-
Notifications
You must be signed in to change notification settings - Fork 0
/
simpleTimeSeries10.py
274 lines (218 loc) · 10.6 KB
/
simpleTimeSeries10.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
from os.path import join, expanduser
from time import sleep
import subprocess
import os
import numpy as np
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
sys.path.append(join(expanduser('~'), 'morphMod'))
from matplotlib.colors import LogNorm
import pbsGridWalker.grid as gr
import pbsGridWalker.tools.algorithms as tal
import pbsGridWalker.tools.fsutils as tfs
import pbsGridWalker.tools.plotutils as tplt
import morphModRoutes as mmr
import classifiers
import gctools
import gccommons
# Tunable hyperparameters
numTrials = 100
segments = 10
# Optional definitions for pbsGridWalker that depend on the number of segments
pointsPerJob = 1
maxJobs = 400
queue = 'workq'
expectedWallClockTime = '30:00:00'
# Constant hyperparameters
evsDefaults = {'individual': 'compositeFixedProbabilities', 'evolver': 'cluneSimplifiedMorphologyControlIndividuals', 'communicator': 'chunkedUnixPipe',
'compositeClass0': 'integerVectorSymmetricRangeMutations', 'probabilityOfMutatingClass0': 0.2,
'lengthClass0': segments, 'initLowerLimitClass0': 0, 'initUpperLimitClass0': segments, 'lowerCapClass0': 0, 'upperCapClass0': segments,
'mutationAmplitudeClass0': 1,
'compositeClass1': 'trinaryWeights',
'lengthClass1': 2*(segments**2), 'initLowerLimitClass1': -1, 'initUpperLimitClass1': 1, 'lowerCapClass1': -1, 'upperCapClass1': 1,
'mutExplorationClass1': 0.8, 'mutInsDelRatioClass1': 1, 'mutationAmplitudeClass1': 1,
'genStopAfter': 4000, 'populationSize': 200,
'initialPopulationType': 'random', 'secondObjectiveProbability': 1.,
'logParetoFront': 'yes', 'logBestIndividual': 'yes', 'logParetoFrontKeepAllGenerations': 'yes', 'logParetoFrontPeriod': 5, 'logParetoSize': 'yes',
'backup': 'no', 'trackAncestry': 'no',
'randomSeed': 0}
evsDefaults['logParetoFrontPeriod'] = 1 # overriding because the field in defaults gets changed during the scripts' autogeneration
arrowbotsDefaults = {'segments': segments, 'sensorAttachmentType': 'variable',
'simulationTime': 10., 'timeStep': 0.1,
'integrateError': 'false', 'writeTrajectories': 'false'}
arrowbotInitialConditions = [[0]*segments]*segments # segmentsXsegments null matrix
arrowbotTargetOrientations = [ [1 if i==j else 0 for i in range(segments)] for j in range(segments) ] # segmentsXsegments identity matrix
# Optional definitions for pbsGridWalker that are constant
involvedGitRepositories = mmr.involvedGitRepositories
# dryRun = False
### Required pbsGridWalker definitions
computationName = 'simpleTimeSeries_N' + str(segments)
gcvsrandGrid = gr.Grid1d('compositeClass0', ['integerVectorSymmetricRangeMutations', 'integerVectorRandomJumps'])*gr.Grid1d('probabilityOfMutatingClass0', [0.2])
constmorphGrid = gr.Grid1d('compositeClass0', ['integerVectorSymmetricRangeMutations'])*gr.Grid1d('probabilityOfMutatingClass0', [0.0])
nonRSGrid = gcvsrandGrid.concatenate(constmorphGrid)
# parametricGrid = nonRSGrid*numTrials + gr.Grid1dFromFile('randomSeed', mmr.randSeedFile, size=len(nonRSGrid)*numTrials)
# Since the order of iteration is not guaranteed in Python, different random seed sets get assigned to different non-RS points on different machines.
