def main():
beta: float = 0.85
convergence: float = 0.01
edges_np: np.ndarray = tfgraph.DataSets.naive_6()
with tf.Session() as sess:
writer: tf.summary.FileWriter = tf.summary.FileWriter(
'logs/tensorflow/.')
graph: tfgraph.Graph = tfgraph.GraphConstructor.from_edges(
sess, "G1", edges_np, writer, is_sparse=False)
pr_iter: tfgraph.PageRank = tfgraph.IterativePageRank(
sess, "PR1", graph, beta)
g_upgradeable: tfgraph.Graph = tfgraph.GraphConstructor.empty(
sess, "G2", graph.n, writer)
pr_upgradeable: tfgraph.PageRank = tfgraph.IterativePageRank(
sess, "PR2", g_upgradeable, beta)
b: np.ndarray = pr_iter.ranks_np(convergence=convergence)
print(b)
for e in edges_np:
g_upgradeable.append(e[0], e[1])
print("[" + str(e[0]) + ", " + str(e[1]) + "]")
e = pr_upgradeable.ranks_np(convergence=convergence)
print(e)
writer.add_graph(sess.graph)
def main():
beta: float = 0.85
convergence: float = 0.0001
edges_np: np.ndarray = tfgraph.DataSets.naive_6()
with tf.Session() as sess:
graph: tfgraph.Graph = tfgraph.GraphConstructor.from_edges(
sess, "G", edges_np, is_sparse=False)
pr_iter: tfgraph.PageRank = tfgraph.IterativePageRank(
sess, "PR1", graph, beta)
v_4: np.ndarray = pr_iter.pagerank_vector_np(convergence=convergence,
topics=[4])
v_5: np.ndarray = pr_iter.pagerank_vector_np(convergence=convergence,
topics=[5])
v_45: np.ndarray = pr_iter.pagerank_vector_np(convergence=convergence,
topics=[4, 5])
v_45_pseudo: np.ndarray = (v_4 + v_5) / 2.0
print(v_4)
print(np.sum(v_4))
print(v_5)
print(np.sum(v_5))
print(v_45)
print(np.sum(v_45))
print(v_45_pseudo)
print(np.sum(v_45_pseudo))
def test_iterative_personalized_pagerank_steps():
with tf.Session() as sess:
beta = 0.85
steps = 100
graph = tfgraph.GraphConstructor.from_edges(sess, "G_proof",
edges_np=tfgraph.DataSets.naive_6())
pageRank = tfgraph.IterativePageRank(sess, "Pr_Proof", graph, beta)
np.testing.assert_array_almost_equal(
pageRank.ranks_np(steps=steps, topics=[4]),
np.array([[4, 0.999186],
[0, 0.000284],
[5, 0.000176],
[1, 0.000150],
[3, 0.000116],
[2, 0.000089]]),
decimal=2
)
def test_iterative_pagerank_convergence():
with tf.Session() as sess:
beta = 0.85
convergence = 0.01
graph = tfgraph.GraphConstructor.from_edges(sess, "G_proof",
edges_np=tfgraph.DataSets.naive_6())
pageRank = tfgraph.IterativePageRank(sess, "Pr_Proof", graph, beta)
np.testing.assert_array_almost_equal(
pageRank.ranks_np(convergence=convergence),
np.array([[0.0, 0.321017],
[5.0, 0.20074403],
[1.0, 0.17054307],
[3.0, 0.13679263],
[2.0, 0.10659166],
[4.0, 0.0643118]]),
decimal=2
)
def main():
beta: float = 0.85
convergence: float = 0.0001
edges_np: np.ndarray = tfgraph.DataSets.naive_6()
with tf.Session() as sess:
writer: tf.summary.FileWriter = tf.summary.FileWriter('logs/tensorflow/')
graph: tfgraph.Graph = tfgraph.GraphConstructor.from_edges(
sess, "G", edges_np, is_sparse=False)
pr_itr: tfgraph.PageRank = tfgraph.IterativePageRank(sess, "PR", graph, beta)
start_time: float = timeit.default_timer()
b: np.ndarray = pr_itr.ranks_np(convergence=convergence)
elapsed: float = timeit.default_timer() - start_time
print(elapsed)
print(b)
writer.add_graph(sess.graph)
def test_iterative_pagerank_upgradeable():
beta = 0.85
convergence=0.01
with tf.Session() as sess:
g_upgradeable: tfgraph.Graph = tfgraph.GraphConstructor.empty(sess, "G", 6)
pr_upgradeable: tfgraph.PageRank = tfgraph.IterativePageRank(
sess, "PR", g_upgradeable, beta)
for e in tfgraph.DataSets.naive_6():
g_upgradeable.append(e[0], e[1])
np.testing.assert_array_almost_equal(
pr_upgradeable.ranks_np(convergence=convergence),
np.array([[0.0, 0.321017],
[5.0, 0.20074403],
[1.0, 0.17054307],
[3.0, 0.13679263],
[2.0, 0.10659166],
[4.0, 0.0643118]]),
decimal=2
)
def main():
beta: float = 0.85
convergence: float = 0.0001
edges_np: np.ndarray = tfgraph.DataSets.naive_6()
with tf.Session() as sess:
writer: tf.summary.FileWriter = tf.summary.FileWriter(
'logs/tensorflow/.')
