def test_taskgenerator_map():
jugfile = os.path.join(_jugdir, 'mapgenerator.py')
store, space = jug.jug.init(jugfile, 'dict_store')
assert not space['s'].can_run()
simple_execute()
assert len(value(space['v2s'])) == 16
assert len(value(space['v4s'])) == 16
def test_nfoldcrossvalidation():
store, space = jug.jug.init('milk/tests/data/jugparallel_jugfile.py',
'dict_store')
options = jug.options.default_options
jug.jug.execute(store, options)
assert len(jug.value(space['classified'])) == 2
assert len(jug.value(space['classified_wpred'])) == 3
def test_util_timed_path():
from jug.hash import hash_one
jug.task.Task.store = dict_store()
system("touch test_file")
t0 = jug.utils.timed_path('test_file')
t1 = jug.utils.timed_path('test_file')
h0 = hash_one(t0)
h1 = hash_one(t1)
assert h0 == h1
sleep(1.1)
system("touch test_file")
h1 = hash_one(t1)
assert h0 != h1
assert value(t0) == 'test_file'
assert value(t1) == 'test_file'
def test_util_timed_path():
from jug.hash import hash_one
jug.task.Task.store = dict_store()
system("touch test_file")
t0 = jug.utils.timed_path('test_file')
t1 = jug.utils.timed_path('test_file')
h0 = hash_one(t0)
h1 = hash_one(t1)
assert h0 == h1
sleep(1.1)
system("touch test_file")
h1 = hash_one(t1)
assert h0 != h1
assert value(t0) == 'test_file'
assert value(t1) == 'test_file'
def test_kmeans():
from jug.task import alltasks
store, space = jug.jug.init('milk/tests/data/jugparallel_kmeans_jugfile.py', 'dict_store')
options = jug.options.default_options
jug.jug.execute(store, options)
assert len(value(space['clustered'])) == 2
def index_metadata(container):
if isinstance(container, str):
container_filename = container
else:
container_filename = container.name
# Init mdat size
mdat_input_offset = _init_mdat_size(container_filename)
if not mdat_input_offset.can_load() and mdat_input_offset.can_run():
mdat_input_offset.run()
mdat_input_offset = jug.value(mdat_input_offset)
# Parse samples mp4 boxes headers b'4c04f56f18ab3a4cd039c28535df69471abab913'
samples_headers = _get_samples_headers(container_filename,
mdat_input_offset)
# Create external moov file to avoid corruptions in the container
moov_filename = _create_moov_file(container_filename, samples_headers)
mdat_input_end = _index_bzna_input(container_filename, moov_filename,
mdat_input_offset)
mdat_target_end = _append_index_bzna_target(container_filename,
moov_filename, mdat_input_end)
mdat_fname_end = _append_index_bzna_fname(container_filename,
moov_filename, mdat_target_end)
mdat_data_end = _index_bzna_thumb(container_filename, moov_filename,
mdat_fname_end)
mdat_data_end = _update_mdat_size(container_filename, mdat_data_end)
return _append_moov(container_filename, moov_filename, mdat_data_end)
def test_currymap():
np.random.seed(33)
jug.task.Task.store = dict_store()
A = np.random.rand(100)
ts = jug.mapreduce.currymap(mapper2, list(zip(A,A)))
simple_execute()
assert np.allclose(np.array(value(ts)) , A*2)
def test_currymap():
np.random.seed(33)
jug.task.Task.store = dict_store()
