body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def send_job_data(self, current_job, data, poll_timeout=None):
'Send a Gearman JOB_DATA update for an inflight job'
current_handler = self._get_handler_for_job(current_job)
current_handler.send_job_data(current_job, data=data)
self.wait_until_updates_sent([current_job], poll_timeout=poll_timeout) | 500,194,096,815,165,760 | Send a Gearman JOB_DATA update for an inflight job | client/python3_gearman/worker.py | send_job_data | aixiwang/gearman_test | python | def send_job_data(self, current_job, data, poll_timeout=None):
current_handler = self._get_handler_for_job(current_job)
current_handler.send_job_data(current_job, data=data)
self.wait_until_updates_sent([current_job], poll_timeout=poll_timeout) |
def send_job_warning(self, current_job, data, poll_timeout=None):
'Send a Gearman JOB_WARNING update for an inflight job'
current_handler = self._get_handler_for_job(current_job)
current_handler.send_job_warning(current_job, data=data)
self.wait_until_updates_sent([current_job], poll_timeout=poll_timeou... | 1,950,741,434,473,703,000 | Send a Gearman JOB_WARNING update for an inflight job | client/python3_gearman/worker.py | send_job_warning | aixiwang/gearman_test | python | def send_job_warning(self, current_job, data, poll_timeout=None):
current_handler = self._get_handler_for_job(current_job)
current_handler.send_job_warning(current_job, data=data)
self.wait_until_updates_sent([current_job], poll_timeout=poll_timeout) |
def create_job(self, command_handler, job_handle, task, unique, data):
'Create a new job using our self.job_class'
current_connection = self.handler_to_connection_map[command_handler]
return self.job_class(current_connection, job_handle, task, unique, data) | 4,949,514,335,659,896,000 | Create a new job using our self.job_class | client/python3_gearman/worker.py | create_job | aixiwang/gearman_test | python | def create_job(self, command_handler, job_handle, task, unique, data):
current_connection = self.handler_to_connection_map[command_handler]
return self.job_class(current_connection, job_handle, task, unique, data) |
def set_job_lock(self, command_handler, lock):
"Set a worker level job lock so we don't try to hold onto 2 jobs at\n anytime"
if (command_handler not in self.handler_to_connection_map):
return False
failed_lock = bool((lock and (self.command_handler_holding_job_lock is not None)))
failed_... | -614,135,064,642,233,300 | Set a worker level job lock so we don't try to hold onto 2 jobs at
anytime | client/python3_gearman/worker.py | set_job_lock | aixiwang/gearman_test | python | def set_job_lock(self, command_handler, lock):
"Set a worker level job lock so we don't try to hold onto 2 jobs at\n anytime"
if (command_handler not in self.handler_to_connection_map):
return False
failed_lock = bool((lock and (self.command_handler_holding_job_lock is not None)))
failed_... |
def check_job_lock(self, command_handler):
'Check to see if we hold the job lock'
return bool((self.command_handler_holding_job_lock == command_handler)) | 5,963,652,033,655,536,000 | Check to see if we hold the job lock | client/python3_gearman/worker.py | check_job_lock | aixiwang/gearman_test | python | def check_job_lock(self, command_handler):
return bool((self.command_handler_holding_job_lock == command_handler)) |
def get_bond_length_distribution_inner(input_fname, output_fname):
'Generate bond length distibutions.\n\n Args:\n input_fname: An existing TFRecord file containing Conformer protos.\n output_fname: An output file that will be created that contains all bond\n length distributions - all bond types, all a... | 1,118,417,521,258,524,800 | Generate bond length distibutions.
Args:
input_fname: An existing TFRecord file containing Conformer protos.
output_fname: An output file that will be created that contains all bond
length distributions - all bond types, all atom types. Requires
post-processing to generate bond length distribution files. | smu/geometry/get_bond_length_distribution.py | get_bond_length_distribution_inner | 10088/google-research | python | def get_bond_length_distribution_inner(input_fname, output_fname):
'Generate bond length distibutions.\n\n Args:\n input_fname: An existing TFRecord file containing Conformer protos.\n output_fname: An output file that will be created that contains all bond\n length distributions - all bond types, all a... |
def get_bond_length_distribution(unused_argv):
'Scan Conformer protos to extract bond length distributions.'
del unused_argv
get_bond_length_distribution_inner(FLAGS.input, FLAGS.output) | -6,997,662,189,550,810,000 | Scan Conformer protos to extract bond length distributions. | smu/geometry/get_bond_length_distribution.py | get_bond_length_distribution | 10088/google-research | python | def get_bond_length_distribution(unused_argv):
del unused_argv
get_bond_length_distribution_inner(FLAGS.input, FLAGS.output) |
@contextmanager
def mktemp(contents):
' Create a temporary file with the given contents, and yield its path '
(_, path) = tempfile.mkstemp()
fp = io.open(path, 'wt+', encoding='utf-8')
fp.write(contents)
fp.flush()
try:
(yield path)
finally:
fp.close()
os.unlink(path) | -1,387,237,215,766,695,400 | Create a temporary file with the given contents, and yield its path | tests/render-test.py | mktemp | arrikto/kolypto-j2cli | python | @contextmanager
def mktemp(contents):
' '
(_, path) = tempfile.mkstemp()
fp = io.open(path, 'wt+', encoding='utf-8')
fp.write(contents)
fp.flush()
try:
(yield path)
finally:
fp.close()
os.unlink(path) |
def _testme(self, argv, expected_output, stdin=None, env=None):
' Helper test shortcut '
with mock_environ((env or {})):
result = render_command(os.getcwd(), (env or {}), stdin, argv)
if isinstance(result, bytes):
result = result.decode('utf-8')
self.assertEqual(result, expected_output) | 1,795,941,189,374,585,600 | Helper test shortcut | tests/render-test.py | _testme | arrikto/kolypto-j2cli | python | def _testme(self, argv, expected_output, stdin=None, env=None):
' '
with mock_environ((env or {})):
result = render_command(os.getcwd(), (env or {}), stdin, argv)
if isinstance(result, bytes):
result = result.decode('utf-8')
self.assertEqual(result, expected_output) |
def test_undefined(self):
' Test --undefined '
self.assertRaises(UndefinedError, self._testme, ['resources/name.j2'], u'Hello !\n', env=dict())
self._testme(['--undefined', 'resources/name.j2'], u'Hello !\n', env=dict()) | -1,063,750,977,480,168,600 | Test --undefined | tests/render-test.py | test_undefined | arrikto/kolypto-j2cli | python | def test_undefined(self):
' '
self.assertRaises(UndefinedError, self._testme, ['resources/name.j2'], u'Hello !\n', env=dict())
self._testme(['--undefined', 'resources/name.j2'], u'Hello !\n', env=dict()) |
def test_jinja2_extensions(self):
' Test that an extension is enabled '
with mktemp('{% do [] %}') as template:
self._testme([template], '') | 2,100,193,027,615,986,400 | Test that an extension is enabled | tests/render-test.py | test_jinja2_extensions | arrikto/kolypto-j2cli | python | def test_jinja2_extensions(self):
' '
with mktemp('{% do [] %}') as template:
self._testme([template], ) |
def test_customize(self):
' Test --customize '
with mktemp('<% if 1 %>1<% else %>2<% endif %>') as template:
self._testme(['--customize=resources/customize.py', template], '1')
with mktemp('<< my_function("hey") >>') as template:
self._testme(['--customize=resources/customize.py', template]... | -1,975,867,084,036,834,000 | Test --customize | tests/render-test.py | test_customize | arrikto/kolypto-j2cli | python | def test_customize(self):
' '
with mktemp('<% if 1 %>1<% else %>2<% endif %>') as template:
self._testme(['--customize=resources/customize.py', template], '1')
with mktemp('<< my_function("hey") >>') as template:
self._testme(['--customize=resources/customize.py', template], 'my function s... |
def _load_coco_keypoint_annotation_kernel(self, img_id):
'load annotation from COCOAPI.\n\n Note:\n bbox:[x1, y1, w, h]\n Args:\n img_id: coco image id\n Returns:\n dict: db entry\n '
img_ann = self.coco.loadImgs(img_id)[0]
width = img_ann['width'... | -3,409,155,877,235,542,500 | load annotation from COCOAPI.
Note:
bbox:[x1, y1, w, h]
Args:
img_id: coco image id
Returns:
dict: db entry | mmpose/datasets/datasets/top_down/topdown_coco_wholebody_dataset.py | _load_coco_keypoint_annotation_kernel | 674106399/mmpose | python | def _load_coco_keypoint_annotation_kernel(self, img_id):
'load annotation from COCOAPI.\n\n Note:\n bbox:[x1, y1, w, h]\n Args:\n img_id: coco image id\n Returns:\n dict: db entry\n '
img_ann = self.coco.loadImgs(img_id)[0]
width = img_ann['width'... |
def _coco_keypoint_results_one_category_kernel(self, data_pack):
'Get coco keypoint results.'
cat_id = data_pack['cat_id']
keypoints = data_pack['keypoints']
cat_results = []
for img_kpts in keypoints:
if (len(img_kpts) == 0):
continue
_key_points = np.array([img_kpt['key... | 911,176,376,589,387,800 | Get coco keypoint results. | mmpose/datasets/datasets/top_down/topdown_coco_wholebody_dataset.py | _coco_keypoint_results_one_category_kernel | 674106399/mmpose | python | def _coco_keypoint_results_one_category_kernel(self, data_pack):
cat_id = data_pack['cat_id']
keypoints = data_pack['keypoints']
cat_results = []
for img_kpts in keypoints:
if (len(img_kpts) == 0):
continue
_key_points = np.array([img_kpt['keypoints'] for img_kpt in img_... |
def _do_python_keypoint_eval(self, res_file):
'Keypoint evaluation using COCOAPI.'
