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03-process-complete-analyses.py
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03-process-complete-analyses.py
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import numpy as np
import matplotlib.pyplot as plt
def read_data(mode, benchmark):
resultsfile = "analysis-" + mode + "-complete-" + benchmark + ".txt"
infile = open(resultsfile)
lines = infile.readlines()
infile.close()
data = {}
i = 0
while i < len(lines):
if lines[i].startswith("Overall"):
i += 1
line = lines[i]
neighborhood = line[:line.find("-")]
i += 1
line = lines[i]
approach = line[len("Paths produced by "):line.find(" Search")]
variant = approach[:approach.find(" ")]
search = approach[len(variant) + 1:]
smoothing = line[line.find(" with ") + len(" with "):line.find(" Smoothing")].strip()
i += 1
line = lines[i]
pathlength = line[len("Mean Path Length = "):].strip()
i += 1
line = lines[i]
runtime = line[len("Mean Runtime (us) = "):].strip()
if neighborhood not in data.keys():
data[neighborhood] = {}
if variant not in data[neighborhood].keys():
data[neighborhood][variant] = {}
if search not in data[neighborhood][variant].keys():
data[neighborhood][variant][search] = {}
data[neighborhood][variant][search][smoothing] = {}
data[neighborhood][variant][search][smoothing]["Pathlength"] = pathlength
data[neighborhood][variant][search][smoothing]["Runtime"] = runtime
i += 1
return data
def report_search_statistics(mode, benchmark, data, search):
searchname = search.replace("*", "-Star").replace(" ", "-")
outputpath = "table-" + mode + "-" + benchmark + "-" + searchname + "-Search.md"
outfile = open(outputpath, "w")
outfile.write("|Neighborhood|Search Method|Smoothing|Path Length|Runtime|\n")
outfile.write("|:----------:|:-----------:|:-------:|:--------:|:-----:|\n")
for smoothing in ["Greedy", "Tentpole"]:
for variant in ["Regular", "Central"]:
for neighborhood in ["4", "8", "16", "32", "64"]:
pathlength = data[neighborhood][variant][search][smoothing]["Pathlength"]
runtime = data[neighborhood][variant][search][smoothing]["Runtime"]
outfile.write("|" + neighborhood + "|" + variant + " " + search + " Search|" + smoothing + "|" + pathlength + "|" + runtime + "|\n")
outfile.close()
def plot_search_statistics(mode, benchmark, data, search, runtime0, pathlength0):
searchname = search.replace("*", "-Star").replace(" ", "-")
outputpath = "plot-" + mode + "-" + benchmark + "-" + searchname + "-Search.png"
neighborhoods = ["4", "8", "16", "32", "64"]
markersizes = ["8", "8", "12", "12", "12"]
fig, ax = plt.subplots(figsize=(8, 8))
ax.set_xlabel("Runtime (relative to reference method)")
ax.set_ylabel("Suboptimality (degrees relative to best)")
if mode == "heuristic":
xmax = 7
ymax = 25
if search == "Mixed A*":
xmax = 12
elif mode == "dijkstra":
xmax = 9
ymax = 25
if search == "Mixed Dijkstra":
xmax = 14
ax.set_xlim([0, xmax])
ax.set_ylim([0, ymax])
ax.set_aspect(0.8*xmax/ymax)
ax.set_xticks(range(xmax + 1))
seriesnames = []
plotdata = {}
for smoothing in ["Greedy", "Tentpole"]:
for variant in ["Regular", "Central"]:
seriesname = variant + " " + search + " Search (" + smoothing + ")"
seriesnames.append(seriesname)
plotdata[seriesname] = {}
plotdata[seriesname]["Speed"] = []
plotdata[seriesname]["Quality"] = []
for neighborhood in neighborhoods:
L = int(neighborhood)
runtime = float(data[neighborhood][variant][search][smoothing]["Runtime"])
speed = runtime/runtime0
pathlength = float(data[neighborhood][variant][search][smoothing]["Pathlength"])
quality = (180/np.pi)*np.arccos(pathlength0/pathlength)
plotdata[seriesname]["Speed"].append(speed)
plotdata[seriesname]["Quality"].append(quality)
for seriesname in seriesnames:
ax.plot(plotdata[seriesname]["Speed"], plotdata[seriesname]["Quality"])
ax.legend(seriesnames)
for seriesname in seriesnames:
for i in range(len(neighborhoods)):
speed = plotdata[seriesname]["Speed"][i]
quality = plotdata[seriesname]["Quality"][i]
ax.plot([speed], [quality], marker=".", markeredgecolor="black", markerfacecolor="black")
ax.plot([speed + 0.1], [quality + 0.5], marker="$" + neighborhoods[i] + "$", markeredgecolor="black", markerfacecolor="black", markersize=markersizes[i])
plt.savefig(outputpath, bbox_inches = "tight")
