class spherefaceAnnoyDatabase():
def __init__(self):
self.network = caffe.Net("pretrainedModels/sphereface_deploy.prototxt", "pretrainedModels/sphereface_model.caffemodel",0)
self.index = AnnoyIndex(512, metric='angular') # 512 is the number of neurons in the last layer of the net
self.indexToName = {}
self.nameToIndex = {}
def getEmbedding(self, imgPath):
img = Image.open(imgPath)
sampleImage = numpy.array(img.resize((net.blobs['data'].data.shape[3],net.blobs['data'].data.shape[2])))
sampleImage = numpy.reshape(sampleImage,(1,)+sampleImage.shape).transpose(0,3,1,2).astype(numpy.float32)
net.blobs['data'].data[...]=sampleImage
net.forward()
return net.blobs['fc5'].data[0].copy()
def addFaceWithName(self, imgPath, name):
embedding = self.getEmbedding(imgPath)
length = self.index.get_n_items()
self.index.add_item(length, embedding)
self.indexToName[length] = name
self.nameToIndex[name] = length
def addEmbeddingWithName(self, embedding, name):
length = self.index.get_n_items()
self.index.add_item(length, embedding)
self.indexToName[length] = name
self.nameToIndex[name] = length
def addFaceWithoutName(self, imgPath):
embedding = self.getEmbedding(imgPath)
length = self.index.get_n_items()
self.index.add_item(length, embedding)
self.indexToName[length] = imgPath
self.nameToIndex[imgPath] = length
def freeze(self, nTrees = 20):
self.index.build(nTrees)
def lookupByFace(self, imgPath, numberOfNeighbours):
embedding = self.getEmbedding(imgPath)
results = self.index.get_nns_by_vector(embedding, numberOfNeighbours, search_k=-1, include_distances=True)
for i in xrange(len(results[0])):
results[0][i] = self.indexToName[results[0][i]]
return results
def lookupByEmbedding(self, embedding, numberOfNeighbours):
if(numberOfNeighbours==-1):
numberOfNeighbours = self.index.get_n_items()
results = self.index.get_nns_by_vector(embedding, numberOfNeighbours, search_k=-1, include_distances=True)
for i in xrange(len(results[0])):
results[0][i] = self.indexToName[results[0][i]]
return results
def lookupByName(self, name, numberOfNeighbours):
if(numberOfNeighbours==-1):
numberOfNeighbours = self.index.get_n_items()
results = self.index.get_nns_by_item(self.nameToIndex[name], numberOfNeighbours, search_k=-1, include_distances=True)
for i in xrange(len(results[0])):
results[0][i] = self.indexToName[results[0][i]]
return results
def create_walks(df,index_file,patient_dict_file,index_dict_file,n_neighbors = 25,walks_per_patient=10,walk_size=50,out_dir="./"):
index = AnnoyIndex(df.shape[1])
index.load(index_file)
patient_dict = {}
for key, val in csv.reader(open(patient_dict_file)):
patient_dict[key] = int(val)
index_dict = {}
for key, val in csv.reader(open(index_dict_file)):
index_dict[int(key)] = val
print("Computing nearest-neighbors...")
neighbor_dict = {}
for i in range(index.get_n_items()):
if i % 1000 == 0:
print str(i)
patient_id = index_dict[i]
neighbors = index.get_nns_by_item(i=i, n=n_neighbors, search_k=-1, include_distances=False)
neighbor_ids = [index_dict[x] for x in neighbors]
neighbor_dict[patient_id] = neighbor_ids
f = open(out_dir+"patient_walks.txt", 'wb')
for i in range(index.get_n_items()):
if i % 1000 == 0:
print str(i)
patient_id = index_dict[i]
patient_sentences = ""
for j in range(walks_per_patient):
sentence = generate_sentence(start=patient_id,neighbor_dict=neighbor_dict,
n_neighbors=n_neighbors,walk_size=walk_size)
patient_sentences = sentence + "\n"
## Write it ##
f.write(patient_sentences)
def build_annoy_indices(input_words, input_vectors):
print("Building Annoy Indices: {0}".format(datetime.now().time()))
sem = AnnoyIndex(99, metric="euclidean")
phon = AnnoyIndex(100, metric="euclidean")
index = 0
print("Reading Data for Semantic Index: {0}".format(datetime.now().time()))
for row in open("semantic_vectors_weighted82.txt"):
spl = row.find("@@@")
line = row[0:spl - 1].lower()
vec = row[spl + 3:-1]
vals = np.array([float(val) for val in vec.split(", ")])
if line not in lookup:
sem.add_item(index, vals)
slines[index] = line
lookup[line] = [index]
index += 1
if index % 100000 == 0:
print("......{0} vectors loaded.".format(index))
last_index = index + 1
for i in range(len(input_words)):
sem.add_item(last_index, input_vectors[i]
