使用scikit learn删除差异较小的功能

Question

scikit learn提供了各种删除描述符的方法，下面的教程提供了一种基本的方法

http://scikit-learn.org/stable/modules/feature_selection.html

但本教程没有提供任何方法或方法来告诉您如何保留已删除或保留的功能列表。

下面的代码取自教程。

    from sklearn.feature_selection import VarianceThreshold
    X = [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]]
    sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
    sel.fit_transform(X)
array([[0, 1],
       [1, 0],
       [0, 0],
       [1, 1],
       [1, 0],
       [1, 1]])

上面给出的示例代码只描述了两个描述符"shape（6，2）"，但在我的情况下，我有一个巨大的数据帧，其形状为（行51，列9000）。在找到合适的模型后，我想跟踪有用和无用的特征，因为在计算测试数据集的特征时，我可以通过只计算有用的特征来节省计算时间。

例如，当您使用WEKA 6.0执行机器学习建模时，它在特征选择方面提供了显著的灵活性，在删除无用特征后，您可以获得一个废弃特征和有用特征的列表。

感谢

Answer 1

那么，如果我没有错的话，你可以做的是：

在VarianceThreshold的情况下，可以调用方法fit而不是fit_transform。这将适合数据，结果方差将存储在vt.variances_中（假设vt是您的对象）。

有了threhold，您可以像fit_transform那样提取转换的特征：

X[:, vt.variances_ > threshold]

或者将索引获取为：

idx = np.where(vt.variances_ > threshold)[0]

或者作为一个掩模

mask = vt.variances_ > threshold

PS：默认阈值为0

编辑：

更直接的方法是使用类VarianceThreshold的方法get_support。来自文件：

get_support([indices])  Get a mask, or integer index, of the features selected

您应该在fit或fit_transform之后调用此方法。

Answer 2

import numpy as np
import pandas as pd
from sklearn.feature_selection import VarianceThreshold
# Just make a convenience function; this one wraps the VarianceThreshold
# transformer but you can pass it a pandas dataframe and get one in return
def get_low_variance_columns(dframe=None, columns=None,
                             skip_columns=None, thresh=0.0,
                             autoremove=False):
    """
    Wrapper for sklearn VarianceThreshold for use on pandas dataframes.
    """
    print("Finding low-variance features.")
    try:
        # get list of all the original df columns
        all_columns = dframe.columns
        # remove `skip_columns`
        remaining_columns = all_columns.drop(skip_columns)
        # get length of new index
        max_index = len(remaining_columns) - 1
        # get indices for `skip_columns`
        skipped_idx = [all_columns.get_loc(column)
                       for column
                       in skip_columns]
        # adjust insert location by the number of columns removed
        # (for non-zero insertion locations) to keep relative
        # locations intact
        for idx, item in enumerate(skipped_idx):
            if item > max_index:
                diff = item - max_index
                skipped_idx[idx] -= diff
            if item == max_index:
                diff = item - len(skip_columns)
                skipped_idx[idx] -= diff
            if idx == 0:
                skipped_idx[idx] = item
        # get values of `skip_columns`
        skipped_values = dframe.iloc[:, skipped_idx].values
        # get dataframe values
        X = dframe.loc[:, remaining_columns].values
        # instantiate VarianceThreshold object
        vt = VarianceThreshold(threshold=thresh)
        # fit vt to data
        vt.fit(X)
        # get the indices of the features that are being kept
        feature_indices = vt.get_support(indices=True)
        # remove low-variance columns from index
        feature_names = [remaining_columns[idx]
                         for idx, _
                         in enumerate(remaining_columns)
                         if idx
                         in feature_indices]
        # get the columns to be removed
        removed_features = list(np.setdiff1d(remaining_columns,
                                             feature_names))
        print("Found {0} low-variance columns."
              .format(len(removed_features)))
        # remove the columns
        if autoremove:
            print("Removing low-variance features.")
            # remove the low-variance columns
            X_removed = vt.transform(X)
            print("Reassembling the dataframe (with low-variance "
                  "features removed).")
            # re-assemble the dataframe
            dframe = pd.DataFrame(data=X_removed,
                                  columns=feature_names)
            # add back the `skip_columns`
            for idx, index in enumerate(skipped_idx):
                dframe.insert(loc=index,
                              column=skip_columns[idx],
                              value=skipped_values[:, idx])
            print("Succesfully removed low-variance columns.")
        # do not remove columns
        else:
            print("No changes have been made to the dataframe.")
    except Exception as e:
        print(e)
        print("Could not remove low-variance features. Something "
              "went wrong.")
        pass
    return dframe, removed_features

Answer 3

这对我来说很有效。如果你想确切地看到阈值后哪些列仍然存在，你可以使用这种方法：

from sklearn.feature_selection import VarianceThreshold
threshold_n=0.95
sel = VarianceThreshold(threshold=(threshold_n* (1 - threshold_n) ))
sel_var=sel.fit_transform(data)
data[data.columns[sel.get_support(indices=True)]] 

Answer 4

在测试特性时，我编写了一个简单的函数，该函数告诉在应用VarianceThreshold之后数据帧中保留了哪些变量。

from sklearn.feature_selection import VarianceThreshold
from itertools import compress
def fs_variance(df, threshold:float=0.1):
    """
    Return a list of selected variables based on the threshold.
    """
    # The list of columns in the data frame
    features = list(df.columns)
    
    # Initialize and fit the method
    vt = VarianceThreshold(threshold = threshold)
    _ = vt.fit(df)
    
    # Get which column names which pass the threshold
    feat_select = list(compress(features, vt.get_support()))
    
    return feat_select

其返回所选择的列名的列表。例如：['col_2','col_14', 'col_17']。