def test_is_datarun_finished(db, dataset, datarun):
r1 = db.get_datarun(1)
worker = Worker(db, r1)
assert worker.is_datarun_finished()
r2 = db.get_datarun(2)
worker = Worker(db, r2)
assert not worker.is_datarun_finished()
deadline = (datetime.datetime.now() - datetime.timedelta(seconds=1)).strftime(TIME_FMT)
worker = get_new_worker(deadline=deadline)
assert worker.is_datarun_finished()
def get_new_worker(**kwargs):
kwargs['methods'] = kwargs.get('methods', ['logreg', 'dt'])
sql_conf = SQLConfig(database=DB_PATH)
run_conf = RunConfig(**kwargs)
run_id = enter_data(sql_conf, run_conf)
db = Database(**vars(sql_conf))
datarun = db.get_datarun(run_id)
return Worker(db, datarun)
def test_save_classifier(db, datarun, model, metrics):
worker = Worker(db, datarun, models_dir=MODEL_DIR, metrics_dir=METRIC_DIR)
hp = db.get_hyperpartitions(datarun_id=worker.datarun.id)[0]
classifier = worker.db.start_classifier(hyperpartition_id=hp.id,
datarun_id=worker.datarun.id,
host='localhost',
hyperparameter_values=DT_PARAMS)
worker.db.complete_classifier = Mock()
worker.save_classifier(classifier.id, model, metrics)
worker.db.complete_classifier.assert_called()
with DBSession(worker.db):
clf = db.get_classifier(classifier.id)
loaded = load_model(clf, MODEL_DIR)
assert isinstance(loaded, Model)
assert loaded.method == model.method
assert loaded.random_state == model.random_state
assert load_metrics(clf, METRIC_DIR) == metrics
def test_save_classifier(db, datarun, model, metrics):
log_conf = LogConfig(model_dir=MODEL_DIR, metric_dir=METRIC_DIR)
worker = Worker(db, datarun, log_config=log_conf)
hp = db.get_hyperpartitions(datarun_id=worker.datarun.id)[0]
classifier = worker.db.start_classifier(hyperpartition_id=hp.id,
datarun_id=worker.datarun.id,
host='localhost',
hyperparameter_values=DT_PARAMS)
worker.db.complete_classifier = Mock()
worker.save_classifier(classifier.id, model, metrics)
worker.db.complete_classifier.assert_called()
with db_session(worker.db):
clf = db.get_classifier(classifier.id)
loaded = load_model(clf, MODEL_DIR)
assert type(loaded) == Model
assert loaded.method == model.method
assert loaded.random_state == model.random_state
assert load_metrics(clf, METRIC_DIR) == metrics
def test_is_datarun_finished(db, dataset, datarun):
r1 = db.get_datarun(1)
worker = Worker(db, r1)
assert worker.is_datarun_finished()
r2 = db.get_datarun(2)
worker = Worker(db, r2)
assert not worker.is_datarun_finished()
deadline = (datetime.datetime.now() - datetime.timedelta(seconds=1)).strftime(TIME_FMT)
worker = get_new_worker(deadline=deadline)
assert worker.is_datarun_finished()
def get_new_worker(**kwargs):
kwargs['dataset_id'] = kwargs.get('dataset_id', None)
kwargs['methods'] = kwargs.get('methods', ['logreg', 'dt'])
sql_conf = SQLConfig({'sql_database': DB_PATH})
run_conf = RunConfig(kwargs)
dataset_conf = DatasetConfig(kwargs)
db = Database(**sql_conf.to_dict())
atm = ATM(sql_conf, None, None)
run_id = atm.enter_data(dataset_conf, run_conf)
datarun = db.get_datarun(run_id.id)
return Worker(db, datarun)
def get_new_worker(**kwargs):
kwargs['dataset_id'] = kwargs.get('dataset_id', None)
kwargs['methods'] = kwargs.get('methods', ['logreg', 'dt'])
run_conf = RunConfig(kwargs)
kwargs['train_path'] = POLLUTION_PATH
dataset_conf = DatasetConfig(kwargs)
db = Database(dialect='sqlite', database=DB_PATH)
atm = ATM(dialect='sqlite', database=DB_PATH)
dataset = atm.add_dataset(**dataset_conf.to_dict())
run_conf.dataset_id = dataset.id
datarun = atm.add_datarun(**run_conf.to_dict())
return Worker(db, datarun)
def work(self, datarun_ids=None, save_files=True, choose_randomly=True,
cloud_mode=False, total_time=None, wait=True, verbose=False):
"""
Check the ModelHub database for unfinished dataruns, and spawn workers to
work on them as they are added. This process will continue to run until it
exceeds total_time or is broken with ctrl-C.