coco_det = self.coco.loadRes(res_file)
cuts = np.cumsum([0, self.body_num, self.foot_num, self.face_num, self.left_hand_num, self.right_hand_num])
coco_eval = COCOeval(self.coco, coco_det, 'keypoints_body', self.sigmas[cu... | 147,649,272,100,721,250 | Keypoint evaluation using COCOAPI. | mmpose/datasets/datasets/top_down/topdown_coco_wholebody_dataset.py | _do_python_keypoint_eval | 674106399/mmpose | python | def _do_python_keypoint_eval(self, res_file):
coco_det = self.coco.loadRes(res_file)
cuts = np.cumsum([0, self.body_num, self.foot_num, self.face_num, self.left_hand_num, self.right_hand_num])
coco_eval = COCOeval(self.coco, coco_det, 'keypoints_body', self.sigmas[cuts[0]:cuts[1]], use_area=True)
c... |
def tearDown(self):
'Reset all bridge blocks in between test method runs.'
for bridge in self.bridges:
bridge._blockedIn = {} | -8,374,115,032,258,045,000 | Reset all bridge blocks in between test method runs. | bridgedb/test/test_https_distributor.py | tearDown | isislovecruft/bridgedb | python | def tearDown(self):
for bridge in self.bridges:
bridge._blockedIn = {} |
def test_HTTPSDistributor_init_with_proxies(self):
'The HTTPSDistributor, when initialised with proxies, should add an\n extra hashring for proxy users.\n '
dist = distributor.HTTPSDistributor(3, self.key, ProxySet(['1.1.1.1', '2.2.2.2']))
self.assertIsNotNone(dist.proxies)
self.assertGrea... | 3,944,217,055,337,707,000 | The HTTPSDistributor, when initialised with proxies, should add an
extra hashring for proxy users. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_init_with_proxies | isislovecruft/bridgedb | python | def test_HTTPSDistributor_init_with_proxies(self):
'The HTTPSDistributor, when initialised with proxies, should add an\n extra hashring for proxy users.\n '
dist = distributor.HTTPSDistributor(3, self.key, ProxySet(['1.1.1.1', '2.2.2.2']))
self.assertIsNotNone(dist.proxies)
self.assertGrea... |
def test_HTTPSDistributor_getSubnet_usingProxy(self):
'HTTPSDistributor.getSubnet(usingProxy=True) should return a proxy\n group number.\n '
clientRequest = self.randomClientRequest()
expectedGroup = ((int(ipaddr.IPAddress(clientRequest.client)) % 4) + 1)
subnet = distributor.HTTPSDistribu... | -178,428,863,725,358,530 | HTTPSDistributor.getSubnet(usingProxy=True) should return a proxy
group number. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_getSubnet_usingProxy | isislovecruft/bridgedb | python | def test_HTTPSDistributor_getSubnet_usingProxy(self):
'HTTPSDistributor.getSubnet(usingProxy=True) should return a proxy\n group number.\n '
clientRequest = self.randomClientRequest()
expectedGroup = ((int(ipaddr.IPAddress(clientRequest.client)) % 4) + 1)
subnet = distributor.HTTPSDistribu... |
def test_HTTPSDistributor_mapSubnetToSubring_usingProxy(self):
'HTTPSDistributor.mapSubnetToSubring() when the client was using a\n proxy should map the client to the proxy subhashring.\n '
dist = distributor.HTTPSDistributor(3, self.key, ProxySet(['1.1.1.1', '2.2.2.2']))
subnet = 'proxy-group... | 1,929,354,051,178,406,100 | HTTPSDistributor.mapSubnetToSubring() when the client was using a
proxy should map the client to the proxy subhashring. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_mapSubnetToSubring_usingProxy | isislovecruft/bridgedb | python | def test_HTTPSDistributor_mapSubnetToSubring_usingProxy(self):
'HTTPSDistributor.mapSubnetToSubring() when the client was using a\n proxy should map the client to the proxy subhashring.\n '
dist = distributor.HTTPSDistributor(3, self.key, ProxySet(['1.1.1.1', '2.2.2.2']))
subnet = 'proxy-group... |
def test_HTTPSDistributor_mapSubnetToSubring_with_proxies(self):
"HTTPSDistributor.mapSubnetToSubring() when the client wasn't using\n a proxy, but the distributor does have some known proxies and a\n proxySubring, should not map the client to the proxy subhashring.\n "
dist = distributor.H... | 9,026,591,236,788,420,000 | HTTPSDistributor.mapSubnetToSubring() when the client wasn't using
a proxy, but the distributor does have some known proxies and a
proxySubring, should not map the client to the proxy subhashring. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_mapSubnetToSubring_with_proxies | isislovecruft/bridgedb | python | def test_HTTPSDistributor_mapSubnetToSubring_with_proxies(self):
"HTTPSDistributor.mapSubnetToSubring() when the client wasn't using\n a proxy, but the distributor does have some known proxies and a\n proxySubring, should not map the client to the proxy subhashring.\n "
dist = distributor.H... |
def test_HTTPSDistributor_prepopulateRings_with_proxies(self):
'An HTTPSDistributor with proxies should prepopulate two extra\n subhashrings (one for each of HTTP-Proxy-IPv4 and HTTP-Proxy-IPv6).\n '
dist = distributor.HTTPSDistributor(3, self.key, ProxySet(['1.1.1.1', '2.2.2.2']))
[dist.inser... | 2,776,454,485,375,904,300 | An HTTPSDistributor with proxies should prepopulate two extra
subhashrings (one for each of HTTP-Proxy-IPv4 and HTTP-Proxy-IPv6). | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_prepopulateRings_with_proxies | isislovecruft/bridgedb | python | def test_HTTPSDistributor_prepopulateRings_with_proxies(self):
'An HTTPSDistributor with proxies should prepopulate two extra\n subhashrings (one for each of HTTP-Proxy-IPv4 and HTTP-Proxy-IPv6).\n '
dist = distributor.HTTPSDistributor(3, self.key, ProxySet(['1.1.1.1', '2.2.2.2']))
[dist.inser... |
def test_HTTPSDistributor_prepopulateRings_without_proxies(self):
'An HTTPSDistributor without proxies should prepopulate\n totalSubrings * 2 subrings.\n '
dist = distributor.HTTPSDistributor(3, self.key)
[dist.insert(bridge) for bridge in self.bridges]
dist.prepopulateRings()
self.ass... | -71,506,198,677,698,360 | An HTTPSDistributor without proxies should prepopulate
totalSubrings * 2 subrings. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_prepopulateRings_without_proxies | isislovecruft/bridgedb | python | def test_HTTPSDistributor_prepopulateRings_without_proxies(self):
'An HTTPSDistributor without proxies should prepopulate\n totalSubrings * 2 subrings.\n '
dist = distributor.HTTPSDistributor(3, self.key)
[dist.insert(bridge) for bridge in self.bridges]
dist.prepopulateRings()
self.ass... |
def test_HTTPSDistributor_getBridges_with_proxy_and_nonproxy_users(self):
'An HTTPSDistributor should give separate bridges to proxy users.'
proxies = ProxySet(['.'.join(['1.1.1', str(x)]) for x in range(1, 256)])
dist = distributor.HTTPSDistributor(3, self.key, proxies)
[dist.insert(bridge) for bridge ... | 6,604,173,368,182,695,000 | An HTTPSDistributor should give separate bridges to proxy users. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_getBridges_with_proxy_and_nonproxy_users | isislovecruft/bridgedb | python | def test_HTTPSDistributor_getBridges_with_proxy_and_nonproxy_users(self):
proxies = ProxySet(['.'.join(['1.1.1', str(x)]) for x in range(1, 256)])
dist = distributor.HTTPSDistributor(3, self.key, proxies)
[dist.insert(bridge) for bridge in self.bridges]
for _ in range(10):
bridgeRequest1 = ... |
def test_HTTPSDistributor_getBridges_same_bridges_to_same_client(self):
'The same client asking for bridges from the HTTPSDistributor\n multiple times in a row should get the same bridges in response each\n time.\n '
dist = distributor.HTTPSDistributor(3, self.key)
[dist.insert(bridge) ... | 7,259,232,939,965,180,000 | The same client asking for bridges from the HTTPSDistributor
multiple times in a row should get the same bridges in response each
time. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_getBridges_same_bridges_to_same_client | isislovecruft/bridgedb | python | def test_HTTPSDistributor_getBridges_same_bridges_to_same_client(self):
'The same client asking for bridges from the HTTPSDistributor\n multiple times in a row should get the same bridges in response each\n time.\n '
dist = distributor.HTTPSDistributor(3, self.key)
[dist.insert(bridge) ... |
def test_HTTPSDistributor_getBridges_ipv4_ipv6(self):
'Asking for bridge addresses which are simultaneously IPv4 and IPv6\n (in that order) should return IPv4 bridges.\n '
dist = distributor.HTTPSDistributor(1, self.key)
[dist.insert(bridge) for bridge in self.bridges[:250]]
bridgeRequest ... | 6,034,760,044,908,151,000 | Asking for bridge addresses which are simultaneously IPv4 and IPv6
(in that order) should return IPv4 bridges. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_getBridges_ipv4_ipv6 | isislovecruft/bridgedb | python | def test_HTTPSDistributor_getBridges_ipv4_ipv6(self):
'Asking for bridge addresses which are simultaneously IPv4 and IPv6\n (in that order) should return IPv4 bridges.\n '
dist = distributor.HTTPSDistributor(1, self.key)
[dist.insert(bridge) for bridge in self.bridges[:250]]
bridgeRequest ... |
def test_HTTPSDistributor_getBridges_ipv6_ipv4(self):
'Asking for bridge addresses which are simultaneously IPv6 and IPv4\n (in that order) should return IPv6 bridges.\n '
dist = distributor.HTTPSDistributor(1, self.key)
[dist.insert(bridge) for bridge in self.bridges[:250]]
bridgeRequest ... | 4,826,968,932,339,698,000 | Asking for bridge addresses which are simultaneously IPv6 and IPv4
(in that order) should return IPv6 bridges. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_getBridges_ipv6_ipv4 | isislovecruft/bridgedb | python | def test_HTTPSDistributor_getBridges_ipv6_ipv4(self):
'Asking for bridge addresses which are simultaneously IPv6 and IPv4\n (in that order) should return IPv6 bridges.\n '
dist = distributor.HTTPSDistributor(1, self.key)
[dist.insert(bridge) for bridge in self.bridges[:250]]
bridgeRequest ... |
def test_HTTPSDistributor_getBridges_ipv6(self):
'A request for IPv6 bridges should return IPv6 bridges.'
