def run(n, k, m, trial, initial_sigmas):
assert isinstance(initial_sigmas, np.ndarray)
gaps = utils.sigmas_to_gaps(initial_sigmas, np.max(initial_sigmas))
logger.info('gaps={}'.format(gaps))
# af_makespan, clp_makespan = 0, 1
init_gaps = gaps
fractional_makespan, clp_res = clp_general.find_strategy(initial_sigmas, k, mode='per_cand')
clp_makespan = utils.makespan(initial_sigmas, clp_res
)
gaps = utils.sigmas_to_gaps(initial_sigmas, clp_makespan)
af_res = average_fit.solve_one_gaps(k, gaps, verbose=False)
af_makespan = utils.makespan(initial_sigmas, af_res)
logger.info(
'n={} k={} m={} trial={} frac={} CLP={} AF={}'.format(n, k, m, trial, fractional_makespan, clp_makespan,
af_makespan))
result_to_append = [n, k, m, trial, initial_sigmas, fractional_makespan, clp_makespan, af_makespan]
df = pd.DataFrame(data=[result_to_append],
columns=['n', 'k', 'm', 'trial', 'initial_sigmas', 'fractional_makespan', 'clp_makespan',
'af_makespan'])
filename = 'results-m{}-k{}-n{}-t{}-tt{}.csv'.format(m, k, n, trials, trial)
if folder is None: # s3
import boto3
csv_buffer = BytesIO()
df.to_csv(csv_buffer, encoding='utf-8')
s3_resource = boto3.resource('s3')
s3_resource.Object('borda-rev', filename).put(Body=csv_buffer.getvalue())
else:
abs_path = os.path.join(folder, filename)
df.to_csv(abs_path, index=False)
return result_to_append
def test_full(self):
initial_sigmas = np.array([5, 6, 6, 6, 7], dtype=np.int32)
k = 2
m = len(initial_sigmas)
assert isinstance(initial_sigmas, np.ndarray)
gaps = utils.sigmas_to_gaps(initial_sigmas, np.max(initial_sigmas))
logger.info('gaps={}'.format(gaps))
init_gaps = gaps
t1 = current_milli_time()
fractional_makespan, clp_res = clp_general.find_strategy(
initial_sigmas, k, mode='per_cand')
t2 = current_milli_time()
logger.warning('Took time {}ms, fractional_makespan={}'.format(
t2 - t1, fractional_makespan))
# fractional_makespan = utils.makespan(initial_sigmas, frac_res)
clp_makespan = utils.makespan(initial_sigmas, clp_res)
gaps = utils.sigmas_to_gaps(initial_sigmas, clp_makespan)
af_res = average_fit.solve_one_gaps(k, gaps, verbose=False)
af_makespan = utils.makespan(initial_sigmas, af_res)
logger.info('k={} m={}frac={} CLP={} AF={}'.format(
k, m, fractional_makespan, clp_makespan, af_makespan))
def test_full2(self):
initial_sigmas = np.array([10, 12, 12, 12, 14], dtype=np.int32)
alpha = utils.borda(5) * 2 # kind-of-Borda
weights = np.array([2, 2])
m = len(initial_sigmas)
assert isinstance(initial_sigmas, np.ndarray)
gaps = utils.sigmas_to_gaps(initial_sigmas, np.max(initial_sigmas))
logger.info('gaps={}'.format(gaps))
init_gaps = gaps
t1 = current_milli_time()
fractional_makespan, clp_res = clp_R_alpha_WCM.find_strategy(
initial_sigmas, alpha, weights, mode='per_cand')
t2 = current_milli_time()
logger.warning('Took time {}ms, fractional_makespan={}'.format(
t2 - t1, fractional_makespan))
# fractional_makespan = utils.weighted_makespan(frac_res, alpha, weights, initial_sigmas)
clp_makespan = utils.weighted_makespan(clp_res, alpha, weights,
initial_sigmas)
logger.info('weights={} m={}frac={} CLP={}'.format(
weights, m, fractional_makespan, clp_makespan))
self.assertLessEqual(fractional_makespan, clp_makespan)
def run(n, alpha, weights, m, trial, initial_sigmas):
assert isinstance(initial_sigmas, np.ndarray)
try:
gaps = utils.sigmas_to_gaps(initial_sigmas, np.max(initial_sigmas))
logger.info('gaps={}'.format(gaps))
# reverse_makespan, clp_makespan = 0, 1
init_gaps = gaps
fractional_makespan, clp_res = clp_R_alpha_WCM.find_strategy(
initial_sigmas, alpha, weights, mode='per_cand')
clp_makespan = utils.weighted_makespan(clp_res, alpha, weights,
initial_sigmas)
