def mean_error_dist(path):
validation_id_traj = load.load_directory_trajectory(path + "training/")
sim_id_traj = load.load_directory_trajectory(path + "sim/")
markov_id_traj = load.load_directory_trajectory(path + "markovchain/")
diff_list = []
min_id = None
validation_trajectoris = random.sample(validation_id_traj.values(), 20)
trajectories = random.sample(markov_id_traj.values(), 20)
count = 0
for validation_id in validation_id_traj.keys():
count += 1
if count == 50:
break
min_dist = sys.maxint
dist = traj_dist(
(random.choice(trajectories), validation_id_traj[validation_id]))
for trajectory in trajectories:
dist = traj_dist((trajectory, validation_id_traj[validation_id]))
if dist < min_dist:
min_dist = dist
min_id = validation_id
print dist
diff_list.append(dist)
validation_id_traj.pop(min_id)
diff_list.sort()
print diff_list
print np.average(diff_list)
def main():
path = "D:/training data/KDDI/#201111.CDR-data/abf7380g/"
train_traj = load.load_directory_trajectory(path + "slot/")
traj_visual(train_traj)
def evaluation(path):
id_traj = load.load_directory_trajectory(
path + "training/") # validation directory
files = os.listdir(path + "param/")
for filename in files:
parampath = path + "param/" + filename
if not os.path.isdir(parampath):
trajectories = random.sample(id_traj.values(), 500)
g = load.load_graph_traj(trajectories)
gw = gridworld.Gridworld(g, 0.9)
feature_matrix = gw.feature_matrix(g)
t_alpha = {}
with open(parampath, 'r') as f:
t = 12
for line in f:
line = line.strip('\n')
tokens = line.split(",")
param = np.zeros(11)
for j in range(11):
if len(tokens) > j:
param[j] = tokens[j]
t_alpha[t] = param.copy()
t += 1
r = dict()
for t in range(12, 48):
r[t] = dict().fromkeys(g.get_edges(), 0)
for edge in g.get_edges():
for t in range(12, 48):
if t in t_alpha.keys():
r[t][edge] = feature_matrix[edge].dot(t_alpha[t])
print "#######################################################"
policy = irl.value_iteration.find_temporal_policy(g,
r,
0.9,
46,
stochastic=True)
nll = irl_nll(policy, trajectories)
m_nll = markov_nll(trajectories)
print len(trajectories), nll, m_nll
def main():
try:
starttime = datetime.datetime.now()
id_traj = load.load_directory_trajectory("D:/PT_Result/trajectory/")
with open("C:/Users/PangYanbo/Desktop/Tokyo/Census5339/2015meshpop.csv"
) as f:
title = f.readline()
for line in f.readlines():
print "#############################"
print line
line = line.strip('\n')
tokens = line.split(',')
mesh_id = tokens[0]
trajectories = []
for uid in id_traj:
if 12 in id_traj[uid].keys():
if id_traj[uid][12][0].__eq__(mesh_id):
trajectories.append(id_traj[uid])
if len(trajectories) == 0:
continue
jobless = int(tokens[1])
workers = int(tokens[2])
students = int(tokens[3])
jobless_path = "D:/PT_Result/others/"
simulation(trajectories, jobless_path, mesh_id, jobless)
workers_path = "D:/PT_Result/commuter/"
simulation(trajectories, workers_path, mesh_id, workers)
students_path = "D:/PT_Result/student/"
simulation(trajectories, students_path, mesh_id, students)
endtime = datetime.datetime.now()
print endtime - starttime
except Exception:
print "main class wrong"
raise
def main(date, discount, epochs, learning_rate, train=True):
"""
Run maximum entropy inverse reinforcement learning on the gridworld MDP.
discount: MDP discount factor. float.
epochs: Gradient descent iterations. int.
learning_rate: Gradient descent learning rate. float.