dist = distributor.HTTPSDistributor(3, self.key)
[dist.insert(bridge) for bridge in self.bridges[:250]]
for i in xrange(500):
bridgeRequest = self.randomClientRequest()
bridgeRequest... | 2,056,632,888,544,047,600 | A request for IPv6 bridges should return IPv6 bridges. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_getBridges_ipv6 | isislovecruft/bridgedb | python | def test_HTTPSDistributor_getBridges_ipv6(self):
dist = distributor.HTTPSDistributor(3, self.key)
[dist.insert(bridge) for bridge in self.bridges[:250]]
for i in xrange(500):
bridgeRequest = self.randomClientRequest()
bridgeRequest.withIPv6()
bridgeRequest.generateFilters()
... |
def test_HTTPSDistributor_getBridges_ipv4(self):
'A request for IPv4 bridges should return IPv4 bridges.'
dist = distributor.HTTPSDistributor(1, self.key)
[dist.insert(bridge) for bridge in self.bridges[:250]]
for i in xrange(500):
bridgeRequest = self.randomClientRequest()
bridgeRequest... | 3,335,645,459,602,166,300 | A request for IPv4 bridges should return IPv4 bridges. | bridgedb/test/test_https_distributor.py | test_HTTPSDistributor_getBridges_ipv4 | isislovecruft/bridgedb | python | def test_HTTPSDistributor_getBridges_ipv4(self):
dist = distributor.HTTPSDistributor(1, self.key)
[dist.insert(bridge) for bridge in self.bridges[:250]]
for i in xrange(500):
bridgeRequest = self.randomClientRequest()
bridgeRequest.generateFilters()
bridges = dist.getBridges(bri... |
def _stash_exp(self, *args, params: Optional[dict]=None, detach_rev: Optional[str]=None, baseline_rev: Optional[str]=None, branch: Optional[str]=None, name: Optional[str]=None, **kwargs):
"Stash changes from the workspace as an experiment.\n\n Args:\n params: Optional dictionary of parameter value... | 1,772,051,208,236,786,200 | Stash changes from the workspace as an experiment.
Args:
params: Optional dictionary of parameter values to be used.
Values take priority over any parameters specified in the
user's workspace.
baseline_rev: Optional baseline rev for this experiment, defaults
to the current SCM rev.
... | dvc/repo/experiments/__init__.py | _stash_exp | esthergold/dvc | python | def _stash_exp(self, *args, params: Optional[dict]=None, detach_rev: Optional[str]=None, baseline_rev: Optional[str]=None, branch: Optional[str]=None, name: Optional[str]=None, **kwargs):
"Stash changes from the workspace as an experiment.\n\n Args:\n params: Optional dictionary of parameter value... |
def _update_params(self, params: dict):
'Update experiment params files with the specified values.'
from benedict import benedict
from dvc.utils.serialize import MODIFIERS
logger.debug("Using experiment params '%s'", params)
for params_fname in params:
path = (PathInfo(self.repo.root_dir) / ... | 6,649,994,754,235,740,000 | Update experiment params files with the specified values. | dvc/repo/experiments/__init__.py | _update_params | esthergold/dvc | python | def _update_params(self, params: dict):
from benedict import benedict
from dvc.utils.serialize import MODIFIERS
logger.debug("Using experiment params '%s'", params)
for params_fname in params:
path = (PathInfo(self.repo.root_dir) / params_fname)
suffix = path.suffix.lower()
... |
def reproduce_one(self, queue=False, **kwargs):
'Reproduce and checkout a single experiment.'
stash_rev = self.new(**kwargs)
if queue:
logger.info("Queued experiment '%s' for future execution.", stash_rev[:7])
return [stash_rev]
results = self.reproduce([stash_rev], keep_stash=False)
... | -7,046,387,588,810,680,000 | Reproduce and checkout a single experiment. | dvc/repo/experiments/__init__.py | reproduce_one | esthergold/dvc | python | def reproduce_one(self, queue=False, **kwargs):
stash_rev = self.new(**kwargs)
if queue:
logger.info("Queued experiment '%s' for future execution.", stash_rev[:7])
return [stash_rev]
results = self.reproduce([stash_rev], keep_stash=False)
exp_rev = first(results)
if (exp_rev is ... |
@scm_locked
def new(self, *args, checkpoint_resume: Optional[str]=None, **kwargs):
"Create a new experiment.\n\n Experiment will be reproduced and checked out into the user's\n workspace.\n "
if (checkpoint_resume is not None):
return self._resume_checkpoint(*args, checkpoint_resume... | 7,423,319,692,721,970,000 | Create a new experiment.
Experiment will be reproduced and checked out into the user's
workspace. | dvc/repo/experiments/__init__.py | new | esthergold/dvc | python | @scm_locked
def new(self, *args, checkpoint_resume: Optional[str]=None, **kwargs):
"Create a new experiment.\n\n Experiment will be reproduced and checked out into the user's\n workspace.\n "
if (checkpoint_resume is not None):
return self._resume_checkpoint(*args, checkpoint_resume... |
def _resume_checkpoint(self, *args, checkpoint_resume: Optional[str]=None, **kwargs):
"Resume an existing (checkpoint) experiment.\n\n Experiment will be reproduced and checked out into the user's\n workspace.\n "
assert checkpoint_resume
if (checkpoint_resume == self.LAST_CHECKPOINT):
... | 8,775,670,305,476,068,000 | Resume an existing (checkpoint) experiment.
Experiment will be reproduced and checked out into the user's
workspace. | dvc/repo/experiments/__init__.py | _resume_checkpoint | esthergold/dvc | python | def _resume_checkpoint(self, *args, checkpoint_resume: Optional[str]=None, **kwargs):
"Resume an existing (checkpoint) experiment.\n\n Experiment will be reproduced and checked out into the user's\n workspace.\n "
assert checkpoint_resume
if (checkpoint_resume == self.LAST_CHECKPOINT):
... |
@scm_locked
def reproduce(self, revs: Optional[Iterable]=None, keep_stash: Optional[bool]=True, **kwargs):
'Reproduce the specified experiments.\n\n Args:\n revs: If revs is not specified, all stashed experiments will be\n reproduced.\n keep_stash: If True, stashed experi... | -8,775,852,405,187,722,000 | Reproduce the specified experiments.
Args:
revs: If revs is not specified, all stashed experiments will be
reproduced.
keep_stash: If True, stashed experiments will be preserved if they
fail to reproduce successfully. | dvc/repo/experiments/__init__.py | reproduce | esthergold/dvc | python | @scm_locked
def reproduce(self, revs: Optional[Iterable]=None, keep_stash: Optional[bool]=True, **kwargs):
'Reproduce the specified experiments.\n\n Args:\n revs: If revs is not specified, all stashed experiments will be\n reproduced.\n keep_stash: If True, stashed experi... |
def _reproduce(self, executors: dict, jobs: Optional[int]=1) -> Mapping[(str, Mapping[(str, str)])]:
'Run dvc repro for the specified BaseExecutors in parallel.\n\n Returns dict containing successfully executed experiments.\n '
result = defaultdict(dict)
manager = Manager()
pid_q = manager... | 5,327,217,892,181,162,000 | Run dvc repro for the specified BaseExecutors in parallel.
Returns dict containing successfully executed experiments. | dvc/repo/experiments/__init__.py | _reproduce | esthergold/dvc | python | def _reproduce(self, executors: dict, jobs: Optional[int]=1) -> Mapping[(str, Mapping[(str, str)])]:
'Run dvc repro for the specified BaseExecutors in parallel.\n\n Returns dict containing successfully executed experiments.\n '
result = defaultdict(dict)
manager = Manager()
pid_q = manager... |
@scm_locked
def get_baseline(self, rev):
'Return the baseline rev for an experiment rev.'
return self._get_baseline(rev) | 7,143,182,011,199,181,000 | Return the baseline rev for an experiment rev. | dvc/repo/experiments/__init__.py | get_baseline | esthergold/dvc | python | @scm_locked
def get_baseline(self, rev):
return self._get_baseline(rev) |
def get_branch_by_rev(self, rev: str, allow_multiple: bool=False) -> str:
'Returns full refname for the experiment branch containing rev.'
ref_infos = list(exp_refs_by_rev(self.scm, rev))
if (not ref_infos):
return None
if ((len(ref_infos) > 1) and (not allow_multiple)):
raise MultipleBr... | 6,905,835,126,619,686,000 | Returns full refname for the experiment branch containing rev. | dvc/repo/experiments/__init__.py | get_branch_by_rev | esthergold/dvc | python | def get_branch_by_rev(self, rev: str, allow_multiple: bool=False) -> str:
ref_infos = list(exp_refs_by_rev(self.scm, rev))
if (not ref_infos):
return None
if ((len(ref_infos) > 1) and (not allow_multiple)):
raise MultipleBranchError(rev)
return str(ref_infos[0]) |
def get_exact_name(self, rev: str):
'Returns preferred name for the specified revision.\n\n Prefers tags, branches (heads), experiments in that orer.\n '
exclude = f'{EXEC_NAMESPACE}/*'
ref = self.scm.describe(rev, base=EXPS_NAMESPACE, exclude=exclude)
if ref:
return ExpRefInfo.fro... | -4,535,959,236,294,254,000 | Returns preferred name for the specified revision.