) #add input vector so its neighbors can be calculated
lookup[input_words[i]] = [last_index]
slines[last_index] = input_words[i]
last_index += 1
print("Building Semantic Index: {0}".format(datetime.now().time()))
sem.build(150)
print("Built: {0}".format(datetime.now().time()))
print("Num items in semantic index: {0}".format(sem.get_n_items()))
print("Reading Data for Phonetic Index: {0}".format(datetime.now().time()))
pindex = 0
for row in open("phonetic_vectors_every2_d100_reformatted.txt"):
spl = row.find("@@@")
line = row[0:spl - 1]
stripped_line = line[2:-1].lower() #skip the b''
vec = row[spl + 3:-1]
vals = np.array([float(val) for val in vec.split(", ")])
if stripped_line in lookup:
phon.add_item(pindex, vals)
lookup[stripped_line].append(pindex)
plines[pindex] = stripped_line
pindex += 1
if pindex % 100000 == 0:
print("......{0} vectors loaded.".format(pindex))
print("Building Phonetic Index: {0}".format(datetime.now().time()))
phon.build(150)
print("Built: {0}".format(datetime.now().time()))
print("Num items in phonetic index: {0}".format(phon.get_n_items()))
print("Done Building Annoy Indices: {0}".format(datetime.now().time()))
return sem, phon
def test_item_vector_after_save(self):
# Issue #279
a = AnnoyIndex(3)
a.verbose(True)
a.add_item(1, [1, 0, 0])
a.add_item(2, [0, 1, 0])
a.add_item(3, [0, 0, 1])
a.build(-1)
self.assertEquals(a.get_n_items(), 4)
a.get_item_vector(3)
a.save('something.annoy')
self.assertEquals(a.get_n_items(), 4)
a.get_item_vector(3)
def test_item_vector_after_save(self):
# Issue #279
a = AnnoyIndex(3, 'angular')
a.verbose(True)
a.add_item(1, [1, 0, 0])
a.add_item(2, [0, 1, 0])
a.add_item(3, [0, 0, 1])
a.build(-1)
self.assertEqual(a.get_n_items(), 4)
self.assertEqual(a.get_item_vector(3), [0, 0, 1])
self.assertEqual(set(a.get_nns_by_item(1, 999)), set([1, 2, 3]))
a.save('something.annoy')
self.assertEqual(a.get_n_items(), 4)
self.assertEqual(a.get_item_vector(3), [0, 0, 1])
self.assertEqual(set(a.get_nns_by_item(1, 999)), set([1, 2, 3]))
def test_get_n_items(self):
print "test_get_n_items"
os.system("rm -rf test_db")
os.system("mkdir test_db")
f = 3
i = AnnoyIndex(f, 2, "test_db", 10, 1000, 3048576000, 0)
#i.verbose(True)
i.create()
i.add_item(0, [0, 0, 1])
self.assertEqual(i.get_n_items(), 1)
i.add_item(1, [0, 1, 0])
self.assertEqual(i.get_n_items(), 2)
i.add_item(2, [1, 0, 0])
self.assertEqual(i.get_n_items(), 3)
def test_get_n_items(self):
print "test_get_n_items"
os.system("rm -rf test_db")
os.system("mkdir test_db")
f = 3
i = AnnoyIndex(f, 2, "test_db", 10, 1000, 3048576000, 0)
#i.verbose(True)
i.create()
i.add_item(0, [0, 0, 1])
self.assertEqual(i.get_n_items(), 1);
i.add_item(1, [0, 1, 0])
self.assertEqual(i.get_n_items(), 2);
i.add_item(2, [1, 0, 0])
self.assertEqual(i.get_n_items(), 3);
def test_item_vector_after_save(self):
# Issue #279
a = AnnoyIndex(3)
a.verbose(True)
a.add_item(1, [1, 0, 0])
a.add_item(2, [0, 1, 0])
a.add_item(3, [0, 0, 1])
a.build(-1)
self.assertEqual(a.get_n_items(), 4)
self.assertEqual(a.get_item_vector(3), [0, 0, 1])
self.assertEqual(set(a.get_nns_by_item(1, 999)), set([1, 2, 3]))
a.save('something.annoy')
self.assertEqual(a.get_n_items(), 4)
self.assertEqual(a.get_item_vector(3), [0, 0, 1])
self.assertEqual(set(a.get_nns_by_item(1, 999)), set([1, 2, 3]))
def main():
t = AnnoyIndex(99, metric='euclidean')
lines = dict()
lookup = dict()
print("loading...")
index = 0
for row in open("semantic_vectors_weighted91.txt"):
spl = row.find("@@@")
line = row[0:spl-1].lower()
vec = row[spl+3:-1]
vals = np.array([float(val) for val in vec.split(", ")])
if line in lookup:
continue
t.add_item(index, vals)
lines[index] = line
lookup[line] = [index]
index += 1
if index % 50000 == 0:
print(line)
print("{0} vectors loaded".format(index))
print("building")
t.build(100)
print("done.")
nums1 = [random.randint(1, t.get_n_items()) for i in range(5)]
nums2 = [random.randint(1, t.get_n_items()) for i in range(5)]
poem = [nums1, nums2]
for s in poem:
for line in s:
print(lines[line])
print("\n")
def k_neighbors(shape_features: {}, db_features: {}, k=s.KNN_SIZE) -> []:
"""
It determines the closest shape to the query shape by computing K-Nearest Neighbors on a
N-dimensional Approximate Nearest Neighbors feature mapping.
