.gitignore

@@ -1,2 +1 @@
 /target
-/landmarks

utils/plot_results.py

@@ -2,11 +2,12 @@
 
 from sys import argv, exit
 import os
+from csv import writer
 from typing import Tuple, List
 import re
-
 import numpy as np
-import pandas as pd
+from pprint import pprint
+
 import matplotlib.pyplot as plt
 
 if len(argv) != 2:
@@ -17,6 +18,8 @@ path = argv[1]
 
 files = [f for f in os.listdir(path) if os.path.isfile(f"{ path }/{f}")]
 
+# files = [f for f in files if re.match(r"greedy_64_.+", f) is not None ]
+
 
 def parse_file(file: str) -> Tuple[float, List[int]]:
 
@@ -38,91 +41,57 @@ def parse_file(file: str) -> Tuple[float, List[int]]:
 
     return time, pops
 
+results = dict()
 
-frames = list()
+with open("times.csv", "w+") as times_file:
+    times = writer(times_file)
 
-for i, file in enumerate(files):
-    time, pops = parse_file(f"{ path }/{ file }")
+    with open("pops.csv", "w+") as pops_file:
+        pops = writer(pops_file)
 
-    parts = file.split(".")[0].split("_")
+        for file in files:
+            name = file.split(".")[0]
+            full_path = f"{ path }/{ file }"
+            time, pop = parse_file(full_path)
 
-    data = {
-            "name": parts[0],
-            "time": time,
-            "pops": int(sum(pops)),
-            }
+            total_pops = sum(pop)/len(pop)
 
-    if len(parts) > 1:
-        data["num_landmarks"] = int(parts[1])
-    if len(parts) > 2:
-        data["best_size"] = int(parts[2])
+            results[name] = (total_pops, time)
 
-    frames.append(pd.DataFrame(data, index=[i]))
+            times.writerow([name, time])
 
-results = pd.concat(frames)
+            pops.writerow([name, *pop])
+rel_pops = list()
+abs_pops = list()
+rel_time = list()
+abs_time = list()
+labels = list()
 
 
-# dijkstra vs A* vs greedy and random ALT
 
-#condition = ((results["name"] == "astar")
-#        | (results["name"] == "dijkstra")
-#        | ((results["num_landmarks"] == 32) & (results["best_size"] == 4)))
-#
-# basic = results[condition].sort_values(by=["time"], ascending=False)
-# names = basic["name"].to_list()
-# for i in range(len(names)):
-#     if names[i] == "greedy":
-#         names[i] = "ALT 32 greedy"
-#     if names[i] == "random":
-#         names[i] = "ALT 32 random"
-# times = basic["time"] * 1000
-# 
-# fig, ax = plt.subplots()
-# 
-# bar = ax.bar(names, times)
-# ax.set_title("Average query time by algorithm")
-# ax.set_ylabel("time in ms")
-# ax.bar_label(bar)
-# fig.savefig("basic.svg")
-# plt.show()
+pprint(results)
+baseline = "dijkstra"
+base_pops = results[baseline][0]
+base_time = results[baseline][1]
+
+for name, values in results.items():
+    pops, time = values
+    labels.append(name)
+    rel_pops.append(pops/base_pops)
+    rel_time.append(time/base_time)
+    abs_pops.append(pops)
+    abs_time.append(time)
 
 
-# comparison greedy vs. random, best_size = 4
 
-condition = (((results["name"] == "greedy") | (results["name"] == "random")) & (results["best_size"] == 4))
-
-data = results[condition]
-greedy = data[data["name"] == "greedy"]
-random = data[data["name"] == "random"]
-
-print(greedy)
-print(random)
-
-labels = greedy["num_landmarks"].to_list()
-labels = [int(label) for label in labels]
-greedy_times = greedy["time"].to_list()
-random_times = random["time"].to_list()
-
-width = 0.35
+x = np.arange(len(labels))  # the label locations
+width = 0.35  # the width of the bars
 
 fig, ax = plt.subplots()
+rects1 = ax.bar(x - width/2, abs_time , width, label='time')
+# rects2 = ax.bar(x + width/2, abs_pops, width, label='pops')
 
-x = np.arange(len(labels))
-
-greedy_bars = ax.bar(x - width/2, greedy_times, width, label="Greedy")
-random_bars = ax.bar(x + width/2, random_times, width, label="Random")
-ax.set_title("Comparison of ALT with random and greedy landmark selection")
-ax.set_ylabel("time in ms")
-ax.set_xlabel("number of landmarks")
-ax.set_xticks(x, labels)
 ax.legend()
-# ax.bar_label(greedy_bars)
-# ax.bar_label(random_bars)
-
-fig.save("random_greedy_comp.svg")
+ax.set_xticks(x, labels)
 
 plt.show()
-
-
-# comparison greedy best_size
-# comparison random best_size