def benchmark_svm(ctx, timer):
print "#worker:", ctx.num_workers
max_iter = 2
#N = 200000 * ctx.num_workers
N = 1000 * 64
D = 64
# create data
data = expr.randn(N, D, dtype=np.float64, tile_hint=(N, util.divup(D, ctx.num_workers)))
labels = expr.shuffle(data, _init_label_mapper, shape_hint=(data.shape[0], 1))
t1 = datetime.now()
w = fit(data, labels, T=max_iter).force()
t2 = datetime.now()
util.log_warn('train time per iteration:%s ms, final w:%s', millis(t1,t2)/max_iter, w.glom().T)
correct = 0
for i in range(10):
new_data = expr.randn(1, D, dtype=np.float64, tile_hint=[1, D])
new_label = predict(w, new_data)
#print 'point %s, predict %s' % (new_data.glom(), new_label)
new_data = new_data.glom()
if new_data[0,0] >= new_data[0,1] and new_label == 1.0 or new_data[0,0] < new_data[0,1] and new_label == -1.0:
correct += 1
print 'predict precision:', correct * 1.0 / 10
def benchmark_cg(ctx, timer):
print "#worker:", ctx.num_workers
l = int(math.sqrt(ctx.num_workers))
n = 2000 * 16
#n = 4000 * l
la = 20
niter = 5
tile_hint = (n, n/ctx.num_workers)
#nonzer = 7
#nz = n * (nonzer + 1) * (nonzer + 1) + n * (nonzer + 2)
#density = 0.5 * nz/(n*n)
A = expr.rand(n, n, tile_hint=tile_hint)
A = (A + expr.transpose(A))*0.5
I = expr.sparse_diagonal((n,n), tile_hint=tile_hint) * la
I.force()
A = expr.eager(A - I)
#x1 = numpy_cg(A.glom(), niter)
util.log_warn('begin cg!')
t1 = datetime.now()
x2 = conj_gradient(A, niter).force()
t2 = datetime.now()
cost_time = millis(t1,t2)
print "total cost time:%s ms, per iter cost time:%s ms" % (cost_time, cost_time/niter)
def benchmark_cg(ctx, timer):
print "#worker:", ctx.num_workers
l = int(math.sqrt(ctx.num_workers))
#n = 2000 * 16
n = 500 * ctx.num_workers
la = 20
niter = 5
#nonzer = 7
#nz = n * (nonzer + 1) * (nonzer + 1) + n * (nonzer + 2)
#density = 0.5 * nz/(n*n)
A = expr.rand(n, n)
A = (A + expr.transpose(A)) * 0.5
I = expr.sparse_diagonal((n, n)) * la
A = A - I
#x1 = numpy_cg(A.glom(), niter)
util.log_warn('begin cg!')
t1 = datetime.now()
x2 = conj_gradient(A, niter).force()
t2 = datetime.now()
cost_time = millis(t1, t2)
print "total cost time:%s ms, per iter cost time:%s ms" % (
cost_time, cost_time / niter)
def benchmark_naive_bayes(ctx, timer):
print "#worker:", ctx.num_workers
#N = 100000 * ctx.num_workers
N = 10000 * 64
D = 128
# create data
data = expr.randint(N, D, low=0, high=D, tile_hint=(N, D/ctx.num_workers))
labels = expr.shuffle(expr.ndarray((data.shape[0], 1), dtype=np.int), _init_label_mapper,
kw={'data': data}, shape_hint=(data.shape[0], 1),
cost_hint={hash(data):{'00': 0, '10': np.prod(data.shape)}}
)
#util.log_warn('data:%s, label:%s', data.glom(), labels.glom())
util.log_warn('begin train')
t1 = datetime.now()
model = fit(data, labels, D)
t2 = datetime.now()
util.log_warn('train time:%s ms', millis(t1,t2))
correct = 0
for i in range(10):
new_data = expr.randint(1, D, low=0, high=D, tile_hint=(1, D))
new_label = predict(model, new_data)
#print 'point %s, predict %s' % (new_data.glom(), new_label)
new_data = new_data.glom()
if np.isclose(new_data[0, new_label], np.max(new_data)):
correct += 1
print 'predict precision:', correct * 1.0 / 10
def benchmark_cholesky(ctx, timer):
print "#worker:", ctx.num_workers
#n = int(math.pow(ctx.num_workers, 1.0 / 3.0))
n = int(math.sqrt(ctx.num_workers))
#ARRAY_SIZE = 1600 * 4
ARRAY_SIZE = 1600 * n
util.log_warn('prepare data!')
