def knn_textual(k, an_id, model, item_type, database, this_vector=None):
nearest = {}
if not this_vector:
this_vector = Vectorizer.text_vector(an_id, database, model,
item_type)
# Get the id's for all items of the same type.
if item_type == 'photo':
an_id = int(an_id)
others = database.get_photo_ids()
elif item_type == 'user':
others = database.get_user_ids()
elif item_type == 'poi':
an_id = int(an_id)
others = database.get_location_ids()
else:
raise ValueError(
'[ERROR] The provided type was invalid.\ntype = ' +
str(item_type))
# remove this id from the list
others.remove(an_id)
# for each other one, get their vector and calculate distance.
for other in others:
other_vector = Vectorizer.text_vector(other, database, model,
item_type)
if len(other_vector
) == 0: # Find any elements with no textual descriptors
continue
distance = Distance.l_p_distance(3, this_vector, other_vector)
if len(nearest) < k:
largest_key, largest_best = None, inf
else:
largest_key, largest_best = max(nearest.items(),
key=itemgetter(1))
if distance < largest_best:
# remove the key with the largest distance if it exists
if largest_key:
nearest.pop(largest_key)
nearest[other] = distance
if all([value == 0 for value in nearest.values()]):
break
# Return your K nearest
return nearest
def nearest_text(self, *args):
"""
arg[0] = type (user, photo, poi)
arg[1] = id
arg[2] = model (tf, df, tf-idf)
arg[3] = k
"""
if not self.database:
print("[ERROR] The database must be loaded for this action.")
return
if not len(args) is 4:
print("Nearest Text expected 4 arguments but got " + str(len(args)) + ".")
print("\targs = " + str(args))
return
# Get the first argument
try:
k = int(args[3])
except:
print("[ERROR] K Value provided is invalid.")
print("\tk = " + str(args[0]))
return
# Get the type of item we are considering.
itype = args[0]
if not itype in self.valid_types:
print("[ERROR] Item Type value provided was invalid.")
print("\tItem Type = " + str(args[1]))
return
# Get the model to use. We do this before the item as it is easier
# to differentiate valid from invalid
model = args[2]
model = model.lower()
if not model in self.valid_txt_models:
print("[ERROR] Model Type value provided was invalid.")
print("\tModel Type = " + str(args[2]))
return
try:
# get vector representing item associated w/ id
an_id = args[1]
this_vector = Vectorizer.text_vector(an_id, self.database, model, itype)
except:
print("[ERROR] The ID specified was not found in the dataset.")
print("\tID = " + an_id + "; Type = " + itype + "; Model = " + model)
return
nearest = Neighbor.knn_textual(k, an_id, model, itype, self.database, this_vector=this_vector)
contribs = Neighbor.similarity_by_id(this_vector, nearest.keys(),
self.database, model, itype)
print(str(k) + " Nearest Neighbors:")
for i, (an_id, distance) in enumerate(nearest.items()):
print('\t' + str(i) + ". " + str(an_id) + "; Distance = " + str(distance))
print('Top 3 Features:')
for i, item in enumerate(contribs):
print('\t' + str(i) + '. ' + str(item))
def similarity_by_id(this_vector, ids, database, model, itemtype, k=3):
other_vectors = [
Vectorizer.text_vector(an_id, database, model, itemtype)
for an_id in ids
]
return Neighbor.similarity_contribution(this_vector, other_vectors, k)