"""
try:
starttime = datetime.datetime.now()
path = "D:/ClosePFLOW/53393575/"
if not os.path.exists(path + "sim/"):
os.mkdir(path + "sim/")
if not os.path.exists(path + "param/"):
os.mkdir(path + "param/")
tools.move_files(path)
if os.path.exists(path + "training/"):
id_traj = load.load_directory_trajectory(path + "training/")
# parameter set numbers
for i in range(26):
trajectories = random.sample(id_traj.values(), 50)
g = load.load_graph_traj(trajectories)
gw = gridworld.Gridworld(g, discount)
feature_matrix = gw.feature_matrix(g)
# train#
print "training ", path
maxent.t_irl(g, feature_matrix, trajectories, epochs,
learning_rate, path + "param/" + str(i))
endtime = datetime.datetime.now()
print "finished reading files with time of" + str(endtime - starttime)
except Exception:
print "mian class wrong"
raise
def simulation(path):
id_traj = load.load_directory_trajectory(path + "slot/")
files = os.listdir(path + "param/")
if not os.path.exists(path + "sim/"):
os.mkdir(path + "sim/")
for filename in files:
parampath = path + "param/" + filename
if not os.path.isdir(parampath):
trajectories = id_traj.values()
g = load.load_graph_traj(trajectories)
gw = gridworld.Gridworld(g, 0.9)
feature_matrix = gw.feature_matrix(g)
t_alpha = {}
with open(parampath, 'r') as f:
t = 12
for line in f:
line = line.strip('\n')
tokens = line.split(",")
param = numpy.zeros(11)
for j in range(11):
if len(tokens) > j:
param[j] = tokens[j]
t_alpha[t] = param.copy()
t += 1
r = dict()
for t in range(12, 48):
r[t] = dict().fromkeys(g.get_edges(), 0)
for edge in g.get_edges():
for t in range(12, 48):
if t in t_alpha.keys():
r[t][edge] = feature_matrix[edge].dot(t_alpha[t])
start = "53397561"
tools.generate_temporal_traj(g, r, start, 0.5, path + "sim/",
filename[0:2])
def main(target):
path_observed = "D:/PT_Result/" + target + "/"
id_traj = load.load_directory_trajectory(path_observed + "training/")
if not os.path.exists(path_observed + "markovchain/"):
os.mkdir(path_observed + "markovchain/")
for i in range(36):
path = "D:/PT_Result/" + target + "/" + '/markovchain/' + str(i) + '.csv'
trajectories = random.sample(id_traj.values(), 50)
initial = []
for traj in trajectories:
if 12 in traj.keys():
initial.append(traj[12][1].get_origin())
pairs = makepairs(trajectories)
cfd = nltk.ConditionalFreqDist(pairs)
generate(cfd, path, random.choice(initial))
print "###################################"
def main(discount, epochs, learning_rate):
"""
Run maximum entropy inverse reinforcement learning on the gridworld MDP.
discount: MDP discount factor. float.
epochs: Gradient descent iterations. int.
learning_rate: Gradient descent learning rate. float.
"""
try:
starttime = datetime.datetime.now()
path = "D:/training data/KDDI/#201111.CDR-data/abf7380g/slot/"
id_traj = load.load_directory_trajectory(path)
print len(id_traj)
trajectories = random.sample(id_traj.values(), 20)
g = load.load_graph_traj(trajectories)
gw = gridworld.Gridworld(g, discount)
feature_matrix = gw.feature_matrix(g)
# train#
print("training ", path)
if not os.path.exists(path + "param/"):
os.mkdir(path + "param/")
maxent.t_irl(g, feature_matrix, trajectories, epochs, learning_rate,
path + "param/")
endtime = datetime.datetime.now()
print("finished reading files with time of" + str(endtime - starttime))
except Exception:
print("mian class wrong")
raise
mode = trajectory[slot][1].get_mode()
temp = (origin, destination)
pairs.append(temp)
return pairs
def generate(cfd, path, word='53393574', num=36):
out = open(path, 'w')
for i in range(num):
# make an array with the words shown by proper count
arr = []
for j in cfd[word]:
for k in range(cfd[word][j]):
arr.append(j)
# choose the word randomly from the conditional distribution
word = arr[int((len(arr)) * random.random())]
out.write(str(12 + i) + ',' + word + '\n')
out.close()
path_observed = "D:/ClosePFLOW/53393574/training/"
id_traj = load.load_directory_trajectory(path_observed)
for i in range(36):
path = 'D:/ClosePFLOW/53393574/markovchain/' + str(i) + '.csv'
trajectories = random.sample(id_traj.values(), 50)
pairs = makePairs(trajectories)
cfd = nltk.ConditionalFreqDist(pairs)
generate(cfd, path)
def main():
root = "/home/ubuntu/Data/pflow_data/pflow-csv/"
