def test_race_challenger_2(self):
"""
Makes sure that a racing configuration with better performance,
that is capped, doesn't substitute the incumbent.
"""
def target(x):
time.sleep(1.5)
return (x["a"] + 1) / 1000.0
taf = ExecuteTAFuncDict(use_pynisher=False,
ta=target,
stats=self.stats,
run_obj="runtime")
taf.runhistory = self.rh
intensifier = Intensifier(
stats=self.stats,
traj_logger=TrajLogger(output_dir=None, stats=self.stats),
rng=np.random.RandomState(12345),
instances=[1],
)
self.rh.add(
config=self.config1,
cost=0.001,
time=0.001,
status=StatusType.SUCCESS,
instance_id=1,
seed=12345,
additional_info=None,
)
intensifier.N = 1
# config2 should have a timeout (due to adaptive capping)
# and config1 should still be the incumbent
inc, instance, seed, cutoff = intensifier._get_next_racer(
challenger=self.config2,
incumbent=self.config1,
run_history=self.rh)
run_info = RunInfo(
config=self.config2,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=True,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(inc, self.config1)
self.assertEqual(intensifier.num_run, 1)
self.assertEqual(intensifier.num_chall_run, 1)
def test_race_challenger_1(self):
"""
Makes sure that a racing configuration with better performance,
is selected as incumbent
No adaptive capping
"""
def target(x):
return (x['a'] + 1) / 1000.
taf = ExecuteTAFuncDict(ta=target, stats=self.stats)
taf.runhistory = self.rh
intensifier = Intensifier(stats=self.stats,
traj_logger=TrajLogger(output_dir=None,
stats=self.stats),
rng=np.random.RandomState(12345),
instances=[1],
run_obj_time=False)
self.rh.add(config=self.config1,
cost=1,
time=1,
status=StatusType.SUCCESS,
instance_id=1,
seed=None,
additional_info=None)
intensifier.N = 1
inc, instance, seed, cutoff = intensifier._get_next_racer(
challenger=self.config2,
incumbent=self.config1,
run_history=self.rh)
run_info = RunInfo(
config=self.config2,
instance=instance,
instance_specific="0",
cutoff=cutoff,
seed=seed,
capped=False,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(inc, self.config2)
self.assertEqual(intensifier.num_run, 1)
self.assertEqual(intensifier.num_chall_run, 1)
def test_eval_challenger_3(self):
"""
test eval_challenger for a resumed SMAC run (first run with incumbent)
"""
def target(x):
return x['a']
taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj="quality")
taf.runhistory = self.rh
intensifier = Intensifier(stats=self.stats,
traj_logger=TrajLogger(output_dir=None,
stats=self.stats),
rng=np.random.RandomState(12345),
instances=[1],
run_obj_time=False,
deterministic=False,
always_race_against=None,
run_limit=1)
self.assertEqual(intensifier.n_iters, 0)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_FIRST_CONFIG)
# adding run for incumbent configuration
self.rh.add(config=self.config1,
cost=1,
time=1,
status=StatusType.SUCCESS,
instance_id=1,
seed=None,
additional_info=None)
# intensification - incumbent will be run, but not as RUN_FIRST_CONFIG stage
intent_, run_info = intensifier.get_next_run(
challengers=[self.config2],
incumbent=self.config1,
run_history=self.rh,
chooser=None)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
self.assertEqual(
len(
self.rh.get_runs_for_config(self.config1,
only_max_observed_budget=True)), 2)
def test_eval_challenger_2(self):
"""
test eval_challenger() - a complete intensification run without a `always_race_against` configuration
"""
def target(x):
return 2 * x["a"] + x["b"]
taf = ExecuteTAFuncDict(use_pynisher=False,
ta=target,
stats=self.stats,
run_obj="quality")
taf.runhistory = self.rh
intensifier = Intensifier(
stats=self.stats,
traj_logger=TrajLogger(output_dir=None, stats=self.stats),
rng=np.random.RandomState(12345),
instances=[1],
run_obj_time=False,
deterministic=True,
always_race_against=None,
run_limit=1,
)
self.assertEqual(intensifier.n_iters, 0)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_FIRST_CONFIG)
# intensification iteration #1
# run first config as incumbent if incumbent is None
intent, run_info = intensifier.get_next_run(
challengers=[self.config3],
incumbent=None,
run_history=self.rh,
chooser=None,
)
self.assertEqual(run_info.config, self.config3)
self.assertEqual(intensifier.stage,
IntensifierStage.PROCESS_FIRST_CONFIG_RUN)
# eval config 2 (=first run)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=None,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(inc, self.config3)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_INCUMBENT)
self.assertEqual(self.stats.inc_changed, 1)
self.assertEqual(intensifier.n_iters,
1) # 1 intensification run complete!
