def plot(js):
fig, ax = plt.subplots(2,2,num='Korali Results', figsize=(8,8))
solver = js['Generations'][0]['Solver']['Type']
numdim = len(js['Variables'])
names = [ js['Variables'][i]['Name'] for i in range(numdim) ]
fval = []
objVec = []
dfval = []
gen = []
width = []
means = []
for s in js['Generations']:
objVec.append(s['Solver']['Internal']['Current Best Variables'])
fval.append(s['Solver']['Internal']['Current Best Value'])
dfval.append(abs(s['Solver']['Internal']['Current Best Value'] - s['Solver']['Internal']['Best Ever Value']))
gen.append(s['Internal']['Current Generation'])
width.append(s['Solver']['Internal']['Max Distances'])
means.append(s['Solver']['Internal']['Current Mean'])
plt.suptitle('DEA Diagnostics', fontweight='bold', fontsize=12 )
colors = hlsColors(numdim)
# Upper Left Plot
ax[0,0].grid(True)
ax[0,0].set_yscale('log')
drawMulticoloredLine(ax[0,0], gen, fval, 0.0, 'r', 'b', '$| F |$')
ax[0,0].plot(gen, dfval, 'x', color = '#34495e', label = '$| F - F_{best} |$')
#if ( (idx == 2) or (updateLegend == False) ):
ax[0,0].legend(bbox_to_anchor=(0,1.00,1,0.2), loc="lower left", mode="expand", ncol = 3, handlelength=1, fontsize = 8)
# Upper Right Plot
ax[0,1].set_title('Objective Variables')
ax[0,1].grid(True)
for i in range(numdim):
ax[0,1].plot(gen, objVec, color = colors[i], label=names[i])
#if ( (idx == 2) or (updateLegend == False) ):
ax[0,1].legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0, handlelength=1)
# Lower Right Plot
ax[1,0].set_title('Width Population')
ax[1,0].grid(True)
for i in range(numdim):
ax[1,0].plot(gen, width, color = colors[i])
# Lower Left Plot
ax[1,1].set_title('Mean Population')
ax[1,1].grid(True)
for i in range(numdim):
ax[1,1].plot(gen, means, color = colors[i], label=names[i])
#if ( (idx == 2) or (updateLegend == False) ):
ax[1,1].legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0, handlelength=1)
plt.show()
def plot(genList, args):
fig, ax = plt.subplots(2, 2, num='Korali Results', figsize=(8, 8))
firstKey = next(iter(genList))
numdim = len(genList[firstKey]['Variables'])
numgens = len(genList)
lastGen = 0
for i in genList:
if genList[i]['Current Generation'] > lastGen:
lastGen = genList[i]['Current Generation']
cond = [0.0] * numgens
absfval = [0.0] * numgens
dfval = [0.0] * numgens
genIds = [0.0] * numgens
sigma = [0.0] * numgens
psL2 = [0.0] * numgens
axis = [None] * numdim
objVec = [None] * numdim
ssdev = [None] * numdim
for i in range(numdim):
axis[i] = [None] * numgens
objVec[i] = [None] * numgens
ssdev[i] = [None] * numgens
curPos = 0
for gen in genList:
genIds[curPos] = genList[gen]['Current Generation']
cond[curPos] = genList[gen]['Solver'][
'Maximum Covariance Eigenvalue'] / genList[gen]['Solver'][
'Minimum Covariance Eigenvalue']
absfval[curPos] = abs(genList[gen]['Solver']['Current Best Value'])
dfval[curPos] = abs(genList[gen]['Solver']['Current Best Value'] -
genList[gen]['Solver']['Best Ever Value'])
sigma[curPos] = genList[gen]['Solver']['Sigma']
psL2[curPos] = genList[gen]['Solver'][
'Conjugate Evolution Path L2 Norm']
for i in range(numdim):
axis[i][curPos] = genList[gen]['Solver']['Axis Lengths'][i]
objVec[i][curPos] = genList[gen]['Solver'][
'Current Best Variables'][i]
ssdev[i][curPos] = genList[gen]['Solver']["Sigma"] * np.sqrt(