mesh_list = read_list("/home/ubuntu/Data/Tokyo/MeshCode/Tokyo.csv")
list_dirs = os.walk(root)
count = 0
print mesh_list
for root, dirs, files in list_dirs:
with open("/home/ubuntu/Data/PT_Result/exp1/result.csv", "w") as f:
for d in dirs:
if d in mesh_list:
file_list = os.listdir(os.path.join(root, d))
if len(file_list) > 100 and "train_irl.csv" in file_list:
count += 1
id_traj = load.load_trajectory(os.path.join(root, d) + "/train_irl.csv")
train, validation = train_test_split(id_traj.values(), test_size=0.4)
g = load.load_graph_traj(train)
gw = gridworld.Gridworld(g, 0.9)
feature_matrix = gw.feature_matrix(g)
path = "/home/ubuntu/Data/PT_Result/exp1/"
# train
if not os.path.exists(path + "parameter/" + d + "param.csv"):
maxent.t_irl(g, feature_matrix, train, 200, 0.2, path + "parameter/" + d)
# simulation
t_alpha = read_param(path + "parameter/" + os.listdir(path+"parameter/")[0])
r = dict()
for t in range(12, 48):
r[t] = dict().fromkeys(g.get_edges(), 0)
for edge in g.get_edges():
for t in range(12, 48):
if t in t_alpha.keys():
r[t][edge] = feature_matrix[edge].dot(t_alpha[t])
if not os.path.exists(path + "sim/" + d + "/"):
os.mkdir(path + "sim/" + d + "/")
for i in range(80):
tools.generate_temporal_traj(g, r, d, 0.5, path + "sim/" + d + "/", d + "_" + str(i))
# markov chain
if not os.path.exists(path + "markov/" + d + "/"):
os.mkdir(path + "markov/" + d + "/")
for i in range(80):
pairs = makepairs(train)
cfd = nltk.ConditionalFreqDist(pairs)
generate(cfd, str(i), path + "markov/" + d + "/" + str(i) + ".csv", d)
# expansion validation
expansion10_trajecotry = random.sample(train, int(len(train)*0.1))
diff_list = []
for validation_traj in validation:
min_dist = sys.maxint
for traj in expansion10_trajecotry:
dist = traj_dist((traj, validation_traj))
if dist < min_dist:
min_dist = dist
diff_list.append(min_dist)
expansion10_score = np.average(diff_list)
expansion50_trajecotry = random.sample(train, int(len(train) * 0.5))
diff_list = []
for validation_traj in validation:
min_dist = sys.maxint
for traj in expansion50_trajecotry:
dist = traj_dist((traj, validation_traj))
if dist < min_dist:
min_dist = dist
diff_list.append(min_dist)
expansion50_score = np.average(diff_list)
# validation
markov_id_traj = load.load_directory_trajectory(path + "markov/" + d + "/")
diff_list = []
print markov_id_traj.keys()
for traj in validation:
min_dist = sys.maxint
for markov_id in markov_id_traj.keys():
dist = traj_dist((traj, markov_id_traj[markov_id]))
if dist < min_dist:
min_dist = dist
diff_list.append(min_dist)
markov_score = np.average(diff_list)
sim_id_traj = load.load_directory_trajectory(path + "sim/" + d + "/")
diff_list = []
for traj in validation:
min_dist = sys.maxint
for sim_id in sim_id_traj.keys():
dist = traj_dist((traj, sim_id_traj[sim_id]))
if dist < min_dist:
min_dist = dist
if min_dist > 10:
continue
diff_list.append(min_dist)
sim_score = np.average(diff_list)
print d+","+str(sim_score)+","+str(markov_score)+","+str(expansion10_score)+","+str(expansion50_score)
f.write(d+","+str(sim_score)+","+str(markov_score)+","+str(expansion10_score)+","+str(expansion50_score))
f.write("\n")
if count > 80:
f.close()
def main():
path_sim = "D:/ClosePFLOW/53393574/sim/"
path_validation = "D:/ClosePFLOW/53393574/validation/"
path_observed = "D:/PT_Result/commuter/sim/"
if not os.path.exists("D:/ClosePFLOW/53393574/comparison/"):
os.mkdir("D:/ClosePFLOW/53393574/comparison/")
observed_id_traj = load.load_directory_trajectory(path_observed)
sim_id_traj = load.load_directory_trajectory(path_sim)
validation_id_traj = load.load_directory_trajectory(path_validation)
markov_id_traj = load_markovchain("D:/ClosePFLOW/53393574/markovchain/")
print len(markov_id_traj)
# diff_list = []
# min_id = None
# trajectories = random.sample(observed_id_traj.values(), 10)
# for validation_id in validation_id_traj.keys():
#
# min_dist = sys.maxint
# dist = traj_dist((random.choice(trajectories), validation_id_traj[validation_id]))
# for trajectory in trajectories:
# dist = traj_dist((trajectory, validation_id_traj[validation_id]))
# if dist < min_dist:
# min_dist = dist
# min_id = validation_id
#
# diff_list.append(dist)
#
# validation_id_traj.pop(min_id)
#
# diff_list.sort()
#
# print diff_list
# print np.average(diff_list)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
count = 0
