View Source Scholar.Impute.KNNImputter (Scholar v0.4.0)

Imputer for completing missing values using k-Nearest Neighbors.

Each sample's missing values are imputed using the mean value from n_neighbors nearest neighbors found in the training set. Two samples are close if the features that neither is missing are close.

Summary

Functions

fit(x, opts \\ [])

Imputter for completing missing values using k-Nearest Neighbors.

transform(knn_imputter, x)

Impute all missing values in x using fitted imputer.

Functions

fit(x, opts \\ [])

Imputter for completing missing values using k-Nearest Neighbors.

Preconditions:

The number of neighbors must be less than the number of valid rows - 1.
A valid row is a row with more than 1 non-NaN values. Otherwise it is better to use a simpler imputter.
When you set a value different than :nan in missing_values there should be no NaNs in the input tensor

Options

:missing_values - The placeholder for the missing values. All occurrences of :missing_values will be imputed.
The default value expects there are no NaNs in the input tensor.
The default value is :nan.
:num_neighbors (pos_integer/0) - The number of nearest neighbors. The default value is 2.

Return Values

The function returns a struct with the following parameters:

:missing_values - the same value as in the :missing_values option
:statistics - The imputation fill value for each feature. Computing statistics can result in values.

Examples

iex> x = Nx.tensor([[40.0, 2.0],[4.0, 5.0],[7.0, :nan],[:nan, 8.0],[11.0, 11.0]])
iex> Scholar.Impute.KNNImputter.fit(x, num_neighbors: 2)
%Scholar.Impute.KNNImputter{
  statistics: Nx.tensor(
    [
            [:nan, :nan],
            [:nan, :nan],
            [:nan, 8.0],
            [7.5, :nan],
            [:nan, :nan]
          ]
  ),
  missing_values: :nan
}

transform(knn_imputter, x)

Impute all missing values in x using fitted imputer.

Return Values

The function returns input tensor with NaN replaced with values saved in fitted imputer.

Examples

iex> x = Nx.tensor([[40.0, 2.0],[4.0, 5.0],[7.0, :nan],[:nan, 8.0],[11.0, 11.0]])
iex> imputer = Scholar.Impute.KNNImputter.fit(x, num_neighbors: 2)
iex> Scholar.Impute.KNNImputter.transform(imputer, x)
Nx.tensor(
  [
    [40.0, 2.0],
    [4.0, 5.0],
    [7.0, 8.0],
    [7.5, 8.0],
    [11.0, 11.0]
  ]
)