RobustScaler函数的使用方法
lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.5, random_state=1))
ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.5, l1_ratio=.9, random_state=3))
sklearn的KFold函数的代码解释、使用方法
KFold函数的代码解释
class KFold Found at: sklearn.model_selection._split
class KFold(_BaseKFold):
"""K-Folds cross-validator
Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
Each fold is then used once as a validation while the k - 1 remaining folds form the training set.
Read more in the :ref:`User Guide <cross_validation>`.
Parameters
----------
n_splits : int, default=3
Number of folds. Must be at least 2.
shuffle : boolean, optional
Whether to shuffle the data before splitting into batches.
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`. Used when ``shuffle`` == True.
在:sklearn.model_select ._split找到的类KFold
类KFold (_BaseKFold):
”““K-Folds cross-validator
提供训练/测试索引来分割训练/测试集中的数据。将数据集分割成k个连续的折叠(默认情况下没有洗牌)。
然后，每条折叠使用一次作为验证，而k - 1条剩余折叠形成训练集。
更多信息参见:ref: ' User Guide <cross_validation> '。</cross_validation>
参数
----------
n_splits :int，默认=3
折叠的数量。必须至少是2。
shuffle :布尔型，可选
在分割成批之前是否打乱数据。
random_state :int, RandomState实例或None，可选，
默认=没有
如果int, random_state是随机数生成器使用的种子;
如果是RandomState实例，则random_state为随机数生成器;
如果没有，随机数生成器是“np.random”使用的RandomState实例。当' ' shuffle ' == True时使用。
Examples
--------
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf) # doctest: +NORMALIZE_WHITESPACE
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
Notes
-----
The first ``n_samples % n_splits`` folds have size
``n_samples // n_splits + 1``, other folds have size
``n_samples // n_splits``, where ``n_samples`` is the number of
samples.
See also
--------
StratifiedKFold
Takes group information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).
GroupKFold: K-fold iterator variant with non-overlapping groups.
RepeatedKFold: Repeats K-Fold n times.
""" 另请参阅
--------
StratifiedKFold
考虑组信息，以避免构建不平衡的类分布的折叠(对于二进制或多类分类任务)。
GroupKFold:不重叠组的K-fold迭代器变体。
RepeatedKFold:重复K-Fold n次。
”“”
def __init__(self, n_splits=3, shuffle=False,
random_state=None):
super(KFold, self).__init__(n_splits, shuffle, random_state)
def _iter_test_indices(self, X, y=None, groups=None):
n_samples = _num_samples(X)
indices = np.arange(n_samples)
if self.shuffle:
check_random_state(self.random_state).shuffle(indices)
n_splits = self.n_splits
fold_sizes = (n_samples // n_splits) * np.ones(n_splits, dtype=np.
int)
fold_sizes[:n_samples % n_splits] += 1
current = 0
for fold_size in fold_sizes:
start, stop = current, current + fold_size
yield indices[start:stop]
current = stop
KFold函数的使用方法
Examples
--------
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf) # doctest: +NORMALIZE_WHITESPACE
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
sklearn的cross_val_score函数的代码解释、使用方法
cross_val_score函数的代码解释
def cross_val_score Found at: sklearn.model_selection._validation
def cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs'):
"""Evaluate a score by cross-validation
Read more in the :ref:`User Guide <cross_validation>`.
通过交叉验证来评估一个分数
更多信息参见:ref: ' User Guide '。
Parameters
----------
estimator : estimator object implementing 'fit' The object to use to fit the data.
X : array-like
The data to fit. Can be for example a list, or an array.
y : array-like, optional, default: None
The target variable to try to predict in the case of supervised learning.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``.
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.
n_jobs : integer, optional
The number of CPUs to use to do the computation. -1 means 'all CPUs'.
verbose : integer, optional
The verbosity level.
fit_params : dict, optional
Parameters to pass to the fit method of the estimator.