Prefers tags, branches (heads), experiments in that orer. | dvc/repo/experiments/__init__.py | get_exact_name | esthergold/dvc | python | def get_exact_name(self, rev: str):
'Returns preferred name for the specified revision.\n\n Prefers tags, branches (heads), experiments in that orer.\n '
exclude = f'{EXEC_NAMESPACE}/*'
ref = self.scm.describe(rev, base=EXPS_NAMESPACE, exclude=exclude)
if ref:
return ExpRefInfo.fro... |
def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
"Creates a CNN model.\n\n Args:\n model_input: 'batch' x 'num_features' matrix of input features.\n vocab_size: The number of classes in the dataset.\n\n Returns:\n A dictionary with a tensor containing the prob... | -8,735,807,252,078,989,000 | Creates a CNN model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_cla... | video_level_models.py | create_model | abdoo8080/youtube-8m | python | def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
"Creates a CNN model.\n\n Args:\n model_input: 'batch' x 'num_features' matrix of input features.\n vocab_size: The number of classes in the dataset.\n\n Returns:\n A dictionary with a tensor containing the prob... |
def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
"Creates a ResNet model.\n\n Args:\n model_input: 'batch' x 'num_features' matrix of input features.\n vocab_size: The number of classes in the dataset.\n\n Returns:\n A dictionary with a tensor containing the p... | 6,562,285,822,841,482,000 | Creates a ResNet model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_... | video_level_models.py | create_model | abdoo8080/youtube-8m | python | def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
"Creates a ResNet model.\n\n Args:\n model_input: 'batch' x 'num_features' matrix of input features.\n vocab_size: The number of classes in the dataset.\n\n Returns:\n A dictionary with a tensor containing the p... |
def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
"Creates a logistic model.\n\n Args:\n model_input: 'batch' x 'num_features' matrix of input features.\n vocab_size: The number of classes in the dataset.\n\n Returns:\n A dictionary with a tensor containing the... | 6,376,754,502,288,288,000 | Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x nu... | video_level_models.py | create_model | abdoo8080/youtube-8m | python | def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
"Creates a logistic model.\n\n Args:\n model_input: 'batch' x 'num_features' matrix of input features.\n vocab_size: The number of classes in the dataset.\n\n Returns:\n A dictionary with a tensor containing the... |
def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-08, **unused_params):
"Creates a Mixture of (Logistic) Experts model.\n\n The model consists of a per-class softmax distribution over a\n configurable number of logistic classifiers. One of the classifiers in the\n mixture ... | -6,447,030,492,059,986,000 | Creates a Mixture of (Logistic) Experts model.
The model consists of a per-class softmax distribution over a
configurable number of logistic classifiers. One of the classifiers in the
mixture is not trained, and always predicts 0.
Args:
model_input: 'batch_size' x 'num_features' matrix of input features.
vocab... | video_level_models.py | create_model | abdoo8080/youtube-8m | python | def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-08, **unused_params):
"Creates a Mixture of (Logistic) Experts model.\n\n The model consists of a per-class softmax distribution over a\n configurable number of logistic classifiers. One of the classifiers in the\n mixture ... |
def get_corner_loss_lidar(pred_bbox3d: torch.Tensor, gt_bbox3d: torch.Tensor):
'\n Args:\n pred_bbox3d: (N, 7) float Tensor.\n gt_bbox3d: (N, 7) float Tensor.\n\n Returns:\n corner_loss: (N) float Tensor.\n '
assert (pred_bbox3d.shape[0] == gt_bbox3d.shape[0])
pred_box_corners ... | 5,956,361,160,264,300,000 | Args:
pred_bbox3d: (N, 7) float Tensor.
gt_bbox3d: (N, 7) float Tensor.
Returns:
corner_loss: (N) float Tensor. | pcdet/utils/loss_utils.py | get_corner_loss_lidar | ocNflag/point2seq | python | def get_corner_loss_lidar(pred_bbox3d: torch.Tensor, gt_bbox3d: torch.Tensor):
'\n Args:\n pred_bbox3d: (N, 7) float Tensor.\n gt_bbox3d: (N, 7) float Tensor.\n\n Returns:\n corner_loss: (N) float Tensor.\n '
assert (pred_bbox3d.shape[0] == gt_bbox3d.shape[0])
pred_box_corners ... |
def __init__(self, gamma: float=2.0, alpha: float=0.25):
'\n Args:\n gamma: Weighting parameter to balance loss for hard and easy examples.\n alpha: Weighting parameter to balance loss for positive and negative examples.\n '
super().__init__()
self.alpha = alpha
self.... | 2,511,071,533,760,564,700 | Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples. | pcdet/utils/loss_utils.py | __init__ | ocNflag/point2seq | python | def __init__(self, gamma: float=2.0, alpha: float=0.25):
'\n Args:\n gamma: Weighting parameter to balance loss for hard and easy examples.\n alpha: Weighting parameter to balance loss for positive and negative examples.\n '
super().__init__()
self.alpha = alpha
self.... |
@staticmethod
def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
' PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:\n max(x, 0) - x * z + log(1 + exp(-abs(x))) in\n https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_log... | -8,905,405,198,205,577,000 | PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:
max(x, 0) - x * z + log(1 + exp(-abs(x))) in
https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anc... | pcdet/utils/loss_utils.py | sigmoid_cross_entropy_with_logits | ocNflag/point2seq | python | @staticmethod
def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
' PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:\n max(x, 0) - x * z + log(1 + exp(-abs(x))) in\n https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_log... |
def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
'\n Args:\n input: (B, #anchors, #classes) float tensor.\n Predicted logits for each class\n target: (B, #anchors, #classes) float tensor.\n One-hot encoded classification ... | -8,421,976,740,454,377,000 | Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classification targets
weights: (B, #anchors) float tensor.
Anchor-wise weights.
Returns:
weighted_loss: (B, #anchors, #classes) float... | pcdet/utils/loss_utils.py | forward | ocNflag/point2seq | python | def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
'\n Args:\n input: (B, #anchors, #classes) float tensor.\n Predicted logits for each class\n target: (B, #anchors, #classes) float tensor.\n One-hot encoded classification ... |
def __init__(self, beta: float=(1.0 / 9.0), code_weights: list=None):
'\n Args:\n beta: Scalar float.\n L1 to L2 change point.\n For beta values < 1e-5, L1 loss is computed.\n code_weights: (#codes) float list if not None.\n Code-wise weights... | 6,300,698,351,211,915,000 | Args:
beta: Scalar float.
L1 to L2 change point.
For beta values < 1e-5, L1 loss is computed.
code_weights: (#codes) float list if not None.
Code-wise weights. | pcdet/utils/loss_utils.py | __init__ | ocNflag/point2seq | python | def __init__(self, beta: float=(1.0 / 9.0), code_weights: list=None):
'\n Args:\n beta: Scalar float.\n L1 to L2 change point.\n For beta values < 1e-5, L1 loss is computed.\n code_weights: (#codes) float list if not None.\n Code-wise weights... |
def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor=None):
'\n Args:\n input: (B, #anchors, #codes) float tensor.\n Ecoded predicted locations of objects.\n target: (B, #anchors, #codes) float tensor.\n Regression targets.\n ... | -3,411,104,254,395,770,400 | Args:
input: (B, #anchors, #codes) float tensor.
Ecoded predicted locations of objects.
target: (B, #anchors, #codes) float tensor.
Regression targets.
weights: (B, #anchors) float tensor if not None.
Returns:
loss: (B, #anchors) float tensor.
Weighted smooth l1 loss without red... | pcdet/utils/loss_utils.py | forward | ocNflag/point2seq | python | def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor=None):
'\n Args:\n input: (B, #anchors, #codes) float tensor.\n Ecoded predicted locations of objects.\n target: (B, #anchors, #codes) float tensor.\n Regression targets.\n ... |
def __init__(self, code_weights: list=None):
'\n Args:\n code_weights: (#codes) float list if not None.\n Code-wise weights.\n '
super(WeightedL1Loss, self).__init__()
if (code_weights is not None):
self.code_weights = np.array(code_weights, dtype=np.float32)
... | -778,590,620,506,814,300 | Args:
code_weights: (#codes) float list if not None.
Code-wise weights. | pcdet/utils/loss_utils.py | __init__ | ocNflag/point2seq | python | def __init__(self, code_weights: list=None):
'\n Args:\n code_weights: (#codes) float list if not None.\n Code-wise weights.\n '
super(WeightedL1Loss, self).__init__()
if (code_weights is not None):
self.code_weights = np.array(code_weights, dtype=np.float32)
... |
def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor=None):
'\n Args:\n input: (B, #anchors, #codes) float tensor.\n Ecoded predicted locations of objects.\n target: (B, #anchors, #codes) float tensor.\n Regression targets.\n ... | -169,834,570,559,773,700 | Args:
input: (B, #anchors, #codes) float tensor.
Ecoded predicted locations of objects.
target: (B, #anchors, #codes) float tensor.
Regression targets.
weights: (B, #anchors) float tensor if not None.
Returns:
loss: (B, #anchors) float tensor.
Weighted smooth l1 loss without red... | pcdet/utils/loss_utils.py | forward | ocNflag/point2seq | python | def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor=None):
'\n Args:\n input: (B, #anchors, #codes) float tensor.\n Ecoded predicted locations of objects.\n target: (B, #anchors, #codes) float tensor.\n Regression targets.\n ... |
def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
'\n Args:\n input: (B, #anchors, #classes) float tensor.\n Predited logits for each class.\n target: (B, #anchors, #classes) float tensor.\n One-hot classification targets.... | 5,017,373,398,995,985,000 | Args:
input: (B, #anchors, #classes) float tensor.
Predited logits for each class.
target: (B, #anchors, #classes) float tensor.
One-hot classification targets.
weights: (B, #anchors) float tensor.
Anchor-wise weights.
Returns:
loss: (B, #anchors) float tensor.
Weighted ... | pcdet/utils/loss_utils.py | forward | ocNflag/point2seq | python | def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
'\n Args:\n input: (B, #anchors, #classes) float tensor.\n Predited logits for each class.\n target: (B, #anchors, #classes) float tensor.\n One-hot classification targets.... |
def _neg_loss(self, pred, gt):
' Modified focal loss. Exactly the same as CornerNet.\n Runs faster and costs a little bit more memory\n Arguments:\n pred (batch x c x h x w)\n gt_regr (batch x c x h x w)\n '
pos_inds = gt.eq(1).float()
neg_inds = gt.lt(... | 389,137,059,020,501,800 | Modified focal loss. Exactly the same as CornerNet.