A = np.random.rand(100)
ts = jug.mapreduce.currymap(mapper2, list(zip(A, A)))
simple_execute()
assert np.allclose(np.array(value(ts)), A * 2)
def test_map():
np.random.seed(33)
jug.task.Task.store = dict_store()
A = np.random.rand(10000)
ts = jug.mapreduce.map(mapper, A)
simple_execute()
ts = value(ts)
assert np.all(ts == np.array(list(map(mapper, A))))
def test_map():
np.random.seed(33)
jug.task.Task.store = dict_store()
A = np.random.rand(10000)
ts = jug.mapreduce.map(mapper, A)
simple_execute()
ts = value(ts)
assert np.all(ts == np.array(list(map(mapper,A))))
def test_map():
np.random.seed(33)
jug.task.Task.store = dict_store()
A = np.random.rand(10000)
t = jug.mapreduce.map(mapper, A)
dfs_run(t)
ts = value(t)
assert np.all(ts == np.array(map(mapper,A)))
def test_nld_access_results(jugdir):
gridjug.grid_jug(jugfile=PRIMES_JUGFILE,
jugdir=jugdir,
**NLD_GRIDMAP_PARAMS)
_, jugspace = jug.init(jugfile=PRIMES_JUGFILE, jugdir=jugdir)
assert jug.value(jugspace['primes10']) == [
True, True, False, True, False, True, False, False, False
]
def test_kmeans():
from jug.task import alltasks
store, space = jug.jug.init('milk/tests/data/jugparallel_kmeans_jugfile.py', 'dict_store')
options = jug.options.default_options
assert len(alltasks) == 5
jug.jug.execute(store, options)
assert len(value(space['clustered'])) == 2
def test_access_results(tmpdir):
jugdir = tmpdir
gridjug.grid_jug(
jugfile=PRIMES_JUGFILE, jugdir=jugdir.strpath, local=True,
)
_, jugspace = jug.init(jugfile=PRIMES_JUGFILE, jugdir=jugdir.strpath)
assert jug.value(jugspace['primes10']) == [
True, True, False, True, False, True, False, False, False
]
def test_nld_access_results(jugdir):
gridjug.grid_jug(
jugfile=PRIMES_JUGFILE,
jugdir=jugdir,
**NLD_GRIDMAP_PARAMS
)
_, jugspace = jug.init(jugfile=PRIMES_JUGFILE, jugdir=jugdir)
assert jug.value(jugspace['primes10']) == [
True, True, False, True, False, True, False, False, False
]
def test_util_timed_path(tmpdir):
from jug.hash import hash_one
jug.task.Task.store = dict_store()
tmpdir = str(tmpdir)
test_file = path.join(tmpdir, 'test_file')
with open(test_file, 'wt') as out:
out.write("Hello World")
t0 = jug.utils.timed_path(test_file)
t1 = jug.utils.timed_path(test_file)
h0 = hash_one(t0)
h1 = hash_one(t1)
assert h0 == h1
sleep(1.1)
with open(test_file, 'wt') as out:
out.write("Hello World")
h1 = hash_one(t1)
assert h0 != h1
assert value(t0) == test_file
assert value(t1) == test_file
def test_access_results(tmpdir):
jugdir = tmpdir
gridjug.grid_jug(
jugfile=PRIMES_JUGFILE,
jugdir=jugdir.strpath,
local=True,
)
_, jugspace = jug.init(jugfile=PRIMES_JUGFILE, jugdir=jugdir.strpath)
assert jug.value(jugspace['primes10']) == [
True, True, False, True, False, True, False, False, False
]
def assemble_sasa_h5(sasas, filename):
import os
import tables
from tqdm import tqdm
if not os.path.isdir(os.path.dirname(filename)):
os.mkdir(os.path.dirname(filename))
if os.path.isfile(filename):
raise FileExistsError(f"File '{filename}' already exists.")
