103 lines
2.6 KiB
Python
103 lines
2.6 KiB
Python
|
# -*- coding: utf-8 -*-
|
||
|
|
#
|
||
|
|
# Author: Yuto Yamaguchi <yuto.ymgc@gmail.com>
|
||
|
|
|
||
|
|
def _propagate(P, F, B):
|
||
|
|
"""Propagate labels by one step
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
P : scipy sparse matrix, shape = [n_samples, n_samples]
|
||
|
|
Propagation matrix
|
||
|
|
F : numpy array, shape = [n_samples, n_classes]
|
||
|
|
Label matrix
|
||
|
|
B : numpy array, shape = [n_samples, n_classes]
|
||
|
|
Base matrix
|
||
|
|
|
||
|
|
Returns
|
||
|
|
----------
|
||
|
|
F_new : array, shape = [n_samples, n_classes]
|
||
|
|
Label matrix
|
||
|
|
"""
|
||
|
|
F_new = P.dot(F) + B
|
||
|
|
return F_new
|
||
|
|
|
||
|
|
|
||
|
|
def _get_label_info(G, label_name):
|
||
|
|
"""Get and return information of labels from the input graph
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : Network X graph
|
||
|
|
label_name : string
|
||
|
|
Name of the target label
|
||
|
|
|
||
|
|
Returns
|
||
|
|
----------
|
||
|
|
labels : numpy array, shape = [n_labeled_samples, 2]
|
||
|
|
Array of pairs of labeled node ID and label ID
|
||
|
|
label_dict : numpy array, shape = [n_classes]
|
||
|
|
Array of labels
|
||
|
|
i-th element contains the label corresponding label ID `i`
|
||
|
|
"""
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
labels = []
|
||
|
|
label_to_id = {}
|
||
|
|
lid = 0
|
||
|
|
for i, n in enumerate(G.nodes(data=True)):
|
||
|
|
if label_name in n[1]:
|
||
|
|
label = n[1][label_name]
|
||
|
|
if label not in label_to_id:
|
||
|
|
label_to_id[label] = lid
|
||
|
|
lid += 1
|
||
|
|
labels.append([i, label_to_id[label]])
|
||
|
|
labels = np.array(labels)
|
||
|
|
label_dict = np.array([label for label, _ in sorted(
|
||
|
|
label_to_id.items(), key=lambda x:x[1])])
|
||
|
|
return (labels, label_dict)
|
||
|
|
|
||
|
|
|
||
|
|
def _init_label_matrix(n_samples, n_classes):
|
||
|
|
"""Create and return zero matrix
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n_samples : integer
|
||
|
|
The number of nodes (samples) on the input graph
|
||
|
|
n_classes : integer
|
||
|
|
The number of classes (distinct labels) on the input graph
|
||
|
|
|
||
|
|
Returns
|
||
|
|
----------
|
||
|
|
F : numpy array, shape = [n_samples, n_classes]
|
||
|
|
Label matrix
|
||
|
|
"""
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
F = np.zeros((n_samples, n_classes))
|
||
|
|
return F
|
||
|
|
|
||
|
|
|
||
|
|
def _predict(F, label_dict):
|
||
|
|
"""Predict labels by learnt label matrix
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
F : numpy array, shape = [n_samples, n_classes]
|
||
|
|
Learnt (resulting) label matrix
|
||
|
|
label_dict : numpy array, shape = [n_classes]
|
||
|
|
Array of labels
|
||
|
|
i-th element contains the label corresponding label ID `i`
|
||
|
|
|
||
|
|
Returns
|
||
|
|
----------
|
||
|
|
predicted : numpy array, shape = [n_samples]
|
||
|
|
Array of predicted labels
|
||
|
|
"""
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
predicted_label_ids = np.argmax(F, axis=1)
|
||
|
|
predicted = label_dict[predicted_label_ids].tolist()
|
||
|
|
return predicted
|