def cols(choice, its, noise, miniters = 0, highlight = -1):
'''Returns an array of colors mapped to iteration by index'''
colors = zeros((its, 3), uint8)
# switches between highlighting individual iterations or just giving a color map
if highlight == -1:
colors[0] = (0, 0, 0)
if choice == 0:
for i in range(miniters, its): colors[i] = (int(remap(i, miniters, its, 0.0, 255.0)), int(remap(i, miniters, its, 0.0, 255.0)), int(remap(i, miniters, its, 0.0, 255.0)))
return colors
if choice == 1:
for i in range(1, its): colors[i] = (colmap(cm.gnuplot2(abs(remap(((i + 256) % 512), 0, 512, -1.0, 1.0)))[0]), colmap(cm.gnuplot2(abs(remap(((i + 256) % 512), 0, 512, -1.0, 1.0)))[1]), colmap(cm.gnuplot2(abs(remap(((i + 256) % 512), 0, 512, -1.0, 1.0)))[2]))
return colors
if choice == 2:
for i in range(1, its): colors[i] = noise.gencolor(i)
return colors
else:
if choice == 0:
colors[highlight] = (255, 255, 255)
return colors
if choice == 1:
colors[highlight] = (colmap(cm.gnuplot2(abs(remap(((highlight + 256) % 512), 0, 512, -1.0, 1.0)))[0]), colmap(cm.gnuplot2(abs(remap(((highlight + 256) % 512), 0, 512, -1.0, 1.0)))[1]), colmap(cm.gnuplot2(abs(remap(((highlight + 256) % 512), 0, 512, -1.0, 1.0)))[2]))
return colors
if choice == 2:
colors[highlight] = noise.gencolor(highlight)
return colors
def get_rgb(temp):
# NOTE: this takes considerable time. Use binning later on.
# Or even give everything to the shader.
# Good ones are afmhot, CMRmap, gist_heat, gnuplot, gnuplot2.
# See http://matplotlib.org/users/colormaps.html for more.
color = cm.gnuplot2(int(temp), bytes=True)
return '{r},{g},{b}'.format(r=color[0], g=color[1], b=color[2])
def get_colors(self, qty):
qty = np.power(qty / qty.max(), 1.0 / CONTRAST)
if COLORMAP == 0:
rgba = cm.gray(qty, alpha=ALPHA)
elif COLORMAP == 1:
rgba = cm.afmhot(qty, alpha=ALPHA)
elif COLORMAP == 2:
rgba = cm.hot(qty, alpha=ALPHA)
elif COLORMAP == 3:
rgba = cm.gist_heat(qty, alpha=ALPHA)
elif COLORMAP == 4:
rgba = cm.copper(qty, alpha=ALPHA)
elif COLORMAP == 5:
rgba = cm.gnuplot2(qty, alpha=ALPHA)
elif COLORMAP == 6:
rgba = cm.gnuplot(qty, alpha=ALPHA)
elif COLORMAP == 7:
rgba = cm.gist_stern(qty, alpha=ALPHA)
elif COLORMAP == 8:
rgba = cm.gist_earth(qty, alpha=ALPHA)
elif COLORMAP == 9:
rgba = cm.spectral(qty, alpha=ALPHA)
return rgba
def get_colors(self, qty):
qty = np.power(qty / qty.max(), 1.0 / CONTRAST)
if COLORMAP == 0:
rgba = cm.gray(qty, alpha=ALPHA)
elif COLORMAP == 1:
rgba = cm.afmhot(qty, alpha=ALPHA)
elif COLORMAP == 2:
rgba = cm.hot(qty, alpha=ALPHA)
elif COLORMAP == 3:
rgba = cm.gist_heat(qty, alpha=ALPHA)
elif COLORMAP == 4:
rgba = cm.copper(qty, alpha=ALPHA)
elif COLORMAP == 5:
rgba = cm.gnuplot2(qty, alpha=ALPHA)
elif COLORMAP == 6:
rgba = cm.gnuplot(qty, alpha=ALPHA)
elif COLORMAP == 7:
rgba = cm.gist_stern(qty, alpha=ALPHA)
elif COLORMAP == 8:
rgba = cm.gist_earth(qty, alpha=ALPHA)
elif COLORMAP == 9:
rgba = cm.spectral(qty, alpha=ALPHA)
return rgba
def draw_agent(agent: ConspiracyAgent) -> dict:
portrayal = {
'Shape': 'circle',
'r': 0.5,
'Filled': 'true',
'Layer': 0,
'Color': c.to_hex(cm.gnuplot2(agent.prior_value)),
}
return portrayal
def main():
f = open('label.txt','w')
#target_names = np.array(args.names)
X, target_names, y = getXY(args.image_dir)
X = np.asfarray(X,dtype='float')
colors = cm.gnuplot2(np.linspace(0, 1, len(target_names)))
#X_pca = PCA(n_components=128).fit_transform(X)
X_pca = X
tsne = TSNE(n_components=2, init='random', random_state=0)
X_r = tsne.fit_transform(X_pca)
for c, i, target_name in zip(colors,
list(range(0, len(target_names))),
target_names):
plt.scatter(X_r[y[i], 0], X_r[y[i], 1],
c=c, label=str(i+1))
f.write(target_name+'\n')
plt.legend()
plt.savefig("{}/10crop1.png".format('./'))
f.close()
def make_plots_3to7(dict_in, param_varied, var_name):
from matplotlib.ticker import MultipleLocator
import matplotlib.cm as cm
fig, ((ax1, ax2), (ax3, ax4)) = pl.subplots(2,
2,
figsize=[12, 12],
sharex=True)
