lale.lib.sklearn.isomap module

class lale.lib.sklearn.isomap.Isomap(*, n_neighbors=5, n_components=2, eigen_solver='auto', tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto', n_jobs=None, metric='minkowski', p=2, metric_params=None, radius=None)

Bases: PlannedIndividualOp

“Isomap embedding from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

  • n_neighbors (integer, >=5 for optimizer, <=20 for optimizer, uniform distribution, default 5) – number of neighbors to consider for each point.

  • n_components (integer, >=2 for optimizer, <='X/items/maxItems', <=256 for optimizer, uniform distribution, default 2) – number of coordinates for the manifold

  • eigen_solver (‘auto’, ‘arpack’, or ‘dense’, default ‘auto’) – ‘auto’ : Attempt to choose the most efficient solver for the given problem

  • tol (float, >=0 for optimizer, <=1 for optimizer, uniform distribution, default 0) – Convergence tolerance passed to arpack or lobpcg

  • max_iter (union type, not for optimizer, default None) –

    Maximum number of iterations for the arpack solver

    • integer

    • or None

  • path_method (‘auto’, ‘FW’, or ‘D’, default ‘auto’) – Method to use in finding shortest path

  • neighbors_algorithm (‘auto’, ‘brute’, ‘kd_tree’, or ‘ball_tree’, default ‘auto’) – Algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance.

  • n_jobs (union type, not for optimizer, default None) –

    The number of parallel jobs to run

    • integer

    • or None

  • metric (Any, not for optimizer, 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 sklearn.metrics.pairwise_distances for its metric parameter. If metric is “precomputed”, X is assumed to be a distance matrix and must be square.

  • p (integer, not for optimizer, 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.

  • metric_params (union type, not for optimizer, default None) –

    Additional keyword arguments for the metric function

    • dict

    • or None

  • radius (union type, optional, not for optimizer, default None) –

    Limiting distance of neighbors to return. If radius is a float, then n_neighbors must be set to None.

    • float

    • or None

Notes

constraint-1 : union type

  • intersection type

    • dict of n_neighors : integer

    • and dict of radius : None

  • or intersection type

    • dict of n_neighors : None

    • and dict of radius : float

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (Any) – Sample data, shape = (n_samples, n_features), in the form of a numpy array, precomputed tree, or NearestNeighbors object.

  • y (any type, optional) –

transform(X, y=None)

Transform the data.

Note: The transform method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (Any) –

Returns

result – Transform X.

Return type

Any