genList[gen]['Solver']['Covariance Matrix'][i * numdim + i])
curPos = curPos + 1
plt.suptitle('CMAES Diagnostics', fontweight='bold', fontsize=12)
names = [genList[firstKey]['Variables'][i]['Name'] for i in range(numdim)]
# Upper Left Plot
ax[0, 0].grid(True)
ax[0, 0].set_yscale('log')
#drawMulticoloredLine(ax[0,0], genIds, absfval, 0.0, 'r', 'b', '$| F |$')
ax[0, 0].plot(genIds, absfval, color='r', label='$| F |$')
ax[0, 0].plot(genIds,
dfval,
'x',
color='#34495e',
label='$| F - F_{best} |$')
ax[0, 0].plot(genIds, cond, color='#98D8D8', label='$\kappa(\mathbf{C})$')
ax[0, 0].plot(genIds, sigma, color='#F8D030', label='$\sigma$')
ax[0, 0].plot(genIds, psL2, color='k', label='$|| \mathbf{p}_{\sigma} ||$')
ax[0, 0].legend(bbox_to_anchor=(0, 1.00, 1, 0.2),
loc="lower left",
mode="expand",
ncol=3,
handlelength=1,
fontsize=8)
colors = hlsColors(numdim)
# Upper Right Plot
ax[0, 1].set_title('Objective Variables')
ax[0, 1].grid(True)
for i in range(numdim):
ax[0, 1].plot(genIds, objVec[i], color=colors[i], label=names[i])
ax[0, 1].legend(bbox_to_anchor=(1.04, 0.5),
loc="center left",
borderaxespad=0,
handlelength=1)
# Lower Right Plot
ax[1, 0].set_title('Square Root of Eigenvalues of $\mathbf{C}$')
ax[1, 0].grid(True)
ax[1, 0].set_yscale('log')
for i in range(numdim):
ax[1, 0].plot(genIds, axis[i], color=colors[i])
# Lower Left Plot
ax[1, 1].set_title('$\sigma \sqrt{diag(\mathbf{C})}$')
ax[1, 1].grid(True)
ax[1, 1].set_yscale('log')
for i in range(numdim):
ax[1, 1].plot(genIds, ssdev[i], color=colors[i], label=names[i])
def plot(js):
fig, ax = plt.subplots(2, 2, num='Korali Results', figsize=(8, 8))
solver = js['Generations'][0]['Solver']['Type']
numdim = len(js['Variables'])
names = [js['Variables'][i]['Name'] for i in range(numdim)]
axis = []
cond = []
fval = []
dfval = []
gen = []
ssdev = []
sigma = []
psL2 = []
objVec = []
for s in js['Generations']:
cond.append(s['Solver']['Internal']['Maximum Covariance Eigenvalue'] /
s['Solver']['Internal']['Minimum Covariance Eigenvalue'])
fval.append(s['Solver']['Internal']['Current Best Value'])
dfval.append(
abs(s['Solver']['Internal']['Current Best Value'] -
s['Solver']['Internal']['Best Ever Value']))
gen.append(s['Internal']['Current Generation'])
sigma.append(s['Solver']['Internal']['Sigma'])
psL2.append(
s['Solver']['Internal']['Conjugate Evolution Path L2 Norm'])
axis.append(s['Solver']['Internal']['Axis Lengths'])
objVec.append(s['Solver']['Internal']['Current Best Variables'])
ssdev = [[] for i in range(numdim)]
for i in range(numdim):
for s in js['Generations']:
ssdev[i].append(
js['Generations'][-1]['Solver']["Internal"]["Sigma"] *
np.sqrt(s['Solver']["Internal"]['Covariance Matrix'][i * numdim
+ i]))
plt.suptitle('CMAES Diagnostics', fontweight='bold', fontsize=12)
# Upper Left Plot
ax[0, 0].grid(True)
ax[0, 0].set_yscale('log')
drawMulticoloredLine(ax[0, 0], gen, fval, 0.0, 'r', 'b', '$| F |$')
ax[0, 0].plot(gen, dfval, 'x', color='#34495e', label='$| F - F_{best} |$')
ax[0, 0].plot(gen, cond, color='#98D8D8', label='$\kappa(\mathbf{C})$')
ax[0, 0].plot(gen, sigma, color='#F8D030', label='$\sigma$')
ax[0, 0].plot(gen, psL2, color='k', label='$|| \mathbf{p}_{\sigma} ||$')
#if ( (idx == 2) or (updateLegend == False) ):
ax[0, 0].legend(bbox_to_anchor=(0, 1.00, 1, 0.2),
loc="lower left",