dashes = [(None, None), (2, 2), (5, 5), (5, 3, 1, 3), (5, 3, 1, 3, 1, 3),
(10, 4)]
colors = cm.gnuplot2(
np.union1d([0], np.linspace(0.3, 0.8,
len(dict_in.keys()) - 1)))
i = 0
ks = [float(val) for val in dict_in.keys()]
ks = [str(val) for val in np.sort(ks)]
for val in ks:
log_mL = dict_in[val]['log_mL']
VRcolor = dict_in[val]['VRcolor']
UVcolor = dict_in[val]['UVcolor']
VKcolor = dict_in[val]['VKcolor']
times = dict_in[val]['times']
ax1.plot(times,
log_mL,
marker='',
dashes=dashes[i],
color=colors[i],
lw=2,
label=param_varied + ' = ' + str(val))
ax2.plot(times,
UVcolor,
marker='',
dashes=dashes[i],
color=colors[i],
lw=2,
label=param_varied + ' = ' + str(val))
ax3.plot(times,
VRcolor,
marker='',
dashes=dashes[i],
color=colors[i],
lw=2,
label=param_varied + ' = ' + str(val))
ax4.plot(times,
VKcolor,
marker='',
dashes=dashes[i],
color=colors[i],
lw=2,
label=param_varied + ' = ' + str(val))
i += 1
# Make figures look nice
# Upper Left: M/L vs time
ax1.set_xlim([0, 15])
ax1.set_ylim([-0.3, 1.0])
ax1.set_xticks([0, 5, 10, 15])
minor_locator = MultipleLocator(1)
ax1.xaxis.set(minor_locator=minor_locator)
ax1.set_yticks(np.arange(-0.2, 1.1, 0.2))
minor_locator = MultipleLocator(0.2 / 4)
ax1.yaxis.set(minor_locator=minor_locator)
ax1.set_ylabel(r'log(M/$\rm L_V$)')
ax1.legend(loc='lower right')
# Upper Right: U - V vs time
ax2.set_ylim([0.6, 1.8])
ax2.set_yticks(np.arange(0.6, 1.81, 0.2))
minor_locator = MultipleLocator(0.2 / 4)
ax2.yaxis.set(minor_locator=minor_locator)
ax2.set_ylabel(r'U - V')
# Lower Left: V - R vs time
ax3.set_ylim(0.4, 0.7)
ax3.set_yticks(np.arange(0.4, 0.71, 0.05))
minor_locator = MultipleLocator(0.05 / 4)
ax3.yaxis.set(minor_locator=minor_locator)
ax3.set_ylabel(r'V - R')
ax3.set_xlabel(r'Time (Gyr)')
# Lower Right: V - K vs time
ax4.set_ylim([2.0, 4.0])
ax4.set_yticks(np.arange(2, 4.1, 0.5))
minor_locator = MultipleLocator(0.5 / 4)
ax4.yaxis.set(minor_locator=minor_locator)
ax4.set_xlabel('Time (Gyr)')
ax4.set_ylabel('V - K')
fig.savefig('test_%s.png' % var_name, bbox='tight')
#pl.show()
pl.close()
return [fig]
import matplotlib.pyplot as plt
import numpy as np
import array as arr
import matplotlib.cm as cm
from itertools import cycle
from matplotlib.figure import figaspect
my_colors = iter(cm.gnuplot2(np.linspace(0, 1, 8)))
thread_default = np.array([88])
runtime_default = np.array([0.0264344])
threads = np.array([1, 2, 4, 8, 16, 22, 44, 88])
af_none = np.array([
0.324832, 0.163288, 0.0842142, 0.0435799, 0.0230997, 0.0194625, 0.0210078,
0.0264214
])
af_sockets_false = np.array([
0.325076, 0.163253, 0.0838667, 0.0436435, 0.023046, 0.0190545, 0.0206504,
0.0238985
])
af_cores_false = np.array([
0.324648, 0.16354, 0.0837889, 0.0434537, 0.0231496, 0.0194563, 0.0212046,
0.0237055
])
af_threads_false = np.array([
0.325007, 0.163234, 0.0840898, 0.0434851, 0.0231797, 0.0194611, 0.0210849,
0.0239731
])
af_sockets_true = np.array([
0.326206, 0.163408, 0.0837538, 0.0521883, 0.0292147, 0.0231815, 0.0199503,
0.0232362
Note: This example assumes that `name i` corresponds to `label i`
in `labels.csv`.
""")
parser = argparse.ArgumentParser()
parser.add_argument('workDir', type=str)
parser.add_argument('--names', type=str, nargs='+', required=True)
args = parser.parse_args()
y = pd.read_csv("{}/labels.csv".format(args.workDir)).as_matrix()[:, 0]
X = pd.read_csv("{}/reps.csv".format(args.workDir)).as_matrix()
target_names = np.array(args.names)
colors = cm.gnuplot2(np.linspace(0, 0.7, len(target_names)))
X_pca = PCA(n_components=50).fit_transform(X, X)
tsne = TSNE(n_components=2, init='random', random_state=0)
X_r = tsne.fit_transform(X_pca)
for c, i, target_name in zip(colors, list(range(1,
len(target_names) + 1)),
target_names):
plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)
plt.legend()
out = "{}/tsne.pdf".format(args.workDir)
plt.savefig(out)
print("Saved to: {}".format(out))
#plt.plot(dG_FEP[1,:],-dG_cumulant[0,1,:]-dG_cumulant[1,1,:],'g.')
#plt.plot(dG_FEP[2,:],-dG_cumulant[0,2,:]-dG_cumulant[1,2,:],'b.')
