snowflake.ml.modeling.impute.IterativeImputer¶

class snowflake.ml.modeling.impute.IterativeImputer(*, estimator=None, missing_values=nan, sample_posterior=False, max_iter=10, tol=0.001, n_nearest_features=None, initial_strategy='mean', fill_value=None, imputation_order='ascending', skip_complete=False, min_value=- inf, max_value=inf, verbose=0, random_state=None, 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, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Multivariate imputer that estimates each feature from all the others For more details on this class, see sklearn.impute.IterativeImputer

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, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

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

This parameter is optional and will be ignored during fit. It is present here for API consistency by convention.

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 must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

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

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

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

estimator: estimator object, default=BayesianRidge()

The estimator to use at each step of the round-robin imputation. If sample_posterior=True, the estimator must support return_std in its predict method.

missing_values: int or np.nan, 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.

sample_posterior: bool, default=False

Whether to sample from the (Gaussian) predictive posterior of the fitted estimator for each imputation. Estimator must support return_std in its predict method if set to True. Set to True if using IterativeImputer for multiple imputations.

max_iter: int, default=10

Maximum number of imputation rounds to perform before returning the imputations computed during the final round. A round is a single imputation of each feature with missing values. The stopping criterion is met once max(abs(X_t - X_{t-1}))/max(abs(X[known_vals])) < tol, where X_t is X at iteration t. Note that early stopping is only applied if sample_posterior=False.

tol: float, default=1e-3

Tolerance of the stopping condition.

n_nearest_features: int, default=None

Number of other features to use to estimate the missing values of each feature column. Nearness between features is measured using the absolute correlation coefficient between each feature pair (after initial imputation). To ensure coverage of features throughout the imputation process, the neighbor features are not necessarily nearest, but are drawn with probability proportional to correlation for each imputed target feature. Can provide significant speed-up when the number of features is huge. If None, all features will be used.

initial_strategy: {‘mean’, ‘median’, ‘most_frequent’, ‘constant’}, default=’mean’

Which strategy to use to initialize the missing values. Same as the strategy parameter in SimpleImputer.

fill_value: str or numerical value, default=None

When strategy=”constant”, fill_value is used to replace all occurrences of missing_values. For string or object data types, fill_value must be a string. If None, fill_value will be 0 when imputing numerical data and “missing_value” for strings or object data types.

imputation_order: {‘ascending’, ‘descending’, ‘roman’, ‘arabic’, ‘random’}, default=’ascending’

The order in which the features will be imputed. Possible values:

• ‘ascending’: From features with fewest missing values to most.

• ‘descending’: From features with most missing values to fewest.

• ‘roman’: Left to right.

• ‘arabic’: Right to left.

• ‘random’: A random order for each round.

skip_complete: bool, default=False

If True then features with missing values during transform() which did not have any missing values during fit() will be imputed with the initial imputation method only. Set to True if you have many features with no missing values at both fit() and transform() time to save compute.

min_value: float or array-like of shape (n_features,), default=-np.inf

Minimum possible imputed value. Broadcast to shape (n_features,) if scalar. If array-like, expects shape (n_features,), one min value for each feature. The default is -np.inf.

max_value: float or array-like of shape (n_features,), default=np.inf

Maximum possible imputed value. Broadcast to shape (n_features,) if scalar. If array-like, expects shape (n_features,), one max value for each feature. The default is np.inf.

verbose: int, default=0

Verbosity flag, controls the debug messages that are issued as functions are evaluated. The higher, the more verbose. Can be 0, 1, or 2.

random_state: int, RandomState instance or None, default=None

The seed of the pseudo random number generator to use. Randomizes selection of estimator features if n_nearest_features is not None, the imputation_order if random, and the sampling from posterior if sample_posterior=True. Use an integer for determinism. See the Glossary.

add_indicator: bool, default=False

If True, a MissingIndicator transform will stack onto 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 except when initial_strategy=”constant” in which case fill_value will be used instead.

Methods

fit(dataset)	Fit the imputer on X and return self For more details on this function, see sklearn.impute.IterativeImputer.fit
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
set_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column setter.
to_sklearn()	Get sklearn.impute.IterativeImputer object.
transform(dataset)	Impute all missing values in X For more details on this function, see sklearn.impute.IterativeImputer.transform

Attributes

model_signatures

Returns model signature of current class.