def find_edges(input, test, K):
print(f"\tbuilding kNN classifier ... ", end=" ")
st_time = time.time()
if kNN_type in [1, 2]:
input, test = input.todense(), test.todense()
if kNN_type == 1:
from sklearn.neighbors import NearestNeighbors
tree = NearestNeighbors(n_neighbors=K + 1, algorithm='ball_tree').fit(input)
elif kNN_type == 2:
from scipy import spatial
tree = spatial.KDTree(input)
elif kNN_type == 3:
from n2 import HnswIndex
tree = HnswIndex(input.shape[1], distance_type) # distance_type in ['angular', 'L2']
for index in tqdm(range(input.shape[0])):
tree.add_data(input[index, :])
tree.build(n_threads=20)
elif kNN_type == 4:
import pysparnn.cluster_index as ci
input_num = input.shape[0]
tree = ci.MultiClusterIndex(input, range(input_num))
elif kNN_type == 5:
import nmslib
M, efC = 30, 100
index_time_params = {'M': M, 'indexThreadQty': num_threads, 'efConstruction': efC, 'post': 0}
space_names = ['l2_sparse', 'cosinesimil_sparse'] # https://github.com/nmslib/nmslib/blob/master/manual/spaces.md
space_name = space_names[0]
data_type = nmslib.DataType.SPARSE_VECTOR
tree = nmslib.init(method='hnsw', space=space_name, data_type=data_type)
'''
def calc_zero_rows(i):
if input[i, :].getnnz() == 0:
return 1
else:
return 0
pool = Pool(num_threads)
zero_row_num = sum(pool.map(calc_zero_rows, range(input.shape[0])))
print(f"# zero rows in input = {zero_row_num}", end=" ")
'''
tree.addDataPointBatch(input)
tree.createIndex(index_time_params, print_progress=True)
# Setting query-time parameters
efS = 100
query_time_params = {'efSearch': efS}
print('Setting query-time parameters', query_time_params, end=" ")
tree.setQueryTimeParams(query_time_params)
else:
raise NotImplementedError
print(f"time={time.time()-st_time:.3f}s")
print("\tfinding indices ... ", end=" ")
if kNN_type == 1:
_, indices = tree.kneighbors(test)
elif kNN_type == 2:
_, indices = tree.query(test, k=K + 1)
elif kNN_type == 3:
indices = []
for i in tqdm(range(test.shape[0])):
indices.append(tree.search_by_vector(test[i, :], k=K + 1))
elif kNN_type == 4:
indices = tree.search(test, k=K+1, k_clusters=100, return_distance=False)
elif kNN_type == 5:
'''
def calc_zero_rows2(i):
if test[i, :].getnnz() == 0:
return 1
else:
return 0
pool = Pool(num_threads)
zero_row_num = sum(pool.map(calc_zero_rows2, range(test.shape[0])))
print(f"# zero rows in test = {zero_row_num}")
'''
indices_ = tree.knnQueryBatch(test, k=K+1, num_threads=num_threads)
indices = [i[0] for i in indices_]
del indices_
else:
raise NotImplementedError
print(f"time={time.time()-st_time:.3f}s")
edge_list = []
for index1, per in enumerate(indices):
assert len(per) == K+1, f"index1={index1} len(per)={len(per)} != K={K}"
for index2 in per:
index2 = int(index2)
if index1 != index2:
edge_list.append((index1, index2))
print(f"\tget edges done! .... time={time.time()-st_time:.3f}s")
return edge_list
class AKNNPredictor:
def __init__(self, params):
self.logFile = params['logFile']
self.seed = params['seed']
def Train(self, X, Y, numThreads=1):
assert (X.shape[0] == Y.shape[0])
if issparse(X):
# The python interface of nmslib library most probably does not support sparse input
# Use KDTree of sklearn package
print(
