def main():
import argparse
import os
import pickle
import sys
key_names = KEYS.keys()
# set up arguments
parser = argparse.ArgumentParser()
parser.add_argument('--latent-d', '-D', type=int, default=5)
parser.add_argument('--steps', '-s', type=int, default=None)
parser.add_argument('--discrete', action='store_true', default=None)
parser.add_argument('--no-discrete', action='store_false', dest='discrete')
parser.add_argument('--subtract-mean', action='store_true', default=True)
parser.add_argument('--no-subtract-mean',
action='store_false',
dest='subtract_mean')
parser.add_argument('--fit', default='batch')
parser.add_argument('--lookahead-fit', default='batch')
parser.add_argument('--samps', '-S', type=int, default=128)
parser.add_argument('--lookahead-samps', type=int, default=128)
parser.add_argument('--threaded', action='store_true', default=True)
parser.add_argument('--unthreaded', action='store_false', dest='threaded')
parser.add_argument('--procs', '-P', type=int, default=None)
parser.add_argument('--test-set', default='all')
parser.add_argument('--load-data', required='True', metavar='FILE')
parser.add_argument('--save-results',
nargs='?',
default=True,
const=True,
metavar='FILE')
parser.add_argument('--no-save-results',
action='store_false',
dest='save_results')
parser.add_argument(
'--note',
action='append',
help="Doesn't do anything, just there to save any notes you'd like "
"in the results file.")
parser.add_argument('keys',
nargs='*',
help="Choices: {}.".format(', '.join(
sorted(key_names))))
args = parser.parse_args()
# check that args.keys are valid
for k in args.keys:
if k not in key_names:
sys.stderr.write("Invalid key name %s; options are %s.\n" %
(k, ', '.join(sorted(key_names))))
sys.exit(1)
if not args.keys:
args.keys = sorted(key_names)
# make directories to save results if necessary
if args.save_results is True:
args.save_results = 'results.pkl'
elif args.save_results:
dirname = os.path.dirname(args.save_results)
if dirname and not os.path.exists(dirname):
os.makedirs(dirname)
# load data
with open(args.load_data, 'rb') as f:
data = np.load(f)
if isinstance(data, np.ndarray):
data = {'_real': data}
real = data['_real']
ratings = data['_ratings']
rating_vals = data['_rating_vals'] if '_rating_vals' in data else None
if args.discrete is None:
args.discrete = rating_vals is not None
# do the comparison
try:
results = compare_active(key_names=args.keys,
latent_d=args.latent_d,
real=real,
ratings=ratings,
rating_vals=rating_vals,
test_set=args.test_set,
num_steps=args.steps,
discrete=args.discrete,
subtract_mean=args.subtract_mean,
fit_type=parse_fit_type(args.fit),
lookahead_fit=args.lookahead_fit,
num_samps=args.samps,
lookahead_samps=args.lookahead_samps,
procs=args.procs,
threaded=args.threaded)
except Exception:
import traceback
print()
traceback.print_exc()
import pdb
print()
pdb.post_mortem()
sys.exit(1)
# save the results file
if args.save_results:
print("\nsaving results in '{}'".format(args.save_results))
results['_args'] = args
with open(args.save_results, 'wb') as f:
pickle.dump(results, f)
def main():
import argparse
import os
import pickle
import sys
key_names = set(KEY_FUNCS.keys())
types = {'float', 'int', 'int-bounds', 'binary'}
parser = argparse.ArgumentParser()
model = parser.add_argument_group("Model Options")
model.add_argument('--latent-d', '-D', type=int, default=5)
model.add_argument('--discrete-integration',
nargs='?',
const=True,
default=False)
model.add_argument('--continuous-integration',
action='store_false',
dest='discrete_integration')
add_bool_opt(model, 'fit-sigmas', default=False)
add_bool_opt(model, 'refit-lookahead', default=False)
model.add_argument('--fit', default='batch')
model.add_argument('--sig-u-mean', type=float, default=0)
model.add_argument('--sig-u-var', type=float, default=-1)
model.add_argument('--sig-v-mean', type=float, default=0)
model.add_argument('--sig-v-var', type=float, default=-1)
model.add_argument('keys',
nargs='*',
help="Choices: {}.".format(', '.join(
sorted(key_names))))
problem_def = parser.add_argument_group("Problem Definiton")
problem_def.add_argument('--load-data', default=None, metavar='FILE')