graph: tfgraph.Graph = tfgraph.GraphConstructor.from_edges(
sess, "G", edges_np, writer, is_sparse=False)
pr_alge: tfgraph.PageRank = tfgraph.AlgebraicPageRank(
sess, "PR1", graph, beta)
pr_iter: tfgraph.PageRank = tfgraph.IterativePageRank(
sess, "PR1", graph, beta)
'''
g_upgradeable: tfgraph.Graph = tfgraph.GraphConstructor.empty(sess,
"Gfollowers",
7, writer)
pr_upgradeable: tfgraph.PageRank = tfgraph.IterativePageRank(sess,
"PRfollowers",
g_upgradeable,
beta)
'''
# a = pr_alge.ranks_np()
start_time: float = timeit.default_timer()
a: np.ndarray = pr_alge.ranks_np(convergence=convergence,
topics=[5],
topics_decrement=True)
elapsed: float = timeit.default_timer() - start_time
print(elapsed)
start_time: float = timeit.default_timer()
b: np.ndarray = pr_iter.ranks_np(convergence=convergence,
topics=[5],
topics_decrement=True)
elapsed: float = timeit.default_timer() - start_time
print(elapsed)
print(a)
print(b)
# print(c)
# print((pr_alge.error_vector_compare_np(pr_iter)))
# print(pr_iter.ranks_np(convergence=convergence))
# print(pr_alge.error_ranks_compare_np(pr_iter))
'''
g_sparse = tfgraph.GraphConstructor.as_sparsifier(sess, graph, 0.75)
pr_sparse = tfgraph.IterativePageRank(sess, "PR_sparse", g_sparse, beta)
start_time: float = timeit.default_timer()
d: np.ndarray = pr_sparse.ranks_np(convergence=convergence)
elapsed: float = timeit.default_timer() - start_time
print(elapsed)
print(pr_iter.error_ranks_compare_np(pr_sparse))
print(graph.m)
print(g_sparse.m)
'''
'''
g_sparse_upgradeable = tfgraph.GraphConstructor.empty_sparsifier(
sess=sess, name="G_su", n=6301, p=0.5)
pr_iter: tfgraph.PageRank = tfgraph.IterativePageRank(
sess, "PR_su", g_sparse_upgradeable, beta)
e = pr_iter.ranks_np(convergence=convergence)
for r in edges_np:
g_sparse_upgradeable.append(r[0], r[1])
e = pr_iter.ranks_np(convergence=convergence)
tfgraph.Utils.save_ranks("logs/csv/sparse_update.csv", e)
print(e)
# tfgraph.Utils.save_ranks("logs/csv/alge.csv",a)
# tfgraph.Utils.save_ranks("logs/csv/sparse.csv", d)
tfgraph.Utils.save_ranks("logs/csv/sparse_update.csv", e)
'''
'''
print(GraphConstructor.unweighted_random(sess, "GRandom", 10 ** 2,
10 ** 3, writer=writer))
'''
tfgraph.Utils.save_ranks("logs/csv/iter.csv", b)
writer.add_graph(sess.graph)