def define_jobs_context(self, context):
options = self.get_options()
config_mdps = get_conftools_tmdp_smdps()
id_mdps = config_mdps.expand_names(options.mdps)
config_solvers = get_conftools_tmdp_smdp_solvers()
id_solvers = config_solvers.expand_names(options.solvers)
for c2, id_mdp in iterate_context_names(context, id_mdps):
c2.add_extra_report_keys(id_mdp=id_mdp)
mdp = c2.comp_config(instance_mdp, id_mdp)
from tmdp.programs.value_iteration import report_mdp_display
r = c2.comp_config(report_mdp_display, mdp)
c2.add_report(r, 'report_mdp_display')
for cc, id_solver in iterate_context_names(c2, id_solvers):
cc.add_extra_report_keys(id_solver=id_solver)
solve_result = jobs_solve(cc, mdp, id_solver)
points = [100, 200, 500]
for c3, num_points in iterate_context_names(cc, points):
c3.add_extra_report_keys(num_points=num_points)
res = c3.comp(resample,
mdp,
solve_result,
num_points=num_points)
c3.add_report(c3.comp(report_resample, mdp, res),
'report_resample')
c3.add_report(c3.comp(report_tension, mdp, res),
'report_tension')
def instance_reports(context):
param1s = ['a', 'b']
param2s = [1, 2]
for c1, param1 in iterate_context_names(context, param1s):
c1.add_extra_report_keys(param1=param1)
for c2, param2 in iterate_context_names(c1, param2s):
c2.add_extra_report_keys(param2=param2)
r = c2.comp(report_example1, param1=param1, param2=param2)
c2.add_report(r, 'report_example1')
r = c2.comp(report_example2, param1=param1, param2=param2)
c2.add_report(r, 'report_example2')
def instance_reports(context):
param1s = ['a', 'b']
param2s = [1, 2]
for c1, param1 in iterate_context_names(context, param1s):
c1.add_extra_report_keys(param1=param1)
for c2, param2 in iterate_context_names(c1, param2s):
c2.add_extra_report_keys(param2=param2)
r = c2.comp(report_example1, param1=param1, param2=param2)
c2.add_report(r, 'report_example1')
r = c2.comp(report_example2, param1=param1, param2=param2)
c2.add_report(r, 'report_example2')
def jobs_registrar(context, cm, create_reports=False):
assert isinstance(cm, ConfigMaster)
# Sep 15: remove name
# context = context.child(cm.name)
context = context.child("")
names = sorted(cm.specs.keys())
names2test_objects = context.comp_config_dynamic(get_testobjects_promises,
cm)
for c, name in iterate_context_names(context, names):
pairs = ComptestsRegistrar.objspec2pairs[name]
functions = ComptestsRegistrar.objspec2tests[name]
some = ComptestsRegistrar.objspec2testsome[name]
some_pairs = ComptestsRegistrar.objspec2testsomepairs[name]
c.comp_config_dynamic(define_tests_for,
cm=cm,
name=name,
names2test_objects=names2test_objects,
pairs=pairs,
functions=functions,
some=some,
some_pairs=some_pairs,
create_reports=create_reports)
jobs_registrar_simple(context)
def jobs_registrar(context, cm, create_reports=False):
assert isinstance(cm, ConfigMaster)
# Sep 15: remove name
# context = context.child(cm.name)
context = context.child("")
names = sorted(cm.specs.keys())
names2test_objects = context.comp_config_dynamic(get_testobjects_promises, cm)
for c, name in iterate_context_names(context, names):
pairs = ComptestsRegistrar.objspec2pairs[name]
functions = ComptestsRegistrar.objspec2tests[name]
some = ComptestsRegistrar.objspec2testsome[name]
some_pairs = ComptestsRegistrar.objspec2testsomepairs[name]
c.comp_config_dynamic(define_tests_for,
cm=cm,
name=name,
names2test_objects=names2test_objects,
pairs=pairs,
functions=functions,
some=some,
some_pairs=some_pairs,
create_reports=create_reports)
jobs_registrar_simple(context)
def define_jobs_context(self, context):
batches_library = get_conftools_batches()