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'
Returns
-------
scores : array of float, shape=(len(list(cv)),)
Array of scores of the estimator for each run of the cross validation.
参数
----------
estimator:实现“适合”对象以适合数据。
X:数组类
需要匹配的数据。可以是列表，也可以是数组。
y : 类似数组，可选，默认:无
在监督学习的情况下，预测的目标变量。
groups : 类数组，形状(n_samples，)，可选
将数据集分割为训练/测试集时使用的样本的标签分组。
scoring : 字符串，可调用或无，可选，默认:无
一个字符串(参见模型评估文档)或签名为' ' scorer(estimator, X, y) ' '的scorer可调用对象/函数。
cv : int，交叉验证生成器或可迭代，可选
确定交叉验证分割策略。
cv可能的输入有:
-无，使用默认的三折交叉验证，
-整数，用于指定“(分层的)KFold”中的折叠数，
-用作交叉验证生成器的对象。
-一个可迭代产生的序列，测试分裂。
对于整数/无输入，如果估计器是一个分类器，并且' ' y ' '是二进制的或多类的，则使用:class: ' StratifiedKFold '。在所有其他情况下，使用:class: ' KFold '。
请参考:ref: ' User Guide '，了解可以在这里使用的各种交叉验证策略。
n_jobs:整数，可选
用于进行计算的cpu数量。-1表示“所有cpu”。
verbose:整数，可选
冗长的水平。
fit_params :dict，可选
参数传递给估计器的拟合方法。
pre_dispatch: int或string，可选
控制并行执行期间分派的作业数量。当分配的作业多于cpu能够处理的任务时，减少这个数量有助于避免内存消耗激增。该参数可以为:
-无，在这种情况下，立即创建并派生所有作业。将此用于轻量级和快速运行的作业，以避免由于按需生成作业而造成的延迟
-一个int，给出生成的作业的确切总数
一个字符串，给出一个作为n_jobs函数的表达式，如'2*n_jobs'
返回
-------
(len(list(cv))，)
交叉验证的每次运行估计器的分数数组。
Examples
--------
>>> from sklearn import datasets, linear_model
>>> from sklearn.model_selection import cross_val_score
>>> diabetes = datasets.load_diabetes()
>>> X = diabetes.data[:150]
>>> y = diabetes.target[:150]
>>> lasso = linear_model.Lasso()
>>> print(cross_val_score(lasso, X, y)) # doctest: +ELLIPSIS
[ 0.33150734 0.08022311 0.03531764]
See Also
---------
:func:`sklearn.model_selection.cross_validate`:
To run cross-validation on multiple metrics and also to return train scores, fit times and score times.
:func:`sklearn.metrics.make_scorer`:
Make a scorer from a performance metric or loss function.
"""
# To ensure multimetric format is not supported
scorer = check_scoring(estimator, scoring=scoring)
cv_results = cross_validate(estimator=estimator, X=X, y=y, groups=groups,
scoring={'score':scorer}, cv=cv,
return_train_score=False,
n_jobs=n_jobs, verbose=verbose,
fit_params=fit_params,
pre_dispatch=pre_dispatch)
return cv_results['test_score'] 另请参阅
---------
:func:“sklearn.model_selection.cross_validate”:
在多个指标上进行交叉验证，并返回训练分数、适应时间和得分时间。
:func:“sklearn.metrics.make_scorer”:
从性能度量或损失函数中制作一个记分员。
”“”
#以确保不支持多度量格式
scoring参数可选的对象
https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter
Scoring
Function
Comment
Classification
‘accuracy’
metrics.accuracy_score
‘balanced_accuracy’
metrics.balanced_accuracy_score
‘average_precision’
metrics.average_precision_score
‘neg_brier_score’
metrics.brier_score_loss
‘f1’
metrics.f1_score
for binary targets
‘f1_micro’
metrics.f1_score
micro-averaged
‘f1_macro’
metrics.f1_score
macro-averaged
‘f1_weighted’
metrics.f1_score
weighted average
‘f1_samples’
metrics.f1_score
by multilabel sample
‘neg_log_loss’
metrics.log_loss
requires predict_proba support
‘precision’ etc.
