benchmark_datreant.py

# Copyright 2017 The Regents of the University of Michigan
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import os
import six
import random
import string
import timeit
import logging
import warnings
from multiprocessing import Pool
from collections import OrderedDict
from contextlib import contextmanager

from tqdm import tqdm
from signac.contrib.hashing import calc_id
import datreant.core as dtr

if six.PY2:
    from tempdir import TemporaryDirectory
else:
    from tempfile import TemporaryDirectory


logger = logging.getLogger(__name__)


class Timer(timeit.Timer):

    def timeit(self, number=10):
        return number, super().timeit(number=number)

    def repeat(self, repeat=3, number=10):
        return super().repeat(repeat=repeat, number=number)


def setup(N, root=None):
    if root is None:
        root = '.'
    for i in tqdm(range(N)):
        sp = dict(a=i)
        s = dtr.Treant(os.path.join(root, 'workspace', calc_id(sp)))
        for key, value in sp.items():
            s.categories[key] = value


def determine_bundle_size(root):
    bundle = dtr.Bundle(os.path.join(root, 'workspace', '*'))
    size = 0
    for t in bundle:
        for c in t.children:
            size += os.path.getsize(str(c))
    return {
        'N': len(bundle),
        'total': size,
    }


def _random_str(size):
    return ''.join(random.choice(string.ascii_lowercase) for _ in range(size))


def _make_doc(i, num_keys=1, data_size=0):
    assert num_keys >= 1
    assert data_size >= 0

    doc = {'b_{}'.format(j): _random_str(data_size) for j in range(num_keys - 1)}
    doc['a'] = '{}{}'.format(i, _random_str(max(0, data_size - len(str(i)))))
    return doc


def _make_treant(root, num_keys, num_doc_keys, data_size, data_std, i):
    size = max(0, int(random.gauss(data_size, data_std)))
    sp = _make_doc(i, num_keys, size)
    _id = calc_id(sp)
    t = dtr.Treant(os.path.join(root, 'workspace', _id))
    t.categories = sp
    assert t.categories == sp


def generate_random_data(root, N_sp, num_keys=1, num_doc_keys=0,
                         data_size=0, data_std=0, parallel=True):
    if six.PY2:
        if parallel:
            warnings.warn("Function 'generate_random_data()' not parallelized for Python 2.")
            parallel = False

    if parallel:
        with Pool() as pool:
            p = [(root, num_keys, num_doc_keys, data_size, data_std, i) for i in range(N_sp)]
            list(pool.starmap(_make_treant, tqdm(p, desc='init random project data')))
    else:
        from functools import partial
        make = partial(_make_treant, root, num_keys, num_doc_keys, data_size, data_std)
        list(map(make, tqdm(range(N_sp), desc='init random project data')))


@contextmanager
def setup_random_bundle(N, num_keys=1, num_doc_keys=0,
                        data_size=0, data_std=0, seed=0, root=None):
    random.seed(seed)
    if not isinstance(N, int):
        raise TypeError("N must be an integer!")

    with TemporaryDirectory(dir=root) as tmp:
        generate_random_data(tmp, N, num_keys, num_doc_keys, data_size, data_std)
        yield tmp


def benchmark_bundle(root, keys=None, skip_rich_filter=False):
    setup = "import datreant.core as dtr; bundle = dtr.Bundle('{}/workspace/*');".format(root)
    setup += "import random;"

    data = OrderedDict()

    def run(key, timer, repeat=3, number=10):
        if keys is None or key in keys:
            logger.info("Run '{}'...".format(key))
            data[key] = timer.repeat(repeat=repeat, number=number)

    run('determine_len', Timer('len(bundle)', setup=setup))

    run('select_by_id', Timer(
        stmt="dtr.Treant(path).categories",
        setup=setup + "path = random.choice(bundle)"))

    run('iterate', Timer(
        stmt='[dtr.Treant(b).categories for b in bundle]',
        setup=setup))

    run('iterate_single_pass', Timer(
        stmt='[dtr.Treant(b).categories for b in bundle]',
        setup=setup), number=1)

    run('search_lean_filter', Timer(
        stmt="bundle.categories.groupby(k)[v]",
        setup=setup + 'import random; sp = dtr.Treant(random.choice(bundle)).categories;'
        'k, v = dict(sp).popitem();'))

    if not skip_rich_filter:
        run('search_rich_filter', Timer(
            stmt="bundle.categories.groupby(keys)[tuple(values)];",
            setup=setup + "sp = dict(dtr.Treant(random.choice(bundle)).categories);"
                          "keys = list(sp); values = [sp[k] for k in keys];"))

    return data