batches = batches_library.expand_names(self.options.batches)
for c, id_batch in iterate_context_names(context, batches):
c.extra_report_keys['batch'] = id_batch
batch = batches_library.instance(id_batch)
create_bench_jobs(c, batch, alltestcases=self.options.alltestcases)
def define_jobs_context(self, context):
batches_library = get_conftools_batches()
batches = batches_library.expand_names(self.options.batches)
for c, id_batch in iterate_context_names(context, batches):
c.extra_report_keys['batch'] = id_batch
batch = batches_library.instance(id_batch)
create_bench_jobs(c, batch, alltestcases=self.options.alltestcases)
def jobs_learnp_and_servo(context, data_central, explogs_learn,
explogs_test, agents, robots):
""" Learn parallel, create servo field """
jobs_learn_parallel(context, data_central, explogs_learn, agents, robots)
for c, id_robot in iterate_context_names(context, robots):
jobs_servo_field_agents(c, data_central=data_central,
id_robot=id_robot,
agents=agents, episodes=explogs_test)
def jobs_servo(context, data_central, combinations, explogs_test):
recipe_agent_servo(context, create_report=True)
for c, id_robot, id_agent in iterate_context_combinations(context, combinations):
for cc, e in iterate_context_names(c, explogs_test):
jobs_servo_field(cc, data_central=data_central,
id_robot=id_robot,
id_agent=id_agent,
id_episode=e.id_episode,
**e.params)
def jobs_parallel_learning(context, data_central, id_agent, id_robot, episodes,
intermediate_reports=True, final_report=True,
episodes_per_tranche=1):
"""
In this way, the agent learns separately on each log, and then
the instances are merged using the merge() function.
Needs:
"episode-ready", id_robot, id_episode
Returns the promise for the agent with learning complete
(tuple agent, state)
"""
agents = []
tranches = get_tranches(episodes, episodes_per_tranche=episodes_per_tranche)
ntranches = len(tranches)
tranches_names = ['tranche%02d' % i for i in range(ntranches)]
for i, (c, _) in enumerate(iterate_context_names(context, tranches_names)):
tranche_episodes = tranches[i]
extra_dep = []
for id_episode in tranche_episodes:
er = c.get_resource(RM_EPISODE_READY, id_robot=id_robot, id_episode=id_episode)
extra_dep.append(er)
agent_i = c.subtask(LearnLogNoSave,
boot_root=data_central.get_boot_root(),
agent=id_agent,
robot=id_robot,
episodes=tranche_episodes,
add_job_prefix='',
extra_dep=extra_dep)
agents.append(agent_i)
if intermediate_reports and ntranches > 1:
progress = '%s' % id_episode
report = c.comp_config(get_agentstate_report, agent_i, progress, job_id='report')
c.add_report(report, 'agent_report_partial',
id_agent=id_agent, id_robot=id_robot,
progress=progress)
agent_state = jobs_merging_recursive(context, agents)
save = context.comp_config(save_state, data_central,
id_agent, id_robot, agent_state)
if final_report:
report = context.comp(get_agentstate_report, agent_state, 'all', job_id='report')
context.add_report(report, 'agent_report', id_agent=id_agent, id_robot=id_robot)
return save
def define_tests_some(context, objspec, names2test_objects, some,
create_reports):
test_objects = names2test_objects[objspec.name]
if not test_objects:
msg = 'No test_objects for objects of kind %r.' % objspec.name
print(msg)
return
if not some:
msg = 'No mcdp_lang_tests specified for objects of kind %r.' % objspec.name
print(msg)
return
db = context.cc.get_compmake_db()
for x in some:
f = x['function']
which = x['which']
dynamic = x['dynamic']
results = {}
c = context.child(f.__name__)
c.add_extra_report_keys(objspec=objspec.name, function=f.__name__)
objects = expand_string(which, list(test_objects))
if not objects:
