sklearn

feature_selection

SequentialFeatureSelector

SequentialFeatureSelector(estimator, 
                          k_features=1, 
                          forward=True, 
                          floating=False, 
                          verbose=0, 
                          scoring=None, 
                          cv=5, 
                          n_jobs=1, 
                          pre_dispatch='2n_jobs', 
                          clone_estimator=True, 
                          fixed_features=None)

用于分类和回归的序列特征选择

Parameters :

estimator : scikit-learn classifier or regressor
k_features : int or tuple or str (default: 1)

Number of features to select, where k_features < the full feature set. New in 0.4.2: A tuple containing a min and max value can be provided, and the SFS will consider return any feature combination between min and max that scored highest in cross-validtion. For example, the tuple (1, 4) will return any combination from 1 up to 4 features instead of a fixed number of features k. New in 0.8.0: A string argument “best” or “parsimonious”. If “best” is provided, the feature selector will return the feature subset with the best cross-validation performance. If “parsimonious” is provided as an argument, the smallest feature subset that is within one standard error of the cross-validation performance will be selected.
forward : bool (default: True)

如果为True，则为正向选择，否则为反向选择
verbose : int (default: 0), 日志记录中使用的详细级别。

如果0，则无输出；如果1当前集合中的特征数；如果2，详细记录，包括步骤中的时间戳和cv分数。
scoring : str, callable, or None (default: None)

If None (default), uses ‘accuracy’ for sklearn classifiers and ‘r2’ for sklearn regressors. If str, uses a sklearn scoring metric string identifier, for example {accuracy, f1, precision, recall, roc_auc} for classifiers, {‘mean_absolute_error’, ‘mean_squared_error’/’neg_mean_squared_error’, ‘median_absolute_error’, ‘r2’} for regressors.
cv : int (default: 5)

Integer or iterable yielding train, test splits. If cv is an integer and estimator is a classifier (or y consists of integer class labels) stratified k-fold. Otherwise regular k-fold cross-validation is performed. No cross-validation if cv is None, False, or 0.
fixed_features : tuple (default: None)

If not None, the feature indices provided as a tuple will be regarded as fixed by the feature selector. For example, if fixed_features=(1, 3, 7), the 2nd, 4th, and 8th feature are guaranteed to be present in the solution. Note that if fixed_features is not None, make sure that the number of features to be selected is greater than len(fixed_features). In other words, ensure that k_features > len(fixed_features).

属性：

k_feature_idx_ : array-like, shape = [n_predictions]

所选特征子集的特征索引。
k_feature_names_ : array-like, shape = [n_predictions]

所选特征的名称，
k_score_ : float

所选子集的交叉验证平均分数。
subsets_ : dict

在穷举选择过程中选择的特征子集的字典，其中字典键是这些特征子集的长度k。字典值是字典本身，具有以下键：“feature_idx”（特征子集的索引元组）“feature_names”（特征名称的元组）。subset）“cv_scores”（列出单个交叉验证得分）“avg_score”（平均交叉验证得分）

Methods：

fit(X, y, custom_feature_names=None, groups=None,* *fit_params)

参数

进行特征选择并从训练数据中训练模型

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training vectors
y : array-like, shape = [n_samples]

Target values

fit_transform(X, y, groups=None, fit_params)

对训练数据进行拟合，然后将X缩减为其最重要的特征。

返回值：

Returns

保留下的特征：shape={n个样本，k个特征}

get_metric_dict(confidence_interval=0.95)

Return metric dictionary

案例

加载数据集

#加载数据集
from mlxtend.feature_selection import SequentialFeatureSelector as SFS #SFS
from mlxtend.data import wine_data #dataset
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler


X, y = wine_data()
X.shape  #(178, 13)

数据预处理

#数据预处理
X_train, X_test, y_train, y_test= train_test_split(X, y, stratify=y, test_size=0.3, random_state=1)
std = StandardScaler()
X_train_std = std.fit_transform(X_train)

循序向前特征选择

#循序向前特征选择
knn = KNeighborsClassifier(n_neighbors=3)

sfs = SFS(estimator=knn, 
          k_features=4, 
          forward=True,
          floating=False, 
          verbose=2, 
          scoring='accuracy', 
          cv=0)

sfs.fit(X_train_std, y_train)  #xy不能是df

查看特征索引

#查看特征索引
sfs.subsets_

可视化#1 Plotting the results

%matplotlib inline
from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs

fig = plot_sfs(sfs.get_metric_dict(), kind='std_err') 

下载 (1)

其中 sfs.get_metric_dict()的结果如下：

sfs.get_metric_dict()

