def _calc_threads(self):
if mp.cpu_count() == 1:
max = 1
else:
max = mp.cpu_count() - 1
return max
def exposure_analysis(rail=False):
"""
Get exposure statistics for all road or railway assets in all regions.
Optional Arguments:
*rail* : Default is **False**. Set to **True** if you would like to
intersect the railway assets in a region.
"""
# set data path
data_path = load_config()['paths']['data']
# load shapefile with all regions
global_regions = geopandas.read_file(
os.path.join(data_path, 'input_data', 'global_regions_v2.shp'))
# load csv with income group data and assign income group to regions
incomegroups = pandas.read_csv(os.path.join(data_path, 'input_data',
'incomegroups_2018.csv'),
index_col=[0])
income_dict = dict(zip(incomegroups.index, incomegroups.GroupCode))
global_regions['wbincome'] = global_regions.GID_0.apply(
lambda x: income_dict[x])
# only keep regions for which we have data
global_regions = global_regions.loc[global_regions.GID_2.isin([
(x.split('.')[0])
for x in os.listdir(os.path.join(data_path, 'region_osm'))
])]
# create dictionary with information on protection standards
prot_lookup = dict(
zip(global_regions['GID_2'], global_regions['prot_stand']))
# create lists for the parallelization
regions = list(global_regions.index)
prot_lookups = [prot_lookup] * len(regions)
data_paths = [data_path] * len(regions)
# run exposure analysis parallel
if not rail:
with Pool(cpu_count() - 1) as pool:
collect_output = pool.starmap(regional_roads,
zip(regions, prot_lookups,
data_paths),
chunksize=1)
pandas.concat(collect_output).to_csv(
os.path.join(data_path, 'summarized', 'total_exposure_road.csv'))
else:
with Pool(cpu_count() - 1) as pool:
collect_output = pool.starmap(regional_railway,
zip(regions, prot_lookups,
data_paths),
chunksize=1)
pandas.concat(collect_output).to_csv(
os.path.join(data_path, 'summarized',
'total_exposure_railway.csv'))
def _calc_num_threads(self,
df_size: int,
query_size: int,
max_threads=None) -> int:
num_queries = df_size * query_size
if mp.cpu_count() == 1:
max = 1
else:
max = mp.cpu_count() - 1
calc = int(num_queries / 5000)
if calc > max:
r = max
elif calc <= 1:
if num_queries > 1000:
r = 2
else:
r = 1
else:
r = calc
if max_threads is not None and r > max_threads:
return max_threads
return r
def generate(self, sample_size):
self.size = sample_size # number of samples to be collected
print(mp.cpu_count())
pool = mp.Pool(mp.cpu_count())
self.samples = pool.map(self.method, range(
self.size)) # container for the collected samples
#print(self.samples) --> debug
pool.close()
pool.join()
def run_preprocess(self, database_destination, table_destination):
"""
:return:
"""
list_of_dfs = []
for journal, path in self._mydict.iteritems():
list_of_dfs.append(self._identify_files_and_titles(path, journal))
dataframe = pd.concat(list_of_dfs)
if self._n_cores != 1:
if self._n_cores == mp.cpu_count():
print "Going to WARP SPEED!!!!"
else:
print "Meh, multiprocessing, but not good enough!"
titles_to_process = dataframe['file_complete_path'].values
list_features = self._pool.map(
self._feat_gen.gen_features_from_pdf_file, titles_to_process)
else:
print "Why you didn't choose multiprocessing??? WHY???? WHY???????!"
print ''
list_features = []
total_size = len(dataframe['file_complete_path'].values)
count_ite = 1
for t in dataframe['file_complete_path'].values:
try:
list_features.append(
self._feat_gen.gen_features_from_pdf_file(t))
count_ite += 1
print 'Done ' + str(t)
print str(count_ite) + ' of ' + str(total_size)
except:
print ' '
print 'Problems with pdf: ' + str(t)
print 'Iteration: ' + str(count_ite)
print ' '
file_features = pd.concat(list_features)
print "Sorry, no multiprocessing implemented for this part :/ "
list_of_features_from_db = []
total_size = len(dataframe['title'].values)
count_ite = 1
for t in dataframe['title'].values:
list_of_features_from_db.append(
self._feat_gen.gen_features_from_database(t))
count_ite += 1
print 'Done ' + str(t)
print str(count_ite) + ' of ' + str(total_size)
db_features = pd.concat(list_of_features_from_db)
rtn_dataframe = file_features.set_index('title').join(
db_features.set_index('title'))
rtn_dataframe = rtn_dataframe.join(dataframe.set_index('title'))
rtn_dataframe.reset_index(level=0, inplace=True)
return rtn_dataframe
def preprocess_from_files(self,shot_files,use_shots):
#all shots, including invalid ones
all_signals = self.conf['paths']['all_signals']
shot_list = ShotList()
shot_list.load_from_shot_list_files_objects(shot_files,all_signals)
shot_list_picked = shot_list.random_sublist(use_shots)
#empty
used_shots = ShotList()
use_cores = max(1,mp.cpu_count()-2)
pool = mp.Pool(use_cores)
print('running in parallel on {} processes'.format(pool._processes))
start_time = time.time()
for (i,shot) in enumerate(pool.imap_unordered(self.preprocess_single_file,shot_list_picked)):
#for (i,shot) in enumerate(map(self.preprocess_single_file,shot_list_picked)):
sys.stdout.write('\r{}/{}'.format(i,len(shot_list_picked)))
used_shots.append_if_valid(shot)
pool.close()
pool.join()
print('Finished Preprocessing {} files in {} seconds'.format(len(shot_list_picked),time.time()-start_time))
print('Omitted {} shots of {} total.'.format(len(shot_list_picked) - len(used_shots),len(shot_list_picked)))
print('{}/{} disruptive shots'.format(used_shots.num_disruptive(),len(used_shots)))
if len(used_shots) == 0:
print("WARNING: All shots were omitted, please ensure raw data is complete and available at {}.".format(self.conf['paths']['signal_prepath']))
return used_shots
def correction_factor(p, number_of_runs, method, X, y, n_jobs=None):
# Setup parallel job
if n_jobs == -1:
n_jobs = cpu_count()
elif n_jobs == None:
n_jobs = 1
pool = Pool(n_jobs, maxtasksperchild=1000)
def run(_):
# Artificially falsify
y_f = falsify(y, p, random_state=_)
# Correct labels
y_corrected = method.fit_transform(X, y_f)
N = X.shape[0]
return ((y == y_corrected).sum() - (1 - p) * N) / (p * N)
factor = np.array(pool.map(run, range(number_of_runs)))
# Close the pool again
pool.close()
pool.join()
pool.clear()
return np.mean(factor), np.std(factor)
def bin_func(raster_folder, raster_filename, output_folder, binary_functions, binary_args):
start_time = datetime.now()
input_raster = os.path.join(raster_folder, raster_filename)
hdr_file = "" # input_raster + ".hdr" # only used for ENVI stacks
outname = os.path.join(output_folder, raster_filename)
if outname.find(".tif") != -1:
outname = outname[0:len(outname) - 4]
# arr: full size numpy array 3D XxYxZ 200x300x100
arr = preprocessing.rio_array(input_raster, hdr_file=hdr_file)
# activate to get list of dates from .hdr file (.hdr file needs to be specified above)
dates = arr[1]
for i, func in enumerate(binary_functions):
# threshold_size = str(statistical_args[i]['threshold_size'])
# creating results with calling wanted algorithm in parallel_apply_along_axis for quick runtime
result = apply_along_axis.parallel_apply_along_axis(func1d=func, arr=arr[0], axis=0,
cores=mp.cpu_count(), **binary_args[i])