Runs faster and costs a little bit more memory
Arguments:
pred (batch x c x h x w)
gt_regr (batch x c x h x w) | pcdet/utils/loss_utils.py | _neg_loss | ocNflag/point2seq | python | def _neg_loss(self, pred, gt):
' Modified focal loss. Exactly the same as CornerNet.\n Runs faster and costs a little bit more memory\n Arguments:\n pred (batch x c x h x w)\n gt_regr (batch x c x h x w)\n '
pos_inds = gt.eq(1).float()
neg_inds = gt.lt(... |
def _reg_loss(self, regr, gt_regr, mask):
' L1 regression loss\n Arguments:\n regr (batch x max_objects x dim)\n gt_regr (batch x max_objects x dim)\n mask (batch x max_objects)\n '
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(gt_regr).float(... | -2,449,150,389,028,161,000 | L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects) | pcdet/utils/loss_utils.py | _reg_loss | ocNflag/point2seq | python | def _reg_loss(self, regr, gt_regr, mask):
' L1 regression loss\n Arguments:\n regr (batch x max_objects x dim)\n gt_regr (batch x max_objects x dim)\n mask (batch x max_objects)\n '
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(gt_regr).float(... |
def __init__(self, gamma: float=2.0, alpha: float=0.25):
'\n Args:\n gamma: Weighting parameter to balance loss for hard and easy examples.\n alpha: Weighting parameter to balance loss for positive and negative examples.\n '
super(ForegroundFocalLoss, self).__init__()
sel... | -5,609,581,717,160,888,000 | Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples. | pcdet/utils/loss_utils.py | __init__ | ocNflag/point2seq | python | def __init__(self, gamma: float=2.0, alpha: float=0.25):
'\n Args:\n gamma: Weighting parameter to balance loss for hard and easy examples.\n alpha: Weighting parameter to balance loss for positive and negative examples.\n '
super(ForegroundFocalLoss, self).__init__()
sel... |
@staticmethod
def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
' PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:\n max(x, 0) - x * z + log(1 + exp(-abs(x))) in\n https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_log... | -8,905,405,198,205,577,000 | PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:
max(x, 0) - x * z + log(1 + exp(-abs(x))) in
https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anc... | pcdet/utils/loss_utils.py | sigmoid_cross_entropy_with_logits | ocNflag/point2seq | python | @staticmethod
def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
' PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:\n max(x, 0) - x * z + log(1 + exp(-abs(x))) in\n https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_log... |
def forward(self, input: torch.Tensor, target: torch.Tensor):
'\n Args:\n input: (B, #anchors, #classes) float tensor.\n Predicted logits for each class\n target: (B, #anchors, #classes) float tensor.\n One-hot encoded classification targets\n we... | -8,527,889,468,556,255,000 | Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classification targets
weights: (B, #anchors) float tensor.
Anchor-wise weights.
Returns:
weighted_loss: (B, #anchors, #classes) float... | pcdet/utils/loss_utils.py | forward | ocNflag/point2seq | python | def forward(self, input: torch.Tensor, target: torch.Tensor):
'\n Args:\n input: (B, #anchors, #classes) float tensor.\n Predicted logits for each class\n target: (B, #anchors, #classes) float tensor.\n One-hot encoded classification targets\n we... |
def _smooth_reg_loss(self, regr, gt_regr, mask, sigma=3):
' L1 regression loss\n Arguments:\n regr (batch x max_objects x dim)\n gt_regr (batch x max_objects x dim)\n mask (batch x max_objects)\n '
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(g... | 7,120,285,669,924,297,000 | L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects) | pcdet/utils/loss_utils.py | _smooth_reg_loss | ocNflag/point2seq | python | def _smooth_reg_loss(self, regr, gt_regr, mask, sigma=3):
' L1 regression loss\n Arguments:\n regr (batch x max_objects x dim)\n gt_regr (batch x max_objects x dim)\n mask (batch x max_objects)\n '
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(g... |
def __init__(self, gamma: float=2.0, alpha: float=0.25, reduction='mean'):
'\n Args:\n gamma: Weighting parameter to balance loss for hard and easy examples.\n alpha: Weighting parameter to balance loss for positive and negative examples.\n '
super(E2ESigmoidFocalClassificati... | 8,031,476,695,436,176,000 | Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples. | pcdet/utils/loss_utils.py | __init__ | ocNflag/point2seq | python | def __init__(self, gamma: float=2.0, alpha: float=0.25, reduction='mean'):
'\n Args:\n gamma: Weighting parameter to balance loss for hard and easy examples.\n alpha: Weighting parameter to balance loss for positive and negative examples.\n '
super(E2ESigmoidFocalClassificati... |
@staticmethod
def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
' PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:\n max(x, 0) - x * z + log(1 + exp(-abs(x))) in\n https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_log... | -8,905,405,198,205,577,000 | PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:
max(x, 0) - x * z + log(1 + exp(-abs(x))) in
https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anc... | pcdet/utils/loss_utils.py | sigmoid_cross_entropy_with_logits | ocNflag/point2seq | python | @staticmethod
def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
' PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:\n max(x, 0) - x * z + log(1 + exp(-abs(x))) in\n https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_log... |
def forward(self, input: torch.Tensor, target: torch.Tensor):
'\n Args:\n input: (B, #anchors, #classes) float tensor.\n Predicted logits for each class\n target: (B, #anchors, #classes) float tensor.\n One-hot encoded classification targets\n we... | 7,519,885,252,206,170,000 | Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classification targets
weights: (B, #anchors) float tensor.
Anchor-wise weights.
Returns:
weighted_loss: (B, #anchors, #classes) float... | pcdet/utils/loss_utils.py | forward | ocNflag/point2seq | python | def forward(self, input: torch.Tensor, target: torch.Tensor):
'\n Args:\n input: (B, #anchors, #classes) float tensor.\n Predicted logits for each class\n target: (B, #anchors, #classes) float tensor.\n One-hot encoded classification targets\n we... |
def parse_model_config_params(model_params, num_settings, random_state):
'\n\n Args:\n model_params:\n num_settings:\n random_state:\n\n Returns:\n\n '
param_distributions = dict()
dist_types = dict(randint=randint, expon=expon, uniform=uniform)
for (param, param_value) in ... | -215,715,886,021,214,560 | Args:
model_params:
num_settings:
random_state:
Returns: | scripts/search_ml_model_params.py | parse_model_config_params | NCAR/mlmicrophysics | python | def parse_model_config_params(model_params, num_settings, random_state):
'\n\n Args:\n model_params:\n num_settings:\n random_state:\n\n Returns:\n\n '
param_distributions = dict()
dist_types = dict(randint=randint, expon=expon, uniform=uniform)
for (param, param_value) in ... |
def validate_model_configuration(classifier_model_name, classifier_model_config, regressor_model_name, regressor_model_config, config_index, train_scaled_input, train_labels, train_scaled_output, val_scaled_input, val_labels, val_scaled_output, classifier_metric_list, regressor_metric_list):
'\n Train a single m... | -7,930,543,682,850,534,000 | Train a single machine learning model configuration to predict each microphysical tendency.
Args:
classifier_model_name:
classifier_model_config:
regressor_model_name:
regressor_model_config:
config_index:
train_scaled_input:
train_labels:
train_scaled_output:
val_scaled_input:
... | scripts/search_ml_model_params.py | validate_model_configuration | NCAR/mlmicrophysics | python | def validate_model_configuration(classifier_model_name, classifier_model_config, regressor_model_name, regressor_model_config, config_index, train_scaled_input, train_labels, train_scaled_output, val_scaled_input, val_labels, val_scaled_output, classifier_metric_list, regressor_metric_list):
'\n Train a single m... |
def test_integers(self):
'Adding an integer as a tag should raise a ValueError (#237).'
apple = self.food_model.objects.create(name='apple')
with self.assertRaisesRegexp(ValueError, "Cannot add 1 \\(<(type|class) 'int'>\\). Expected <class 'django.db.models.base.ModelBase'> or str."):
apple.tags.add... | 2,789,897,774,914,508,000 | Adding an integer as a tag should raise a ValueError (#237). | tests/tests.py | test_integers | Immensa/django-taggit | python | def test_integers(self):
apple = self.food_model.objects.create(name='apple')
with self.assertRaisesRegexp(ValueError, "Cannot add 1 \\(<(type|class) 'int'>\\). Expected <class 'django.db.models.base.ModelBase'> or str."):
apple.tags.add(1) |
def test_similarity_by_tag(self):
'Test that pears are more similar to apples than watermelons'
apple = self.food_model.objects.create(name='apple')
apple.tags.add('green', 'juicy', 'small', 'sour')
pear = self.food_model.objects.create(name='pear')
pear.tags.add('green', 'juicy', 'small', 'sweet')
... | 1,360,618,908,624,745,200 | Test that pears are more similar to apples than watermelons | tests/tests.py | test_similarity_by_tag | Immensa/django-taggit | python | def test_similarity_by_tag(self):
apple = self.food_model.objects.create(name='apple')
apple.tags.add('green', 'juicy', 'small', 'sour')
pear = self.food_model.objects.create(name='pear')
pear.tags.add('green', 'juicy', 'small', 'sweet')
watermelon = self.food_model.objects.create(name='waterme... |
def test_with_simple_space_delimited_tags(self):
'\n Test with simple space-delimited tags.\n '
self.assertEqual(parse_tags('one'), ['one'])
self.assertEqual(parse_tags('one two'), ['one', 'two'])
self.assertEqual(parse_tags('one two three'), ['one', 'three', 'two'])
self.assertEqual(p... | -1,707,843,335,423,203,600 | Test with simple space-delimited tags. | tests/tests.py | test_with_simple_space_delimited_tags | Immensa/django-taggit | python | def test_with_simple_space_delimited_tags(self):
'\n \n '
self.assertEqual(parse_tags('one'), ['one'])
self.assertEqual(parse_tags('one two'), ['one', 'two'])
self.assertEqual(parse_tags('one two three'), ['one', 'three', 'two'])
self.assertEqual(parse_tags('one one two two'), ['one', ... |
def test_with_comma_delimited_multiple_words(self):
'\n Test with comma-delimited multiple words.\n An unquoted comma in the input will trigger this.\n '
self.assertEqual(parse_tags(',one'), ['one'])
self.assertEqual(parse_tags(',one two'), ['one two'])
self.assertEqual(parse_tags('... | 2,158,762,115,256,022,300 | Test with comma-delimited multiple words.
