Class: FunctionTransformer

Constructs a transformer from an arbitrary callable.

A FunctionTransformer forwards its X (and optionally y) arguments to a user-defined function or function object and returns the result of this function. This is useful for stateless transformations such as taking the log of frequencies, doing custom scaling, etc.

Note: If a lambda is used as the function, then the resulting transformer will not be pickleable.

Python Reference

Constructors

new FunctionTransformer()

new FunctionTransformer(opts?): FunctionTransformer

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.accept_sparse`?	`boolean`	Indicate that func accepts a sparse matrix as input. If validate is `false`, this has no effect. Otherwise, if accept_sparse is false, sparse matrix inputs will cause an exception to be raised.
`opts.check_inverse`?	`boolean`	Whether to check that or `func` followed by `inverse_func` leads to the original inputs. It can be used for a sanity check, raising a warning when the condition is not fulfilled.
`opts.feature_names_out`?	`"one-to-one"`	Determines the list of feature names that will be returned by the `get_feature_names_out` method. If it is ‘one-to-one’, then the output feature names will be equal to the input feature names. If it is a callable, then it must take two positional arguments: this `FunctionTransformer` (`self`) and an array-like of input feature names (`input_features`). It must return an array-like of output feature names. The `get_feature_names_out` method is only defined if `feature_names_out` is not `undefined`. See `get_feature_names_out` for more details.
`opts.func`?	`any`	The callable to use for the transformation. This will be passed the same arguments as transform, with args and kwargs forwarded. If func is `undefined`, then func will be the identity function.
`opts.inv_kw_args`?	`any`	Dictionary of additional keyword arguments to pass to inverse_func.
`opts.inverse_func`?	`any`	The callable to use for the inverse transformation. This will be passed the same arguments as inverse transform, with args and kwargs forwarded. If inverse_func is `undefined`, then inverse_func will be the identity function.
`opts.kw_args`?	`any`	Dictionary of additional keyword arguments to pass to func.
`opts.validate`?	`boolean`	Indicate that the input X array should be checked before calling `func`. The possibilities are:

Returns FunctionTransformer

Defined in generated/preprocessing/FunctionTransformer.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/preprocessing/FunctionTransformer.ts:23
`_isInitialized`	`boolean`	`false`	generated/preprocessing/FunctionTransformer.ts:22
`_py`	`PythonBridge`	`undefined`	generated/preprocessing/FunctionTransformer.ts:21
`id`	`string`	`undefined`	generated/preprocessing/FunctionTransformer.ts:18
`opts`	`any`	`undefined`	generated/preprocessing/FunctionTransformer.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/preprocessing/FunctionTransformer.ts:451

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/preprocessing/FunctionTransformer.ts:424

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/FunctionTransformer.ts:78

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/FunctionTransformer.ts:134

fit()

fit(opts): Promise<any>

Fit transformer by checking X.

If validate is true, X will be checked.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Input array.
`opts.y`?	`any`	Not used, present here for API consistency by convention.

Returns Promise<any>

Defined in generated/preprocessing/FunctionTransformer.ts:153

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).