LocallyLinearEmbedding
¶
-
class
ibex.sklearn.manifold.
LocallyLinearEmbedding
(n_neighbors=5, n_components=2, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, neighbors_algorithm='auto', random_state=None, n_jobs=1)¶ Bases:
sklearn.manifold.locally_linear.LocallyLinearEmbedding
,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
.
Locally Linear Embedding
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
- reg : float
- regularization constant, multiplies the trace of the local covariance matrix of the distances.
- eigen_solver : string, {‘auto’, ‘arpack’, ‘dense’}
auto : algorithm will attempt to choose the best method for input data
- arpack : use arnoldi iteration in shift-invert mode.
- For this method, M may be a dense matrix, sparse matrix, or general linear operator. Warning: ARPACK can be unstable for some problems. It is best to try several random seeds in order to check results.
- dense : use standard dense matrix operations for the eigenvalue
- decomposition. For this method, M must be an array or matrix type. This method should be avoided for large problems.
- tol : float, optional
- Tolerance for ‘arpack’ method Not used if eigen_solver==’dense’.
- max_iter : integer
- maximum number of iterations for the arpack solver. Not used if eigen_solver==’dense’.
- method : string (‘standard’, ‘hessian’, ‘modified’ or ‘ltsa’)
- standard : use the standard locally linear embedding algorithm. see
- reference [1]
- hessian : use the Hessian eigenmap method. This method requires
n_neighbors > n_components * (1 + (n_components + 1) / 2
see reference [2]- modified : use the modified locally linear embedding algorithm.
- see reference [3]
- ltsa : use local tangent space alignment algorithm
- see reference [4]
- hessian_tol : float, optional
- Tolerance for Hessian eigenmapping method.
Only used if
method == 'hessian'
- modified_tol : float, optional
- Tolerance for modified LLE method.
Only used if
method == 'modified'
- neighbors_algorithm : string [‘auto’|’brute’|’kd_tree’|’ball_tree’]
- algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance
- random_state : int, RandomState instance or None, optional (default=None)
- If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by np.random. Used when
eigen_solver
== ‘arpack’. - 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_vectors_ : array-like, shape [n_components, n_samples]
- Stores the embedding vectors
- reconstruction_error_ : float
- Reconstruction error associated with embedding_vectors_
- nbrs_ : NearestNeighbors object
- Stores nearest neighbors instance, including BallTree or KDtree if applicable.
[1] Roweis, S. & Saul, L. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323 (2000). [2] Donoho, D. & Grimes, C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci U S A. 100:5591 (2003). [3] Zhang, Z. & Wang, J. MLLE: Modified Locally Linear Embedding Using Multiple Weights. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382 [4] Zhang, Z. & Zha, H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Journal of Shanghai Univ. 8:406 (2004) -
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 of shape [n_samples, n_features]
- training set.
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
.
Compute the embedding vectors for data X and transform X.
- X : array-like of shape [n_samples, n_features]
- training set.
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 new points into embedding space.
X : array-like, shape = [n_samples, n_features]
X_new : array, shape = [n_samples, n_components]
Because of scaling performed by this method, it is discouraged to use it together with methods that are not scale-invariant (like SVMs)
- A parameter
- A parameter