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 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