# regular intensification begins - run incumbent
# Normally a challenger will be given, which in this case is the incumbent
# But no more instances are available. So to prevent cicles
# where No iteration happens, provide the challengers
intent, run_info = intensifier.get_next_run(
challengers=[
self.config2,
self.config1,
], # since incumbent is run, no configs required
incumbent=inc,
run_history=self.rh,
chooser=None,
)
# no new TA runs as there are no more instances to run
self.assertEqual(inc, self.config3)
self.assertEqual(self.stats.inc_changed, 1)
self.assertEqual(
len(
self.rh.get_runs_for_config(self.config3,
only_max_observed_budget=True)),
1,
)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
# run challenger now that the incumbent has been executed
# So this call happen above, to save one iteration
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
self.assertEqual(run_info.config, self.config2)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# challenger has a better performance, so incumbent has changed
self.assertEqual(inc, self.config2)
self.assertEqual(self.stats.inc_changed, 2)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_INCUMBENT
) # since there is no `always_race_against`
self.assertFalse(intensifier.continue_challenger)
self.assertEqual(
intensifier.n_iters,
1) # iteration continues as `min_chall` condition is not met
# intensification continues running incumbent again in same iteration...
# run incumbent
# Same here, No further instance-seed pairs for incumbent available
# so above call gets the new config to run
self.assertEqual(run_info.config, self.config2)
# There is a transition from:
# IntensifierStage.RUN_FIRST_CONFIG-> IntensifierStage.RUN_INCUMBENT
# Because after the first run, incumbent is run.
# Nevertheless, there is now a transition:
# IntensifierStage.RUN_INCUMBENT->IntensifierStage.RUN_CHALLENGER
# because in add_inc_run, there are more available instance pairs
# FROM: IntensifierStage.RUN_INCUMBENT TO: IntensifierStage.RUN_INCUMBENT WHY: no more to run
# if all <instance, seed> have been run, compare challenger performance
# self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_INCUMBENT)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# run challenger
intent, run_info = intensifier.get_next_run(
challengers=None, # don't need a new list here as old one is cont'd
incumbent=inc,
run_history=self.rh,
chooser=None,
)
self.assertEqual(run_info.config, self.config1)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(inc, self.config1)
self.assertEqual(self.stats.inc_changed, 3)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_INCUMBENT)
self.assertEqual(intensifier.n_iters,
2) # 2 intensification run complete!