compression = tables.Filters(complevel=9, complib='zlib', shuffle=True)
n_zeros = len(str(len(sasas))) + 1
print(filename)
with tables.open_file(filename, 'a') as handle:
shape = None
for i, sasa in enumerate(tqdm(sasas)):
data = jug.value(sasa.t)
atom = tables.Atom.from_dtype(data.dtype)
tag = 'sasas_' + str(i).zfill(n_zeros)
if tag in handle.root:
logger.warn('Tag %s already existed in %s. Overwriting.', tag,
filename)
handle.remove_node('/', name=tag)
if shape is None:
shape = data.shape
elif len(shape) > 1:
assert shape[1] == data.shape[
1], "We had %s residues, but then loaded trajectory %s and it had %s." % (
shape[1], i, data.shape[1])
node = handle.create_carray(where='/',
name=tag,
atom=atom,
shape=data.shape,
filters=compression)
node[:] = data
sasa.t.unload()
return filename
def dump(jugfile):
# get absolute path and import the Tasks from the associated source code
jugpath, ext = os.path.splitext(os.path.abspath(jugfile))
modulename = os.path.basename(jugpath)
# set path to results and load execution script
# NOTE: jugdir must be set *before* loading the source
jug.set_jugdir(jugpath + '.jugdata')
results = imp.load_source(modulename, jugpath + ext).results
# initialize dictionary of completed results to be incrementally built below
completed = OrderedDict()
incomplete = False
for name, experiment in results.items():
completed[name] = OrderedDict()
for key, task in experiment.items():
completed[name][key] = []
for run in task:
try:
completed[name][key].append(jug.value(run))
except:
incomplete = True
pass
# get rid of empty keys
if len(completed[name][key]) == 0:
completed[name].pop(key)
if not completed[name]:
completed.pop(name)
# if dictionary is not empty, dump to pickle file
if completed:
if incomplete:
jugpath += '.tmp'
with open(jugpath + '.pkl', 'w') as fp:
pickle.dump(completed, fp)
return completed
def _index_bzna_input(filename, moov_filename, mdat_input_offset):
moov = _load_moov(moov_filename)
mvhd = next(find_boxes(moov.boxes, [b"mvhd"]))
samples_headers = _get_samples_headers(filename, mdat_input_offset)
samples_headers = jug.value(samples_headers)
# bzna_input trak
if next(find_traks(moov.boxes, [b"bzna_input\0"]), None) is not None:
trak = next(find_traks(moov.boxes, [b"bzna_input\0"]))
moov.boxes = [box for box in moov.boxes if box is not trak]
samples_size = 0
sample_size = -1
sizes = []
for sample_header in samples_headers:
# Every sample starts with a ftyp box
if sample_header.type == b"ftyp":
if sample_size >= 0:
sizes.append(sample_size)
samples_size += sample_size
sample_size = 0
sample_size += sample_header.box_size
sizes.append(sample_size)
samples_size += sample_size
# MOOV.TRAK
trak = _make_bzna_input_trak(sizes, mdat_input_offset, mvhd.next_track_id)
moov.append(trak)
mvhd.next_track_id += 1
moov.refresh_box_size()
with open(moov_filename, "wb") as moov_file:
moov_file.write(bytes(moov))
return mdat_input_offset + samples_size
def test_kmeans():
store, space = jug.jug.init("milk/tests/data/jugparallel_kmeans_jugfile.py", "dict_store")
simple_execute()
assert len(value(space["clustered"])) == 2
def test_empty_mapreduce():
store, space = jug.jug.init('jug/tests/jugfiles/empty_mapreduce.py',
'dict_store')
simple_execute()
assert value(space['two']) == []
def twice(x):
x2 = double(x)