# selecting dtype based on result
dtype = type(result[0][0])
func_name_end = str(func).find(" at")
func_name_start = 10
func_name = str(func)[func_name_start:func_name_end]
# exporting result to new raster
export_arr.functions_out_array(outname=outname + "_" + func_name + str(17), arr=result, input_file=input_raster,
dtype=dtype)
# print time to this point
statistics_time = datetime.now()
print("breakpoint-time = ", statistics_time - start_time, "Hr:min:sec")
def _on_done(artifacts, graph, node, done, rem, value, apf=False, **kwargs):
done.add(node)
artifacts[node] = value
frontier = rem | set(graph[node])
batch = {n for n in frontier if not _pendencies(graph, n, done)}
rem = frontier - batch
if not batch:
return
pool = mp.Pool(processes=min(max(1, mp.cpu_count() - 1), len(batch)))
each(
lambda node: pool.apply_async(
_resolve,
args=(node, artifacts),
callback=partial(
_on_done, artifacts, graph, node, done, rem, apf=apf, **kwargs
),
),
batch,
)
pool.close()
pool.join()
def _build_async(artifacts, apf=False, processes=None):
graph = u.to_graph(artifacts)
frontier = u.initial(graph)
if not frontier:
return {}
done, rem = set(), set()
if processes is None:
processes = min(max(1, mp.cpu_count() - 1), len(frontier))
pool = mp.Pool(processes=processes)
each(
lambda node: pool.apply_async(
partial(_resolve, apf=apf),
args=(node, artifacts),
callback=partial(
_on_done, artifacts, graph, node, done, rem, apf=apf),
),
frontier,
)
pool.close()
pool.join()
artifacts.update({n: _resolve(n, artifacts, apf=apf) for n in rem})
return artifacts
def clear_stack(cls):
cpu = cpu_count()
pool = Pool(cpu)
def classify_set(c, pd, pc):
pd = Directory.classify_product_auto(config, pd)
# if product.part_id:
pc.product = pd
Directory.set_price(pc)
# else:
# manuals.append((config, product, price))
# manuals = []
for config, product, price in cls.STACK:
pool.apply_async(classify_set, args=(config, product, price))
# for config, product, price in manuals:
# product = Directory.classify_product_manual(config, product)
# if product.part_id:
# price.product = product
# Directory.set_price(price)
# else:
# Directory.set_product(product)
cls.STACK = []
def construct_adjacency(fips_data,
filename=os.path.join(resourceDir,
'fips_2019_adj.pkl.gz')):
"""
Creates, and then stores (or loads) the adjacency dictionary of all US counties and territorial units. If the storage file, which is by default :download:`fips_2019_adj.pkl.gz </_static/gis/fips_2019_adj.pkl.gz>`, does not exist, then will create and store this data into the storage file. Will return the data in the end.
:param dict fips_data: the US county :py:class:`dict` produced by, for example, :py:meth:`create_and_store_fips_2018 <covid19_stats.engine.gis.create_and_store_fips_2018>`.
:param str filename: the location of the adjacency dictionary file, which is by default :download:`fips_2019_adj.pkl.gz </_static/gis/fips_2019_adj.pkl.gz>` located in the ``covid19_stats`` resource directory.
:returns: a :py:class:`dict` of adjacency. Each key is a `FIPS code`_ of a county, and each value is a :py:class:`set` of counties and other territories adjacent to it. See :py:meth:`get_fips_adjacency <covid19_stats.engine.gis.get_fips_adjacency>` to see an example of this adjacency information for a single county.
:rtype: dict
"""
if os.path.isfile(filename):
return pickle.load(gzip.open(filename, 'rb'))
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
all_adj = dict(
map(lambda fips: (fips, get_fips_adjacency(fips, fips_data)),
fips_data))
set_of_adjacents = set(
chain.from_iterable(
map(
lambda fips: map(
lambda fips2: tuple(sorted([fips, fips2])), all_adj[
fips]), all_adj)))
if filename is not None:
pickle.dump(set_of_adjacents, gzip.open(filename, 'wb'))
return set_of_adjacents
def map_list_as_chunks(l, f, extra_data, cpus=None, max_chunk_size=None):
'''
A wrapper around `pathos.multiprocessing.ProcessPool.uimap` that processes a list in chunks.
Differs from `map_list_in_chunks` in that this method calls `f` once for each chunk.
uimap already chunks but if you have extra data to pass in it will pickle
it for every item. This function passes in the extra data to each chunk
which significantly saves on pickling.
https://stackoverflow.com/questions/53604048/iterating-the-results-of-a-multiprocessing-list-is-consuming-large-amounts-of-me
Parameters
----------
l : list
the list
f : function
the function to process each item
takes two parameters: chunk, extra_data
extra_data : object
the extra data to pass to each f
cpus : int
the number of cores to use to split the chunks across
max_chunk_size : int
the maximum size for each chunk
'''
cpus = cpu_count() if cpus is None else cpus
max_chunk_size = float('inf') if max_chunk_size is None else max_chunk_size
chunk_length = min(max_chunk_size, max(1, ceil(len(l) / cpus)))
chunks = [l[x:x + chunk_length] for x in range(0, len(l), chunk_length)]
pool = Pool(nodes=cpus)
f_dumps = cloudpickle.dumps(f)
tuples = [(chunk, f_dumps, extra_data) for chunk in chunks]
return pool.map(_process_whole_chunk, tuples)
def preprocess_from_files(self,shot_files,use_shots):
#all shots, including invalid ones
all_signals = self.conf['paths']['all_signals']
shot_list = ShotList()
shot_list.load_from_shot_list_files_objects(shot_files,all_signals)
shot_list_picked = shot_list.random_sublist(use_shots)
#empty
used_shots = ShotList()
use_cores = max(1,mp.cpu_count()-2)
pool = mp.Pool(use_cores)
print('running in parallel on {} processes'.format(pool._processes))
start_time = time.time()
for (i,shot) in enumerate(pool.imap_unordered(self.preprocess_single_file,shot_list_picked)):
#for (i,shot) in enumerate(map(self.preprocess_single_file,shot_list_picked)):
sys.stdout.write('\r{}/{}'.format(i,len(shot_list_picked)))
used_shots.append_if_valid(shot)
pool.close()
pool.join()
print('Finished Preprocessing {} files in {} seconds'.format(len(shot_list_picked),time.time()-start_time))
print('Omitted {} shots of {} total.'.format(len(shot_list_picked) - len(used_shots),len(shot_list_picked)))
print('{}/{} disruptive shots'.format(used_shots.num_disruptive(),len(used_shots)))
if len(used_shots) == 0:
print("WARNING: All shots were omitted, please ensure raw data is complete and available at {}.".format(self.conf['paths']['signal_prepath']))
return used_shots
def _movie_casedeaths( msas_or_conus, dirname, time0, type_disp = 'cases' ):
all_msas = set(map(lambda msa_or_conus: msa_or_conus.lower( ), msas_or_conus))
with ThreadPool(processes = min(8, cpu_count( ), len( all_msas ) ) ) as pool:
_ = list(pool.map(lambda msa_or_conus: _movie_casedeaths_metro_or_conus(
msa_or_conus, dirname, time0, type_disp = type_disp ), all_msas ) )
logging.info( 'at %0.3f seconds to create all %d movies of %s.' % (
time.time( ) - time0, len( all_msas ), type_disp.upper( ) ) )
def parallel_search(self, query):
self.parallel = 1
processes = cpu_count()
data_set_split = []
query_filename = "query"
query_bytes = objectToBytes(query, self.predinstance.group)