可视化#2 Selecting the “best” feature combination in a k-range

knn = KNeighborsClassifier(n_neighbors=3)
sfs2 = SFS(estimator=knn, 
           k_features=(3, 10), # 特征的数量 in （3，10）
           forward=True, 
           floating=True,   
           verbose=0,
           scoring='accuracy',
           cv=5)  # 5 Fold

sfs2.fit(X_train_std, y_train)
fig = plot_sfs(sfs2.get_metric_dict(), kind='std_err')

下载 (2)

此时带交叉验证的 get_metric_dict() 返回的结果：

sfs2.get_metric_dict()

结果：

{1: {'feature_idx': (6,),
  'cv_scores': array([0.8       , 0.72      , 0.84      , 0.76      , 0.79166667]),
  'avg_score': 0.7823333333333333,
  'feature_names': ('6',),
  'ci_bound': 0.05176033893650721,
  'std_dev': 0.04027130216143722,
  'std_err': 0.02013565108071861},
 2: {'feature_idx': (6, 9),
  'cv_scores': array([0.92      , 0.92      , 0.92      , 1.        , 0.91666667]),
  'avg_score': 0.9353333333333333,
  'feature_names': ('6', '9'),
  'ci_bound': 0.04159085255450659,
  'std_dev': 0.03235909626536425,
  'std_err': 0.016179548132682124},
 3: {'feature_idx': (6, 9, 12),
  'cv_scores': array([0.96, 0.92, 0.96, 1.  , 1.  ]),
  'avg_score': 0.968,
  'feature_names': ('6', '9', '12'),
  'ci_bound': 0.03847294606910585,
  'std_dev': 0.029933259094191523,
  'std_err': 0.01496662954709576},
 4: {'feature_idx': (3, 6, 9, 12),
  'cv_scores': array([1.        , 0.96      , 1.        , 1.        , 0.95833333]),
  'avg_score': 0.9836666666666666,
  'feature_names': ('3', '6', '9', '12'),
  'ci_bound': 0.025720095411605862,
  'std_dev': 0.020011108026404847,
  'std_err': 0.010005554013202423},
 5: {'feature_idx': (3, 6, 9, 10, 12),
  'cv_scores': array([0.96, 0.96, 1.  , 1.  , 1.  ]),
  'avg_score': 0.984,
  'feature_names': ('3', '6', '9', '10', '12'),
  'ci_bound': 0.025186455367090216,
  'std_dev': 0.019595917942265444,
  'std_err': 0.009797958971132722},
 6: {'feature_idx': (0, 3, 6, 9, 10, 12),
  'cv_scores': array([0.96, 1.  , 1.  , 1.  , 1.  ]),
  'avg_score': 0.992,
  'feature_names': ('0', '3', '6', '9', '10', '12'),
  'ci_bound': 0.020564654692917933,
  'std_dev': 0.016000000000000014,
  'std_err': 0.008000000000000007},
 7: {'feature_idx': (0, 2, 3, 6, 9, 10, 12),
  'cv_scores': array([0.96, 0.96, 1.  , 1.  , 1.  ]),
  'avg_score': 0.984,
  'feature_names': ('0', '2', '3', '6', '9', '10', '12'),
  'ci_bound': 0.025186455367090216,
  'std_dev': 0.019595917942265444,
  'std_err': 0.009797958971132722},
 8: {'feature_idx': (0, 2, 3, 6, 7, 9, 10, 12),
  'cv_scores': array([0.96, 0.96, 1.  , 1.  , 1.  ]),
  'avg_score': 0.984,
  'feature_names': ('0', '2', '3', '6', '7', '9', '10', '12'),
  'ci_bound': 0.025186455367090216,
  'std_dev': 0.019595917942265444,
  'std_err': 0.009797958971132722},
 9: {'feature_idx': (0, 2, 3, 5, 6, 7, 9, 10, 12),
  'cv_scores': array([0.96, 0.96, 1.  , 1.  , 1.  ]),
  'avg_score': 0.984,
  'feature_names': ('0', '2', '3', '5', '6', '7', '9', '10', '12'),
  'ci_bound': 0.025186455367090216,
  'std_dev': 0.019595917942265444,
  'std_err': 0.009797958971132722},
 10: {'feature_idx': (0, 2, 3, 5, 6, 7, 8, 9, 10, 12),
  'cv_scores': array([0.96      , 0.96      , 1.        , 1.        , 0.95833333]),
  'avg_score': 0.9756666666666666,
  'feature_names': ('0', '2', '3', '5', '6', '7', '8', '9', '10', '12'),
  'ci_bound': 0.025548244297351058,
  'std_dev': 0.019877402021167435,
  'std_err': 0.009938701010583717}}