# Source code for graspy.embed.lse

# Copyright 2019 NeuroData (http://neurodata.io)
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import warnings

from .base import BaseEmbed
from ..utils import import_graph, to_laplace, is_fully_connected

[docs]class LaplacianSpectralEmbed(BaseEmbed):
r"""
Class for computing the laplacian spectral embedding of a graph.

The laplacian spectral embedding (LSE) is a k-dimensional Euclidean representation
of the graph based on its Laplacian matrix. It relies on an SVD to reduce
the dimensionality to the specified k, or if k is unspecified, can find a number
of dimensions automatically.

Read more in the :ref:tutorials <embed_tutorials>

Parameters
----------
Specifies the type of Laplacian normalization to use.

n_components : int or None, default = None
Desired dimensionality of output data. If "full",
n_components must be <= min(X.shape). Otherwise, n_components must be
< min(X.shape). If None, then optimal dimensions will be chosen by
:func:~graspy.embed.select_dimension using n_elbows argument.

n_elbows : int, optional, default: 2
If n_components=None, then compute the optimal embedding dimension using
:func:~graspy.embed.select_dimension. Otherwise, ignored.

algorithm : {'randomized' (default), 'full', 'truncated'}, optional
SVD solver to use:

- 'randomized'
Computes randomized svd using
:func:sklearn.utils.extmath.randomized_svd
- 'full'
Computes full svd using :func:scipy.linalg.svd
- 'truncated'
Computes truncated svd using :func:scipy.sparse.linalg.svds

n_iter : int, optional (default = 5)
Number of iterations for randomized SVD solver. Not used by 'full' or
'truncated'. The default is larger than the default in randomized_svd
to handle sparse matrices that may have large slowly decaying spectrum.

check_lcc : bool , optional (defult = True)
Whether to check if input graph is connected. May result in non-optimal
results if the graph is unconnected. If True and input is unconnected,
a UserWarning is thrown. Not checking for connectedness may result in
faster computation.

regularizer: int, float or None, optional (default=None)
Constant to be added to the diagonal of degree matrix. If None, average
node degree is added. If int or float, must be >= 0. Only used when
form == 'R-DAD'.

Attributes
----------
latent_left_ : array, shape (n_samples, n_components)
Estimated left latent positions of the graph.

latent_right_ : array, shape (n_samples, n_components), or None
Only computed when the graph is directed, or adjacency matrix is assymetric.
Estimated right latent positions of the graph. Otherwise, None.

singular_values_ : array, shape (n_components)
Singular values associated with the latent position matrices.

--------
graspy.embed.selectSVD
graspy.embed.select_dimension
graspy.utils.to_laplace

Notes
-----
The singular value decomposition:

.. math:: A = U \Sigma V^T

is used to find an orthonormal basis for a matrix, which in our case is the
Laplacian matrix of the graph. These basis vectors (in the matrices U or V) are
ordered according to the amount of variance they explain in the original matrix.
By selecting a subset of these basis vectors (through our choice of dimensionality
reduction) we can find a lower dimensional space in which to represent the graph.

References
----------
.. [1] Sussman, D.L., Tang, M., Fishkind, D.E., Priebe, C.E.  "A
Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs,"
Journal of the American Statistical Association, Vol. 107(499), 2012.
.. [2] Von Luxburg, Ulrike. "A tutorial on spectral clustering," Statistics
and computing, Vol. 17(4), pp. 395-416, 2007.
.. [3] Rohe, Karl, Sourav Chatterjee, and Bin Yu. "Spectral clustering and
the high-dimensional stochastic blockmodel," The Annals of Statistics,
Vol. 39(4), pp. 1878-1915, 2011.
"""

def __init__(
self,
n_components=None,
n_elbows=2,
algorithm="randomized",
n_iter=5,
check_lcc=True,
regularizer=None,
):
super().__init__(
n_components=n_components,
n_elbows=n_elbows,
algorithm=algorithm,
n_iter=n_iter,
check_lcc=check_lcc,
)
self.form = form
self.regularizer = regularizer

[docs]    def fit(self, graph, y=None):
"""
Fit LSE model to input graph

By default, uses the Laplacian normalization of the form:

.. math:: L = D^{-1/2} A D^{-1/2}

Parameters
----------
graph : array_like or networkx.Graph
Input graph to embed. see graspy.utils.import_graph

Returns
-------
self : object
Returns an instance of self.
"""
A = import_graph(graph)

if self.check_lcc:
if not is_fully_connected(A):
msg = (
"Input graph is not fully connected. Results may not"
+ "be optimal. You can compute the largest connected component by"
+ "using graspy.utils.get_lcc."
)
warnings.warn(msg, UserWarning)

L_norm = to_laplace(A, form=self.form, regularizer=self.regularizer)
self._reduce_dim(L_norm)
return self