msg = 'Which = %r did not give anything in %r.' % (which,
test_objects)
raise ValueError(msg)
print('Testing %s for %s' % (f, objects))
it = iterate_context_names(c, objects, key=objspec.name)
for cc, id_object in it:
ob_job_id = test_objects[id_object]
assert_job_exists(ob_job_id, db)
ob = Promise(ob_job_id)
# bjob_id = 'f' # XXX
job_id = '%s-%s' % (f.__name__, id_object)
params = dict(job_id=job_id, command_name=f.__name__)
if dynamic:
res = cc.comp_config_dynamic(wrap_func_dyn, f, id_object, ob,
**params)
else:
res = cc.comp_config(wrap_func, f, id_object, ob, **params)
results[id_object] = res
if create_reports:
r = c.comp(report_results_single, f, objspec.name, results)
c.add_report(r, 'some')
def define_jobs_context(self, context):
options = self.get_options()
config_mdps = get_conftools_tmdp_smdps()
id_mdps = config_mdps.expand_names(options.mdps)
config_solvers = get_conftools_tmdp_smdp_solvers()
id_solvers = config_solvers.expand_names(options.solvers)
for c2, id_mdp in iterate_context_names(context, id_mdps):
c2.add_extra_report_keys(id_mdp=id_mdp)
mdp = c2.comp_config(instance_mdp, id_mdp)
from tmdp.programs.value_iteration import report_mdp_display
r = c2.comp_config(report_mdp_display, mdp)
c2.add_report(r, 'report_mdp_display')
for cc, id_solver in iterate_context_names(c2, id_solvers):
cc.add_extra_report_keys(id_solver=id_solver)
jobs_solve(cc, mdp, id_solver)
def define_tests_some(context, objspec, names2test_objects,
some, create_reports):
test_objects = names2test_objects[objspec.name]
if not test_objects:
msg = 'No test_objects for objects of kind %r.' % objspec.name
print(msg)
return
if not some:
msg = 'No mcdp_lang_tests specified for objects of kind %r.' % objspec.name
print(msg)
return
db = context.cc.get_compmake_db()
for x in some:
f = x['function']
which = x['which']
dynamic = x['dynamic']
results = {}
c = context.child(f.__name__)
c.add_extra_report_keys(objspec=objspec.name, function=f.__name__)
objects = expand_string(which, list(test_objects))
if not objects:
msg = 'Which = %r did not give anything in %r.' % (which, test_objects)
raise ValueError(msg)
print('Testing %s for %s' % (f, objects))
it = iterate_context_names(c, objects, key=objspec.name)
for cc, id_object in it:
ob_job_id = test_objects[id_object]
assert_job_exists(ob_job_id, db)
ob = Promise(ob_job_id)
# bjob_id = 'f' # XXX
job_id = '%s-%s' % (f.__name__, id_object)
params = dict(job_id=job_id, command_name=f.__name__)
if dynamic:
res = cc.comp_config_dynamic(wrap_func_dyn, f, id_object, ob,
**params)
else:
res = cc.comp_config(wrap_func, f, id_object, ob,
**params)
results[id_object] = res
if create_reports:
r = c.comp(report_results_single, f, objspec.name, results)
c.add_report(r, 'some')
def define_jobs_context(self, context):
options = self.get_options()
config_mdps = get_conftools_tmdp_smdps()
id_mdps = config_mdps.expand_names(options.mdps)
for cc, id_mdp in iterate_context_names(context, id_mdps):
cc.add_extra_report_keys(id_mdp=id_mdp)
# jobs_vit_display(cc, id_mdp)
jobs_vit_solve(cc, id_mdp)
context.create_dynamic_index_job()
def define_jobs_context(self, context):
options = self.get_options()
config_mdps = get_conftools_tmdp_smdps()
id_mdps = config_mdps.expand_names(options.mdps)
for cc, id_mdp in iterate_context_names(context, id_mdps):
cc.add_extra_report_keys(id_mdp=id_mdp)
# jobs_vit_display(cc, id_mdp)
jobs_vit_solve(cc, id_mdp)
context.create_dynamic_index_job()
def define_jobs_context(self, context):
options = self.get_options()
config_mdps = get_conftools_tmdp_smdps()
id_mdps = config_mdps.expand_names(options.mdps)