#A = np.random.randn(ARRAY_SIZE, ARRAY_SIZE)
#A = np.dot(A, A.T)
#A = expr.force(from_numpy(A, tile_hint=(ARRAY_SIZE/n, ARRAY_SIZE/n)))
#A = expr.randn(ARRAY_SIZE, ARRAY_SIZE, tile_hint=(ARRAY_SIZE/n, ARRAY_SIZE/n))
A = expr.randn(ARRAY_SIZE, ARRAY_SIZE)
# FIXME: Ideally we should be able to get rid of tile_hint.
# However, current extent.change_partition_axis relies on the
# information of one-dimentional size to change tiling to grid tiling.
# It assumes that every extent should be partitioned in the same size.
# Trace extent.pyx to think about how to fix it!
A = expr.dot(A,
expr.transpose(A),
tile_hint=(ARRAY_SIZE, ARRAY_SIZE / ctx.num_workers)).force()
util.log_warn('begin cholesky!')
t1 = datetime.now()
L = cholesky(A).glom()
t2 = datetime.now()
assert np.all(np.isclose(A.glom(), np.dot(L, L.T.conj())))
cost_time = millis(t1, t2)
print "total cost time:%s ms, per iter cost time:%s ms" % (cost_time,
cost_time / n)
def benchmark_lda(ctx, timer):
print "#worker:", ctx.num_workers
NUM_TERMS = 160
NUM_DOCS = 200 * ctx.num_workers
#NUM_DOCS = 10 * 64
# create data
# NUM_TERMS = 41807
# NUM_DOCS = 21578
# terms_docs_matrix = from_file("/scratch/cq/numpy_dense_matrix", sparse = False, tile_hint = (NUM_TERMS, int((NUM_DOCS + ctx.num_workers - 1) / ctx.num_workers))).evaluate()
terms_docs_matrix = expr.randint(NUM_TERMS, NUM_DOCS, low=0, high=100)
max_iter = 3
k_topics = 16
t1 = datetime.now()
doc_topics, topic_term_count = learn_topics(terms_docs_matrix,
k_topics,
max_iter=max_iter)
doc_topics.optimized().evaluate()
topic_term_count.optimized().evaluate()
t2 = datetime.now()
time_cost = millis(t1, t2)
util.log_warn('total_time:%s ms, train time per iteration:%s ms',
time_cost, time_cost / max_iter)
def benchmark_naive_bayes(ctx, timer):
print "#worker:", ctx.num_workers
N = 100000 * ctx.num_workers
D = 128
# create data
data = expr.randint(N, D, low=0, high=D, tile_hint=(N/ctx.num_workers, D))
labels = expr.eager(expr.shuffle(data, _init_label_mapper))
#util.log_warn('data:%s, label:%s', data.glom(), labels.glom())
util.log_warn('begin train')
t1 = datetime.now()
model = fit(data, labels, D)
t2 = datetime.now()
util.log_warn('train time:%s ms', millis(t1,t2))
correct = 0
for i in range(10):
new_data = expr.randint(1, D, low=0, high=D, tile_hint=(1, D))
new_label = predict(model, new_data)
#print 'point %s, predict %s' % (new_data.glom(), new_label)
new_data = new_data.glom()
if np.isclose(new_data[0, new_label], np.max(new_data)):
correct += 1
print 'predict precision:', correct * 1.0 / 10
def numpy_cg(A, num_iter):
x = np.ones((A.shape[1], 1))
for iter in range(num_iter):
util.log_warn('iteration:%d', iter)
z = numpy_cgit(A, x)
x = z / np.linalg.norm(z, 2)
return x
def numpy_cg(A, num_iter):
x = np.ones((A.shape[1],1))
for iter in range(num_iter):
util.log_warn('iteration:%d', iter)
z = numpy_cgit(A, x)
x = z / np.linalg.norm(z,2)
return x
def sparse_multiply(wts, p, p_tile_hint):
avg_time = 0.0