add_bool_opt(problem_def, 'load-model', default=False)
problem_def.add_argument('--gen-rank', '-R', type=int, default=5)
problem_def.add_argument(
'--type',
default='float',
help="An integer (meaning values are from 0 to that integer) or "
"one of {}".format(', '.join(sorted(types))))
problem_def.add_argument('--u-mean', type=float, default=0)
problem_def.add_argument('--u-std', type=float, default=2)
problem_def.add_argument('--v-mean', type=float, default=0)
problem_def.add_argument('--v-std', type=float, default=2)
problem_def.add_argument('--noise', '-n', type=float, default=.25)
problem_def.add_argument('--num-users', '-N', type=int, default=10)
problem_def.add_argument('--num-items', '-M', type=int, default=10)
problem_def.add_argument('--mask', '-m', default=0)
running = parser.add_argument_group("Running")
running.add_argument('--processes', '-P', type=int, default=None)
add_bool_opt(running, 'threading', True)
running.add_argument('--steps', '-s', type=int, default=None)
results = parser.add_argument_group("Results")
results.add_argument('--save-results',
nargs='?',
default=None,
const=True,
metavar='FILE')
results.add_argument('--no-save-results',
action='store_false',
dest='save_results')
results.add_argument(
'--note',
action='append',
help="Doesn't do anything, just there to save any notes you'd like "
"in the results file.")
args = parser.parse_args()
# is args.mask supposed to be a float?
try:
args.mask = float(args.mask)
except ValueError:
pass
# check args.type
try:
args.type = int(args.type)
except ValueError:
if args.type not in types:
raise ValueError("--type must be integer or one of {}".format(
', '.join(sorted(types))))
# check that args.keys are valid
for k in args.keys:
if k not in key_names:
sys.stderr.write("Invalid key name %s; options are %s.\n" %
(k, ', '.join(sorted(key_names))))
sys.exit(1)
if not args.keys:
args.keys = sorted(key_names)
# make directories to save results if necessary
if args.save_results is True:
args.save_results = 'results.pkl'
elif args.save_results:
dirname = os.path.dirname(args.save_results)
if dirname and not os.path.exists(dirname):
os.makedirs(dirname)
# load previous data, if we're doing that
real_ratings_vals = None
apmf = None
knowable = None
if args.load_data:
with open(args.load_data, 'rb') as f:
data = np.load(f)
if isinstance(data, np.ndarray):
data = {'_real': data}
real = data['_real']
real_ratings_vals = (
real,
data['_ratings'] if '_ratings' in data else get_ratings(
real, args.mask),
data['_rating_vals'] if '_rating_vals' in data else None,
)
if args.load_model:
apmf = data['_initial_apmf']
knowable = np.isfinite(real)
knowable[real == 0] = 0
knowable = zip(*knowable.nonzero())
# get results
try:
results = compare(args.keys,
num_users=args.num_users,
num_items=args.num_items,
real_ratings_vals=real_ratings_vals,
apmf=apmf,
u_mean=args.u_mean,
u_std=args.u_std,
v_mean=args.v_mean,
v_std=args.v_std,
noise=args.noise,
mask_type=args.mask,
rank=args.gen_rank,
latent_d=args.latent_d,
discrete_exp=args.discrete_integration,
refit_lookahead=args.refit_lookahead,
fit_sigmas=args.fit_sigmas,
sig_u_mean=args.sig_u_mean,
sig_u_var=args.sig_u_var,
sig_v_mean=args.sig_v_mean,
sig_v_var=args.sig_v_var,
data_type=args.type,
steps=args.steps,
fit_type=parse_fit_type(args.fit),
processes=args.processes,
do_threading=args.threading)
except Exception:
import traceback
print()
traceback.print_exc()
import pdb
print()
pdb.post_mortem()
sys.exit(1)
# save the results file
if args.save_results:
print("saving results in '{}'".format(args.save_results))
results['_args'] = args
with open(args.save_results, 'wb') as f:
pickle.dump(results, f)
def main():
import argparse
import os
import pickle
import sys
key_names = KEYS.keys()
# set up arguments
parser = argparse.ArgumentParser()
parser.add_argument('--latent-d', '-D', type=int, default=5)
parser.add_argument('--steps', '-s', type=int, default=None)
parser.add_argument('--discrete', action='store_true', default=None)
parser.add_argument('--no-discrete', action='store_false', dest='discrete')
parser.add_argument('--subtract-mean', action='store_true', default=True)
