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.20 ``cv`` default value if None will change from 3-fold to 5-fold in v0.22. refit : boolean, or string, 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 is used to find the best parameters for refitting the estimator at the end. The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``GridSearchCV`` 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. verbose : integer Controls the verbosity: the higher, the more messages. 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 'raise' but from version 0.22 it will change to np.nan. return_train_score : boolean, optional If ``False``, the ``cv_results_`` attribute will not include training scores. Current default is ``'warn'``, which behaves as ``True`` in addition to raising a warning when a training score is looked up. That default will be changed to ``False`` in 0.21. 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. Examples -------- >>> from sklearn import svm, datasets >>> from sklearn.model_selection import GridSearchCV >>> iris = datasets.load_iris() >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} >>> svc = svm.SVC(gamma="scale") >>> clf = GridSearchCV(svc, parameters, cv=5) >>> clf.fit(iris.data, iris.target) ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS GridSearchCV(cv=5, error_score=..., estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., decision_function_shape='ovr', degree=..., gamma=..., kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=..., verbose=False), fit_params=None, iid=..., n_jobs=None, param_grid=..., pre_dispatch=..., refit=..., return_train_score=..., scoring=..., verbose=...) >>> sorted(clf.cv_results_.keys()) ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS ['mean_fit_time', 'mean_score_time', 'mean_test_score',... 'mean_train_score', 'param_C', 'param_kernel', 'params',... 'rank_test_score', 'split0_test_score',... 'split0_train_score', 'split1_test_score', 'split1_train_score',... 'split2_test_score', 'split2_train_score',... 'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...] 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|param_degree|split0_test_score|...|rank_t...| +============+===========+============+=================+===+=========+ | 'poly' | -- | 2 | 0.80 |...| 2 | +------------+-----------+------------+-----------------+---+---------+ | 'poly' | -- | 3 | 0.70 |...| 4 | +------------+-----------+------------+-----------------+---+---------+ | 'rbf' | 0.1 | -- | 0.80 |...| 3 | +------------+-----------+------------+-----------------+---+---------+ | 'rbf' | 0.2 | -- | 0.93 |...| 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' : [0.80, 0.70, 0.80, 0.93], 'split1_test_score' : [0.82, 0.50, 0.70, 0.78], 'mean_test_score' : [0.81, 0.60, 0.75, 0.85], 'std_test_score' : [0.01, 0.10, 0.05, 0.08], 'rank_test_score' : [2, 4, 3, 1], 'split0_train_score' : [0.80, 0.92, 0.70, 0.93], 'split1_train_score' : [0.82, 0.55, 0.70, 0.87], 'mean_train_score' : [0.81, 0.74, 0.70, 0.90], 'std_train_score' : [0.01, 0.19, 0.00, 0.03], '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' : [0.01, 0.06, 0.04, 0.04], 'std_score_time' : [0.00, 0.00, 0.00, 0.01], 'params' : [{'kernel': 'poly', 'degree': 2}, ...], } 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 (``'_'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.) best_estimator_ : estimator or dict 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``. 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 present only if ``refit`` is specified. best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is present only if ``refit`` is specified. 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 present only if ``refit`` is specified. 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 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.model_selection.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. RXis2*n_jobssraise-deprecatingc C sltt|ƒjd|d|d|d|d|d|d|d| d | d | d | ƒ ||_t|ƒdS( NRjR€RdRR‚RƒR„RoR…RfR†(tsuperR"R1R-Rx( R.RjR-R€RdRR‚RƒR„RoR…RfR†((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR1™s  cC s|t|jƒƒdS(s#Search all candidates in param_gridN(R#R-(R.R™((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyRš¥s(RNRORPRcRgR1Rš(((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR"bsÿ6 c B sDeZdZdeeededddeddd„ Zd„ZRS(sØ4Randomized 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. Note that before SciPy 0.16, 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 `. 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, 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 default scorer (if available) is used. fit_params : dict, optional Parameters to pass to the fit method. .. deprecated:: 0.19 ``fit_params`` as a constructor argument was deprecated in version 0.19 and will be removed in version 0.21. Pass fit parameters to the ``fit`` method instead. 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 ` 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='warn' 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. If False, return the average score across folds. Default is True, but will change to False in version 0.21, to correspond to the standard definition of cross-validation. .. versionchanged:: 0.20 Parameter ``iid`` will change from True to False by default in version 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 3-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 ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.20 ``cv`` default value if None will change from 3-fold to 5-fold in v0.22. refit : boolean, or string 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. 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. 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 'raise' but from version 0.22 it will change to np.nan. return_train_score : boolean, optional If ``False``, the ``cv_results_`` attribute will not include training scores. Current default is ``'warn'``, which behaves as ``True`` in addition to raising a warning when a training score is looked up. That default will be changed to ``False`` in 0.21. 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 (``'_'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.) best_estimator_ : estimator or dict 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. 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. i RXis2*n_jobssraise-deprecatingcC st||_||_| |_tt|ƒjd|d|d|d|d|d|d| d| d | d | d |ƒ dS( NRjR€RdRR‚RƒR„RoR…RfR†(RQRRRSRðR&R1(R.RjRQRRR€RdRR‚RƒR„RoR…RSRfR†((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR1Ùs    cC s&|t|j|jd|jƒƒdS(s1Search n_iter candidates from param_distributionsRSN(R%RQRRRS(R.R™((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyRšçs(RNRORPRcRgR1Rš(((s>lib/python2.7/site-packages/sklearn/model_selection/_search.pyR&ªsÿ.   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