for i in range(num_iter):
util.log_warn('iteration %d begin!', i)
t1 = datetime.now()
p = expr.dot(wts, p, tile_hint=p_tile_hint).force()
t2 = datetime.now()
time_cost = millis(t1, t2)
print "iteration %d sparse * dense: %s ms" % (i, time_cost)
avg_time += time_cost
return avg_time / num_iter
def benchmark_lreg(ctx, timer):
print "#worker:", ctx.num_workers
FLAGS.opt_parakeet_gen = 0
N_EXAMPLES = 4000000 * ctx.num_workers
#N_EXAMPLES = 5000000 * 64
x = expr.rand(N_EXAMPLES, N_DIM)
y = expr.rand(N_EXAMPLES, 1)
start = time.time()
linear_regression.linear_regression(x, y, ITERATION)
total = time.time() - start
util.log_warn("time cost : %s s" % (total*1.0/ITERATION,))
def benchmark_ridgereg(ctx, timer):
print "#worker:", ctx.num_workers
#N_EXAMPLES = 100000000 * ctx.num_workers
N_EXAMPLES = 90000000 * ctx.num_workers
x = expr.rand(N_EXAMPLES, N_DIM)
y = expr.rand(N_EXAMPLES, 1)
start = time.time()
ridge_regression.ridge_regression(x, y, 1, ITERATION)
total = time.time() - start
util.log_warn("time cost : %s s" % (total * 1.0 / ITERATION, ))
def benchmark_logreg(ctx, timer):
print "#worker:", ctx.num_workers
#N_EXAMPLES = 40000000 * ctx.num_workers
N_EXAMPLES = 5000000 * 64
x = expr.eager(expr.rand(N_EXAMPLES, N_DIM, tile_hint=(N_EXAMPLES / ctx.num_workers, N_DIM)))
y = expr.eager(expr.rand(N_EXAMPLES, 1, tile_hint=(N_EXAMPLES / ctx.num_workers, 1)))
start = time.time()
logistic_regression.logistic_regression(x, y, ITERATION)
total = time.time() - start
util.log_warn("time cost : %s s" % (total*1.0/ITERATION,))
def benchmark_ridgereg(ctx, timer):
print "#worker:", ctx.num_workers
#N_EXAMPLES = 100000000 * ctx.num_workers
N_EXAMPLES = 90000000 * ctx.num_workers
x = expr.rand(N_EXAMPLES, N_DIM)
y = expr.rand(N_EXAMPLES, 1)
start = time.time()
ridge_regression.ridge_regression(x, y, 1, ITERATION)
total = time.time() - start
util.log_warn("time cost : %s s" % (total*1.0/ITERATION,))
def _cholesky_dsyrk_dgemm_mapper(extents, tiles):
util.log_warn("dgemm %s" % str(extents))
input = tiles[0]
ex = extents[0]
A_mk = tiles[1].T
if ex.ul[0] == ex.ul[1] and ex.lr[0] == ex.lr[1]:
# diag block
return ex, linalg.blas.dsyrk(-1.0, A_mk, 1.0, input, lower=1)
else:
# other block
A_lk = tiles[2]
return ex, linalg.blas.dgemm(-1.0, A_lk, A_mk.T, 1.0, input)
def _cholesky_dsyrk_dgemm_mapper(extents, tiles):
util.log_warn("dgemm %s" % str(extents))
input = tiles[0]
ex = extents[0]
A_mk = tiles[1].T
if ex.ul[0] == ex.ul[1] and ex.lr[0] == ex.lr[1]:
# diag block
return ex, linalg.blas.dsyrk(-1.0, A_mk, 1.0, input, lower=1)
else:
# other block
A_lk = tiles[2]
return ex, linalg.blas.dgemm(-1.0, A_lk, A_mk.T, 1.0, input)
def benchmark_als(ctx, timer):
print "#worker:", ctx.num_workers
#USER_SIZE = 400 * ctx.num_workers
USER_SIZE = 200 * 64
MOVIE_SIZE = 12800
num_features = 20
num_iter = 5
A = expr.eager(expr.randint(USER_SIZE, MOVIE_SIZE, low=0, high=5, tile_hint=(USER_SIZE/ctx.num_workers, MOVIE_SIZE)))
util.log_warn('begin als!')
