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, thenn_neighbors
must be set toNone
.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