for j in range(processes):
start = ceil(j * self.num_records / processes)
end = ceil((j + 1) * self.num_records / processes)
if end > self.num_records:
end = self.num_records
data_set_split.append((start, end))
overall_return_list = []
(matrix_str, generator_bytes) = self.serialize_key()
with Pool(processes) as p:
with concurrent.futures.ProcessPoolExecutor(processes) as executor:
future_list = {
executor.submit(self.augment_search, self.vector_length, self.predicate_scheme, self.group_name,
matrix_str, generator_bytes, query_bytes, self.public_parameters, start,
end)
for (start, end) in data_set_split
}
for future in concurrent.futures.as_completed(future_list):
res = future.result()
if res is not None and len(res) > 0:
overall_return_list = overall_return_list + res
return overall_return_list
def _summarize( msas_or_conus, dirname, time0 ):
all_msas = set(map(lambda msa_or_conus: msa_or_conus.lower( ), msas_or_conus))
with ThreadPool(processes = min(8, cpu_count( ), len( all_msas ) ) ) as pool:
_ = list(pool.map(lambda msa_or_conus: _summarize_metro_or_conus(
msa_or_conus, dirname, time0 ), all_msas ) )
logging.info( 'at %0.3f seconds to create all %d summaries.' % (
time.time( ) - time0, len( all_msas ) ) )
def runLGB(train_X, train_y, test_X, test_y, test_X2, label, dev_index,
val_index):
d_train = lgb.Dataset(train_X, label=train_y)
d_valid = lgb.Dataset(test_X, label=test_y)
watchlist = [d_train, d_valid]
params = {
'learning_rate': 0.05,
'application': 'binary',
'num_leaves': 31,
'verbosity': -1,
'metric': 'auc',
'data_random_seed': 3,
'bagging_fraction': 1.0,
'feature_fraction': 0.4,
'nthread': min(mp.cpu_count() - 1, 6),
'lambda_l1': 1,
'lambda_l2': 1
}
rounds_lookup = {
'toxic': 1400,
'severe_toxic': 500,
'obscene': 550,
'threat': 380,
'insult': 500,
'identity_hate': 480
}
model = lgb.train(params,
train_set=d_train,
num_boost_round=rounds_lookup[label],
valid_sets=watchlist,
verbose_eval=10)
print(model.feature_importance())
pred_test_y = model.predict(test_X)
pred_test_y2 = model.predict(test_X2)
return pred_test_y, pred_test_y2
def ProcessFrag( f ):
with file_handle(f) as fi:
Tmp = [l.rstrip() for l in fi]
Frags = Tmp[10:] # remove first 10 lines, which are just file formatting
del Tmp
mpi = multiprocessing.Pool(processes=multiprocessing.cpu_count())
start = time.perf_counter()
# Tmp = [frag(x) for x in tqdm(Frags, total=len(Frags))]
Tmp = [x for x in tqdm(mpi.imap(read_in_json, Frags, chunk),total=len(Frags))]
mpi.close()
mpi.join()
end = time.perf_counter()
print((end-start)/1000, 'ms')
Tmp2 = [frag for frag in Tmp if frag is not None]
Tmp3 = list(itertools.chain.from_iterable(Tmp2)) # flatten nested lists
FgSele = list(set(tuple(Tmp3))) # select unique smiles
del Tmp
del Tmp2
del Tmp3
del Frags
gc.collect()
return FgSele
def get_n_cpu():
try:
available_cpus = int(getenv('SLURM_NTASKS'))
except:
available_cpus = int(pp.cpu_count() / 2)
return available_cpus
def evaluate_summarizer(self, parsers, **kwargs):
"""
:param parsers: list
List stores newssum.parser.StoryParser.
:param kwargs:
See below
:Keyword Arguments:
* *w_threshod* (``int``)
word length threshold
* *s_threshod* (``int``)
sentence length threshold
"""
print("Evaluating summarizer...")
p = multiprocessing.ProcessingPool(multiprocessing.cpu_count() - 2)
def get_rouges(parser):
print("Get rouges...")
print(parser)
selected_sents = self.get_best_sents(parser, **kwargs)
rouge = Rouge(selected_sents, parser.highlights).get_rouge()
return rouge
rouges = p.map(get_rouges, parsers)
# p.close()
# p.join()
avg_rouge = Rouge.cal_avg_rouge(rouges)
Rouge.print("InfoFilter", avg_rouge)
def single_show_summary_dataframe( df_sub, showname, mode_dataformat = DATAFORMAT.IS_LATER ):
df_show = df_sub[ df_sub.shows == showname ].copy( )
assert( len( df_show ) != 0 )
if mode_dataformat == DATAFORMAT.IS_AVI_OR_MPEG:
return df_show
#
def get_ffprobe_json( filename ):
stdout_val = subprocess.check_output(
[ _ffprobe_exec, '-v', 'quiet', '-show_streams',
'-show_format', '-print_format', 'json', filename ],
stderr = subprocess.STDOUT )
file_info = json.loads( stdout_val )
return file_info
def is_hevc( filename ):
data = get_ffprobe_json( filename )
return data['streams'][0]['codec_name'].lower( ) == 'hevc'
with Pool( processes = min( cpu_count( ), len( df_show ) ) ) as pool:
dict_of_episodes_hevc = dict( pool.map(
lambda filename: ( filename, is_hevc( filename ) ),
list( df_show.paths ) ) )
df_show[ 'is hevc' ] = list(map(lambda filename: dict_of_episodes_hevc[ filename ],
list( df_show.paths ) ) )
return df_show
def direct(self):
def browse_each(config):
log.info(Directory.INFO_MAP[0] % (self.market.name, config.name))
try:
map_strs = self.PRODUCT_MAP[config.name]
for map_str in map_strs:
results = self.get_products_prices(map_str)
for product, price in results:
product = Directory.check_product(product)
if not product.id:
Directory.STACK.append((config, product, price))
elif product.part_id:
price.product = product
Directory.set_price(price)
except KeyError:
log.error(Directory.ERROR_MAP[1] % config.name)
cpu = cpu_count()
pool = _ThreadPool(cpu)
for c in self.configs:
pool.apply_async(browse_each, args=(c, ))
pool.close()
pool.join()
def __init__(self,
journal_paths_dict,
feature_generation_instance,
n_cores=-1):
"""
Class constructor
:param mypath: system path to the folder with the files to be be organized
"""
if isinstance(journal_paths_dict, dict):
self._mypaths = journal_paths_dict.keys()
self._myjournals = journal_paths_dict.values()
self._mydict = journal_paths_dict
else:
print 'Input must be a dictionary, with the keys being the journal and the values being the respective ' \
'paths'
raise ValueError
self._feat_gen = feature_generation_instance
if n_cores == 1:
self._n_cores = 1
self._pool = None
elif n_cores == -1:
self._n_cores = mp.cpu_count()
self._pool = mp.ProcessingPool(self._n_cores)
else:
self._n_cores = n_cores
self._pool = mp.ProcessingPool(self._n_cores)
def DomainDistances(Ref_Coords, PDB_Coords, RefReg2, Reg2, Data, parm, output):
# Input_Coords = [pdb_name, H_Crds, N_Crds, C_Crds, G_Crds, R_Crds, T_Crds]
# x_Coords = [resname, resid, bb_crds, ca_crd, cg_crd, avg_crd, cb_crd]
print(
'##################################################################\n')
# Create distance object for MPI
Ref = CalculateDist([Ref_Coords, RefReg2])
if parm['MPICPU'][0] == 1:
Tmp = [
CalculateDist([Tgt, Reg2[idx]])
for idx, Tgt in enumerate(PDB_Coords)
]
else:
if parm['MPICPU'][0] == 0:
mpi_cpu = multiprocessing.cpu_count()
else:
mpi_cpu = parm['MPICPU'][0]
mpi = multiprocessing.Pool(mpi_cpu)
Tmp = [
x
for x in tqdm(mpi.imap(CalculateDist, list(zip(PDB_Coords, Reg2))),
total=len(Reg2))
]
mpi.close()
mpi.join()
Tgt_List = [x for x in Tmp if x is not None]
print('\n ## Domain Distances return: {0}\n'.format(len(Tgt_List)))
CollectDomain(Ref, Tgt_List, Data)
def map_list_in_chunks(l, f, extra_data):
'''
A wrapper around ProcessPool.uimap that processes a list in chunks.