# To circumvent that, I pickle the grid (produced by the commented line above) on the machine that does the calculations and unpickle it on the data processing machine.
import pickle
with open(join(mmr.morphModPath, 'rawgrid'), 'r') as rgf:
parametricGrid = pickle.load(rgf)
for par in parametricGrid.paramNames():
evsDefaults.pop(par)
def prepareEnvironment(experiment):
gccommons.prepareEnvironment(experiment)
def runComputationAtPoint(worker, params):
return gccommons.runComputationAtPoint(worker, params,
evsDefaults, arrowbotsDefaults,
arrowbotInitialConditions,
arrowbotTargetOrientations)
def plotTTCvsMMMD(experiment):
import scipy
fitnessThreshold = -8
def TTCvsMMMD(gridPoint):
if not (gridPoint['compositeClass0'] == 'integerVectorSymmetricRangeMutations' and gridPoint['probabilityOfMutatingClass0'] == 0.2):
return
# determining the time of convergence
bilogFileName = 'bestIndividual{}.log'.format(gridPoint['randomSeed'])
bilog = np.loadtxt(bilogFileName)
toc = None # time of convergence
# TOME OF CONVERGENCE
for i in range(bilog.shape[0]):
if -1.*bilog[i,1] <= fitnessThreshold:
toc = i
break
if toc is None:
with open('../results/nonconverged runs', 'a') as ncrf:
ncrf.write(str(gridPoint['randomSeed']) + '\n')
return
# making a scatter of points of TTC vs MMMD
with open('../results/ttcvsmmmd', 'a') as tmfile:
for i in range(evsDefaults['genStopAfter']+1):
tmfile.write('{} {}\n'.format(toc-i, gctools.minParetoFrontHammingDistanceToMMM(i)))
experiment.executeAtEveryGridPointDir(TTCvsMMMD)
os.chdir('results')
ttcmmmddata = np.loadtxt('ttcvsmmmd')
mmmd = ttcmmmddata[:,1]
ttc = ttcmmmddata[:,0]
mmmdrange = [0,6]
ttcrange = [ min(ttc), max(ttc) ]
mmmdbins = 6
ttcbins = 100
xdat = mmmd
ydat = ttc
xrange = mmmdrange
yrange = ttcrange
xbins = mmmdbins
ybins = ttcbins
# https://stackoverflow.com/questions/10439961
xyrange = [ xrange, yrange ]
bins = [xbins, ybins]
thresh = 1
hh, locx, locy = scipy.histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
# select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
fig, ax = plt.subplots(figsize=(7,6))
cax = fig.add_axes() # https://stackoverflow.com/questions/32462881
#im = ax.imshow(np.flipud(hh.T),cmap='jet',extent=np.array(xyrange).flatten(), interpolation='none', origin='upper',norm=LogNorm(vmin=1, vmax=10000))
im = ax.imshow(np.flipud(hh.T),cmap='jet',extent=np.array(xyrange).flatten(), interpolation='none', origin='upper',norm=LogNorm(vmin=1))
cb = fig.colorbar(im, cax=cax)
cb.set_label('# of points')
ax.plot(xdat1, ydat1, '.',color='darkblue')
ax.set_xlabel(r'$\mu$', x=1.0, fontsize=20)
ax.set_ylabel(r'$\tau_{conv}$', y=1.0, fontsize=20)
# ax.set_ylim([0,420])
ax.set_aspect(0.006)
plt.savefig('ttcvsmmmd.png', dpi=300)
plt.clf()
os.chdir('..')
def plotErrorTSs(experiment, prefixFun):
import shutil
gridFileNamePrefix = prefixFun
##### Extracting and plotting fitness time series #####
xlabel = r'$T$'
#figureDims = [7,4]
figureDims = None
xlimit = evsDefaults['genStopAfter']
margins = 0.5
strips = 'conf95'
title = None
legendLocation = 1
def fitnessFileName(gp):
return gridFileNamePrefix(gp) + '_fitness'
def columnExtractor(gp):
outFile = fitnessFileName(gp)
subprocess.call('cut -d \' \' -f 2 bestIndividual*.log | tail -n +4 | tr \'\n\' \' \' >> ../results/' + outFile, shell=True)
subprocess.call('echo >> ../results/' + outFile, shell=True)
experiment.executeAtEveryGridPointDir(columnExtractor)
os.chdir('results')
ylabel = r'$\log_{10} E$'
forcedYLabelPos = [0.05, 1.]