t1 = datetime.now()
U, M = als(A, implicit_feedback=True, num_features=num_features, num_iter=num_iter)
U.force()
M.force()
t2 = datetime.now()
cost_time = millis(t1,t2)
print "total cost time:%s ms, per iter cost time:%s ms" % (cost_time, cost_time/num_iter)
def numpy_cgit(A, x):
z = np.zeros(x.shape)
r = x
rho = np.dot(r.T, r)
util.log_warn('rho:%s', rho)
p = r
for i in xrange(15):
q = np.dot(A, p)
alpha = rho / np.dot(p.T, q)
#util.log_warn('alpha:%s', alpha)
z = z + p * alpha
rho0 = rho
r = r - q * alpha
rho = np.dot(r.T, r)
beta = rho / rho0
#util.log_warn('beta:%s', beta)
p = r + p * beta
return z
def benchmark_als(ctx, timer):
print "#worker:", ctx.num_workers
#USER_SIZE = 100 * ctx.num_workers
USER_SIZE = 320
#USER_SIZE = 200 * 64
MOVIE_SIZE = 12800
num_features = 20
num_iter = 2
A = expr.randint(USER_SIZE, MOVIE_SIZE, low=0, high=5, tile_hint=(USER_SIZE, util.divup(MOVIE_SIZE, ctx.num_workers)))
#A = expr.randint(USER_SIZE, MOVIE_SIZE, low=0, high=5)
util.log_warn('begin als!')
t1 = datetime.now()
U, M = als(A, implicit_feedback=True, num_features=num_features, num_iter=num_iter)
U.force()
M.force()
t2 = datetime.now()
cost_time = millis(t1,t2)
print "total cost time:%s ms, per iter cost time:%s ms" % (cost_time, cost_time/num_iter)
def numpy_cgit(A, x):
z = np.zeros(x.shape)
r = x
rho = np.dot(r.T, r)
util.log_warn('rho:%s', rho)
p = r
for i in xrange(15):
q = np.dot(A, p)
alpha = rho / np.dot(p.T, q)
#util.log_warn('alpha:%s', alpha)
z = z + p * alpha
rho0 = rho
r = r - q * alpha
rho = np.dot(r.T, r)
beta = rho / rho0
#util.log_warn('beta:%s', beta)
p = r + p * beta
return z
def benchmark_cholesky(ctx, timer):
print "#worker:", ctx.num_workers
#n = int(math.pow(ctx.num_workers, 1.0 / 3.0))
n = int(math.sqrt(ctx.num_workers))
#ARRAY_SIZE = 1600 * 4
ARRAY_SIZE = 900 * n
util.log_warn('prepare data!')
#A = np.random.randn(ARRAY_SIZE, ARRAY_SIZE)
#A = np.dot(A, A.T)
A = expr.randn(ARRAY_SIZE, ARRAY_SIZE)
A = expr.dot(A, expr.transpose(A))
util.log_warn('begin cholesky!')
t1 = datetime.now()
L = cholesky(A).optimized().glom()
t2 = datetime.now()
#assert np.all(np.isclose(A.glom(), np.dot(L, L.T.conj())))
cost_time = millis(t1, t2)
print "total cost time:%s ms, per iter cost time:%s ms" % (cost_time, cost_time/n)
def benchmark_cholesky(ctx, timer):
print "#worker:", ctx.num_workers
# n = int(math.pow(ctx.num_workers, 1.0 / 3.0))
n = int(math.sqrt(ctx.num_workers))
ARRAY_SIZE = 1600 * 4
# ARRAY_SIZE = 1600 * n
util.log_warn("prepare data!")
# A = np.random.randn(ARRAY_SIZE, ARRAY_SIZE)
# A = np.dot(A, A.T)
# A = expr.force(from_numpy(A, tile_hint=(ARRAY_SIZE/n, ARRAY_SIZE/n)))
A = expr.randn(ARRAY_SIZE, ARRAY_SIZE, tile_hint=(ARRAY_SIZE / n, ARRAY_SIZE / n))
A = expr.dot(A, expr.transpose(A)).force()
util.log_warn("begin cholesky!")
t1 = datetime.now()
L = cholesky(A).glom()
t2 = datetime.now()
assert np.all(np.isclose(A.glom(), np.dot(L, L.T.conj())))
cost_time = millis(t1, t2)
print "total cost time:%s ms, per iter cost time:%s ms" % (cost_time, cost_time / n)
def benchmark_cholesky(ctx, timer):
print "#worker:", ctx.num_workers
#n = int(math.pow(ctx.num_workers, 1.0 / 3.0))
n = int(math.sqrt(ctx.num_workers))
#ARRAY_SIZE = 1600 * 4
ARRAY_SIZE = 900 * n
util.log_warn('prepare data!')
