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experiment.py
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/
experiment.py
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import argparse
import multiprocessing as mp
import os
import pickle
import numpy as np
from logistic import LogisticSGD
from logistic_parallel import LogisticParallelSGD
from parameters import Parameters
from utils import pickle_it
X, y = None, None
def run_logistic(param):
m = LogisticSGD(param)
res = m.fit(X, y)
print('{} - score: {}'.format(param, m.score(X, y)))
return res
def run_experiment(directory, dataset_pickle, params, nproc=None):
global X, y
if not os.path.exists(directory):
os.makedirs(directory)
pickle_it(params, 'params', directory)
print('load dataset')
with open(dataset_pickle, 'rb') as f:
X, y = pickle.load(f)
print('start experiment')
with mp.Pool(nproc) as pool:
results = pool.map(run_logistic, params)
pickle_it(results, 'results', directory)
print('results saved in "{}"'.format(directory))
def run_parallel_experiment(directory, dataset_pickle, models, cores, baseline, repeat=3):
if not os.path.exists(directory):
os.makedirs(directory)
pickle_it([m(1) for m in models], 'models', directory)
pickle_it(cores, 'cores', directory)
print('load dataset')
with open(dataset_pickle, 'rb') as f:
X, y = pickle.load(f)
print('start experiment')
chronos = np.zeros((len(models), len(cores), repeat))
stop_times = np.zeros((len(models), len(cores), repeat), dtype=int)
for r in range(repeat):
for c_idx, core in enumerate(cores):
for m_idx, model in enumerate(models):
p = model(core)
print("{} - cores {} - repeat {}".format(p, core, r))
m = LogisticParallelSGD(p)
timing, epoch, iteration, losses = m.fit_until(X, y, num_features=X.shape[1], num_samples=X.shape[0],
baseline=baseline)
chronos[m_idx, c_idx, r] = timing
stop_times[m_idx, c_idx, r] = epoch * X.shape[0] + iteration
pickle_it(chronos, 'chronos', directory)
pickle_it(stop_times, 'stop_times', directory)
pickle_it(chronos, 'chronos', directory)
pickle_it(stop_times, 'stop_times', directory)
print('results saved in "{}"'.format(directory))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('experiment', type=str)
parser.add_argument('directory', type=str)
parser.add_argument('--nproc', type=int, default=1)
args = parser.parse_args()
assert args.experiment in ['epsilon-th', 'epsilon-quant', 'epsilon-parallel',
'rcv1-th', 'rcv1-quant', 'rcv1-parallel']
# dataset
if args.experiment.startswith('epsilon'):
dataset = os.path.expanduser('/mlodata1/jb/data/epsilon_normalized_1.pickle')
n, d = 400000, 2000
elif args.experiment.startswith('rcv1'):
dataset = os.path.expanduser('/mlodata1/jb/data/rcv1-test-1.pickle')
n, d = 677399, 47236
# parameters to evaluate
if args.experiment == 'epsilon-th':
params = [
Parameters(name="full-sgd", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2'),
Parameters(name="full-sgd-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=1,
regularizer=1 / n, estimate='(t+tau)^2'),
Parameters(name="top1", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=1, with_memory=True, take_top=True),
Parameters(name="top1-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=1,
regularizer=1 / n, estimate='(t+tau)^2', take_k=1, with_memory=True, take_top=True),
Parameters(name="rand1", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=1, with_memory=True),
Parameters(name="rand1-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=1,
regularizer=1 / n, estimate='(t+tau)^2', take_k=1, with_memory=True),
Parameters(name="rand2", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=2, with_memory=True),
Parameters(name="rand2-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=1,
regularizer=1 / n, estimate='(t+tau)^2', take_k=2, with_memory=True),
Parameters(name="rand3", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=3, with_memory=True),
Parameters(name="rand3-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=1,
regularizer=1 / n, estimate='(t+tau)^2', take_k=3, with_memory=True),
]
elif args.experiment == 'epsilon-quant':
params = [
Parameters(name="full-sgd", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2'),
Parameters(name="qsgd-8bits", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', qsgd_s=2 ** 8),
Parameters(name="qsgd-4bits", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', qsgd_s=2 ** 4),
