def get_keywords_keywords_distance(prod_tuple, q_tuple):
try:
p_keywords = np.array(
MyUtils_strings.fromlls_toarrays(prod_tuple.kwsVectors))
q_keywords = np.array(
MyUtils_strings.fromlls_toarrays(q_tuple.kwsVectors))
m = len(p_keywords)
n = len(q_keywords)
sim_matrix = np.ones(shape=(m, n)) * -1
for i in range(m):
kw_vec_1 = p_keywords[i]
for j in range(n):
kw_vec_2 = q_keywords[j]
sim_matrix[i][j] = 1 - distance.cosine(u=kw_vec_1, v=kw_vec_2)
# logging.debug("\nThe sim.matrix : %s", sim_matrix)
max_similarities = MyUtils.pick_maxmatches_matrix(sim_matrix)
min_distances = list(map(lambda sim: 1 - sim, max_similarities))
avg_min_distance = np.average(min_distances)
return avg_min_distance
except NameError:
return None
def get_kws_similarity(kwsVectors_stringls_1, kwsVectors_stringls_2):
#logging.debug(kwsVectors_stringls_1)
#logging.debug(type(kwsVectors_stringls_1))
kwsVectors_ls_1 = MyUtils_strings.fromlls_toarrays(kwsVectors_stringls_1)
kwsVectors_ls_2 = MyUtils_strings.fromlls_toarrays(kwsVectors_stringls_2)
m = len(kwsVectors_ls_1)
n = len(kwsVectors_ls_2)
sim_matrix = numpy.ones(shape=(m, n)) * -1
for i in range(m):
kw_vec_1 = kwsVectors_ls_1[i]
#logging.debug("Vector 1 : %s, kw_vec_1)
for j in range(n):
kw_vec_2 = kwsVectors_ls_2[j]
#logging.debug(kw_vec_2)
sim_matrix[i][j] = 1 - scipy.spatial.distance.cosine(u=kw_vec_1,
v=kw_vec_2)
logging.debug("\nThe keywords sim.matrix : %s", sim_matrix)
kws_aggregated_sim = numpy.average(
MyUtils.pick_maxmatches_matrix(sim_matrix))
logging.debug("Aggregated keywords' similarity: %s", kws_aggregated_sim)
return kws_aggregated_sim