for cc, id_mdp in iterate_context_names(context, id_mdps):
cc.add_extra_report_keys(id_mdp=id_mdp)
mdp = cc.comp_config(instance_mdp, id_mdp)
cc.add_report(cc.comp(report_start_dist, mdp), 'start_dist')
cc.add_report(cc.comp(report_actions, mdp), 'actions')
context.create_dynamic_index_job()
def define_jobs_context(self, context):
options = self.get_options()
config_mdps = get_conftools_tmdp_smdps()
id_mdps = config_mdps.expand_names(options.mdps)
for cc, id_mdp in iterate_context_names(context, id_mdps):
cc.add_extra_report_keys(id_mdp=id_mdp)
mdp = cc.comp_config(instance_mdp, id_mdp)
cc.add_report(cc.comp(report_start_dist, mdp), 'start_dist')
cc.add_report(cc.comp(report_actions, mdp), 'actions')
context.create_dynamic_index_job()
def define_jobs_context(self, context):
library = get_conftools_uncertain_image_distances()
distances = library.expand_names(self.options.distances)
library = get_conftools_testcases()
testcases = library.expand_names(self.options.testcases)
self.info('Using distances: %s' % distances)
self.info('Using testcases: %s' % testcases)
for c, id_distance in iterate_context_names(context, distances):
c.comp_config(benchmark_distance, id_distance, testcases,
repeat=self.options.repeat)
def define_jobs_context(self, context):
distances_library = get_conftools_uncertain_image_distances()
distances = distances_library.expand_names(self.options.distances)
streams_library = get_conftools_streams()
streams = streams_library.expand_names(self.options.streams)
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.dds)
for c, id_discdds in iterate_context_names(context, discdds):
create_predstats_jobs(context=c, distances=distances,
id_discdds=id_discdds,
streams=streams, maxd=10)
def jobs_vit_solve(context, id_mdp):
config_smdps = get_conftools_tmdp_smdps()
mdp = config_smdps.instance(id_mdp)
vit_res = context.comp(vit_solve, mdp, gamma=1.0)
context.add_report(context.comp(report_vit, mdp, vit_res), 'vit')
context.add_report(context.comp(report_policy, mdp, vit_res), 'policy')
betas = [0.001, 0.05, 0.051, 0.052, 0.15, 0.5, 5]
its = [10, 75, 75, 100, 75, 75, 50]
for i, (c, beta) in enumerate(iterate_context_names(context, betas)):
c.add_extra_report_keys(beta=beta)
iterations = its[i]
fe_res = c.comp(free_energy_iteration, mdp,
min_iterations=iterations, max_iterations=iterations,
beta=beta)
c.add_report(c.comp(report_free_energy, mdp, fe_res), 'report_free_energy')
def define_jobs_context(self, context):
library = get_conftools_uncertain_image_distances()
distances = library.expand_names(self.options.distances)
library = get_conftools_testcases()
testcases = library.expand_names(self.options.testcases)
self.info('Using distances: %s' % distances)
self.info('Using testcases: %s' % testcases)
for c, id_distance in iterate_context_names(context, distances):
c.comp_config(benchmark_distance,
id_distance,
testcases,
repeat=self.options.repeat)
def define_jobs_context(self, context):
distances_library = get_conftools_uncertain_image_distances()
distances = distances_library.expand_names(self.options.distances)
streams_library = get_conftools_streams()
streams = streams_library.expand_names(self.options.streams)
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.dds)
for c, id_discdds in iterate_context_names(context, discdds):
create_predstats_jobs(context=c,
distances=distances,
id_discdds=id_discdds,
streams=streams,
maxd=10)
def jobs_parallel_learning_concurrent(context, data_central, id_agent, id_robot, episodes,
n, intermediate_reports=True, final_report=True):
"""
In this way, there are "n" instances of each agent.
