def test_neighbor_features(self):
g = data4tests.get_small_graph()
ia = data4tests.MockIMBD(g)
ratings = fame.neighbor_features(
g, PersonNode(194), lambda m: ia.get_movie(m.id)["rating"])
self.assertAlmostEqual(4.699903862142268, ratings[WorkNode(160513)])
self.assertAlmostEqual(7.70552832501407, ratings[WorkNode(1658801)])
self.assertEqual(57, len(ratings))
def test_actors_only_weights(self):
g = self.make_graph()
weight = "actor_weight"
weights.weight_only_actors(g, weight=weight)
self.assertEqual(1, g.edges[(PersonNode(1), WorkNode(43044))][weight])
self.assertEqual(1, g.edges[(PersonNode(1), WorkNode(50419))][weight])
self.assertEqual(1, g.edges[(PersonNode(2), WorkNode(117057))][weight])
self.assertEqual(
0, g.edges[(PersonNode(861703), WorkNode(43044))][weight])
def test_rating_weights(self):
g = self.make_graph()
weight = "rating_weight"
weights.weight_by_rating(g, weight=weight)
self.assertAlmostEqual(
0.69, g.edges[(PersonNode(1), WorkNode(43044))][weight])
self.assertAlmostEqual(
0.70, g.edges[(PersonNode(1), WorkNode(50419))][weight])
self.assertAlmostEqual(
0.66, g.edges[(PersonNode(2), WorkNode(117057))][weight])
self.assertAlmostEqual(
0.69, g.edges[(PersonNode(861703), WorkNode(43044))][weight])
def test_order(self):
g = self.make_graph()
e = g.edges[(PersonNode(1), WorkNode(43044))]
self.assertEqual(1, imdb_tsv.get_order(e))
e = g.edges[(PersonNode(1), WorkNode(50419))]
self.assertEqual(2, imdb_tsv.get_order(e))
e = g.edges[(PersonNode(2), WorkNode(117057))]
self.assertEqual(3, imdb_tsv.get_order(e))
e = g.edges[(PersonNode(861703), WorkNode(43044))]
self.assertEqual(5, imdb_tsv.get_order(e))
def test_movie_node(self):
n0 = ProfessionalNode(42, False)
n1 = WorkNode(42)
self.assertFalse(n1.is_person)
self.assertEqual(n0, n1)
self.assertEqual(hash(n0), hash(n1))
def test_get_works(self):
g = data4tests.get_small_graph()
works = fame.get_works(g)
self.assertEqual(300, len(works))
self.assertTrue(WorkNode(119567) in works)
for w in works:
self.assertFalse(w.is_person)
def test_credit_order_weights(self):
def activation(x):
y = (1 - x) / 3
return math.tanh(y) + 1
g = self.make_graph()
weight = "credit_weight"
weights.weight_credit_order(g, weight=weight)
self.assertEqual(activation(1),
g.edges[(PersonNode(1), WorkNode(43044))][weight])
self.assertEqual(activation(2),
g.edges[(PersonNode(1), WorkNode(50419))][weight])
self.assertEqual(activation(3),
g.edges[(PersonNode(2), WorkNode(117057))][weight])
self.assertEqual(
activation(5),
g.edges[(PersonNode(861703), WorkNode(43044))][weight])
def test_acted_in(self):
g = self.make_graph()
thorpe = PersonNode(861703)
astaire = PersonNode(1)
bacall = PersonNode(2)
three_little_words = WorkNode(43044)
the_mirror_has_two_faces = WorkNode(117057)
self.assertFalse(imdb_tsv.acted_in_in_graph(g, thorpe, three_little_words))
self.assertFalse(
imdb_tsv.acted_in_in_graph(g, thorpe, the_mirror_has_two_faces)
)
self.assertTrue(imdb_tsv.acted_in_in_graph(g, astaire, three_little_words))
self.assertFalse(
imdb_tsv.acted_in_in_graph(g, astaire, the_mirror_has_two_faces)
)
self.assertFalse(imdb_tsv.acted_in_in_graph(g, bacall, three_little_words))
self.assertTrue(imdb_tsv.acted_in_in_graph(g, bacall, the_mirror_has_two_faces))
def test_add_person(self):
g = nx.Graph()
imdb_tsv.add_person(g, self.names[0])
expected = {
"name": "Fred Astaire",
"birth": "1899",
"death": "1987",
"professions": {"miscellaneous", "actor", "soundtrack"},
"known": {
WorkNode(53137),
WorkNode(50419),
WorkNode(72308),
WorkNode(43044),
},
}
actual = g.nodes[PersonNode(1)]
self.assertEqual(expected, actual)
def test_weight_by_cast_order(self):
g = data4tests.get_small_graph()
ia = data4tests.MockIMBD(g)
fame.weight_by_cast_order(g, ia)
for edge in g.edges:
self.assertIn("weight", g.edges[edge])
neighbors = list(g.neighbors((WorkNode(206634))))
self.assertEqual(4, len(neighbors))
neighbors.sort(key=lambda p: g.edges[(WorkNode(206634), p)]["weight"],
reverse=True)
actual = [g.edges[(m, WorkNode(206634))]["weight"] for m in neighbors]
expected = fame.normalized_exponential_decay(4)
self.assertAlmostEqual(0, norm(actual - expected))
# order of cast should be same as order of weights from highest to lowest
cast = ia.get_movie(206634)["cast"]