parser.add_argument('--no-subtract-mean',
action='store_false', dest='subtract_mean')
parser.add_argument('--fit', default='batch')
parser.add_argument('--lookahead-fit', default='batch')
parser.add_argument('--samps', '-S', type=int, default=128)
parser.add_argument('--lookahead-samps', type=int, default=128)
parser.add_argument('--threaded', action='store_true', default=True)
parser.add_argument('--unthreaded', action='store_false', dest='threaded')
parser.add_argument('--procs', '-P', type=int, default=None)
parser.add_argument('--test-set', default='all')
parser.add_argument('--load-data', required='True', metavar='FILE')
parser.add_argument('--save-results', nargs='?', default=True, const=True,
metavar='FILE')
parser.add_argument('--no-save-results',
action='store_false', dest='save_results')
parser.add_argument('--note', action='append',
help="Doesn't do anything, just there to save any notes you'd like "
"in the results file.")
parser.add_argument('keys', nargs='*',
help="Choices: {}.".format(', '.join(sorted(key_names))))
args = parser.parse_args()
# check that args.keys are valid
for k in args.keys:
if k not in key_names:
sys.stderr.write("Invalid key name %s; options are %s.\n" % (
k, ', '.join(sorted(key_names))))
sys.exit(1)
if not args.keys:
args.keys = sorted(key_names)
# make directories to save results if necessary
if args.save_results is True:
args.save_results = 'results.pkl'
elif args.save_results:
dirname = os.path.dirname(args.save_results)
if dirname and not os.path.exists(dirname):
os.makedirs(dirname)
# load data
with open(args.load_data, 'rb') as f:
data = np.load(f)
if isinstance(data, np.ndarray):
data = { '_real': data }
real = data['_real']
ratings = data['_ratings']
rating_vals = data['_rating_vals'] if '_rating_vals' in data else None
if args.discrete is None:
args.discrete = rating_vals is not None
# do the comparison
try:
results = compare_active(
key_names=args.keys,
latent_d=args.latent_d,
real=real, ratings=ratings, rating_vals=rating_vals,
test_set=args.test_set, num_steps=args.steps,
discrete=args.discrete, subtract_mean=args.subtract_mean,
fit_type=parse_fit_type(args.fit),
lookahead_fit=args.lookahead_fit,
num_samps=args.samps, lookahead_samps=args.lookahead_samps,
procs=args.procs, threaded=args.threaded)
except Exception:
import traceback
print()
traceback.print_exc()
import pdb
print()
pdb.post_mortem()
sys.exit(1)
# save the results file
if args.save_results:
print("\nsaving results in '{}'".format(args.save_results))
results['_args'] = args
with open(args.save_results, 'wb') as f:
pickle.dump(results, f)
def main():
import argparse
import os
import pickle
import sys
key_names = set(KEY_FUNCS.keys())
types = {'float', 'int', 'int-bounds', 'binary'}
parser = argparse.ArgumentParser()
parser.add_argument('--load-data', default=None, metavar='FILE')
model = parser.add_argument_group("Model Options")
model.add_argument('--latent-d', '-D', type=int, default=5)
model.add_argument('--discrete-integration',
nargs='?',
const=True,
default=False)
model.add_argument('--continuous-integration',
action='store_false',
dest='discrete_integration')
add_bool_opt(model, 'fit-sigmas', default=False)
add_bool_opt(model, 'refit-lookahead', default=False)
model.add_argument('--fit', default='batch')
model.add_argument('--sig-u-mean', type=float, default=0)
model.add_argument('--sig-u-var', type=float, default=-1)
model.add_argument('--sig-v-mean', type=float, default=0)
model.add_argument('--sig-v-var', type=float, default=-1)
model.add_argument('keys',
nargs='*',
help="Choices: {}.".format(', '.join(
sorted(key_names))))
running = parser.add_argument_group("Running")
running.add_argument('--processes', '-P', type=int, default=None)
add_bool_opt(running, 'threading', True)
running.add_argument('--steps', '-s', type=int, default=None)
results = parser.add_argument_group("Results")
results.add_argument('--save-results', default=True, metavar='FILE')
results.add_argument('--no-save-results',
action='store_false',
dest='save_results')
results.add_argument(
'--note',
action='append',
help="Doesn't do anything, just there to save any notes you'd like "
"in the results file.")