An unquoted comma in the input will trigger this. | tests/tests.py | test_with_comma_delimited_multiple_words | Immensa/django-taggit | python | def test_with_comma_delimited_multiple_words(self):
'\n Test with comma-delimited multiple words.\n An unquoted comma in the input will trigger this.\n '
self.assertEqual(parse_tags(',one'), ['one'])
self.assertEqual(parse_tags(',one two'), ['one two'])
self.assertEqual(parse_tags('... |
def test_with_double_quoted_multiple_words(self):
'\n Test with double-quoted multiple words.\n A completed quote will trigger this. Unclosed quotes are ignored.\n '
self.assertEqual(parse_tags('"one'), ['one'])
self.assertEqual(parse_tags('"one two'), ['one', 'two'])
self.assertEq... | 5,358,388,618,481,210,000 | Test with double-quoted multiple words.
A completed quote will trigger this. Unclosed quotes are ignored. | tests/tests.py | test_with_double_quoted_multiple_words | Immensa/django-taggit | python | def test_with_double_quoted_multiple_words(self):
'\n Test with double-quoted multiple words.\n A completed quote will trigger this. Unclosed quotes are ignored.\n '
self.assertEqual(parse_tags('"one'), ['one'])
self.assertEqual(parse_tags('"one two'), ['one', 'two'])
self.assertEq... |
def test_with_no_loose_commas(self):
'\n Test with no loose commas -- split on spaces.\n '
self.assertEqual(parse_tags('one two "thr,ee"'), ['one', 'thr,ee', 'two']) | 6,121,131,418,971,766,000 | Test with no loose commas -- split on spaces. | tests/tests.py | test_with_no_loose_commas | Immensa/django-taggit | python | def test_with_no_loose_commas(self):
'\n \n '
self.assertEqual(parse_tags('one two "thr,ee"'), ['one', 'thr,ee', 'two']) |
def test_with_loose_commas(self):
'\n Loose commas - split on commas\n '
self.assertEqual(parse_tags('"one", two three'), ['one', 'two three']) | 4,144,708,498,068,357,000 | Loose commas - split on commas | tests/tests.py | test_with_loose_commas | Immensa/django-taggit | python | def test_with_loose_commas(self):
'\n \n '
self.assertEqual(parse_tags('"one", two three'), ['one', 'two three']) |
def test_tags_with_double_quotes_can_contain_commas(self):
'\n Double quotes can contain commas\n '
self.assertEqual(parse_tags('a-one "a-two, and a-three"'), ['a-one', 'a-two, and a-three'])
self.assertEqual(parse_tags('"two", one, one, two, "one"'), ['one', 'two']) | 2,664,990,977,619,601,400 | Double quotes can contain commas | tests/tests.py | test_tags_with_double_quotes_can_contain_commas | Immensa/django-taggit | python | def test_tags_with_double_quotes_can_contain_commas(self):
'\n \n '
self.assertEqual(parse_tags('a-one "a-two, and a-three"'), ['a-one', 'a-two, and a-three'])
self.assertEqual(parse_tags('"two", one, one, two, "one"'), ['one', 'two']) |
def test_with_naughty_input(self):
'\n Test with naughty input.\n '
self.assertEqual(parse_tags(None), [])
self.assertEqual(parse_tags(''), [])
self.assertEqual(parse_tags('"'), [])
self.assertEqual(parse_tags('""'), [])
self.assertEqual(parse_tags(('"' * 7)), [])
self.assertEq... | 6,806,080,594,845,519,000 | Test with naughty input. | tests/tests.py | test_with_naughty_input | Immensa/django-taggit | python | def test_with_naughty_input(self):
'\n \n '
self.assertEqual(parse_tags(None), [])
self.assertEqual(parse_tags(), [])
self.assertEqual(parse_tags('"'), [])
self.assertEqual(parse_tags(''), [])
self.assertEqual(parse_tags(('"' * 7)), [])
self.assertEqual(parse_tags(',,,,,,'), []... |
@pytest.fixture
def cache(request):
'\n Return a cache object that can persist state between testing sessions.\n\n cache.get(key, default)\n cache.set(key, value)\n\n Keys must be a ``/`` separated value, where the first part is usually the\n name of your plugin or application to avoid clashes with o... | -824,272,688,077,182,200 | Return a cache object that can persist state between testing sessions.
cache.get(key, default)
cache.set(key, value)
Keys must be a ``/`` separated value, where the first part is usually the
name of your plugin or application to avoid clashes with other cache users.
Values can be any object handled by the json stdli... | src/_pytest/cacheprovider.py | cache | bigbZik/pytest | python | @pytest.fixture
def cache(request):
'\n Return a cache object that can persist state between testing sessions.\n\n cache.get(key, default)\n cache.set(key, value)\n\n Keys must be a ``/`` separated value, where the first part is usually the\n name of your plugin or application to avoid clashes with o... |
def makedir(self, name):
' return a directory path object with the given name. If the\n directory does not yet exist, it will be created. You can use it\n to manage files likes e. g. store/retrieve database\n dumps across test sessions.\n\n :param name: must be a string not containing ... | -4,155,896,975,680,624,600 | return a directory path object with the given name. If the
directory does not yet exist, it will be created. You can use it
to manage files likes e. g. store/retrieve database
dumps across test sessions.
:param name: must be a string not containing a ``/`` separator.
Make sure the name contains your plugin or a... | src/_pytest/cacheprovider.py | makedir | bigbZik/pytest | python | def makedir(self, name):
' return a directory path object with the given name. If the\n directory does not yet exist, it will be created. You can use it\n to manage files likes e. g. store/retrieve database\n dumps across test sessions.\n\n :param name: must be a string not containing ... |
def get(self, key, default):
' return cached value for the given key. If no value\n was yet cached or the value cannot be read, the specified\n default is returned.\n\n :param key: must be a ``/`` separated value. Usually the first\n name is the name of your plugin or your applicat... | 1,390,270,008,379,693,000 | return cached value for the given key. If no value
was yet cached or the value cannot be read, the specified
default is returned.
:param key: must be a ``/`` separated value. Usually the first
name is the name of your plugin or your application.
:param default: must be provided in case of a cache-miss or
in... | src/_pytest/cacheprovider.py | get | bigbZik/pytest | python | def get(self, key, default):
' return cached value for the given key. If no value\n was yet cached or the value cannot be read, the specified\n default is returned.\n\n :param key: must be a ``/`` separated value. Usually the first\n name is the name of your plugin or your applicat... |
def set(self, key, value):
' save value for the given key.\n\n :param key: must be a ``/`` separated value. Usually the first\n name is the name of your plugin or your application.\n :param value: must be of any combination of basic\n python types, including nested types\n ... | 1,058,034,138,620,027,000 | save value for the given key.
:param key: must be a ``/`` separated value. Usually the first
name is the name of your plugin or your application.
:param value: must be of any combination of basic
python types, including nested types
like e. g. lists of dictionaries. | src/_pytest/cacheprovider.py | set | bigbZik/pytest | python | def set(self, key, value):
' save value for the given key.\n\n :param key: must be a ``/`` separated value. Usually the first\n name is the name of your plugin or your application.\n :param value: must be of any combination of basic\n python types, including nested types\n ... |
def __init__(self, policyId=None, policyType=None):
'\n :param policyId: (Optional) 自动任务策略ID。\n :param policyType: (Optional) 自动任务策略类型，当前只支持 `AutoImage` 自动备份镜像。\n '
self.policyId = policyId
self.policyType = policyType | -6,499,586,989,692,238,000 | :param policyId: (Optional) 自动任务策略ID。
:param policyType: (Optional) 自动任务策略类型，当前只支持 `AutoImage` 自动备份镜像。 | jdcloud_sdk/services/vm/models/Policy.py | __init__ | jdcloud-api/jdcloud-sdk-python | python | def __init__(self, policyId=None, policyType=None):
'\n :param policyId: (Optional) 自动任务策略ID。\n :param policyType: (Optional) 自动任务策略类型，当前只支持 `AutoImage` 自动备份镜像。\n '
self.policyId = policyId
self.policyType = policyType |
def _compute_delta(log_moments, eps):
'Compute delta for given log_moments and eps.\n\n Args:\n log_moments: the log moments of privacy loss, in the form of pairs\n of (moment_order, log_moment)\n eps: the target epsilon.\n Returns:\n delta\n '
min_delta = 1.0
for (moment_order, log_moment)... | 8,862,847,200,555,492,000 | Compute delta for given log_moments and eps.
Args:
log_moments: the log moments of privacy loss, in the form of pairs
of (moment_order, log_moment)
eps: the target epsilon.
Returns:
delta | CIFAR_tests/gaussian_moments.py | _compute_delta | DPBayes/ADADP | python | def _compute_delta(log_moments, eps):
'Compute delta for given log_moments and eps.\n\n Args:\n log_moments: the log moments of privacy loss, in the form of pairs\n of (moment_order, log_moment)\n eps: the target epsilon.\n Returns:\n delta\n '
min_delta = 1.0
for (moment_order, log_moment)... |
def _compute_eps(log_moments, delta):
'Compute epsilon for given log_moments and delta.\n\n Args:\n log_moments: the log moments of privacy loss, in the form of pairs\n of (moment_order, log_moment)\n delta: the target delta.\n Returns:\n epsilon\n '
min_eps = float('inf')
for (moment_order... | -6,687,883,152,029,463,000 | Compute epsilon for given log_moments and delta.