#A = np.random.randn(ARRAY_SIZE, ARRAY_SIZE)
#A = np.dot(A, A.T)
A = expr.randn(ARRAY_SIZE, ARRAY_SIZE)
A = expr.dot(A, expr.transpose(A))
util.log_warn('begin cholesky!')
t1 = datetime.now()
L = cholesky(A).optimized().glom()
t2 = datetime.now()
#assert np.all(np.isclose(A.glom(), np.dot(L, L.T.conj())))
cost_time = millis(t1, t2)
print "total cost time:%s ms, per iter cost time:%s ms" % (cost_time,
cost_time / n)
def benchmark_lda(ctx, timer):
print "#worker:", ctx.num_workers
NUM_TERMS = 160
NUM_DOCS = 200 * ctx.num_workers
#NUM_DOCS = 10 * 64
# create data
# NUM_TERMS = 41807
# NUM_DOCS = 21578
# terms_docs_matrix = from_file("/scratch/cq/numpy_dense_matrix", sparse = False, tile_hint = (NUM_TERMS, int((NUM_DOCS + ctx.num_workers - 1) / ctx.num_workers))).force()
terms_docs_matrix = expr.randint(NUM_TERMS, NUM_DOCS, low=0, high=100)
max_iter = 3
k_topics = 16
t1 = datetime.now()
doc_topics, topic_term_count = learn_topics(terms_docs_matrix, k_topics, max_iter=max_iter)
doc_topics.optimized().force()
topic_term_count.optimized().force()
t2 = datetime.now()
time_cost = millis(t1,t2)
util.log_warn('total_time:%s ms, train time per iteration:%s ms', time_cost, time_cost/max_iter)
def benchmark_jacobi(ctx, timer):
global base, ITERATION
util.log_warn('util.log_warn: %s', ctx.num_workers)
A, b = jacobi.jacobi_init(base * ctx.num_workers)
A, b = A.evaluate(), b.evaluate()
start = time.time()
result = jacobi.jacobi_method(A, b, ITERATION).glom()
cost = time.time() - start
util.log_info('\nresult =\n%s', result)
util.log_warn('time cost: %s s', cost)
util.log_warn('cost per iteration: %s s\n', cost / ITERATION)
def fit(self, X, centers=None, implementation='outer'):
"""Compute k-means clustering.
Parameters
----------
X : spartan matrix, shape=(n_samples, n_features). It should be tiled by rows.
centers : numpy.ndarray. The initial centers. If None, it will be randomly generated.
"""
num_dim = X.shape[1]
num_points = X.shape[0]
labels = expr.zeros((num_points, 1), dtype=np.int)
if implementation == 'map2':
if centers is None:
centers = np.random.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
labels = expr.map2(X, 0, fn=kmeans_map2_dist_mapper, fn_kw={"centers": centers},
shape=(X.shape[0], ))
counts = expr.map2(labels, 0, fn=kmeans_count_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], ))
new_centers = expr.map2((X, labels), (0, 0), fn=kmeans_center_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], centers.shape[1]))
counts = counts.optimized().glom()
centers = new_centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
return centers, labels
elif implementation == 'outer':
if centers is None:
centers = expr.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
labels = expr.outer((X, centers), (0, None), fn=kmeans_outer_dist_mapper,
shape=(X.shape[0],))
#labels = expr.argmin(distances, axis=1)
counts = expr.map2(labels, 0, fn=kmeans_count_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], ))
new_centers = expr.map2((X, labels), (0, 0), fn=kmeans_center_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], centers.shape[1]))
counts = counts.optimized().glom()
centers = new_centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
centers = expr.from_numpy(centers)
return centers, labels
elif implementation == 'broadcast':
if centers is None:
centers = expr.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
util.log_warn("k_means_ %d %d", i, time.time())
X_broadcast = expr.reshape(X, (X.shape[0], 1, X.shape[1]))
centers_broadcast = expr.reshape(centers, (1, centers.shape[0],
centers.shape[1]))
distances = expr.sum(expr.square(X_broadcast - centers_broadcast), axis=2)
labels = expr.argmin(distances, axis=1)
center_idx = expr.arange((1, centers.shape[0]))
matches = expr.reshape(labels, (labels.shape[0], 1)) == center_idx
matches = matches.astype(np.int64)
counts = expr.sum(matches, axis=0)
centers = expr.sum(X_broadcast * expr.reshape(matches, (matches.shape[0],
matches.shape[1], 1)),
axis=0)
counts = counts.optimized().glom()
centers = centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
centers = expr.from_numpy(centers)
return centers, labels
elif implementation == 'shuffle':
if centers is None:
