import numpy as np
from sklearn import datasets# 载入数据包
digits = datasets.load_digits()#读取数据
X = digits.data#定义X
y = digits.target#定义y
from sklearn.model_selection import train_test_split #载入数据切分工具
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2)#数据切分
Signature: train_test_split(arrays, *options) Docstring: Split arrays or matrices into random train and test subsets
Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner.
Read more in the :ref:User Guide <cross_validation>.
Parameters
*arrays : sequence of indexables with same length / shape[0] Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.
test_size : float, int, None, optional If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. By default, the value is set to 0.25. The default will change in version 0.21. It will remain 0.25 only if train_size is unspecified, otherwise it will complement the specified train_size.
train_size : float, int, or None, default None If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.
random_state : int, RandomState instance or None, optional (default=None) 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.
shuffle : boolean, optional (default=True) Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
stratify : array-like or None (default is None) If not None, data is split in a stratified fashion, using this as the class labels.
Returns
splitting : list, length=2 * len(arrays) List containing train-test split of inputs.
.. versionadded:: 0.16
If the input is sparse, the output will be a
``scipy.sparse.csr_matrix``. Else, output type is the same as the
input type.Examples
import numpy as np from sklearn.model_selection import train_test_split X, y = np.arange(10).reshape((5, 2)), range(5) X array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) list(y) [0, 1, 2, 3, 4]
X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.33, random_state=42) ... X_train array([[4, 5], [0, 1], [6, 7]]) y_train [2, 0, 3] X_test array([[2, 3], [8, 9]]) y_test [1, 4]
train_test_split(y, shuffle=False) [[0, 1, 2], [3, 4]]
from sklearn.neighbors import KNeighborsClassifier #载入KNN分类器
knn_clf = KNeighborsClassifier(n_neighbors=3)# 设置分类器
knn_clf.fit(X_train,y_train)
Signature: knn_clf.fit(X, y) Docstring: Fit the model using X as training data and y as target values
Parameters
X : {array-like, sparse matrix, BallTree, KDTree} Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric='precomputed'.
y : {array-like, sparse matrix} Target values of shape = [n_samples] or [n_samples, n_outputs] File: c:usersqq123anaconda3libsite-packagessklearn eighborsase.py Type: method
knn_clf.score(X_test,y_test)
Signature: knn_clf.score(X, y, sample_weight=None) Docstring: Returns the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters
X : array-like, shape = (n_samples, n_features) Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns
score : float Mean accuracy of self.predict(X) wrt. y.
超参数网格搜索
para_grid = [
{
'weights':['uniform'],
'n_neighbors':[i for i in range(1,11)]
},
{
'weights':['distance'],
'n_neighbors':[i for i in range(1,11)],
'p':[i for i in range(1,6)]
}
]
knn_clf = KNeighborsClassifier(n_jobs=-1)
from sklearn.model_selection import GridSearchCV #导入网格搜索工具
grid_search = GridSearchCV(knn_clf,para_grid,verbose=3)#设置网格搜索工具
Init signature: GridSearchCV(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', return_train_score='warn') Docstring:
Exhaustive 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 <grid_search>.
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, 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 in a `(Stratified)KFold`,
- 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:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.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_parameters_`` 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' (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.
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() clf = GridSearchCV(svc, 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=None, iid=..., n_jobs=1, 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 | 0.8 |...| 2 |
+------------+-----------+------------+-----------------+---+---------+
| 'poly' | -- | 3 | 0.7 |...| 4 |
+------------+-----------+------------+-----------------+---+---------+
| 'rbf' | 0.1 | -- | 0.8 |...| 3 |
+------------+-----------+------------+-----------------+---+---------+
| 'rbf' | 0.2 | -- | 0.9 |...| 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' : [0.8, 0.7, 0.8, 0.9],
'split1_test_score' : [0.82, 0.5, 0.7, 0.78],
'mean_test_score' : [0.81, 0.60, 0.75, 0.82],
'std_test_score' : [0.02, 0.01, 0.03, 0.03],
'rank_test_score' : [2, 4, 3, 1],
'split0_train_score' : [0.8, 0.9, 0.7],
'split1_train_score' : [0.82, 0.5, 0.7],
'mean_train_score' : [0.81, 0.7, 0.7],
'std_train_score' : [0.03, 0.03, 0.04],
'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' : [0.007, 0.06, 0.04, 0.04],
'std_score_time' : [0.001, 0.002, 0.003, 0.005],
'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 (``'_<scorer_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).
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.
%%time
grid_search.fit(X_train,y_train)#寻找最优参数
grid_search.best_estimator_#最优参数
grid_search.best_params_
knn_clf_best = grid_search.best_estimator_
knn_clf_best.predict(X_test)
knn_clf_best.score(X_test,y_test)
归一化
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2)
from sklearn.preprocessing import StandardScaler
stdscl =StandardScaler()
stdscl.fit(X_train)
stdscl.mean_
stdscl.scale_
X_train_std = stdscl.transform(X_train)
X_test_std = stdscl.transform(X_test)
knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(X_train_std,y_train)
knn_clf.score(X_test_std,y_test)
两者合一
knn_clf_grid = KNeighborsClassifier(n_jobs=-1)
grid_search = GridSearchCV(knn_clf_grid,para_grid)
%%time
grid_search.fit(X_train_std,y_train)
grid_search.best_estimator_
grid_search.best_params_
knn_clf_grid.fit(X_test_std,y_test)
knn_clf_grid = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
knn_clf_grid.fit(X_test_std,y_test)
knn_clf_grid.score(X_test_std,y_test)