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drawing.py
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drawing.py
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import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.pylab as pl
import math
from matplotlib.gridspec import GridSpec
plt.rcParams["font.family"] = "serif"
plt.rcParams.update({'font.size': 12})
matplotlib.use('agg')
class Drawing(object):
def __init__(
self,
game,
true_rewards,
rewardssquence,
model,
model_solutions,
example_samples,
mirl_solutions,
path
):
self.game = game
self.true_rewards = true_rewards
self.rewardssquence = rewardssquence
self.model = model
self.model_solutions = model_solutions
self.example_samples = example_samples
self.mirl_solutions = mirl_solutions
self.path = path
self.g = self.visible_examples()
self.worlddraw()
def visible_examples(self):
Eo = np.zeros((self.game.states))
N = np.shape(self.example_samples)[0]
T = np.shape(self.example_samples)[1]
for i in range(N):
for t in range(T):
Eo[self.example_samples[i][t][0]] = 1
g = np.ones((self.game.states))*0.5 + Eo*0.5
g = np.ones((self.game.states))
return g
def worlddraw(self):
fig = plt.figure(constrained_layout=True)
gs = GridSpec(2, 6, figure=fig)
Gr = len(self.model_solutions)
Pr = len(self.mirl_solutions)
for gr in range(Gr):
ax = fig.add_subplot(gs[0, gr])
r_true = self.true_rewards[gr]
p_true = self.model_solutions[gr].p
self.rewarddraw(r_true, ax, 'True \n reward function '+str(gr+1))
self.policydraw(p_true, ax)
self.objectdraw(ax)
for pr in range(Pr):
ax = fig.add_subplot(gs[1, pr])
r_pred_mirl = self.mirl_solutions[pr].r
p_pred_mirl = self.mirl_solutions[pr].p
self.rewarddraw(r_pred_mirl, ax, 'Predicted \n reward function '+str(pr+1))
self.policydraw(p_pred_mirl, ax)
self.objectdraw(ax)
fig.set_size_inches((18, 10), forward=False)
plt.savefig(self.path, dpi=500)
def rewarddraw(self,r, ax, name):
n = self.game.n
maxr = np.max(r)
minr = np.min(r)
rngr = maxr - minr
xticks = np.arange(0,n+1,1)
yticks = np.arange(0,n+1,1)
ax.set_xlim(0, n)
ax.set_ylim(0, n)
ax.set_aspect(1)
ax.set_xticks(xticks)
ax.set_yticks(yticks)
ax.tick_params(axis='both', which='both', length=0)
ax.grid(True, color='black')
ax.set_title(name)
ax.set_yticklabels([])
ax.set_xticklabels([])
for y in range(n):
for x in range(n):
if rngr == 0:
v = 0
else:
temp1 = r[y*n+x,:]
temp2 = np.mean(temp1)
v = ( temp2 - minr)/rngr
color = np.array([v,v,v])
temp = np.maximum(np.zeros((3)), color)
color = np.minimum(np.ones((3)),temp)
left, bottom, width, height = (x, y, 1, 1)
rect = plt.Rectangle((left, bottom), width, height,facecolor=color)
ax.add_patch(rect)
def policydraw(self, p, ax):
p = np.argmax(p, axis=1)
n = self.game.n
g = self.g
for y in range(n):
for x in range(n):
s = y*n + x
a = p[s]
color = np.array([g[s],g[s],g[s]])
w = 1
if a == 0:
nx = x + 0.5
ny = y + 0.5
xy = np.array([
[nx + 0.25,ny],
[nx, ny - 0.25],
[nx - 0.25, ny],
[nx, ny + 0.25]
])
poly = plt.Polygon(xy,facecolor=color, linewidth=w, edgecolor='black')
ax.add_patch(poly)
else:
if a == 4:
nx = x - 1
ny = y
elif a == 3:
nx = x
ny = y - 1
elif a == 2:
nx = x + 1
ny = y
elif a == 1:
nx = x
ny = y + 1
vec = np.array([(nx - x)*0.25,(ny - y)*0.25])
normal1 = np.array([vec[1],-vec[0]])
normal2 = -normal1
xy = np.array([
