Isomap
¶
-
class
ibex.sklearn.manifold.
Isomap
(n_neighbors=5, n_components=2, eigen_solver='auto', tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto', n_jobs=1)¶ Bases:
sklearn.manifold.isomap.Isomap
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Isomap Embedding
Non-linear dimensionality reduction through Isometric Mapping
Read more in the User Guide.
- 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, optional (default = 1)
- The number of parallel jobs to run.
If
-1
, then the number of jobs is set to the number of CPU cores.
- embedding_ : array-like, shape (n_samples, n_components)
- Stores the embedding vectors.
- kernel_pca_ : object
- KernelPCA object used to implement the embedding.
- training_data_ : array-like, shape (n_samples, n_features)
- Stores the training data.
- 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.
[1] Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 (5500) -
fit
(X, y=None)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Compute the embedding vectors for data X
- X : {array-like, sparse matrix, BallTree, KDTree, NearestNeighbors}
- Sample data, shape = (n_samples, n_features), in the form of a numpy array, precomputed tree, or NearestNeighbors object.
y: Ignored.
self : returns an instance of self.
- A parameter
-
fit_transform
(X, y=None)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Fit the model from data in X and transform X.
- X : {array-like, sparse matrix, BallTree, KDTree}
- Training vector, where n_samples in the number of samples and n_features is the number of features.
y: Ignored.
X_new : array-like, shape (n_samples, n_components)
- A parameter
-
transform
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
X : array-like, shape (n_samples, n_features)
X_new : array-like, shape (n_samples, n_components)
- A parameter
- A parameter