def main():
logger.info("Starting on %s with %d CPU's.", socket.gethostname(),
multiprocessing.cpu_count())
args = parse_args()
if args.link_run is not None:
run_json = link_json(args.link_run, args.data_path)
run_json.has_runinfo = True
else:
json_path = os.path.join(args.data_path, 'input', 'AppSession.json')
try:
with open(json_path, 'r') as json_file:
run_json = parse_json(json_file)
except:
if os.path.exists(json_path):
# copy the input file to the output dir for postmortem analysis
logger.error("Error occurred while parsing '%s'" % json_path)
with open(json_path, 'r') as json_file:
file_cont = json_file.read()
out_path = os.path.join(args.data_path, 'logs',
'AppSession.json')
with open(out_path, 'w') as json_file:
json_file.write(file_cont)
else:
logger.error("Error: no such file as '%s'" % json_path)
raise
# Do we have run_ids for all sample_ids ?
if run_json.run_id is None:
run_json.has_runinfo = False
else:
bs = BSrequest()
sample_id_set = bs.check_run_sample_ids(
[run_json.run_id], [s["Id"] for s in run_json.samples])
run_json.has_runinfo = (len(sample_id_set) == len(
run_json.samples))
logger.info("setting json.has_run_info to %s" % run_json.has_runinfo)
pssm = Pssm()
scratch_path = os.path.join(args.data_path, 'scratch')
makedirs(scratch_path)
for filename in os.listdir(scratch_path):
filepath = os.path.join(scratch_path, filename)
if os.path.isdir(filepath):
shutil.rmtree(filepath)
else:
os.remove(filepath)
args.g2p_path = args.qc_path = create_app_result(args.data_path,
run_json,
suffix='results')
if run_json.run_id is None:
run_summary = None
else:
logger.info('Summarizing run.')
run_summary = summarize_run(args, run_json)
pool = Pool()
pool.map(
functools.partial(try_sample, run_info=run_json, args=args, pssm=pssm),
range(len(run_json.samples)))
pool.close()
pool.join()
collate_samples(args, run_json)
if run_json.run_id is not None:
summarize_samples(args, run_json, run_summary)
logger.info('Done.')
def quality_over_theta():
number_dataset = 1
data, target, enable_i = datasets[number_dataset]
pool = Pool(processes=5)
# if we want to average
nb_launched = 5
theta = 0.1
data_final = {'WRAcc': [], 'theta': [], 'Algorithm': []}
for i in range(10):
print('Iteration: {}'.format(i))
for i in range(nb_launched):
results_misere = pool.apply_async(misere, (data, target), {
'time_budget': TIME_BUDGET_XP,
'theta': theta
})
results_beam = pool.apply_async(
beam_search, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP,
'theta': theta
})
result_ucb_opti = pool.apply_async(
seq_scout, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP,
'theta': theta
})
results_misere = results_misere.get()
results_beam = results_beam.get()
result_ucb_opti = result_ucb_opti.get()
if len(results_beam) < TOP_K:
print("Too few beam: {}".format(len(results_beam)))
if len(result_ucb_opti) < TOP_K:
print("Too few seqscout: {}".format(len(result_ucb_opti)))
if len(results_misere) < TOP_K:
print("Too few misere: {}".format(len(results_misere)))
data_add_generic(data_final,
WRAcc=max(0, average_results(results_misere)),
theta=theta,
Algorithm='misere')
data_add_generic(data_final,
WRAcc=max(0, average_results(results_beam)),
theta=theta,
Algorithm='beam')
data_add_generic(data_final,
WRAcc=max(0, average_results(result_ucb_opti)),
theta=theta,
Algorithm='seqscout')
theta += 0.1
df = pd.DataFrame(data=data_final)
sns.set(rc={'figure.figsize': (8, 6.5)})
plt.clf()
ax = sns.lineplot(data=df, x='theta', y='WRAcc', hue='Algorithm')
plt.savefig('./theta/over_theta.png')
df.to_pickle('./theta/result')
if SHOW:
plt.show()
def boxplot_dataset_iterations():
pool = Pool(processes=5)
xp_repeat = 5
data_final = {'WRAcc': [], 'dataset': [], 'Algorithm': []}
for i, (data, target, enable_i) in enumerate(datasets):
print("Dataset {}".format(datasets_names[i]))
for j in range(xp_repeat):
results_misere = pool.apply_async(misere, (data, target),
{'time_budget': TIME_BUDGET_XP})
results_beam = pool.apply_async(beam_search, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP
})
result_ucb_opti = pool.apply_async(seq_scout, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP
})
results_misere = results_misere.get()
results_beam = results_beam.get()
result_ucb_opti = result_ucb_opti.get()
if len(results_misere) < TOP_K:
print("Too few example on misere on dataset {}: {} results".
format(datasets_names[i], len(results_misere)))
if len(results_beam) < TOP_K:
print(
"Too few example on beam_search on dataset {}: {} results".
format(datasets_names[i], len(results_beam)))
if len(result_ucb_opti) < TOP_K:
print("Too few example on seqscout on dataset {}: {} results".
format(datasets_names[i], len(result_ucb_opti)))
data_add_generic(data_final,
WRAcc=max(0, average_results(results_misere)),
dataset=datasets_names[i],
Algorithm='misere')
data_add_generic(data_final,
WRAcc=max(0, average_results(results_beam)),
dataset=datasets_names[i],
Algorithm='beam')
data_add_generic(data_final,
WRAcc=max(0, average_results(result_ucb_opti)),
dataset=datasets_names[i],
Algorithm='seqscout')
df = pd.DataFrame(data=data_final)
sns.set(rc={'figure.figsize': (8, 6.5)})
plt.clf()
ax = sns.barplot(x='dataset', y='WRAcc', hue='Algorithm', data=df)
plt.savefig('./wracc_datasets/iterations_boxplot.png')
df.to_pickle('./wracc_datasets/result')
if SHOW:
plt.show()
def _read_obs(self, stns_ids=None):
