def main():
manipulatedVariables = OrderedDict()
manipulatedVariables['alphaForStateWidening'] = [0.25, 0.5, 0.75]
#manipulatedVariables['attentionType'] = ['idealObserver']
manipulatedVariables['attentionType'] = ['hybrid4']
#manipulatedVariables['attentionType'] = ['preAttention', 'attention4', 'hybrid4']
manipulatedVariables['CForStateWidening'] = [2]
manipulatedVariables['minAttentionDistance'] = [7.5, 10.5, 13.5]
manipulatedVariables['rangeAttention'] = [2, 4, 8]
manipulatedVariables['numTrees'] = [2]
manipulatedVariables['numSimulationTimes'] = [33]
productedValues = it.product(*[[(key, value) for value in values] for key, values in manipulatedVariables.items()])
parametersAllCondtion = [dict(list(specificValueParameter)) for specificValueParameter in productedValues]
DIRNAME = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(DIRNAME, '..', 'data', 'mcts',
'trajectories')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
trajectoryExtension = '.pickle'
getTrajectorySavePathByCondition = lambda condition: tsl.GetSavePath(trajectoryDirectory, trajectoryExtension, condition)
measurementEscapeExtension = '.csv'
getCSVSavePathByCondition = lambda condition: tsl.GetSavePath(trajectoryDirectory, measurementEscapeExtension, condition)
runOneCondition = RunOneCondition(getTrajectorySavePathByCondition, getCSVSavePathByCondition)
#runOneCondition(parametersAllCondtion[0])
numCpuCores = os.cpu_count()
numCpuToUse = int(0.9 * numCpuCores)
runPool = mp.Pool(numCpuToUse)
runPool.map(runOneCondition, parametersAllCondtion)
precisionToSubtletyDict={500:0,50:5,11:30,3.3:60,1.83:90,0.92:120,0.31:150,0.001: 180}
def main():
manipulatedVariables = OrderedDict()
manipulatedVariables['alpha'] = [0.25]
manipulatedVariables['attType'] = ['idealObserver', 'hybrid4']
#manipulatedVariables['attType'] = ['hybrid4']#, 'preAttention']
#manipulatedVariables['attType'] = ['preAttention']
#manipulatedVariables['attType'] = ['idealObserver', 'preAttention', 'attention4', 'hybrid4']
#manipulatedVariables['attType'] = ['preAttentionMem0.65', 'preAttentionMem0.25', 'preAttentionPre0.5', 'preAttentionPre4.5']
manipulatedVariables['C'] = [2]
manipulatedVariables['minAttDist'] = [5.0, 10.0, 20.0, 40.0]
manipulatedVariables['rangeAtt'] = [5.0, 20.0]
manipulatedVariables['cBase'] = [50]
manipulatedVariables['numTrees'] = [4]
manipulatedVariables['numSim'] = [154]
manipulatedVariables['actRatio'] = [1.0]
manipulatedVariables['burnTime'] = [0]
manipulatedVariables['softId'] = [1.0]
manipulatedVariables['softSubtlety'] = [1.0]
manipulatedVariables['actCost'] = [0.05, 0.1, 0.15, 0.2]
manipulatedVariables['damp'] = [1.0]
productedValues = it.product(
*[[(key, value) for value in values]
for key, values in manipulatedVariables.items()])
parametersAllCondtion = [
dict(list(specificValueParameter))
for specificValueParameter in productedValues
]
DIRNAME = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(DIRNAME, '..', 'data', 'mcts',
'trajectories')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
trajectoryExtension = '.pickle'
getTrajectorySavePathByCondition = lambda condition: tsl.GetSavePath(
trajectoryDirectory, trajectoryExtension, condition)
measurementEscapeExtension = '.csv'
getCSVSavePathByCondition = lambda condition: tsl.GetSavePath(
trajectoryDirectory, measurementEscapeExtension, condition)
runOneCondition = RunOneCondition(getTrajectorySavePathByCondition,
getCSVSavePathByCondition)
#runOneCondition(parametersAllCondtion[0])
numCpuCores = os.cpu_count()
numCpuToUse = int(numCpuCores)
runPool = mp.Pool(numCpuToUse)
runPool.map(runOneCondition, parametersAllCondtion)
precisionToSubtletyDict = {
500: 0,
50: 5,