----------------------------
Args:
shape_features (obj: 'dict'): The dictionary containing the feature metrics of the shape
db_features (obj: 'dict): The dictionary containing the feature metrics of the shapes
k (int): The number of neighbors to return, the default value specified in Settings
Returns:
neighbors (obj: 'dict'): The dictionary containing the closest shapes (key) and the respective
distance to the query shape (value)
"""
ann = AnnoyIndex(56, 'euclidean') # 56 features
for id, featureList in db_features.items():
features_flatten = flatten_features_array(featureList)
ann.add_item(id, features_flatten)
shape_features_flat = flatten_features_array(shape_features)
# To get the neighbors, it is necessary to add the new item to the mapping first
shape_id = ann.get_n_items()
ann.add_item(shape_id, shape_features_flat)
ann.build(s.CATEGORIES)
neighbors = ann.get_nns_by_item(shape_id, k, include_distances=True)
return neighbors
def AddToTrain(individual):
global annoy_train
global test_db
global IND_SIZE
global config
max_memory = 5
if set.get_master_volume() == 1:
print set.get_master_volume()
set.set_master_volume(0.85)
test_db.append(individual)
print "SAVING TO TRAINING SET. TestDB Size: " + str(len(test_db))
annoy_train = AnnoyIndex(IND_SIZE)
annoy_train.add_item(annoy_train.get_n_items(), individual)
annoy_train.build(config["annoy_tree"]) # 10 trees
if len(test_db) > max_memory:
test_db.pop(0)
print "delete old memory entry"
if set.get_master_volume() == 0:
test_db = []
# gen_record = []
annoy_train = AnnoyIndex(IND_SIZE)
annoy_train.build(config["annoy_tree"]) # 10 trees
print "clean set"
set.set_master_volume(0.85)
def build_index(embedding_fun, batch_size, sentences):
ann = AnnoyIndex(D)
batch_sentences = []
batch_indexes = []
last_indexed = 0
num_batches = 0
with tf.Session() as sess:
sess.run([tf.global_variables_initializer(), tf.tables_initializer()])
with open('wiki.txt.uniq', 'r') as fr:
for sindex, sentence in enumerate(fr):
batch_sentences.append(sentence)
batch_indexes.append(sindex)
if len(batch_sentences) == batch_size:
context_embed = sess.run(
embedding_fun, feed_dict={sentences: batch_sentences})
for index in batch_indexes:
ann.add_item(index,
context_embed[index - last_indexed])
batch_sentences = []
batch_indexes = []
last_indexed += batch_size
if num_batches % 10000 == 0:
print_with_time('sindex: {} annoy_size: {}'.format(
sindex, ann.get_n_items()))
num_batches += 1
if batch_sentences:
context_embed = sess.run(
embedding_fun, feed_dict={sentences: batch_sentences})
for index in batch_indexes:
ann.add_item(index, context_embed[index - last_indexed])
return ann
def test_no_items(self):
idx = AnnoyIndex(100)
idx.build(n_trees=10)
idx.save('foo.idx')
idx = AnnoyIndex(100)
idx.load('foo.idx')
self.assertEquals(idx.get_n_items(), 0)
self.assertEquals(idx.get_nns_by_vector(vector=numpy.random.randn(100), n=50, include_distances=False), [])
def test_no_items(self):
idx = AnnoyIndex(100)
idx.build(n_trees=10)
idx.save('foo.idx')
idx = AnnoyIndex(100)
idx.load('foo.idx')
self.assertEquals(idx.get_n_items(), 0)
self.assertEquals(idx.get_nns_by_vector(vector=numpy.random.randn(100), n=50, include_distances=False), [])
def test_get_n_item(self):
print "test_get_n_item"
os.system("rm -rf test_db")
os.system("mkdir test_db")
f = 3
i = AnnoyIndex(f, 2, "test_db", 10, 1000, 3048576000, 0)
i.create()
i.add_item(0, [0, 0, 1])
i.add_item(1, [0, 1, 0])
i.add_item(2, [1, 0, 0])
i1 = i.get_n_items(0)
self.assertEqual(i1, [0, 0, 1])
i2 = i.get_n_items(1)
self.assertEqual(i2, [0, 1, 0])
i3 = i.get_n_items(2)
self.assertEqual(i3, [1, 0, 0])
def test_get_n_item(self):
print "test_get_n_item"
os.system("rm -rf test_db")
os.system("mkdir test_db")
f = 3
i = AnnoyIndex(f, 2, "test_db", 10, 1000, 3048576000, 0)
i.create()
i.add_item(0, [0, 0, 1])
i.add_item(1, [0, 1, 0])
i.add_item(2, [1, 0, 0])
i1 = i.get_n_items(0);
self.assertEqual(i1, [0, 0, 1]);
i2 = i.get_n_items(1);
self.assertEqual(i2, [0, 1, 0]);
i3 = i.get_n_items(2);
self.assertEqual(i3, [1, 0, 0]);
def test_only_one_item(self):
# reported to annoy-user by Kireet Reddy
idx = AnnoyIndex(100)
idx.add_item(0, numpy.random.randn(100))
idx.build(n_trees=10)
idx.save('foo.idx')
idx = AnnoyIndex(100)
idx.load('foo.idx')
self.assertEquals(idx.get_n_items(), 1)
self.assertEquals(idx.get_nns_by_vector(vector=numpy.random.randn(100), n=50, include_distances=False), [0])
def test_only_one_item(self):
# reported to annoy-user by Kireet Reddy
idx = AnnoyIndex(100)
idx.add_item(0, numpy.random.randn(100))
idx.build(n_trees=10)
idx.save('foo.idx')
idx = AnnoyIndex(100)
idx.load('foo.idx')
self.assertEquals(idx.get_n_items(), 1)
self.assertEquals(idx.get_nns_by_vector(vector=numpy.random.randn(100), n=50, include_distances=False), [0])
def main():
args = setup_args()
print_with_time(args)
start_time = time.time()
ann = AnnoyIndex(args.vector_size, metric='angular')
ann.load(args.ann)
end_time = time.time()
print('Load Time: {}'.format(end_time - start_time))
print_with_time('Annoy Index: {}'.format(ann.get_n_items()))
start_time = time.time()
df = read_data(args.csv_file_path, args.filter_data)
content_array = df.to_numpy()
end_time = time.time()
print_with_time('Sentences: {} Time: {}'.format(len(content_array), end_time - start_time))
# start_time = time.time()
# embed_fn = hub.load(args.use_model)
# end_time = time.time()
# print_with_time('Model loaded time: {}'.format(end_time - start_time))
random_projection_matrix = None
if args.random_projection:
if os.path.exists('random_projection_matrix'):
print("Loading random projection matrix...")
with open('random_projection_matrix', 'rb') as handle:
random_projection_matrix = pickle.load(handle)
print('random projection matrix is loaded.')
