def benchmark_slice(ctx, timer):
TEST_SIZE = 1000 * ctx.num_workers
# force arange to evaluate first.
x = expr.eager(expr.zeros((TEST_SIZE,10000)))
for i in range(5):
timer.time_op('slice-rows', lambda: expr.evaluate(x[200:300, :].sum()))
timer.time_op('slice-cols', lambda: expr.evaluate(x[:, 200:300].sum()))
timer.time_op('slice-box', lambda: expr.evaluate(x[200:300, 200:300].sum()))
def benchmark_slice(ctx, timer):
TEST_SIZE = 1000 * ctx.num_workers
# force arange to evaluate first.
x = expr.eager(expr.zeros((TEST_SIZE, 10000)))
for i in range(5):
timer.time_op('slice-rows', lambda: expr.evaluate(x[200:300, :].sum()))
timer.time_op('slice-cols', lambda: expr.evaluate(x[:, 200:300].sum()))
timer.time_op('slice-box',
lambda: expr.evaluate(x[200:300, 200:300].sum()))
def test_index(self):
a = expr.arange((TEST_SIZE, TEST_SIZE))
b = expr.ones((10, ), dtype=np.int)
z = a[b]
val = expr.evaluate(z)
nx = np.arange(TEST_SIZE * TEST_SIZE).reshape(TEST_SIZE, TEST_SIZE)
ny = np.ones((10, ), dtype=np.int)
Assert.all_eq(val.glom(), nx[ny])
def test_index(self):
a = expr.arange((TEST_SIZE, TEST_SIZE))
b = expr.ones((10,), dtype=np.int)
z = a[b]
val = expr.evaluate(z)
nx = np.arange(TEST_SIZE * TEST_SIZE).reshape(TEST_SIZE, TEST_SIZE)
ny = np.ones((10,), dtype=np.int)
Assert.all_eq(val.glom(), nx[ny])
def _step():
y = expr.evaluate(x * x)
def train_smo_2005(self, data, labels):
"""
Train an SVM model using the SMO (2005) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
"""
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
alpha = expr.zeros((N, 1), dtype=np.float64, tile_hint=[N / self.ctx.num_workers, 1]).evaluate()
# linear kernel
kernel_results = expr.dot(data, expr.transpose(data), tile_hint=[N / self.ctx.num_workers, N])
gradient = expr.ones((N, 1), dtype=np.float64, tile_hint=[N / self.ctx.num_workers, 1]) * -1.0
expr_labels = expr.lazify(labels)
util.log_info("Starting SMO")
pv1 = pv2 = -1
it = 0
while it < self.maxiter:
util.log_info("Iteration:%d", it)
minObj = 1e100
expr_alpha = expr.lazify(alpha)
G = expr.multiply(labels, gradient) * -1.0
v1_mask = (expr_labels > self.tol) * (expr_alpha < self.C) + (expr_labels < -self.tol) * (
expr_alpha > self.tol
)
v1 = expr.argmax(G[v1_mask - True]).glom().item()
maxG = G[v1, 0].glom()
print "maxv1:", v1, "maxG:", maxG
v2_mask = (expr_labels > self.tol) * (expr_alpha > self.tol) + (expr_labels < -self.tol) * (
expr_alpha < self.C
)
min_v2 = expr.argmin(G[v2_mask - True]).glom().item()
minG = G[min_v2, 0].glom()
# print 'minv2:', min_v2, 'minG:', minG
set_v2 = v2_mask.glom().nonzero()[0]
# print 'actives:', set_v2.shape[0]
v2 = -1
for v in set_v2:
b = maxG - G[v, 0].glom()
if b > self.tol:
na = (kernel_results[v1, v1] + kernel_results[v, v] - 2 * kernel_results[v1, v]).glom()[0][0]
if na < self.tol:
na = 1e12
obj = -(b * b) / na
if obj <= minObj and v1 != pv1 or v != pv2:
v2 = v
a = na
minObj = obj
if v2 == -1:
break
if maxG - minG < self.tol:
break
print "opt v1:", v1, "v2:", v2
pv1 = v1
pv2 = v2
y1 = labels[v1, 0]
y2 = labels[v2, 0]
oldA1 = alpha[v1, 0]
oldA2 = alpha[v2, 0]
# Calculate new alpha values, to reduce the objective function...
b = y2 * expr.glom(gradient[v2, 0]) - y1 * expr.glom(gradient[v1, 0])
if y1 != y2:
a += 4 * kernel_results[v1, v2].glom()
newA1 = oldA1 + y1 * b / a
newA2 = oldA2 - y2 * b / a
# Correct for alpha being out of range...
sum = y1 * oldA1 + y2 * oldA2
if newA1 < self.tol:
newA1 = 0.0
elif newA1 > self.C:
newA1 = self.C
newA2 = y2 * (sum - y1 * newA1)
if newA2 < self.tol:
newA2 = 0.0
elif newA2 > self.C:
newA2 = self.C
newA1 = y1 * (sum - y2 * newA2)
