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 : 
  ?n_components:int ->
  ?init:[ `S of string | `Arr of Arr.t ] ->
  ?warm_start:bool ->
  ?max_iter:int ->
  ?tol:float ->
  ?callback:Py.Object.t ->
  ?verbose:int ->
  ?random_state:int ->
  unit ->
  t

Neighborhood Components Analysis

Neighborhood Component Analysis (NCA) is a machine learning algorithm for metric learning. It learns a linear transformation in a supervised fashion to improve the classification accuracy of a stochastic nearest neighbors rule in the transformed space.

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

Parameters ---------- n_components : int, optional (default=None) Preferred dimensionality of the projected space. If None it will be set to ``n_features``.

init : string or numpy array, optional (default='auto') Initialization of the linear transformation. Possible options are 'auto', 'pca', 'lda', 'identity', 'random', and a numpy array of shape (n_features_a, n_features_b).

'auto' Depending on ``n_components``, the most reasonable initialization will be chosen. If ``n_components <= n_classes`` we use 'lda', as it uses labels information. If not, but ``n_components < min(n_features, n_samples)``, we use 'pca', as it projects data in meaningful directions (those of higher variance). Otherwise, we just use 'identity'.

'pca' ``n_components`` principal components of the inputs passed to :meth:`fit` will be used to initialize the transformation. (See :class:`~sklearn.decomposition.PCA`)

'lda' ``min(n_components, n_classes)`` most discriminative components of the inputs passed to :meth:`fit` will be used to initialize the transformation. (If ``n_components > n_classes``, the rest of the components will be zero.) (See :class:`~sklearn.discriminant_analysis.LinearDiscriminantAnalysis`)

'identity' If ``n_components`` is strictly smaller than the dimensionality of the inputs passed to :meth:`fit`, the identity matrix will be truncated to the first ``n_components`` rows.

'random' The initial transformation will be a random array of shape `(n_components, n_features)`. Each value is sampled from the standard normal distribution.

numpy array n_features_b must match the dimensionality of the inputs passed to :meth:`fit` and n_features_a must be less than or equal to that. If ``n_components`` is not None, n_features_a must match it.

warm_start : bool, optional, (default=False) If True and :meth:`fit` has been called before, the solution of the previous call to :meth:`fit` is used as the initial linear transformation (``n_components`` and ``init`` will be ignored).

max_iter : int, optional (default=50) Maximum number of iterations in the optimization.

tol : float, optional (default=1e-5) Convergence tolerance for the optimization.

callback : callable, optional (default=None) If not None, this function is called after every iteration of the optimizer, taking as arguments the current solution (flattened transformation matrix) and the number of iterations. This might be useful in case one wants to examine or store the transformation found after each iteration.

verbose : int, optional (default=0) If 0, no progress messages will be printed. If 1, progress messages will be printed to stdout. If > 1, progress messages will be printed and the ``disp`` parameter of :func:`scipy.optimize.minimize` will be set to ``verbose - 2``.

random_state : int or numpy.RandomState or None, optional (default=None) A pseudo random number generator object or a seed for it if int. If ``init='random'``, ``random_state`` is used to initialize the random transformation. If ``init='pca'``, ``random_state`` is passed as an argument to PCA when initializing the transformation.

Attributes ---------- components_ : array, shape (n_components, n_features) The linear transformation learned during fitting.

n_iter_ : int Counts the number of iterations performed by the optimizer.

random_state_ : numpy.RandomState Pseudo random number generator object used during initialization.

Examples -------- >>> from sklearn.neighbors import NeighborhoodComponentsAnalysis >>> from sklearn.neighbors import KNeighborsClassifier >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import train_test_split >>> X, y = load_iris(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, ... stratify=y, test_size=0.7, random_state=42) >>> nca = NeighborhoodComponentsAnalysis(random_state=42) >>> nca.fit(X_train, y_train) NeighborhoodComponentsAnalysis(...) >>> knn = KNeighborsClassifier(n_neighbors=3) >>> knn.fit(X_train, y_train) KNeighborsClassifier(...) >>> print(knn.score(X_test, y_test)) 0.933333... >>> knn.fit(nca.transform(X_train), y_train) KNeighborsClassifier(...) >>> print(knn.score(nca.transform(X_test), y_test)) 0.961904...

References ---------- .. 1 J. Goldberger, G. Hinton, S. Roweis, R. Salakhutdinov. "Neighbourhood Components Analysis". Advances in Neural Information Processing Systems. 17, 513-520, 2005. http://www.cs.nyu.edu/~roweis/papers/ncanips.pdf

.. 2 Wikipedia entry on Neighborhood Components Analysis https://en.wikipedia.org/wiki/Neighbourhood_components_analysis

val fit : x:Arr.t -> y:Arr.t -> t -> t

Fit the model according to the given training data.

Parameters ---------- X : array-like, shape (n_samples, n_features) The training samples.

y : array-like, shape (n_samples,) The corresponding training labels.

Returns ------- self : object returns a trained NeighborhoodComponentsAnalysis model.

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 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

Applies the learned transformation to the given data.

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

Returns ------- X_embedded: array, shape (n_samples, n_components) The data samples transformed.

Raises ------ NotFittedError If :meth:`fit` has not been called before.

val components_ : t -> Arr.t

Attribute components_: get value or raise Not_found if None.

val components_opt : t -> Arr.t option

Attribute components_: get value as an option.

val n_iter_ : t -> int

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> int option

Attribute n_iter_: get value as an option.

val random_state_ : t -> Py.Object.t

Attribute random_state_: get value or raise Not_found if None.

val random_state_opt : t -> Py.Object.t option

Attribute random_state_: 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.