while True:
input_sentence_id = input('Enter sentence id: ').strip()
if input_sentence_id == 'q':
return
print_with_time('Input Sentence: {}'.format(input_sentence_id))
query_filter = 'GUID == "' + input_sentence_id + '"'
input_data_object = df.query(query_filter)
input_sentence = input_data_object['CONTENT']
start_time = time.time()
query_sentence_vector = generate_embeddings(input_sentence.values[0], args.use_model, random_projection_matrix)
print_with_time('vec done')
similar_sentences = find_similar_items(ann, query_sentence_vector, content_array, args.k)
end_time = time.time()
print_with_time('nns done: Time: {}'.format(end_time-start_time))
for sentence in similar_sentences[1:]:
if args.filter_data:
if sentence[2] in ['country-related', 'person-related']:
print(sentence[0])
else:
print(sentence[0])
def test_dense_annoy_index(annoy_index_file):
data = np.random.choice([0, 1], size=(10, 5))
index = build_annoy_index(data, annoy_index_file)
assert os.path.exists(annoy_index_file)
loaded_index = AnnoyIndex(5)
loaded_index.load(annoy_index_file)
assert index.f == loaded_index.f == 5
assert index.get_n_items() == loaded_index.get_n_items() == 10
assert index.get_nns_by_item(0, 5) == loaded_index.get_nns_by_item(0, 5)
def test_build_unbuid(self):
f = 10
i = AnnoyIndex(f, 'euclidean')
for j in xrange(1000):
i.add_item(j, [random.gauss(0, 1) for x in xrange(f)])
i.build(10)
for j in xrange(100):
i.unbuild()
i.build(10)
self.assertEqual(i.get_n_items(), 1000)
def test_build_unbuid(self):
f = 10
i = AnnoyIndex(f, 'euclidean')
for j in xrange(1000):
i.add_item(j, [random.gauss(0, 1) for x in xrange(f)])
i.build(10)
for j in xrange(100):
i.unbuild()
i.build(10)
self.assertEqual(i.get_n_items(), 1000)
def get_similar_sentences(query):
embeddings = load_embeddings()
sentence_ids = load_sentence_ids()
index = AnnoyIndex(get_embeddings_dim(embeddings), "angular")
index.load("index.ann")
print("Found {} items in the index.".format(index.get_n_items()))
print("The index uses {} trees.".format(index.get_n_trees()))
print("")
closest, dists = index.get_nns_by_vector(
embeddings[query], 10, include_distances=True) # noqa: E501
assert (len(closest) == len(dists))
closest = map(lambda sid: sentence_ids[sid], closest)
return zip(closest, dists)
def generate_extra_pair_basis(basis,
X,
n_neighbors,
tree: AnnoyIndex,
distance='euclidean',
verbose=True):
'''Generate pairs that connects the extra set of data to the fitted basis.
'''
npr, dimp = X.shape
assert (
basis is not None or tree is not None
), "If the annoyindex is not cached, the original dataset must be provided."
# Build the tree again if not cached
if tree is None:
n, dim = basis.shape
assert dimp == dim, "The dimension of the original dataset is different from the new one's."
tree = AnnoyIndex(dim, metric=distance)
if _RANDOM_STATE is not None:
tree.set_seed(_RANDOM_STATE)
for i in range(n):
tree.add_item(i, basis[i, :])
tree.build(20)
else:
n = tree.get_n_items()
n_neighbors_extra = min(n_neighbors + 50, n - 1)
nbrs = np.zeros((npr, n_neighbors_extra), dtype=np.int32)
knn_distances = np.empty((npr, n_neighbors_extra), dtype=np.float32)
for i in range(npr):
nbrs[i, :], knn_distances[i, :] = tree.get_nns_by_vector(
X[i, :], n_neighbors_extra, include_distances=True)
print_verbose("Found nearest neighbor", verbose)
# sig = np.maximum(np.mean(knn_distances[:, 3:6], axis=1), 1e-10)
# print_verbose("Calculated sigma", verbose)
# Debug
# print_verbose(f"Sigma is of the scale of {sig.shape}", verbose)
# print_verbose(f"KNN dist is of shape scale of {knn_distances.shape}", verbose)
# print_verbose(f"nbrs max: {nbrs.max()}", verbose)
# scaling the distances is not possible since we don't always track the basis
# scaled_dist = scale_dist(knn_distances, sig, nbrs)
print_verbose("Found scaled dist", verbose)
pair_neighbors = sample_neighbors_pair_basis(n, X, knn_distances, nbrs,
n_neighbors)
return pair_neighbors
def merge_indicies(self, index_file_a, index_file_b, sender_urn):
logger.info("Merging {0} and {1} for {2} index".format(index_file_a, index_file_b, sender_urn))
index_a = AnnoyIndex(self.feat_size, metric='euclidean')
index_b = AnnoyIndex(self.feat_size, metric='euclidean')
new_index = AnnoyIndex(self.feat_size, metric='euclidean')
index_a.load(index_file_a)
index_b.load(index_file_b)
cnt = 0
for i in range(index_a.get_n_items()):
new_index.add_item(cnt, index_a.get_item_vector(i))
cnt += 1
for i in range(index_b.get_n_items()):
new_index.add_item(cnt, index_b.get_item_vector(i))
cnt += 1
new_index_file = index_file_a + ".merged"
index_a.unload()
index_b.unload()
new_index.build(self.n_trees)
new_index.save(new_index_file)
logger.info("Merging {0} and {1} for {2} index, total number of items: {3}".format(
index_file_a,
index_file_b,
sender_urn,
cnt))
new_index.unload()
pykka.ActorRegistry.get_by_urn(sender_urn).proxy().complete_compaction(
new_index_file=new_index_file,
index_file_a=index_file_a,
index_file_b=index_file_b
)
def make_pairs(id):
index = AnnoyIndex(dim, 'euclidean')
index.load(model_path)
k = min(index.get_n_items() - 1,
max(50, min(index.get_n_items() / 5,
200))) #eh idk if this is necessary lol
last_k = 0
found = 0
i = 0
while found < 15 and i < 5 and k < index.get_n_items(
): #loop until we get a satisfactory amount (15 for now), or for 5 iterations
i += 1
neighbors = get_neighbors(id, k)[last_k:]
for neighbor in neighbors:
if neighbor == id:
continue
p = Pair.query.filter_by(hash=str(
hash(f'{min(id, neighbor)}-{max(id, neighbor)}'))).first()
print(p)
if not p:
found += 1
new_pair = Pair(min(id, neighbor), max(id, neighbor))
db_session.add(new_pair)
db_session.commit()
def test_build_sparse_annoy_index(annoy_index_file):
data = np.random.choice([0, 1], size=(10, 5))
sparse_data = csr_matrix(data)
index = build_annoy_index(sparse_data, annoy_index_file)
assert os.path.exists(annoy_index_file)
loaded_index = AnnoyIndex(5, metric='angular')
loaded_index.load(annoy_index_file)
assert index.f == loaded_index.f == 5
assert index.get_n_items() == loaded_index.get_n_items() == 10
assert index.get_nns_by_item(0, 5) == loaded_index.get_nns_by_item(0, 5)
index.unload()
loaded_index.unload()
def baseline_train(olddata, f, trees):
"""" olddata to train with using f number of features of the data and building an index with trees number of trees """
t = AnnoyIndex(f) # Length of item vector that will be indexed
if (os.path.isfile(saving_model)):
print "Loading in a pre-made, large read-only data structure we previously made with training data to use for approximate nearest neighbors on holdout data..."