Each instance sees all logs, but is given a hint using the
parallell_hint(i, n) function.
The instances are merged using the merge() function.
Needs:
RM_EPISODE_READY, id_robot, id_episode
"""
agents = []
contexts = ['learn%02dof%02d' % (i + 1, n) for i in range(n)]
for i, (c, progress) in enumerate(iterate_context_names(context, contexts)):
for id_episode in episodes:
c.needs(RM_EPISODE_READY, id_robot=id_robot, id_episode=id_episode)
agent_i = c.subtask(LearnLogNoSaveHint,
boot_root=data_central.get_boot_root(),
agent=id_agent, robot=id_robot,
episodes=episodes,
parallel_hint=[i, n],
add_job_prefix='')
if intermediate_reports:
report = c.comp(get_agentstate_report, agent_i, progress, job_id='report')
c.add_report(report, 'agent_report_partial',
id_agent=id_agent, id_robot=id_robot,
progress=progress)
agents.append(agent_i)
agent_state = jobs_merging_recursive(context, agents)
if final_report:
report = context.comp(get_agentstate_report, agent_state, 'all', job_id='report')
context.add_report(report, 'agent_report', id_agent=id_agent, id_robot=id_robot)
save = context.comp(save_state, data_central,
id_agent, id_robot, agent_state)
return save
def jobs_parallel_learning_concurrent_reps(context, data_central, id_agent, id_robot, episodes,
n, max_reps, intermediate_reports=True,
final_report=True):
"""
This one cycles multiple times in the data.
"""
agents = []
contexts = ['learn%02dof%02d' % (i + 1, n) for i in range(n)]
for i, (c, progress) in enumerate(iterate_context_names(context, contexts)):
extra_dep = []
for id_episode in episodes:
er = c.get_resource(RM_EPISODE_READY, id_robot=id_robot, id_episode=id_episode)
extra_dep.append(er)
agent_i = c.subtask(LearnLogNoSaveHintRepeated,
boot_root=data_central.get_boot_root(),
agent=id_agent, robot=id_robot,
episodes=episodes,
parallel_hint=[i, n],
max_reps=max_reps,
intermediate_reports=True,
add_job_prefix='',
extra_dep=extra_dep)
if intermediate_reports:
report = c.comp(get_agentstate_report, agent_i, progress, job_id='report')
c.add_report(report, 'agent_report_partial',
id_agent=id_agent, id_robot=id_robot,
progress=progress)
agents.append(agent_i)
agent_state = jobs_merging_recursive(context, agents)
if final_report:
report = context.comp(get_agentstate_report, agent_state, 'all', job_id='report')
context.add_report(report, 'agent_report', id_agent=id_agent, id_robot=id_robot)
save = context.comp(save_state, data_central,
id_agent, id_robot, agent_state)
return save
def jobs_vit_solve(context, id_mdp):
config_smdps = get_conftools_tmdp_smdps()
mdp = config_smdps.instance(id_mdp)
vit_res = context.comp(vit_solve, mdp, gamma=1.0)
context.add_report(context.comp(report_vit, mdp, vit_res), 'vit')
context.add_report(context.comp(report_policy, mdp, vit_res), 'policy')
betas = [0.001, 0.05, 0.051, 0.052, 0.15, 0.5, 5]
its = [10, 75, 75, 100, 75, 75, 50]
for i, (c, beta) in enumerate(iterate_context_names(context, betas)):
c.add_extra_report_keys(beta=beta)
iterations = its[i]
fe_res = c.comp(free_energy_iteration,
mdp,
min_iterations=iterations,
max_iterations=iterations,
beta=beta)
c.add_report(c.comp(report_free_energy, mdp, fe_res),
'report_free_energy')
def define_tests_single(context, objspec, names2test_objects,
functions, create_reports):