# no configs should be left at the end
with self.assertRaises(StopIteration):
next(intensifier.configs_to_run)
self.assertEqual(
len(
self.rh.get_runs_for_config(self.config1,
only_max_observed_budget=True)),
1,
)
self.assertEqual(
len(
self.rh.get_runs_for_config(self.config2,
only_max_observed_budget=True)),
1,
)
self.assertEqual(
len(
self.rh.get_runs_for_config(self.config3,
only_max_observed_budget=True)),
1,
)
def test_eval_challenger_1(self):
"""
test eval_challenger() - a complete intensification run with a `always_race_against` configuration
"""
print(self.rh)
def target(x):
if x["a"] == 100:
time.sleep(1)
return x["a"]
taf = ExecuteTAFuncDict(use_pynisher=False,
ta=target,
stats=self.stats,
run_obj="runtime")
taf.runhistory = self.rh
intensifier = Intensifier(
stats=self.stats,
traj_logger=TrajLogger(output_dir=None, stats=self.stats),
rng=np.random.RandomState(12345),
instances=[1, 2],
run_obj_time=True,
cutoff=2,
deterministic=False,
always_race_against=self.config3,
run_limit=1,
)
self.assertEqual(intensifier.n_iters, 0)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_FIRST_CONFIG)
# intensification iteration #1
# run first config as incumbent if incumbent is None
intent, run_info = intensifier.get_next_run(
incumbent=None,
run_history=self.rh,
challengers=[self.config2],
chooser=None,
)
self.assertEqual(run_info.config, self.config2)
self.assertEqual(intensifier.stage,
IntensifierStage.PROCESS_FIRST_CONFIG_RUN)
# eval config 2 (=first run)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=None,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(inc, self.config2)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_INCUMBENT)
self.assertEqual(self.stats.inc_changed, 1)
self.assertEqual(intensifier.n_iters,
1) # 1 intensification run complete!
# intensification iteration #2
# regular intensification begins - run incumbent first
intent, run_info = intensifier.get_next_run(
challengers=None, # don't need a new list here as old one is cont'd
incumbent=inc,
run_history=self.rh,
chooser=None,
)
self.assertEqual(run_info.config, inc)
self.assertEqual(intensifier.stage,
IntensifierStage.PROCESS_INCUMBENT_RUN)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
self.assertEqual(self.stats.inc_changed, 1)
# run challenger now that the incumbent has been executed
intent, run_info = intensifier.get_next_run(challengers=[self.config1],
incumbent=inc,
run_history=self.rh,
chooser=None)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
self.assertEqual(run_info.config, self.config1)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# challenger has a better performance, but not run on all instances yet. so incumbent stays the same
self.assertEqual(inc, self.config2)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
self.assertTrue(intensifier.continue_challenger)
# run challenger again on the other instance
intent, run_info = intensifier.get_next_run(
challengers=None, # don't need a new list here as old one is cont'd
incumbent=inc,
run_history=self.rh,
chooser=None,
)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
self.assertEqual(run_info.config, self.config1)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# challenger better than incumbent in both instances. so incumbent changed
self.assertEqual(inc, self.config1)
self.assertEqual(self.stats.inc_changed, 2)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_BASIS)
self.assertFalse(intensifier.continue_challenger)
# run basis configuration (`always_race_against`)
intent, run_info = intensifier.get_next_run(
challengers=None, # don't need a new list here as old one is cont'd
incumbent=inc,
run_history=self.rh,
chooser=None,
)
self.assertEqual(run_info.config, self.config3)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_BASIS)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# the basis configuration (config3) not better than incumbent, so can move on
self.assertEqual(inc, self.config1)
self.assertEqual(self.stats.inc_changed, 2)
self.assertEqual(intensifier.stage,
IntensifierStage.RUN_INCUMBENT,
msg=self.rh.data.items())
self.assertEqual(list(self.rh.data.values())[4][2], StatusType.CAPPED)
self.assertEqual(
intensifier.n_iters,
1) # iteration continues as `min_chall` condition is not met
self.assertIsInstance(intensifier.configs_to_run,
collections.abc.Iterator)
# no configs should be left at the end
with self.assertRaises(StopIteration):
next(intensifier.configs_to_run)