for uid in observed_id_traj.keys():
count += 1
if count > 80:
break
X = []
Y = []
Z = []
trajectory = observed_id_traj[uid]
# print "trajectory", trajectory
# print "adjusted trajectory", adjust_traj(trajectory)
for i in range(12, 47):
if i in trajectory.keys():
x, y = tools.parse_MeshCode(trajectory[i][0])
X.append(x)
Y.append(y)
Z.append(i-12)
ax.plot(X, Y, Z)
ax.set_zlabel('Hour')
ax.set_ylabel('Lat')
ax.set_xlabel('Lon')
plt.show()
# for uid in validation_id_traj.keys():
# X = []
# Y = []
# Z = []
#
#
# trajectory = validation_id_traj[uid]
# # print "trajectory", trajectory
# # print "adjusted trajectory", adjust_traj(trajectory)
# for i in range(12, 47):
# if i in trajectory.keys():
# x, y = tools.parse_MeshCode(trajectory[i][0])
# X.append(x)
# Y.append(y)
# Z.append(i-12)
# ax.plot(X, Y, Z)
#
# plt.show()
#
# for uid in sim_id_traj.keys():
# X = []
# Y = []
# Z = []
# trajectory = sim_id_traj[uid]
# for i in range(12, 46):
# if i in trajectory.keys():
# x, y = tools.parse_MeshCode(trajectory[i][0])
# X.append(x)
# Y.append(y)
# Z.append(i-12)
# ax.plot(X, Y, Z, color="blue")
#
# plt.show()
diff_list = []
#
# for ob_id in observed_id_traj:
# fig = plt.figure()
#
# ax = fig.add_subplot(111, projection='3d')
#
# min_dist = sys.maxint
# for validation_id in validation_id_traj.keys():
# dist = traj_dist((observed_id_traj[ob_id], validation_id_traj[validation_id]))
# if dist < min_dist:
# min_dist = dist
# min_id = validation_id
#
# diff_list.append(min_dist)
# if min_id in validation_id_traj.keys():
# X = []
# Y = []
# Z = []
# trajectory = validation_id_traj[min_id]
# # print "trajectory", trajectory
# # print "adjusted trajectory", adjust_traj(trajectory)
# for i in range(12, 47):
# if i in trajectory.keys():
# x, y = tools.parse_MeshCode(trajectory[i][0])
# print x, y
# X.append(x)
# Y.append(y)
# Z.append(i - 12)
# ax.plot(X, Y, Z, color="red")
#
# X = []
# Y = []
# Z = []
# trajectory = observed_id_traj[ob_id]
# for i in range(12, 46):
# if i in trajectory.keys():
# x, y = tools.parse_MeshCode(trajectory[i][0])
# X.append(x)
# Y.append(y)
# Z.append(i - 12)
# ax.plot(X, Y, Z, color="blue")
#
# ax.set_zlabel('time')
# ax.set_ylabel('lat')
# ax.set_xlabel('lon')
# plt.show()
#
# validation_id_traj.pop(min_id)
#
# diff_list.sort()
#
# print len(diff_list)
#
# x = range(len(diff_list))
# plt.plot(x, diff_list)
# plt.show()
diff_list = []
for sim_id in sim_id_traj:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
min_dist = sys.maxint
for validation_id in validation_id_traj.keys():
dist = traj_dist((sim_id_traj[sim_id], validation_id_traj[validation_id]))
if dist < min_dist:
min_dist = dist
min_id = validation_id
diff_list.append(min_dist)
print min_id
if min_id in validation_id_traj.keys():
X = []
Y = []
Z = []
trajectory = validation_id_traj[min_id]
# print "trajectory", trajectory
# print "adjusted trajectory", adjust_traj(trajectory)
for i in range(12, 47):
if i in trajectory.keys():
x, y = tools.parse_MeshCode(trajectory[i][0])
X.append(x)
Y.append(y)
Z.append(i - 12)
ax.plot(X, Y, Z, color="red", linewidth=3.5)
X = []
Y = []
Z = []
trajectory = sim_id_traj[sim_id]
for i in range(12, 46):
print trajectory.keys()
if i in trajectory.keys():
x, y = tools.parse_MeshCode(trajectory[i][0])
print i,";;;;;;;;;;;;;;;"
print trajectory[i]
X.append(x)
Y.append(y)
Z.append(i - 12)
ax.plot(X, Y, Z, color="blue", linewidth=3.5)
ax.set_zlabel('time')
ax.set_ylabel('lat')
ax.set_xlabel('lon')
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(6)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(6)
for tick in ax.zaxis.get_major_ticks():
tick.label1.set_fontsize(6)
ax.xaxis.set_ticks_position('none')
plt.xlim((139, 140))
plt.ylim((35.3, 36))
plt.title(min_dist)
plt.savefig("D:/ClosePFLOW/53393574/comparison/traj_compare"+sim_id+"_"+".png")
plt.show()
validation_id_traj.pop(min_id)
diff_list.sort()
print np.average(diff_list)
def main(date, discount, epochs, learning_rate, train=True):
"""
Run maximum entropy inverse reinforcement learning on the gridworld MDP.
discount: MDP discount factor. float.
epochs: Gradient descent iterations. int.
learning_rate: Gradient descent learning rate. float.