test_objects = names2test_objects[objspec.name]
if not test_objects:
msg = 'No test_objects for objects of kind %r.' % objspec.name
print(msg)
return
if not functions:
msg = 'No mcdp_lang_tests specified for objects of kind %r.' % objspec.name
print(msg)
db = context.cc.get_compmake_db()
for x in functions:
f = x['function']
dynamic = x['dynamic']
results = {}
c = context.child(f.__name__)
c.add_extra_report_keys(objspec=objspec.name, function=f.__name__)
it = iterate_context_names(c, list(test_objects), key=objspec.name)
for cc, id_object in it:
ob_job_id = test_objects[id_object]
assert_job_exists(ob_job_id, db)
ob = Promise(ob_job_id)
job_id = 'f'
params = dict(job_id=job_id, command_name=f.__name__)
if dynamic:
res = cc.comp_config_dynamic(wrap_func_dyn, f, id_object, ob,
**params)
else:
res = cc.comp_config(wrap_func, f, id_object, ob,
**params)
results[id_object] = res
if create_reports:
r = c.comp(report_results_single, f, objspec.name, results)
c.add_report(r, 'single')
def define_tests_single(context, objspec, names2test_objects, functions,
create_reports):
test_objects = names2test_objects[objspec.name]
if not test_objects:
msg = 'No test_objects for objects of kind %r.' % objspec.name
print(msg)
return
if not functions:
msg = 'No mcdp_lang_tests specified for objects of kind %r.' % objspec.name
print(msg)
db = context.cc.get_compmake_db()
for x in functions:
f = x['function']
dynamic = x['dynamic']
results = {}
c = context.child(f.__name__)
c.add_extra_report_keys(objspec=objspec.name, function=f.__name__)
it = iterate_context_names(c, list(test_objects), key=objspec.name)
for cc, id_object in it:
ob_job_id = test_objects[id_object]
assert_job_exists(ob_job_id, db)
ob = Promise(ob_job_id)
job_id = 'f'
params = dict(job_id=job_id, command_name=f.__name__)
if dynamic:
res = cc.comp_config_dynamic(wrap_func_dyn, f, id_object, ob,
**params)
else:
res = cc.comp_config(wrap_func, f, id_object, ob, **params)
results[id_object] = res
if create_reports:
r = c.comp(report_results_single, f, objspec.name, results)
c.add_report(r, 'single')
def cdtest_alt_obsmodel(context, t):
""" In this test we look for alternative observations models. """
# id_pomdp = t['id_pomdp']
pomdp = t['pomdp']
results = []
cc = context.child('original')
res = cc.comp(pomdp_list_states, pomdp)
res = cc.comp(find_minimal_policy, res, pomdp)
results.append(('original', res))
# normal agent
horizons = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
# horizons = [3, 2, 1, 0]
for cc, horizon in iterate_context_names(context, horizons):
pomdp2 = cc.comp(get_alternative_pomdp, pomdp, horizon)
res2 = cc.comp(alternate_observersations_an, res, pomdp, pomdp2)
desc = 'horizon = %d ' % horizon
results.append((desc, res2))
context.comp(cdtest_alt_obsmodel_check, results)
def cdtest_alt_obsmodel(context, t):
""" In this test we look for alternative observations models. """
# id_pomdp = t['id_pomdp']
pomdp = t['pomdp']
results = []
cc = context.child('original')
res = cc.comp(pomdp_list_states, pomdp)
res = cc.comp(find_minimal_policy, res, pomdp)
results.append(('original', res))
# normal agent
horizons = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
# horizons = [3, 2, 1, 0]
for cc, horizon in iterate_context_names(context, horizons):