datarun_ids (optional): list of IDs of dataruns to compute on. If None,
this will work on all unfinished dataruns in the database.
choose_randomly: if True, work on all highest-priority dataruns in random
order. If False, work on them in sequential order (by ID)
cloud_mode: if True, save processed datasets to AWS.
total_time (optional): if set to an integer, this worker will only work for
total_time seconds. Otherwise, it will continue working until all
dataruns are complete (or indefinitely).
wait: if True, once all dataruns in the database are complete, keep spinning
and wait for new runs to be added. If False, exit once all dataruns are
complete.
"""
start_time = datetime.now()
# main loop
while True:
# get all pending and running dataruns, or all pending/running dataruns
# from the list we were given
dataruns = self.db.get_dataruns(include_ids=datarun_ids, ignore_complete=True)
if not dataruns:
if wait:
LOGGER.debug('No dataruns found. Sleeping %d seconds and trying again.',
ATM.LOOP_WAIT)
time.sleep(ATM.LOOP_WAIT)
continue
else:
LOGGER.info('No dataruns found. Exiting.')
break
# either choose a run randomly between priority, or take the run with the lowest ID
if choose_randomly:
run = random.choice(dataruns)
else:
run = sorted(dataruns, key=attrgetter('id'))[0]
# say we've started working on this datarun, if we haven't already
self.db.mark_datarun_running(run.id)
LOGGER.info('Computing on datarun %d' % run.id)
# actual work happens here
worker = Worker(self.db, run, save_files=save_files,
cloud_mode=cloud_mode, aws_access_key=self.aws_access_key,
aws_secret_key=self.aws_secret_key, s3_bucket=self.s3_bucket,
s3_folder=self.s3_folder, models_dir=self.models_dir,
metrics_dir=self.metrics_dir, verbose_metrics=self.verbose_metrics)
try:
if run.budget_type == 'classifier':
pbar = tqdm(
total=run.budget,
ascii=True,
initial=run.completed_classifiers,
disable=not verbose
)
while run.status != RunStatus.COMPLETE:
worker.run_classifier()
run = self.db.get_datarun(run.id)
if verbose and run.completed_classifiers > pbar.last_print_n:
pbar.update(run.completed_classifiers - pbar.last_print_n)
pbar.close()
elif run.budget_type == 'walltime':
pbar = tqdm(
disable=not verbose,
ascii=True,
initial=run.completed_classifiers,
unit=' Classifiers'
)
while run.status != RunStatus.COMPLETE:
worker.run_classifier()
run = self.db.get_datarun(run.id) # Refresh the datarun object.
if verbose and run.completed_classifiers > pbar.last_print_n:
pbar.update(run.completed_classifiers - pbar.last_print_n)
pbar.close()
except ClassifierError:
# the exception has already been handled; just wait a sec so we
# don't go out of control reporting errors
LOGGER.error('Something went wrong. Sleeping %d seconds.', ATM.LOOP_WAIT)
time.sleep(ATM.LOOP_WAIT)
elapsed_time = (datetime.now() - start_time).total_seconds()
if total_time is not None and elapsed_time >= total_time:
LOGGER.info('Total run time for worker exceeded; exiting.')
break
def work(self,
datarun_ids=None,
save_files=False,
choose_randomly=True,
cloud_mode=False,
total_time=None,
wait=True):
"""
Check the ModelHub database for unfinished dataruns, and spawn workers to
work on them as they are added. This process will continue to run until it
exceeds total_time or is broken with ctrl-C.
datarun_ids (optional): list of IDs of dataruns to compute on. If None,
this will work on all unfinished dataruns in the database.
choose_randomly: if True, work on all highest-priority dataruns in random
order. If False, work on them in sequential order (by ID)
cloud_mode: if True, save processed datasets to AWS. If this option is set,
aws_config must be supplied.
total_time (optional): if set to an integer, this worker will only work for
total_time seconds. Otherwise, it will continue working until all
dataruns are complete (or indefinitely).
wait: if True, once all dataruns in the database are complete, keep spinning
and wait for new runs to be added. If False, exit once all dataruns are
complete.