for i, actor in enumerate(cast):
self.assertEqual(actor.getID(), neighbors[i].id)
def test_contributed_as(self):
g = self.make_graph()
thorpe = PersonNode(861703)
three_little_words = WorkNode(43044)
the_mirror_has_two_faces = WorkNode(117057)
self.assertIn(three_little_words, g.nodes)
self.assertIn((thorpe, three_little_words), g.edges)
self.assertTrue(
imdb_tsv.contributed_as_in_graph(g, thorpe, three_little_words, "director")
)
self.assertFalse(
imdb_tsv.contributed_as_in_graph(g, thorpe, three_little_words, "cook")
)
self.assertFalse(
imdb_tsv.contributed_as_in_graph(
g, thorpe, the_mirror_has_two_faces, "director"
)
)
def test_works_by_pagerank(self):
g = data4tests.get_small_graph()
works_rank, _ = fame.works_by_pagerank(g)
actual = [w for w, _ in works_rank[:4]]
expected = [
WorkNode(3154822),
WorkNode(118901),
WorkNode(1233192),
WorkNode(98638),
]
self.assertEqual(expected, actual)
actual = [r for _, r in works_rank[:4]]
expected = np.array([
0.0033347298102977384,
0.0019010827959628127,
0.0019010827959628127,
0.0019010827959628127,
])
self.assertAlmostEqual(0, norm(actual - expected))
def test_normalize_votes_weights(self):
def activate(x):
y = (x - mu) / sigma
y = (math.tanh(y) + 1) / 2
return y
g = self.make_graph()
weight = "vote_weight"
mu, sigma = 13005.75, 8347.634017342878
weights.weight_by_normalized_votes(g, weight=weight)
self.assertAlmostEqual(
activate(1497), g.edges[(PersonNode(1), WorkNode(43044))][weight])
self.assertAlmostEqual(
activate(24896), g.edges[(PersonNode(1), WorkNode(50419))][weight])
self.assertAlmostEqual(
activate(14369),
g.edges[(PersonNode(2), WorkNode(117057))][weight])
self.assertAlmostEqual(
activate(1497),
g.edges[(PersonNode(861703), WorkNode(43044))][weight])
def test_add_work(self):
g = nx.Graph()
imdb_tsv.add_work(g, self.basics[0])
expected = {
"title": "Three Little Words",
"adult": 0,
"start": "1950",
"end": "\\N",
"runtime": "102",
"genres": {"Biography", "Musical", "Comedy"},
}
actual = g.nodes[WorkNode(43044)]
self.assertEqual(expected, actual)
def test_add_arc(self):
g = nx.Graph()
grapher.add_arc(g, "The Air I Breathe", "Kevin Bacon", job="actor")
grapher.add_arc(g, "The Air I Breathe", "Sarah Michelle Geller", job="actress")
grapher.add_arc(g, "A Powerful Noise Live", "Sarah Michelle Geller", job="self")
grapher.add_arc(g, "A Powerful Noise Live", "Natalie Portman", job="self")
self.assertEqual(5, g.number_of_nodes())
self.assertEqual(4, g.number_of_edges())
self.assertIn(WorkNode("The Air I Breathe"), g.nodes)
self.assertIn(WorkNode("A Powerful Noise Live"), g.nodes)
self.assertIn(PersonNode("Kevin Bacon"), g.nodes)
self.assertIn(PersonNode("Sarah Michelle Geller"), g.nodes)
self.assertIn(PersonNode("Natalie Portman"), g.nodes)
self.assertEqual(
{"actor"},
g.edges[(WorkNode("The Air I Breathe"), PersonNode("Kevin Bacon"))]["job"],
)
self.assertEqual(
{"actress"},
g.edges[
(WorkNode("The Air I Breathe"), PersonNode("Sarah Michelle Geller"))
]["job"],
)
self.assertEqual(
{"self"},
g.edges[
(
WorkNode("A Powerful Noise Live"),
PersonNode("Sarah Michelle Geller"),
)
]["job"],
)
self.assertEqual(
{"self"},
g.edges[
(WorkNode("A Powerful Noise Live"), PersonNode("Natalie Portman"))
]["job"],
)
def fetch_contributors(self, work):
return self.fetch_neighbors(WorkNode(work))
def test_rating(self):
g = self.make_graph()
three_little_words = g.nodes[WorkNode(43044)]
the_mirror_has_two_faces = g.nodes[WorkNode(117057)]
self.assertEqual(6.9, imdb_tsv.get_rating(three_little_words))
self.assertEqual(6.6, imdb_tsv.get_rating(the_mirror_has_two_faces))
def test_mock_movie(self):
one = data4tests.MockImdbObject.mock_movie(WorkNode(1))
self.assertEqual(1, one.getID())
self.assertEqual('1', one['title'])
self.assertEqual(38, one['votes'])
self.assertAlmostEqual(9.971848109388686, one['rating'])
def test_add_contribution(self):
g = self.make_graph()
expected = {"ordering": 1, "contributions": {"actor": {"job": "\\N"}}}
actual = g.edges[(PersonNode(1), WorkNode(43044))]
self.assertEqual(expected, actual)
def __init__(self, tconst):
WorkNode.__init__(self, int(tconst[2:]))
def get_movie(self, id):
movie_node = WorkNode(id)
m = MockImdbObject.mock_movie(movie_node)
MockImdbObject.mock_cast(m, movie_node, self.g)
return m