def plot_comparison_statistics(mode, benchmark, data, searches, runtime0, pathlength0):
outputpath = "plot-" + mode + "-" + benchmark + ".png"
neighborhoods = ["4", "8", "16", "32", "64"]
markersizes = ["8", "8", "12", "12", "12"]
fig, ax = plt.subplots(figsize=(8, 8))
ax.set_xlabel("Runtime (relative to standard)")
ax.set_ylabel("Suboptimality (degrees relative to best)")
if mode == "heuristic":
xmax = 5
ymax = 8
elif mode == "dijkstra":
xmax = 7
ymax = 8
ax.set_xlim([0, xmax])
ax.set_ylim([0, ymax])
ax.set_aspect(0.8*xmax/ymax)
ax.set_xticks(range(xmax + 1))
seriesnames = []
plotdata = {}
smoothing = "Tentpole"
for variant in ["Regular", "Central"]:
for search in searches:
seriesname = variant + " " + search + " Search"
seriesnames.append(seriesname)
plotdata[seriesname] = {}
plotdata[seriesname]["Speed"] = []
plotdata[seriesname]["Quality"] = []
for neighborhood in neighborhoods:
L = int(neighborhood)
runtime = float(data[neighborhood][variant][search][smoothing]["Runtime"])
speed = runtime/runtime0
pathlength = float(data[neighborhood][variant][search][smoothing]["Pathlength"])
quality = (180/np.pi)*np.arccos(pathlength0/pathlength)
plotdata[seriesname]["Speed"].append(speed)
plotdata[seriesname]["Quality"].append(quality)
for seriesname in seriesnames:
ax.plot(plotdata[seriesname]["Speed"], plotdata[seriesname]["Quality"])
ax.legend(seriesnames)
for seriesname in seriesnames:
for i in range(len(neighborhoods)):
speed = plotdata[seriesname]["Speed"][i]
quality = plotdata[seriesname]["Quality"][i]
ax.plot([speed], [quality], marker=".", markeredgecolor="black", markerfacecolor="black")
ax.plot([speed + 0.1], [quality + 0.2], marker="$" + neighborhoods[i] + "$", markeredgecolor="black", markerfacecolor="black", markersize=markersizes[i])
plt.savefig(outputpath, bbox_inches = "tight")
def visualize_analysis(mode, benchmark):
data = read_data(mode, benchmark)
if mode == "heuristic":
runtime0 = float(data["8"]["Regular"]["A*"]["Greedy"]["Runtime"])
pathlength0 = float(data["64"]["Central"]["Bounded Jump Point"]["Tentpole"]["Pathlength"])
report_search_statistics(mode, benchmark, data, "A*")
plot_search_statistics(mode, benchmark, data, "A*", runtime0, pathlength0)
report_search_statistics(mode, benchmark, data, "Jump Point")
plot_search_statistics(mode, benchmark, data, "Jump Point", runtime0, pathlength0)
report_search_statistics(mode, benchmark, data, "Bounded Jump Point")
plot_search_statistics(mode, benchmark, data, "Bounded Jump Point", runtime0, pathlength0)
report_search_statistics(mode, benchmark, data, "Mixed A*")
plot_search_statistics(mode, benchmark, data, "Mixed A*", runtime0, pathlength0)
report_search_statistics(mode, benchmark, data, "Mixed Jump Point")
plot_search_statistics(mode, benchmark, data, "Mixed Jump Point", runtime0, pathlength0)
searches = ["A*", "Jump Point", "Bounded Jump Point", "Mixed Jump Point"]
plot_comparison_statistics(mode, benchmark, data, searches, runtime0, pathlength0)
elif mode == "dijkstra":
runtime0 = float(data["8"]["Regular"]["Dijkstra"]["Greedy"]["Runtime"])
pathlength0 = float(data["64"]["Central"]["Bounded Canonical Dijkstra"]["Tentpole"]["Pathlength"])
report_search_statistics(mode, benchmark, data, "Dijkstra")
plot_search_statistics(mode, benchmark, data, "Dijkstra", runtime0, pathlength0)
report_search_statistics(mode, benchmark, data, "Canonical Dijkstra")
plot_search_statistics(mode, benchmark, data, "Canonical Dijkstra", runtime0, pathlength0)
report_search_statistics(mode, benchmark, data, "Bounded Canonical Dijkstra")
plot_search_statistics(mode, benchmark, data, "Bounded Canonical Dijkstra", runtime0, pathlength0)
report_search_statistics(mode, benchmark, data, "Mixed Dijkstra")
plot_search_statistics(mode, benchmark, data, "Mixed Dijkstra", runtime0, pathlength0)
report_search_statistics(mode, benchmark, data, "Mixed Canonical Dijkstra")
plot_search_statistics(mode, benchmark, data, "Mixed Canonical Dijkstra", runtime0, pathlength0)
searches = ["Dijkstra", "Canonical Dijkstra", "Bounded Canonical Dijkstra", "Mixed Canonical Dijkstra"]
plot_comparison_statistics(mode, benchmark, data, searches, runtime0, pathlength0)
visualize_analysis("heuristic", "dao")
visualize_analysis("dijkstra", "dao")