pomdp2 = cc.comp(get_alternative_pomdp, pomdp, horizon)
res2 = cc.comp(alternate_observersations_an, res, pomdp, pomdp2)
desc = 'horizon = %d ' % horizon
results.append((desc, res2))
context.comp(cdtest_alt_obsmodel_check, results)
def define_jobs_context(self, context):
names1 = ['a', 'b']
for c1, name1 in iterate_context_names(context, names1):
c1.comp_dynamic(define_jobs1, name1)
def define_jobs_context(self, context):
options = self.get_options()
context.activate_dynamic_reports()
config_mdps = get_conftools_tmdp_smdps()
id_mdps = config_mdps.expand_names(options.mdps)
for cc, id_mdp in iterate_context_names(context, id_mdps):
# put all videos in the same place
outdir = os.path.join(context.get_output_dir(), id_mdp)
cc.add_extra_report_keys(id_mdp=id_mdp)
pomdp = cc.comp_config(instance_mdp, id_mdp)
res = cc.comp(pomdp_list_states, pomdp)
""" Returns res['builder'] as a MDPBuilder """
res = cc.comp(find_minimal_policy, res, pomdp)
cc.add_report(cc.comp(report_trajectories, res, name_obs=False),
'report_trajectories', sensing='original',
named=False)
cc.add_report(cc.comp(report_trajectories, res, name_obs=True),
'report_trajectories', sensing='original',
named=True)
cc.add_report(cc.comp(report_agent, res, pomdp),
'report_agent')
#
# cc.add_report(cc.comp(report_aliasing, res, pomdp),
# 'report_aliasing')
# Too long (too many states)
# cc.add_report(cc.comp(report_sampled_mdp, res, pomdp),
# 'sampled_mdp')
# Too long (too many iterations)
# cc.add_report(cc.comp(report_pictures, res, pomdp),
# 'report_pictures')
prefix = '%s-or' % (id_mdp)
cc.comp_dynamic(jobs_videos, res, pomdp, outdir=outdir, prefix=prefix)
# See if we can do the same policy with different
# observation model
results = []
results.append(('original', res))
if options.horizons:
horizons = [0, 1, 2, 3, 4][::-1]
for ch, horizon in iterate_context_names(cc, horizons, key='sensing'):
pomdp2 = ch.comp(get_alternative_pomdp, pomdp, horizon)
res2 = ch.comp(alternate_observersations_an, res, pomdp, pomdp2,
rename_obs=False)
ch.add_report(ch.comp(report_agent, res2, pomdp, job_id='report_agent_z'),
'report_agent_z')
prefix = '%s-h%d' % (id_mdp, horizon)
ch.comp_dynamic(jobs_videos, res2, pomdp2, outdir=outdir, prefix=prefix)
ch.add_report(ch.comp(report_trajectories, res2, name_obs=False),
'report_trajectories', named=False)
ch.add_report(ch.comp(report_trajectories, res2, name_obs=True),
'report_trajectories', named=True)
results.append(('hor-%d' % horizon, res2))
cc.add_report(cc.comp(report_summary, results), 'summary')
def instance_reports1(context):
param1s = ['a', 'b']
for c1, param1 in iterate_context_names(context, param1s, key='param1'):
c1.comp_dynamic(instance_reports2, param1=param1)
def define_jobs_context(self, context):
names1 = ['a', 'b']
names2 = ['m', 'n']
for c1, name1 in iterate_context_names(context, names1):
for c2, name2 in iterate_context_names(c1, names2):
c2.comp_dynamic(define_jobs1, name1 + name2)
def define_jobs_context(self, context):
names = ['a', 'b', 'c']
for c, id_name in iterate_context_names(context, names):
define_jobs(c, id_name)
def define_jobs_context(self, context):
names = ['a', 'b']
for c, id_name in iterate_context_names(context, names):
c.comp_dynamic(define_jobs1, id_name)