Differs from `map_list_as_chunks` in that this method calls `f` once for each item in `l`.
uimap already chunks but if you have extra data to pass in it will pickle
it for every item. This function passes in the extra data to each chunk
which significantly saves on pickling.
https://stackoverflow.com/questions/53604048/iterating-the-results-of-a-multiprocessing-list-is-consuming-large-amounts-of-me
Parameters
----------
l : list
the list
f : function
the function to process each item
takes two parameters: item, extra_data
extra_data : object
the extra data to pass to each f
'''
cpus = cpu_count()
chunk_length = max(1, int(len(l) / cpus))
chunks = [l[x:x + chunk_length] for x in range(0, len(l), chunk_length)]
pool = Pool(nodes=cpus)
f_dumps = cloudpickle.dumps(f)
tuples = [(chunk, f_dumps, extra_data) for chunk in chunks]
mapped_chunks = pool.map(_process_chunk, tuples)
return (item for chunk in mapped_chunks for item in chunk)
def relative_flammability( firescar_list, output_filename, ncores=None, mask_arr=None, mask_value=None, crs=None ):
'''
run relative flammability.
Arguments:
firescar_list = [list] string paths to all GeoTiff FireScar outputs to be processed
output_filename = [str] path to output relative flammability filename to be generated.
* only GTiff supported. *
ncores = [int] number of cores to use if None multiprocessing.cpu_count() used.
mask_arr = [numpy.ndarray] numpy ndarray with dimensions matching the rasters' arrays
listed in firescar_list and masked where 1=dontmask 0=mask (this is opposite
numpy mask behavior, but follows common GIS patterns ) * THIS MAY CHANGE. *
mask_value = [numeric] single numeric value determining the value of the newly masked out
regions. If None, the nodata value from the firescar outputs will be used and
if this is an invalid value, it will be set to -9999.
crs=[dict] rasterio-compatible crs dict object i.e.: {'init':'epsg:3338'}
Returns:
output_filename, with the side effect of the relative flammability raster being written to
disk in that location.
'''
tmp_rst = rasterio.open( firescar_list[0] )
if ncores == None:
ncores = multiprocessing.cpu_count() - 1
out = sum_firescars( firescar_list, ncores=ncores )
# calculate the relative flammability -- and fill in the mask with -9999
relative_flammability = ( out.astype( np.float32 ) / len( firescar_list ) ).filled()
if mask_value == None:
mask_value = tmp_rst.nodata
if mask_value == None or mask_value == '':
print( 'setting mask_value to -9999')
mask_value = -9999
if mask_arr:
relative_flammability[ mask_arr == 0 ] = mask_value
meta = tmp_rst.meta
# pop out transform to overcome warning
if 'transform' in meta.keys():
_ = meta.pop( 'transform' )
meta.update( compress='lzw', count=1, dtype='float32', nodata=mask_value )
if crs:
meta.update( crs=crs )
try:
dirname = os.path.dirname( output_filename )
if not os.path.exists( dirname ):
os.makedirs( dirname )
except:
pass
with rasterio.open( output_filename, 'w', **meta ) as out_rst:
out_rst.write( np.around( relative_flammability, 4 ), 1 )
return output_filename
def __init__(self,
directory=os.getcwd(),
model_file=os.path.join(os.getcwd(), 'model'),
classifierType='gradientboosting',
verbose=False,
num_threads=mp.cpu_count()):
self.directory = directory
self.model_file = model_file
self.classifierType = classifierType
self.verbose = verbose
self.num_threads = num_threads
try:
db = MySQLdb.connect(host=host,
user=user,
passwd=passwd,
db=database)
except _mysql_exceptions.OperationalError, e:
if e[0] != 1049:
raise
else:
with MySQLdb.connect(host=host,
user=user,
passwd=passwd,
db='') as cur:
cur.execute("CREATE DATABASE %s;" % database)
def bootstrap(data, fun, n_resamples=10000, alpha=0.05):
"""Compute confidence interval for values of function fun
Parameters
==========
data: list of arguments to fun
"""
assert isinstance(data, list)
n_samples = len(data[0])
idx = np.random.randint(0, n_samples, (n_resamples, n_samples))
def select(data, sample):
return [d[sample] for d in data]
def evaluate(sample):
return fun(*select(data, sample))
pool = multiprocessing.Pool(multiprocessing.cpu_count())
values = pool.map(evaluate, idx)
pool.terminate()
idx = idx[np.argsort(values, axis=0, kind='mergesort')]
values = np.sort(values, axis=0, kind='mergesort')
stat = namedtuple('stat', ['value', 'index'])
low = stat(value=values[int((alpha/2.0)*n_resamples)],
index=idx[int((alpha/2.0)*n_resamples)])
high = stat(value=values[int((1-alpha/2.0)*n_resamples)],
index=idx[int((1-alpha/2.0)*n_resamples)])
return low, high
def encrypt_dataset_parallel(self, data_set):
self.parallel = 1
for data_item in data_set:
if len(data_item) != self.vector_length:
raise ValueError("Improper Vector Size")
self.enc_data = {}
processes = cpu_count()
data_set_split = []
data_set_len = len(data_set)
self.num_records = data_set_len
for j in range(processes):
start = ceil(j * data_set_len / processes)
end = ceil((j + 1) * data_set_len / processes)
if end > data_set_len:
end = data_set_len
data_set_split.append((start, end, data_set[start:end]))
total_data_size = 0
(matrix_str, generator_bytes) = self.serialize_key()
with Pool(processes) as p:
with concurrent.futures.ProcessPoolExecutor(processes) as executor:
future_list = {executor.submit(self.augment_encrypt, self.vector_length, self.predicate_scheme,
self.group_name, matrix_str, generator_bytes,
self.public_parameters, data_set_component, start, end)
for (start, end, data_set_component) in data_set_split
}
for future in concurrent.futures.as_completed(future_list):
res = future.result()
if res is not None:
total_data_size = total_data_size + res
self.enc_data_size = total_data_size
def createPNGPicObjects(cls, pImgClient):
"""
:param PlexIMGClient pImgClient: the :py:class:`PlexIMGClient <nprstuff.npremail.email_imgur.NPRStuffIMGClient>` used to access and manipulate (add, delete, rename) images in the main Imgur_ album.
