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How to use fast_time method in hypothesis

Best Python code snippet using hypothesis

main.py

Source:main.py

1import itertools2import torch3import time4import matplotlib.pyplot as plt5# Get minimal representation of a string6def getminimal(terms):7    c = 'a'8    skip = [',', "-", ">"]9    dict = {x : x for x in skip}10    for x in terms:11        if x not in skip and x not in dict:12            dict[x] = c13            c = chr(ord(c) + 1)14    return ''.join(dict[c] for c in terms)15def get_mask(term):16    return sum(1 << (ord(c) - ord('a')) for c in term)17def evaluate_path(terms):18    terms = getminimal(terms)19    [prefix, suffix] = terms.split("->")20    vals = [get_mask(term) for term in [*prefix.split(","), suffix]]21    n = len(vals)22    pmask = vals.copy()23    for i in range(1, n):24        pmask[i] = pmask[i-1] | pmask[i]25    smask = vals.copy()26    for i in reversed(range(0, n - 1)):27        smask[i] = smask[i+1] | smask[i]28    res = 029    for i in range(n - 1):30        p = 0 if i == 0 else pmask[i - 1]31        s = smask[i + 1]32        curmask = (p & s) | vals[i]33        res = max(res, bin(curmask).count("1"))34    return res35def find_opt_path_brute_force(terms):36    [prefix, suffix] = terms.split("->")37    ops = prefix.split(",")38    idx = range(len(ops))39    min_score = -140    permute = -141    for per in list(itertools.permutations(idx)):42        path = "->".join([','.join(ops[per[i]] for i in range(len(ops))), suffix])43        # print(path)44        cur_score = evaluate_path(path)45        if min_score == -1 or cur_score < min_score:46            min_score = cur_score47            permute = per48    return (min_score, permute)49def find_opt_path_dp(terms):50    terms = getminimal(terms)51    [prefix, suffix] = terms.split("->")52    vals = [get_mask(term) for term in prefix.split(",")]53    final_mask = get_mask(suffix)54    n = len(vals)55    sum_mask = [0] * (1 << n)56    for mask in range(1 << n):57        for j in range(n):58            if ((mask >> j) & 1) == 1:59                sum_mask[mask] |= vals[j]60    dp = [-1] * (1 << n)61    back = [-1] * (1 << n)62    dp[0] = 063    for mask in range(1 << n):64        for j in range(n):65            if ((mask >> j) & 1) == 0:66                smask = (sum_mask[((1 << n) - 1) ^ (mask | (1 << j))] | final_mask)67                nxtdp = max(dp[mask], bin((sum_mask[mask] & smask) | vals[j]).count("1"))68                if dp[mask | (1 << j)] == -1 or nxtdp < dp[mask | (1 << j)]:69                    dp[mask | (1 << j)] = nxtdp70                    back[mask | (1 << j)] = j71    permute = []72    cur = (1 << n) - 173    while cur != 0:74        permute.append(back[cur])75        cur ^= (1 << back[cur])76    return (dp[(1 << n) - 1], tuple(reversed(permute)))77def permute_terms(terms, per):78    [prefix, suffix] = terms.split("->")79    ops = prefix.split(",")80    return "->".join([','.join(ops[per[i]] for i in range(len(ops))), suffix])81def fast_einsum(equation, operands):82    [_, p] = find_opt_path_dp(equation)83    new_equation = permute_terms(equation, p)84    print("fast_einsum using new equation: " + new_equation)85    return torch.einsum(new_equation, [operands[i] for i in p])86def benchmark(equation, operands):87    print("benchmarking on equation " + equation + "\n")88    total_einsum = 089    total_fast = 090    for i in range(10):91        print("torch.einsum: ")92        einsum_time = -time.perf_counter()93        torch.einsum(equation, operands)94        einsum_time += time.perf_counter()95        if i >= 5:96            total_einsum += einsum_time97        print("fast_einsum:")98        fast_time = -time.perf_counter()99        fast_einsum(equation, operands)100        fast_time += time.perf_counter()101        if i >= 5:102            total_fast += fast_time103    return total_einsum / 5, total_fast / 5104def plot_time(xs, fast_ein_time, ein_time, plot_title):105    plt.plot(xs, fast_ein_time, label='fast einsum')106    plt.plot(xs, ein_time, label='Pytorch einsum')107    title = plot_title108    plt.title(title)109    plt.yscale('log', basey=10)110    plt.xticks(xs)111    plt.xlabel("n")112    plt.ylabel("Time (ms)")113    plt.legend()114    plt.savefig( f"./path_opt/results/{title}.png")115    plt.show(block=False)116    plt.close() 117y1s = []118y1_ein = []119y2s = []120y2_ein = []121for dim in range(1, 20):122    einsum_time, fast_time = benchmark('bdik,acaj,ikab,ajac,ikbd->abjk', [torch.rand(dim, dim, dim, dim) for i in range(5)])123    y1_ein.append(einsum_time)124    y1s.append(fast_time)125    einsum_time, fast_time = benchmark("ab,cd,ef,fe,dc,ba->a", [torch.rand(dim, dim), torch.rand(dim, dim),126                                    torch.rand(dim, dim), torch.rand(dim, dim),127                                    torch.rand(dim, dim), torch.rand(dim, dim)])128    y2_ein.append(einsum_time)129    y2s.append(fast_time)130x = [i for i in range(1, 20)]131plot_time(x, y1s, y1_ein, "bdik,acaj,ikab,ajac,ikbd->abjk")...