t.load(saving_model)
else:
print "Creating a large read-only data structure with training data to use for approximate nearest neighbors on holdout data..."
for i in olddata.index:
v = list(olddata.ix[i, ['latitude', 'longitude', 'time_period']])
t.add_item(i, v)
print "Building the trees..."
t.build(trees)
assert t.get_n_items() == olddata.shape[0]
print "Saving the model..."
t.save(saving_model) # Can easily be loaded into memory later.
return (t)
def baseline_train(olddata, f, trees):
"""" olddata to train with using f number of features of the data and building an index with trees number of trees """
t = AnnoyIndex(f) # Length of item vector that will be indexed
if os.path.isfile(saving_model):
print "Loading in a pre-made, large read-only data structure we previously made with training data to use for approximate nearest neighbors on holdout data..."
t.load(saving_model)
else:
print "Creating a large read-only data structure with training data to use for approximate nearest neighbors on holdout data..."
for i in olddata.index:
v = list(olddata.ix[i, ["latitude", "longitude", "time_period"]])
t.add_item(i, v)
print "Building the trees..."
t.build(trees)
assert t.get_n_items() == olddata.shape[0]
print "Saving the model..."
t.save(saving_model) # Can easily be loaded into memory later.
return t
def main():
t = AnnoyIndex(99, metric='euclidean')
lines = dict()
lookup = dict()
prompt_word = input("Get the nearest semantic neighbors of: ")
prompt_vec = find_glove_vector(prompt_word)
print("loading...")
index = 0
for row in open("semantic_vectors_weighted91.txt"):
spl = row.find("@@@")
line = row[0:spl - 1].lower()
vec = row[spl + 3:-1]
vals = np.array([float(val) for val in vec.split(", ")])
if line in lookup:
continue
t.add_item(index, vals)
lines[index] = line
lookup[line] = [index]
index += 1
if index % 50000 == 0:
print(line)
print("{0} vectors loaded".format(index))
last_index = index + 1
t.add_item(
last_index,
prompt_vec) #add input vector so its neighbors can be calculated
lookup[prompt_word] = [last_index]
lines[last_index] = prompt_word
t.build(100)
print("done.")
print("Num dict items: {0}".format(len(lookup)))
print("Num list items: {0}".format(len(lines)))
print("Num index items: {0}".format(t.get_n_items()))
try:
vec = prompt_vec
print(nn_lookup(t, vec))
print([lines[i[0]] for i in nn_lookup(t, vec)])
except KeyError:
print("not found")
def build_index(annoy_vector_dimension, embedding_fun, batch_size, sentences,
content_array, stop_words, content_index):
ann = AnnoyIndex(annoy_vector_dimension, metric='angular')
batch_sentences = []
batch_indexes = []
last_indexed = 0
num_batches = 0
content = ''
with tf.compat.v1.Session() as sess:
sess.run([
tf.compat.v1.global_variables_initializer(),
tf.compat.v1.tables_initializer()
])
for sindex, sentence in enumerate(content_array):
content = sentence[content_index]
if stop_words:
content = remove_stopwords(sentence[1])
batch_sentences.append(content)
batch_indexes.append(sindex)
if len(batch_sentences) == batch_size:
context_embed = sess.run(
embedding_fun, feed_dict={sentences: batch_sentences})
for index in batch_indexes:
ann.add_item(index, context_embed[index - last_indexed])
batch_sentences = []
batch_indexes = []
last_indexed += batch_size
if num_batches % 10000 == 0:
print_with_time('sindex: {} annoy_size: {}'.format(
sindex, ann.get_n_items()))
num_batches += 1
if batch_sentences:
context_embed = sess.run(embedding_fun,
feed_dict={sentences: batch_sentences})
for index in batch_indexes:
ann.add_item(index, context_embed[index - last_indexed])
return ann
def load(self):
self.prev_id = -1
self.indexes = []
logger.info("Loading index {0}".format(self.actor_urn))
for index in self.indexes:
index.unload()
for f in sorted(listdir(self.index_dir)):
if f.endswith(".ann"):
self.index_files.append(join(self.index_dir,f))
index = AnnoyIndex(self.feat_size, metric='euclidean')
index.load(join(self.index_dir, f))
self.indexes.append(index)
self.prev_id += index.get_n_items()
elif f.endswith('saved_state'):
self.mem_store = np.load(join(self.index_dir, f)).tolist()
logger.info("Loaded {0} files with total {1} records for index {2}"
.format(len(self.indexes), self.prev_id + 1, self.actor_urn))
def annotate_all_questions():
embeddings = load_embeddings()
sentence_ids = load_sentence_ids()
index = AnnoyIndex(get_embeddings_dim(embeddings), "angular")
index.load("index.ann")
print("Found {} items in the index.".format(index.get_n_items()))
print("The index uses {} trees.".format(index.get_n_trees()))
print("")
df = pd.concat(map(dm.ALLEN_AI_OBQA, list(OBQAType)))
annotations = {}
for _, row in tqdm.tqdm(df.iterrows(), total=len(df)):
for answer in row.answers:
sent = row.question + " " + answer
closest = index.get_nns_by_vector(embeddings[sent], 75)