:returns: a :py:class:`list` of :py:class:`PNGPicObject <nprstuff.npremail.PNGPicObject>` representing the images in the main Imgur_ album.
:rtype: list
"""
pngPICObjects = []
def _create_object(imgMD5):
imgName, imgID, imgurlLink, imgDateTime = pImgClient.imghashes[
imgMD5]
try:
newObj = PNGPicObject(
{
'initialization': 'SERVER',
'imgurlLink': imgurlLink,
'imgName': imgName,
'imgMD5': imgMD5,
'imgDateTime': imgDateTime
}, pImgClient)
return newObj
except:
return None
with multiprocessing.Pool(
processes=multiprocessing.cpu_count()) as pool:
#
## doesn't work with multiprocessing for some reason...
pngPICObjects = list(
filter(None, map(_create_object, pImgClient.imghashes)))
return pngPICObjects
def calculatePearsonCorrelationMatrixMultiprocessing(matrix, axis=0, symmetrical=True, getpvalmat=False):
if axis == 1:
matrix = matrix.T
nRows = matrix.shape[0]
# create shared array that can be used from multiple processes
output_r_arr = Array(ctypes.c_double, matrix.shape[0] * matrix.shape[0])
# then in each new process create a new numpy array using:
output_r = np.frombuffer(output_r_arr.get_obj()) # mp_arr and arr share the same memory
# make it two-dimensional
output_r = output_r.reshape((matrix.shape[0], matrix.shape[0])) # b and arr share the same memory
# output_r = np.zeros((nRows,nRows)) # old version
output_p_arr = Array(ctypes.c_double, matrix.shape[0] * matrix.shape[0])
output_p = np.frombuffer(output_p_arr.get_obj())
output_p = output_p.reshape((matrix.shape[0], matrix.shape[0]))
print 'Calculating Pearson R for each row, multithreaded'
print mp.cpu_count(), 'processes in pool'
pool = None
try:
pool = mp.Pool(mp.cpu_count(),
initializer=_init_pool,
initargs=(matrix, output_r_arr, output_p_arr,
nRows, symmetrical))
# bar = tqdm(total=nRows*nRows/2)
# tqdm.write('Calculating Pearson R for each row, multithreaded')
for result in tqdm(pool.imap_unordered(_f, range(0, nRows)), total=nRows):
# bar.update(result)
pass
# bar.close()
finally: # To make sure processes are closed in the end, even if errors happen
pool.close()
pool.join()
print output_r
if getpvalmat:
return output_r, output_p
else:
return output_r
def _check_threads(self, message_end=None):
"""
Checks number of threads
:param message_end: closing part of the error message.
"""
if PATHOS_FOUND:
threads = AbinsModules.AbinsParameters.threads
if not (isinstance(threads, six.integer_types) and 1 <= threads <= mp.cpu_count()):
raise RuntimeError("Invalid number of threads for parallelisation over atoms" + message_end)
def _calc_num_threads(self, df_size: int, query_size: int) -> int:
num_queries = df_size * query_size
if mp.cpu_count() == 1:
max = 1
else:
max = mp.cpu_count() - 1
calc = int(num_queries / 5000)
if calc > max:
r = max
elif calc <= 1:
if num_queries > 1000:
r = 2
else:
r = 1
else:
r = calc
return r
def parallelmap(func, data, nodes = None):
"""
Return the averaged signal and background (based on blank frames) over the given runs
"""
if not nodes:
nodes = multiprocessing.cpu_count() - 2
pool = ProcessingPool(nodes=nodes)
try:
return pool.map(func, data)
except KeyboardInterrupt:
pool.terminate()
pool.join()
def parallelmap(func, lst, nodes = None):
"""
Return the averaged signal and background (based on blank frames) over the given runs using
multiprocessing (as opposed to MPI).
"""
from pathos.multiprocessing import ProcessingPool
from pathos import multiprocessing
if not nodes:
nodes = multiprocessing.cpu_count() - 2
pool = ProcessingPool(nodes=nodes)
try:
return pool.map(func, lst)
except KeyboardInterrupt:
pool.terminate()
pool.join()
def make_predictions(conf,shot_list,loader):
loader.set_inference_mode(True)
use_cores = max(1,mp.cpu_count()-2)
if backend == 'tf' or backend == 'tensorflow':
first_time = "tensorflow" not in sys.modules
if first_time:
import tensorflow as tf
os.environ['KERAS_BACKEND'] = 'tensorflow'
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto(device_count={"CPU":use_cores})
set_session(tf.Session(config=config))
else:
os.environ['THEANO_FLAGS'] = 'device=cpu'
import theano
from plasma.models.builder import ModelBuilder
specific_builder = ModelBuilder(conf)
y_prime = []
y_gold = []
disruptive = []
model = specific_builder.build_model(True)
model.compile(optimizer=optimizer_class(),loss=conf['data']['target'].loss)
specific_builder.load_model_weights(model)
model_save_path = specific_builder.get_latest_save_path()
start_time = time.time()
pool = mp.Pool(use_cores)
fn = partial(make_single_prediction,builder=specific_builder,loader=loader,model_save_path=model_save_path)
print('running in parallel on {} processes'.format(pool._processes))
for (i,(y_p,y,is_disruptive)) in enumerate(pool.imap(fn,shot_list)):
print('Shot {}/{}'.format(i,len(shot_list)))
sys.stdout.flush()
y_prime.append(y_p)
y_gold.append(y)
disruptive.append(is_disruptive)
pool.close()
pool.join()
print('Finished Predictions in {} seconds'.format(time.time()-start_time))
loader.set_inference_mode(False)
return y_prime,y_gold,disruptive
def __init__(self, logger, directory, urls, max_cores = 0):
self.logger = logger # default logger
self.directory = directory # working directory
self.urls = urls # service URLs
self.results = [] # parallel execution results
self.returnVal = {} # final execution results
self.task_list =[] # parallel task list
#self.clearCommonParams()
# user defined parameters
self.common_params = None
self.method_params = None
self.num_threads = None
self.num_jobs = None
#self.callback_url = os.environ['SDK_CALLBACK_URL']
# default parameters
self.num_cores = mp.cpu_count()
self.max_cores = max_cores
def bootstrap(data, fun, n_resamples=10000, alpha=0.05):
"""
Compute confidence interval for values of function fun on data_preprocess_scripts
Args:
data : list of numpy arrays
Each numpy array will be subsampled and then passed to fun.
fun :
Function taking len(data_preprocess_scripts) numpy arrays -> float
n_resamples : int, 10000 is default
Number of times to resample data_preprocess_scripts to produce intervals.
alpha : float, 0.5 is default
Confidence level parameter for confidence intervals.
