snowflake.ml.modeling.impute.MissingIndicator¶

class snowflake.ml.modeling.impute.MissingIndicator(*, missing_values=nan, features='missing-only', sparse='auto', error_on_new=True, 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

Binary indicators for missing values For more details on this class, see sklearn.impute.MissingIndicator

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.

features: {‘missing-only’, ‘all’}, default=’missing-only’

Whether the imputer mask should represent all or a subset of features.

• If ‘missing-only’ (default), the imputer mask will only represent features containing missing values during fit time.

• If ‘all’, the imputer mask will represent all features.

sparse: bool or ‘auto’, default=’auto’

Whether the imputer mask format should be sparse or dense.

• If ‘auto’ (default), the imputer mask will be of same type as input.

• If True, the imputer mask will be a sparse matrix.

• If False, the imputer mask will be a numpy array.

error_on_new: bool, default=True

If True, transform() will raise an error when there are features with missing values that have no missing values in fit(). This is applicable only when features=’missing-only’.

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 transformer on X For more details on this function, see sklearn.impute.MissingIndicator.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.impute.MissingIndicator object.
transform(dataset)	Generate missing values indicator for X For more details on this function, see sklearn.impute.MissingIndicator.transform

Attributes

model_signatures

Returns model signature of current class.