Parameters(name="qsgd-2bits", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', qsgd_s=2 ** 2),
Parameters(name="qsgd-sqrt-d-bits", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', qsgd_s=44),
Parameters(name="top1", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=1, with_memory=True, take_top=True),
Parameters(name="rand1", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=1, with_memory=True),
]
elif args.experiment == 'epsilon-parallel':
models = [
lambda n_cores: Parameters(name="rand1", num_epoch=5, lr_type='constant', initial_lr=.05, n_cores=n_cores,
regularizer=1 / n, take_k=1, with_memory=True, estimate='final'),
lambda n_cores: Parameters(name="top1", num_epoch=5, lr_type='constant', initial_lr=.05, n_cores=n_cores,
regularizer=1 / n, take_k=1, take_top=True, with_memory=True, estimate='final'),
lambda n_cores: Parameters(name="hogwild", num_epoch=5, lr_type='constant', initial_lr=.05, n_cores=n_cores,
regularizer=1 / n, estimate='final'),
]
cores = [1, 2, 3, 5, 8, 10, 12, 14, 16, 18, 20, 22, 24]
baseline = 0.305
elif args.experiment == 'rcv1-th':
params = [
Parameters(name="full-sgd", num_epoch=3, lr_type='decay', initial_lr=2, tau=10,
regularizer=1 / n, estimate='(t+tau)^2'),
Parameters(name="top10", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d / 10,
regularizer=1 / n, estimate='(t+tau)^2', take_k=10, with_memory=True, take_top=True),
Parameters(name="top10-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=10,
regularizer=1 / n, estimate='(t+tau)^2', take_k=10, with_memory=True, take_top=True),
Parameters(name="rand10", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d / 10,
regularizer=1 / n, estimate='(t+tau)^2', take_k=10, with_memory=True),
Parameters(name="rand10-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=10,
regularizer=1 / n, estimate='(t+tau)^2', take_k=10, with_memory=True),
Parameters(name="rand20", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=20, with_memory=True),
Parameters(name="rand20-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=10,
regularizer=1 / n, estimate='(t+tau)^2', take_k=20, with_memory=True),
Parameters(name="rand30", num_epoch=3, lr_type='decay', initial_lr=2, tau=d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=30, with_memory=True),
Parameters(name="rand30-no-shift", num_epoch=3, lr_type='decay', initial_lr=2, tau=10,
regularizer=1 / n, estimate='(t+tau)^2', take_k=30, with_memory=True),
]
elif args.experiment == 'rcv1-quant':
params = [
Parameters(name="full-sgd", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2'),
Parameters(name="qsgd-8bits", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2', qsgd_s=2 ** 8),
Parameters(name="qsgd-4bits", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2', qsgd_s=2 ** 4),
Parameters(name="qsgd-2bits", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2', qsgd_s=2 ** 2),
Parameters(name="qsgd-sqrt-d-bits", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2', qsgd_s=217),
Parameters(name="top1", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=1, with_memory=True, take_top=True),
Parameters(name="rand1", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=1, with_memory=True),
Parameters(name="top10", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=10, with_memory=True, take_top=True),
Parameters(name="rand10", num_epoch=3, lr_type='decay', initial_lr=2, tau=10 * d,
regularizer=1 / n, estimate='(t+tau)^2', take_k=10, with_memory=True),
]
elif args.experiment == 'rcv1-parallel':
models = [
lambda n_cores: Parameters(name="top100", num_epoch=6, lr_type='decay', initial_lr=2., n_cores=n_cores,
tau=10 / 100 * d,
regularizer=1 / n, estimate='final', take_k=100, take_top=True,
with_memory=True),
lambda n_cores: Parameters(name="rand100", num_epoch=6, lr_type='decay', initial_lr=2., n_cores=n_cores,
tau=10 / 100 * d,
regularizer=1 / n, estimate='final', take_k=100, take_top=False,
with_memory=True),
lambda n_cores: Parameters(name="hogwild", num_epoch=6, lr_type='decay', initial_lr=2., n_cores=n_cores,
tau=10, regularizer=1 / n,
estimate='final'),
]
cores = [1, 2, 3, 5, 8, 10, 12, 14, 16, 18, 20, 22, 24]
baseline = 0.101
if 'parallel' in args.experiment:
run_parallel_experiment(args.directory, dataset, models, cores, baseline, repeat=3)
else:
run_experiment(args.directory, dataset, params, nproc=args.nproc)