closest = list(map(lambda sid: sentence_ids[sid], closest))
annotations[sent] = closest
pickle.dump(annotations, open("annotations.pkl", "wb"))
print("Annotations written to annotations.pkl")
def main():
args = setup_args()
print_with_time(args)
start_time = time.time()
ann = AnnoyIndex(D)
ann.load(args.ann)
end_time = time.time()
print('Load Time: {}'.format(end_time - start_time))
print_with_time('Annoy Index: {}'.format(ann.get_n_items()))
start_time = time.time()
sentences = load_sentences(args.sentences)
end_time = time.time()
print_with_time('Sentences: {} Time: {}'.format(len(sentences), end_time - start_time))
start_time = time.time()
embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder/1")
sentences_ph = tf.placeholder(dtype=tf.string, shape=[None])
embedding_fun = embed(sentences_ph)
sess = tf.Session()
sess.run([tf.global_variables_initializer(), tf.tables_initializer()])
end_time = time.time()
print_with_time('Ready! TF setup time: {}'.format(end_time - start_time))
while True:
input_sentence = input('Enter sentence: ').strip()
if input_sentence == 'q':
return
print_with_time('Input Sentence: {}'.format(input_sentence))
start_time = time.time()
sentence_vector = sess.run(embedding_fun, feed_dict={sentences_ph:[input_sentence]})
print_with_time('vec done')
nns = ann.get_nns_by_vector(sentence_vector[0], args.k)
end_time = time.time()
print_with_time('nns done: Time: {}'.format(end_time-start_time))
similar_sentences = [sentences[nn] for nn in nns]
for sentence in similar_sentences:
print(sentence)
class AnnoyClient:
DIMENSION = 100
def __init__(self, index_file: str, id_list: List[str]):
print('Initializing AnnoyIndex...')
self.index = AnnoyIndex(self.DIMENSION, 'angular')
self.index.load(index_file)
self.id_list = id_list
print('Done')
def search(self, query: List[float], n: int = 100) -> List[dict]:
items = self.index.get_nns_by_vector(query, n, include_distances=False)
print(items)
return [{
'id': self.id_list[i],
'rank': r + 1
} for (r, i) in enumerate(items)]
def get_total_count(self) -> int:
return self.index.get_n_items()
def build_save_ann_from_iter_lookup(sentence_id_iter,
lookup_fun,
ann_file,
num_trees=10,
log_freq=1000,
batch_size=32,
encoder=None):
if not encoder:
encoder = USEEncoder()
ann = AnnoyIndex(encoder.dim())
sentences = []
sentences_ids = []
for sentence_id in sentence_id_iter:
sentence = lookup_fun[sentence_id]
sentence = sentence.strip()
sentences.append(sentence)
sentences_ids.append(sentence_id)
if len(sentences) == batch_size:
vectors = encoder.encode(sentences)
for vector, sid in zip(vectors, sentences_ids):
ann.add_item(sid, vector)
sentences = []
sentences_ids = []
if ann.get_n_items() % (batch_size * log_freq) == 0:
logging.info(f'Indexed: {ann.get_n_items()}')
if sentences:
vectors = encoder.encode(sentences)
for vector, sid in zip(vectors, sentences_ids):
ann.add_item(sid, vector)
logging.info(f'Final Indexed: {ann.get_n_items()}')
ann.build(num_trees)
ann.save(ann_file)
return ann
def build_index(batch_size, content_array, model_url, random_projection_matrix):
VECTOR_LENGTH = 512
if random_projection_matrix is not None:
VECTOR_LENGTH = 64
ann = AnnoyIndex(VECTOR_LENGTH, metric=METRIC)
batch_sentences = []
batch_indexes = []
last_indexed = 0
num_batches = 0
for sindex, sentence in enumerate(content_array):
# sentence_embedding = generate_embeddings(sentence[1], model_url, random_projection_matrix)
# ann.add_item(sindex, sentence_embedding[0])
batch_sentences.append(sentence[1])
batch_indexes.append(sindex)
if len(batch_sentences) == batch_size:
context_embed = generate_embeddings(batch_sentences, model_url, random_projection_matrix)
for index in batch_indexes:
ann.add_item(index, context_embed[index - last_indexed])
batch_sentences = []
batch_indexes = []
last_indexed += batch_size
if num_batches % 10000 == 0:
print_with_time('sindex: {} annoy_size: {}'.format(sindex, ann.get_n_items()))
num_batches += 1
if batch_sentences:
context_embed = generate_embeddings(batch_sentences, model_url, random_projection_matrix)
for index in batch_indexes:
ann.add_item(index, context_embed[index - last_indexed])
return ann
def main():
t = AnnoyIndex(200, metric='euclidean')
lines = list()
lookup = dict()
print("loading...")
index = 0
for row in open("phonetic_vectors_every2_d200_reformatted.txt"):
spl = row.find("@@@")
line = row[0:spl - 1]
stripped_line = line[2:-1].lower() #skip the b''
vec = row[spl + 3:-1]
vals = np.array([float(val) for val in vec.split(", ")])
if stripped_line in lookup:
continue
lookup[stripped_line] = index
lines.append(stripped_line)
t.add_item(index, vals)
index += 1
if index % 50000 == 0:
print(stripped_line.lower())
print("{0} vectors loaded".format(index))
t.build(100)
print("done.")
