def make_train_test_set(graph, radius,
test_proportion=.3, ratio_neg_to_pos=10):
"""make_train_test_set."""
pos = [(u, v) for u, v in graph.edges()]
neg = [(u, v) for u, v in nx.non_edges(graph)]
random.shuffle(pos)
random.shuffle(neg)
pos_dim = len(pos)
neg_dim = len(neg)
max_n_neg = min(pos_dim * ratio_neg_to_pos, neg_dim)
neg = neg[:max_n_neg]
neg_dim = len(neg)
tr_pos = pos[:-int(pos_dim * test_proportion)]
te_pos = pos[-int(pos_dim * test_proportion):]
tr_neg = neg[:-int(neg_dim * test_proportion)]
te_neg = neg[-int(neg_dim * test_proportion):]
# remove edges
tr_graph = graph.copy()
tr_graph.remove_edges_from(te_pos)
tr_pos_graphs = list(_make_subgraph_set(tr_graph, radius, tr_pos))
tr_neg_graphs = list(_make_subgraph_set(tr_graph, radius, tr_neg))
te_pos_graphs = list(_make_subgraph_set(tr_graph, radius, te_pos))
te_neg_graphs = list(_make_subgraph_set(tr_graph, radius, te_neg))
tr_graphs = tr_pos_graphs + tr_neg_graphs
te_graphs = te_pos_graphs + te_neg_graphs
tr_targets = [1] * len(tr_pos_graphs) + [0] * len(tr_neg_graphs)
te_targets = [1] * len(te_pos_graphs) + [0] * len(te_neg_graphs)
tr_graphs, tr_targets = paired_shuffle(tr_graphs, tr_targets)
te_graphs, te_targets = paired_shuffle(te_graphs, te_targets)
return (tr_graphs, np.array(tr_targets)), (te_graphs, np.array(te_targets))
def make_train_test_set(graph, radius,
test_proportion=.3, ratio_neg_to_pos=10):
"""make_train_test_set."""
pos = [(u, v) for u, v in graph.edges()]
neg = [(u, v) for u, v in nx.non_edges(graph)]
random.shuffle(pos)
random.shuffle(neg)
pos_dim = len(pos)
neg_dim = len(neg)
max_n_neg = min(pos_dim * ratio_neg_to_pos, neg_dim)
neg = neg[:max_n_neg]
neg_dim = len(neg)
tr_pos = pos[:-int(pos_dim * test_proportion)]
te_pos = pos[-int(pos_dim * test_proportion):]
tr_neg = neg[:-int(neg_dim * test_proportion)]
te_neg = neg[-int(neg_dim * test_proportion):]
# remove edges
tr_graph = graph.copy()
tr_graph.remove_edges_from(te_pos)
tr_pos_graphs = list(_make_subgraph_set(tr_graph, radius, tr_pos))
tr_neg_graphs = list(_make_subgraph_set(tr_graph, radius, tr_neg))
te_pos_graphs = list(_make_subgraph_set(tr_graph, radius, te_pos))
te_neg_graphs = list(_make_subgraph_set(tr_graph, radius, te_neg))
tr_graphs = tr_pos_graphs + tr_neg_graphs
te_graphs = te_pos_graphs + te_neg_graphs
tr_targets = [1] * len(tr_pos_graphs) + [0] * len(tr_neg_graphs)
te_targets = [1] * len(te_pos_graphs) + [0] * len(te_neg_graphs)
tr_graphs, tr_targets = paired_shuffle(tr_graphs, tr_targets)
te_graphs, te_targets = paired_shuffle(te_graphs, te_targets)
return (tr_graphs, np.array(tr_targets)), (te_graphs, np.array(te_targets))
def learning_curve(self, graphs, targets,
cv=5, n_steps=10, start_fraction=0.1):
"""learning_curve."""
graphs, targets = paired_shuffle(graphs, targets)
x = self.transform(graphs)
train_sizes = np.linspace(start_fraction, 1.0, n_steps)
scoring = 'roc_auc'
train_sizes, train_scores, test_scores = learning_curve(
self.model, x, targets,
cv=cv, train_sizes=train_sizes,
scoring=scoring, n_jobs=self.n_jobs)
return train_sizes, train_scores, test_scores
def learning_curve(self, graphs, targets,
cv=5, n_steps=10, start_fraction=0.1):
"""learning_curve."""
graphs, targets = paired_shuffle(graphs, targets)
x = self.transform(graphs)
train_sizes = np.linspace(start_fraction, 1.0, n_steps)
scoring = 'roc_auc'
train_sizes, train_scores, test_scores = learning_curve(
self.model, x, targets,
cv=cv, train_sizes=train_sizes,
scoring=scoring)
return train_sizes, train_scores, test_scores