"""
start_time = datetime.now()
public_ip = get_public_ip()
# main loop
while True:
# get all pending and running dataruns, or all pending/running dataruns
# from the list we were given
dataruns = self.db.get_dataruns(include_ids=datarun_ids,
ignore_complete=True)
if not dataruns:
if wait:
logger.warning(
'No dataruns found. Sleeping %d seconds and trying again.',
ATM.LOOP_WAIT)
time.sleep(ATM.LOOP_WAIT)
continue
else:
logger.warning('No dataruns found. Exiting.')
break
max_priority = max([datarun.priority for datarun in dataruns])
priority_runs = [r for r in dataruns if r.priority == max_priority]
# either choose a run randomly, or take the run with the lowest ID
if choose_randomly:
run = random.choice(priority_runs)
else:
run = sorted(dataruns, key=attrgetter('id'))[0]
# say we've started working on this datarun, if we haven't already
self.db.mark_datarun_running(run.id)
logger.info('Computing on datarun %d' % run.id)
# actual work happens here
worker = Worker(self.db,
run,
save_files=save_files,
cloud_mode=cloud_mode,
aws_config=self.aws_conf,
log_config=self.log_conf,
public_ip=public_ip)
try:
worker.run_classifier()
except ClassifierError:
# the exception has already been handled; just wait a sec so we
# don't go out of control reporting errors
logger.warning('Something went wrong. Sleeping %d seconds.',
ATM.LOOP_WAIT)
time.sleep(ATM.LOOP_WAIT)
elapsed_time = (datetime.now() - start_time).total_seconds()
if total_time is not None and elapsed_time >= total_time:
logger.warning('Total run time for worker exceeded; exiting.')
break
def worker(db, datarun):
return Worker(db, datarun)
def work(self, datarun_ids=None, save_files=True, choose_randomly=True,
cloud_mode=False, total_time=None, wait=True, verbose=False):
"""Get unfinished Dataruns from the database and work on them.
Check the ModelHub Database for unfinished Dataruns, and work on them
as they are added. This process will continue to run until it exceeds
total_time or there are no more Dataruns to process or it is killed.
Args:
datarun_ids (list):
list of IDs of Dataruns to work on. If ``None``, this will work on any
unfinished Dataruns found in the database. Optional. Defaults to ``None``.
save_files (bool):
Whether to save the fitted classifiers and their metrics or not.
Optional. Defaults to True.
choose_randomly (bool):
If ``True``, work on all the highest-priority dataruns in random order.
Otherwise, work on them in sequential order (by ID).
Optional. Defaults to ``True``.
cloud_mode (bool):
Save the models and metrics in AWS S3 instead of locally. This option
works only if S3 configuration has been provided on initialization.
Optional. Defaults to ``False``.
total_time (int):
Total time to run the work process, in seconds. If ``None``, continue to
run until interrupted or there are no more Dataruns to process.
Optional. Defaults to ``None``.
wait (bool):
If ``True``, wait for more Dataruns to be inserted into the Database
once all have been processed. Otherwise, exit the worker loop
when they run out.
Optional. Defaults to ``False``.
verbose (bool):
Whether to be verbose about the process. Optional. Defaults to ``True``.
"""
start_time = datetime.now()
# main loop
while True:
# get all pending and running dataruns, or all pending/running dataruns
# from the list we were given
dataruns = self.db.get_dataruns(include_ids=datarun_ids, ignore_complete=True)
if not dataruns:
if wait:
LOGGER.debug('No dataruns found. Sleeping %d seconds and trying again.',
self._LOOP_WAIT)
time.sleep(self._LOOP_WAIT)
continue
else:
LOGGER.info('No dataruns found. Exiting.')