# intensification continues running incumbent again in same iteration...
intent, run_info = intensifier.get_next_run(
challengers=None, # don't need a new list here as old one is cont'd
incumbent=inc,
run_history=self.rh,
chooser=None,
)
self.assertEqual(intensifier.stage,
IntensifierStage.PROCESS_INCUMBENT_RUN)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(inc, self.config1)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
self.assertEqual(
len(
self.rh.get_runs_for_config(self.config1,
only_max_observed_budget=True)),
3,
)
self.assertEqual(
len(
self.rh.get_runs_for_config(self.config2,
only_max_observed_budget=True)),
2,
)
self.assertEqual(
len(
self.rh.get_runs_for_config(self.config3,
only_max_observed_budget=True)),
0,
) # capped
def test_add_inc_run_nondet(self):
"""
test _add_inc_run()
"""
def target(x):
return (x["a"] + 1) / 1000.0
taf = ExecuteTAFuncDict(use_pynisher=False,
ta=target,
stats=self.stats,
run_obj="solution_quality")
intensifier = Intensifier(
stats=self.stats,
traj_logger=TrajLogger(output_dir=None, stats=self.stats),
rng=np.random.RandomState(12345),
instances=[1, 2],
deterministic=False,
)
instance, seed, cutoff = intensifier._get_next_inc_run(
available_insts=intensifier._get_inc_available_inst(
incumbent=self.config1, run_history=self.rh))
run_info = RunInfo(
config=self.config1,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=False,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(len(self.rh.data), 1, self.rh.data)
instance, seed, cutoff = intensifier._get_next_inc_run(
available_insts=intensifier._get_inc_available_inst(
incumbent=self.config1, run_history=self.rh))
run_info = RunInfo(
config=self.config1,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=False,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(len(self.rh.data), 2, self.rh.data)
runs = self.rh.get_runs_for_config(config=self.config1,
only_max_observed_budget=True)
# exactly one run on each instance
self.assertIn(1, [runs[0].instance, runs[1].instance])
self.assertIn(2, [runs[0].instance, runs[1].instance])
instance, seed, cutoff = intensifier._get_next_inc_run(
available_insts=intensifier._get_inc_available_inst(
incumbent=self.config1, run_history=self.rh))
run_info = RunInfo(
config=self.config1,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=False,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(len(self.rh.data), 3, self.rh.data)
# The number of runs performed should be 3
# No Next iteration call as an incumbent is provided
self.assertEqual(intensifier.num_run, 2)
self.assertEqual(intensifier.num_chall_run, 0)
def test_add_inc_run_det(self):
"""
test _add_inc_run()
"""
def target(x):
return (x["a"] + 1) / 1000.0
taf = ExecuteTAFuncDict(use_pynisher=False,
ta=target,
stats=self.stats,
run_obj="solution_quality")
taf.runhistory = self.rh
intensifier = Intensifier(
stats=self.stats,
traj_logger=TrajLogger(output_dir=None, stats=self.stats),
rng=np.random.RandomState(12345),
instances=[1],
deterministic=True,
)
instance, seed, cutoff = intensifier._get_next_inc_run(
available_insts=intensifier._get_inc_available_inst(
incumbent=self.config1, run_history=self.rh))
run_info = RunInfo(
config=self.config1,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=False,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
intensifier.stage = IntensifierStage.PROCESS_FIRST_CONFIG_RUN
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(len(self.rh.data), 1, self.rh.data)
# since we assume deterministic=1,
# the second call should not add any more runs
# given only one instance
# So the returned seed/instance is None so that a new
# run to be triggered is not launched
available_insts = intensifier._get_inc_available_inst(
incumbent=self.config1, run_history=self.rh)
# Make sure that the list is empty, and hence no new call
# of incumbent will be triggered
self.assertFalse(available_insts)
# The following two tests evaluate to zero because _next_iteration is triggered by _add_inc_run
# as it is the first evaluation of this intensifier
# After the above incumbent run, the stage is
# IntensifierStage.RUN_CHALLENGER. Change it to test next iteration
intensifier.stage = IntensifierStage.PROCESS_FIRST_CONFIG_RUN