"""
"""
# this part is used for calculate uniform reward parameter
id_trajectory = load.load_trajectory(10000)
print tools.motion_model_policy(id_trajectory)
for i in range(1000):
graph_trajectories = tools.choose_trajectory(1000, id_trajectory)
g = load.load_graph_traj(graph_trajectories)
sample_trajectories = sample(graph_trajectories, 100)
gw = gridworld.Gridworld(g, 0.9)
feature_matrix = gw.feature_matrix(g)
alpha = maxent.irl(g, feature_matrix, sample_trajectories, 40, 0.05)
path = str("D:/Ubicomp/alpha" + str(i) + ".txt")
numpy.savetxt(path, alpha)
"""
"""
this part is usedfor temporal reward parameter training
"""
try:
starttime = datetime.datetime.now()
path = "D:/ClosePFLOW/"
dirs = os.listdir(path)
for dirname in dirs:
directory = path + dirname + "/"
print directory
if not os.path.exists(directory + "sim/"):
os.mkdir(directory + "sim/")
tools.move_files(directory)
if os.path.exists(directory + "training/"):
id_traj = load.load_directory_trajectory(directory +
"training/")
if (len(id_traj) >= 40
and not os.path.exists(directory + "param.csv")
) or os.path.getsize(directory + "param.csv") > 2038:
trajectories = id_traj.values()
g = load.load_graph_traj(trajectories)
gw = gridworld.Gridworld(g, discount)
feature_matrix = gw.feature_matrix(g)
# train#
print "training ", directory
maxent.t_irl(g, feature_matrix, trajectories, epochs,
learning_rate, directory)
indicator = 0
i = 0
while indicator <= 5000:
sample_id = []
trajectories = []
for k in range(indicator, indicator + 100):
sample_id.append(id_list[k])
for sid in sample_id:
trajectories.append(id_traj.get(sid))
start_state = []
for traj in trajectories:
start_state.append(traj[12][0])
training_data = "C:/Users/PangYanbo/Desktop/UbiResult/TrainingTrajectoriesGroup_" + str(
i) + ".csv"
with open(training_data, "wb") as f:
for k in range(100):
for j in range(12, 47):
if j in trajectories[k].keys():
f.write(
str(j) + ',' +
trajectories[k][j][1].get_origin() + ',' +
trajectories[k][j][1].get_destination() + ',' +
trajectories[k][j][1].get_mode() + '\n')
# initial environment based on trajectories
g = load.load_graph_traj(trajectories)
gw = gridworld.Gridworld(g, discount)
feature_matrix = gw.feature_matrix(g)
print g
if train:
# training the model
maxent.t_irl(g, feature_matrix, trajectories, epochs,
learning_rate, date)
else:
# simulation
for start in start_state:
# read alpha from saved file
root = "C:/Users/PangYanbo/Desktop/UbiResult/param/"
para_list = list(
os.path.join(root, name) for name in os.listdir(root))
for filename in para_list:
if os.path.isdir(filename):
para_list.remove(filename)
param_path = random.choice(para_list)
agent_id = param_path[43:-4]
print agent_id, param_path
t_alpha = {}
with open(param_path, 'r') as f:
t = 12
for line in f:
line = line.strip('\n')
tokens = line.split(",")
param = numpy.zeros(11)
for j in range(11):
if len(tokens) > j:
param[j] = tokens[j]
t_alpha[t] = param.copy()
t += 1
r = dict()
for t in range(12, 48):
r[t] = dict().fromkeys(g.get_edges(), 0)
for edge in g.get_edges():
for t in range(12, 48):
if t in t_alpha.keys():
r[t][edge] = feature_matrix[edge].dot(
t_alpha[t])
tools.generate_temporal_traj(g, r, start, 0.5, i, agent_id)
i += 1
indicator += 50
endtime = datetime.datetime.now()
print "finished reading files with time of" + str(endtime - starttime)
except Exception:
print "something wrong"
raise