"""
assert isinstance(data, list)
n_samples = len(data[0])
idx = np.random.randint(0, n_samples, (n_resamples, n_samples))
def select(data, sample):
return [d[sample] for d in data]
def evaluate(sample):
return fun(*select(data, sample))
pool = multiprocessing.Pool(multiprocessing.cpu_count())
values = pool.map(evaluate, idx)
pool.terminate()
idx = idx[np.argsort(values, axis=0, kind='mergesort')]
values = np.sort(values, axis=0, kind='mergesort')
stat = namedtuple('stat', ['value', 'index'])
low = stat(
value=values[int((alpha / 2.0) * n_resamples)],
index=idx[int((alpha / 2.0) * n_resamples)])
high = stat(
value=values[int((1 - alpha / 2.0) * n_resamples)],
index=idx[int((1 - alpha / 2.0) * n_resamples)])
return low, high
def train_on_files(self,shot_files,use_shots,all_machines):
conf = self.conf
all_signals = conf['paths']['all_signals']
shot_list = ShotList()
shot_list.load_from_shot_list_files_objects(shot_files,all_signals)
shot_list_picked = shot_list.random_sublist(use_shots)
previously_saved,machines_saved = self.previously_saved_stats()
machines_to_compute = all_machines - machines_saved
recompute = conf['data']['recompute_normalization']
if recompute:
machines_to_compute = all_machines
previously_saved = False
if not previously_saved or len(machines_to_compute) > 0:
if previously_saved:
self.load_stats()
print('computing normalization for machines {}'.format(machines_to_compute))
use_cores = max(1,mp.cpu_count()-2)
pool = mp.Pool(use_cores)
print('running in parallel on {} processes'.format(pool._processes))
start_time = time.time()
for (i,stats) in enumerate(pool.imap_unordered(self.train_on_single_shot,shot_list_picked)):
#for (i,stats) in enumerate(map(self.train_on_single_shot,shot_list_picked)):
if stats.machine in machines_to_compute:
self.incorporate_stats(stats)
self.machines.add(stats.machine)
sys.stdout.write('\r' + '{}/{}'.format(i,len(shot_list_picked)))
pool.close()
pool.join()
print('Finished Training Normalizer on {} files in {} seconds'.format(len(shot_list_picked),time.time()-start_time))
self.save_stats()
else:
self.load_stats()
print(self)
def calculate_centroids(self, p=None):
"""
Perform integration to find centroid at all turns up to N. Multiprocessing pool used to calculate independent
turn values.
Will automatically use `integrate_first_order` or `integrate_second_order` if appropriate.
Args:
p: Specify number of processes for pool. If not given then `cpu_count` is used.
Returns:
array of floats
"""
if p:
pool_size = p
else:
pool_size = cpu_count()
pool = Pool(pool_size)
# attempt to speed things up by spreading out difficult integration values at the end of range
# appeared to not work
# x = []
# for i in range(cpu_count()):
# x += range(N)[i::4]
if len(self.mu) == 1:
integration_function = self.integrate_first_order
elif len(self.mu) == 2:
integration_function = self.integrate_second_order
else:
integration_function = self.integrate_any_order
x = range(self.N)
results = pool.map(integration_function, x)
pool.close()
return results
def __init__(self,process):
self.size = mp.cpu_count()
self.process = process
self.phase=None
self.pool = mp.ProcessingPool(mp.cpu_count())
def populate(centres, masses, halomodel=None, profile=None, hodmod=None, edges=None):
"""
Populate a series of DM halos with galaxies given a HOD model.
Parameters
----------
centres : (N,3)-array
The cartesian co-ordinates of the centres of the halos
masses : array_like
The masses (in M_sun/h) of the halos
halomodel : type :class:`halomod.HaloModel`
A HaloModel object pre-instantiated. One can either use this, or
*both* `profile` and `hodmod` arguments.
profile : type :class:`profile.Profile`
A density profile to use.
hodmod : object of type :class:`hod.HOD`
A HOD model to use to populate the dark matter.
edges : float, len(2) iterable, or (2,3)-array
Periodic box edges. If float, defines the upper limit of cube, with lower limit at zero.
If len(2) iterable, defines edges of cube.
If (2,3)-array, specifies edges of arbitrary rectangular prism.
Returns
-------
pos : array
(N,3)-array of positions of galaxies.
halo : array
(N)-array of associated haloes (by index)
H : int
Number of central galaxies. The first H galaxies in pos/halo correspond to centrals.
"""
if halomodel is not None:
profile = halomodel.profile
hodmod = halomodel.hod
masses = np.array(masses)
# Define which halos have central galaxies.
cgal = np.random.binomial(1, hodmod.nc(masses))
cmask = cgal > 0
central_halos = np.arange(len(masses))[cmask]
if hodmod._central:
masses = masses[cmask]
centres = centres[cmask]
# Calculate the number of satellite galaxies in halos
# Using _ns, rather than ns, gives the correct answer for both central condition and not.
# Note that other parts of the algorithm also need to be changed if central condition is not true.
# if hodmod._central:
# sgal = poisson.rvs(hodmod._ns(masses[cmask]))
# else:
sgal = poisson.rvs(hodmod._ns(masses))
# Get an array ready, hopefully speeds things up a bit
ncen = np.sum(cgal)
nsat = np.sum(sgal)
pos = np.empty((ncen + nsat, 3))
halo = np.empty(ncen+nsat)
# Assign central galaxy positions
halo[:ncen] = central_halos
if hodmod._central:
pos[:ncen, :] = centres
else:
pos[:ncen, :] = centres[cmask]
smask = sgal > 0
# if hodmod._central:
# sat_halos = central_halos[np.arange(len(masses[cmask]))[smask]]
# else:
if hodmod._central:
sat_halos = central_halos[np.arange(len(masses))[smask]]
else:
sat_halos = np.arange(len(masses))[smask]
sgal = sgal[smask]
centres = centres[smask]
masses = masses[smask]
# Now go through each halo and calculate galaxy positions
start = time.time()
halo[ncen:] = np.repeat(sat_halos,sgal)
indx = np.concatenate(([0],np.cumsum(sgal))) + ncen
# print "SMASHING THIS NOW"
def fill_array(i):
m,n,ctr = masses[i], sgal[i],centres[i]
pos[indx[i]:indx[i+1],:] = profile.populate(n, m, ba=1, ca=1, centre=ctr)
if HAVE_POOL:
mp.ProcessingPool(mp.cpu_count()).map(fill_array,range(len(masses)))
else:
for i in range(len(masses)):
fill_array(i)
nhalos_with_gal = len(set(central_halos.tolist()+sat_halos.tolist()))
print "Took ", time.time() - start, " seconds, or ", (time.time() - start)/nhalos_with_gal, " each halo."
print "NhalosWithGal: ", nhalos_with_gal, ", Ncentrals: ", ncen,", NumGal: ", len(halo), ", MeanGal: ", float(
len(halo))/nhalos_with_gal, ", MostGal: ", sgal.max() + 1 if len(sgal)>0 else 1
if edges is None:
pass
elif np.isscalar(edges):
edges = np.array([[0, 0, 0], [edges, edges, edges]])
elif np.array(edges).shape == (2,):
edges = np.array([[edges[0]]*3, [edges[1]]*3])
if edges is not None:
for j in range(3):
d = pos[:, j] - edges[0][j]
pos[d < 0, j] = edges[1][j] + d[d < 0]
d = pos[:, j] - edges[1][j]
pos[d > 0, j] = edges[0][j] + d[d > 0]
return pos, halo.astype("int"), ncen
# parsing configuration file to import dir of cross-corr results
from seissuite.ant import (pscrosscorr, psutils)
process_type = "serial"
multiprocess = True
#import multiprocessing as mp
if multiprocess:
try:
import pathos.multiprocessing as mp
except:
import multiprocessing as mp
no_of_cores = int(mp.cpu_count())
process_type = "parallel"
# import CONFIG class initalised in ./configs/tmp_config.pickle
config_pickle = 'configs/tmp_config.pickle'
f = open(name=config_pickle, mode='rb')
CONFIG = pickle.load(f)
f.close()