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=None,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(intensifier.num_run, 0)
self.assertEqual(intensifier.num_chall_run, 0)
def test_race_challenger_large_blocked_seed(self):
"""
test _race_challenger whether seeds are blocked for challenger runs
"""
def target(x):
return 1
taf = ExecuteTAFuncDict(use_pynisher=False,
ta=target,
stats=self.stats)
taf.runhistory = self.rh
intensifier = Intensifier(
stats=self.stats,
traj_logger=TrajLogger(output_dir=None, stats=self.stats),
rng=np.random.RandomState(12345),
instances=list(range(10)),
run_obj_time=False,
deterministic=False,
)
for i in range(10):
self.rh.add(
config=self.config1,
cost=i + 1,
time=1,
status=StatusType.SUCCESS,
instance_id=i,
seed=i,
additional_info=None,
)
intensifier.stage = IntensifierStage.RUN_CHALLENGER
# tie on first instances and then challenger should always win
# and be returned as inc
while True:
if intensifier.continue_challenger:
config = intensifier.current_challenger
else:
config, _ = intensifier.get_next_challenger(
challengers=[self.config2, self.config3], chooser=None)
inc, instance, seed, cutoff = intensifier._get_next_racer(
challenger=config, incumbent=self.config1, run_history=self.rh)
run_info = RunInfo(
config=config,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=False,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# stop when challenger evaluation is over
if not intensifier.stage == IntensifierStage.RUN_CHALLENGER:
break
self.assertEqual(inc, self.config2)
self.assertEqual(self.rh.get_cost(self.config2), 1)
# get data for config2 to check that the correct run was performed
runs = self.rh.get_runs_for_config(self.config2,
only_max_observed_budget=True)
self.assertEqual(len(runs), 10)
seeds = sorted([r.seed for r in runs])
self.assertEqual(seeds, list(range(10)), seeds)
self.assertEqual(intensifier.num_run, 10)
self.assertEqual(intensifier.num_chall_run, 10)
def test_race_challenger_3(self):
"""
test _race_challenger with adaptive capping on a previously capped configuration
"""
def target(config: Configuration, seed: int, instance: str):
if instance == 1:
time.sleep(2.1)
else:
time.sleep(0.6)
return (config["a"] + 1) / 1000.0
taf = ExecuteTAFuncDict(
use_pynisher=False,
ta=target,
stats=self.stats,
run_obj="runtime",
par_factor=1,
)
taf.runhistory = self.rh
intensifier = Intensifier(
stats=self.stats,
traj_logger=TrajLogger(output_dir=None, stats=self.stats),
rng=np.random.RandomState(12345),
cutoff=2,
instances=[1],
)
self.rh.add(
config=self.config1,
cost=0.5,
time=0.5,
status=StatusType.SUCCESS,
instance_id=1,
seed=12345,
additional_info=None,
)
# config2 should have a timeout (due to adaptive capping)
# and config1 should still be the incumbent
config, _ = intensifier.get_next_challenger(
challengers=[self.config2, self.config3], chooser=None)
inc, instance, seed, cutoff = intensifier._get_next_racer(
challenger=config, incumbent=self.config1, run_history=self.rh)
run_info = RunInfo(
config=config,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=True,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(inc, self.config1)
# further run for incumbent
self.rh.add(
config=self.config1,
cost=2,
time=2,
status=StatusType.TIMEOUT,
instance_id=2,
seed=12345,
additional_info=None,
)
# give config2 a second chance - now it should run on both instances
# run on instance 1
config, _ = intensifier.get_next_challenger(
challengers=[self.config2, self.config3], chooser=None)
inc, instance, seed, cutoff = intensifier._get_next_racer(
challenger=config, incumbent=self.config1, run_history=self.rh)
run_info = RunInfo(
config=config,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=False,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# run on instance 2
config, _ = intensifier.get_next_challenger(challengers=[self.config3],
chooser=None)
self.assertEqual(config, self.config2)
self.assertTrue(intensifier.continue_challenger)
inc, instance, seed, cutoff = intensifier._get_next_racer(
challenger=config, incumbent=self.config1, run_history=self.rh)
run_info = RunInfo(
config=config,
instance=instance,
instance_specific="0",
seed=seed,
cutoff=cutoff,
capped=False,
budget=0.0,
