def taxiny_evaluation():
# parameters
DATAPATH = '../data'
CACHEDATA = True # cache data or NOT
T = 24 # number of time intervals in one day
lr = 0.00035 # learning rate
len_closeness = len_c = 4 # length of closeness dependent sequence - should be 6
len_period = len_p = 4 # length of peroid dependent sequence
len_trend = len_t = 4 # length of trend dependent sequence
nb_flow = 2 # there are two types of flows: inflow and outflow
days_test = 7 * 4
len_test = T * days_test
map_height, map_width = 16, 8 # grid size
path_cache = os.path.join(DATAPATH, 'CACHE', 'MST3D') # cache path
if CACHEDATA and os.path.isdir(path_cache) is False:
os.mkdir(path_cache)
# load data
print("loading data...")
fname = os.path.join(path_cache,
'TaxiNYC_C{}_P{}_T{}.h5'.format(len_c, len_p, len_t))
if os.path.exists(fname) and CACHEDATA:
X_train, Y_train, \
X_test, Y_test, mmn, external_dim, \
timestamp_train, timestamp_test = read_cache(
fname, 'preprocessing_taxinyc.pkl')
print("load %s successfully" % fname)
else:
X_train, Y_train, \
X_test, Y_test, mmn, external_dim, \
timestamp_train, timestamp_test = TaxiNYC.load_data(
T=T, nb_flow=nb_flow, len_closeness=len_c, len_period=len_p, len_trend=len_t, len_test=len_test,
preprocess_name='preprocessing_taxinyc.pkl', meta_data=True,
meteorol_data=True, holiday_data=True, datapath=DATAPATH)
if CACHEDATA:
cache(fname, X_train, Y_train, X_test, Y_test, external_dim,
timestamp_train, timestamp_test)
print("\n days (test): ", [v[:8] for v in timestamp_test[0::T]])
print('=' * 10)
# build model
model = build_model('NY', len_c, len_p, len_t, nb_flow, map_height,
map_width, external_dim)
model_fname = 'TaxiNYC1.c4.p4.t4.lr_0.00034.batchsize_16.best.h5'
model.load_weights(os.path.join('../best_models', 'MST3D', model_fname))
# evaluate and save results
dict_multi_score = multi_step_2D(model, X_test, Y_test, mmn, len_c, step=5)
for i in range(len(dict_multi_score)):
csv_name = os.path.join('results', f'taxiny_step{i+1}.csv')
save_to_csv(dict_multi_score[i], csv_name)
def bikenyc_evaluation():
# parameters
DATAPATH = '../data' # data path, you may set your own data path with the global envirmental variable DATAPATH
CACHEDATA = True # cache data or NOT
path_cache = os.path.join(DATAPATH, 'CACHE', 'ST-ResNet') # cache path
T = 24 # number of time intervals in one day
lr = 0.0002 # learning rate
len_closeness = 3 # length of closeness dependent sequence
len_period = 4 # length of peroid dependent sequence
len_trend = 4 # length of trend dependent sequence
nb_residual_unit = 4 # paper says 4 for BikeNYC
nb_flow = 2
days_test = 10
len_test = T * days_test
map_height, map_width = 16, 8 # grid size
if CACHEDATA and os.path.isdir(path_cache) is False:
os.mkdir(path_cache)
# load data
print("loading data...")