# Saw extreme decreased performance due to garbage collection when
# pandas ran checks for a chained assignment. Turn off this check
# temporarily.
opt_val = pd.get_option('mode.chained_assignment')
pd.set_option('mode.chained_assignment', None)
try:
if stns_ids is None:
stns_obs = self.stns
else:
stns_obs = self.stns.loc[stns_ids]
nstns = len(stns_obs.station_id)
nprocs = self.nprocs if nstns >= self.nprocs else nstns
if self.has_start_end_dates:
start_end = (self.start_date, self.end_date)
else:
start_end = None
if nprocs > 1:
# http://stackoverflow.com/questions/24171725/
# scikit-learn-multicore-attributeerror-stdin-instance-
# has-no-attribute-close
if not hasattr(sys.stdin, 'close'):
def dummy_close():
pass
sys.stdin.close = dummy_close
iter_stns = [(None, a_id, self.elems, start_end)
for a_id in stns_obs.station_id]
pool = Pool(processes=nprocs)
obs = pool.map(_parse_ghcnd_dly_star_remote, iter_stns)
pool.close()
pool.join()
else:
obs = []
for a_id in stns_obs.station_id:
abuf = open_remote_file('https://www1.ncdc.noaa.gov/'
'pub/data/ghcn/daily/all/%s.dly' % a_id)
obs_stn = _parse_ghcnd_dly(abuf, a_id, self.elems, start_end)
obs.append(obs_stn)
df_obs = pd.concat(obs, ignore_index=True)
finally:
pd.set_option('mode.chained_assignment', opt_val)
df_obs = df_obs.set_index(['station_id', 'elem', 'time'])
df_obs = df_obs.sortlevel(0, sort_remaining=True)
return df_obs
def show_quality_over_iterations_ucb(number_dataset):
data, target, enable_i = datasets[number_dataset]
# if we want to average
nb_launched = 5
pool = Pool(processes=3)
iterations_limit = 50
iterations_step = 1000
data_final = {'WRAcc': [], 'iterations': [], 'Algorithm': []}
for i in range(12):
print('Iteration: {}'.format(i))
for i in range(nb_launched):
results_misere = pool.apply_async(
misere, (data, target), {
'time_budget': TIME_BUDGET_XP,
'iterations_limit': iterations_limit
})
results_beam = pool.apply_async(
beam_search, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP,
'iterations_limit': iterations_limit
})
result_ucb_opti = pool.apply_async(
seq_scout, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP,
'iterations_limit': iterations_limit
})
data_add_generic(data_final,
WRAcc=max(0,
average_results(results_misere.get())),
iterations=iterations_limit,
Algorithm='misere')
data_add_generic(data_final,
WRAcc=max(0, average_results(results_beam.get())),
iterations=iterations_limit,
Algorithm='beam')
data_add_generic(data_final,
WRAcc=max(0,
average_results(result_ucb_opti.get())),
iterations=iterations_limit,
Algorithm='seqscout')
iterations_limit += iterations_step
df = pd.DataFrame(data=data_final)
sns.set(rc={'figure.figsize': (8, 6.5)})
plt.clf()
ax = sns.lineplot(data=df,
x='iterations',
y='WRAcc',
hue='Algorithm',
markers=True)
plt.savefig('./iterations_ucb/over_iterations{}.png'.format(
datasets_names[number_dataset]))
df.to_pickle('./iterations_ucb/result{}'.format(
datasets_names[number_dataset]))
if SHOW:
plt.show()
def get_data_for_linker(
catalog: str,
entity: str,
qids: Set[str],
url_pids: Set[str],
ext_id_pids_to_urls: Dict,
qids_and_tids: Dict,
fileout: TextIO,
) -> None:
"""Collect relevant data for linking Wikidata to a given catalog.
Dump the result to a given output stream.
This function uses multithreaded parallel processing.
:param catalog: ``{'discogs', 'imdb', 'musicbrainz'}``.
A supported catalog
:param entity: ``{'actor', 'band', 'director', 'musician', 'producer',
'writer', 'audiovisual_work', 'musical_work'}``.
A supported entity
:param qids: a set of QIDs
:param url_pids: a set of PIDs holding URL values.
Returned by :py:func:`soweego.wikidata.sparql_queries.url_pids`
:param ext_id_pids_to_urls: a
``{PID: {formatter_URL: (id_regex, url_regex,)} }`` dict.
Returned by
:py:func:`soweego.wikidata.sparql_queries.external_id_pids_and_urls`
:param fileout: a file stream open for writing
:param qids_and_tids: a ``{QID: {'tid': {catalog_ID_set} }`` dict.
Populated by
:py:func:`soweego.commons.data_gathering.gather_target_ids`
"""
qid_buckets, request_params = _prepare_request(
qids, 'labels|aliases|descriptions|sitelinks|claims')
# Catalog-specific data needs
if catalog in constants.REQUIRE_OCCUPATION.keys():
needs_occupation = entity in constants.REQUIRE_OCCUPATION[catalog]
else:
needs_occupation = False
needs_genre = entity in constants.REQUIRE_GENRE
needs_publication_date = entity in constants.REQUIRE_PUBLICATION_DATE
# Initialize 7 counters to 0
# Indices legend:
# 0 = claims
# 1 = labels
# 2 = aliases
# 3 = descriptions
# 4 = sitelinks
# 5 = third-party URLs
# 6 = third-party IDs
counters = [0] * 7
# Create a partial function where all parameters
# but the data bucket are passed to `_process_bucket`,
# so that we only pass the data bucket
# when we call `pool_function`.
# In this way, it becomes trivial to use
# `multiprocessing.Pool` map functions, like `imap_unordered`
pool_function = partial(
_process_bucket,
request_params=request_params,
url_pids=url_pids,
ext_id_pids_to_urls=ext_id_pids_to_urls,
qids_and_tids=qids_and_tids,
needs=(needs_occupation, needs_genre, needs_publication_date),
counters=counters,
)
# Create a pool of threads and map the list of buckets via `pool_function`
with Pool() as pool:
# `processed_bucket` will be a list of dicts, where each dict
# is a processed entity from the bucket
for processed_bucket in pool.imap_unordered(
pool_function, tqdm(qid_buckets, total=len(qid_buckets))):
# Join results into a string so that we can write them to
# the dump file
to_write = ''.join(
json.dumps(result, ensure_ascii=False) + '\n'
for result in processed_bucket)
fileout.write(to_write)
fileout.flush()
LOGGER.info(
'QIDs: got %d with no expected claims, %d with no labels, '
'%d with no aliases, %d with no descriptions, %d with no sitelinks, '
'%d with no third-party links, %d with no external ID links',
*counters)
def _build_tobs_hdfs(path_out, fpaths_yrly, elems, nprocs=1):
fpaths_yrly = np.array(fpaths_yrly)
nprocs = nprocs if fpaths_yrly.size >= nprocs else fpaths_yrly.size
stn_nums = pd.DataFrame([(np.nan, np.nan)], columns=['station_id', 'station_num'])
num_inc = 0
first_append = {elem:True for elem in elems}
# assume ~1.5 millions rows per year to estimate expected number of rows
erows = 1500000 * len(fpaths_yrly)
def write_data(df_tobs, num_inc, stn_nums):
hdfs = {elem:pd.HDFStore(os.path.join(path_out, '%s.hdf' % elem), 'a')
for elem in elems}
df_tobs.set_index('station_id', inplace=True)
df_tobs['obs_value'] = df_tobs.obs_value.astype(np.int16)
uids = pd.DataFrame(df_tobs.index.unique(), columns=['station_id'])
uids = uids.merge(stn_nums, how='left', on='station_id')
mask_nonum = uids.station_num.isnull()
if mask_nonum.any():
nums = np.arange(num_inc, (num_inc + mask_nonum.sum()))
uids.loc[mask_nonum, 'station_num'] = nums
num_inc = nums[-1] + 1
stn_nums = pd.concat([stn_nums, uids[mask_nonum]], ignore_index=True)
uids.set_index('station_id', inplace=True)
uids['station_num'] = uids.station_num.astype(np.int)
df_tobs = df_tobs.join(uids, how='left').set_index('station_num')
grped = df_tobs.groupby('elem')
for elem in elems:
try:
grp = grped.get_group(elem)[['time', 'obs_value']].copy()
except KeyError:
# no observation for element
continue
if first_append[elem]:
hdfs[elem].append('df_tobs', grp, data_columns=['time'],
expectedrows=erows, index=False)
first_append[elem] = False
else:
hdfs[elem].append('df_tobs', grp, data_columns=['time'], index=False)
for store in hdfs.values():
store.close()