11: 30,
3.3: 60,
1.83: 90,
0.92: 120,
0.31: 150,
0.001: 180
}
def main():
manipulatedVariables = OrderedDict()
manipulatedVariables['alpha'] = [0.25]
#manipulatedVariables['attType'] = ['idealObserver']#, 'hybrid4']
#manipulatedVariables['attType'] = ['hybrid4']#, 'preAttention']
#manipulatedVariables['attType'] = ['preAttention']
manipulatedVariables['attType'] = ['idealObserver', 'preAttention', 'attention4', 'hybrid4']
#manipulatedVariables['attType'] = ['preAttentionMem0.65', 'preAttentionMem0.25', 'preAttentionPre0.5', 'preAttentionPre4.5']
manipulatedVariables['C'] = [2]
manipulatedVariables['minAttDist'] = [10.0, 40.0]#[10.0, 20.0, 40.0]
manipulatedVariables['rangeAtt'] = [10.0]
manipulatedVariables['cBase'] = [50]
manipulatedVariables['numTrees'] = [2]
manipulatedVariables['numSim'] = [144]
manipulatedVariables['actRatio'] = [1.0]
manipulatedVariables['burnTime'] = [0]
manipulatedVariables['softId'] = [1]
manipulatedVariables['softSubtlety'] = [1]
manipulatedVariables['actCost'] = [0.1, 0.4, 0.7, 1.0]
manipulatedVariables['damp'] = [0.0]
manipulatedVariables['measure'] = ['escape']
productedValues = it.product(*[[(key, value) for value in values] for key, values in manipulatedVariables.items()])
parametersAllCondtion = [dict(list(specificValueParameter)) for specificValueParameter in productedValues]
DIRNAME = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(DIRNAME, '..', 'data', 'mcts',
'trajectories')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
measurementEscapeExtension = '.csv'
getCSVSavePathByCondition = lambda condition: tsl.GetSavePath(trajectoryDirectory, measurementEscapeExtension, condition)
#columnNames = [500.0, 11.0, 3.3, 1.83, 0.92, 0.31, 0.001]
#columnNames = [500.0, 3.3, 0.92]
columnNames = [500.0, 3.3, 0.92, 0.01]
readcsv = Readcsv(getCSVSavePathByCondition, columnNames)
precisionToSubtletyDict={500.0:0, 50.0:5, 11.0:30, 3.3:60, 1.83:90, 0.92:120, 0.31:150, 0.01: 180}
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index = modelIndex)
modelResultDf = toSplitFrame.groupby(levelNames).apply(readcsv)
toDropLevels = ['alpha', 'C', 'cBase', 'numTrees', 'rangeAtt', 'damp', 'burnTime', 'softId', 'softSubtlety',
'measure', 'actRatio', 'numSim']
modelResultDf.index = modelResultDf.index.droplevel(toDropLevels)
fig = plt.figure()
numColumns = len(manipulatedVariables['actCost'])
numRows = len(manipulatedVariables['minAttDist'])
plotCounter = 1
for key, group in modelResultDf.groupby(['minAttDist', 'actCost']):
columnNamesAsSubtlety = [precisionToSubtletyDict[precision] for precision in group.columns]
group.columns = columnNamesAsSubtlety
group = group.stack()
group.index.names = ['attType', 'actCost', 'minAttDist', 'chasingSubtlety']
group.index = group.index.droplevel(['actCost', 'minAttDist'])
group = group.to_frame()
group.columns = ['model']
axForDraw = fig.add_subplot(numRows, numColumns, plotCounter)
if (plotCounter) % max(numColumns, 2) == 1:
axForDraw.set_ylabel(str(key[0]))
if plotCounter <= numColumns:
axForDraw.set_title(str(key[1]))
for attentionType, grp in group.groupby('attType'):
grp.index = grp.index.droplevel('attType')
#if str(attentionType) == manipulatedVariables['attType'][-1]:
# grp['human'] = [0.24, 0.51]
# grp['human'] = [0.6, 0.48, 0.37, 0.25, 0.24, 0.42, 0.51]
# grp.plot.line(ax = axForDraw, y = 'human', label = 'human', ylim = (0, 0.7), marker = 'o', rot = 0 )
grp.plot.line(ax = axForDraw, y = 'model', label = str(attentionType), ylim = (0, 1.1), marker = 'o', rot = 0 )
plotCounter = plotCounter + 1
#plt.suptitle('Measurement = Perception Rate')
#plt.suptitle('Measurement = Action Deviation')
#plt.suptitle('Measurement = Velocity Diff')
