def test_solvers(self):
for solver in ['piteration', 'lanczos', 'bicgstab', 'RH']:
pagerank = PageRank(solver=solver)
scores = pagerank.fit_transform(self.adjacency)
self.assertAlmostEqual(0, np.linalg.norm(scores - self.truth))
with self.assertRaises(ValueError):
PageRank(solver='toto').fit_transform(self.adjacency)
def test_seeding(self):
pr = PageRank()
seeds_array = np.zeros(self.n)
seeds_array[0] = 1.
seeds_dict = {0: 1}
scores1 = pr.fit_transform(self.adjacency, seeds_array)
scores2 = pr.fit_transform(self.adjacency, seeds_dict)
self.assertAlmostEqual(np.linalg.norm(scores1 - scores2), 0.)
def test_copagerank(self):
seeds = {0: 1}
biadjacency = test_bigraph()
adjacency = co_neighbor_graph(biadjacency,
method='exact',
normalized=True)
pagerank = PageRank(damping_factor=0.85, solver='lanczos')
pagerank.fit(biadjacency, seeds_row=seeds)
scores1 = pagerank.scores_row_
scores1 /= scores1.sum()
scores2 = PageRank(damping_factor=0.85**2,
solver='lanczos').fit_transform(adjacency, seeds)
self.assertAlmostEqual(np.linalg.norm(scores1 - scores2), 0., places=6)
def test_copagerank(self):
seeds = {0: 1}
biadjacency = test_bigraph()
adjacency = co_neighbors_graph(biadjacency, method='exact')
scores1 = CoPageRank().fit_transform(biadjacency, seeds)
scores2 = PageRank().fit_transform(adjacency, seeds)
self.assertAlmostEqual(np.linalg.norm(scores1 - scores2), 0.)
adjacency = co_neighbors_graph(biadjacency.T.tocsr(), method='exact')
scores1 = CoPageRank().fit(biadjacency, seeds_col=seeds).scores_col_
scores2 = PageRank().fit_transform(adjacency, seeds)
self.assertAlmostEqual(np.linalg.norm(scores1 - scores2), 0.)
with self.assertRaises(ValueError):
CoPageRank(solver='diteration').fit_transform(biadjacency, seeds)
def __init__(self,
damping_factor: float = 0.85,
solver: str = 'piteration',
n_iter: int = 10,
tol: float = 0.,
n_jobs: Optional[int] = None,
verbose: bool = False):
algorithm = PageRank(damping_factor, solver, n_iter, tol)
super(PageRankClassifier, self).__init__(algorithm, n_jobs, verbose)
def test_damping(self):
pr = PageRank(damping_factor=0.99)
scores = pr.fit_transform(self.adjacency)
self.assertAlmostEqual(np.linalg.norm(scores - self.truth), 0.)
pr = PageRank(damping_factor=0.01)
scores = pr.fit_transform(self.adjacency)
self.assertAlmostEqual(np.linalg.norm(scores - self.truth), 0.)
def test_params(self):
with self.assertRaises(ValueError):
PageRank(damping_factor=1789)
def test_input(self):
pagerank = PageRank()
scores = pagerank.fit_transform(self.adjacency, force_bipartite=True)
self.assertEqual(len(scores), len(pagerank.scores_col_))
def test_pagerank(self):
ground_truth = np.ones(3) / 3
adjacency = rock_paper_scissors()
pagerank_sps = PageRank(solver='spsolve')
pagerank_sps.fit(adjacency)
scores = pagerank_sps.scores_
self.assertAlmostEqual(np.linalg.norm(scores - ground_truth), 0.)
pagerank_sps.fit(adjacency, personalization=np.array([0, 1, 0]))
pagerank_sps.fit(adjacency, personalization={1: 1})
pagerank_high_damping = PageRank(damping_factor=0.99)
pagerank_high_damping.fit(adjacency)
scores = pagerank_high_damping.scores_
self.assertAlmostEqual(np.linalg.norm(scores - ground_truth),
0.,
places=1)
pagerank_lcz = PageRank(solver='lanczos')
pagerank_lcz.fit(adjacency)
scores = pagerank_lcz.scores_
self.assertAlmostEqual(np.linalg.norm(scores - ground_truth), 0.)
pagerank_lsq = PageRank(solver='lsqr')
pagerank_lsq.fit(adjacency)
scores = pagerank_lsq.scores_
self.assertAlmostEqual(np.linalg.norm(scores - ground_truth), 0.)
pagerank_naive = PageRank(solver=None)
pagerank_naive.fit(adjacency)