return num_inc, stn_nums
# Initialize output hdfs
hdfs = [pd.HDFStore(os.path.join(path_out, '%s.hdf' % elem), 'w')
for elem in elems]
for store in hdfs:
store.close()
if nprocs > 1:
# http://stackoverflow.com/questions/24171725/
# scikit-learn-multicore-attributeerror-stdin-instance-
# has-no-attribute-close
if not hasattr(sys.stdin, 'close'):
def dummy_close():
pass
sys.stdin.close = dummy_close
for i in np.arange(fpaths_yrly.size, step=nprocs):
fpaths = fpaths_yrly[i:(i + nprocs)]
gc.collect()
pool = Pool(processes=nprocs)
iter_files = [(fpath, elems) for fpath in fpaths]
ls_tobs = pool.map(_parse_ghcnd_yrly_star, iter_files, chunksize=1)
pool.close()
pool.join()
for df_tobs in ls_tobs:
num_inc, stn_nums = write_data(df_tobs, num_inc, stn_nums)
del df_tobs
del ls_tobs
else:
for fpath in fpaths_yrly:
df_tobs = _parse_ghcnd_yrly(fpath, elems)
num_inc, stn_nums = write_data(df_tobs, num_inc, stn_nums)
stn_nums = stn_nums.dropna()
store_stnnums = pd.HDFStore(os.path.join(path_out, 'stn_nums.hdf'), 'w')
store_stnnums.put('df_stnnums', stn_nums)
store_stnnums.close()
# Create indexess
for elem in elems:
with pd.HDFStore(os.path.join(path_out, '%s.hdf' % elem)) as store:
store.create_table_index('df_tobs', optlevel=9, kind='full')
response = client.rpc_add(1, 2)
return response.data.result
def long_time_task():
for ii in range(1000):
# response = client.rpc_add(1, 3)
client = get_client2()
res = main2(client)
# client = get_client()
# res = main(client)
print(ii)
if __name__ == '__main__':
t = time.time()
from multiprocessing.pool import Pool
p = Pool()
for i in range(4):
p.apply_async(long_time_task, args=())
p.close()
p.join()
print(time.time() - t)
time.sleep(2)
tracer.close()
def predict(self, prediction_object):
threshold = self.prediction_threshold
predictions = list()
if isinstance(prediction_object, Commit):
# Predict
open_issues = [
i for i in self.repository_obj.issues if
# (len(i.states) == 0 or i.states[-1].to_ == IssueStates.open)
# or
(min([
abs(entity.timestamp - prediction_object.timestamp
) if hasattr(entity, 'timestamp') and entity.timestamp
else timedelta(days=self.net_size_in_days, seconds=1)
for entity in [i.original_post] + i.states + i.actions
]) <= timedelta(days=self.net_size_in_days))
]
open_issues += [null_issue]
prediction_data = list()
if len(open_issues) > 128:
with Pool(processes=os.cpu_count() - 1) as wp:
for point in wp.map(func=Issue_Closure(
prediction_object, self.feature_generator),
iterable=open_issues,
chunksize=128):
prediction_data.append(point)
else:
for issue in open_issues:
prediction_data.append(
self.feature_generator.generate_features_commit(
issue, prediction_object, False))
for point in prediction_data:
probabilities = self.clf.predict_proba(
np.array(
tuple([
v for k, v in point.items()
if k not in ['linked', 'issue', 'commit']
])).reshape(1, -1))
if point['issue'] == 'null_issue':
threshold = max(threshold, probabilities[0][1])
else:
prediction = (point['issue'], float(probabilities[0][1]))
predictions.append(prediction)
predictions = sorted([p for p in predictions if p[1] >= threshold],
key=lambda p: (p[1], p[0]),
reverse=True)
response = prediction_object.c_hash, predictions
elif isinstance(prediction_object, Issue):
# Predict
candidates = [
c for c in self.repository_obj.commits if (min([
abs(entity.timestamp - c.timestamp
) if hasattr(entity, 'timestamp') and entity.timestamp
else timedelta(days=self.net_size_in_days, seconds=1)
for entity in [prediction_object.original_post] +
prediction_object.states + prediction_object.actions
]) <= timedelta(days=self.net_size_in_days))
]
candidates += [null_commit]
prediction_data = list()
if len(candidates) > 128:
with Pool(processes=os.cpu_count() - 1) as wp:
for point in wp.map(func=Commit_Closure(
prediction_object, self.feature_generator),
iterable=candidates,
chunksize=128):
prediction_data.append(point)
else:
for commit in candidates:
prediction_data.append(
self.feature_generator.generate_features_commit(
prediction_object, commit, False))
for point in prediction_data:
probabilities = self.clf.predict_proba(
np.array(
tuple([
v for k, v in point.items()
if k not in ['linked', 'issue', 'commit']
])).reshape(1, -1))
if point['commit'] == 'null_commit':
threshold = max(threshold, probabilities[0][1])
else:
prediction = (point['commit'], float(probabilities[0][1]))
predictions.append(prediction)
predictions = sorted([p for p in predictions if p[1] >= threshold],
key=lambda p: (p[1], p[0]),
reverse=True)
response = prediction_object.id_, predictions
if self.use_sim_cs or self.use_sim_j or self.use_sim_d or self.use_file:
if self.predictions_from_last_tf_idf_update < self.predictions_between_updates:
self.predictions_from_last_tf_idf_update += 1
else:
self.predictions_from_last_tf_idf_update = 0
temporal_config = None
self.model, self.dictionary, new_cache = generate_tfidf_commit(
self.repository_obj,
self.stopwords,
self.min_tok_len,
cache=self.feature_generator.text_cache)
similarity_config = {
'dict': self.dictionary,
'model': self.model,
'min_len': self.min_tok_len,
'stopwords': self.stopwords,
}
if self.use_temporal:
self.fingerprint = None
temporal_config = {
'fingerprint': self.fingerprint,
'net_size_in_days': self.net_size_in_days,
}
self.feature_generator = FeatureGenerator(
use_file=self.use_file,
use_sim_cs=self.use_sim_cs,
use_sim_j=self.use_sim_j,
use_sim_d=self.use_sim_d,
use_social=self.use_social,
use_temporal=self.use_temporal,
use_pr_only=self.use_pr_only,
use_issue_only=self.use_issue_only,
similarity_config=similarity_config,
temporal_config=temporal_config,
text_cache=new_cache,
selected=self.features,
)
return response
def execute(self):
yesterday = (datetime.datetime.now() -
datetime.timedelta(days=1)).strftime("%Y-%m-%d 00:00:00")
today = datetime.datetime.now().strftime("%Y-%m-%d 00:00:00")
tomorrow = (datetime.datetime.now() +
datetime.timedelta(days=1)).strftime("%Y-%m-%d 00:00:00")
# self.etl()
sql = (
"select * from {} where SBMT_TMSTMP >= to_timestamp('{}', 'yyyy-mm-dd hh24:mi:ss') "
"and SBMT_TMSTMP < to_timestamp('{}', 'yyyy-mm-dd hh24:mi:ss') ")
# KC21 empi
batch_rows = self.batch_rows(sql.format(KC21, yesterday, today))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.empi, (row, "KC21"))
task.close()
task.join()
except StopIteration:
break
batch_rows = self.batch_rows(sql.format(N041, yesterday, today))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.process, (row, "N041"))
task.close()
task.join()
except StopIteration:
break
# pairing check
# N041
batch_rows = self.batch_rows(sql.format(N041, yesterday, today))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.pairing_check, (row, "N041"))
task.close()
task.join()
except StopIteration:
break
# KC21
batch_rows = self.batch_rows(sql.format(KC21, yesterday, today))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.pairing_check, (row, "KC21"))
task.close()
task.join()
except StopIteration:
break
# KC24
batch_rows = self.batch_rows(sql.format(KC24, yesterday, today))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.pairing_check, (row, "KC24"))
task.close()
task.join()
except StopIteration:
break
sql = (
"select * from {} where etl_date >= to_date('{}', 'yyyy-mm-dd hh24:mi:ss') "
"and etl_date < to_date('{}', 'yyyy-mm-dd hh24:mi:ss')").format(
ETL, today, tomorrow)
row = self.row(sql)
row["RUN"] = "True"
self.insert_or_update(ETL, row, key="RUN")
def getting_start(self):
sql = ("select * from {}")
# KC21 EMPI
print("Start processing...")
