def main():
parser = argparse.ArgumentParser(description='Placement')
parser.add_argument("-c",
"--config",
help="path to config file",
default="")
parser.add_argument("-i", "--input", help="path to data file", default="")
parser.add_argument("-d",
"--debug",
help="debug mode",
action="store_const",
dest="loglevel",
const=logging.DEBUG,
default=logging.WARNING)
parser.add_argument("-p",
"--pickle",
help="create pickle files",
action="store_true",
dest="create_pickle")
parser.add_argument("-v",
"--verbose",
help="verbose mode",
action="store_const",
dest="loglevel",
const=logging.INFO)
args = parser.parse_args(sys.argv[1:])
logging.basicConfig(format='%(asctime)s main: %(message)s',
level=args.loglevel)
config = pandas.read_csv(args.config, sep=',')
X = pandas.read_csv(args.input, sep=',',
header=None).values.astype(np.float64)
"""
discretize the space into cells
"""
geo_utils = helpers.GeoUtilities(config['lat_min'].iloc[0],
config['lat_max'].iloc[0],
config['lng_min'].iloc[0],
config['lng_max'].iloc[0],
cell_length_meters)
geo_utils.set_grids()
"""
segregate data based on time bins
"""
train_time_utils = helpers.TimeUtilities(time_bin_width_secs)
train_time_utils.set_bounds(X)
logging.info("Loaded %d data points", len(X))
sim = Sim(X, len(geo_utils.lng_grids), train_time_utils, geo_utils,
action_dim, time_bins_per_hour)
"""
starting time-step for training and testing time bins (tb)
"""
test_tb_starts = [time_bins_per_hour]
train_tb_starts = [
time_bins_per_day * 4 + time_bins_per_hour,
time_bins_per_day * 5 + time_bins_per_hour,
time_bins_per_day * 6 + time_bins_per_hour
]
train_bins = range(time_bins_per_day * 4 + time_bins_per_hour,
time_bins_per_day * 7, time_bins_per_day)
if args.create_pickle:
helpers.load_create_pickle(sim, train_time_utils, geo_utils, X,
train_tb_starts, test_tb_starts)
with open(r"rrs.pickle", "rb") as input_file:
sim.rrs = cPickle.load(input_file)
sim.n_reqs = cPickle.load(input_file)
sim.pre_sim_pickups = cPickle.load(input_file)
with tf.Session() as sess:
worker = Worker('worker', sim, 10, train_bins, test_tb_starts,
sim.num_cells, sim.n_actions)
sess.run(tf.global_variables_initializer())
worker.train(sess)
"""
model = A2C(sim, 10,
train_windows, test_window,
sim.num_cells,
sim.n_actions, hidden_units)
model.train()
"""
'''
