ML之SVM：利用SVM算法(超參數(shù)組合進(jìn)行單線(xiàn)程網(wǎng)格搜索+3fCrVa)對(duì)20類(lèi)新聞文本數(shù)據(jù)集進(jìn)行分類(lèi)預(yù)測(cè)、評(píng)估

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目錄

輸出結(jié)果

設(shè)計(jì)思路

核心代碼

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輸出結(jié)果

Fitting 3 folds for each of 12 candidates, totalling 36 fits [CV] svc__C=0.1, svc__gamma=0.01 ..................................... [CV] ............................ svc__C=0.1, svc__gamma=0.01 - 6.2s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 6.2s remaining: 0.0s [CV] svc__C=0.1, svc__gamma=0.01 ..................................... [CV] ............................ svc__C=0.1, svc__gamma=0.01 - 7.1s [CV] svc__C=0.1, svc__gamma=0.01 ..................................... [CV] ............................ svc__C=0.1, svc__gamma=0.01 - 7.0s [CV] svc__C=0.1, svc__gamma=0.1 ...................................... [CV] ............................. svc__C=0.1, svc__gamma=0.1 - 6.9s [CV] svc__C=0.1, svc__gamma=0.1 ...................................... [CV] ............................. svc__C=0.1, svc__gamma=0.1 - 6.8s [CV] svc__C=0.1, svc__gamma=0.1 ...................................... [CV] ............................. svc__C=0.1, svc__gamma=0.1 - 6.3s [CV] svc__C=0.1, svc__gamma=1.0 ...................................... [CV] ............................. svc__C=0.1, svc__gamma=1.0 - 6.3s [CV] svc__C=0.1, svc__gamma=1.0 ...................................... [CV] ............................. svc__C=0.1, svc__gamma=1.0 - 7.0s [CV] svc__C=0.1, svc__gamma=1.0 ...................................... [CV] ............................. svc__C=0.1, svc__gamma=1.0 - 8.1s [CV] svc__C=0.1, svc__gamma=10.0 ..................................... [CV] ............................ svc__C=0.1, svc__gamma=10.0 - 8.8s [CV] svc__C=0.1, svc__gamma=10.0 ..................................... [CV] ............................ svc__C=0.1, svc__gamma=10.0 - 10.7s [CV] svc__C=0.1, svc__gamma=10.0 ..................................... [CV] ............................ svc__C=0.1, svc__gamma=10.0 - 9.4s [CV] svc__C=1.0, svc__gamma=0.01 ..................................... [CV] ............................ svc__C=1.0, svc__gamma=0.01 - 8.4s [CV] svc__C=1.0, svc__gamma=0.01 ..................................... [CV] ............................ svc__C=1.0, svc__gamma=0.01 - 6.7s [CV] svc__C=1.0, svc__gamma=0.01 ..................................... [CV] ............................ svc__C=1.0, svc__gamma=0.01 - 6.9s [CV] svc__C=1.0, svc__gamma=0.1 ...................................... [CV] ............................. svc__C=1.0, svc__gamma=0.1 - 6.6s [CV] svc__C=1.0, svc__gamma=0.1 ...................................... [CV] ............................. svc__C=1.0, svc__gamma=0.1 - 6.2s [CV] svc__C=1.0, svc__gamma=0.1 ...................................... [CV] ............................. svc__C=1.0, svc__gamma=0.1 - 6.8s [CV] svc__C=1.0, svc__gamma=1.0 ...................................... [CV] ............................. svc__C=1.0, svc__gamma=1.0 - 7.6s [CV] svc__C=1.0, svc__gamma=1.0 ...................................... [CV] ............................. svc__C=1.0, svc__gamma=1.0 - 7.7s [CV] svc__C=1.0, svc__gamma=1.0 ...................................... [CV] ............................. svc__C=1.0, svc__gamma=1.0 - 8.2s [CV] svc__C=1.0, svc__gamma=10.0 ..................................... [CV] ............................ svc__C=1.0, svc__gamma=10.0 - 6.7s [CV] svc__C=1.0, svc__gamma=10.0 ..................................... [CV] ............................ svc__C=1.0, svc__gamma=10.0 - 8.4s [CV] svc__C=1.0, svc__gamma=10.0 ..................................... [CV] ............................ svc__C=1.0, svc__gamma=10.0 - 9.5s [CV] svc__C=10.0, svc__gamma=0.01 .................................... [CV] ........................... svc__C=10.0, svc__gamma=0.01 - 10.1s [CV] svc__C=10.0, svc__gamma=0.01 .................................... [CV] ........................... svc__C=10.0, svc__gamma=0.01 - 9.9s [CV] svc__C=10.0, svc__gamma=0.01 .................................... [CV] ........................... svc__C=10.0, svc__gamma=0.01 - 8.8s [CV] svc__C=10.0, svc__gamma=0.1 ..................................... [CV] ............................ svc__C=10.0, svc__gamma=0.1 - 9.2s [CV] svc__C=10.0, svc__gamma=0.1 ..................................... [CV] ............................ svc__C=10.0, svc__gamma=0.1 - 7.7s [CV] svc__C=10.0, svc__gamma=0.1 ..................................... [CV] ............................ svc__C=10.0, svc__gamma=0.1 - 6.9s [CV] svc__C=10.0, svc__gamma=1.0 ..................................... [CV] ............................ svc__C=10.0, svc__gamma=1.0 - 8.0s [CV] svc__C=10.0, svc__gamma=1.0 ..................................... [CV] ............................ svc__C=10.0, svc__gamma=1.0 - 9.5s [CV] svc__C=10.0, svc__gamma=1.0 ..................................... [CV] ............................ svc__C=10.0, svc__gamma=1.0 - 9.0s [CV] svc__C=10.0, svc__gamma=10.0 .................................... [CV] ........................... svc__C=10.0, svc__gamma=10.0 - 8.6s [CV] svc__C=10.0, svc__gamma=10.0 .................................... [CV] ........................... svc__C=10.0, svc__gamma=10.0 - 8.1s [CV] svc__C=10.0, svc__gamma=10.0 .................................... [CV] ........................... svc__C=10.0, svc__gamma=10.0 - 9.0s [Parallel(n_jobs=1)]: Done 36 out of 36 | elapsed: 4.8min finished 單線(xiàn)程：輸出最佳模型在測(cè)試集上的準(zhǔn)確性： 0.8226666666666667