# Update the gradient...
dA1 = newA1 - oldA1
dA2 = newA2 - oldA2
gradient += (
expr.multiply(labels, kernel_results[:, v1]) * y1 * dA1
+ expr.multiply(labels, kernel_results[:, v2]) * y2 * dA2
)
alpha[v1, 0] = newA1
alpha[v2, 0] = newA2
# print 'alpha:', alpha.glom().T
it += 1
# print 'gradient:', gradient.glom().T
self.w = expr.zeros((D, 1), dtype=np.float64).evaluate()
for i in xrange(D):
self.w[i, 0] = expr.reduce(
alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels, data=expr.evaluate(data[:, i])),
).glom()
self.b = 0.0
E = (labels - self.margins(data)).evaluate()
minB = -1e100
maxB = 1e100
actualB = 0.0
numActualB = 0
for i in xrange(N):
ai = alpha[i, 0]
yi = labels[i, 0]
Ei = E[i, 0]
if ai < 1e-3:
if yi < self.tol:
maxB = min((maxB, Ei))
else:
minB = max((minB, Ei))
elif ai > self.C - 1e-3:
if yi < self.tol:
minB = max((minB, Ei))
else:
maxB = min((maxB, Ei))
else:
numActualB += 1
actualB += (Ei - actualB) / float(numActualB)
if numActualB > 0:
self.b = actualB
else:
self.b = 0.5 * (minB + maxB)
self.usew_ = True
print "iteration finish:", it
print "b:", self.b
print "w:", self.w.glom()
def train_smo_1998(self, data, labels):
"""
Train an SVM model using the SMO (1998) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
"""
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
self.alpha = expr.zeros((N, 1), dtype=np.float64, tile_hint=[N / self.ctx.num_workers, 1]).evaluate()
# linear kernel
kernel_results = expr.dot(data, expr.transpose(data), tile_hint=[N / self.ctx.num_workers, N])
labels = labels.evaluate()
self.E = expr.zeros((N, 1), dtype=np.float64, tile_hint=[N / self.ctx.num_workers, 1]).evaluate()
for i in xrange(N):
self.E[i, 0] = (
self.b
+ expr.reduce(
self.alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels, data=kernel_results[:, i].evaluate()),
).glom()
- labels[i, 0]
)
util.log_info("Starting SMO")
it = 0
num_changed = 0
examine_all = True
while (num_changed > 0 or examine_all) and (it < self.maxiter):
util.log_info("Iteration:%d", it)
num_changed = 0
if examine_all:
for i in xrange(N):
num_changed += self.examine_example(i, N, labels, kernel_results)
else:
for i in xrange(N):
if self.alpha[i, 0] > 0 and self.alpha[i, 0] < self.C:
num_changed += self.examine_example(i, N, labels, kernel_results)
it += 1
if examine_all:
examine_all = False
elif num_changed == 0:
examine_all = True
self.w = expr.zeros((D, 1), dtype=np.float64).evaluate()
for i in xrange(D):
self.w[i, 0] = expr.reduce(
self.alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels, data=expr.evaluate(data[:, i])),
).glom()
self.usew_ = True
print "iteration finish:", it
print "b:", self.b
print "w:", self.w.glom()
def _step():
expr.evaluate(expr.dot(x, y))
def _step():
expr.evaluate(expr.dot(x, y))
def train_smo_2005(self, data, labels):
'''
Train an SVM model using the SMO (2005) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
'''
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
alpha = expr.zeros((N, 1),
dtype=np.float64,
tile_hint=[N / self.ctx.num_workers, 1]).evaluate()
# linear kernel
kernel_results = expr.dot(data,
expr.transpose(data),
tile_hint=[N / self.ctx.num_workers, N])
gradient = expr.ones(
(N, 1), dtype=np.float64, tile_hint=[N / self.ctx.num_workers, 1
]) * -1.0
expr_labels = expr.lazify(labels)
util.log_info("Starting SMO")
pv1 = pv2 = -1
it = 0
while it < self.maxiter:
util.log_info("Iteration:%d", it)
minObj = 1e100
expr_alpha = expr.lazify(alpha)
G = expr.multiply(labels, gradient) * -1.0
v1_mask = ((expr_labels > self.tol) * (expr_alpha < self.C) +
(expr_labels < -self.tol) * (expr_alpha > self.tol))
v1 = expr.argmax(G[v1_mask - True]).glom().item()
maxG = G[v1, 0].glom()
print 'maxv1:', v1, 'maxG:', maxG
v2_mask = ((expr_labels > self.tol) * (expr_alpha > self.tol) +
(expr_labels < -self.tol) * (expr_alpha < self.C))
min_v2 = expr.argmin(G[v2_mask - True]).glom().item()
minG = G[min_v2, 0].glom()
#print 'minv2:', min_v2, 'minG:', minG
set_v2 = v2_mask.glom().nonzero()[0]
#print 'actives:', set_v2.shape[0]
v2 = -1
for v in set_v2:
b = maxG - G[v, 0].glom()
if b > self.tol:
na = (kernel_results[v1, v1] + kernel_results[v, v] -
2 * kernel_results[v1, v]).glom()[0][0]
if na < self.tol: na = 1e12
obj = -(b * b) / na
if obj <= minObj and v1 != pv1 or v != pv2:
v2 = v
a = na
minObj = obj
if v2 == -1: break
if maxG - minG < self.tol: break
print 'opt v1:', v1, 'v2:', v2
pv1 = v1
pv2 = v2
y1 = labels[v1, 0]
y2 = labels[v2, 0]
oldA1 = alpha[v1, 0]
oldA2 = alpha[v2, 0]