ylimit = None
title = None
dataDict = {gridFileNamePrefix(p): -1.*np.loadtxt(fitnessFileName(p)) for p in nonRSGrid}
# Plotting averages
yscale = 'lin'
xscale = 'lin'
tplt.plotAverageTimeSeries(dataDict, ylabel, 'errorComparisonLinLin.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, strips=strips, figureDims=figureDims, forcedYLabelPos=forcedYLabelPos)
xscale = 'log'
tplt.plotAverageTimeSeries(dataDict, ylabel, 'errorComparisonLogLin.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, strips=strips, figureDims=figureDims, forcedYLabelPos=forcedYLabelPos)
# Plotting the trajectory scatter
alpha = 0.3
yscale = 'lin'
xscale = 'lin'
tplt.plotAllTimeSeries(dataDict, ylabel, 'errorAllTrajectoriesLinLog.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, alpha=alpha, figureDims=figureDims, forcedYLabelPos=forcedYLabelPos)
xscale = 'log'
tplt.plotAllTimeSeries(dataDict, ylabel, 'errorAllTrajectoriesLogLog.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, alpha=alpha, figureDims=figureDims, forcedYLabelPos=forcedYLabelPos)
plt.clf()
os.chdir('..')
def plotMinMMMDistTSs(experiment, prefixFun):
'''Extracting and plotting time series for distance to the maximally modular morphology (MMM)'''
xlabel = r'$T$'
figureDims = [7,4]
#figureDims = None
xlimit = evsDefaults['genStopAfter']
margins = 0.5
strips = 'conf95'
title = None
legendLocation = 1
gridFileNamePrefix = prefixFun
def minMMMDistFileName(gridPoint):
return '../results/' + gridFileNamePrefix(gridPoint) + '_minMMMDist'
def generateMinMMMDistTimeSeries(gridPoint):
minMMMDistTS = [ gctools.minParetoFrontHammingDistanceToMMM(gen) for gen in range(1, evsDefaults['genStopAfter']+1) ]
filename = minMMMDistFileName(gridPoint)
with open(filename, 'a') as file:
file.write(' '.join(map(str, minMMMDistTS)) + '\n')
experiment.executeAtEveryGridPointDir(generateMinMMMDistTimeSeries)
os.chdir('results')
ylabel = r'$\mu$'
ylimit = None
title = None
dataDict = {gridFileNamePrefix(p): np.loadtxt(minMMMDistFileName(p)) for p in nonRSGrid}
# Plotting averages in linear time scales on y
yscale = 'lin'
xscale = 'lin'
tplt.plotAverageTimeSeries(dataDict, ylabel, 'minMMMDistTS.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, strips=strips, figureDims=figureDims)
plt.clf()
os.chdir('..')
def processResults(experiment):
tfs.makeDirCarefully('results', maxBackups=100)
plotTTCvsMMMD(experiment)
def gridFileNamePrefix(gridPoint):
if gridPoint['probabilityOfMutatingClass0'] == 0.2:
if gridPoint['compositeClass0'] == 'integerVectorSymmetricRangeMutations':
return 'move_sensor_by_1_segment'
elif gridPoint['compositeClass0'] == 'integerVectorRandomJumps':
return 're-generate_morphology'
elif gridPoint['probabilityOfMutatingClass0'] == 0 and gridPoint['compositeClass0'] == 'integerVectorSymmetricRangeMutations':
return 'no_morphological_mutation'
raise ValueError('Wrong point {} in the non-RS grid'.format(gridPoint))
plotErrorTSs(experiment, gridFileNamePrefix)
plotMinMMMDistTSs(experiment, gridFileNamePrefix)