# import variables from initialised CONFIG class.
MSEED_DIR = CONFIG.MSEED_DIR
DATABASE_DIR = CONFIG.DATABASE_DIR
DATALESS_DIR = CONFIG.DATALESS_DIR
def __init__(self, source, config, processes=cpu_count()):
self._pool = Pool(processes)
self._results = self._pool.imap(_merge_records, izip(source, repeat(config)))
def genseq(idx):
first = np.where(np.random.multinomial(1,pvals=pops)==1)[0][0]
last = first
last_ts = datetime.now()
result = {'artist_idx':[first],'ts':[last_ts]}
for i in xrange(seq_length-1):
next_listen = draw(last)
last = next_listen
gap_bin = 120*np.where(np.random.multinomial(1,pvals=td)==1)[0][0]
gap = np.random.randint(gap_bin,gap_bin+120)
result['artist_idx'].append(next_listen)
new_ts = last_ts+timedelta(0,gap)
result['ts'].append(new_ts)
last_ts = new_ts
df = pd.DataFrame(result)
df['block'] = ((df['artist_idx'].shift(1) != df['artist_idx']).astype(int).cumsum())-1
df.to_pickle(str(idx)+'.pkl')
logging.info('idx {} complete'.format(idx))
pool = Pool(cpu_count())
indices = range(n)
pool.map(genseq,indices)
pool.close()
# if os.path.isfile(filename):
# data = _merge(filename, data)
if data:
dill_save_obj(data, filename)
def load_finland_ids():
with open('finland_ids.csv', 'r') as f:
lines = f.readlines()
return map(lambda l: l.split(',')[0].strip(), lines)
if __name__ == '__main__':
chunk_size = pmp.cpu_count() - 2
id_list = load_finland_ids()
start_time = datetime(2013,11,1)
end_time = datetime(2013,11,02)
id_list_group = (lambda l, s=chunk_size: [l[i:i+s] for i in range(0, len(l), s)])(id_list)
print "chuck size : " + str(chunk_size) + " len(id_list_group) : " + str(len(id_list_group))
try:
for id_group in id_list_group:
p = pmp.Pool(chunk_size)
p.map(lambda fin_id: read_vtt_data(fin_id, start_time, end_time), id_group)
p.close()
p.join()
p.terminate()
print('done reading simulation cases')
df['sampled_std']=None
df['mse']=None
df['error_per']=None
df['pearson']=None
if 0:
'''removing non interesting cases'''
df=df[(df.quant_size*df.number_of_bins<15)&(df.quant_size*df.number_of_bins>6)]
df=df[df.quant_size*df.number_of_bins>8.9]
print('dropping non interesting cases - running only %d simulations'%df.shape[0])
df.reset_index(drop=True,inplace=True)
if not u.run_serial and u.number_of_splits==1:
'''multi cores'''
num_of_cpu = mp.cpu_count()
if 0:
print('disabling parallel work!!!!!')
num_of_cpu = 1
print('number of cpus: %d' % num_of_cpu)
p = mp.Pool(num_of_cpu)
args = [dict(df=df.iloc[inx], A_rows=A_rows, samples=u.samples) for inx in np.array_split(range(df.shape[0]), num_of_cpu)]
df[['sampled_std','mse','error_per','pearson']] = pd.concat(p.map(lambda y: y['df'].apply(lambda x:pd.Series(globals()[x.method](samples=y['samples'] ,quant_size=x.quant_size, std_threshold=x.std_threshold,number_of_bins=x.number_of_bins,A_rows=y['A_rows']),index=['sampled_std','mse','error_per','pearson']),axis=1),args))
else:
for inx in tqdm(np.array_split(range(df.shape[0]),100)):
df.loc[inx+df.index.values[0],['sampled_std','mse','error_per','pearson']]=df.iloc[inx].apply(lambda x:pd.Series(globals()[x.method](samples=u.samples ,quant_size=x.quant_size,number_of_bins=x.number_of_bins, std_threshold=x.std_threshold,A_rows=A_rows),index=['sampled_std','mse','error_per','pearson']),axis=1)
print(df.head())
df.to_csv('resutls_%08d.csv.gz'%u.number_of_split,compression='gzip')
# df.dropna(how='any',inplace=True)
# res=df.pivot_table(values='mse',columns=['number_of_bins','method'],index=['quant_size'],aggfunc=[np.mean,np.median])
# res.to_csv('7.2 resutls.csv')
def __init__(self, source, config, single_process=False, processes=cpu_count()):
if single_process:
self.results = imap(transform, izip(source, repeat(config)))
else:
self.pool = Pool(processes)
self.results = self.pool.imap(transform, izip(source, repeat(config)))
print(clock)
'''
# --------------------------------------- #
# This approach uses multiple cores #
from pathos.multiprocessing import ProcessingPool, cpu_count
if __name__ == '__main__': # This is essential if Used on windows!
clock = time() # starting time
# creates a worker pool from given comand line parameter. If the given
# parameter is to large all detectable CPUs will be utilised. If the given
# parameter is nonsense only 1 core will be utilized.
workers = 1
if len(sys.argv) >= 2 and sys.argv[1].isdigit() and int(sys.argv[1]) > 0:
workers = cpu_count()
if int(sys.argv[1]) <= workers:
workers = int(sys.argv[1])
print 'N: ' + str(N)
print 'PW: ' + str(workers)
sleep(3) # just 3 seconds pause to read the input again.
# All the magic happens here:
pool = ProcessingPool(workers)
Ys = pool.map(steadyState,y0)
clock = time()-clock # elapsed time
print 'Seconds: ' + str(clock) # Not essential but useful.
# Serilisation of results and stats:
def set_experiment(self, experiment, options, mstats, logbook, pset, toolbox, population, invalid_ind, hof, START_GEN=None, POP_SIZE=None, SEED=42):
if options:
self.options = options
if not self.re_positive_dataset:
print "Loading datasets"
self.load_dataset(self.options)
if experiment:
self.experiment = experiment
if hof:
self.hof = hof
else:
self.hof = deap.tools.ParetoFront(self.pareto_similar)
if START_GEN:
self.START_GEN = START_GEN
if POP_SIZE:
self.POP_SIZE = POP_SIZE
self.CXPB = options.cxpb
self.MUTPB = options.mutpb
if mstats:
self.mstats = mstats
else:
stats = {}
def lambda_factory(idx):
return lambda ind: ind.fitness.values[idx]
for tag in self.FITNESS_TAGS:
s = deap.tools.Statistics( key=lambda_factory(
self.FITNESS_TAGS.index(tag)
))
stats[tag] = s
#stats["Discrimination"] = deap.tools.Statistics(key=lambda ind: ind.fitness.values[0])
#stats["Support"] = deap.tools.Statistics(key=lambda ind: ind.fitness.values[1])
#stats_accuracy = deap.tools.Statistics(key=lambda ind: ind.fitness.values[0])
#stats_coverage = deap.tools.Statistics(key=lambda ind: ind.fitness.values[1])
self.mstats = deap.tools.MultiStatistics( **stats ) #accuracy=stats_accuracy, coverage=stats_coverage )
self.mstats.register("avg", numpy.mean, axis=0)
self.mstats.register("std", numpy.std , axis=0)
self.mstats.register("min", numpy.min , axis=0)
self.mstats.register("max", numpy.max , axis=0)
if logbook:
self.logbook = logbook
else:
self.logbook = deap.tools.Logbook()
#self.logbook.header = "gen", "evals", "hypervolume", "memoize"
self.logbook.header = "gen", "evals", "memoize"
self.logbook.header += tuple((self.FITNESS_TAGS)) #tuple(["accuracy","coverage"]) #
if pset:
self.pset = pset
if population:
self.population = population
if invalid_ind:
self.invalid_ind = invalid_ind
#creator.create("FitnessMax", base.Fitness, weights=self.FITNESS_WEIGHTS)
#creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax, pset=self.pset)
if toolbox:
self.toolbox = toolbox
else:
self.toolbox = deap.base.Toolbox()
if self.options.ncpu and ( self.options.ncpu == "auto" or int(self.options.ncpu) > 1):
"""
Check if PATHOS was loaded
"""
global PATHOS
if PATHOS:
if self.options.ncpu == "auto":
n_cpu = cpu_count()
else:
n_cpu = int(self.options.ncpu)
print "Using pathos.multiprocessing with", n_cpu, "cpus."
pool = Pool(n_cpu)
self.toolbox.register("map", pool.map)
else:
print "WARNING: Failed to load pathos; using a single processor."