Args:
log_moments: the log moments of privacy loss, in the form of pairs
of (moment_order, log_moment)
delta: the target delta.
Returns:
epsilon | CIFAR_tests/gaussian_moments.py | _compute_eps | DPBayes/ADADP | python | def _compute_eps(log_moments, delta):
'Compute epsilon for given log_moments and delta.\n\n Args:\n log_moments: the log moments of privacy loss, in the form of pairs\n of (moment_order, log_moment)\n delta: the target delta.\n Returns:\n epsilon\n '
min_eps = float('inf')
for (moment_order... |
def compute_log_moment(q, sigma, steps, lmbd, verify=False, verbose=False):
'Compute the log moment of Gaussian mechanism for given parameters.\n\n Args:\n q: the sampling ratio.\n sigma: the noise sigma.\n steps: the number of steps.\n lmbd: the moment order.\n verify: if False, only compute the sy... | -2,424,217,040,214,786,000 | Compute the log moment of Gaussian mechanism for given parameters.
Args:
q: the sampling ratio.
sigma: the noise sigma.
steps: the number of steps.
lmbd: the moment order.
verify: if False, only compute the symbolic version. If True, computes
both symbolic and numerical solutions and verifies the results... | CIFAR_tests/gaussian_moments.py | compute_log_moment | DPBayes/ADADP | python | def compute_log_moment(q, sigma, steps, lmbd, verify=False, verbose=False):
'Compute the log moment of Gaussian mechanism for given parameters.\n\n Args:\n q: the sampling ratio.\n sigma: the noise sigma.\n steps: the number of steps.\n lmbd: the moment order.\n verify: if False, only compute the sy... |
def get_privacy_spent(log_moments, target_eps=None, target_delta=None):
'Compute delta (or eps) for given eps (or delta) from log moments.\n\n Args:\n log_moments: array of (moment_order, log_moment) pairs.\n target_eps: if not None, the epsilon for which we would like to compute\n corresponding delta v... | 7,915,246,685,305,713,000 | Compute delta (or eps) for given eps (or delta) from log moments.
Args:
log_moments: array of (moment_order, log_moment) pairs.
target_eps: if not None, the epsilon for which we would like to compute
corresponding delta value.
target_delta: if not None, the delta for which we would like to compute
corres... | CIFAR_tests/gaussian_moments.py | get_privacy_spent | DPBayes/ADADP | python | def get_privacy_spent(log_moments, target_eps=None, target_delta=None):
'Compute delta (or eps) for given eps (or delta) from log moments.\n\n Args:\n log_moments: array of (moment_order, log_moment) pairs.\n target_eps: if not None, the epsilon for which we would like to compute\n corresponding delta v... |
def create(self, validated_data):
'Create a new user with encrypted password and return it'
return get_user_model().objects.create_user(**validated_data) | -1,686,897,521,577,608,400 | Create a new user with encrypted password and return it | app/user/serializers.py | create | siddharthisaiah/recipe-app-api | python | def create(self, validated_data):
return get_user_model().objects.create_user(**validated_data) |
def update(self, instance, validated_data):
'Update a user, setting the password correctly and return it'
password = validated_data.pop('password', None)
user = super().update(instance, validated_data)
if password:
user.set_password(password)
user.save()
return user | -203,708,244,252,171,460 | Update a user, setting the password correctly and return it | app/user/serializers.py | update | siddharthisaiah/recipe-app-api | python | def update(self, instance, validated_data):
password = validated_data.pop('password', None)
user = super().update(instance, validated_data)
if password:
user.set_password(password)
user.save()
return user |
def validate(self, attrs):
'Validate and authenticate the user'
email = attrs.get('email')
password = attrs.get('password')
user = authenticate(request=self.context.get('request'), username=email, password=password)
if (not user):
msg = _('Unable to authenticate with provided credentials')
... | 6,380,639,698,687,869,000 | Validate and authenticate the user | app/user/serializers.py | validate | siddharthisaiah/recipe-app-api | python | def validate(self, attrs):
email = attrs.get('email')
password = attrs.get('password')
user = authenticate(request=self.context.get('request'), username=email, password=password)
if (not user):
msg = _('Unable to authenticate with provided credentials')
raise serializers.ValidationE... |
def testProjectWorkflowStepDtoV2(self):
'Test ProjectWorkflowStepDtoV2'
pass | 5,664,496,987,346,935,000 | Test ProjectWorkflowStepDtoV2 | test/test_project_workflow_step_dto_v2.py | testProjectWorkflowStepDtoV2 | unofficial-memsource/memsource-cli | python | def testProjectWorkflowStepDtoV2(self):
pass |
def binarize(tensor: tf.Tensor, bitsize: Optional[int]=None) -> tf.Tensor:
'Extract bits of values in `tensor`, returning a `tf.Tensor` with same\n dtype.'
with tf.name_scope('binarize'):
bitsize = (bitsize or (tensor.dtype.size * 8))
bit_indices_shape = (([1] * len(tensor.shape)) + [bitsize])
... | 3,900,341,414,611,682,300 | Extract bits of values in `tensor`, returning a `tf.Tensor` with same
dtype. | tf_encrypted/tensor/shared.py | binarize | Arash-Afshar/tf-encrypted | python | def binarize(tensor: tf.Tensor, bitsize: Optional[int]=None) -> tf.Tensor:
'Extract bits of values in `tensor`, returning a `tf.Tensor` with same\n dtype.'
with tf.name_scope('binarize'):
bitsize = (bitsize or (tensor.dtype.size * 8))
bit_indices_shape = (([1] * len(tensor.shape)) + [bitsize])
... |
def bits(tensor: tf.Tensor, bitsize: Optional[int]=None) -> list:
'Extract bits of values in `tensor`, returning a list of tensors.'
with tf.name_scope('bits'):
bitsize = (bitsize or (tensor.dtype.size * 8))
the_bits = [tf.bitwise.bitwise_and(tf.bitwise.right_shift(tensor, i), 1) for i in range(... | 7,872,300,279,742,978,000 | Extract bits of values in `tensor`, returning a list of tensors. | tf_encrypted/tensor/shared.py | bits | Arash-Afshar/tf-encrypted | python | def bits(tensor: tf.Tensor, bitsize: Optional[int]=None) -> list:
with tf.name_scope('bits'):
bitsize = (bitsize or (tensor.dtype.size * 8))
the_bits = [tf.bitwise.bitwise_and(tf.bitwise.right_shift(tensor, i), 1) for i in range(bitsize)]
return the_bits |
def im2col(x: Union[(tf.Tensor, np.ndarray)], h_filter: int, w_filter: int, padding: str, stride: int) -> tf.Tensor:
'Generic implementation of im2col on tf.Tensors.'
with tf.name_scope('im2col'):
nhwc_tensor = tf.transpose(x, [0, 2, 3, 1])
channels = int(nhwc_tensor.shape[3])
patch_tens... | 426,579,149,317,277,900 | Generic implementation of im2col on tf.Tensors. | tf_encrypted/tensor/shared.py | im2col | Arash-Afshar/tf-encrypted | python | def im2col(x: Union[(tf.Tensor, np.ndarray)], h_filter: int, w_filter: int, padding: str, stride: int) -> tf.Tensor:
with tf.name_scope('im2col'):
nhwc_tensor = tf.transpose(x, [0, 2, 3, 1])
channels = int(nhwc_tensor.shape[3])
patch_tensor = tf.extract_image_patches(nhwc_tensor, ksizes... |
def conv2d(x: AbstractTensor, y: AbstractTensor, stride, padding) -> AbstractTensor:
'Generic convolution implementation with im2col over AbstractTensors.'
with tf.name_scope('conv2d'):
(h_filter, w_filter, in_filters, out_filters) = map(int, y.shape)
(n_x, c_x, h_x, w_x) = map(int, x.shape)
... | -1,181,663,389,733,783,800 | Generic convolution implementation with im2col over AbstractTensors. | tf_encrypted/tensor/shared.py | conv2d | Arash-Afshar/tf-encrypted | python | def conv2d(x: AbstractTensor, y: AbstractTensor, stride, padding) -> AbstractTensor:
with tf.name_scope('conv2d'):
(h_filter, w_filter, in_filters, out_filters) = map(int, y.shape)
(n_x, c_x, h_x, w_x) = map(int, x.shape)
if (c_x != in_filters):
out_filters = in_filters
... |
def _autolag(mod, endog, exog, startlag, maxlag, method, modargs=(), fitargs=(), regresults=False):
"\n Returns the results for the lag length that maximizes the info criterion.\n\n Parameters\n ----------\n mod : Model class\n Model estimator class\n endog : array-like\n nobs array con... | -3,476,040,984,998,727,700 | Returns the results for the lag length that maximizes the info criterion.
Parameters
----------
mod : Model class
Model estimator class
endog : array-like
nobs array containing endogenous variable
exog : array-like
nobs by (startlag + maxlag) array containing lags and possibly other
variables
startlag ... | statsmodels/tsa/stattools.py | _autolag | josef-pkt/statsmodels | python | def _autolag(mod, endog, exog, startlag, maxlag, method, modargs=(), fitargs=(), regresults=False):
"\n Returns the results for the lag length that maximizes the info criterion.\n\n Parameters\n ----------\n mod : Model class\n Model estimator class\n endog : array-like\n nobs array con... |
def adfuller(x, maxlag=None, regression='c', autolag='AIC', store=False, regresults=False):
'\n Augmented Dickey-Fuller unit root test\n\n The Augmented Dickey-Fuller test can be used to test for a unit root in a\n univariate process in the presence of serial correlation.\n\n Parameters\n ----------\... | -4,962,697,273,032,497,000 | Augmented Dickey-Fuller unit root test
The Augmented Dickey-Fuller test can be used to test for a unit root in a
univariate process in the presence of serial correlation.