#plt.plot([0],[0],'r.',label="U")
#plt.plot([0],[0],'g.',label="TS")
#plt.plot([0],[0],'b.',label="N")
plt.show()
raise SystemExit
Y = dcontact_E/(0.5*(stdEij_U**2 + stdEij_N**2))
plt.figure()
for i in range(Eij.shape[1]):
plt.plot(Qavg,Y[:,i],color=cm.gnuplot2((relative_TS_fluct[i] - relative_TS_fluct.min())/relative_TS_fluct.max()))
plt.xlabel("$Q$",fontsize=18)
plt.ylabel("$\\frac{2\\langle\\delta\\epsilon_{ij}^2\\rangle(Q)}{\\langle\\delta\\epsilon_{ij}^2\\rangle_U + \\langle\\delta\\epsilon_{ij}^2\\rangle_N}$",fontsize=22)
#plt.title("$\\frac{2\\langle\\delta\\epsilon_{ij}^2\\rangle}{\\left(\\langle\\delta\\epsilon_{ij}^2\\rangle_U + \\langle\\delta\\epsilon_{ij}^2\\rangle_N\\right)}$")
plt.title("Energy Fluctuations Relative to average of U and N")
plt.savefig("eps_fluct_relative_U_and_N.png")
plt.savefig("eps_fluct_relative_U_and_N.pdf")
plt.savefig("eps_fluct_relative_U_and_N.eps")
Z = dcontact_E/(stdEij_N**2)
plt.figure()
for i in range(Eij.shape[1]):
plt.plot(Qavg,Z[:,i],color=cm.gnuplot2((relative_TS_fluct[i] - relative_TS_fluct.min())/relative_TS_fluct.max()))
plt.xlabel("Q")
#plt.ylabel("$\\langle\\delta\\epsilon_{ij}^2\\rangle / \\langle\\delta\\epsilon_{ij}^2\\rangle_N $")
np.savetxt("contact_Q.dat", contact_Q)
np.savetxt("dQ2.dat", dQ2)
np.savetxt("contact_avgE.dat", contact_avgE)
np.savetxt("dcontact_E.dat", dcontact_E)
np.savetxt("dcontact_S.dat", dcontact_E)
np.savetxt("ddG_vs_Q.dat", ddG_vs_Q)
np.savetxt("ddG_deriv_vs_Q.dat", ddG_deriv_vs_Q)
Y = dcontact_E / (0.5 * (stdEij_U**2 + stdEij_N**2))
plt.figure()
for i in range(Eij.shape[1]):
plt.plot(Qavg,
Y[:, i],
color=cm.gnuplot2(
(relative_TS_fluct[i] - relative_TS_fluct.min()) /
relative_TS_fluct.max()))
plt.xlabel("$Q$", fontsize=18)
plt.ylabel(
"$\\frac{2\\langle\\delta\\epsilon_{ij}^2\\rangle(Q)}{\\langle\\delta\\epsilon_{ij}^2\\rangle_U + \\langle\\delta\\epsilon_{ij}^2\\rangle_N}$",
fontsize=22)
#plt.title("$\\frac{2\\langle\\delta\\epsilon_{ij}^2\\rangle}{\\left(\\langle\\delta\\epsilon_{ij}^2\\rangle_U + \\langle\\delta\\epsilon_{ij}^2\\rangle_N\\right)}$")
plt.title("Energy Fluctuations Relative to average of U and N")
plt.savefig("eps_fluct_relative_U_and_N.png")
plt.savefig("eps_fluct_relative_U_and_N.pdf")
plt.savefig("eps_fluct_relative_U_and_N.eps")
Z = dcontact_E / (stdEij_N**2)
plt.figure()
for i in range(Eij.shape[1]):
plt.plot(Qavg,
S = S - 1
if j == 1:
S == S
b = np.random.choice([2, 0], I, p=[γ, 1 - γ])
#infected individuals have chances to recover or stay ill
for e in b:
if e == 2:
R = +1 #to append the newly recovered people and delete it in the infectious number
I = I - 1
if e == 0:
I == I
B.append(I) #store the result of I for a vaccination rate
BB.append(B) #to store infectious population of different vaccination rate
B = [1]
#plot
#print('the number of infectious',B)
plt.figure(figsize=(6, 4), dpi=150)
for j in range(0, 11):
X = range(0, 1001)
f = plt.plot(
X, BB[j], label=str(j * 10) + '%', color=cm.gnuplot2(j * 20)
) #the plot function is inside the loop, so it can be ploted in one figure
#plt.savefig ("markflow" ,type="png")
#plt.plot(x, y, format_string, **kwargs):
#x is the X-axis data, can be a list or array;Y in the same way;Format string for control curve, **kwargs second group or more (x, y, format_string)
plt.xlabel("times", fontsize=14)
plt.ylabel("number of people", fontsize=14)
plt.title("SIR model with different vaccination rates")
plt.legend()
plt.show()
import os
import sys
# sys.path.insert(0, r'./')
import synthwave_parts as synth
matplotlib.matplotlib_fname()
rcParams['animation.convert_path'] = '/usr/local/bin/convert'
# import synthwave_parts as synth
background = '#000000'
# black to purple gradient
horizon = {'grad': cm.gnuplot(range(28)), 'dist': 16}
ground = {'grad': cm.gnuplot2(range(20)), 'dist': 50}
matplotlib.rcParams['axes.facecolor'] = background
# key paramters
perspective = (0, 5)
motion_lines = 10
frames = 200
xlim = (-100, 100)
ylim = (-50, 70)
# figure and axes setup
fig = plt.figure(figsize=(20, 12))
ax = plt.axes(xlim=xlim, ylim=ylim)
exp_debug = False
rho = 28.0
beta = 8.0 / 3.0
""" Create PyGnosis System """
references = {}
variables = {
'x': [[1.0]], # rate of convection, formally 'x'
'y': [[1.0]], # horizontal temperature variation, formally 'y'
'z': [[1.0]] # vertical temperature variation, formally 'z'
}
sys = pygnosis.System(references, variables)
""" Define system dynamics """
dx_dt = lambda: sigma * (sys.y - sys.x)
dy_dt = lambda: sys.x * (rho - sys.z) - sys.y
dz_dt = lambda: sys.x * sys.y - beta * sys.z
dynamics = {'x': dx_dt, 'y': dy_dt, 'z': dz_dt}
sys.setup_dynamics(dynamics)
""" Build simulation """
sim = pygnosis.Simulation(sys, timestep=0.01, timespan=40.0)
""" Run simulation """
states = sim()
""" 3-D Scatter """
from matplotlib import cm
t = sim.time
ax, fig = sim.scatter('x',
'y',
'z',
s=1,
c=cm.gnuplot2((t - t.min()) / (t.ptp())))
""" Plot lines """
ax, fig = sim.plot(t, 'x', t, 'y', t, 'z')
for t in range(cfg['T']):
nmis[i * cfg['J'] + j, t] = MI[t] / np.sqrt(Hz[t] * Hgt[t])
# nmis[i*cfg['J']+j,t] = MI*2. / (Hz+Hgt)
mis[i * cfg['J'] + j, t] = MI[t]
vMeasures[i * cfg['J'] + j, t] = 2. * MI[t] / (Hz[t] + Hgt[t])
print nmis[i * cfg['J'] + j, t], 2. * MI[t], Hz[t], Hgt[t]
