Class: KBinsDiscretizer

Bin continuous data into intervals.

Constructors

new KBinsDiscretizer()

new KBinsDiscretizer(opts?): KBinsDiscretizer

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.dtype`?	`any`	The desired data-type for the output. If `undefined`, output dtype is consistent with input dtype. Only np.float32 and np.float64 are supported.
`opts.encode`?	`"onehot"` \| `"onehot-dense"` \| `"ordinal"`	Method used to encode the transformed result.
`opts.n_bins`?	`number` \| `ArrayLike`	The number of bins to produce. Raises ValueError if `n_bins < 2`.
`opts.random_state`?	`number`	Determines random number generation for subsampling. Pass an int for reproducible results across multiple function calls. See the `subsample` parameter for more details. See Glossary.
`opts.strategy`?	`"uniform"` \| `"quantile"` \| `"kmeans"`	Strategy used to define the widths of the bins.
`opts.subsample`?	`number`	Maximum number of samples, used to fit the model, for computational efficiency. `subsample=None` means that all the training samples are used when computing the quantiles that determine the binning thresholds. Since quantile computation relies on sorting each column of `X` and that sorting has an `n log(n)` time complexity, it is recommended to use subsampling on datasets with a very large number of samples.

Returns KBinsDiscretizer

Defined in generated/preprocessing/KBinsDiscretizer.ts:23

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/preprocessing/KBinsDiscretizer.ts:21
`_isInitialized`	`boolean`	`false`	generated/preprocessing/KBinsDiscretizer.ts:20
`_py`	`PythonBridge`	`undefined`	generated/preprocessing/KBinsDiscretizer.ts:19
`id`	`string`	`undefined`	generated/preprocessing/KBinsDiscretizer.ts:16
`opts`	`any`	`undefined`	generated/preprocessing/KBinsDiscretizer.ts:17

Accessors

bin_edges_

Get Signature

get bin_edges_(): Promise<any[]>

The edges of each bin. Contain arrays of varying shapes (n_bins_, ) Ignored features will have empty arrays.

Returns Promise<any[]>

Defined in generated/preprocessing/KBinsDiscretizer.ts:456

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/preprocessing/KBinsDiscretizer.ts:537

n_bins_

Get Signature

get n_bins_(): Promise<ArrayLike>

Number of bins per feature. Bins whose width are too small (i.e., <= 1e-8) are removed with a warning.

Returns Promise<ArrayLike>

Defined in generated/preprocessing/KBinsDiscretizer.ts:483

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/preprocessing/KBinsDiscretizer.ts:510

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/preprocessing/KBinsDiscretizer.ts:66

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/preprocessing/KBinsDiscretizer.ts:120

fit()

fit(opts): Promise<any>

Fit the estimator.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Contains weight values to be associated with each sample. Cannot be used when `strategy` is set to `"uniform"`.
`opts.X`?	`ArrayLike`[]	Data to be discretized.
`opts.y`?	`any`	Ignored. This parameter exists only for compatibility with `Pipeline`.

Returns Promise<any>

Defined in generated/preprocessing/KBinsDiscretizer.ts:137

fit_transform()

fit_transform(opts): Promise<any[]>

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	Additional fit parameters.
`opts.X`?	`ArrayLike`[]	Input samples.
`opts.y`?	`ArrayLike`	Target values (`undefined` for unsupervised transformations).

Returns Promise<any[]>

Defined in generated/preprocessing/KBinsDiscretizer.ts:183

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Get output feature names.

Parameters

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

Returns Promise<any>

Defined in generated/preprocessing/KBinsDiscretizer.ts:229

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

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

Returns Promise<any>

Defined in generated/preprocessing/KBinsDiscretizer.ts:267

init()

init(py): Promise<void>

Initializes the underlying Python resources.

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

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/preprocessing/KBinsDiscretizer.ts:79

inverse_transform()

inverse_transform(opts): Promise<ArrayLike>

Transform discretized data back to original feature space.

Note that this function does not regenerate the original data due to discretization rounding.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Transformed data in the binned space.
`opts.Xt`?	`ArrayLike`[]	Transformed data in the binned space.

Returns Promise<ArrayLike>

Defined in generated/preprocessing/KBinsDiscretizer.ts:305

set_fit_request()

set_fit_request(opts): Promise<any>

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Parameters