args = parser.parse_args()
# check that args.keys are valid
for k in args.keys:
if k not in key_names:
sys.stderr.write("Invalid key name %s; options are %s.\n" %
(k, ', '.join(sorted(key_names))))
sys.exit(1)
if not args.keys:
args.keys = sorted(key_names)
# make directories to save results if necessary
if args.save_results is True:
args.save_results = 'results.pkl'
elif args.save_results:
dirname = os.path.dirname(args.save_results)
if dirname and not os.path.exists(dirname):
os.makedirs(dirname)
# load previous data
with open(args.load_data, 'rb') as f:
data = np.load(f)
if isinstance(data, np.ndarray):
data = {'_real': data}
real = data['_real']
ratings = data['_ratings']
rating_vals = data['_rating_vals'] if '_rating_vals' in data else None
test_on = data['_test_on'] if '_test_on' in data else None
is_new_item = data['_is_new_item'] if '_is_new_item' in data else None
# XXX handle is_new_item
knowable = np.isfinite(real)
knowable[real == 0] = False
if test_on is not None:
print("using test set from file")
knowable[test_on] = False
knowable = zip(*knowable.nonzero())
# get results
try:
results = compare(args.keys,
real=real,
ratings=ratings,
rating_vals=rating_vals,
knowable=knowable,
test_on=test_on,
latent_d=args.latent_d,
discrete_exp=args.discrete_integration,
refit_lookahead=args.refit_lookahead,
fit_sigmas=args.fit_sigmas,
sig_u_mean=args.sig_u_mean,
sig_u_var=args.sig_u_var,
sig_v_mean=args.sig_v_mean,
sig_v_var=args.sig_v_var,
steps=args.steps,
fit_type=parse_fit_type(args.fit),
processes=args.processes,
do_threading=args.threading)
except Exception:
import traceback
print()
traceback.print_exc()
import pdb
print()
pdb.post_mortem()
sys.exit(1)
# save the results file
if args.save_results:
print("saving results in '{}'".format(args.save_results))
results['_args'] = args
with open(args.save_results, 'wb') as f:
pickle.dump(results, f)
def main():
import argparse
import os
import pickle
import sys
key_names = set(KEY_FUNCS.keys())
types = {'float', 'int', 'int-bounds', 'binary'}
parser = argparse.ArgumentParser()
model = parser.add_argument_group("Model Options")
model.add_argument('--latent-d', '-D', type=int, default=5)
model.add_argument('--discrete-integration',
nargs='?', const=True, default=False)
model.add_argument('--continuous-integration',
action='store_false', dest='discrete_integration')
add_bool_opt(model, 'fit-sigmas', default=False)
add_bool_opt(model, 'refit-lookahead', default=False)
model.add_argument('--fit', default='batch')
model.add_argument('--sig-u-mean', type=float, default=0)
model.add_argument('--sig-u-var', type=float, default=-1)
model.add_argument('--sig-v-mean', type=float, default=0)
model.add_argument('--sig-v-var', type=float, default=-1)
model.add_argument('keys', nargs='*',
help="Choices: {}.".format(', '.join(sorted(key_names))))
problem_def = parser.add_argument_group("Problem Definiton")
problem_def.add_argument('--load-data', default=None, metavar='FILE')
add_bool_opt(problem_def, 'load-model', default=False)
problem_def.add_argument('--gen-rank', '-R', type=int, default=5)
problem_def.add_argument('--type', default='float',
help="An integer (meaning values are from 0 to that integer) or "
"one of {}".format(', '.join(sorted(types))))
problem_def.add_argument('--u-mean', type=float, default=0)