print("Num dict items: {0}".format(len(lookup)))
print("Num list items: {0}".format(len(lines)))
print("Num index items: {0}".format(t.get_n_items()))
try:
vec = lookup["skating on thin ice"]
print(vec)
print(t.get_item_vector(vec))
print(nn_lookup(t, t.get_item_vector(vec)))
print([lines[i[0]] for i in nn_lookup(t, t.get_item_vector(vec))])
except KeyError:
print("not found")
def r_neighbors(shape_features: {}, db_features: {}, r=s.RNN_RANGE) -> []:
"""
It determines the closest shape to the query shape by computing R-Nearest Neighbors on a
N-dimensional Approximate Nearest Neighbors feature mapping.
----------------------------
Args:
shape_features (obj: 'dict'): The dictionary containing the feature metrics of the shape
db_features (obj: 'dict): The dictionary containing the feature metrics of the shapes
r (int): The distance range, the default value specified in Settings
Returns:
neighbors (obj: 'dict'): The dictionary containing the closest shapes (key) and the respective
distance to the query shape (value)
"""
ann = AnnoyIndex(56, 'euclidean') # 56 features
for id, featureList in db_features.items():
features_flatten = flatten_features_array(featureList)
ann.add_item(id, features_flatten)
shape_features_flat = flatten_features_array(shape_features)
# To get the neighbors, it is necessary to add the new item to the mapping first
shape_id = ann.get_n_items()
ann.add_item(shape_id, shape_features_flat)
ann.build(s.CATEGORIES)
neighbors = ann.get_nns_by_item(shape_id, 200, include_distances=True)
range_neighbors = ([], [])
for i, distance in enumerate(neighbors[1]):
if distance < r:
range_neighbors[0].append(neighbors[0][i])
range_neighbors[1].append(distance)
return range_neighbors
class Memory:
def __init__(self, capacity, state_dim, value_dim):
self.capacity = capacity
print("state_dim:", state_dim)
self.states = np.zeros((capacity, state_dim))
self.values = np.zeros((capacity, value_dim))
self.curr_capacity = 0
self.curr_ = 0
self.lru = np.zeros(capacity)
self.tm = 0
self.cached_states = []
self.cached_values = []
self.cached_indices = []
self.index = AnnoyIndex(state_dim)
self.index.set_seed(123)
self.update_size = 1
self.build_capacity = 0
def sample_knn_test(self, state, k):
inds, dists = self.index.get_nns_by_vector(state,
k,
include_distances=True)
self.tm += 0.01
self.lru[inds] = self.tm
return self.states[inds], self.values[inds], dists
def sample_knn(self, states, k):
dists = []
inds = []
for state in states:
ind, dist = self.index.get_nns_by_vector(state,
k,
include_distances=True)
inds.append(ind)
dists.append(dist)
# inds = np.reshape(np.array(inds), -1)
self.tm += 0.01
self.lru[inds] = self.tm
return self.states[inds], self.values[inds], dists
def sample(self, n_samples):
if self.curr_capacity < n_samples or n_samples == 0:
idx = np.random.choice(np.arange(len(self.states)),
n_samples,
replace=False)
else:
idx = np.random.choice(np.arange(self.curr_capacity),
n_samples,
replace=False)
self.tm += 0.01
self.lru[idx] = self.tm
embs = self.states[idx]
values = self.values[idx]
return embs, values
def add_knn(self, states, values):
self._add_knn(states, values)
def add_knn_lru(self, states, values):
self._add_knn(states, values, lru=True)
def add(self, states, values):
self._add(states, values)
def add_lru(self, states, values):
self._add(states, values, lru=True)
def add_rand(self, states, values):
self._add(states, values, rand=True)
def _insert(self, states, values, indices):
self.cached_states = self.cached_states + states
self.cached_values = self.cached_values + values
self.cached_indices = self.cached_indices + indices
if len(self.cached_states) >= self.update_size:
self._update_index()
def _update_index(self):
self.index.unbuild()
for i, ind in enumerate(self.cached_indices):
self.states[ind] = self.cached_states[i]
self.values[ind] = self.cached_values[i]
self.index.add_item(ind, self.cached_states[i])
self.index.build(50)
self.build_capacity = self.curr_capacity
self.cached_states = []
self.cached_values = []
self.cached_indices = []
def _rebuild_index(self):
self.index.unbuild()
for ind, state in enumerate(self.states[:self.curr_capacity]):
self.index.add_item(ind, state)
self.index.build(50)
self.build_capacity = self.curr_capacity
def _add_knn(self, states, values, lru=False):
# print(states)
indices = []
states_ = []
values_ = []
for i, _ in enumerate(states):
if lru:
if self.curr_capacity >= self.capacity:
ind = np.argmin(self.lru)
else:
ind = self.curr_capacity
self.curr_capacity += 1
else:
if self.curr_capacity >= self.capacity:
self.curr_ = (self.curr_ + 1) % self.capacity
ind = self.curr_
else:
ind = self.curr_capacity
self.curr_capacity += 1
self.lru[ind] = self.tm
indices.append(ind)
states_.append(states[i])
values_.append(values[i])
self._insert(states_, values_, indices)
def _add(self, states, values, rand=False, lru=False):
for i, state in enumerate(states):
if self.curr_capacity < self.capacity:
self.curr_ = (self.curr_ + 1) % self.capacity
# self.states[self.curr_] = state
# self.values[self.curr_] = values[i]
if self.curr_capacity < self.capacity:
