#!/usr/bin/env python
"""Provides link prediction utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import networkx as nx
import numpy as np
from eden.util import timeit
from eden.display import draw_graph
import matplotlib.pyplot as plt
import logging
logger = logging.getLogger()
def filter_if_degree_smaller_then(g, th=1):
"""filter_if_degree_smaller_then."""
subset = [u for u in g.nodes() if len(g.neighbors(u)) >= th]
return nx.Graph(g.subgraph(subset))
def filter_if_degree_greater_then(g, th=1):
"""filter_if_degree_greater_then."""
subset = [u for u in g.nodes() if len(g.neighbors(u)) < th]
return nx.Graph(g.subgraph(subset))
def show_graph(g, vertex_color='typeof', size=15, vertex_label=None):
"""show_graph."""
degrees = [len(g.neighbors(u)) for u in g.nodes()]
print(('num nodes=%d' % len(g)))
print(('num edges=%d' % len(g.edges())))
print(('num non edges=%d' % len(list(nx.non_edges(g)))))
print(('max degree=%d' % max(degrees)))
print(('median degree=%d' % np.percentile(degrees, 50)))
draw_graph(g, size=size,
vertex_color=vertex_color, vertex_label=vertex_label,
vertex_size=200, edge_label=None)
# display degree distribution
size = int((max(degrees) - min(degrees)) / 1.5)
plt.figure(figsize=(size, 3))
plt.title('Degree distribution')
_bins = np.arange(min(degrees), max(degrees) + 2) - .5
n, bins, patches = plt.hist(degrees, _bins,
alpha=0.3,
facecolor='navy', histtype='bar',
rwidth=0.8, edgecolor='k')
labels = np.array([str(int(i)) for i in n])
for xi, yi, label in zip(bins, n, labels):
plt.text(xi + 0.5, yi, label, ha='center', va='bottom')
plt.xticks(bins + 0.5)
plt.xlim((min(degrees) - 1, max(degrees) + 1))
plt.ylim((0, max(n) * 1.1))
plt.xlabel('Node degree')
plt.ylabel('Counts')
plt.grid(linestyle=":")
plt.show()
@timeit
def display_edge_predictions(g, tr_graphs, tr_targets,
te_graphs, te_targets, preds,
vertex_color='typeof', size=15):
"""display_edge_predictions."""
tr_roots = [gg.graph['roots'] for gg in tr_graphs]
graph = g.copy()
for (u, v), t in zip(tr_roots, tr_targets):
if t == 1:
graph.edge[u][v]['color'] = 'gray'
te_roots = [gg.graph['roots'] for gg in te_graphs]
for (u, v), p, t in zip(te_roots, preds, te_targets):
# for all predicted edges
if p == 1:
# true positive
if t == 1:
graph.edge[u][v]['color'] = 'limegreen'
# false positive
if t == 0:
graph.add_edge(u, v)
graph.edge[u][v]['nesting'] = True
graph.edge[u][v]['color'] = 'gray'
if p == 0:
# false negative
if t == 1:
graph.edge[u][v]['color'] = 'crimson'
draw_graph(graph, size=size, vertex_color=vertex_color,
vertex_size=100, vertex_label=None, edge_label=None,
edge_color='color', edge_alpha=1,
ignore_for_layout='nesting', dark_edge_alpha=.9,
dark_edge_color='color')
return graph