mode="expand",
ncol=3,
handlelength=1,
fontsize=8)
colors = hlsColors(numdim)
# Upper Right Plot
#if (plot_mean):
# ax[0,1].set_title('Mean of Objective Variables')
# objVec = mu
#else:
ax[0, 1].set_title('Objective Variables')
ax[0, 1].grid(True)
for i in range(numdim):
ax[0, 1].plot(gen, objVec, color=colors[i], label=names[i])
#if ( (idx == 2) or (updateLegend == False) ):
ax[0, 1].legend(bbox_to_anchor=(1.04, 0.5),
loc="center left",
borderaxespad=0,
handlelength=1)
# Lower Right Plot
ax[1, 0].set_title('Square Root of Eigenvalues of $\mathbf{C}$')
ax[1, 0].grid(True)
ax[1, 0].set_yscale('log')
for i in range(numdim):
ax[1, 0].plot(gen, axis, color=colors[i])
# Lower Left Plot
ax[1, 1].set_title('$\sigma \sqrt{diag(\mathbf{C})}$')
ax[1, 1].grid(True)
ax[1, 1].set_yscale('log')
for i in range(numdim):
ax[1, 1].plot(gen, ssdev[i], color=colors[i], label=names[i])
plt.show()
def plot(genList, args):
firstKey = next(iter(genList))
fig, ax = plt.subplots(2, 2, num='Korali Results', figsize=(8, 8))
numdim = len(genList[firstKey]['Variables'])
numgens = len(genList)
lastGen = 0
for i in genList:
if genList[i]['Current Generation'] > lastGen:
lastGen = genList[i]['Current Generation']
cond = [0.0] * numgens
fval = [0.0] * numgens
dfval = [0.0] * numgens
genIds = [0.0] * numgens
width = [None] * numdim
means = [None] * numdim
objVec = [None] * numdim
for i in range(numdim):
objVec[i] = [0.0] * numgens
width[i] = [0.0] * numgens
means[i] = [0.0] * numgens
curPos = 0
for gen in genList:
genIds[curPos] = genList[gen]['Current Generation']
fval[curPos] = genList[gen]['Solver']['Current Best Value']
dfval[curPos] = abs(genList[gen]['Solver']['Current Best Value'] -
genList[gen]['Solver']['Best Ever Value'])
for i in range(numdim):
means[i][curPos] = genList[gen]['Solver']['Current Mean'][i]
width[i][curPos] = genList[gen]['Solver']['Max Distances'][i]
objVec[i][curPos] = genList[gen]['Solver'][
'Current Best Variables'][i]
curPos = curPos + 1
plt.suptitle('DEA Diagnostics', fontweight='bold', fontsize=12)
names = [genList[firstKey]['Variables'][i]['Name'] for i in range(numdim)]
colors = hlsColors(numdim)
# Upper Left Plot
ax[0, 0].grid(True)
ax[0, 0].set_yscale('log')
drawMulticoloredLine(ax[0, 0], genIds, fval, 0.0, 'r', 'b', '$| F |$')
ax[0, 0].plot(genIds,
dfval,
'x',
color='#34495e',
label='$| F - F_{best} |$')
#if ( (idx == 2) or (updateLegend == False) ):
ax[0, 0].legend(bbox_to_anchor=(0, 1.00, 1, 0.2),
loc="lower left",
mode="expand",
ncol=3,
handlelength=1,
fontsize=8)
# Upper Right Plot
ax[0, 1].set_title('Objective Variables')
ax[0, 1].grid(True)
for i in range(numdim):
ax[0, 1].plot(genIds, objVec[i], color=colors[i], label=names[i])
#if ( (idx == 2) or (updateLegend == False) ):
ax[0, 1].legend(bbox_to_anchor=(1.04, 0.5),
loc="center left",
borderaxespad=0,
handlelength=1)
# Lower Right Plot
ax[1, 0].set_title('Width Population')
ax[1, 0].grid(True)
for i in range(numdim):
ax[1, 0].plot(genIds, width[i], color=colors[i])
# Lower Left Plot
ax[1, 1].set_title('Mean Population')
ax[1, 1].grid(True)
for i in range(numdim):
ax[1, 1].plot(genIds, means[i], color=colors[i], label=names[i])
#if ( (idx == 2) or (updateLegend == False) ):
ax[1, 1].legend(bbox_to_anchor=(1.04, 0.5),
loc="center left",
borderaxespad=0,
handlelength=1)