ts = time.time()
fname = os.path.join(
path_cache, 'BikeNYC_C{}_P{}_T{}.h5'.format(len_closeness, len_period,
len_trend))
if os.path.exists(fname) and CACHEDATA:
X_train, Y_train, X_test, Y_test, mmn, external_dim, timestamp_train, timestamp_test = read_cache(
fname, 'preprocessing_bikenyc.pkl')
print("load %s successfully" % fname)
else:
X_train, Y_train, X_test, Y_test, mmn, external_dim, timestamp_train, timestamp_test = BikeNYC.load_data(
T=T,
nb_flow=nb_flow,
len_closeness=len_closeness,
len_period=len_period,
len_trend=len_trend,
len_test=len_test,
preprocess_name='preprocessing_bikenyc.pkl',
meta_data=True,
datapath=DATAPATH)
if CACHEDATA:
cache(fname, X_train, Y_train, X_test, Y_test, external_dim,
timestamp_train, timestamp_test)
print("\n days (test): ", [v[:8] for v in timestamp_test[0::T]])
print("\nelapsed time (loading data): %.3f seconds\n" % (time.time() - ts))
# build model
model = build_model(external_dim, nb_residual_unit, map_height, map_width,
len_closeness, len_period, len_trend)
model_fname = 'BikeNYC.c3.p4.t4.resunit4.iter0.cont.best.h5'
model.load_weights(os.path.join('../best_models', 'ST-ResNet',
model_fname))
# evaluate and save results
dict_multi_score = multi_step_2D(model,
X_test,
Y_test,
mmn,
len_closeness,
step=5)
for i in range(len(dict_multi_score)):
csv_name = os.path.join('results', f'bikenyc_step{i+1}.csv')
save_to_csv(dict_multi_score[i], csv_name)
def taxiny_evaluation():
# params
DATAPATH = '../data'
CACHEDATA = True
T = 24 # number of time intervals in one day
len_closeness = 3 # length of closeness dependent sequence
len_period = 1 # length of peroid dependent sequence
len_trend = 1 # length of trend dependent sequence
nb_residual_unit = 4
nb_flow = 2
days_test = 7 * 4
len_test = T * days_test
map_height, map_width = 16, 8 # grid size
path_cache = os.path.join(DATAPATH, 'CACHE', 'ST-ResNet') # cache path
if CACHEDATA and os.path.isdir(path_cache) is False:
os.mkdir(path_cache)
# load data
print("loading data...")
fname = os.path.join(
path_cache, 'TaxiNYC_C{}_P{}_T{}.h5'.format(len_closeness, len_period,
len_trend))
if os.path.exists(fname) and CACHEDATA:
X_train, Y_train, X_test, Y_test, mmn, external_dim, timestamp_train, timestamp_test = read_cache(
fname, 'preprocessing_taxinyc.pkl')
print("load %s successfully" % fname)
else:
X_train, Y_train, X_test, Y_test, mmn, external_dim, timestamp_train, timestamp_test = TaxiNYC.load_data(
T=T,
nb_flow=nb_flow,
len_closeness=len_closeness,
len_period=len_period,
len_trend=len_trend,
len_test=len_test,
preprocess_name='preprocessing_taxinyc.pkl',
meta_data=True,
meteorol_data=True,
holiday_data=True,
datapath=DATAPATH)
if CACHEDATA:
cache(fname, X_train, Y_train, X_test, Y_test, external_dim,
timestamp_train, timestamp_test)
print("\n days (test): ", [v[:8] for v in timestamp_test[0::T]])
print('=' * 10)
# build model
model = build_model(external_dim,
nb_residual_unit,
map_height,
map_width,
len_closeness,
len_period,
len_trend,
bn=True)
model_fname = 'TaxiNYC4.c3.p1.t1.resunits_4.lr_0.0001.batchsize_16.best.h5'
model.load_weights(os.path.join('../best_models', 'ST-ResNet',
model_fname))
# evaluate and save results
dict_multi_score = multi_step_2D(model,
X_test,
Y_test,
mmn,
len_closeness,
step=5)
for i in range(len(dict_multi_score)):
csv_name = os.path.join('results', f'taxiny_step{i+1}.csv')
save_to_csv(dict_multi_score[i], csv_name)
def taxibj_evaluation():
# parameters
DATAPATH = '../data' # data path, you may set your own data path with the global envirmental variable DATAPATH
CACHEDATA = True # cache data or NOT
path_cache = os.path.join(DATAPATH, 'CACHE', 'ST-ResNet') # cache path
T = 48 # number of time intervals in one day
lr = 0.0002 # learning rate
len_closeness = 3 # length of closeness dependent sequence
len_period = 1 # length of peroid dependent sequence
len_trend = 1 # length of trend dependent sequence
nb_residual_unit = 12 # paper says 12 for taxiBJ
nb_flow = 2
days_test = 7 * 4
len_test = T * days_test
map_height, map_width = 32, 32 # grid size
if CACHEDATA and os.path.isdir(path_cache) is False:
os.mkdir(path_cache)
# load data
print("loading data...")