plt.suptitle('Measurement = Escape rate')
fig.text(x = 0.5, y = 0.92, s = 'Action Cost', ha = 'center', va = 'center')
#fig.text(x = 0.5, y = 0.92, s = 'Min Attention Distance', ha = 'center', va = 'center')
#fig.text(x = 0.05, y = 0.5, s = 'Attention Range', ha = 'center', va = 'center', rotation=90)
fig.text(x = 0.05, y = 0.5, s = 'minAttDist', ha = 'center', va = 'center', rotation=90)
plt.show()
def main():
manipulatedVariables = OrderedDict()
manipulatedVariables['alphaForStateWidening'] = [0.25]
#manipulatedVariables['attentionType'] = ['idealObserver']
manipulatedVariables['attentionType'] = ['hybrid4']
#manipulatedVariables['attentionType'] = ['preAttention', 'attention4', 'hybrid4']
manipulatedVariables['CForStateWidening'] = [2]
manipulatedVariables['minAttentionDistance'] = [6.5, 11.5, 16.5]
manipulatedVariables['rangeAttention'] = [2, 4, 8]
manipulatedVariables['cBase'] = [50]
manipulatedVariables['numTrees'] = [2]
manipulatedVariables['numSimulationTimes'] = [74]
manipulatedVariables['actionRatio'] = [0.2]
manipulatedVariables['burnTime'] = [3, 6, 12]
productedValues = it.product(
*[[(key, value) for value in values]
for key, values in manipulatedVariables.items()])
parametersAllCondtion = [
dict(list(specificValueParameter))
for specificValueParameter in productedValues
]
DIRNAME = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(DIRNAME, '..', 'data', 'mcts',
'trajectories')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
measurementEscapeExtension = '.csv'
getCSVSavePathByCondition = lambda condition: tsl.GetSavePath(
trajectoryDirectory, measurementEscapeExtension, condition)
columnNames = [500.0, 3.3, 1.83, 0.92, 0.001]
readcsv = Readcsv(getCSVSavePathByCondition, columnNames)
precisionToSubtletyDict = {
500.0: 0,
50.0: 5,
11: 30,
3.3: 60,
1.83: 90,
0.92: 120,
0.31: 150,
0.001: 180
}
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index=modelIndex)
modelResultDf = toSplitFrame.groupby(levelNames).apply(readcsv)
toDropLevels = [
'alphaForStateWidening', 'attentionType', 'CForStateWidening', 'cBase',
'numTrees', 'numSimulationTimes', 'actionRatio'
]
modelResultDf.index = modelResultDf.index.droplevel(toDropLevels)
fig = plt.figure()
numColumns = len(manipulatedVariables['minAttentionDistance'])
numRows = len(manipulatedVariables['rangeAttention'])
plotCounter = 1
for key, group in modelResultDf.groupby(
['rangeAttention', 'minAttentionDistance']):
columnNamesAsSubtlety = [
precisionToSubtletyDict[precision] for precision in group.columns
]
group.columns = columnNamesAsSubtlety
group = group.stack()
group.index.names = [
'rangeAttention', 'minAttentionDistance', 'burnTime',
'chasingSubtlety'
]
group.index = group.index.droplevel(
['minAttentionDistance', 'rangeAttention'])
group = group.to_frame()
group.columns = ['model']
axForDraw = fig.add_subplot(numRows, numColumns, plotCounter)
if (plotCounter) % max(numColumns, 2) == 1:
axForDraw.set_ylabel(str(key[0]))
if plotCounter <= numColumns:
axForDraw.set_title(str(key[1]))
for burnTime, grp in group.groupby('burnTime'):
grp.index = grp.index.droplevel('burnTime')
if burnTime == max(manipulatedVariables['burnTime']):
grp['human'] = [0.6, 0.37, 0.25, 0.24, 0.52]
grp.plot.line(ax=axForDraw,
y='human',
label='human',
ylim=(0, 0.9),
marker='o',
rot=0)
grp.plot.line(ax=axForDraw,
y='model',
label='burnTime=' + str((burnTime - 1) * 0.2) + 's',
ylim=(0, 0.9),
marker='o',
rot=0)
plotCounter = plotCounter + 1
plt.suptitle('Measurement = Escape Rate of Hybrid')
fig.text(x=0.5,
y=0.92,
s='Min Attention Distance',
ha='center',
va='center')
fig.text(x=0.05,
y=0.5,
s='Attention Range',
ha='center',
va='center',
rotation=90)
plt.show()