batch_rows = self.batch_rows(sql.format(KC21))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.empi, (row, "KC21"))
task.close()
task.join()
except StopIteration:
break
batch_rows = self.batch_rows(sql.format(N041))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.process, (row, "N041"))
task.close()
task.join()
except StopIteration:
break
# pairing check
# N041
batch_rows = self.batch_rows(sql.format(N041))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.pairing_check, (row, "N041"))
task.close()
task.join()
except StopIteration:
break
# KC21
batch_rows = self.batch_rows(sql.format(KC21))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.pairing_check, (row, "KC21"))
task.close()
task.join()
except StopIteration:
break
# KC24
batch_rows = self.batch_rows(sql.format(KC24))
while True:
try:
batch = next(batch_rows)
m = Manager()
share_ls = m.list()
share_ls.extend(batch)
del batch
task = Pool(self.__process_count)
for row in share_ls:
task.apply_async(self.pairing_check, (row, "KC24"))
task.close()
task.join()
except StopIteration:
break
meanexp = getMean(prediction_test)
meanofdiff = getMeanofDiffs(ground_test, prediction_test)
pvarfe = getPvar(ground_test, meanfe)
pvarexp = getPvar(prediction_test, meanexp)
ccc_test = getCCC(pvarfe, pvarexp, meanofdiff, meanfe, meanexp)
print(f"CV = {i}, Test >>> gamma = {best_gamma}, C = {best_c}, RMSE. ={rmse_test}, Spearman = {spearman_test}, CCC = {ccc_test}")
logger.info(
f"CV = {i}, Test >>> gamma = {best_gamma}, C = {best_c}, RMSE. ={rmse_test}, Spearman = {spearman_test}, CCC = {ccc_test}")
# Save
df = pd.DataFrame(data={"vggish_prediction_D": prediction_test, "vggish_groundtruth_D": test_y.values.tolist()})
df.to_csv(f"./Prediction_202106_Ratio631/CV{i}_vggish_Dominance_0621.csv")
print("save success!")
print(f">>>>>>> CV = {i}/10, Over Training >>>>>>>\n")
logger.info(f">>>>>>> CV = {i}/10,Over Training >>>>>>>")
return [rmse_test,spearman_values_test,ccc_test]
if __name__ == '__main__':
pool = Pool(int(os.getenv('N_PROC', os.cpu_count())))
futures = [pool.apply_async(func=svr, args=[i]) for i in range(1, 11)]
pool.close() # 关闭pool，使其不在接受新的（主进程）任务
average_rmse_test, average_pearson_test, average_ccc_test = [], [], []
for item in futures:
result = item.get()
average_rmse_test.append(result[0])
average_pearson_test.append(result[1])
average_ccc_test.append(result[2])
print(f"Vggish Regression Average Results of Dominance: RMSE.= {mean(average_rmse_test)}, Spearman = {mean(average_pearson_test)}, CCC = {mean(average_ccc_test)}")
logger.info(
f"/n/n/n Vggish Regression Average Results of Dominance: RMSE.= {mean(average_rmse_test)}, Spearman = {mean(average_pearson_test)}, CCC = {mean(average_ccc_test)}")
pool.join()
def validate(self,
do_mirroring: bool = True,
use_sliding_window: bool = True,
step_size: float = 0.5,
save_softmax: bool = True,
use_gaussian: bool = True,
overwrite: bool = True,
validation_folder_name: str = 'validation_raw',
debug: bool = False,
all_in_gpu: bool = False,
segmentation_export_kwargs: dict = None):
current_mode = self.network.training
self.network.eval()
assert self.was_initialized, "must initialize, ideally with checkpoint (or train first)"
if self.dataset_val is None:
self.load_dataset()
self.do_split()
if segmentation_export_kwargs is None:
if 'segmentation_export_params' in self.plans.keys():
force_separate_z = self.plans['segmentation_export_params'][
'force_separate_z']
interpolation_order = self.plans['segmentation_export_params'][
'interpolation_order']
interpolation_order_z = self.plans[
'segmentation_export_params']['interpolation_order_z']
else:
force_separate_z = None
interpolation_order = 1
interpolation_order_z = 0
else:
force_separate_z = segmentation_export_kwargs['force_separate_z']
interpolation_order = segmentation_export_kwargs[
'interpolation_order']
interpolation_order_z = segmentation_export_kwargs[
'interpolation_order_z']
output_folder = join(self.output_folder, validation_folder_name)
maybe_mkdir_p(output_folder)
if do_mirroring:
mirror_axes = self.data_aug_params['mirror_axes']
else:
mirror_axes = ()
pred_gt_tuples = []
export_pool = Pool(2)
results = []
transpose_backward = self.plans.get('transpose_backward')
for k in self.dataset_val.keys():
properties = load_pickle(self.dataset[k]['properties_file'])
data = np.load(self.dataset[k]['data_file'])['data']
# concat segmentation of previous step
seg_from_prev_stage = np.load(
join(self.folder_with_segs_from_prev_stage,
k + "_segFromPrevStage.npz"))['data'][None]
print(data.shape)
data[-1][data[-1] == -1] = 0
data_for_net = np.concatenate(
(data[:-1],
to_one_hot(seg_from_prev_stage[0], range(1,
self.num_classes))))
softmax_pred = self.predict_preprocessed_data_return_seg_and_softmax(
data_for_net,
do_mirroring=do_mirroring,
mirror_axes=mirror_axes,
use_sliding_window=use_sliding_window,
step_size=step_size,
use_gaussian=use_gaussian,
all_in_gpu=all_in_gpu,
mixed_precision=self.fp16)[1]
if transpose_backward is not None:
transpose_backward = self.plans.get('transpose_backward')
softmax_pred = softmax_pred.transpose(
[0] + [i + 1 for i in transpose_backward])
fname = properties['list_of_data_files'][0].split("/")[-1][:-12]
if save_softmax:
softmax_fname = join(output_folder, fname + ".npz")
else:
softmax_fname = None
"""There is a problem with python process communication that prevents us from communicating obejcts
larger than 2 GB between processes (basically when the length of the pickle string that will be sent is
communicated by the multiprocessing.Pipe object then the placeholder (\%i I think) does not allow for long
enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually
patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will
then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either
filename or np.ndarray and will handle this automatically"""
if np.prod(softmax_pred.shape) > (2e9 / 4 *
0.85): # *0.85 just to be save
np.save(fname + ".npy", softmax_pred)
softmax_pred = fname + ".npy"
results.append(
export_pool.starmap_async(
save_segmentation_nifti_from_softmax,
((softmax_pred, join(output_folder, fname + ".nii.gz"),
properties, interpolation_order,
self.regions_class_order, None, None, softmax_fname,
None, force_separate_z, interpolation_order_z), )))
pred_gt_tuples.append([
join(output_folder, fname + ".nii.gz"),
join(self.gt_niftis_folder, fname + ".nii.gz")
])
_ = [i.get() for i in results]
task = self.dataset_directory.split("/")[-1]
job_name = self.experiment_name
_ = aggregate_scores(pred_gt_tuples,
labels=list(range(self.num_classes)),
json_output_file=join(output_folder,
"summary.json"),
json_name=job_name,
json_author="Fabian",
json_description="",
json_task=task)
# in the old nnunet we would stop here. Now we add a postprocessing. This postprocessing can remove everything
# except the largest connected component for each class. To see if this improves results, we do this for all
# classes and then rerun the evaluation. Those classes for which this resulted in an improved dice score will
# have this applied during inference as well
self.print_to_log_file("determining postprocessing")
determine_postprocessing(self.output_folder,
self.gt_niftis_folder,
validation_folder_name,
final_subf_name=validation_folder_name +
"_postprocessed",
debug=debug)