fname = os.path.join(
path_cache,
'TaxiBJ_onlyMeta_C{}_P{}_T{}.h5'.format(len_closeness, len_period,
len_trend))
if os.path.exists(fname) and CACHEDATA:
X_train, Y_train, X_test, Y_test, mmn, external_dim, timestamp_train, timestamp_test = read_cache(
fname, 'preprocessing_taxibj.pkl')
print("load %s successfully" % fname)
else:
X_train, Y_train, X_test, Y_test, mmn, external_dim, timestamp_train, timestamp_test = TaxiBJ.load_data(
T=T,
nb_flow=nb_flow,
len_closeness=len_closeness,
len_period=len_period,
len_trend=len_trend,
len_test=len_test,
preprocess_name='preprocessing_taxibj.pkl',
meta_data=True,
meteorol_data=False,
holiday_data=False,
datapath=DATAPATH)
if CACHEDATA:
cache(fname, X_train, Y_train, X_test, Y_test, external_dim,
timestamp_train, timestamp_test)
print("\n days (test): ", [v[:8] for v in timestamp_test[0::T]])
print('=' * 10)
# build model
model = build_model(external_dim, nb_residual_unit, map_height, map_width,
len_closeness, len_period, len_trend)
model_fname = 'TaxiBJ.c3.p1.t1.resunit12.iter8.cont.best.h5'
model.load_weights(os.path.join('../best_models', 'ST-ResNet',
model_fname))
# evaluate and save results
dict_multi_score = multi_step_2D(model,
X_test,
Y_test,
mmn,
len_closeness,
step=5)
for i in range(len(dict_multi_score)):
csv_name = os.path.join('results', f'taxibj_step{i+1}.csv')
save_to_csv(dict_multi_score[i], csv_name)
def taxibj_evaluation():
# parameters
DATAPATH = '../data'
CACHEDATA = True # cache data or NOT
T = 48 # number of time intervals in one day
lr = 0.0002 # learning rate
len_closeness = 4 # length of closeness dependent sequence - should be 6
len_period = 4 # length of peroid dependent sequence
len_trend = 4 # length of trend dependent sequence
nb_flow = 2
days_test = 7 * 4
len_test = T * days_test
map_height, map_width = 32, 32 # grid size
path_cache = os.path.join(DATAPATH, 'CACHE', 'MST3D') # cache path
if CACHEDATA and os.path.isdir(path_cache) is False:
os.mkdir(path_cache)
# load data
print("loading data...")
ts = time.time()
fname = os.path.join(
path_cache, 'TaxiBJ_C{}_P{}_T{}.h5'.format(len_closeness, len_period,
len_trend))
if os.path.exists(fname) and CACHEDATA:
X_train, Y_train, X_test, Y_test, mmn, external_dim, timestamp_train, timestamp_test = read_cache(
fname, 'preprocessing.pkl')
print("load %s successfully" % fname)
else:
X_train, Y_train, X_test, Y_test, mmn, external_dim, timestamp_train, timestamp_test = TaxiBJ.load_data(
T=T,
nb_flow=nb_flow,
len_closeness=len_closeness,
len_period=len_period,
len_trend=len_trend,
len_test=len_test,
preprocess_name='preprocessing.pkl',
meta_data=True,
meteorol_data=True,
holiday_data=True,
datapath=DATAPATH)
if CACHEDATA:
cache(fname, X_train, Y_train, X_test, Y_test, external_dim,
timestamp_train, timestamp_test)
print("\n days (test): ", [v[:8] for v in timestamp_test[0::T]])
print("\nelapsed time (loading data): %.3f seconds\n" % (time.time() - ts))
print('=' * 10)
mmn._max = 1292 # just to be sure it's correct
# build model
model = build_model('BJ', len_closeness, len_period, len_trend, nb_flow,
map_height, map_width, external_dim)
model_fname = 'TaxiBJ.c4.p4.t4.iter6.best.h5'
model.load_weights(os.path.join('../best_models', 'MST3D', model_fname))
# evaluate and save results
dict_multi_score = multi_step_2D(model,
X_test,
Y_test,
mmn,
len_closeness,
step=5)
for i in range(len(dict_multi_score)):
csv_name = os.path.join('results', f'taxibj_step{i+1}.csv')
save_to_csv(dict_multi_score[i], csv_name)