spider.py

Source:spider.py

1# -*- coding: utf-8 -*-2#!/usr/bin/env python3import argparse4import json5import os6import re7import sys8import urllib9import urllib210from dojang.util import parse_config_file11from tornado.options import options12PROJDIR = os.path.abspath(os.path.dirname(__file__))13ROOTDIR = os.path.split(PROJDIR)[0]14try:15    import keepcd16    print('Start keepcd version: %s' % keepcd.__version__)17except ImportError:18    import site19    site.addsitedir(ROOTDIR)20    site.addsitedir(ROOTDIR+"/keepcd")21    print('Development of keepcd: ' + ROOTDIR)22def spider_callback(self, message):23    print message24def run_command(name, second_arg):25    if name == 'dl_eslpod':26        download_eslmp3()27    elif name == "parse_elspod":28        parse_eslmp3()29    else:30        from dojang.util import parse_config_file, import_object31        from easycrawl.models import CrawlSite32        # from keepcd.admin.easycrawl.douban.app import worker33        site = CrawlSite.query.filter_by(name=name).first()34        worker_name = "easycrawl.%s.app.worker" % name35        print worker_name36        worker = import_object(worker_name)37        if second_arg == 'debug':38            print "\nexec debug()\n\n"39            worker.debug()40            worker.finished()41        elif second_arg == 'parse':42            print "\nexec parse()\n\n"43            worker.parse()44        else:45            print "\nexec run()\n\n"46            worker.run()47            worker.finished()48def download_file(url, filename):49    try:50        u = urllib.FancyURLopener()51        u.retrieve(url, filename)52    except IOError as e:53        url = u.headers['Location']54        print e55        print url56        57def download_eslmp3():58    from easycrawl.eslpod.models import EslPod59    pods = EslPod.query.filter_by().all()60    for pod in pods:61        if pod.media:62            media = pod.media63            media_file = media.split('/')64            mp3_file = media_file[-1]65            dest_file = "/Users/zhenng/tmp/eslpod/" + mp3_file 66            if not os.path.exists(dest_file):67                print media68                download_file(media, dest_file)69def parse_eslmp3():70    from easycrawl.eslpod.models import EslPod71    pods = EslPod.query.filter_by().all()72    for pod in pods:73        if pod.media:74            media = pod.media75            media_file = media.split('/')76            mp3_file = media_file[-1]77            mp3_filename = mp3_file.split('.')[0]78            src_file = "/Users/zhenng/tmp/eslpod/old/" + mp3_file79            dest_file = "normal/" + mp3_filename80            #print pod.fast_dialog81            if not os.path.exists(src_file):82                #print "not exists", src_file83                continue84            if pod.fast_dialog:85                fast_time = pod.fast_dialog86                #print "fasttime: type", type(fast_time)87                fast_time = fast_time.replace(":", ".")88                cmd = "mp3splt -o %s %s %s EOF-0.30" % (dest_file, src_file, fast_time)89                print cmd90                os.system(cmd)91if __name__ == "__main__":92    # reload(sys)93    # sys.setdefaultencoding('utf8') 94    parser = argparse.ArgumentParser(95        prog='keep cd',96        description='keep',97    )98    parser.add_argument('command', nargs="*")99    args = parser.parse_args()100    name = args.command[0]101    second_arg = None102    if len(args.command) >= 2:103        second_arg = args.command[1]104        105    parse_config_file("../settings.py")106    107    run_command(name, second_arg)108    # r = "^/movies/\d+[/]*$ || http://imax.im/movies/\d+[/]*$"109    # # r = "^http://movie.douban.com/subject/\d+[/]*$"110    # url = 'http://imax.im/movies/36172'111    # # url = 'http://movie.douban.com/subject/2345'112    # ps = r.split('||')113    # for patten in ps:114    #     patten = patten.strip()115    #     print patten116    #     if re.search(patten, url):117    #         print "true", url118    #     else:119    #         print "false", url120    121    ...

bitz.py

Source:bitz.py

1# -*- coding: utf-8 -*-2"""3Created on Mon Sep  7 14:37:34 20204@author: maximos5"""6import pandas as pd7import os8import matplotlib.pyplot as plt9import scipy.stats as spst10results = pd.read_csv( os.path.join( 'datasets' , 'bitzounis_results.csv' ) , decimal='.' )11stages = ['circuit', 'carmona', 'sprint', 'laguna']12for s in stages:13    # max14    fast_max = results[ s + '_' + 'fast_max']15    fast_max = fast_max[ ~pd.isna(fast_max) ]16    17    slow_max = results[ s + '_' + 'slow_max']18    slow_max = slow_max[ ~pd.isna(slow_max) ]19    20    w_max = spst.wilcoxon( fast_max , slow_max )[1]21    22    plt.clf()23    plt.boxplot( [slow_max, fast_max] ); plt.title(str( w_max ))24    plt.savefig(os.path.join('figs_bitz',  s + '_' + 'max.png'), dpi=300)25    26    # avg27    fast_avg = results[ s + '_' + 'fast_avg']28    fast_avg = fast_avg[ ~pd.isna(fast_avg) ]29    30    slow_avg = results[ s + '_' + 'slow_avg']31    slow_avg = slow_avg[ ~pd.isna(slow_avg) ]32    33    w_avg = spst.wilcoxon( fast_avg , slow_avg )[1]34    35    plt.clf()36    plt.boxplot( [slow_avg, fast_avg] ); plt.title(str( w_avg ))37    plt.savefig(os.path.join('figs_bitz',  s + '_' + 'avg.png'), dpi=300)38    39    # time40    fast_time = results[ s + '_' + 'fast_time']41    fast_time = fast_time[ ~pd.isna(fast_time) ]42    43    slow_time = results[ s + '_' + 'slow_time']44    slow_time = slow_time[ ~pd.isna(slow_time) ]45    46    w_time = spst.wilcoxon( fast_time , slow_time )[1]47    48    plt.clf()49    plt.boxplot( [slow_time, fast_time] ); plt.title(str( w_time ))...

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