def similarity_to(self, other, metric = "cosine"):
"""
set-to-set similarity
Param:
docs: KeywordList
Return:
float
"""
assert type(other) in (scinet3.model.Keyword, KeywordList), "`other` should be either Keyword or KeywordList, but is %r" %other
if isinstance(other, scinet3.model.Keyword):
other = KeywordList([other])
if metric == "cosine":
return cosine_similarity(self.centroid, other.centroid)
else:
raise NotImplementedError("Only cosine similarity metric is implemented for now")
def similarity_to(self, other, metric="cosine"):
"""
set-to-set similarity
Param:
docs: KeywordList
Return:
float
"""
assert type(other) in (
scinet3.model.Keyword, KeywordList
), "`other` should be either Keyword or KeywordList, but is %r" % other
if isinstance(other, scinet3.model.Keyword):
other = KeywordList([other])
if metric == "cosine":
return cosine_similarity(self.centroid, other.centroid)
else:
raise NotImplementedError(
"Only cosine similarity metric is implemented for now")
def test_sparse_matrix(self):
v1 = csr_matrix([self.row1])
v2 = csr_matrix([self.row2])
self.assertAlmostEqual(self.expected,
cosine_similarity(v1, v2))
def test_array(self):
v1 = np.array(self.row1)
v2 = np.array(self.row2)
self.assertAlmostEqual(self.expected,
cosine_similarity(v1, v2))
def test_list(self):
v1 = self.row1
v2 = self.row2
self.assertAlmostEqual(self.expected,
cosine_similarity(v1, v2))
def test_sparse_matrix(self):
v1 = csr_matrix([self.row1])
v2 = csr_matrix([self.row2])
self.assertAlmostEqual(self.expected, cosine_similarity(v1, v2))
def test_array(self):
v1 = np.array(self.row1)
v2 = np.array(self.row2)
self.assertAlmostEqual(self.expected, cosine_similarity(v1, v2))
def test_list(self):
v1 = self.row1
v2 = self.row2
self.assertAlmostEqual(self.expected, cosine_similarity(v1, v2))