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data_charts.py
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data_charts.py
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#!/usr/bin/env python2.7
##
## Generate .dat files for charts (data-charts folder).
##
## Uses:
# For especific domains:
# ./data_charts.py "delta-cl delta-o-cl1" blocks-world-optimal depots-optimal -stats [-fast]
# For all domains:
# ./data_charts.py "delta-cl delta-o-cl1" all -stats [-fast]
# For method groups:
# ./data_charts.py lm optimal -scatter -stats [-fast]
# ./data_charts.py fl optimal -scatter -stats [-fast]
# ./data_charts.py dr optimal -stats [-fast]
##
import os, sys
import data_domain as dd
import data_output as do
class ObsStats:
#
# The results for the set of goal recognition tasks for a single observatility level.
#
def __init__(self, obs):
self.level = obs
self.points = dict()
self.win = [0, 0, 0] # better, worse, draw
self.quads = [0, 0, 0, 0] # Q1, Q2, Q3, Q4
self.axis = [0, 0, 0, 0, 0] # Origin, X left, X right, Y bottom, Y right
self.sum_points = [[], [], []]
self.sum_diag = [0, 0, 0] * len(self.sum_points)
self.sum_axis = [0, 0] * len(self.sum_points)
def count_hvalues(self, problem, experiments):
for h in range(len(problem.hyps)):
hc = [m.problem_outputs[problem.name].scores[h][1] \
if h in m.problem_outputs[problem.name].scores else 45 \
for m in experiments]
line = ' '.join([str(x) for x in hc])
if line in self.points:
self.points[line] += 1
else:
self.points[line] = 1
agr = [m.problem_outputs[problem.name].agreement for m in experiments]
if hc[0] > hc[1]:
self.win[0] += 1
if agr[0] > agr[1]:
self.quads[2] += 1
elif agr[1] > agr[0]:
self.quads[1] += 1
else:
self.axis[1] += 1
elif hc[1] > hc[0]:
self.win[1] += 1
if agr[0] > agr[1]:
self.quads[3] += 1
elif agr[1] > agr[0]:
self.quads[0] += 1
else:
self.axis[2] += 1
else:
self.win[2] += 1
if agr[0] > agr[1]:
self.axis[3] += 1
elif agr[1] > agr[0]:
self.axis[4] += 1
else:
self.axis[0] += 1
def print_points(self):
return '\n'.join([line + " " + str(c) for line, c in self.points.items()])
def print_stats(self, methods):
content = methods[0] + " vs " + methods[1] + '\n'
content += "%s higher than %s: %s" % (methods[0], methods[1], self.win[0]) + "\n"
content += "%s higher than %s: %s" % (methods[1], methods[0], self.win[1]) + "\n"
content += "%s equal to %s: %s" % (methods[0], methods[1], self.win[2]) + "\n"
for i in range(0, 4):
content += "Q%s: %s" % (i + 1, self.quads[i]) + '\n'
content += "Axis X (left): %s" % self.axis[1] + '\n'
content += "Axis X (right): %s" % self.axis[2] + '\n'
content += "Axis Y (bottom): %s" % self.axis[3] + '\n'
content += "Axis Y (top): %s" % self.axis[4] + '\n'
content += "Origin: %s" % self.axis[0] + '\n'
return content
def count_sums(self, problem, experiments):
ref = frozenset(problem.get_solution_indexes())
nonref = frozenset(problem.get_hyp_indexes()) - ref
if len(ref) <= 0 or len(nonref) <= 0:
return [], []
# (\sum h1(s_0,s*_i) > h2(s_0,s*_i) for i in \Gamma*) / |\Gamma*|
sums_ref = []
# (\sum h1(s_0,s*_i) > h2(s_0,s*_i) for i in \Gamma* - \Gamma) / |\Gamma* - \Gamma|
sums_nonref = []
for exp in experiments:
if len(ref) > 0:
sums_ref.append(sum([ 1.0 for hyp in ref if \
exp.problem_outputs[problem.name].scores[hyp][1] > \
exp.problem_outputs[problem.name].scores[hyp][0] ]) / len(ref))
if len(nonref) > 0:
sums_nonref.append(sum([ 1.0 for hyp in nonref if \
hyp not in exp.problem_outputs[problem.name].scores or \
exp.problem_outputs[problem.name].scores[hyp][1] > \
exp.problem_outputs[problem.name].scores[hyp][0] ]) / len(nonref))
if len(ref) > 0 and len(nonref) > 0:
sums_ref.append(sum([ 1.0 for hyp in ref if \
experiments[1].problem_outputs[problem.name].scores[hyp][1] > \
experiments[0].problem_outputs[problem.name].scores[hyp][1] ]) / len(ref))
sums_nonref.append(sum([ 1.0 for hyp in nonref if \
hyp not in experiments[1].problem_outputs[problem.name].scores or \
hyp in experiments[0].problem_outputs[problem.name].scores and \