baseMap={'spkm':'spkm','kmeans':'k-means','NiwSphere':'DirSNIW', \
'DpNiw':'DP-GMM','DpNiwSphere':'DpSNIW opt','DpNiwSphereFull':'DP-TGMM', \
'DPvMFmeans':'DPvMFmeans',"DirNiwSphereFull":"Dir-TGMM"}
#cl = cm.gnuplot2(np.arange(len(bases)))
cl = cm.hsv(np.arange(255))
cl = cm.brg(np.arange(255))
cl = cm.gist_rainbow(np.arange(255))
cl = cm.gnuplot2(np.arange(255))
cl = cm.gnuplot(np.arange(255))
cl = cm.spectral(np.arange(255))
#print cltlib
I = len(bases) + 1
print nmis.shape
fig = plt.figure(figsize=figSize, dpi=80, facecolor='w', edgecolor='k')
for i, base in enumerate(bases):
if not re.search('_', base) is None:
info = base.split('_')
base = info[0]
Ki = int(info[1])
plt.plot(np.arange(cfg['T']),
nmis[i * cfg['J'], :],
label=baseMap[base] + ' ($K_0={}$)'.format(Ki),
def plot(args):
limit = 1000
env_name = args.env_name
if args.env_id == 9:
limit = 500
""" get config about trajectory file"""
if args.env_id != 21:
discriminator_updater = IRL(1, 1, args, False)
traj_name = discriminator_updater.traj_name
m_return_list = discriminator_updater.m_return_list
else:
traj_name = "traj10"
m_return_list = None
seed_list = [1, 2, 3, 4, 5]
seed_num = len(seed_list)
print(seed_list)
load_robo = 0
plot_methods = [
"vild_per0", "vild_per2", "meirl", "airl", "gail", "vail",
"infogail_mean", "infogail_best"
]
if args.env_id == 9:
args.info_loss_type = "linear"
args.rl_method = "sac"
args.hidden_size = (100, 100)
args.activation = "relu"
args.q_step = 1
if args.env_id == 0:
plot_methods = [
"vild_per0", "vild_per2", "gail", "infogail_mean", "infogail_best"
]
args.vild_loss_type = "bce"
if args.env_id == -3:
plot_methods = [
"vild_per0", "vild_per2", "gail", "infogail_mean", "infogail_best"
]
args.vild_loss_type = "bce"
args.rl_method = "ppo"
args.bce_negative = 1
args.hidden_size = (100, 100)
args.activation = "relu"
args.gp_lambda = 0
if args.env_id == 21:
plot_methods = [
"vild_per0", "vild_per2", "gail", "infogail_mean", "infogail_best",
"meirl", "airl", "vail"
] # for paper
# plot_methods = ["vild_per2", "gail", "meirl", "airl", "vail"] # for slide
args.vild_loss_type = "bce"
env_name += "_reach"
args.hidden_size = (100, 100)
args.activation = "relu"
load_robo = 1
if args.env_id == 15:
plot_methods = ["vild_per2", "gail", "meirl"]
R_test_all = []
gail_legend = []
c_tmp = []
l_tmp = []
max_len = 10000
if any("vild_per2" in s for s in plot_methods):
""" VILD """
R_test_avg = []
cat = 1
noise_array_is = []
args.il_method = "vild"
args.per_alpha = 2
args.clip_discriminator = 5
method_name = args.il_method.upper() + "_" + args.rl_method.upper()
if args.vild_loss_type.lower() != "linear":
method_name += "_" + args.vild_loss_type.upper()
args.clip_discriminator = 0
hypers = rl_hypers_parser(args) + "_" + irl_hypers_parser(args)
for seed in seed_list:
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers, seed)
if load_robo:
filename = "./results_IL/test/%s/%s-%s" % (env_name, env_name,
exp_name)
else:
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if not load_robo and args.c_data == 7:
noise_array_is += [load_noise(filename)]
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all += [R_test_avg]
gail_legend += ["VILD (IS)"]
c_tmp += ["r"]
l_tmp += ["-"]
if any("vild_per0" in s for s in plot_methods):
""" VILD no IS """
R_test_avg = []
cat = 1
noise_array = []
args.il_method = "vild"
args.per_alpha = 0
args.clip_discriminator = 5
method_name = args.il_method.upper() + "_" + args.rl_method.upper()
if args.vild_loss_type.lower() != "linear":
method_name += "_" + args.vild_loss_type.upper()
args.clip_discriminator = 0
hypers = rl_hypers_parser(args) + "_" + irl_hypers_parser(args)
for seed in seed_list:
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers, seed)
if load_robo:
filename = "./results_IL/test/%s/%s-%s" % (env_name, env_name,
exp_name)
else:
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if not load_robo:
noise_array += [load_noise(filename)]
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all += [R_test_avg]
gail_legend += ["VILD (w/o IS)"]
c_tmp += ["blueviolet"]
l_tmp += ["-"]
if any("gail" in s for s in plot_methods):
""" GAIL """
R_test_avg = []
cat = 1
args.il_method = "gail"
args.clip_discriminator = 0
hypers = rl_hypers_parser(args) + "_" + irl_hypers_parser(args)
method_name = args.il_method.upper() + "_" + args.rl_method.upper()
for seed in seed_list:
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers, seed)
if load_robo:
filename = "./results_IL/test/%s/%s-%s" % (env_name, env_name,
exp_name)
else:
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all += [R_test_avg]
gail_legend += ["GAIL"]
c_tmp += ["darkgoldenrod"]
l_tmp += ["--"]
if any("airl" in s for s in plot_methods):
""" AIRL """
R_test_avg = []
cat = 1
args.il_method = "airl"
args.clip_discriminator = 5
hypers = rl_hypers_parser(args) + "_" + irl_hypers_parser(args)
method_name = args.il_method.upper() + "_" + args.rl_method.upper()
for seed in seed_list:
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers, seed)
if load_robo:
filename = "./results_IL/test/%s/%s-%s" % (env_name, env_name,
exp_name)
else:
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all += [R_test_avg]
gail_legend += ["AIRL"]
c_tmp += ["b"]
l_tmp += ["-."]