barrier()
return double(value(x2))
def sum_partials(ts):
total = 0.0
for t in ts:
total+= jug.value(t.t)
return total
def test_taskgenerator_map():
store, space = jug.jug.init('jug/tests/jugfiles/mapgenerator.py', 'dict_store')
simple_execute()
assert len(value(space['v2s'])) == 16
args = parser.parse_args()
# set where the data is stored
path = os.path.realpath(args.path)
jugdata = os.path.join(path, 'execute.jugdata')
set_jugdir(jugdata)
# import data place holder
execute_path = os.path.join(path, 'execute.py')
data = imp.load_source('execute', execute_path).data
for function in data.keys():
ax = pl.figure(1).gca()
ax.cla()
for method in data[function].keys():
runs = np.array(value(data[function][method]))
N = runs.shape[0]
T = runs.shape[1]
x = np.arange(1, T+1)
y = runs.mean(axis=0)
e = runs.std(axis=0) / np.sqrt(N) * 3
ax.plot(x, y, lw=2, label=method)
ax.fill_between(x, y-e, y+e, color=ax.lines[-1].get_color(), alpha=0.1)
ax.axis('tight')
ax.axis(xmin=0, xmax=T)
ax.set_xlabel('iterations')
ax.set_ylabel('function value')
ax.legend(loc='best')
ax.figure.canvas.draw()
import benchfunk
import matplotlib.pyplot as plt
import numpy as np
from example import results
import jug
jug.set_jugdir('example.jugdata')
fig, axs = plt.subplots(1,
len(results),
figsize=(5 * len(results), 4),
sharex=True)
for ax, (name, res) in zip(axs, results.items()):
func, sn2 = name.split('(')
sn2 = float(sn2[:-1])
obj = getattr(benchfunk.functions, func)(sn2)
for key, xbest in res.items():
xbest = jug.value(xbest)
xbest = np.array(xbest)
ybest = np.array([obj.get_f(run) for run in xbest])
ax.plot(ybest.mean(0), label=key)
ax.set_title(name)
plt.show()
def test_empty_mapreduce():
jugfile = os.path.join(_jugdir, 'empty_mapreduce.py')
store, space = jug.jug.init(jugfile, 'dict_store')
simple_execute()
assert value(space['two']) == []
def test_empty_mapreduce():
jugfile = os.path.join(_jugdir, 'empty_mapreduce.py')
store, space = jug.jug.init(jugfile, 'dict_store')
simple_execute()
assert value(space['two']) == []
# This tests an important regression:
# adding the module to the module map *before* execfile()ing the jugfile makes
# this not work.
from jug import barrier, Task, value
import jug.mapreduce
import math
def double(x):
val = math.sqrt(2.)*math.sqrt(2.)
return x*val
two = jug.mapreduce.map(double, range(20))
barrier()
def product(vals):
import operator
return reduce(operator.mul, vals)
values = product(value(two))
def twice(x):
x2 = double(x)
barrier()
return double(value(x2))
def test_taskgenerator_map():
jugfile = os.path.join(_jugdir, 'mapgenerator.py')
store, space = jug.jug.init(jugfile, 'dict_store')
simple_execute()
assert len(value(space['v2s'])) == 16
def __call__(self, i):
return milk.nfoldcrossvalidation(value(self.features), value(self.labels), folds=[i], **value(self.kwargs))
def test_empty_mapreduce():
store, space = jug.jug.init('jug/tests/jugfiles/empty_mapreduce.py', 'dict_store')
simple_execute()
assert value(space['two']) == []
# This tests an important regression:
# adding the module to the module map *before* execfile()ing the jugfile makes
# this not work.
from jug import barrier, Task, value
import jug.mapreduce
import math
from functools import reduce
def double(x):
val = math.sqrt(2.)*math.sqrt(2.)
