L = CoordinateMatrix(edges).toBlockMatrix()
L_transpose = CoordinateMatrix(edges_transpose).toBlockMatrix()
h_init = []
for i in range(1000):
h_init.append((i, 0, 1))
h = CoordinateMatrix(sc.parallelize(h_init)).toBlockMatrix()
a = None
for i in range(40):
a_new = L_transpose.multiply(h)
a_new_max = np.max(np.array(a_new.toLocalMatrix().toArray()))
a_new_max_inverse = []
for j in range(1000):
a_new_max_inverse.append((j, j, 1 / a_new_max))
a_new_max_inverse = CoordinateMatrix(sc.parallelize(a_new_max_inverse)).toBlockMatrix()
a = a_new_max_inverse.multiply(a_new)
h_new = L.multiply(a)
h_new_max = np.max(np.array(h_new.toLocalMatrix().toArray()))
h_new_max_inverse = []
for j in range(1000):
h_new_max_inverse.append((j, j, 1 / h_new_max))
h_new_max_inverse = CoordinateMatrix(sc.parallelize(h_new_max_inverse)).toBlockMatrix()
h = h_new_max_inverse.multiply(h_new)
end = time.time()
elapsed_seconds = float("%.4f" % (end - start))
logging.info('%s: elapsed seconds: %s', name, elapsed_seconds)
logging.getLogger().setLevel(logging.INFO)
def to_matrix_entry(x):
i, j, v = x.split(',')
return MatrixEntry(i, j, v)
sc = pyspark.SparkContext(appName="Matrix Multiplication")
for i in range(1, 10):
with time_usage("temps matrix multiplication"):
matrix_a_raw = sc.textFile(sys.argv[1])
matrix_b_raw = sc.textFile(sys.argv[2])
spark = SparkSession(sc)
entries_a = matrix_a_raw.map(to_matrix_entry)
entries_b = matrix_b_raw.map(to_matrix_entry)
mat_a = CoordinateMatrix(entries_a).toBlockMatrix()
mat_b = CoordinateMatrix(entries_b).toBlockMatrix()
product = mat_a.multiply(mat_b)
product.toLocalMatrix()
#for t in result:
#print('%s, %s, %s' % (t[0], t[1], t[2]))
# create the context
sc = pyspark.SparkContext(conf=conf)
spark = SparkSession.builder.getOrCreate()
small_data = sc.textFile('graph-small.txt')
full_data = sc.textFile('graph-full.txt')
BETA = 0.8
source_dest_pair = full_data.map(lambda x: (int(x.split('\t')[0]) - 1, int(x.split('\t')[1]) - 1)).distinct()
edges = source_dest_pair.map(lambda x: (x[1], x[0], 1))
degrees = source_dest_pair.map(lambda x: (x[0], 1)).reduceByKey(lambda x, y: x + y).map(lambda x: (x[0], x[0], 1 / x[1]))
edge_matrix = CoordinateMatrix(edges).toBlockMatrix()
degree_inverse_matrix = CoordinateMatrix(degrees).toBlockMatrix()
M = edge_matrix.multiply(degree_inverse_matrix)
r_init = []
beta_init = []
teleport_init = []
for i in range(1000):
r_init.append((i, 0, 1 / 1000))
beta_init.append((i, i, BETA))
teleport_init.append((i, 0, (1 - BETA) / 1000))
r = CoordinateMatrix(sc.parallelize(r_init)).toBlockMatrix()
beta = CoordinateMatrix(sc.parallelize(beta_init)).toBlockMatrix()
teleport = CoordinateMatrix(sc.parallelize(teleport_init)).toBlockMatrix()
for i in range(40):
r = teleport.add(beta.multiply(M).multiply(r))
W = CoordinateMatrix(upper_entries.union(lower_entries), numCols=N, numRows=N)
# XXX:
laplacian = sys.argv[1]
if laplacian == 'unnormalized':
entries = degrees.map(lambda x: MatrixEntry(x[0], x[0], x[1]))
D = CoordinateMatrix(entries, numCols=N, numRows=N)
L = D.toBlockMatrix().subtract(W.toBlockMatrix()).toCoordinateMatrix()
elif laplacian == 'normalized':
entries = degrees.map(lambda x: MatrixEntry(x[0], x[0], 1 / x[1]))
D_inv = CoordinateMatrix(entries, numCols=N, numRows=N).toBlockMatrix()
I = CoordinateMatrix(sc.range(N).map(lambda i: MatrixEntry(i, i, 1.0)),
numCols=N,
numRows=N).toBlockMatrix()
L = I.subtract(D_inv.multiply(W.toBlockMatrix())).toCoordinateMatrix()
elif laplacian == 'symmetric':
entries = degrees.map(lambda x: MatrixEntry(x[0], x[0], 1 / sqrt(x[1])))
D_invsq = CoordinateMatrix(entries, numCols=N, numRows=N).toBlockMatrix()
I = sc.range(N).map(lambda i: MatrixEntry(i, i, 1.0), N, N)
tmp = D_invsq.multiply(W.toBlockMatrix()).multiply(D_invsq)
L = I.toBlockMatrix().subtract(tmp)
else:
raise ValueError('Unknown type of Laplacian.')
## SVD, and transform from dense matrix to dataframe.
svd = L.toRowMatrix().computeSVD(k=K, computeU=False)
V = svd.V.toArray().tolist()
VV = spark.createDataFrame(V)
kmeans = KMeans().setK(K).setSeed(1)
vecAssembler = VectorAssembler(inputCols=VV.schema.names, outputCol='features')