if any("meirl" in s for s in plot_methods):
""" ME-IRL """
R_test_avg = []
cat = 1
args.il_method = "irl"
args.clip_discriminator = 5
hypers = rl_hypers_parser(args) + "_" + irl_hypers_parser(args)
method_name = args.il_method.upper() + "_" + args.rl_method.upper()
for seed in seed_list:
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers, seed)
if load_robo:
filename = "./results_IL/test/%s/%s-%s" % (env_name, env_name,
exp_name)
else:
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all += [R_test_avg]
gail_legend += ["ME-IRL"]
c_tmp += ["olive"]
l_tmp += [":"]
if any("vail" in s for s in plot_methods):
""" VAIL """
R_test_avg = []
cat = 1
args.il_method = "vail"
args.clip_discriminator = 0
hypers = rl_hypers_parser(args) + "_" + irl_hypers_parser(args)
method_name = args.il_method.upper() + "_" + args.rl_method.upper()
for seed in seed_list:
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers, seed)
if load_robo:
filename = "./results_IL/test/%s/%s-%s" % (env_name, env_name,
exp_name)
else:
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all += [R_test_avg]
gail_legend += ["VAIL"]
c_tmp += ["m"]
l_tmp += ["--"]
if any("infogail_mean" in s for s in plot_methods):
""" InfoGAIL """
R_test_avg = []
cat = 1
args.il_method = "infogail"
args.clip_discriminator = 5 if args.info_loss_type == "linear" else 0
hypers = rl_hypers_parser(args) + "_" + irl_hypers_parser(args)
method_name = args.il_method.upper() + "_" + args.rl_method.upper()
if args.il_method == "infogail" and args.info_loss_type.lower(
) != "bce":
method_name += "_" + args.info_loss_type.upper()
for seed in seed_list:
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers, seed)
if load_robo:
filename = "./results_IL/test/%s/%s-%s" % (env_name, env_name,
exp_name)
else:
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load_info(
filename, limit,
load_robo) # [iter, 10, test_episode (1?) ] array
if R_test_avg_i[0, 0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
# R_test_avg_x = np.stack(R_test_avg, axis=2) # array [iter, 10, test_episode * seed]
R_test_avg_x = np.concatenate(R_test_avg, axis=2)
# average over uniform contexts
R_test_avg_mean = np.mean(R_test_avg_x, axis=1)
R_test_all += [R_test_avg_mean]
gail_legend += ["InfoGAIL"]
c_tmp += ["black"]
l_tmp += ["-."]
if any("infogail_best" in s for s in plot_methods):
R_test_avg_sort = []
## For each seed, sort array along context descending based on performance
for i in range(0, len(seed_list)):
R_test_x = R_test_avg[
i] # R_test_avg is a list. R_test_x is a np array shape [test_iter, 10, test_epi ]
test_last = np.mean(
np.mean(R_test_x, axis=2)[-100:, :], axis=0
) # final performance average over last 100 iterations
sort_idx = np.argsort(test_last)[::-1] # descending indices
R_test_x_sort = R_test_x[:, sort_idx, :]
R_test_avg_sort += [R_test_x_sort]
R_test_avg_sort = np.concatenate(
R_test_avg_sort,
axis=2) # array [iter, 10, test_episode * seed]
R_test_avg_sort_best = R_test_avg_sort[:, 0, :]
R_test_all += [R_test_avg_sort_best]
gail_legend += ["InfoGAIL (best)"]
c_tmp += ["dodgerblue"]
l_tmp += [":"]
if any("infogail_worst" in s for s in plot_methods):
R_test_avg_sort_worst = R_test_avg_sort[:, -1, :]
R_test_all += [R_test_avg_sort_worst]
gail_legend += ["InfoGAIL (worst)"]
c_tmp += ["teal"]
l_tmp += ["--"]
## infogail_all
R_test_info = []
R_test_info_mean = []
R_test_info_std = []
for i in range(0, 10):
R_test_info += [R_test_avg_sort[:, i, :]]
R_test_info_mean += [np.mean(R_test_info[i], 1)]
R_test_info_std += [
np.std(R_test_info[i], 1) / np.sqrt(R_test_info[i].shape[1])
]
""" Compute statistics for plotting """
R_test_mean = []
R_test_std = []
R_test_last = []
R_test_last_mean = []
R_test_last_std = []
best_idx = -1
best_R = -1e6
for i in range(0, len(R_test_all)):
R_test = R_test_all[i]
""" This is for plotting """
R_test_mean += [np.mean(R_test, 1)]
R_test_std += [np.std(R_test, 1) / np.sqrt(R_test.shape[1])]
## Average last final 100 iteration.
R_test_last += [np.mean(R_test[-100:, :], 0)]
last_mean = np.mean(R_test_last[i], 0)
if last_mean > best_R:
best_idx = i
best_R = last_mean
R_test_last_mean += [last_mean]
R_test_last_std += [
np.std(R_test_last[i], 0) / np.sqrt(R_test.shape[1])
]
### Print statistics
# print("%s: %0.1f(%0.1f)" % (gail_legend[i], R_test_mean_last, R_test_std_last))
""" For t_test """
## paired t-test
from scipy import stats
best_m = R_test_last[best_idx]
p_list = []
for i in range(0, len(R_test_all)):
# if gail_legend[i] == "InfoGAIL (best)": continue
if i != best_idx:
_, p = stats.ttest_ind(best_m, R_test_last[i], nan_policy="omit")
else:
p = 1
p_list += [p]
## copied able latex format
latex = ""
for i in range(0, len(R_test_all)):
# if gail_legend[i] == "InfoGAIL (best)": continue
print("%-70s: Sum %0.0f(%0.0f) with p-value %f" %
(gail_legend[i], R_test_last_mean[i], R_test_last_std[i],
p_list[i]))
if p_list[i] > 0.01: # 1 percent confidence
latex += " & \\textbf{%0.0f (%0.0f)}" % (R_test_last_mean[i],
R_test_last_std[i])
else:
latex += " & %0.0f (%0.0f)" % (R_test_last_mean[i],
R_test_last_std[i])
print(latex + " \\\\")
plot_large = args.plot_large
skipper = 10 # Use sliding window to compute running mean and std for clearer figures. skipper 1 = no running.