return x*val
two = jug.mapreduce.map(double, list(range(20)))
barrier()
def product(vals):
import operator
return reduce(operator.mul, vals)
values = product(value(two))
def test_kmeans():
store, space = jug.jug.init(
'milk/tests/data/jugparallel_kmeans_jugfile.py', 'dict_store')
simple_execute()
assert len(value(space['clustered'])) == 2
def test_nfoldcrossvalidation():
store, space = jug.jug.init('milk/tests/data/jugparallel_jugfile.py',
'dict_store')
simple_execute()
assert len(jug.value(space['classified'])) == 2
assert len(jug.value(space['classified_wpred'])) == 3
import benchfunk
import matplotlib.pyplot as plt
import numpy as np
from example import results
import jug
jug.set_jugdir('example.jugdata')
fig, axs = plt.subplots(1, len(results),
figsize=(5*len(results), 4),
sharex=True)
for ax, (name, res) in zip(axs, results.items()):
func, sn2 = name.split('(')
sn2 = float(sn2[:-1])
obj = getattr(benchfunk.functions, func)(sn2)
for key, xbest in res.items():
xbest = jug.value(xbest)
xbest = np.array(xbest)
ybest = np.array([obj.get_f(run) for run in xbest])
ax.plot(ybest.mean(0), label=key)
ax.set_title(name)
plt.show()
git_hash = subprocess.check_output(['git', 'rev-parse', 'HEAD'])[:6]
jug_dict = jug.init('experiments_explore.py', 'experiments_explore.jugdata/')[1]
exs_stut = jug_dict['stut_experiments']
exs_gauss = jug_dict['gauss_experiments']
exs_survival = jug_dict['survival_experiments']
exs = exs_stut + exs_gauss + exs_survival
filename = 'results/experiments_explore_data_all-{}-{}.pkl'.format(datetime.datetime.now().strftime('%d%m%y_%H:%M:%S'), git_hash)
es = []
i = 0
for e in exs:
try:
es.append(jug.value(e.__dict__))
except Exception, e:
print "Something went wrong"
print e
print i
i += 1
#es are all our experiments wrapped up
#Lets make a dataframe of results
import pandas as pd
exs_df = pd.DataFrame(columns=['fold', 'seed', 'num_inducing', 'fixZ', 'f_bias', 'g_bias', 'f_rbf_len', 'g_rbf_len', 'g_mean', 'f_rbf_var'])
for e in es:
default_d = {'fold':e['fold'],
'seed':e['seed'],
'num_inducing':e['num_inducing'],
'fixZ':e['fixZ'],
def test_taskgenerator_map():
store, space = jug.jug.init('jug/tests/jugfiles/mapgenerator.py',
'dict_store')
simple_execute()
assert len(value(space['v2s'])) == 16
def _run_tasks(tasks) -> list:
for task in tasks:
_run_tasks(recursive_dependencies(task))
if not task.can_load() and task.can_run():
task.run()
return jug.value(tasks)
import cPickle as pkl
import argparse
import os.path
import imp
from jug import set_jugdir, value
# parse the path of the experiment to plot
parser = argparse.ArgumentParser()
parser.add_argument('path')
args = parser.parse_args()
# set where the data is stored
path = os.path.realpath(args.path.rstrip('/'))
jugdata = os.path.join(path, 'execute.jugdata')
set_jugdir(jugdata)
# import data place holder and fill in values
execute_path = os.path.join(path, 'execute.py')
data = imp.load_source('execute', execute_path).data
data = value(data)
# save to results directory
root_path = os.path.dirname(os.path.realpath(__file__))
expt_name = os.path.basename(path)
results_path = os.path.join(root_path, 'results')
if not os.path.isdir(results_path):
os.mkdir(results_path)
pkl_file = os.path.join(results_path, expt_name)
with open(pkl_file + '.pkl', 'wb') as f:
pkl.dump(data, f)
def test_nfoldcrossvalidation():
store, space = jug.jug.init('milk/tests/data/jugparallel_jugfile.py', 'dict_store')
options = jug.options.default_options
jug.jug.execute(store, options)
assert len(jug.value(space['classified'])) == 2
assert len(jug.value(space['classified_wpred'])) ==3
def sum_partials(ts):
total = 0.0
for t in ts:
total+= jug.value(t.base)
return total
def test_nfoldcrossvalidation():
store, space = jug.jug.init("milk/tests/data/jugparallel_jugfile.py", "dict_store")
simple_execute()
assert len(jug.value(space["classified"])) == 2
assert len(jug.value(space["classified_wpred"])) == 3