)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=self.config1,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# the incumbent should still be config1 because
# config2 should get on inst 1 a full timeout
# such that c(config1) = 1.25 and c(config2) close to 1.3
self.assertEqual(inc, self.config1)
# the capped run should not be counted in runs_perf_config
self.assertAlmostEqual(self.rh.num_runs_per_config[2], 2)
self.assertFalse(intensifier.continue_challenger)
self.assertEqual(intensifier.num_run, 3)
self.assertEqual(intensifier.num_chall_run, 3)
def test_no_new_intensification_wo_challenger_run(self):
"""
This test ensures that no new iteration is started if no challenger run was conducted
"""
def target(x):
return 2 * x["a"] + x["b"]
taf = ExecuteTAFuncDict(use_pynisher=False,
ta=target,
stats=self.stats,
run_obj="quality")
taf.runhistory = self.rh
intensifier = Intensifier(
stats=self.stats,
traj_logger=TrajLogger(output_dir=None, stats=self.stats),
rng=np.random.RandomState(12345),
instances=[1],
run_obj_time=False,
deterministic=True,
always_race_against=None,
run_limit=1,
min_chall=1,
)
self.assertEqual(intensifier.n_iters, 0)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_FIRST_CONFIG)
intent, run_info = intensifier.get_next_run(
challengers=[self.config3],
incumbent=None,
run_history=self.rh,
chooser=None,
)
self.assertEqual(run_info.config, self.config3)
self.assertEqual(intensifier.stage,
IntensifierStage.PROCESS_FIRST_CONFIG_RUN)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=None,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
self.assertEqual(inc, self.config3)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_INCUMBENT)
self.assertEqual(intensifier.n_iters,
1) # 1 intensification run complete!
# regular intensification begins - run incumbent
# No further instance-seed pairs for incumbent available
# Here None challenger is suggested. Code jumps to next iteration
# This causes a transition from RUN_INCUMBENT->RUN_CHALLENGER
# But then, the next configuration to run is the incumbent
# We don't rerun the incumbent (see message):
# Challenger was the same as the current incumbent; Skipping challenger
# Then, we try to get more challengers, but below all challengers
# Provided are config3, the incumbent which means nothing more to run
intent, run_info = intensifier.get_next_run(
challengers=[self.config3
], # since incumbent is run, no configs required
incumbent=inc,
run_history=self.rh,
chooser=None,
)
self.assertEqual(run_info.config, None)
self.assertEqual(intensifier.stage, IntensifierStage.RUN_CHALLENGER)
intensifier._next_iteration()
# Add a configuration, then try to execute it afterwards
self.assertEqual(intensifier.n_iters, 2)
self.rh.add(
config=self.config1,
cost=1,
time=1,
status=StatusType.SUCCESS,
instance_id=1,
seed=0,
additional_info=None,
)
intensifier.stage = IntensifierStage.RUN_CHALLENGER
# In the upcoming get next run, the stage is RUN_CHALLENGER
# so the intensifier tries to run config1. Nevertheless,
# there are no further instances for this configuration available.
# In this scenario, the intensifier produces a SKIP intent as an indication
# that a new iteration must be initiated, and for code simplicity,
# relies on a new call to get_next_run to yield more configurations
intent, run_info = intensifier.get_next_run(challengers=[self.config1],
incumbent=inc,
run_history=self.rh,
chooser=None)
self.assertEqual(intent, RunInfoIntent.SKIP)
# This doesn't return a config because the array of configs is exhausted
intensifier.stage = IntensifierStage.RUN_CHALLENGER
config, _ = intensifier.get_next_challenger(challengers=None,
chooser=None)
self.assertIsNone(config)
# This finally gives a runable configuration
intent, run_info = intensifier.get_next_run(challengers=[self.config2],
incumbent=inc,
run_history=self.rh,
chooser=None)
result = eval_challenger(run_info, taf, self.stats, self.rh)
inc, perf = intensifier.process_results(
run_info=run_info,
incumbent=inc,
run_history=self.rh,
time_bound=np.inf,
result=result,
)
# 4 Iterations due to the proactive runs
# of get next challenger
self.assertEqual(intensifier.n_iters, 3)
self.assertEqual(intensifier.num_chall_run, 1)