running = 1
plot_name = "%s_%s_%s" % (args.rl_method, env_name, args.noise_type)
title = "%s" % (env_name[:-3])
x_max_iter = len(R_test_mean[0])
""" Plot """
if plot_large:
linewidth = 2
fontsize = 21
f = plt.figure(figsize=(8, 6)) # for Figure 2, ...
else:
linewidth = 1 # for Figure 2, ...
fontsize = 14 # for Figure 2, ...
f = plt.figure(figsize=(4, 3)) # for Figure 2, ...
ax = f.gca()
""" Plot """
cc_tmp = ["black"] + ["gray"] + ["darkgray"] + ["dimgray"] + ["silver"]
for i in range(0, len(R_test_all)):
if running:
y_plot = running_mean(R_test_mean[i][:x_max_iter], skipper)
y_err = running_mean(R_test_std[i][:x_max_iter], skipper)
else:
y_plot = R_test_mean[i][:x_max_iter:skipper]
y_err = R_test_std[i][:x_max_iter:skipper]
x_ax = np.arange(0, len(y_plot)) * 10000 // 2
if args.rl_method == "sac":
x_ax = np.arange(0, len(y_plot)) * 10000 // 2 // 5
if args.env_id == 21:
x_ax = np.arange(0, len(y_plot)) * 100000 * 5 / 4
if i == 0 and m_return_list is not None:
ic_k = 0
for i_k in range(0, len(m_return_list)):
opt_plot = (x_ax * 0) + m_return_list[i_k]
if i_k == 0 or i_k == 2 or i_k == 4 or i_k == 6 or i_k == 9:
# if i_k == 0 or i_k == 4 or i_k == 8:
ax.plot(x_ax,
opt_plot,
color=cc_tmp[ic_k],
linestyle=":",
linewidth=linewidth + 0.5)
# plt.text(x=0, y=m_return_list[i_k], s="k=%d" % (i_k+1), fontsize=fontsize-3, color=cc_tmp[ic_k])
ic_k += 1
errorfill(x_ax,
y_plot,
yerr=y_err,
color=c_tmp[i],
linestyle=l_tmp[i],
linewidth=linewidth,
label=gail_legend[i],
shade=1)
if plot_large == 1:
if args.env_id == 0:
ax.legend(prop={"size": fontsize - 3},
frameon=True,
loc='lower right',
framealpha=1,
ncol=2)
elif args.env_id == -3:
# ax.legend(prop={"size":fontsize-4}, frameon=True, loc = 'lower right', framealpha=1, ncol=2)
ax.legend(prop={"size": fontsize - 3},
frameon=True,
loc='lower right',
framealpha=1,
ncol=2)
elif args.env_id == 21:
ax.legend(prop={"size": fontsize - 6.3},
frameon=True,
loc='lower right',
framealpha=1,
ncol=3)
# handles, labels = ax.get_legend_handles_labels()
# leg_order = [0, 1, 2, 3, 4, 5, 6]
# leg_order = [0, 1, 6, 3, 4, 5, 2]
# handles, labels = [handles[i] for i in leg_order], [labels[i] for i in leg_order]
# ax.legend(handles, labels, prop={"size":fontsize-4}, frameon=True, loc = 'lower right', framealpha=1, ncol=3)
else:
ax.legend(prop={"size": fontsize - 3},
frameon=True,
loc='upper left',
framealpha=1)
else:
if args.env_id == -3:
ax.legend(prop={"size": fontsize - 3},
frameon=True,
loc='lower right',
framealpha=1,
ncol=2)
if args.env_id == 21:
ax.legend(prop={"size": fontsize - 6},
frameon=True,
loc='lower right',
framealpha=1,
ncol=3)
if args.env_id == -3:
title = "%s" % ("LunarLander")
# ax.set_ylim([-200,250])
# ax.set_ylim([-200,300])
# f.legend(prop={"size":fontsize-3}, frameon=True, loc="lower right", framealpha=1, ncol=3)
if args.env_id == 21:
title = "%s" % ("RobosuiteReacher")
plot_name = "%s_%s" % (args.rl_method, env_name)
ax.set_ylim([-10, 45]) # paper
# ax.set_ylim([-2.4, 29]) # slide
if args.env_id == 15:
title = "%s" % ("AntBullet")
fig_name = "./figures/%s" % (plot_name)
if plot_large:
fig_name += "_large"
# fig_name += "_slide"
print(fig_name)
if plot_large:
plt.title(title, fontsize=fontsize + 1)
else:
plt.title(title, fontsize=fontsize)
plt.xlabel("Transition samples", fontsize=fontsize - 2)
plt.ylabel('Cumulative rewards', fontsize=fontsize - 2)
plt.xticks(fontsize=fontsize - 2)
plt.yticks(fontsize=fontsize - 2)
plt.tight_layout()
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
## plot legend.
if plot_large == 2:
figlegend = pylab.figure(figsize=(18, 1))
#pylab.figlegend(*ax.get_legend_handles_labels(), prop={"size":fontsize-3}, ncol=7, handlelength=2.8, )
pylab.figlegend(
*ax.get_legend_handles_labels(),
prop={"size": fontsize - 3},
ncol=10,
handlelength=2.0,
)
figlegend.savefig("./figures/legend.pdf",
bbox_inches='tight',
pad_inches=-0.25)
if args.plot_save:
f.savefig(fig_name + ".pdf", bbox_inches='tight', pad_inches=0)
## plot covariance.
if not load_robo and args.c_data == 7:
f_cov = plt.figure(figsize=(4, 3)) # for Figure 2, ...