experiments[1].problem_outputs[problem.name].scores[hyp][1] > \
experiments[0].problem_outputs[problem.name].scores[hyp][1] ]) / len(nonref))
return sums_ref, sums_nonref
def add_sum_point(self, i, x, y, z):
self.sum_points[i].append((x, y, z))
if x < y:
self.sum_diag[i*3] += 1
elif x > y:
self.sum_diag[i*3 + 1] += 1
else:
self.sum_diag[i* 3 + 2] += 1
if y == 0:
self.sum_axis[i*2] += 1
if x == 0:
self.sum_axis[i*2 + 1] += 1
def compute_sum_points(self, problems, experiments):
for p in problems:
sums_ref, sums_nonref = self.count_sums(p, experiments)
# sums0: (hc1 > h1 [ref], hc2 > h2 [ref])
# sums1: (hc1 > h1 [nonref], hc2 > h2 [nonref])
# sums2: (hc2 > hc1 [nonref], hc2 > hc1 [ref])
if len(sums_ref) >= 1:
self.add_sum_point(0, sums_ref[0], sums_ref[1], p.name)
if len(sums_nonref) >= 1:
self.add_sum_point(1, sums_nonref[0], sums_nonref[1], p.name)
if len(sums_ref) >= 2 and len(sums_nonref) >= 2:
self.add_sum_point(2, sums_nonref[2], sums_ref[2], p.name)
def print_sum_points(self, i):
return '\n'.join(["%s\t%s\t%s" % p for p in self.sum_points[i]])
def print_diag_axis(self, i):
return '\t'.join([str(d) for d in self.sum_diag[i*3:i*3+3]]) \
+ '\t' + '\t'.join([str(a) for a in self.sum_axis[i*2:i*2+2]])
def write_dat_files(all_domain_data, methods, observabilities, chart_name = None, scatter = True, stats = True, sums = True):
obs_stats = [ObsStats(o) for o in range(len(observabilities))]
for domain_data in all_domain_data.values():
method_outputs = [do.MethodOutput(method, domain_data) for method in methods]
for o in range(len(observabilities)):
method_experiments = [m.experiments[o] for m in method_outputs]
problems = domain_data.data[observabilities[o]].values()
if scatter or stats:
for p in problems:
obs_stats[o].count_hvalues(p, method_experiments)
if sums:
obs_stats[o].compute_sum_points(problems, method_experiments)
if not chart_name:
chart_name = ' vs '.join(methods)
header = ' '.join(["x%s" % i for i in range(len(methods))]) + " w \n"
for o in range(len(observabilities)):
if scatter:
content = obs_stats[o].print_points()
with open("data-charts/" + chart_name + "-" + observabilities[o] + "-scatter-all.dat", 'w') as f:
f.write(header + content)
if stats:
content = obs_stats[o].print_stats(methods)
with open("data-charts/" + chart_name + "-" + observabilities[o] + "-stats.dat", 'w') as f:
f.write(content)
if sums:
for i in range(0, 3):
content = obs_stats[o].print_sum_points(i)
with open("data-charts/" + chart_name + "-" + observabilities[o] + "-sums%d.dat" % i, 'w') as f:
f.write(content)
for i in range(0, 3):
print('fig %s:\t' % (i + 1) + '\tAbove\tBelow\tDiag\tX\tY')
for o in range(len(observabilities)):
print(observabilities[o] + "%: " + obs_stats[o].print_diag_axis(i))
if __name__ == '__main__':
observabilities = ['10', '30', '50', '70']
base_path = '../goal-plan-recognition-dataset/'
# Flags
test = False
scatter = False
stats = False
sums = False
if '-fast' in sys.argv:
set_filter(True)
dd.set_filter(True)
sys.argv.remove('-fast')
if '-test' in sys.argv:
test = True
sys.argv.remove('-test')
base_path = 'experiments/'
if '-scatter' in sys.argv:
scatter = True
sys.argv.remove('-scatter')
if '-stats' in sys.argv:
stats = True
sys.argv.remove('-stats')
if '-sums' in sys.argv:
sums = True
sys.argv.remove('-sums')
# Domains
domains = dd.parse_domains(sys.argv[2:], test)
all_domain_data = {}
for d in domains:
domain_data = dd.DomainData(d, observabilities)
if os.path.exists("data-domains/" + d + ".txt"):
domain_data.read("data-domains/")
else:
domain_data.load(base_path)
all_domain_data[d] = domain_data
# Methods
methods = sys.argv[1]
chart_name = None
if methods == 'lm':
methods = ["delta-cl", "delta-o-cl1"]
chart_name = "LMC-vs-LMC+soft"
elif methods == 'dr':
methods = ["delta-o-cdt", "delta-o-cdto"]
chart_name = "DEL+1-vs-DEL+2"
elif methods == 'fl':
methods = ["delta-cf1", "delta-o-cf17"]
chart_name = "F1-vs-FOPxEIntra"
else:
methods = methods.split()
write_dat_files(all_domain_data, methods, observabilities, \
chart_name, scatter, stats, sums)