package sklearn

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sklearn
- Sklearn

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type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?func:Py.Object.t ->
  ?inverse_func:Py.Object.t ->
  ?validate:bool ->
  ?accept_sparse:bool ->
  ?check_inverse:bool ->
  ?kw_args:Dict.t ->
  ?inv_kw_args:Dict.t ->
  unit ->
  t

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.

.. versionadded:: 0.17

Read more in the :ref:`User Guide <function_transformer>`.

Parameters ---------- func : callable, optional default=None The callable to use for the transformation. This will be passed the same arguments as transform, with args and kwargs forwarded. If func is None, then func will be the identity function.

inverse_func : callable, optional default=None 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 None, then inverse_func will be the identity function.

validate : bool, optional default=False Indicate that the input X array should be checked before calling ``func``. The possibilities are:

If False, there is no input validation.
If True, then X will be converted to a 2-dimensional NumPy array or sparse matrix. If the conversion is not possible an exception is raised.

.. versionchanged:: 0.22 The default of ``validate`` changed from True to False.

accept_sparse : boolean, optional 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.

check_inverse : bool, default=True 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.

.. versionadded:: 0.20

kw_args : dict, optional Dictionary of additional keyword arguments to pass to func.

inv_kw_args : dict, optional Dictionary of additional keyword arguments to pass to inverse_func.

Examples -------- >>> import numpy as np >>> from sklearn.preprocessing import FunctionTransformer >>> transformer = FunctionTransformer(np.log1p) >>> X = np.array([0, 1], [2, 3]) >>> transformer.transform(X) array([0. , 0.6931...], [1.0986..., 1.3862...])

val fit : ?y:Py.Object.t -> x:Arr.t -> t -> t

Fit transformer by checking X.

If ``validate`` is ``True``, ``X`` will be checked.

Parameters ---------- X : array-like, shape (n_samples, n_features) Input array.

Returns ------- self

val fit_transform : 
  ?y:Arr.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:Arr.t ->
  t ->
  Arr.t

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 ---------- X : numpy array of shape n_samples, n_features Training set.

y : numpy array of shape n_samples Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.

val get_params : ?deep:bool -> t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val inverse_transform : x:Arr.t -> t -> Arr.t

Transform X using the inverse function.

Parameters ---------- X : array-like, shape (n_samples, n_features) Input array.

Returns ------- X_out : array-like, shape (n_samples, n_features) Transformed input.

val set_params : ?params:(string * Py.Object.t) list -> t -> t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val transform : x:Arr.t -> t -> Arr.t

Transform X using the forward function.

Parameters ---------- X : array-like, shape (n_samples, n_features) Input array.

Returns ------- X_out : array-like, shape (n_samples, n_features) Transformed input.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.