ax_cov = plt.gca()
noise_mean_is = np.mean(np.array(noise_array_is), axis=0)
noise_mean = np.mean(np.array(noise_array), axis=0)
true_variance = np.array(
args.noise_level_list)**2 # convert to variance
if args.env_id == -3:
1
# true_variance = [0.021, 0.022, 0.023, 0.024, 0.025, 0.026, 0.027, 0.028, 0.029, 0.030]
# true_variance = true_variance * 0.034
x = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']
x_pos = [i for i, _ in enumerate(x)]
plt.ylabel('Covariance', fontsize=fontsize - 2)
plt.xlabel('Demonstrator number k', fontsize=fontsize - 2)
plt.plot(x_pos, noise_mean_is, 'rx', markersize=8, label="VILD (IS)")
plt.plot(x_pos, noise_mean, 'm+', markersize=8, label="VILD (w/o IS)")
if args.env_id == -3:
plt.legend(prop={"size": fontsize - 3},
frameon=True,
loc='lower right',
framealpha=1)
elif args.noise_type == "SDNTN":
plt.legend(prop={"size": fontsize - 3},
frameon=True,
loc='upper left',
framealpha=1)
else:
plt.plot(x_pos,
true_variance,
'ko',
markersize=5,
label="Ground-truth")
plt.legend(prop={"size": fontsize - 3},
frameon=True,
loc='upper left',
framealpha=1)
plt.yticks(fontsize=fontsize - 2)
plt.tight_layout()
plt.xticks(x_pos, x, fontsize=fontsize - 2)
fig_name_cov = "./figures/%s_cov" % (plot_name)
if plot_large:
plt.title(title + " (Covariance)", fontsize=fontsize + 1)
else:
plt.title(title + " (Covariance)", fontsize=fontsize)
print(fig_name_cov)
if args.plot_save:
f_cov.savefig(fig_name_cov + ".pdf",
bbox_inches='tight',
pad_inches=0)
## plot bc.
if args.env_id == 2 or args.env_id == 5 or args.env_id == 7 or args.env_id == 9: # mujoco tasks.
c_tmp_2 = []
l_tmp_2 = []
R_test_all_2 = []
gail_legend_2 = []
""" BC """
R_test_avg = []
cat = 1
method_name = "BC"
for seed in seed_list:
hypers_tmp = bc_hypers_parser(args)
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers_tmp,
seed)
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all_2 += [R_test_avg]
gail_legend_2 += ["BC"]
c_tmp_2 += ["blue"]
l_tmp_2 += ["--"]
""" D-BC """
R_test_avg = []
cat = 1
method_name = "DBC"
for seed in seed_list:
hypers_tmp = bc_hypers_parser(args)
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers_tmp,
seed)
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all_2 += [R_test_avg]
gail_legend_2 += ["BC-D"]
c_tmp_2 += ["orange"]
l_tmp_2 += ["-."]
""" Co-BC """
R_test_avg = []
cat = 1
method_name = "COBC"
for seed in seed_list:
hypers_tmp = bc_hypers_parser(args)
exp_name = "%s-%s-%s_s%d" % (traj_name, method_name, hypers_tmp,
seed)
filename = "./results_IL/%s/%s/%s-%s" % (method_name, env_name,
env_name, exp_name)
R_test_avg_i = load(filename, limit, load_robo)
if R_test_avg_i[0, 0] == -999:
cat = 0
print("cannot load %s" % exp_name)
else:
load_iter = R_test_avg_i.shape[0]
if load_iter < max_len:
max_len = load_iter
for i in range(0, seed - 1):
R_test_avg[i] = R_test_avg[i][0:max_len, :]
R_test_avg_i = R_test_avg_i[0:max_len, :]
R_test_avg += [R_test_avg_i]
if cat:
R_test_avg = np.concatenate(
R_test_avg, axis=1) # array [iter, test_episode * seed]
R_test_all_2 += [R_test_avg]
gail_legend_2 += ["Co-teaching"]
c_tmp_2 += ["darkgreen"]
l_tmp_2 += [":"]
R_test_mean_2 = []
R_test_std_2 = []
for i in range(0, len(R_test_all_2)):
R_test = R_test_all_2[i]
""" This is for plotting """
R_test_mean_2 += [np.mean(R_test, 1)]
R_test_std_2 += [np.std(R_test, 1) / np.sqrt(R_test.shape[1])]
if plot_large:
linewidth = 2
fontsize = 21
f_2 = plt.figure(figsize=(8, 6)) # for Figure 2, ...
else:
linewidth = 1 # for Figure 2, ...
fontsize = 14 # for Figure 2, ...
f_2 = plt.figure(figsize=(4, 3)) # for Figure 2, ...
ax_2 = f_2.gca()
from matplotlib import cm
for i in range(0, len(R_test_all_2)):
if running:
y_plot = running_mean(R_test_mean_2[i][:x_max_iter], skipper)
y_err = running_mean(R_test_std_2[i][:x_max_iter], skipper)
else:
y_plot = R_test_mean_2[i][:x_max_iter:skipper]
y_err = R_test_std_2[i][:x_max_iter:skipper]
x_ax = np.arange(0, len(y_plot)) * 10000 // 2 // 5
if i == 0:
errorfill(x_ax, (x_ax * 0) + R_test_last_mean[0],
yerr=(x_ax * 0) + R_test_last_std[0],
color=c_tmp[0],
linestyle="-",
linewidth=linewidth,
shade=1)
errorfill(x_ax,
y_plot,
yerr=y_err,
color=c_tmp_2[i],
linestyle=l_tmp_2[i],
linewidth=linewidth,
label=gail_legend_2[i],
shade=1)
# if plot_large:
# ax_2.legend(prop={"size":fontsize-3}, frameon=True, loc = 'lower right', framealpha=1, ncol=2)
if args.env_id == 2:
ax_2.legend(prop={"size": fontsize - 3},
frameon=True,
loc='lower right',
framealpha=1,
ncol=2)
if args.env_id == 2:
ax_2.set_ylim([-999, 5000])
fig_name_bc = "./figures/%s_bc" % (plot_name)
if plot_large:
fig_name_bc += "_large"
print(fig_name_bc)
# title += " (Supervised)"
if plot_large:
plt.title(title, fontsize=fontsize + 1)
else:
plt.title(title, fontsize=fontsize)
plt.xlabel("Gradient steps", fontsize=fontsize - 2)
plt.ylabel('Cumulative rewards', fontsize=fontsize - 2)
plt.xticks(fontsize=fontsize - 2)
plt.yticks(fontsize=fontsize - 2)
plt.tight_layout()
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
## plot legend.
if plot_large == 2:
figlegend = pylab.figure(figsize=(18, 1))
pylab.figlegend(
*ax_2.get_legend_handles_labels(),
prop={"size": fontsize - 3},
ncol=10,
handlelength=2.0,
)
figlegend.savefig("./figures/legend_bc.pdf",
bbox_inches='tight',
pad_inches=-0.25)
if args.plot_save:
f_2.savefig(fig_name_bc + ".pdf",
bbox_inches='tight',
pad_inches=0)
## plot infogail sorted performance.
if any("infogail_mean" in s for s in plot_methods):
if plot_large:
linewidth = 2
fontsize = 21
f_3 = plt.figure(figsize=(8, 6)) # for Figure 2, ...