# Calculate new alpha values, to reduce the objective function...
b = y2 * expr.glom(gradient[v2, 0]) - y1 * expr.glom(gradient[v1,
0])
if y1 != y2:
a += 4 * kernel_results[v1, v2].glom()
newA1 = oldA1 + y1 * b / a
newA2 = oldA2 - y2 * b / a
# Correct for alpha being out of range...
sum = y1 * oldA1 + y2 * oldA2
if newA1 < self.tol: newA1 = 0.0
elif newA1 > self.C: newA1 = self.C
newA2 = y2 * (sum - y1 * newA1)
if newA2 < self.tol: newA2 = 0.0
elif newA2 > self.C: newA2 = self.C
newA1 = y1 * (sum - y2 * newA2)
# Update the gradient...
dA1 = newA1 - oldA1
dA2 = newA2 - oldA2
gradient += expr.multiply(
labels, kernel_results[:, v1]) * y1 * dA1 + expr.multiply(
labels, kernel_results[:, v2]) * y2 * dA2
alpha[v1, 0] = newA1
alpha[v2, 0] = newA2
#print 'alpha:', alpha.glom().T
it += 1
#print 'gradient:', gradient.glom().T
self.w = expr.zeros((D, 1), dtype=np.float64).evaluate()
for i in xrange(D):
self.w[i, 0] = expr.reduce(alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels,
data=expr.evaluate(
data[:, i]))).glom()
self.b = 0.0
E = (labels - self.margins(data)).evaluate()
minB = -1e100
maxB = 1e100
actualB = 0.0
numActualB = 0
for i in xrange(N):
ai = alpha[i, 0]
yi = labels[i, 0]
Ei = E[i, 0]
if ai < 1e-3:
if yi < self.tol:
maxB = min((maxB, Ei))
else:
minB = max((minB, Ei))
elif ai > self.C - 1e-3:
if yi < self.tol:
minB = max((minB, Ei))
else:
maxB = min((maxB, Ei))
else:
numActualB += 1
actualB += (Ei - actualB) / float(numActualB)
if numActualB > 0:
self.b = actualB
else:
self.b = 0.5 * (minB + maxB)
self.usew_ = True
print 'iteration finish:', it
print 'b:', self.b
print 'w:', self.w.glom()
def train_smo_1998(self, data, labels):
'''
Train an SVM model using the SMO (1998) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
'''
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
self.alpha = expr.zeros((N, 1),
dtype=np.float64,
tile_hint=[N / self.ctx.num_workers,
1]).evaluate()
# linear kernel
kernel_results = expr.dot(data,
expr.transpose(data),
tile_hint=[N / self.ctx.num_workers, N])
labels = labels.evaluate()
self.E = expr.zeros((N, 1),
dtype=np.float64,
tile_hint=[N / self.ctx.num_workers,
1]).evaluate()
for i in xrange(N):
self.E[i, 0] = self.b + expr.reduce(
self.alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels, data=kernel_results[:, i].evaluate()
)).glom() - labels[i, 0]
util.log_info("Starting SMO")
it = 0
num_changed = 0
examine_all = True
while (num_changed > 0 or examine_all) and (it < self.maxiter):
util.log_info("Iteration:%d", it)
num_changed = 0
if examine_all:
for i in xrange(N):
num_changed += self.examine_example(
i, N, labels, kernel_results)
else:
for i in xrange(N):
if self.alpha[i, 0] > 0 and self.alpha[i, 0] < self.C:
num_changed += self.examine_example(
i, N, labels, kernel_results)
it += 1
if examine_all: examine_all = False
elif num_changed == 0: examine_all = True
self.w = expr.zeros((D, 1), dtype=np.float64).evaluate()
for i in xrange(D):
self.w[i, 0] = expr.reduce(self.alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels,
data=expr.evaluate(
data[:, i]))).glom()
self.usew_ = True
print 'iteration finish:', it
print 'b:', self.b
print 'w:', self.w.glom()