def define_jobs_context(self, context):
options = self.get_options()
context.activate_dynamic_reports()
config_mdps = get_conftools_tmdp_smdps()
id_mdps = config_mdps.expand_names(options.mdps)
for cc, id_mdp in iterate_context_names(context, id_mdps):
# put all videos in the same place
outdir = os.path.join(context.get_output_dir(), id_mdp)
cc.add_extra_report_keys(id_mdp=id_mdp)
pomdp = cc.comp_config(instance_mdp, id_mdp)
res = cc.comp(pomdp_list_states, pomdp)
""" Returns res['builder'] as a MDPBuilder """
res = cc.comp(find_minimal_policy, res, pomdp)
cc.add_report(cc.comp(report_trajectories, res, name_obs=False),
'report_trajectories',
sensing='original',
named=False)
cc.add_report(cc.comp(report_trajectories, res, name_obs=True),
'report_trajectories',
sensing='original',
named=True)
cc.add_report(cc.comp(report_agent, res, pomdp), 'report_agent')
#
# cc.add_report(cc.comp(report_aliasing, res, pomdp),
# 'report_aliasing')
# Too long (too many states)
# cc.add_report(cc.comp(report_sampled_mdp, res, pomdp),
# 'sampled_mdp')
# Too long (too many iterations)
# cc.add_report(cc.comp(report_pictures, res, pomdp),
# 'report_pictures')
prefix = '%s-or' % (id_mdp)
cc.comp_dynamic(jobs_videos,
res,
pomdp,
outdir=outdir,
prefix=prefix)
# See if we can do the same policy with different
# observation model
results = []
results.append(('original', res))
if options.horizons:
horizons = [0, 1, 2, 3, 4][::-1]
for ch, horizon in iterate_context_names(cc,
horizons,
key='sensing'):
pomdp2 = ch.comp(get_alternative_pomdp, pomdp, horizon)
res2 = ch.comp(alternate_observersations_an,
res,
pomdp,
pomdp2,
rename_obs=False)
ch.add_report(
ch.comp(report_agent,
res2,
pomdp,
job_id='report_agent_z'), 'report_agent_z')
prefix = '%s-h%d' % (id_mdp, horizon)
ch.comp_dynamic(jobs_videos,
res2,
pomdp2,
outdir=outdir,
prefix=prefix)
ch.add_report(ch.comp(report_trajectories,
res2,
name_obs=False),
'report_trajectories',
named=False)
ch.add_report(ch.comp(report_trajectories,
res2,
name_obs=True),
'report_trajectories',
named=True)
results.append(('hor-%d' % horizon, res2))
cc.add_report(cc.comp(report_summary, results), 'summary')
def define_jobs1(context, id_name):
names2 = ['m', 'n']
for c2, name2 in iterate_context_names(context, names2):
#assert c2.currently_executing == context.currently_executing
c2.comp_dynamic(define_jobs2, id_name + name2)
def instance_reports2(context, param1):
param2s = [1, 2]
for c2, param2 in iterate_context_names(context, param2s, key='param2'):
c2.comp_dynamic(instance_reports3, param1=param1, param2=param2)
def define_jobs_context(self, context):
names = ['a', 'b', 'c']
for c, id_name in iterate_context_names(context, names):
define_jobs(c, id_name)
def instance_reports2(context, param1):
param2s = [1, 2]
for c2, param2 in iterate_context_names(context, param2s, key='param2'):
c2.comp_dynamic(instance_reports3, param1=param1, param2=param2)
def define_jobs_context(self, context):
names = ['a', 'b']
for c, id_name in iterate_context_names(context, names):
c.comp_dynamic(define_jobs1, id_name)
def instance_reports1(context):
param1s = ['a', 'b']
for c1, param1 in iterate_context_names(context, param1s, key='param1'):
c1.comp_dynamic(instance_reports2, param1=param1)
