def main():
args = parse_cli()
config = yread(args.conf)
config['experiment_name'] = {}
clfs, param_space, _ = get_param_space()
for clf_name in clfs:
print("processing {}".format(clf_name))
# sampling max_evals parameters
param_iter = ParameterSampler(param_space[clf_name], n_iter=config['max_evals'])
# create the database collection's skeleton
experiment_name = param_space_to_experiment_name(clf_name, user.param_space[clf_name])
jobs = JobsDB(config['project_name'], experiment_name,
host=config['MongoDB']['host'],
port=config['MongoDB']['port'])
jobs.create_jobs(config['features'], param_iter)
# collect database collection's name
config['experiment_name'][clf_name] = experiment_name
# add collection's info to config.
ywrite(config, args.save)
def main():
args = parse_cli()
config = yread(args.conf)
config['experiment_name'] = {}
clfs, param_space, _ = get_param_space()
for clf_name in clfs:
print("processing {}".format(clf_name))
# sampling max_evals parameters
param_iter = ParameterSampler(param_space[clf_name],
n_iter=config['max_evals'])
# create the database collection's skeleton
experiment_name = param_space_to_experiment_name(
clf_name, user.param_space[clf_name])
jobs = JobsDB(config['project_name'],
experiment_name,
host=config['MongoDB']['host'],
port=config['MongoDB']['port'])
jobs.create_jobs(config['features'], param_iter)
# collect database collection's name
config['experiment_name'][clf_name] = experiment_name
# add collection's info to config.
ywrite(config, args.save)
def main():
args = parse_cli()
config = yread(args.configure)
config['experiment_name'] = {}
param_space = get_param_space()
meta_param = list(param_space.keys())
for clf_name in meta_param:
print("processing {}".format(clf_name))
# sampling max_evals parameters
param_iter = ParameterSampler(param_space[clf_name], n_iter=config['max_evals'])
# create the database collection's skeleton
experiment_name = clf_name + ':' + chain_dict_keys(param_space[clf_name])
jobs = JobsDB(config['project_name'], experiment_name,
host=config['MongoDB']['host'],
port=config['MongoDB']['port'])
jobs.create_jobs(config['features'], param_iter)
# collect database collection's name
config['experiment_name'][clf_name] = experiment_name
# add collection's info to config.
ywrite(config, args.save)
def main():
args = parse_cli()
config = yread(args.conf)
w = Worker(config['project_name'],
config['experiment_name'],
objective_random_sleep,
host=config['MongoDB']['host'],
port=config['MongoDB']['port'],
loop_forever=args.loop)
# do the tasks
w.start_worker()
def main():
args = parse_cli()
config = yread(args.conf)
# sample parameters
param_space = get_param_space()
param_iter = ParameterSampler(param_space, n_iter=config['number_examples'])
# create MongoDB collection
jobs = JobsDB(config['project_name'],
config['experiment_name'],
host=config['MongoDB']['host'],
port=config['MongoDB']['port'])
jobs.create_jobs(None, param_iter)
def main():
args = parse_cli()
config = yread(args.conf)
# sample parameters
param_space = get_param_space()
param_iter = ParameterSampler(param_space,
n_iter=config['number_examples'])
# create MongoDB collection
jobs = JobsDB(config['project_name'],
config['experiment_name'],
host=config['MongoDB']['host'],
port=config['MongoDB']['port'])
jobs.create_jobs(None, param_iter)
def main():
args = parse_cli()
config = yread(args.conf)
# connect to collection
jobs = JobsDB(config['project_name'],
config['experiment_name'],
host=config['MongoDB']['host'],
port=config['MongoDB']['port'])
jobs.print_job_stats()
# TODO: need to introduce a Results class to handle this stuff...
# we'll use the results multiple times, so cache it (TODO: catch OOM)
completed = list(jobs.get_completed_jobs())
for idx, trial in enumerate(completed):
print 'Trial #{}: params: {} / results: {} / run-on: {}'.format(idx,
trial['params'], trial['aux_data'], trial['owner'])
def main():
args = parse_cli()
config = yread(args.conf)
# connect to collection
jobs = JobsDB(config['project_name'],
config['experiment_name'],
host=config['MongoDB']['host'],
port=config['MongoDB']['port'])
jobs.print_job_stats()
# TODO: need to introduce a Results class to handle this stuff...
# we'll use the results multiple times, so cache it (TODO: catch OOM)
completed = list(jobs.get_completed_jobs())
for idx, trial in enumerate(completed):
print 'Trial #{}: params: {} / results: {} / run-on: {}'.format(
idx, trial['params'], trial['aux_data'], trial['owner'])
def main():
args = parse_cli()
config = yread(args.conf)
for clf, db_collection in config['experiment_name'].items():
print('\nProcessing {}, experiment name: {}'.format(
clf, db_collection))
p = Plotting(config['project_name'], db_collection,
user.param_space[clf], user.param_types[clf])
print('\nPlotting loss vs time')
p.plot_loss_vs_time()
for param in p.param_names:
print('\nPlotting loss vs. {}'.format(param))
p.plot_loss_vs_param(param)
print('\nPlotting loss vs. two best params')
p.plot_loss_vs_param()
def main():
args = parse_cli()
config = yread(args.configure)
for clf, db_collection in config['experiment_name'].items():
p = Plotting(config['project_name'],
clf + ':' + db_collection,
user.param_space[clf],
user.param_types[clf])
print('plotting loss vs time')
p.plot_loss_vs_time()
for param in p.param_names:
print('plotting loss vs {}'.format(param))
p.plot_loss_vs_param(param)
print('plotting loss vs two params')
p.plot_loss_vs_param()
def main():
args = parse_cli()
config = yread(args.conf)
for clf, db_collection in config['experiment_name'].items():
print('\nProcessing {}, experiment name: {}'.format(clf, db_collection))
p = Plotting(config['project_name'],
db_collection,
user.param_space[clf],
user.param_types[clf])
print('\nPlotting loss vs time')
p.plot_loss_vs_time()
for param in p.param_names:
print('\nPlotting loss vs. {}'.format(param))
p.plot_loss_vs_param(param)
print('\nPlotting loss vs. two best params')
p.plot_loss_vs_param()
def main():
args = parse_cli()
config = yread(args.configure)
config["experiment_name"] = {}
clfs, param_space, _ = get_param_space()
for clf_name in clfs:
print("processing {}".format(clf_name))
# sampling max_evals parameters
param_iter = ParameterSampler(param_space[clf_name], n_iter=config["max_evals"])
# create the database collection's skeleton
experiment_name = clf_name + ":" + chain_dict_keys(param_space[clf_name])
jobs = JobsDB(
config["project_name"], experiment_name, host=config["MongoDB"]["host"], port=config["MongoDB"]["port"]
)
jobs.create_jobs(config["features"], param_iter)
# collect database collection's name
config["experiment_name"][clf_name] = experiment_name
# add collection's info to config.
ywrite(config, args.save)