str(datetime.now()) + " : " +
"Creating Approximate KNN graph over train examples using sklearn functions"
)
self.graph = NearestNeighbors(n_neighbors=10,
radius=5,
algorithm='auto',
metric='l2',
n_jobs=numThreads)
self.graph.fit(X)
else:
print(
str(datetime.now()) + " : " +
"Creating Approximate KNN graph over train examples using HANN"
)
self.graph = nmslib.init(method='hnsw', space='l2')
self.graph.addDataPointBatch(X)
self.graph.createIndex({
'post': 2,
'M': 10,
'maxM0': 20
},
print_progress=False)
self.Y = Y
def Predict(self, Xt, nnTest, numThreads=1):
# Compute K nearest neighbors for input data
print(str(datetime.now()) + " : " + "Computing Approximate KNN")
knn = self.ComputeAKNN(Xt, nnTest, numThreads)
# Predict labels for input data
print(str(datetime.now()) + " : " + "Performing prediction")
predYt = self.ComputeLabelScore(knn, nnTest, numThreads)
return predYt
def ComputeLabelScore(self, KNN, nnTest, numThreads=1):
if (KNN.shape[0] == 0):
return lil_matrix((0, self.Y.shape[1]), dtype=float)
Y = self.Y
nt = KNN.shape[0]
L = Y.shape[1]
batchSize = int(math.ceil(float(nt) / numThreads))
numBatches = int(math.ceil(float(nt) / batchSize))
startIdx = [i * batchSize for i in range(numBatches)]
endIdx = [min((i + 1) * batchSize, nt) for i in range(numBatches)]
numCores = numThreads
resultList = Parallel(n_jobs=numCores)(
delayed(ComputeLabelScoreInner)(Y, KNN[s:e, :], nnTest)
for s, e in zip(startIdx, endIdx))
predYt = vstack(resultList, format='lil')
assert (predYt.shape[0] == nt)
return predYt
def ComputePrecision(self, predYt, Yt, K, numThreads):
assert (predYt.shape == Yt.shape)
if (predYt.shape[0] == 0):
return np.zeros((K), dtype=float)
nt, L = Yt.shape
batchSize = int(math.ceil(float(nt) / numThreads))
numBatches = int(math.ceil(float(nt) / batchSize))
startIdx = [i * batchSize for i in range(numBatches)]
endIdx = [min((i + 1) * batchSize, nt) for i in range(numBatches)]
resultList = Parallel(n_jobs=numThreads)(
delayed(ComputePrecisionInner)(predYt[s:e, :], Yt[s:e, :], K)
for s, e in zip(startIdx, endIdx))
precision = np.zeros((K, 1))
for i, res in enumerate(resultList):
precision += res * (endIdx[i] - startIdx[i])
precision /= float(nt)
return precision
def ComputeAKNN(self, Xt, nnTest, numThreads=1):
if (Xt.shape[0] == 0):
return np.zeros((0, nnTest), dtype=np.int64)
if (issparse(Xt)):
KNN = self.graph.kneighbors(Xt,
min(nnTest, Xt.shape[0]),
return_distance=False)
if (KNN.shape[1] < nnTest):
rf = int(nnTest / KNN.shape[1])
KNN = np.hstack(tuple([KNN] * rf))
KNN = np.hstack((KNN, KNN[:, :(nnTest - KNN.shape[1])]))
else:
neighbors = self.graph.knnQueryBatch(Xt,
min(nnTest, Xt.shape[0]),
num_threads=numThreads)
# Create the KNN matrix
KNN = np.zeros((Xt.shape[0], nnTest), dtype=np.int64)
for i, nei in enumerate(neighbors):
nn = nei[0].shape[1]
KNN[i, :nn] = nei[0]
if (nn < nnTest):
for j in range(nn, nnTest):
KNN[i, j] = nei[0][j % nn]
return KNN
def PredictAndComputePrecision(self, Xt, Yt, nnTestList, maxTestSamples,
numThreads):
assert (Xt.shape[0] == Yt.shape[0])
# Perform down sampling of input data
if (maxTestSamples > 0):
Xt, Yt, testSample = DownSampleData(Xt, Yt, maxTestSamples)
