Basic method of missing data filling (1) -- k-nearest neighbors (KNN) filling

install ：

pip install missingpy

One 、kNN Introduce

kNNImputer Class provides the k-Nearest Neighbors（KNN） The algorithm completes the filling of missing values . The missing values of each sample are found in the training set n_neighbors The value of the nearest neighbor , Please note that , If a sample lacks multiple features , Then the sample can have multiple groups n_neighbors Neighborhood donor , It depends on the specific characteristics of the filling .

then , Each missing feature is filled in as the weighted or unweighted average of these neighbors . If donor neighbors The number of is less than n_neighbors, The average value of the training set of the feature is used for filling . Of course , The total number of samples in the training set is always greater than or equal to the number of nearest neighbors that can be used for filling . This depends on the population sample size and the number of samples excluded from the nearest neighbor calculation due to too many missing features （ from row_max_missing control ）

Two 、 Code example

The following code snippet demonstrates how to replace missing values with np.nan, Use the average eigenvalue of the two nearest neighbors of the row containing the missing value ：

>>> import numpy as np
>>> from missingpy import KNNImputer
>>> nan = np.nan
>>> X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]]
>>> imputer = KNNImputer(n_neighbors=2, weights="uniform")
>>> imputer.fit_transform(X)
array([[1. , 2. , 4. ],
       [3. , 4. , 3. ],
       [5.5, 6. , 5. ],
       [8. , 8. , 7. ]])

3、 ... and 、API example

KNNImputer Of API as follows ：

KNNImputer(missing_values="NaN", n_neighbors=5, weights="uniform", 
                 metric="masked_euclidean", row_max_missing=0.5, 
                 col_max_missing=0.8, copy=True)
             
Parameters
----------
missing_values : integer or "NaN", optional (default = "NaN")
    The placeholder for the missing values. All occurrences of
    `missing_values` will be imputed. For missing values encoded as
    ``np.nan``, use the string value "NaN".

n_neighbors : int, optional (default = 5)
    Number of neighboring samples to use for imputation.

weights : str or callable, optional (default = "uniform")
    Weight function used in prediction.  Possible values:

    - 'uniform' : uniform weights.  All points in each neighborhood
      are weighted equally.
    - 'distance' : weight points by the inverse of their distance.
      in this case, closer neighbors of a query point will have a
      greater influence than neighbors which are further away.
    - [callable] : a user-defined function which accepts an
      array of distances, and returns an array of the same shape
      containing the weights.

metric : str or callable, optional (default = "masked_euclidean")
    Distance metric for searching neighbors. Possible values:
    - 'masked_euclidean'
    - [callable] : a user-defined function which conforms to the
    definition of _pairwise_callable(X, Y, metric, **kwds). In other
    words, the function accepts two arrays, X and Y, and a
    ``missing_values`` keyword in **kwds and returns a scalar distance
    value.

row_max_missing : float, optional (default = 0.5)
    The maximum fraction of columns (i.e. features) that can be missing
    before the sample is excluded from nearest neighbor imputation. It
    means that such rows will not be considered a potential donor in
    ``fit()``, and in ``transform()`` their missing feature values will be
    imputed to be the column mean for the entire dataset.

col_max_missing : float, optional (default = 0.8)
    The maximum fraction of rows (or samples) that can be missing
    for any feature beyond which an error is raised.

copy : boolean, optional (default = True)
    If True, a copy of X will be created. If False, imputation will
    be done in-place whenever possible. Note that, if metric is
    "masked_euclidean" and copy=False then missing_values in the
    input matrix X will be overwritten with zeros.

Attributes
----------
statistics_ : 1-D array of length {
    n_features}
    The 1-D array contains the mean of each feature calculated using
    observed (i.e. non-missing) values. This is used for imputing
    missing values in samples that are either excluded from nearest
    neighbors search because they have too many ( > row_max_missing)
    missing features or because all of the sample's k-nearest neighbors
    (i.e., the potential donors) also have the relevant feature value
    missing.

Methods
-------
fit(X, y=None):
    Fit the imputer on X.

    Parameters
    ----------
    X : {
    array-like}, shape (n_samples, n_features)
        Input data, where ``n_samples`` is the number of samples and
        ``n_features`` is the number of features.

    Returns
    -------
    self : object
        Returns self.
        
        
transform(X):
    Impute all missing values in X.

    Parameters
    ----------
    X : {
    array-like}, shape = [n_samples, n_features]
        The input data to complete.

    Returns
    -------
    X : {
    array-like}, shape = [n_samples, n_features]
        The imputed dataset.


fit_transform(X, y=None, **fit_params):
    Fit KNNImputer and impute all missing values in X.

    Parameters
    ----------
    X : {
    array-like}, shape (n_samples, n_features)
        Input data, where ``n_samples`` is the number of samples and
        ``n_features`` is the number of features.

    Returns
    -------
    X : {
    array-like}, shape (n_samples, n_features)
        Returns imputed dataset.       

Reference resources ：

1. Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing value estimation methods for DNA microarrays, BIOINFORMATICS Vol. 17 no. 6, 2001 Pages 520-525.