reverse_result = reverse_algorithm.find_strategy(
initial_sigmas, alpha, weights)
reverse_makespan = utils.weighted_makespan(reverse_result, alpha,
weights, initial_sigmas)
logger.info('n={} k={} m={} trial={} frac={} CLP={} AF={}'.format(
n, len(weights), m, trial, fractional_makespan, clp_makespan,
reverse_makespan))
result_to_append = [
n,
len(weights), m, 50 + trial, initial_sigmas, weights,
fractional_makespan, clp_makespan, reverse_makespan
]
df = pd.DataFrame(data=[result_to_append],
columns=[
'n', 'k', 'm', 'trial', 'initial_sigmas',
'weights', 'fractional_makespan', 'clp_makespan',
'reverse_makespan'
])
filename = 'results-m{}-k{}-n{}-t{}-tt{}.csv'.format(
m, np.sum(weights), n, trials, 50 + trial)
if folder is None: # s3
import boto3
csv_buffer = BytesIO()
df.to_csv(csv_buffer, encoding='utf-8')
s3_resource = boto3.resource('s3')
s3_resource.Object('borda-zipf3',
filename).put(Body=csv_buffer.getvalue())
else:
abs_path = os.path.join(folder, filename)
df.to_csv(abs_path, index=False)
return result_to_append
except Exception:
logger.error(
'Failed on params n={} weights={} m={} trial={} sigmas={}'.format(
n, weights, m, trial, initial_sigmas))
raise
def test_full(self):
initial_sigmas = np.array([5, 6, 6, 6, 7], dtype=np.int32)
k = 2
m = len(initial_sigmas)
alpha = utils.borda(m) # borda
assert isinstance(initial_sigmas, np.ndarray)
gaps = utils.sigmas_to_gaps(initial_sigmas, np.max(initial_sigmas))
logger.info('gaps={}'.format(gaps))
init_gaps = gaps
t1 = current_milli_time()
fractional_makespan, clp_res = clp_R_alpha_UCM.find_strategy(
initial_sigmas, alpha, k, mode='per_cand')
t2 = current_milli_time()
logger.warning('Took time {}ms, fractional_makespan={}'.format(
t2 - t1, fractional_makespan))
# fractional_makespan = utils.makespan(initial_sigmas, frac_res, alpha=alpha)
clp_makespan = utils.makespan(initial_sigmas, clp_res, alpha=alpha)
gaps = utils.sigmas_to_gaps(initial_sigmas, clp_makespan)
af_res = average_fit.solve_one_gaps(k, gaps, verbose=False)
af_makespan = utils.makespan(initial_sigmas, af_res, alpha=alpha)
logger.info('k={} m={} frac={} CLP={} AF={}'.format(
k, m, fractional_makespan, clp_makespan, af_makespan))
# self.assertListEqual(clp_res.tolist(),
# [[0, 0, 1, 1, 0],
# [0, 1, 0, 1, 0],
# [1, 0, 0, 0, 1],
# [1, 0, 0, 0, 1],
# [0, 1, 1, 0, 0]]
# )
self.assertAlmostEqual(fractional_makespan, 10.0, delta=1e-3)
self.assertEquals(clp_makespan, 10)
self.assertEquals(af_makespan, 12)
def test_full2(self):
initial_sigmas = np.array([10, 12, 12, 12, 14], dtype=np.int32)
alpha = np.arange(5) * 2 # kind-of-Borda
exa = utils.makespan(
initial_sigmas,
np.array([[0, 0, 1, 1, 0], [0, 1, 0, 1, 0], [1, 0, 0, 0, 1],
[1, 0, 0, 0, 1], [0, 1, 1, 0, 0]]), alpha)
k = 2
m = len(initial_sigmas)
assert isinstance(initial_sigmas, np.ndarray)
gaps = utils.sigmas_to_gaps(initial_sigmas, np.max(initial_sigmas))
logger.info('gaps={}'.format(gaps))
init_gaps = gaps
t1 = current_milli_time()
fractional_makespan, clp_res = clp_R_alpha_UCM.find_strategy(
initial_sigmas, alpha, k, mode='per_cand')
t2 = current_milli_time()
logger.warning('Took time {}ms, fractional_makespan={}'.format(
t2 - t1, fractional_makespan))
# fractional_makespan = utils.fractional_makespan(initial_sigmas, frac_res, alpha=alpha)
clp_makespan = utils.makespan(initial_sigmas, clp_res, alpha=alpha)
logger.info('k={} m={} frac={} CLP={}'.format(k, m,
fractional_makespan,
clp_makespan))
# self.assertListEqual(clp_res.tolist(),
# [[0, 0, 1, 1, 0],
# [0, 1, 0, 1, 0],
# [1, 0, 0, 0, 1],
# [1, 0, 0, 0, 1],
# [0, 1, 1, 0, 0]]
# )
self.assertAlmostEqual(fractional_makespan, 20.0, delta=1e-3)
self.assertEquals(clp_makespan, 20)