Class: 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

new IterativeImputer()

new IterativeImputer(opts?): IterativeImputer

Parameters

Parameter	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.
`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:
`opts.initial_strategy`?	`"most_frequent"` \| `"constant"` \| `"mean"` \| `"median"`	Which strategy to use to initialize the missing values. Same as the `strategy` parameter in `SimpleImputer`.
`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.
`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`.
`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.
`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.
`opts.tol`?	`number`	Tolerance of the stopping condition.
`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.

Returns IterativeImputer

Defined in generated/impute/IterativeImputer.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/impute/IterativeImputer.ts:23
`_isInitialized`	`boolean`	`false`	generated/impute/IterativeImputer.ts:22
`_py`	`PythonBridge`	`undefined`	generated/impute/IterativeImputer.ts:21
`id`	`string`	`undefined`	generated/impute/IterativeImputer.ts:18
`opts`	`any`	`undefined`	generated/impute/IterativeImputer.ts:19

Accessors

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/impute/IterativeImputer.ts:548

imputation_sequence_

Get Signature

get imputation_sequence_(): Promise<any>

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_.

Returns Promise<any>

Defined in generated/impute/IterativeImputer.ts:467

indicator_

Get Signature

get indicator_(): Promise<any>

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

Returns Promise<any>

Defined in generated/impute/IterativeImputer.ts:602

initial_imputer_

Get Signature

get initial_imputer_(): Promise<any>

Imputer used to initialize the missing values.

Returns Promise<any>

Defined in generated/impute/IterativeImputer.ts:440

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/impute/IterativeImputer.ts:521

n_features_with_missing_

Get Signature

get n_features_with_missing_(): Promise<number>

Number of features with missing values.

Returns Promise<number>

Defined in generated/impute/IterativeImputer.ts:575

n_iter_

Get Signature

get n_iter_(): Promise<number>

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

Returns Promise<number>

Defined in generated/impute/IterativeImputer.ts:494

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/impute/IterativeImputer.ts:133

random_state_

Get Signature

get random_state_(): Promise<any>

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

Returns Promise<any>

Defined in generated/impute/IterativeImputer.ts:629

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

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

Returns Promise<void>

Defined in generated/impute/IterativeImputer.ts:187

fit()

fit(opts): Promise<any>

Fit the imputer on X and return self.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	Parameters routed to the `fit` method of the sub-estimator via the metadata routing API.
`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:204

fit_transform()

fit_transform(opts): Promise<ArrayLike>

Fit the imputer on X and return the transformed X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.params`?	`any`	Parameters routed to the `fit` method of the sub-estimator via the metadata routing API.
`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.