""" Basic toolbox functions"""
self.toolbox.register("expr", deap.gp.genHalfAndHalf, pset=self.pset, type_=self.pset.ret, min_=1, max_=4)
self.toolbox.register("individual", deap.tools.initIterate, deap.creator.Individual, self.toolbox.expr)
self.toolbox.register("population", deap.tools.initRepeat, list, self.toolbox.individual)
self.toolbox.register("compile", deap.gp.compile, pset=self.pset)
# Set evaluation
if self.options.grammar == "pssm":
self.toolbox.register("evaluate", self.eval_pssm_match)
else:
try:
#self.toolbox.register("evaluate", self.memoize_pfm_match)
#self.toolbox.register("evaluate", self.eval_regex_match)
#self.toolbox.register("evaluate", self.memoize_regex_match)
#self.toolbox.register("evaluate", self.memoize_python_matcher)
if self.options.matcher == "grep":
print "Loading grep matcher"
self.test_grep()
self.toolbox.register("evaluate", self.memoize_grep_match)
elif self.options.matcher == "python" :
print "Loading Python matcher"
self.toolbox.register("evaluate", self.memoize_python_matcher)
else:
raise Exception("Unknown matching mode" + str(self.options.matcher))
print "Loaded."
except Exception as e:
print "Exception occured while setting matcher: ({0}): {1}".format( e.message, str(e.args))
print "Use --matcher=python as argument."
exit(-1)
#print "Falling back to python matcher"
#self.toolbox.register("evaluate", self.memoize_python_matcher)
# Add penalties
for penalty_feasible in self.FITNESS_PENALTIES:
if penalty_feasible is not None:
self.toolbox.decorate("evaluate",
deap.tools.DeltaPenality(penalty_feasible, (0.0,)*len(self.FITNESS_TAGS)))
#self.toolbox.register("evaluate", self.eval_pfm_match)
#self.toolbox.register("evaluate", self.eval_pfm_match)
#self.toolbox.register("evaluate", self.eval_precomputed_pfm_matches)
#self.toolbox.register("mate", deap.gp.cxOnePoint)
self.toolbox.register("mate", deap.gp.cxOnePointLeafBiased, termpb=0.1 )
self.toolbox.register("expr_mut", deap.gp.genGrow, min_=1, max_=4, pset=self.pset)
self.toolbox.register("mutate", deap.gp.mutUniform, expr=self.toolbox.expr_mut, pset=self.pset)
#self.toolbox.register("mutate", self.multimutate, pset=self.pset)
""" Static Limit for GP Tree """
MAX_HEIGHT = 17 # Koza
#MAX_HEIGHT = 8
#MAX_HEIGHT = 89
#MAX_HEIGHT = 25
self.toolbox.decorate("mate", deap.gp.staticLimit(operator.attrgetter('height'), MAX_HEIGHT))
self.toolbox.decorate("mutate", deap.gp.staticLimit(operator.attrgetter('height'), MAX_HEIGHT))
""" Seeding """
self.toolbox.register("init_seeds", self.initSeeds, deap.creator.Individual)
self.toolbox.register("init_seeded_population", self.initSeededPopulation, list, self.toolbox.init_seeds)
self.population = self.toolbox.population(self.POP_SIZE) #Random pop for algorithms that need a initial pop
self.toolbox.register("memoizecount", lambda: self.memoize_count)
if self.options.moo == "SPEA2":
self.toolbox.register("select", deap.tools.selSPEA2)
elif self.options.moo == "NSGA2":
self.toolbox.register("select", deap.tools.selNSGA2)
elif self.options.moo == "MOEAD":
pass
else:
if self.options.moo == "ESCMA":
from deap import cma
"""
# We start with a centroid in [-4, 4]**D
sigma = 2 * 10**(-2 * numpy.random.rand())
strategy = cma.StrategyMultiObjective(self.population,
#centroid=numpy.random.uniform(-4, 4, len(self.population)),
sigma=sigma,
lambda_=len(self.population)
)
#self.toolbox.register("generate", strategy.generate, deap.creator.Individual)
self.toolbox.register("update", strategy.update)
for ind in self.population:
ind.fitness.values = self.toolbox.evaluate(ind)
"""
strategy = cma.StrategyMultiObjective(self.population, sigma=1.0, mu=self.POP_SIZE, lambda_=self.POP_SIZE)
self.toolbox.register("generate", strategy.generate, deap.creator.Individual)
self.toolbox.register("update", strategy.update)
pass
else: #DEFAULT
if self.options.moo != "NSGAR":
print "Unknown algorithm", self.options.moo, ". Defaulting to NSGAR"
self.toolbox.register("preselect", nsgafortin.selTournamentFitnessDCD)
self.toolbox.register("select", nsgafortin.selNSGA2)
# Evaluate the individuals with an invalid fitness
self.population = self.toolbox.population(self.POP_SIZE) #Random pop
invalid_ind = [ind for ind in self.population if not ind.fitness.valid]
fitnesses = self.toolbox.map(self.toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
self.population = self.toolbox.select(self.population, self.POP_SIZE)
# Set REVCOMP
self.REVCOMP = options.revcomp
if not self.options.no_seed:
seed_options = copy.deepcopy(self.options)
seed_options.no_seed=True
seed_options.grammar="alpha"
#seed_options.moo="SPEA2" #FIXME There's currently a bug preventing seeds from being produced by the FORTIN selection algorithm # Nope, doesn't work at all, just fix it already.
seedengine = Engine(None)
#seedengine
#seedengine.boot(self.options)
#print self.population
#raw_input()
seedengine.set_experiment("regex", seed_options,None, None, None, self.toolbox, self.toolbox.population(self.POP_SIZE), None, None, 0)
ENGINE_GEN = 50
print "Seeded for", ENGINE_GEN," generations using grammar:", seed_options.grammar
self_seeds, self.logbook = seedengine.run(ENGINE_GEN)
self_seeds = [ind for ind in seedengine.hof]
#print self_seeds
#raw_input()
self.population = self_seeds #TODO: Problem with small seed counts
pass
else:
if len(self.initial_seeds) > 0:
#self.initial_seeds = ["add,C,A", "C", "add,A,add,C,add,T,G" ]
print "Seeded population initated"
self.population = self.toolbox.init_seeded_population()
elif not self.population:
self.population = self.toolbox.population(self.POP_SIZE)
print "Initial pop size:", len(self.population)
