snowflake.ml.modeling.impute.KNNImputer¶

class snowflake.ml.modeling.impute.KNNImputer(*, missing_values=nan, n_neighbors=5, weights='uniform', metric='nan_euclidean', copy=True, add_indicator=False, keep_empty_features=False, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Imputation for completing missing values using k-Nearest Neighbors For more details on this class, see sklearn.impute.KNNImputer

missing_values: int, float, str, np.nan or None, default=np.nan

The placeholder for the missing values. All occurrences of missing_values will be imputed. For pandas’ dataframes with nullable integer dtypes with missing values, missing_values should be set to np.nan, since pd.NA will be converted to np.nan.

n_neighbors: int, default=5

Number of neighboring samples to use for imputation.

weights: {‘uniform’, ‘distance’} or callable, 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: {‘nan_euclidean’} or callable, default=’nan_euclidean’

Distance metric for searching neighbors. Possible values:

• ‘nan_euclidean’

• callable: a user-defined function which conforms to the definition of _pairwise_callable(X, Y, metric, **kwds). The function accepts two arrays, X and Y, and a missing_values keyword in kwds and returns a scalar distance value.

copy: bool, default=True

If True, a copy of X will be created. If False, imputation will be done in-place whenever possible.

add_indicator: bool, default=False

If True, a MissingIndicator transform will stack onto the output of the imputer’s transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won’t appear on the missing indicator even if there are missing values at transform/test time.

keep_empty_features: bool, default=False

If True, features that consist exclusively of missing values when fit is called are returned in results when transform is called. The imputed value is always 0.

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

Methods

fit(dataset)	Fit the imputer on X For more details on this function, see sklearn.impute.KNNImputer.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.impute.KNNImputer object.
transform(dataset)	Impute all missing values in X For more details on this function, see sklearn.impute.KNNImputer.transform

Attributes

model_signatures

Returns model signature of current class.