metrics.precision_score
suffixes apply as with ‘f1’
‘recall’ etc.
metrics.recall_score
suffixes apply as with ‘f1’
‘jaccard’ etc.
metrics.jaccard_score
suffixes apply as with ‘f1’
‘roc_auc’
metrics.roc_auc_score
‘roc_auc_ovr’
metrics.roc_auc_score
‘roc_auc_ovo’
metrics.roc_auc_score
‘roc_auc_ovr_weighted’
metrics.roc_auc_score
‘roc_auc_ovo_weighted’
metrics.roc_auc_score
Clustering
‘adjusted_mutual_info_score’
metrics.adjusted_mutual_info_score
‘adjusted_rand_score’
metrics.adjusted_rand_score
‘completeness_score’
metrics.completeness_score
‘fowlkes_mallows_score’
metrics.fowlkes_mallows_score
‘homogeneity_score’
metrics.homogeneity_score
‘mutual_info_score’
metrics.mutual_info_score
‘normalized_mutual_info_score’
metrics.normalized_mutual_info_score
‘v_measure_score’
metrics.v_measure_score
Regression
‘explained_variance’
metrics.explained_variance_score
‘max_error’
metrics.max_error
‘neg_mean_absolute_error’
metrics.mean_absolute_error
‘neg_mean_squared_error’
metrics.mean_squared_error
‘neg_root_mean_squared_error’
metrics.mean_squared_error
‘neg_mean_squared_log_error’
metrics.mean_squared_log_error
‘neg_median_absolute_error’
metrics.median_absolute_error
‘r2’
metrics.r2_score
‘neg_mean_poisson_deviance’
metrics.mean_poisson_deviance
‘neg_mean_gamma_deviance’
metrics.mean_gamma_deviance
cross_val_score函数的使用方法
1、分类预测——糖尿病
>>> from sklearn import datasets, linear_model
>>> from sklearn.model_selection import cross_val_score
>>> diabetes = datasets.load_diabetes()
>>> X = diabetes.data[:150]
>>> y = diabetes.target[:150]
>>> lasso = linear_model.Lasso()
>>> print(cross_val_score(lasso, X, y)) # doctest: +ELLIPSIS
[ 0.33150734 0.08022311 0.03531764]
2、分类预测——iris鸢尾花
from sklearn import datasets #自带数据集
from sklearn.model_selection import train_test_split,cross_val_score #划分数据 交叉验证
from sklearn.neighbors import KNeighborsClassifier #一个简单的模型，只有K一个参数，类似K-means
import matplotlib.pyplot as plt
iris = datasets.load_iris() #加载sklearn自带的数据集
X = iris.data #这是数据
y = iris.target #这是每个数据所对应的标签
train_X,test_X,train_y,test_y = train_test_split(X,y,test_size=1/3,random_state=3) #这里划分数据以1/3的来划分 训练集训练结果 测试集测试结果
k_range = range(1,31)
cv_scores = [] #用来放每个模型的结果值
for n in k_range:
knn = KNeighborsClassifier(n) #knn模型，这里一个超参数可以做预测，当多个超参数时需要使用另一种方法GridSearchCV
scores = cross_val_score(knn,train_X,train_y,cv=10,scoring='accuracy') #cv：选择每次测试折数 accuracy：评价指标是准确度,可以省略使用默认值，具体使用参考下面。
cv_scores.append(scores.mean())
plt.plot(k_range,cv_scores)
plt.xlabel('K')
plt.ylabel('Accuracy') #通过图像选择最好的参数
plt.show()
best_knn = KNeighborsClassifier(n_neighbors=3) # 选择最优的K=3传入模型
best_knn.fit(train_X,train_y) #训练模型
print(best_knn.score(test_X,test_y)) #看看评分