else:
linewidth = 1 # for Figure 2, ...
fontsize = 14 # for Figure 2, ...
f_3 = plt.figure(figsize=(4, 3)) # for Figure 2, ...
ax_3 = f_3.gca()
# info_color = ["black"] + ["gray"] + ["darkgray"] + ["dimgray"] + ["silver"] \
# + ["r"] + ["g"] + ["b"] + ["m"] + ["c"]
from matplotlib import cm
info_color = cm.gnuplot2(np.linspace(0, 1, 12))
for i in range(0, 10):
if running:
y_plot = running_mean(R_test_info_mean[i][:x_max_iter],
skipper)
y_err = running_mean(R_test_info_std[i][:x_max_iter], skipper)
else:
y_plot = R_test_info_mean[i][:x_max_iter:skipper]
y_err = R_test_info_std[i][:x_max_iter:skipper]
x_ax = np.arange(0, len(y_plot)) * 10000 // 2
if args.env_id == 21:
x_ax = np.arange(0, len(y_plot)) * 100000 * 5 / 4
if i == 0 and m_return_list is not None:
ic_k = 0
for i_k in range(0, len(m_return_list)):
opt_plot = (x_ax * 0) + m_return_list[i_k]
if i_k == 0 or i_k == 2 or i_k == 4 or i_k == 6 or i_k == 9:
# if i_k == 0 or i_k == 4 or i_k == 8:
ax_3.plot(x_ax,
opt_plot,
color=cc_tmp[ic_k],
linestyle=":",
linewidth=linewidth + 0.5)
# plt.text(x=0, y=m_return_list[i_k], s="k=%d" % (i_k+1), fontsize=fontsize-3, color=cc_tmp[ic_k])
ic_k += 1
if i != 8:
errorfill(x_ax,
y_plot,
yerr=y_err,
color=info_color[i],
linestyle="-",
linewidth=linewidth,
shade=1)
else:
errorfill(x_ax,
y_plot,
yerr=y_err,
color="goldenrod",
linestyle="-",
linewidth=linewidth,
shade=1)
fig_name_info = "./figures/%s_info" % (plot_name)
if plot_large:
fig_name_info += "_large"
print(fig_name_info)
if args.env_id == 21:
title = "%s" % ("RobosuiteReacher")
if "infogail_best" in plot_methods:
ax_3.set_ylim([-10, 45]) # for paper
else:
ax_3.set_ylim([0, 35]) # for slide
if plot_large:
plt.title(title + " (InfoGAIL)", fontsize=fontsize + 1)
else:
plt.title(title + " (InfoGAIL)", fontsize=fontsize)
plt.xlabel("Transition samples", fontsize=fontsize - 2)
plt.ylabel('Cumulative rewards', fontsize=fontsize - 2)
plt.xticks(fontsize=fontsize - 2)
plt.yticks(fontsize=fontsize - 2)
plt.tight_layout()
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
if args.plot_save:
f_3.savefig(fig_name_info + ".pdf",
bbox_inches='tight',
pad_inches=0)
if args.plot_show:
plt.show()
else:
sp = [N - vc - 1]
ift = [1]
rcv = [0]
for t in range(1, 1000):
count1 = np.random.choice(
range(2),
sp[t - 1],
p=[1 - beta * ift[t - 1] / N,
beta * ift[t - 1] / N]).sum() #yield infected people
count2 = np.random.choice(range(2), ift[t - 1],
p=[1 - gamma,
gamma]).sum() #yield recovered people
sp.append(sp[t - 1] - count1)
rcv.append(rcv[t - 1] + count2)
ift.append(ift[t - 1] + count1 - count2)
count1, count2 = 0, 0 #reset
list_inf.append(ift)
#========================plot==================================================
plt.figure(figsize=(6, 4), dpi=150)
x = [y for y in range(1, 1001)]
for u in range(0, 11):
plt.plot(x,
list_inf[u],
label=str(u * 10) + '%',
color=cm.gnuplot2(u * 20)) #plot graphs in one figure with loops
plt.xlabel('time')
plt.ylabel('number of people')
plt.title('SIR model with different vaccination rates')
plt.legend()
plt.savefig("SIR model with different vaccination rates", type="png")
Note: This example assumes that `name i` corresponds to `label i`
in `labels.csv`.
""")
parser = argparse.ArgumentParser()
parser.add_argument('workDir', type=str)
parser.add_argument('--names', type=str, nargs='+', required=True)
args = parser.parse_args()
y = pd.read_csv("{}/labels.csv".format(args.workDir)).as_matrix()[:, 0]
X = pd.read_csv("{}/reps.csv".format(args.workDir)).as_matrix()
target_names = np.array(args.names)
colors = cm.gnuplot2(np.linspace(0, 0.7, len(target_names)))
X_pca = PCA(n_components=50).fit_transform(X, X)
tsne = TSNE(n_components=2, init='random', random_state=0)
X_r = tsne.fit_transform(X_pca)
for c, i, target_name in zip(colors,
list(range(1, len(target_names) + 1)),
target_names):
plt.scatter(X_r[y == i, 0], X_r[y == i, 1],
c=c, label=target_name)
plt.legend()
out = "{}/tsne.pdf".format(args.workDir)
plt.savefig(out)
print("Saved to: {}".format(out))