self.curr_capacity += 1
else:
if lru:
self.curr_ = np.argmin(self.lru)
if rand:
self.curr_ = np.random.choice(np.arange(
self.curr_capacity),
1,
replace=False)
if not lru and not rand:
self.curr_ = (self.curr_ + 1) % self.capacity
self.states[self.curr_] = state
self.values[self.curr_] = values[i]
@property
def length(self):
# assert self.index.get_n_items() == self.curr_capacity
# return self.curr_capacity
return self.index.get_n_items()
class NearSentence(object):
def __init__(self, fn_word, model_name, model_path):
self.model = QueryModel(fn_word, model_name, model_path)
self.queries = []
self.titles = []
self.query_index = 0
self.title_index = 0
self.query_ann = AnnoyIndex(self.model.dim, metric='euclidean')
self.title_ann = AnnoyIndex(self.model.dim, metric='euclidean')
def load_queries(self, fn_query, column):
print '[In load_queries] Load candidate queries'
sentences = []
chunk = []
vecs = []
with open(fn_query) as fin:
for line in fin:
ll = line.decode('utf8').strip().split('\t')
if len(ll) < column:
continue
chunk.append(ll[column - 1])
if len(chunk) == 1000:
vec, valid_sentence = self.model.get_query_vec(chunk)
vec = vec / np.sqrt(np.sum(vec**2, 1, keepdims=True))
vecs.extend(list(vec))
sentences.extend(valid_sentence)
chunk = []
if len(chunk) > 0:
vec, valid_sentence = self.model.get_query_vec(chunk)
vecs.extend(list(vec))
sentences.extend(valid_sentence)
print '[In load_queries] Build query annoy tree'
for s, v in izip(sentences, vecs):
self.queries.append(s)
# if vecs == [0] * self.vectorizer.dim:
# continue
self.query_ann.add_item(self.query_index, v)
self.query_index += 1
self.query_ann.build(10)
print '[In load_queries] Size of tree =', self.query_ann.get_n_items()
def load_titles(self, fn_title, column):
print '[In load_titles] Load candidate titles'
sentences = []
chunk = []
vecs = []
with open(fn_title) as fin:
for line in fin:
ll = line.decode('utf8').strip().split('\t')
if len(ll) < column:
continue
chunk.append(ll[column - 1])
if len(chunk) == 1000:
vec, valid_sentence = self.model.get_title_vec(chunk)
vec = vec / np.sqrt(np.sum(vec ** 2, 1, keepdims=True))
vecs.extend(list(vec))
sentences.extend(valid_sentence)
chunk = []
if len(chunk) > 0:
vec, valid_sentence = self.model.get_title_vec(chunk)
vec = vec / np.sqrt(np.sum(vec ** 2, 1, keepdims=True))
vecs.extend(list(vec))
sentences.extend(valid_sentence)
print '[In load_titles] Build titles annoy tree, size =', len(vecs)
for s, v in izip(sentences, vecs):
self.titles.append(s)
self.title_ann.add_item(self.title_index, v) # v is a list
self.title_index += 1
self.title_ann.build(10)
print '[In load_titles] Size of tree =', self.title_ann.get_n_items()
def get_k_nearest_query(self, query, k):
if isinstance(query, unicode):
query = query.encode('utf8')
cut_data = text_cutter.process({'title': query})
cut_query = cut_data['cut_title'].decode('utf8')
vecs, valid_queries= self.model.get_query_vec([cut_query])
if len(valid_queries) == 0:
return []
vecs = vecs / np.sqrt(np.sum(vecs ** 2, 1, keepdims=True))
vec = list(vecs)[0]
k_neighbors, scores = self.query_ann.get_nns_by_vector(vec, n=k, include_distances=True)
neighbors = []
for i in k_neighbors:
neighbors.append(self.queries[i])
return sorted(zip(neighbors, scores), key=lambda x: x[-1])
# def sim(self, u, v):
# norm_u = u / np.sqrt(np.sum(u ** 2, keepdims=True))
# norm_v = u /np.sqrt(np.sum(v ** 2, keepdims=True))
# return np.dot(norm_u, norm_v)
def get_k_nearest_title(self, title, k):
if isinstance(title, unicode):
title = title.encode('utf8')
cut_data = text_cutter.process({'title': title})
title = cut_data['cut_title'].decode('utf8')
vecs, valid_titles = self.model.get_title_vec([title])
if len(valid_titles) == 0:
return []
vecs = vecs / np.sqrt(np.sum(vecs ** 2, 1, keepdims=True))
vec = list(vecs)[0]
k_neighbors, scores = self.title_ann.get_nns_by_vector(vec, n=k, include_distances=True)
neighbors = []
for i in k_neighbors:
neighbors.append(self.titles[i])
return sorted(zip(neighbors, scores), key=lambda x: x[-1])
def get_answers(self, query, k):
if isinstance(query, unicode):
query = query.encode('utf8')
cut_data = text_cutter.process({'title': query})
cut_query = cut_data['cut_title'].decode('utf8')
vecs, valid_queries = self.model.get_query_vec([cut_query])
if len(valid_queries)==0:
return []
vecs = vecs / np.sqrt(np.sum(vecs ** 2, 1, keepdims=True))
vec = list(vecs)[0]
# recall titles according to cosine similarity
candidate_titles_index, scores = self.title_ann.get_nns_by_vector(vec, n=k*10, include_distances=True)
# rank candidate titles using model
candidate_titles = []
for i in candidate_titles_index:
candidate_titles.append(self.titles[i])
ranks = self.model.rank_titles(cut_query, candidate_titles)[:k]
return ranks
def process(self, data):
res = {}
if 'titles' in data:
res['title_nns'] = self.get_k_nearest_title(data['titles'], 10)
if 'queries' in data:
res['query_nns'] = self.get_k_nearest_query(data['queries'], 10)
return json.dumps(res, ensure_ascii=False).encode('utf8')
