162 lines
4.6 KiB
Python
162 lines
4.6 KiB
Python
# -*- coding: utf-8 -*-
|
|
#
|
|
# Author: Yuto Yamaguchi <yuto.ymgc@gmail.com>
|
|
"""Function for computing Local and global consistency algorithm by Zhou et al.
|
|
|
|
References
|
|
----------
|
|
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004).
|
|
Learning with local and global consistency.
|
|
Advances in neural information processing systems, 16(16), 321-328.
|
|
"""
|
|
import networkx as nx
|
|
|
|
from networkx.utils.decorators import not_implemented_for
|
|
from networkx.algorithms.node_classification.utils import (
|
|
_get_label_info,
|
|
_init_label_matrix,
|
|
_propagate,
|
|
_predict,
|
|
)
|
|
|
|
__all__ = ['local_and_global_consistency']
|
|
|
|
|
|
@not_implemented_for('directed')
|
|
def local_and_global_consistency(G, alpha=0.99,
|
|
max_iter=30,
|
|
label_name='label'):
|
|
"""Node classification by Local and Global Consistency
|
|
|
|
Parameters
|
|
----------
|
|
G : NetworkX Graph
|
|
alpha : float
|
|
Clamping factor
|
|
max_iter : int
|
|
Maximum number of iterations allowed
|
|
label_name : string
|
|
Name of target labels to predict
|
|
|
|
Raises
|
|
----------
|
|
`NetworkXError` if no nodes on `G` has `label_name`.
|
|
|
|
Returns
|
|
----------
|
|
predicted : array, shape = [n_samples]
|
|
Array of predicted labels
|
|
|
|
Examples
|
|
--------
|
|
>>> from networkx.algorithms import node_classification
|
|
>>> G = nx.path_graph(4)
|
|
>>> G.nodes[0]['label'] = 'A'
|
|
>>> G.nodes[3]['label'] = 'B'
|
|
>>> G.nodes(data=True)
|
|
NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}})
|
|
>>> G.edges()
|
|
EdgeView([(0, 1), (1, 2), (2, 3)])
|
|
>>> predicted = node_classification.local_and_global_consistency(G)
|
|
>>> predicted
|
|
['A', 'A', 'B', 'B']
|
|
|
|
|
|
References
|
|
----------
|
|
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004).
|
|
Learning with local and global consistency.
|
|
Advances in neural information processing systems, 16(16), 321-328.
|
|
"""
|
|
try:
|
|
import numpy as np
|
|
except ImportError:
|
|
raise ImportError(
|
|
"local_and_global_consistency() requires numpy: ",
|
|
"http://scipy.org/ ")
|
|
try:
|
|
from scipy import sparse
|
|
except ImportError:
|
|
raise ImportError(
|
|
"local_and_global_consistensy() requires scipy: ",
|
|
"http://scipy.org/ ")
|
|
|
|
def _build_propagation_matrix(X, labels, alpha):
|
|
"""Build propagation matrix of Local and global consistency
|
|
|
|
Parameters
|
|
----------
|
|
X : scipy sparse matrix, shape = [n_samples, n_samples]
|
|
Adjacency matrix
|
|
labels : array, shape = [n_samples, 2]
|
|
Array of pairs of node id and label id
|
|
alpha : float
|
|
Clamping factor
|
|
|
|
Returns
|
|
----------
|
|
S : scipy sparse matrix, shape = [n_samples, n_samples]
|
|
Propagation matrix
|
|
|
|
"""
|
|
degrees = X.sum(axis=0).A[0]
|
|
degrees[degrees == 0] = 1 # Avoid division by 0
|
|
D2 = np.sqrt(sparse.diags((1.0 / degrees), offsets=0))
|
|
S = alpha * D2.dot(X).dot(D2)
|
|
return S
|
|
|
|
def _build_base_matrix(X, labels, alpha, n_classes):
|
|
"""Build base matrix of Local and global consistency
|
|
|
|
Parameters
|
|
----------
|
|
X : scipy sparse matrix, shape = [n_samples, n_samples]
|
|
Adjacency matrix
|
|
labels : array, shape = [n_samples, 2]
|
|
Array of pairs of node id and label id
|
|
alpha : float
|
|
Clamping factor
|
|
n_classes : integer
|
|
The number of classes (distinct labels) on the input graph
|
|
|
|
Returns
|
|
----------
|
|
B : array, shape = [n_samples, n_classes]
|
|
Base matrix
|
|
"""
|
|
|
|
n_samples = X.shape[0]
|
|
B = np.zeros((n_samples, n_classes))
|
|
B[labels[:, 0], labels[:, 1]] = 1 - alpha
|
|
return B
|
|
|
|
X = nx.to_scipy_sparse_matrix(G) # adjacency matrix
|
|
labels, label_dict = _get_label_info(G, label_name)
|
|
|
|
if labels.shape[0] == 0:
|
|
raise nx.NetworkXError(
|
|
"No node on the input graph is labeled by '" + label_name + "'.")
|
|
|
|
n_samples = X.shape[0]
|
|
n_classes = label_dict.shape[0]
|
|
F = _init_label_matrix(n_samples, n_classes)
|
|
|
|
P = _build_propagation_matrix(X, labels, alpha)
|
|
B = _build_base_matrix(X, labels, alpha, n_classes)
|
|
|
|
remaining_iter = max_iter
|
|
while remaining_iter > 0:
|
|
F = _propagate(P, F, B)
|
|
remaining_iter -= 1
|
|
|
|
predicted = _predict(F, label_dict)
|
|
|
|
return predicted
|
|
|
|
|
|
# fixture for pytest
|
|
def setup_module(module):
|
|
import pytest
|
|
numpy = pytest.importorskip('numpy')
|
|
scipy = pytest.importorskip('scipy')
|