[x + 0.5 + vec[0], y + 0.5 + vec[1]],
[x + 0.5 + normal1[0], y + 0.5 + normal1[1]],
[x + 0.5 + normal2[0], y + 0.5 + normal2[1]]
])
poly = plt.Polygon(xy,facecolor=color, linewidth=w, edgecolor='black')
ax.add_patch(poly)
def objectdraw(self, ax):
n = self.game.n
c1array = self.game.c1array
shapeColors = pl.cm.jet(np.linspace(0,1,self.game.c1 + self.game.c2))
for i in range(len(c1array)):
for j in range(len(c1array[i])):
s = c1array[i][j] + 1
c1 = i
c2 = self.game.map2[s-1] - 1
y = math.ceil(s/n) - 1
x = s - (y)*n - 1
xy = np.array([x + 0.5, y + 0.5])
shcol = np.squeeze(shapeColors[self.game.c1+c2,:], axis=0)
ecol = shapeColors[c1,:]
ccl = plt.Circle(xy, 0.12, facecolor=shcol,linewidth=2,edgecolor=ecol)
ax.add_patch(ccl)
class Drawing_Prediction(object):
def __init__(
self,
game,
pred_sample,
gt_sample,
obs_sample,
):
self.game = game
self.pred_sample = pred_sample
self.gt_sample = gt_sample
self.obs_sample = obs_sample
self.worlddraw()
def worlddraw(self):
_, ax = plt.subplots(1,3)
self.obspathdraw(-self.obs_sample, ax[0])
self.obspathdraw(-self.gt_sample, ax[1])
self.obspathdraw(-self.pred_sample, ax[2])
self.objectdraw(ax[0])
self.objectdraw(ax[1])
self.objectdraw(ax[2])
plt.show()
def obspathdraw(self,path, ax):
n = self.game.n
maxpath = np.max(path)
minpath = np.min(path)
rngpath = maxpath - minpath
xticks = np.arange(0,n+1,1)
yticks = np.arange(0,n+1,1)
ax.set_xlim(0, n)
ax.set_ylim(0, n)
ax.set_aspect(1)
ax.set_xticks(xticks)
ax.set_yticks(yticks)
ax.tick_params(axis='both', which='both', length=0)
ax.grid(True, color='black')
for y in range(n):
for x in range(n):
if rngpath == 0:
v = 0
else:
temp1 = path[y*n+x,:]
temp2 = np.mean(temp1)
v = ( temp2 - minpath)/rngpath
color = np.array([v,v,v])
temp = np.maximum(np.zeros((3)), color)
color = np.minimum(np.ones((3)),temp)
left, bottom, width, height = (x, y, 1, 1)
rect = plt.Rectangle((left, bottom), width, height,facecolor=color)
ax.add_patch(rect)
def policydraw(self, p, ax):
p = np.argmax(p, axis=1)
n = self.game.n
g = self.g
for y in range(n):
for x in range(n):
s = y*n + x
a = p[s]
color = np.array([g[s],g[s],g[s]])
w = 1
if a == 0:
nx = x + 0.5
ny = y + 0.5
xy = np.array([
[nx + 0.25,ny],
[nx, ny - 0.25],
[nx - 0.25, ny],
[nx, ny + 0.25]
])
poly = plt.Polygon(xy,facecolor=color, linewidth=w, edgecolor='black')
ax.add_patch(poly)
else:
if a == 4:
nx = x - 1
ny = y
elif a == 3:
nx = x
ny = y - 1
elif a == 2:
nx = x + 1
ny = y
elif a == 1:
nx = x
ny = y + 1
vec = np.array([(nx - x)*0.25,(ny - y)*0.25])
normal1 = np.array([vec[1],-vec[0]])
normal2 = -normal1
xy = np.array([
[x + 0.5 + vec[0], y + 0.5 + vec[1]],
[x + 0.5 + normal1[0], y + 0.5 + normal1[1]],
[x + 0.5 + normal2[0], y + 0.5 + normal2[1]]
])
poly = plt.Polygon(xy,facecolor=color, linewidth=w, edgecolor='black')
ax.add_patch(poly)
def objectdraw(self, ax):
n = self.game.n
c1array = self.game.c1array
shapeColors = pl.cm.jet(np.linspace(0,1,self.game.c1 + self.game.c2))
for i in range(len(c1array)):
for j in range(len(c1array[i])):
s = c1array[i][j] + 1
c1 = i
c2 = self.game.map2[s-1] - 1
y = math.ceil(s/n) - 1
x = s - (y)*n - 1
xy = np.array([x + 0.5, y + 0.5])
shcol = np.squeeze(shapeColors[self.game.c1+c2,:], axis=0)
ecol = shapeColors[c1,:]
ccl = plt.Circle(xy, 0.12, facecolor=shcol,linewidth=2,edgecolor=ecol)
ax.add_patch(ccl)