centers = np.random.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
# Reset them to zero.
new_centers = expr.ndarray((self.n_clusters, num_dim),
reduce_fn=lambda a, b: a + b)
new_counts = expr.ndarray((self.n_clusters, 1), dtype=np.int,
reduce_fn=lambda a, b: a + b)
_ = expr.shuffle(X,
_find_cluster_mapper,
kw={'d_pts': X,
'old_centers': centers,
'new_centers': new_centers,
'new_counts': new_counts,
'labels': labels},
shape_hint=(1,),
cost_hint={hash(labels): {'00': 0,
'01': np.prod(labels.shape)}})
_.force()
new_counts = new_counts.glom()
new_centers = new_centers.glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (new_counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
new_counts[zcount_indices] = 1
new_centers[zcount_indices, :] = np.random.randn(n_points, num_dim)
new_centers = new_centers / new_counts
centers = new_centers
return centers, labels
def sparse_multiply(wts, p, p_tile_hint):
for i in range(num_iter):
util.log_warn('iteration %d begin!', i)
p = expr.dot(wts, p).optimized()
p.evaluate()
return
def sparse_multiply(wts, p, p_tile_hint):
for i in range(num_iter):
util.log_warn('iteration %d begin!', i)
p = expr.dot(wts, p).optimized()
p.evaluate()
return
def fit(self, X, centers=None, implementation='map2'):
"""Compute k-means clustering.
Parameters
----------
X : spartan matrix, shape=(n_samples, n_features). It should be tiled by rows.
centers : numpy.ndarray. The initial centers. If None, it will be randomly generated.
"""
num_dim = X.shape[1]
num_points = X.shape[0]
labels = expr.zeros((num_points, 1), dtype=np.int)
if implementation == 'map2':
if centers is None:
centers = np.random.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
labels = expr.map2(X,
0,
fn=kmeans_map2_dist_mapper,
fn_kw={"centers": centers},
shape=(X.shape[0], ))
counts = expr.map2(labels,
0,
fn=kmeans_count_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], ))
new_centers = expr.map2(
(X, labels), (0, 0),
fn=kmeans_center_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], centers.shape[1]))
counts = counts.optimized().glom()
centers = new_centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(
n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
return centers, labels
elif implementation == 'outer':
if centers is None:
centers = expr.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
labels = expr.outer((X, centers), (0, None),
fn=kmeans_outer_dist_mapper,
shape=(X.shape[0], ))
#labels = expr.argmin(distances, axis=1)
counts = expr.map2(labels,
0,
fn=kmeans_count_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], ))
new_centers = expr.map2(
(X, labels), (0, 0),
fn=kmeans_center_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], centers.shape[1]))
counts = counts.optimized().glom()
centers = new_centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(
n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
centers = expr.from_numpy(centers)
return centers, labels
elif implementation == 'broadcast':
if centers is None:
centers = expr.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
util.log_warn("k_means_ %d %d", i, time.time())
X_broadcast = expr.reshape(X, (X.shape[0], 1, X.shape[1]))
centers_broadcast = expr.reshape(
centers, (1, centers.shape[0], centers.shape[1]))
distances = expr.sum(expr.square(X_broadcast -
centers_broadcast),
axis=2)
labels = expr.argmin(distances, axis=1)
center_idx = expr.arange((1, centers.shape[0]))
matches = expr.reshape(labels,
(labels.shape[0], 1)) == center_idx
matches = matches.astype(np.int64)
counts = expr.sum(matches, axis=0)
centers = expr.sum(
X_broadcast *
expr.reshape(matches,
(matches.shape[0], matches.shape[1], 1)),
axis=0)
counts = counts.optimized().glom()
centers = centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(
n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
centers = expr.from_numpy(centers)
return centers, labels
elif implementation == 'shuffle':
if centers is None:
centers = np.random.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
# Reset them to zero.
new_centers = expr.ndarray((self.n_clusters, num_dim),
reduce_fn=lambda a, b: a + b)
new_counts = expr.ndarray((self.n_clusters, 1),
dtype=np.int,
reduce_fn=lambda a, b: a + b)
_ = expr.shuffle(X,
_find_cluster_mapper,
kw={
'd_pts': X,
'old_centers': centers,
'new_centers': new_centers,
'new_counts': new_counts,
'labels': labels
},
shape_hint=(1, ),
cost_hint={
hash(labels): {
'00': 0,
'01': np.prod(labels.shape)
}
})
_.force()
new_counts = new_counts.glom()
new_centers = new_centers.glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (new_counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
new_counts[zcount_indices] = 1
new_centers[zcount_indices, :] = np.random.randn(
n_points, num_dim)
new_centers = new_centers / new_counts
centers = new_centers
return centers, labels
