IterativeImputer

Multivariate imputer that estimates each feature from all the others.

A strategy for imputing missing values by modeling each feature with missing values as a function of other features in a round-robin fashion.

Constructors

constructor()

Signature

new IterativeImputer(opts?: object): IterativeImputer;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.add_indicator?`	`boolean`	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. `Default Value` `false`
`opts.estimator?`	`any`	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.
`opts.fill_value?`	`string`	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 `undefined`, `fill\_value` will be 0 when imputing numerical data and “missing_value” for strings or object data types.
`opts.imputation_order?`	`"random"` \| `"ascending"` \| `"descending"` \| `"roman"` \| `"arabic"`	The order in which the features will be imputed. Possible values: `Default Value` `'ascending'`
`opts.initial_strategy?`	`"most_frequent"` \| `"constant"` \| `"mean"` \| `"median"`	Which strategy to use to initialize the missing values. Same as the `strategy` parameter in `SimpleImputer`. `Default Value` `'mean'`
`opts.keep_empty_features?`	`boolean`	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. `Default Value` `false`
`opts.max_iter?`	`number`	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`. `Default Value` `10`
`opts.max_value?`	`number` \| `ArrayLike`	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`.
`opts.min_value?`	`number` \| `ArrayLike`	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`.
`opts.missing_values?`	`number`	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`.
`opts.n_nearest_features?`	`number`	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 `undefined`, all features will be used.
`opts.random_state?`	`number`	The seed of the pseudo random number generator to use. Randomizes selection of estimator features if `n\_nearest\_features` is not `undefined`, the `imputation\_order` if `random`, and the sampling from posterior if `sample\_posterior=True`. Use an integer for determinism. See the Glossary.
`opts.sample_posterior?`	`boolean`	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. `Default Value` `false`
`opts.skip_complete?`	`boolean`	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. `Default Value` `false`
`opts.tol?`	`number`	Tolerance of the stopping condition. `Default Value` `0.001`
`opts.verbose?`	`number`	Verbosity flag, controls the debug messages that are issued as functions are evaluated. The higher, the more verbose. Can be 0, 1, or 2. `Default Value` `0`

Returns

IterativeImputer

Defined in: generated/impute/IterativeImputer.ts:25

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/impute/IterativeImputer.ts:214

fit()

Fit the imputer on X and return self.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Input data, where `n\_samples` is the number of samples and `n\_features` is the number of features.
`opts.y?`	`any`	Not used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/impute/IterativeImputer.ts:231

fit_transform()

Fit the imputer on X and return the transformed X.

Signature

fit_transform(opts: object): Promise<ArrayLike>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Input data, where `n\_samples` is the number of samples and `n\_features` is the number of features.
`opts.y?`	`any`	Not used, present for API consistency by convention.

Returns

Promise<ArrayLike>

Defined in: generated/impute/IterativeImputer.ts:271

get_feature_names_out()

Get output feature names for transformation.

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.input_features?`	`any`	Input features.

Returns

Promise<any>

Defined in: generated/impute/IterativeImputer.ts:313

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/impute/IterativeImputer.ts:353

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/impute/IterativeImputer.ts:146

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

set_output(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.transform?`	`"default"` \| `"pandas"`	Configure output of `transform` and `fit\_transform`.

Returns

Promise<any>

Defined in: generated/impute/IterativeImputer.ts:392

transform()

Impute all missing values in X.

Note that this is stochastic, and that if random\_state is not fixed, repeated calls, or permuted input, results will differ.

Signature

transform(opts: object): Promise<ArrayLike>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	The input data to complete.

Returns

Promise<ArrayLike>

Defined in: generated/impute/IterativeImputer.ts:429

Properties

_isDisposed

boolean = false

Defined in: generated/impute/IterativeImputer.ts:23

_isInitialized

boolean = false

Defined in: generated/impute/IterativeImputer.ts:22

_py

PythonBridge

Defined in: generated/impute/IterativeImputer.ts:21

id

string

Defined in: generated/impute/IterativeImputer.ts:18

opts

any

Defined in: generated/impute/IterativeImputer.ts:19

Accessors

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/impute/IterativeImputer.ts:572

imputation_sequence_

Each tuple has (feat\_idx, neighbor\_feat\_idx, estimator), where feat\_idx is the current feature to be imputed, neighbor\_feat\_idx is the array of other features used to impute the current feature, and estimator is the trained estimator used for the imputation. Length is self.n\_features\_with\_missing\_ \* self.n\_iter\_.

Signature

imputation_sequence_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/impute/IterativeImputer.ts:491

indicator_

Indicator used to add binary indicators for missing values. undefined if add\_indicator=False.

Signature

indicator_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/impute/IterativeImputer.ts:626

initial_imputer_

Imputer used to initialize the missing values.

Signature

initial_imputer_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/impute/IterativeImputer.ts:464

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/impute/IterativeImputer.ts:545

n_features_with_missing_

Number of features with missing values.

Signature

n_features_with_missing_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/impute/IterativeImputer.ts:599

n_iter_

Number of iteration rounds that occurred. Will be less than self.max\_iter if early stopping criterion was reached.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/impute/IterativeImputer.ts:518

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/impute/IterativeImputer.ts:133

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/impute/IterativeImputer.ts:137

random_state_

RandomState instance that is generated either from a seed, the random number generator or by np.random.

Signature

random_state_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/impute/IterativeImputer.ts:653

Files

IterativeImputer.md

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History

IterativeImputer.md

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IterativeImputer

Constructors

constructor()

Signature

Parameters

Returns

Methods

dispose()

Signature

Returns

fit()

Signature

Parameters

Returns

fit_transform()

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Parameters

Returns

get_feature_names_out()

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Parameters

Returns

get_metadata_routing()

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Parameters

Returns

init()

Signature

Parameters

Returns

set_output()

Signature

Parameters

Returns

transform()

Signature

Parameters

Returns

Properties

_isDisposed

_isInitialized

_py

id

opts

Accessors

feature_names_in_

Signature

Returns

imputation_sequence_

Signature

Returns

indicator_

Signature

Returns

initial_imputer_

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Returns

n_features_in_

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n_features_with_missing_

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n_iter_

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Returns

py

Signature

Returns

Signature

Parameters

Returns

random_state_

Signature