Isomap
Isomap Embedding.
Non-linear dimensionality reduction through Isometric Mapping
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new Isomap(opts?: object): Isomap;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.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. Default Value 'auto' |
opts.max_iter? | number | Maximum number of iterations for the arpack solver. not used if eigen_solver == ‘dense’. |
opts.metric? | any | 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. X may be a Glossary. Default Value 'minkowski' |
opts.metric_params? | any | Additional keyword arguments for the metric function. |
opts.n_components? | number | Number of coordinates for the manifold. Default Value 2 |
opts.n_jobs? | number | The number of parallel jobs to run. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details. |
opts.n_neighbors? | number | Number of neighbors to consider for each point. If n\_neighbors is an int, then radius must be undefined. Default Value 5 |
opts.neighbors_algorithm? | "auto" | "ball_tree" | "kd_tree" | "brute" | Algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance. Default Value 'auto' |
opts.p? | number | 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. Default Value 2 |
opts.path_method? | "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. Default Value 'auto' |
opts.radius? | number | Limiting distance of neighbors to return. If radius is a float, then n\_neighbors must be set to undefined. |
opts.tol? | number | Convergence tolerance passed to arpack or lobpcg. not used if eigen_solver == ‘dense’. Default Value 0 |
Returns
Defined in: generated/manifold/Isomap.ts:25 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/manifold/Isomap.ts:180 (opens in a new tab)
fit()
Compute the embedding vectors for data X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Sample data, shape = (n_samples, n_features), in the form of a numpy array, sparse matrix, precomputed tree, or NearestNeighbors object. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/manifold/Isomap.ts:197 (opens in a new tab)
fit_transform()
Fit the model from data in X and transform X.
Signature
fit_transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Training vector, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<ArrayLike>
Defined in: generated/manifold/Isomap.ts:235 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class\_name0", "class\_name1", "class\_name2"\].
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Only used to validate feature names with the names seen in fit. |
Returns
Promise<any>
Defined in: generated/manifold/Isomap.ts:275 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/manifold/Isomap.ts:310 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/manifold/Isomap.ts:129 (opens in a new tab)
reconstruction_error()
Compute the reconstruction error for the embedding.
Signature
reconstruction_error(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.reconstruction_error? | number | Reconstruction error. |
Returns
Promise<any>
Defined in: generated/manifold/Isomap.ts:343 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/manifold/Isomap.ts:379 (opens in a new tab)
transform()
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.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | If neighbors_algorithm=’precomputed’, X is assumed to be a distance matrix or a sparse graph of shape (n_queries, n_samples_fit). |
Returns
Promise<ArrayLike>
Defined in: generated/manifold/Isomap.ts:414 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/manifold/Isomap.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/manifold/Isomap.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/manifold/Isomap.ts:21 (opens in a new tab)
id
string
Defined in: generated/manifold/Isomap.ts:18 (opens in a new tab)
opts
any
Defined in: generated/manifold/Isomap.ts:19 (opens in a new tab)
Accessors
dist_matrix_
Stores the geodesic distance matrix of training data.
Signature
dist_matrix_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/manifold/Isomap.ts:513 (opens in a new tab)
embedding_
Stores the embedding vectors.
Signature
embedding_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/manifold/Isomap.ts:445 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/manifold/Isomap.ts:559 (opens in a new tab)
kernel_pca_
KernelPCA object used to implement the embedding.
Signature
kernel_pca_(): Promise<any>;Returns
Promise<any>
Defined in: generated/manifold/Isomap.ts:468 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/manifold/Isomap.ts:536 (opens in a new tab)
nbrs_
Stores nearest neighbors instance, including BallTree or KDtree if applicable.
Signature
nbrs_(): Promise<any>;Returns
Promise<any>
Defined in: generated/manifold/Isomap.ts:491 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/manifold/Isomap.ts:116 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/manifold/Isomap.ts:120 (opens in a new tab)