break
# either choose a run randomly between priority, or take the run with the lowest ID
if choose_randomly:
run = random.choice(dataruns)
else:
run = sorted(dataruns, key=attrgetter('id'))[0]
# say we've started working on this datarun, if we haven't already
self.db.mark_datarun_running(run.id)
LOGGER.info('Computing on datarun %d' % run.id)
# actual work happens here
worker = Worker(self.db, run, save_files=save_files,
cloud_mode=cloud_mode, aws_access_key=self.aws_access_key,
aws_secret_key=self.aws_secret_key, s3_bucket=self.s3_bucket,
s3_folder=self.s3_folder, models_dir=self.models_dir,
metrics_dir=self.metrics_dir, verbose_metrics=self.verbose_metrics)
try:
if run.budget_type == 'classifier':
pbar = tqdm(
total=run.budget,
ascii=True,
initial=run.completed_classifiers,
disable=not verbose
)
while run.status != RunStatus.COMPLETE:
worker.run_classifier()
run = self.db.get_datarun(run.id)
if verbose and run.completed_classifiers > pbar.last_print_n:
pbar.update(run.completed_classifiers - pbar.last_print_n)
pbar.close()
elif run.budget_type == 'walltime':
pbar = tqdm(
disable=not verbose,
ascii=True,
initial=run.completed_classifiers,
unit=' Classifiers'
)
while run.status != RunStatus.COMPLETE:
worker.run_classifier()
run = self.db.get_datarun(run.id) # Refresh the datarun object.
if verbose and run.completed_classifiers > pbar.last_print_n:
pbar.update(run.completed_classifiers - pbar.last_print_n)
pbar.close()
except ClassifierError:
# the exception has already been handled; just wait a sec so we
# don't go out of control reporting errors
LOGGER.error('Something went wrong. Sleeping %d seconds.', self._LOOP_WAIT)
time.sleep(self._LOOP_WAIT)
elapsed_time = (datetime.now() - start_time).total_seconds()
if total_time is not None and elapsed_time >= total_time:
LOGGER.info('Total run time for worker exceeded; exiting.')
break
def get_datarun_steps_info(datarun_id,
classifier_start=None,
classifier_end=None,
nice=False):
"""
Get the scores of the hyperpartitions/method in each step.
:param datarun_id: the id of the datarun
:param classifier_start: only return the scores of and after the `classifier_start` th classifier
:param classifier_end: only return the scores before the `classifier_end` th classifier,
Note that :classifier_start and :classifier_end are not ids, they starts from 1.
(This is because the caller may not know the classifier ids of the datarun)
:param nice: A flag for return nice format result
:return:
if nice is False,
[
{"1": 0.2, "2": 0.3, ...},
...
]
if nice is True,
[
{
"knn": [0.2, 0.3],
"logreg": [0.1],
...
},
...
]
"""
if classifier_start is None:
classifier_start = -np.inf
if classifier_end is None:
classifier_end = np.inf
db = get_db()
datarun = db.get_datarun(datarun_id=datarun_id)
hyperpartitions = db.get_hyperpartitions(datarun_id=datarun_id)
# load classifiers and build scores lists
# make sure all hyperpartitions are present in the dict, even ones that
# don't have any classifiers. That way the selector can choose hyperpartitions
# that haven't been scored yet.
hyperpartition_scores = {fs.id: [] for fs in hyperpartitions}
classifiers = db.get_classifiers(datarun_id=datarun_id,
status=ClassifierStatus.COMPLETE)
selected_classifiers = [
c for c in classifiers if c.hyperpartition_id in hyperpartition_scores
]
# Create a temporary worker
worker = Worker(db, datarun, public_ip=get_public_ip())
bandit_scores_of_steps = []
for i, c in enumerate(selected_classifiers):
if i >= classifier_end:
break
# the cast to float is necessary because the score is a Decimal;
# doing Decimal-float arithmetic throws errors later on.
score = float(getattr(c, datarun.score_target) or 0)
hyperpartition_scores[c.hyperpartition_id].append(score)
bandit_scores = selector_bandit_scores(worker.selector,
hyperpartition_scores)
bandit_scores = {
key: float("%.5f" % val)
for key, val in bandit_scores.items()
}
if i < classifier_start:
continue
bandit_scores_of_steps.append(bandit_scores)
# For a nicer formatted output
if nice:
results = []
hp_id2method = {fs.id: fs.method for fs in hyperpartitions}
for bandit_scores in bandit_scores_of_steps:
res = defaultdict(list)
for hp_id, score in bandit_scores.items():
res[hp_id2method[hp_id]].append(score)
results.append(res)
return results
return bandit_scores_of_steps