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sklearn
- Sklearn

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type tag = [

| `AdditiveChi2Sampler

]

type t =
  [ `AdditiveChi2Sampler | `BaseEstimator | `Object | `TransformerMixin ] Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_transformer : t -> [ `TransformerMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val create : ?sample_steps:int -> ?sample_interval:float -> unit -> t

Approximate feature map for additive chi2 kernel.

Uses sampling the fourier transform of the kernel characteristic at regular intervals.

Since the kernel that is to be approximated is additive, the components of the input vectors can be treated separately. Each entry in the original space is transformed into 2*sample_steps+1 features, where sample_steps is a parameter of the method. Typical values of sample_steps include 1, 2 and 3.

Optimal choices for the sampling interval for certain data ranges can be computed (see the reference). The default values should be reasonable.

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

Parameters ---------- sample_steps : int, optional Gives the number of (complex) sampling points. sample_interval : float, optional Sampling interval. Must be specified when sample_steps not in

,2,3

Attributes ---------- sample_interval_ : float Stored sampling interval. Specified as a parameter if sample_steps not in

,2,3

Examples -------- >>> from sklearn.datasets import load_digits >>> from sklearn.linear_model import SGDClassifier >>> from sklearn.kernel_approximation import AdditiveChi2Sampler >>> X, y = load_digits(return_X_y=True) >>> chi2sampler = AdditiveChi2Sampler(sample_steps=2) >>> X_transformed = chi2sampler.fit_transform(X, y) >>> clf = SGDClassifier(max_iter=5, random_state=0, tol=1e-3) >>> clf.fit(X_transformed, y) SGDClassifier(max_iter=5, random_state=0) >>> clf.score(X_transformed, y) 0.9499...

Notes ----- This estimator approximates a slightly different version of the additive chi squared kernel then ``metric.additive_chi2`` computes.

See also -------- SkewedChi2Sampler : A Fourier-approximation to a non-additive variant of the chi squared kernel.

sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.

sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi squared kernel.

References ---------- See `'Efficient additive kernels via explicit feature maps' <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>`_ A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, 2011

val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

Set the parameters

Parameters ---------- X : array-like, shape (n_samples, n_features) Training data, where n_samples in the number of samples and n_features is the number of features.

Returns ------- self : object Returns the transformer.

val fit_transform : 
  ?y:[> `ArrayLike ] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.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 : array-like, sparse matrix, dataframe of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), default=None Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

val get_params : ?deep:bool -> [> tag ] Obj.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 set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.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:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Apply approximate feature map to X.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features)

Returns ------- X_new : array, sparse matrix, shape = (n_samples, n_features * (2*sample_steps + 1)) Whether the return value is an array of sparse matrix depends on the type of the input X.

val sample_interval_ : t -> float

Attribute sample_interval_: get value or raise Not_found if None.

val sample_interval_opt : t -> float option

Attribute sample_interval_: get value as an option.

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.