# after this the final predictions for the vlaidation set can be found in validation_folder_name_base + "_postprocessed"
# They are always in that folder, even if no postprocessing as applied!
# detemining postprocesing on a per-fold basis may be OK for this fold but what if another fold finds another
# postprocesing to be better? In this case we need to consolidate. At the time the consolidation is going to be
# done we won't know what self.gt_niftis_folder was, so now we copy all the niftis into a separate folder to
# be used later
gt_nifti_folder = join(self.output_folder_base, "gt_niftis")
maybe_mkdir_p(gt_nifti_folder)
for f in subfiles(self.gt_niftis_folder, suffix=".nii.gz"):
success = False
attempts = 0
while not success and attempts < 10:
try:
shutil.copy(f, gt_nifti_folder)
success = True
except OSError:
attempts += 1
sleep(1)
self.network.train(current_mode)
export_pool.close()
export_pool.join()
def run(self):
# On compte le nombre de fichiers à traiter
deb = time.time()
self.fenetre.setpourcent(0)
if len(os.listdir(self.data_entree)) == 0:
self.fenetre.setInstruction("le dossier \"./results\" est vide.")
return
pas = 100. / len(os.listdir(self.data_entree))
self.fenetre.setInstruction("Debut de la synthese des fichiers.")
# pour chaque fichier dans./results/
MultiListe = []
for file in sorted(os.listdir(self.data_entree)):
if file.endswith('.csv'):
MultiListe.append([file, self.frequency])
multiprocessing = Pool(self.nbproc)
Values = multiprocessing.map(description, MultiListe)
for resultat in Values:
self.summary.append({
'name': (resultat[0])[:(resultat[0]).rfind('-')],
'id': (resultat[0])[(resultat[0]).rfind('-') +
1:(resultat[0]).rfind('.')],
'category':
"\(O_O)/",
'measurement_tools':
self.measurement_tools,
'measurement_mode':
self.measurement_mode,
'measurement_system':
self.measurement_system,
'measurement_method':
self.measurement_method,
'measurement_protocol':
self.measurement_protocol,
'min_value':
resultat[1],
'number_of_samples':
resultat[11],
'max_value':
resultat[2],
'mean':
resultat[3],
'stdev':
resultat[4],
'median_value':
resultat[5],
'sum_square':
resultat[6],
'sum_square/frequency':
resultat[7],
'percentile_25':
resultat[8],
'percentile_50':
resultat[9],
'percentile_75':
resultat[10]
})
self.fenetre.setpourcent(self.fenetre.getpourcent() + pas)
sorted(self.summary, key=lambda k: (k['name'], k['id']))
summary = pd.DataFrame(
self.summary,
columns=[
'name', 'id', 'category', 'measurement_tools',
'measurement_system', 'measurement_mode', 'measurement_method',
'measurement_protocol', 'number_of_samples', 'min_value',
'max_value', 'mean', 'stdev', 'median_value', 'sum_square',
'sum_square/frequency', 'percentile_25', 'percentile_50',
'percentile_75'
])
summary.to_csv('./synthese/{}_summary.csv'.format(self.data_sortie),
index=False,
sep=';')
self.fenetre.setInstruction(
"Travail termine.\nTemps total pris pour la synthese : " +
str(round((time.time() - deb), 2)) + " secs")
def Pool(processes=None, initializer=None, initargs=()):
'''
Returns a process pool object
'''
from multiprocessing.pool import Pool
return Pool(processes, initializer, initargs)
def run(name):
print("%s子进程开始，进程ID：%d" % (name, os.getpid()))
start = time()
# 可以通过这个方式，保证每个程序开始运行的时间有微小的差异，
# 可应用于爬虫。
sleep(random.choice([1, 2, 3, 4]))
end = time()
print("%s子进程结束，进程ID：%d。耗时%0.2f" % (name, os.getpid(), end - start))
if __name__ == "__main__":
print("父进程开始")
# 创建多个进程，表示可以同时执行的进程数量。默认大小是CPU的核心数
# 通常获取逻辑cpu个数，然后设置进程数量。
p = Pool(4)
for i in range(10):
# 创建进程，放入进程池统一管理
# 通过异步运行的方式，每运行一个run，创建一个进程，这些进程由进程池管理。
p.apply_async(run, args=(i, ))
# 也可以用同步的方式启动，每个进程需要等待程序结束再继续加载。
# p.apply(run, args=(i,))
# 如果我们用的是进程池，在调用join()之前必须要先close()，
# 并且在close()之后不能再继续往进程池添加新的进程
p.close()
# 进程池对象调用join，会等待进程池中所有的子进程结束完毕再去结束父进程
p.join()
print("父进程结束。")
p.terminate()
#
def train(self, dump_reader, parallel=True,
pool_size=multiprocessing.cpu_count(), chunk_size=100):
self._word_counter = multiprocessing.Value(c_uint64, 0)
self._word_alpha = multiprocessing.RawValue(
c_float, self._word_initial_alpha
)
self._entity_alpha = multiprocessing.RawValue(
c_float, self._entity_initial_alpha
)
logger.info('Initializing weights...')
syn0_shared = multiprocessing.RawArray(
c_float, len(self.dictionary) * self._size
)
syn0 = np.frombuffer(syn0_shared, dtype=REAL)
syn0 = syn0.reshape(len(self.dictionary), self._size)
for w in self.dictionary:
if isinstance(w, Word):
np.random.seed(np.uint32(hash(w.text)))
elif isinstance(w, Entity):
np.random.seed(np.uint32(hash(w.title)))
else:
RuntimeError('Unknown type')
syn0[w.index] = (np.random.rand(self._size) - 0.5) / self._size
syn1_shared = multiprocessing.RawArray(
c_float, len(self.dictionary) * self._size
)
syn1 = np.frombuffer(syn1_shared, dtype=REAL)
syn1 = syn1.reshape(len(self.dictionary), self._size)
syn1.fill(0)
self._total_words = int(sum(
w.count for w in self.dictionary.words()
))
self._total_words *= self._iteration
logger.info('Total number of words: %d', self._total_words)
word_neg_table = self._build_word_neg_table()
entity_neg_table = self._build_entity_neg_table()
logger.info('Starting to train a model...')
