178 lines
6.9 KiB
Python
178 lines
6.9 KiB
Python
import json
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import random
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from time import time
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from collections import namedtuple
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import util
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from gift_wrapper import rapper
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from graham import graham_scan
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from mbc import mbc, mbc_no_shuffle, mbc2_no_shuffle, mbc2
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from profile import Profiler
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from quick_hull import quick_hull
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import os.path
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#random.seed(1337_420)
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TimedResult = namedtuple("TimedResult", "algorithm points running_time")
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def time_it(f: callable, args: tuple = ()):
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start = time()
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f(*args)
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return str(time() - start)
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def initiate_file(file):
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with open(file, "w+") as tmp:
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tmp.write("algorithm\t\tpoints\t\ttime")
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def write_to_log(file, data):
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if not os.path.isfile(file):
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initiate_file(file)
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tmp = []
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for res in data:
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line = str.join("\t\t", res)
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print(line)
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tmp.append(line)
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write_string = "\n" + str.join("\n", tmp)
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with open(file, "a+") as open_file:
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open_file.write(write_string)
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def calculate_hulls(number_of_points, points):
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return [TimedResult("graham", number_of_points, time_it(graham_scan, args=(points,))),
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TimedResult("gift", number_of_points, time_it(rapper, args=(points,))),
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TimedResult("quick", number_of_points, time_it(quick_hull, args=(points,))),
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TimedResult("mbch", number_of_points, time_it(mbc, args=(points,))),
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TimedResult("mbch2", number_of_points, time_it(mbc2, args=(points,)))]
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def do_square_tests(number_of_points):
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points_square = {util.gen_point(0, 100) for _ in range(number_of_points)}
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number_of_points = str(number_of_points)
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results = calculate_hulls(number_of_points, points_square)
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write_to_log("square_tests.log", results)
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def do_circular_tests(number_of_points):
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points_circular = {util.gen_point(0, 100) for _ in range(number_of_points)}
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results = calculate_hulls(number_of_points, points_circular)
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write_to_log("circular_tests.log", results)
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def do_triangular_tests(number_of_points):
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left, right, top = util.Point(1,1), util.Point(51,1), util.Point(26,40)
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points = {util.gen_triangular_point(left, right, top) for _ in range(number_of_points)}
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results = calculate_hulls(number_of_points, points)
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write_to_log("triangular_tests.log", results)
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def do_quadratic_tests(number_of_points):
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points = {util.gen_weird_point(-10, 10) for _ in range(number_of_points)}
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results = calculate_hulls(number_of_points, points)
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write_to_log("quadratic_tests.log", results)
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def sanity_check():
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points = {util.gen_point(1, 50) for i in range(100)}
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graham = set(graham_scan(points))
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gift = set(rapper(points))
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quick = quick_hull(points)
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mbch = set.union(mbc(points))
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mbch2 = set.union(mbc2(points))
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assert gift == graham == quick == mbch == mbch2
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def do_one_profile(num_points):
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print(f"==================================== PROFILE ({num_points}) ====================================")
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random.seed(6)
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points = {util.gen_point(0, 100) for _ in range(num_points)}
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tests = [
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#("graham_scan", graham_scan),
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#("gift_wrapper", rapper),
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#("quick_hull", quick_hull),
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("mbc", mbc),
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("mbc2", mbc2),
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("mbc_no_shuffle", mbc_no_shuffle),
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("mbc2_no_shuffle", mbc2_no_shuffle),
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]
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results = {}
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for algorithm, func in tests:
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Profiler.reset()
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func(points)
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times = dict(Profiler.results)
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print(f"-------------- {algorithm} --------------")
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print("Times:", times)
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total = times[algorithm]
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print("Total:", total)
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sum_profiled = sum(times.values()) - total
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print("Total Profiled:", sum_profiled)
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unaccounted = total - sum_profiled
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print("Unaccounted:", unaccounted)
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times["other"] = unaccounted
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results[algorithm] = {
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"times": times,
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"total": total,
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"total_profiled": sum_profiled,
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"unaccounted": unaccounted,
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}
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return results
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def do_profile():
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num_points = 60_000
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#results = do_one_profile(num_points)
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results = {"mbc": {"times": {"finding median": 0.006870746612548828, "partitioning set": 0.066436767578125, "flipping constraints": 0.13354206085205078, "shuffling constraints": 0.08272123336791992, "solving LP": 0.13545918464660645, "finding bridge points": 0.06052136421203613, "pruning between line points": 0.04519915580749512, "mbc": 0.5643129348754883, "other": 0.033562421798706055}, "total": 0.5643129348754883, "total_profiled": 0.5307505130767822, "unaccounted": 0.033562421798706055}, "mbc2": {"times": {"extra pruning step": 0.15866398811340332, "finding median": 0.00189971923828125, "partitioning set": 0.022511959075927734, "flipping constraints": 0.04117441177368164, "shuffling constraints": 0.030447006225585938, "solving LP": 0.05805325508117676, "finding bridge points": 0.025882959365844727, "pruning between line points": 0.014936208724975586, "mbc2": 0.3658268451690674, "other": 0.01225733757019043}, "total": 0.3658268451690674, "total_profiled": 0.35356950759887695, "unaccounted": 0.01225733757019043}, "mbc_no_shuffle": {"times": {"finding median": 0.006849050521850586, "partitioning set": 0.06539726257324219, "flipping constraints": 0.13605880737304688, "solving LP": 0.06955385208129883, "finding bridge points": 0.06419634819030762, "pruning between line points": 0.044390201568603516, "mbc_no_shuffle": 0.397723913192749, "other": 0.011278390884399414}, "total": 0.397723913192749, "total_profiled": 0.3864455223083496, "unaccounted": 0.011278390884399414}, "mbc2_no_shuffle": {"times": {"extra pruning step": 0.16416001319885254, "finding median": 0.002000570297241211, "partitioning set": 0.022954702377319336, "flipping constraints": 0.0455164909362793, "solving LP": 0.02709197998046875, "finding bridge points": 0.022936582565307617, "pruning between line points": 0.017188310623168945, "mbc2_no_shuffle": 0.30688953399658203, "other": 0.005040884017944336}, "total": 0.30688953399658203, "total_profiled": 0.3018486499786377, "unaccounted": 0.005040884017944336}}
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print("================== RESULTS ==================")
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print(json.dumps(results))
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algorithms = list(results.keys())
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steps = (
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"other",
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"finding median",
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"partitioning set",
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"flipping constraints",
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"solving 2D",
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"finding bridge points",
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"pruning between line points",
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"shuffling constraints",
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"extra pruning step",
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)
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data = [[result["times"].get(step, 0) * 1000 for result in results.values()]
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for step in steps]
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util.stacked_bar(data=data,
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series_labels=steps,
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category_labels=algorithms,
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show_values=True,
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value_format="{:.1f}",
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y_label="time (ms)",
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grid=False,
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reverse=False)
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if __name__ == '__main__':
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sanity_check()
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do_profile()
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exit()
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for i in range(50, 1000, 50):
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do_square_tests(i)
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