BerGeo/h2/tmptest.py

import json
import random
from time import time
from collections import namedtuple
import util
from gift_wrapper import rapper
from graham import graham_scan
from mbc import mbc, mbc_no_shuffle, mbc2_no_shuffle, mbc2
from profile import Profiler
from quick_hull import quick_hull
import os.path

#random.seed(1337_420)

TimedResult = namedtuple("TimedResult", "algorithm points running_time")


def time_it(f: callable, args: tuple = ()):
    start = time()
    f(*args)
    return str(time() - start)


def initiate_file(file):
    with open(file, "w+") as tmp:
        tmp.write("algorithm\t\tpoints\t\ttime")


def write_to_log(file, data):
    if not os.path.isfile(file):
        initiate_file(file)

    tmp = []
    for res in data:
        line = str.join("\t\t", res)
        print(line)
        tmp.append(line)

    write_string = "\n" + str.join("\n", tmp)

    with open(file, "a+") as open_file:
        open_file.write(write_string)


def calculate_hulls(number_of_points, points):
    return [TimedResult("graham", number_of_points, time_it(graham_scan, args=(points,))),
            TimedResult("gift", number_of_points, time_it(rapper, args=(points,))),
            TimedResult("quick", number_of_points, time_it(quick_hull, args=(points,))),
            TimedResult("mbch", number_of_points, time_it(mbc, args=(points,))),
            TimedResult("mbch2", number_of_points, time_it(mbc2, args=(points,)))]


def do_square_tests(number_of_points):
    points_square = {util.gen_point(0, 100) for _ in range(number_of_points)}
    number_of_points = str(number_of_points)

    results = calculate_hulls(number_of_points, points_square)
    write_to_log("square_tests.log", results)


def do_circular_tests(number_of_points):
    points_circular = {util.gen_point(0, 100) for _ in range(number_of_points)}

    results = calculate_hulls(number_of_points, points_circular)
    write_to_log("circular_tests.log", results)


def do_triangular_tests(number_of_points):
    left, right, top = util.Point(1,1), util.Point(51,1), util.Point(26,40)
    points = {util.gen_triangular_point(left, right, top) for _ in range(number_of_points)}

    results = calculate_hulls(number_of_points, points)
    write_to_log("triangular_tests.log", results)


def do_quadratic_tests(number_of_points):
    points = {util.gen_weird_point(-10, 10) for _ in range(number_of_points)}

    results = calculate_hulls(number_of_points, points)
    write_to_log("quadratic_tests.log", results)


def sanity_check():
    points = {util.gen_point(1, 50) for i in range(100)}
    graham = set(graham_scan(points))
    gift = set(rapper(points))
    quick = quick_hull(points)
    mbch = set.union(mbc(points))
    mbch2 = set.union(mbc2(points))
    assert gift == graham == quick == mbch == mbch2


def do_one_profile(num_points):
    print(f"==================================== PROFILE ({num_points}) ====================================")
    random.seed(6)
    points = {util.gen_point(0, 100) for _ in range(num_points)}

    tests = [
        #("graham_scan", graham_scan),
        #("gift_wrapper", rapper),
        #("quick_hull", quick_hull),
        ("mbc", mbc),
        ("mbc2", mbc2),
        ("mbc_no_shuffle", mbc_no_shuffle),
        ("mbc2_no_shuffle", mbc2_no_shuffle),
    ]

    results = {}
    for algorithm, func in tests:
        Profiler.reset()
        func(points)

        times = dict(Profiler.results)

        print(f"-------------- {algorithm} --------------")
        print("Times:", times)

        total = times[algorithm]
        print("Total:", total)

        sum_profiled = sum(times.values()) - total
        print("Total Profiled:", sum_profiled)

        unaccounted = total - sum_profiled
        print("Unaccounted:", unaccounted)
        times["other"] = unaccounted

        results[algorithm] = {
            "times": times,
            "total": total,
            "total_profiled": sum_profiled,
            "unaccounted": unaccounted,
        }

    return results


def do_profile():
    num_points = 60_000
    #results = do_one_profile(num_points)
    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}}
    print("================== RESULTS ==================")
    print(json.dumps(results))

    algorithms = list(results.keys())

    steps = (
        "other",
        "finding median",
        "partitioning set",
        "flipping constraints",
        "solving 2D",
        "finding bridge points",
        "pruning between line points",
        "shuffling constraints",
        "extra pruning step",
    )

    data = [[result["times"].get(step, 0) * 1000 for result in results.values()]
            for step in steps]

    util.stacked_bar(data=data,
                     series_labels=steps,
                     category_labels=algorithms,
                     show_values=True,
                     value_format="{:.1f}",
                     y_label="time (ms)",
                     grid=False,
                     reverse=False)


if __name__ == '__main__':
    sanity_check()
    do_profile()
    exit()
    for i in range(50, 1000, 50):
        do_square_tests(i)