def iter_dump_reader():
for n in range(self._iteration):
logger.info('Iteration: %d', n)
for page in dump_reader:
yield page
init_args = (
self, syn0_shared, syn1_shared, word_neg_table, entity_neg_table
)
if parallel:
pool = Pool(pool_size, initializer=init_worker, initargs=init_args)
imap_func = partial(pool.imap_unordered, chunksize=chunk_size)
else:
init_worker(*init_args)
imap_func = imap
for (n, _) in enumerate(imap_func(train_page, iter_dump_reader())):
if n % 10000 == 0:
prog = float(self._word_counter.value) / self._total_words
logger.info(
'Proccessing page #%d progress: %.1f%% '
'word alpha: %.3f entity alpha: %.3f',
n, prog * 100, self._word_alpha.value,
self._entity_alpha.value
)
if parallel:
pool.close()
self.syn0 = syn0
self.syn1 = syn1
self._word_neg_table = word_neg_table
self._entity_neg_table = entity_neg_table
initClosure(closurePath)
initAnnotations(annotationsFilePath)
# cafaFiles= ["/mnt/home/hampt/workspace/doctorProject/src/CAFA/sprot_go.fasta.test.outM1.txt","/mnt/home/hampt/workspace/doctorProject/src/CAFA/sprot_go.fasta.test.outM2.txt","/mnt/home/hampt/workspace/doctorProject/src/CAFA/sprot_go.fasta.test.outR.txt","/mnt/home/hampt/workspace/doctorProject/src/CAFA/sprot_go.fasta.test.outX.txt", "/mnt/home/hampt/workspace/doctorProject/src/CAFA/sprot_go.fasta.test.outY.txt", "/mnt/home/hampt/workspace/doctorProject/src/CAFA/sprot_go.fasta.test.outZ.txt", "/mnt/home/hampt/workspace/doctorProject/src/CAFA/sprot_go.fasta.test.outM2R.txt" ]
cafaFiles= ["/mnt/home/hampt/workspace/doctorProject/src/CAFA/sprot_go.fasta.test.outX.txt" ]
methodToTargetToTermToScore = {}
inputs = []
for filePath in cafaFiles:
inputs.append((filePath))
print inputs
pool = Pool(processes=10)
resultMaps = pool.map(fillData, inputs, chunksize=1)
for resultMap in resultMaps:
for key, val in resultMap.iteritems():
methodToTargetToTermToScore[key] = val
targetToTermToMethodToScore = collections.defaultdict(dict)
for method, methodDict in methodToTargetToTermToScore.iteritems():
for target, targetDict in methodDict.iteritems():
for term, score in targetDict.iteritems():
targetToTermToMethodToScore[target].setdefault(term,{})[method] = score
# outMeta = open("/mnt/home/hampt/workspace/doctorProject/src/CAFA/meta2.out",'w')
# i=0
# for target, targetDict in targetToTermToMethodToScore.iteritems():
# for term, termDict in targetDict.iteritems():
import subprocess
from multiprocessing.pool import Pool
# mocking your code
modules = ["mod1.onnx", "mod2.onnx"]
def run(args):
# pool.map will call run() with a tuple, deconstruct it
idx, mod = args
with open("output.txt", "w") as fp:
subprocess.run(["./SOME_PROGRAM", str(idx)], stdout=fp)
print("module", mod, "has finished")
# use the maximum number of threads by default
with Pool() as pool:
pool.map(run, enumerate(modules))
wer_inst = decoder.wer(transcript, reference)
cer_inst = decoder.cer(transcript, reference)
total_cer += cer_inst
total_wer += wer_inst
num_tokens += len(reference.split())
num_chars += len(reference)
wer = float(total_wer) / num_tokens
cer = float(total_cer) / num_chars
return [lm_alpha, lm_beta, wer * 100, cer * 100]
if __name__ == '__main__':
p = Pool(args.num_workers, init,
[args.beam_width,
model.labels.index('_'), args.lm_path])
cand_alphas = np.linspace(args.lm_alpha_from, args.lm_alpha_to,
args.lm_num_alphas)
cand_betas = np.linspace(args.lm_beta_from, args.lm_beta_to,
args.lm_num_betas)
params_grid = [(float(alpha), float(beta)) for alpha in cand_alphas
for beta in cand_betas]
scores = []
for params in tqdm(p.imap(decode_dataset, params_grid),
total=len(params_grid)):
scores.append(list(params))
print("Saving tuning results to: {}".format(args.output_path))
with open(args.output_path, "w") as fh:
def _read_obs(self, stns_ids=None):
# Saw extreme decreased performance due to garbage collection when
# pandas ran checks for a chained assignment. Turn off this check
# temporarily.
opt_val = pd.get_option('mode.chained_assignment')
pd.set_option('mode.chained_assignment', None)
try:
if stns_ids is None:
stns_obs = self.stns
else:
stns_obs = self.stns.loc[stns_ids]
nstns = len(stns_obs.station_id)
nprocs = self.nprocs if nstns >= self.nprocs else nstns
if self.has_start_end_dates:
start_end = (self.start_date, self.end_date)
else:
start_end = None
if nprocs > 1:
# http://stackoverflow.com/questions/24171725/
# scikit-learn-multicore-attributeerror-stdin-instance-
# has-no-attribute-close
if not hasattr(sys.stdin, 'close'):
def dummy_close():
pass
sys.stdin.close = dummy_close
iter_stns = [(os.path.join(self.path_ghcnd_data, 'ghcnd_all',
'%s.dly' % a_id), a_id, self._elems,
start_end) for a_id in stns_obs.station_id]
pool = Pool(processes=nprocs)
obs = pool.map(_parse_ghcnd_dly_star, iter_stns)
pool.close()
pool.join()
else:
obs = []
for a_id in stns_obs.station_id:
fpath = os.path.join(self.path_ghcnd_data, 'ghcnd_all',
'%s.dly' % a_id)
obs_stn = _parse_ghcnd_dly(fpath, a_id, self._elems, start_end)
obs.append(obs_stn)
df_obs = pd.concat(obs, ignore_index=True)
if self._has_tobs:
stnnums = stns_obs.join(self._df_tobs_stnnums).dropna(subset=['station_num'])
if not stnnums.empty:
stnnums = stnnums.reset_index(drop=True).set_index('station_num')
select_str = "index = a_num"
df_tobs = []
path_yrly = os.path.join(self.path_ghcnd_data, 'by_year')
for elem in self._elems_tobs:
store = pd.HDFStore(os.path.join(path_yrly, '%s.hdf' % elem))
