ML之SVM：利用SVM算法(超参数组合进行单线程网格搜索+3fCrVa)对20类新闻文本数据集进行分类预测、评估

ML之SVM：利用SVM算法(超参数组合进行单线程网格搜索+3fCrVa)对20类新闻文本数据集进行分类预测、评估

目录

输出结果

设计思路

核心代码

输出结果

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
单线程：输出最佳模型在测试集上的准确性： 0.8226666666666667

设计思路

核心代码

class GridSearchCV(-title class_ inherited__">BaseSearchCV):
    """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 :
    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:: 0.17
    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='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 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.