package sklearn

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package sklearn

sklearn
- Sklearn

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type tag = [

| `Isomap

]

type t = [ `BaseEstimator | `Isomap | `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 : 
  ?n_neighbors:int ->
  ?n_components:int ->
  ?eigen_solver:[ `Auto | `Arpack | `Dense ] ->
  ?tol:float ->
  ?max_iter:int ->
  ?path_method:[ `Auto | `FW | `D ] ->
  ?neighbors_algorithm:[ `Auto | `Brute | `Kd_tree | `Ball_tree ] ->
  ?n_jobs:int ->
  ?metric:[ `S of string | `Callable of Py.Object.t ] ->
  ?p:int ->
  ?metric_params:Dict.t ->
  unit ->
  t

Isomap Embedding

Non-linear dimensionality reduction through Isometric Mapping

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

Parameters ---------- n_neighbors : integer number of neighbors to consider for each point.

n_components : integer number of coordinates for the manifold

eigen_solver : 'auto'|'arpack'|'dense' 'auto' : Attempt to choose the most efficient solver for the given problem.

'arpack' : Use Arnoldi decomposition to find the eigenvalues and eigenvectors.

'dense' : Use a direct solver (i.e. LAPACK) for the eigenvalue decomposition.

tol : float Convergence tolerance passed to arpack or lobpcg. not used if eigen_solver == 'dense'.

max_iter : integer Maximum number of iterations for the arpack solver. not used if eigen_solver == 'dense'.

path_method : string 'auto'|'FW'|'D' Method to use in finding shortest path.

'auto' : attempt to choose the best algorithm automatically.

'FW' : Floyd-Warshall algorithm.

'D' : Dijkstra's algorithm.

neighbors_algorithm : string 'auto'|'brute'|'kd_tree'|'ball_tree' Algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance.

n_jobs : int or None, default=None The number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

metric : string, or callable, default='minkowski' The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by :func:`sklearn.metrics.pairwise_distances` for its metric parameter. If metric is 'precomputed', X is assumed to be a distance matrix and must be square. X may be a :term:`Glossary <sparse graph>`.

.. versionadded:: 0.22

p : int, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

.. versionadded:: 0.22

metric_params : dict, default=None Additional keyword arguments for the metric function.

.. versionadded:: 0.22

Attributes ---------- embedding_ : array-like, shape (n_samples, n_components) Stores the embedding vectors.

kernel_pca_ : object :class:`~sklearn.decomposition.KernelPCA` object used to implement the embedding.

nbrs_ : sklearn.neighbors.NearestNeighbors instance Stores nearest neighbors instance, including BallTree or KDtree if applicable.

dist_matrix_ : array-like, shape (n_samples, n_samples) Stores the geodesic distance matrix of training data.

Examples -------- >>> from sklearn.datasets import load_digits >>> from sklearn.manifold import Isomap >>> X, _ = load_digits(return_X_y=True) >>> X.shape (1797, 64) >>> embedding = Isomap(n_components=2) >>> X_transformed = embedding.fit_transform(X:100) >>> X_transformed.shape (100, 2)

References ----------

.. 1 Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 (5500)

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

Compute the embedding vectors for data X

Parameters ---------- X : array-like, sparse graph, BallTree, KDTree, NearestNeighbors Sample data, shape = (n_samples, n_features), in the form of a numpy array, sparse graph, precomputed tree, or NearestNeighbors object.

y : Ignored

Returns ------- self : returns an instance of self.

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

Fit the model from data in X and transform X.

Parameters ---------- X : array-like, sparse graph, BallTree, KDTree Training vector, where n_samples in the number of samples and n_features is the number of features.

y : Ignored

Returns ------- X_new : array-like, shape (n_samples, n_components)

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 reconstruction_error : [> tag ] Obj.t -> float

Compute the reconstruction error for the embedding.

Returns ------- reconstruction_error : float

Notes ----- The cost function of an isomap embedding is

``E = frobenius_normK(D) - K(D_fit) / n_samples``

Where D is the matrix of distances for the input data X, D_fit is the matrix of distances for the output embedding X_fit, and K is the isomap kernel:

``K(D) = -0.5 * (I - 1/n_samples) * D^2 * (I - 1/n_samples)``

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

Transform X.

This is implemented by linking the points X into the graph of geodesic distances of the training data. First the `n_neighbors` nearest neighbors of X are found in the training data, and from these the shortest geodesic distances from each point in X to each point in the training data are computed in order to construct the kernel. The embedding of X is the projection of this kernel onto the embedding vectors of the training set.

Parameters ---------- X : array-like, shape (n_queries, n_features) If neighbors_algorithm='precomputed', X is assumed to be a distance matrix or a sparse graph of shape (n_queries, n_samples_fit).

Returns ------- X_new : array-like, shape (n_queries, n_components)

val embedding_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute embedding_: get value or raise Not_found if None.

val embedding_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute embedding_: get value as an option.

val kernel_pca_ : t -> Py.Object.t

Attribute kernel_pca_: get value or raise Not_found if None.

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

Attribute kernel_pca_: get value as an option.

val nbrs_ : t -> Py.Object.t

Attribute nbrs_: get value or raise Not_found if None.

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

Attribute nbrs_: get value as an option.

val dist_matrix_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute dist_matrix_: get value or raise Not_found if None.

val dist_matrix_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute dist_matrix_: 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 : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.