problem_def.add_argument('--u-std', type=float, default=2)
problem_def.add_argument('--v-mean', type=float, default=0)
problem_def.add_argument('--v-std', type=float, default=2)
problem_def.add_argument('--noise', '-n', type=float, default=.25)
problem_def.add_argument('--num-users', '-N', type=int, default=10)
problem_def.add_argument('--num-items', '-M', type=int, default=10)
problem_def.add_argument('--mask', '-m', default=0)
running = parser.add_argument_group("Running")
running.add_argument('--processes', '-P', type=int, default=None)
add_bool_opt(running, 'threading', True)
running.add_argument('--steps', '-s', type=int, default=None)
results = parser.add_argument_group("Results")
results.add_argument('--save-results', nargs='?', default=None, const=True,
metavar='FILE')
results.add_argument('--no-save-results',
action='store_false', dest='save_results')
results.add_argument('--note', action='append',
help="Doesn't do anything, just there to save any notes you'd like "
"in the results file.")
args = parser.parse_args()
# is args.mask supposed to be a float?
try:
args.mask = float(args.mask)
except ValueError:
pass
# check args.type
try:
args.type = int(args.type)
except ValueError:
if args.type not in types:
raise ValueError("--type must be integer or one of {}".format(
', '.join(sorted(types))))
# check that args.keys are valid
for k in args.keys:
if k not in key_names:
sys.stderr.write("Invalid key name %s; options are %s.\n" % (
k, ', '.join(sorted(key_names))))
sys.exit(1)
if not args.keys:
args.keys = sorted(key_names)
# make directories to save results if necessary
if args.save_results is True:
args.save_results = 'results.pkl'
elif args.save_results:
dirname = os.path.dirname(args.save_results)
if dirname and not os.path.exists(dirname):
os.makedirs(dirname)
# load previous data, if we're doing that
real_ratings_vals = None
apmf = None
knowable = None
if args.load_data:
with open(args.load_data, 'rb') as f:
data = np.load(f)
if isinstance(data, np.ndarray):
data = {'_real': data}
real = data['_real']
real_ratings_vals = (
real,
data['_ratings'] if '_ratings' in data
else get_ratings(real, args.mask),
data['_rating_vals'] if '_rating_vals' in data else None,
)
if args.load_model:
apmf = data['_initial_apmf']
knowable = np.isfinite(real)
knowable[real == 0] = 0
knowable = zip(*knowable.nonzero())
# get results
try:
results = compare(args.keys,
num_users=args.num_users, num_items=args.num_items,
real_ratings_vals=real_ratings_vals, apmf=apmf,
u_mean=args.u_mean, u_std=args.u_std,
v_mean=args.v_mean, v_std=args.v_std,
noise=args.noise, mask_type=args.mask,
rank=args.gen_rank, latent_d=args.latent_d,
discrete_exp=args.discrete_integration,
refit_lookahead=args.refit_lookahead,
fit_sigmas=args.fit_sigmas,
sig_u_mean=args.sig_u_mean, sig_u_var=args.sig_u_var,
sig_v_mean=args.sig_v_mean, sig_v_var=args.sig_v_var,
data_type=args.type,
steps=args.steps,
fit_type=parse_fit_type(args.fit),
processes=args.processes, do_threading=args.threading)
except Exception:
import traceback
print()
traceback.print_exc()
import pdb
print()
pdb.post_mortem()
sys.exit(1)
# save the results file
if args.save_results:
print("saving results in '{}'".format(args.save_results))
results['_args'] = args
with open(args.save_results, 'wb') as f:
pickle.dump(results, f)