# Perform separate read for each station.
# Had this in a single call using "index in stnnums"
# but memory usage was too high
for a_num in stnnums.index:
elem_tobs = store.select('df_tobs', select_str).reset_index()
elem_tobs['elem'] = elem
elem_tobs['station_id'] = stnnums.station_id.loc[a_num]
df_tobs.append(elem_tobs[['time', 'elem', 'obs_value',
'station_id']])
store.close()
del store
gc.collect()
df_tobs = pd.concat(df_tobs, ignore_index=True)
if self.has_start_end_dates:
df_tobs = df_tobs[(df_tobs.time >= self.start_date) &
(df_tobs.time <= self.end_date)]
df_obs = pd.concat([df_obs, df_tobs], ignore_index=True)
finally:
pd.set_option('mode.chained_assignment', opt_val)
df_obs = df_obs.set_index(['station_id', 'elem', 'time'])
df_obs = df_obs.sortlevel(0, sort_remaining=True)
return df_obs
def run_cv(f, n_proc):
p = Pool(n_proc)
p.map(f, range(len(configs)))
p.close() # no more tasks
p.join() # wrap up current tasks
try:
local_image_url = item.get('image')
new_image_url = local_image_url.replace('list', 'large')
response = requests.get('http:' + new_image_url)
if response.status_code == 200:
file_path = '{0}/{1}.{2}'.format(item.get('title'),
md5(response.content).hexdigest(),
'jpg')
if not os.path.exists(file_path):
with open(file_path, 'wb') as f:
f.write(response.content)
else:
print('Already Downloaded', file_path)
except requests.ConnectionError:
print('Failed to save image')
def main(offset):
json = get_page(offset)
for item in get_images(json):
print(item)
save_image(item)
if __name__ == '__main__':
pool = Pool()
groups = ([x * 20 for x in range(GROUP_START, GROUP_END + 1)])
pool.map(main, groups)
pool.close()
pool.join()
def Pool(processes=None, initializer=None, initargs=(), maxtasksperchild=None):
'''
Returns a process pool object
'''
from multiprocessing.pool import Pool
return Pool(processes, initializer, initargs, maxtasksperchild)
def other_measures():
pool = Pool(processes=5)
xp_repeat = 5
nb_iterations = 1000
for i, (data, target, enable_i) in enumerate(datasets):
print("Dataset {}".format(datasets_names[i]))
for measure in ['Informedness', 'F1']:
mean_misere = 0
mean_beam = 0
mean_seqscout = 0
for j in range(xp_repeat):
results_misere = pool.apply_async(
misere, (data, target), {
'time_budget': TIME_BUDGET_XP,
'quality_measure': measure,
'iterations_limit': nb_iterations
})
results_beam = pool.apply_async(
beam_search, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP,
'quality_measure': measure,
'iterations_limit': nb_iterations
})
result_ucb_opti = pool.apply_async(
seq_scout, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP,
'quality_measure': measure,
'iterations_limit': nb_iterations
})
results_misere = results_misere.get()
results_beam = results_beam.get()
result_ucb_opti = result_ucb_opti.get()
if len(results_misere) < TOP_K:
print(
"Too few example on misere on dataset {}: {} results".
format(datasets_names[i], len(results_misere)))
if len(results_beam) < TOP_K:
print(
"Too few example on beam_search on dataset {}: {} results"
.format(datasets_names[i], len(results_beam)))
if len(result_ucb_opti) < TOP_K:
print(
"Too few example on seqscout on dataset {}: {} results"
.format(datasets_names[i], len(result_ucb_opti)))
mean_misere += average_results(results_misere)
mean_beam += average_results(results_beam)
mean_seqscout += average_results(result_ucb_opti)
mean_misere = mean_misere / xp_repeat
mean_beam = mean_beam / xp_repeat
mean_seqscout = mean_seqscout / xp_repeat
print(
'For datasets {}, measure {}, algorithm misere the means score is: {}'
.format(datasets_names[i], measure, mean_misere))
print(
'For datasets {}, measure {}, algorithm beam_search the means score is: {}'
.format(datasets_names[i], measure, mean_beam))
print(
'For datasets {}, measure {}, algorithm seqscout the means score is: {}'
.format(datasets_names[i], measure, mean_seqscout))
import subprocess
import pickle
import sys
import ntpath
from threading import Lock
from multiprocessing.pool import ThreadPool as Pool
import xml.etree.ElementTree as ET
import urllib2
checksums_dict = {}
checksums_dict_lock = Lock()
file_path_dict = {}
file_path_dict_lock = Lock()
pool_size = 5 # your "parallelness"
pool = Pool(pool_size)
class FileAttributes:
def __init__(self):
self.file_path=None
self.file_name=None
self.checksum=None
self.itunes_key=-1
self.itunes_file_path=None
def path_leaf(path):
''' Taken from https://stackoverflow.com/questions/8384737/extract-file-name-from-path-no-matter-what-the-os-path-format'''
head, tail = ntpath.split(path)
return tail or ntpath.basename(head)
def quality_over_size():
number_dataset = 6
data_origin, target, enable_i = datasets[number_dataset]
pool = Pool(processes=3)
# if we want to average
nb_launched = 5
size = 15
size_step = 4
data_final = {'WRAcc': [], 'size': [], 'Algorithm': []}
for i in range(10):
print('Iteration: {}'.format(i))
data = reduce_k_length(size, data_origin)
for i in range(nb_launched):
results_misere = pool.apply_async(misere, (data, target),
{'time_budget': TIME_BUDGET_XP})
results_beam = pool.apply_async(beam_search, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP
})
result_ucb_opti = pool.apply_async(seq_scout, (data, target), {
'enable_i': enable_i,
'time_budget': TIME_BUDGET_XP
})
results_misere = results_misere.get()
results_beam = results_beam.get()
result_ucb_opti = result_ucb_opti.get()
if len(results_beam) < TOP_K:
print("Too few beam: {}".format(len(results_beam)))
if len(result_ucb_opti) < TOP_K:
print("Too few seqscout: {}".format(len(result_ucb_opti)))
if len(results_misere) < TOP_K:
print("Too few misere: {}".format(len(results_misere)))
data_add_generic(data_final,
WRAcc=max(0, average_results(results_misere)),
size=size,
Algorithm='misere')
data_add_generic(data_final,
WRAcc=max(0, average_results(results_beam)),
size=size,
Algorithm='beam')
data_add_generic(data_final,
WRAcc=max(0, average_results(result_ucb_opti)),
size=size,
Algorithm='seqscout')
size += size_step
df = pd.DataFrame(data=data_final)
sns.set(rc={'figure.figsize': (8, 6.5)})
plt.clf()
ax = sns.lineplot(data=df, x='size', y='WRAcc', hue='Algorithm')
ax.set(xlabel='Length max', ylabel='WRAcc')
# ax.set(xlabel='Time(s)', ylabel='Average WRAcc top-10 patterns')
plt.savefig('./space_size/over_size.png')
df.to_pickle('./space_size/result')
if SHOW:
plt.show()
def validate(self,
do_mirroring: bool = True,
use_train_mode: bool = False,
tiled: bool = True,
step: int = 2,
save_softmax: bool = True,
use_gaussian: bool = True,
overwrite: bool = True,
validation_folder_name: str = 'validation_raw',
debug: bool = False,
all_in_gpu: bool = False,
force_separate_z: bool = None,
interpolation_order: int = 3,
interpolation_order_z=0):
assert self.was_initialized, "must initialize, ideally with checkpoint (or train first)"
# save whether network is in deep supervision mode or not
ds = self.network.do_ds
# disable deep supervision
self.network.do_ds = False
if self.dataset_val is None:
self.load_dataset()
self.do_split()
output_folder = join(self.output_folder, validation_folder_name)
maybe_mkdir_p(output_folder)
# this is for debug purposes
my_input_args = {
'do_mirroring': do_mirroring,
'use_train_mode': use_train_mode,
'tiled': tiled,
'step': step,
'save_softmax': save_softmax,
'use_gaussian': use_gaussian,
'overwrite': overwrite,