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設(shè)計(jì)思路

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核心代碼

class GridSearchCV(BaseSearchCV):"""Exhaustive search over specified parameter values for an estimator... deprecated:: 0.18This 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 theestimator used.The parameters of the estimator used to apply these methods are optimizedby 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 dictionariesDictionary with parameters names (string) as keys and lists ofparameter settings to try as values, or a list of suchdictionaries, in which case the grids spanned by each dictionaryin the list are explored. This enables searching over any sequenceof parameter settings.scoring : string, callable or None, default=NoneA string (see model evaluation documentation) ora scorer callable object / function with signature``scorer(estimator, X, y)``.If ``None``, the ``score`` method of the estimator is used.fit_params : dict, optionalParameters to pass to the fit method.n_jobs: int, default: 1 :The maximum number of estimators fit in parallel.- If -1 all CPUs are used.- If 1 is given, no parallel computing code is used at all,which is useful for debugging.- For ``n_jobs`` below -1, ``(n_cpus + n_jobs + 1)`` are used.For example, with ``n_jobs = -2`` all CPUs but one are used... versionchanged:: 0.17Upgraded to joblib 0.9.3.pre_dispatch : int, or string, optionalControls the number of jobs that get dispatched during parallelexecution. Reducing this number can be useful to avoid anexplosion of memory consumption when more jobs get dispatchedthan CPUs can process. This parameter can be:- None, in which case all the jobs are immediatelycreated and spawned. Use this for lightweight andfast-running jobs, to avoid delays due to on-demandspawning of the jobs- An int, giving the exact number of total jobs that arespawned- A string, giving an expression as a function of n_jobs,as in '2*n_jobs'iid : boolean, default=TrueIf True, the data is assumed to be identically distributed acrossthe 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, optionalDetermines 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`` iseither binary or multiclass,:class:`sklearn.model_selection.StratifiedKFold` is used. In allother cases, :class:`sklearn.model_selection.KFold` is used.Refer :ref:`User Guide <cross_validation>` for the variouscross-validation strategies that can be used here.refit : boolean, default=TrueRefit the best estimator with the entire dataset.If "False", it is impossible to make predictions usingthis GridSearchCV instance after fitting.verbose : integerControls the verbosity: the higher, the more messages.error_score : 'raise' (default) or numericValue 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 refitstep, 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 +ELLIPSISGridSearchCV(cv=None, 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={}, iid=..., n_jobs=1,param_grid=..., pre_dispatch=..., refit=...,scoring=..., verbose=...)Attributes----------grid_scores_ : list of named tuplesContains 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 thecross-validation folds* ``cv_validation_scores``, the list of scores for each foldbest_estimator_ : estimatorEstimator that was chosen by the search, i.e. estimatorwhich gave highest score (or smallest loss if specified)on the left out data. Not available if refit=False.best_score_ : floatScore of best_estimator on the left out data.best_params_ : dictParameter setting that gave the best results on the hold out data.scorer_ : functionScorer function used on the held out data to choose the bestparameters for the model.Notes------The parameters selected are those that maximize the score of the left outdata, 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 eachpoint in the grid (and not `n_jobs` times). This is done for efficiencyreasons if individual jobs take very little time, but may raise errors ifthe dataset is large and not enough memory is available. A workaround inthis 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 usablefor fitting a GridSearchCV instance and an evaluation set forits final evaluation.:func:`sklearn.metrics.make_scorer`:Make a scorer from a performance metric or loss function."""def __init__(self, estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise'):super(GridSearchCV, self).__init__(estimator, scoring, fit_params, n_jobs, iid, refit, cv, verbose, pre_dispatch, error_score)self.param_grid = param_grid_check_param_grid(param_grid)def fit(self, X, y=None):"""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 andn_features is the number of features.y : array-like, shape = [n_samples] or [n_samples, n_output], optionalTarget relative to X for classification or regression;None for unsupervised learning."""return self._fit(X, y, ParameterGrid(self.param_grid))

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