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
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(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.
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.
"""
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 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.
"""
return self._fit(X, y, ParameterGrid(self.param_grid))