'validation_folder_name': validation_folder_name,
'debug': debug,
'all_in_gpu': all_in_gpu,
'force_separate_z': force_separate_z,
'interpolation_order': interpolation_order,
'interpolation_order_z': interpolation_order_z,
}
save_json(my_input_args, join(output_folder, "validation_args.json"))
if do_mirroring:
if not self.data_aug_params['do_mirror']:
raise RuntimeError(
"We did not train with mirroring so you cannot do inference with mirroring enabled"
)
mirror_axes = self.data_aug_params['mirror_axes']
else:
mirror_axes = ()
pred_gt_tuples = []
export_pool = Pool(default_num_threads)
results = []
for k in self.dataset_val.keys():
properties = self.dataset[k]['properties']
fname = properties['list_of_data_files'][0].split("/")[-1][:-12]
if overwrite or (not isfile(join(output_folder, fname + ".nii.gz"))) or \
(save_softmax and not isfile(join(output_folder, fname + ".npz"))):
data = np.load(self.dataset[k]['data_file'])['data']
# concat segmentation of previous step
seg_from_prev_stage = np.load(
join(self.folder_with_segs_from_prev_stage,
k + "_segFromPrevStage.npz"))['data'][None]
print(k, data.shape)
data[-1][data[-1] == -1] = 0
data_for_net = np.concatenate(
(data[:-1],
to_one_hot(seg_from_prev_stage[0],
range(1, self.num_classes))))
softmax_pred = self.predict_preprocessed_data_return_softmax(
data_for_net,
do_mirroring,
1,
use_train_mode,
1,
mirror_axes,
tiled,
True,
step,
self.patch_size,
use_gaussian=use_gaussian,
all_in_gpu=all_in_gpu)
softmax_pred = softmax_pred.transpose(
[0] + [i + 1 for i in self.transpose_backward])
if save_softmax:
softmax_fname = join(output_folder, fname + ".npz")
else:
softmax_fname = None
"""There is a problem with python process communication that prevents us from communicating obejcts
larger than 2 GB between processes (basically when the length of the pickle string that will be sent is
communicated by the multiprocessing.Pipe object then the placeholder (\%i I think) does not allow for long
enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually
patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will
then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either
filename or np.ndarray and will handle this automatically"""
if np.prod(softmax_pred.shape) > (
2e9 / 4 * 0.85): # *0.85 just to be save
np.save(join(output_folder, fname + ".npy"), softmax_pred)
softmax_pred = join(output_folder, fname + ".npy")
results.append(
export_pool.starmap_async(
save_segmentation_nifti_from_softmax,
((softmax_pred, join(output_folder,
fname + ".nii.gz"), properties,
interpolation_order, None, None, None, softmax_fname,
force_separate_z, interpolation_order_z), )))
pred_gt_tuples.append([
join(output_folder, fname + ".nii.gz"),
join(self.gt_niftis_folder, fname + ".nii.gz")
])
_ = [i.get() for i in results]
self.print_to_log_file("finished prediction")
# evaluate raw predictions
self.print_to_log_file("evaluation of raw predictions")
task = self.dataset_directory.split("/")[-1]
job_name = self.experiment_name
_ = aggregate_scores(
pred_gt_tuples,
labels=list(range(self.num_classes)),
json_output_file=join(output_folder, "summary.json"),
json_name=job_name + " val tiled %s" % (str(tiled)),
json_author="Fabian",
json_task=task,
num_threads=default_num_threads)
# in the old nnunet we would stop here. Now we add a postprocessing. This postprocessing can remove everything
# except the largest connected component for each class. To see if this improves results, we do this for all
# classes and then rerun the evaluation. Those classes for which this resulted in an improved dice score will
# have this applied during inference as well
self.print_to_log_file("determining postprocessing")
determine_postprocessing(self.output_folder,
self.gt_niftis_folder,
validation_folder_name,
final_subf_name=validation_folder_name +
"_postprocessed",
debug=debug)
# after this the final predictions for the vlaidation set can be found in validation_folder_name_base + "_postprocessed"
# They are always in that folder, even if no postprocessing as applied!
# detemining postprocesing on a per-fold basis may be OK for this fold but what if another fold finds another
# postprocesing to be better? In this case we need to consolidate. At the time the consolidation is going to be
# done we won't know what self.gt_niftis_folder was, so now we copy all the niftis into a separate folder to
# be used later
gt_nifti_folder = join(self.output_folder_base, "gt_niftis")
maybe_mkdir_p(gt_nifti_folder)
for f in subfiles(self.gt_niftis_folder, suffix=".nii.gz"):
success = False
attempts = 0
e = None
while not success and attempts < 10:
try:
shutil.copy(f, gt_nifti_folder)
success = True
except OSError as e:
attempts += 1
sleep(1)
if not success:
print("Could not copy gt nifti file %s into folder %s" %
(f, gt_nifti_folder))
if e is not None:
raise e
# restore network deep supervision mode
self.network.do_ds = ds
def mmap_(fn: Callable[[A], B], iter: Iterable[A]) -> List[B]:
return Pool().map(fn, iter)
def post(self):
try:
# set up post request parameters
parser.add_argument('gre')
parser.add_argument('toefl')
parser.add_argument('grade')
parser.add_argument('email')
parser.add_argument('uid')
parser.add_argument('work_ex')
parser.add_argument('lor1')
parser.add_argument('lor2')
parser.add_argument('lor3')
parser.add_argument('lor4')
# Setup google storage for access
storage_client = storage.Client.from_service_account_json(
'key1.json')
bucket = storage_client.bucket('mf-frontend.appspot.com')
# Retrieve post request values
args = parser.parse_args()
gre = args['gre']
toefl = args['toefl']
grade = args['grade']
email = args['email']
uid = args['uid']
work_ex = args['work_ex']
lor1 = args['lor1']
lor2 = args['lor2']
lor3 = args['lor3']
lor4 = args['lor4']
# Retrieve resume and convert to image
blob = bucket.blob('resume/' + uid + '/' + uid + '_resume.pdf')
blob.download_to_filename('/tmp/resume.pdf')
doc = fitz.open('/tmp/resume.pdf')
mat = fitz.Matrix(fitz.Identity)
resume = doc[0].getPixmap(alpha=False, matrix=mat)
resume.writePNG("/tmp/resume.png")
resume = np.array(Image.open('/tmp/resume.png'))
logging.info("Loaded resume")
# Retrieve sop and extract text
blob = bucket.blob('sop/' + uid + '/' + uid + '_sop.pdf')
blob.download_to_filename('/tmp/sop.pdf')
doc = fitz.open('/tmp/sop.pdf')
pages = len(doc)
sop = ""
for page in range(pages):
sop += doc[page].getText()
logging.info("Loaded sop")
# Make calls to second backend for all universities
data = {
"gre": gre,
"toefl": toefl,
"grade": grade,
"email": email,
"uid": uid,
"work_ex": work_ex,
"lor1": lor1,
"lor2": lor2,
"lor3": lor3,
"lor4": lor4
}
data["resume"] = resume.tolist()
data["sop"] = sop
universities = ["mit", "neu", "ncsu", "utd", "usc"]
# Parallel requests
pool = Pool(len(universities))
async_result = [
pool.apply_async(self.send_requests, (
data,
univ,
)) for univ in universities
]
pool.close()
pool.join()
return_val = sorted([ar.get() for ar in async_result],
key=itemgetter('score'),
reverse=True)
print(return_val)
resp = {}
for d in return_val:
univ = d['univ']
nd = copy.deepcopy(data)
nd['score'] = d['score']
resp[univ] = nd
# rets = [self.send_requests(data,x,) for x in universities]
# resp = {}
# for i,u in enumerate(universities):
# resp[u] = rets[i]
return resp
except Exception as e:
print(e)
return {}