maxNNTest = max(nnTestList)
# Compute K nearest neighbors for input data
print(str(datetime.now()) + " : " + "Computing KNN")
knn = self.ComputeAKNN(Xt, maxNNTest, numThreads)
resList = []
for nnTest in nnTestList:
# Predict labels for input data
print(
str(datetime.now()) + " : " +
"Performing prediction for nnTest = " + str(nnTest))
predYt = self.ComputeLabelScore(knn, nnTest, numThreads)
# Compute precisions for input data
print(
str(datetime.now()) + " : " +
"Computing precisions for nnTest = " + str(nnTest))
precision = self.ComputePrecision(predYt, Yt, 5, numThreads)
#resList.append({'Y': Yt, 'predY': predYt, 'scoreY': scoreYt, 'precision': precision, 'testSample': testSample})
resList.append({'precision': precision})
return resList
def UpdateLogFile(self, logFile):
self.logFile = logFile
def UpdateSeed(self, seed):
self.seed = seed
def find_edges(input, test, K, cluster_ids, query_ids):
print(f"\tbuilding kNN classifier ... ", end=" ")
st_time = time.time()
if kNN_type in [1, 2]:
input, test = input.todense(), test.todense()
if kNN_type == 1:
from sklearn.neighbors import NearestNeighbors
tree = NearestNeighbors(n_neighbors=K + 1, algorithm='ball_tree').fit(input)
elif kNN_type == 2:
from scipy import spatial
tree = spatial.KDTree(input)
elif kNN_type == 3:
from n2 import HnswIndex
tree = HnswIndex(input.shape[1], distance_type) # distance_type in ['angular', 'L2']
for index in tqdm(range(input.shape[0])):
tree.add_data(input[index, :])
tree.build(n_threads=20)
elif kNN_type == 4:
import pysparnn.cluster_index as ci
input_num = input.shape[0]
tree = ci.MultiClusterIndex(input, range(input_num))
elif kNN_type == 5:
import nmslib
M, efC, num_threads = 30, 100, 10
index_time_params = {'M': M, 'indexThreadQty': num_threads, 'efConstruction': efC, 'post': 0}
space_name = 'cosinesimil_sparse'
data_type = nmslib.DataType.SPARSE_VECTOR
tree = nmslib.init(method='hnsw', space=space_name, data_type=data_type)
print(f"type(input) = {type(input)} type(test)={type(test)}", end=" ")
tree.addDataPointBatch(input)
tree.createIndex(index_time_params)
# Setting query-time parameters
efS = 100
query_time_params = {'efSearch': efS}
print('Setting query-time parameters', query_time_params)
tree.setQueryTimeParams(query_time_params)
else:
raise NotImplementedError
print(f"time={time.time()-st_time:.3f}s")
print("\tfinding indices ... ", end=" ")
if kNN_type == 1:
_, indices = tree.kneighbors(test)
elif kNN_type == 2:
_, indices = tree.query(test, k=K + 1)
elif kNN_type == 3:
indices = []
for i in tqdm(range(test.shape[0])):
indices.append(tree.search_by_vector(test[i, :], k=K + 1))
elif kNN_type == 4:
indices = tree.search(test, k=K+1, k_clusters=100, return_distance=False)
elif kNN_type == 5:
indices_ = tree.knnQueryBatch(test, k=K, num_threads=num_threads)
indices = [i[0] for i in indices_]
del indices_
else:
raise NotImplementedError
print(f"time={time.time()-st_time:.3f}s")
edge_list = []
for index1, per in enumerate(indices):
for index2 in per:
index2 = int(index2)
if index1 != index2:
edge_list.append((query_ids[index1], center_ids[index2]))
print(f"\tdone! .... time={time.time()-st_time:.3f}s")
return edge_list