Parameters
----------
x : array_like, 1d
data series
maxlag : int
Maximum lag which is included in test, default 12*(nobs/100)^{1/4}
regres... | statsmodels/tsa/stattools.py | adfuller | josef-pkt/statsmodels | python | def adfuller(x, maxlag=None, regression='c', autolag='AIC', store=False, regresults=False):
'\n Augmented Dickey-Fuller unit root test\n\n The Augmented Dickey-Fuller test can be used to test for a unit root in a\n univariate process in the presence of serial correlation.\n\n Parameters\n ----------\... |
def acovf(x, unbiased=False, demean=True, fft=False, missing='none'):
"\n Autocovariance for 1D\n\n Parameters\n ----------\n x : array\n Time series data. Must be 1d.\n unbiased : bool\n If True, then denominators is n-k, otherwise n\n demean : bool\n If True, then subtract t... | -5,403,537,379,756,743,000 | Autocovariance for 1D
Parameters
----------
x : array
Time series data. Must be 1d.
unbiased : bool
If True, then denominators is n-k, otherwise n
demean : bool
If True, then subtract the mean x from each element of x
fft : bool
If True, use FFT convolution. This method should be preferred
for lon... | statsmodels/tsa/stattools.py | acovf | josef-pkt/statsmodels | python | def acovf(x, unbiased=False, demean=True, fft=False, missing='none'):
"\n Autocovariance for 1D\n\n Parameters\n ----------\n x : array\n Time series data. Must be 1d.\n unbiased : bool\n If True, then denominators is n-k, otherwise n\n demean : bool\n If True, then subtract t... |
def q_stat(x, nobs, type='ljungbox'):
"\n Return's Ljung-Box Q Statistic\n\n x : array-like\n Array of autocorrelation coefficients. Can be obtained from acf.\n nobs : int\n Number of observations in the entire sample (ie., not just the length\n of the autocorrelation function results... | -7,626,575,831,499,459,000 | Return's Ljung-Box Q Statistic
x : array-like
Array of autocorrelation coefficients. Can be obtained from acf.
nobs : int
Number of observations in the entire sample (ie., not just the length
of the autocorrelation function results.
Returns
-------
q-stat : array
Ljung-Box Q-statistic for autocorrela... | statsmodels/tsa/stattools.py | q_stat | josef-pkt/statsmodels | python | def q_stat(x, nobs, type='ljungbox'):
"\n Return's Ljung-Box Q Statistic\n\n x : array-like\n Array of autocorrelation coefficients. Can be obtained from acf.\n nobs : int\n Number of observations in the entire sample (ie., not just the length\n of the autocorrelation function results... |
def acf(x, unbiased=False, nlags=40, qstat=False, fft=False, alpha=None, missing='none'):
"\n Autocorrelation function for 1d arrays.\n\n Parameters\n ----------\n x : array\n Time series data\n unbiased : bool\n If True, then denominators for autocovariance are n-k, otherwise n\n nlag... | -6,031,796,841,099,306,000 | Autocorrelation function for 1d arrays.
Parameters
----------
x : array
Time series data
unbiased : bool
If True, then denominators for autocovariance are n-k, otherwise n
nlags: int, optional
Number of lags to return autocorrelation for.
qstat : bool, optional
If True, returns the Ljung-Box q statistic ... | statsmodels/tsa/stattools.py | acf | josef-pkt/statsmodels | python | def acf(x, unbiased=False, nlags=40, qstat=False, fft=False, alpha=None, missing='none'):
"\n Autocorrelation function for 1d arrays.\n\n Parameters\n ----------\n x : array\n Time series data\n unbiased : bool\n If True, then denominators for autocovariance are n-k, otherwise n\n nlag... |
def pacf_yw(x, nlags=40, method='unbiased'):
"Partial autocorrelation estimated with non-recursive yule_walker\n\n Parameters\n ----------\n x : 1d array\n observations of time series for which pacf is calculated\n nlags : int\n largest lag for which pacf is returned\n method : 'unbiase... | 7,176,742,949,149,395,000 | Partial autocorrelation estimated with non-recursive yule_walker
Parameters
----------
x : 1d array
observations of time series for which pacf is calculated
nlags : int
largest lag for which pacf is returned
method : 'unbiased' (default) or 'mle'
method for the autocovariance calculations in yule walker
R... | statsmodels/tsa/stattools.py | pacf_yw | josef-pkt/statsmodels | python | def pacf_yw(x, nlags=40, method='unbiased'):
"Partial autocorrelation estimated with non-recursive yule_walker\n\n Parameters\n ----------\n x : 1d array\n observations of time series for which pacf is calculated\n nlags : int\n largest lag for which pacf is returned\n method : 'unbiase... |
def pacf_ols(x, nlags=40):
'Calculate partial autocorrelations\n\n Parameters\n ----------\n x : 1d array\n observations of time series for which pacf is calculated\n nlags : int\n Number of lags for which pacf is returned. Lag 0 is not returned.\n\n Returns\n -------\n pacf : 1d... | 6,238,843,439,734,900,000 | Calculate partial autocorrelations
Parameters
----------
x : 1d array
observations of time series for which pacf is calculated
nlags : int
Number of lags for which pacf is returned. Lag 0 is not returned.
Returns
-------
pacf : 1d array
partial autocorrelations, maxlag+1 elements
Notes
-----
This solves... | statsmodels/tsa/stattools.py | pacf_ols | josef-pkt/statsmodels | python | def pacf_ols(x, nlags=40):
'Calculate partial autocorrelations\n\n Parameters\n ----------\n x : 1d array\n observations of time series for which pacf is calculated\n nlags : int\n Number of lags for which pacf is returned. Lag 0 is not returned.\n\n Returns\n -------\n pacf : 1d... |
def pacf(x, nlags=40, method='ywunbiased', alpha=None):
"\n Partial autocorrelation estimated\n\n Parameters\n ----------\n x : 1d array\n observations of time series for which pacf is calculated\n nlags : int\n largest lag for which pacf is returned\n method : {'ywunbiased', 'ywmle'... | 2,415,925,801,062,447,000 | Partial autocorrelation estimated
Parameters
----------
x : 1d array
observations of time series for which pacf is calculated
nlags : int
largest lag for which pacf is returned
method : {'ywunbiased', 'ywmle', 'ols'}
specifies which method for the calculations to use:
- yw or ywunbiased : yule walker ... | statsmodels/tsa/stattools.py | pacf | josef-pkt/statsmodels | python | def pacf(x, nlags=40, method='ywunbiased', alpha=None):
"\n Partial autocorrelation estimated\n\n Parameters\n ----------\n x : 1d array\n observations of time series for which pacf is calculated\n nlags : int\n largest lag for which pacf is returned\n method : {'ywunbiased', 'ywmle'... |
def ccovf(x, y, unbiased=True, demean=True):
' crosscovariance for 1D\n\n Parameters\n ----------\n x, y : arrays\n time series data\n unbiased : boolean\n if True, then denominators is n-k, otherwise n\n\n Returns\n -------\n ccovf : array\n autocovariance function\n\n No... | 4,675,003,449,293,840,000 | crosscovariance for 1D
Parameters
----------
x, y : arrays
time series data
unbiased : boolean
if True, then denominators is n-k, otherwise n
Returns
-------
ccovf : array
autocovariance function
Notes
-----
This uses np.correlate which does full convolution. For very long time
series it is recommended to ... | statsmodels/tsa/stattools.py | ccovf | josef-pkt/statsmodels | python | def ccovf(x, y, unbiased=True, demean=True):
' crosscovariance for 1D\n\n Parameters\n ----------\n x, y : arrays\n time series data\n unbiased : boolean\n if True, then denominators is n-k, otherwise n\n\n Returns\n -------\n ccovf : array\n autocovariance function\n\n No... |
def ccf(x, y, unbiased=True):
'cross-correlation function for 1d\n\n Parameters\n ----------\n x, y : arrays\n time series data\n unbiased : boolean\n if True, then denominators for autocovariance is n-k, otherwise n\n\n Returns\n -------\n ccf : array\n cross-correlation fun... | -2,296,032,143,774,643,000 | cross-correlation function for 1d
Parameters
----------
x, y : arrays
time series data
unbiased : boolean
if True, then denominators for autocovariance is n-k, otherwise n
Returns
-------
ccf : array
cross-correlation function of x and y
Notes
-----
This is based np.correlate which does full convolution. F... | statsmodels/tsa/stattools.py | ccf | josef-pkt/statsmodels | python | def ccf(x, y, unbiased=True):
'cross-correlation function for 1d\n\n Parameters\n ----------\n x, y : arrays\n time series data\n unbiased : boolean\n if True, then denominators for autocovariance is n-k, otherwise n\n\n Returns\n -------\n ccf : array\n cross-correlation fun... |
def periodogram(X):
'\n Returns the periodogram for the natural frequency of X\n\n Parameters\n ----------\n X : array-like\n Array for which the periodogram is desired.\n\n Returns\n -------\n pgram : array\n 1./len(X) * np.abs(np.fft.fft(X))**2\n\n\n References\n ---------... | 6,612,493,813,122,968,000 | Returns the periodogram for the natural frequency of X
Parameters
----------
X : array-like
Array for which the periodogram is desired.
Returns
-------
pgram : array
1./len(X) * np.abs(np.fft.fft(X))**2
References
----------
Brockwell and Davis. | statsmodels/tsa/stattools.py | periodogram | josef-pkt/statsmodels | python | def periodogram(X):
'\n Returns the periodogram for the natural frequency of X\n\n Parameters\n ----------\n X : array-like\n Array for which the periodogram is desired.\n\n Returns\n -------\n pgram : array\n 1./len(X) * np.abs(np.fft.fft(X))**2\n\n\n References\n ---------... |
def levinson_durbin(s, nlags=10, isacov=False):
'Levinson-Durbin recursion for autoregressive processes\n\n Parameters\n ----------\n s : array_like\n If isacov is False, then this is the time series. If iasacov is true\n then this is interpreted as autocovariance starting with lag 0\n nla... | -1,512,750,122,343,626,200 | Levinson-Durbin recursion for autoregressive processes
Parameters
----------
s : array_like
If isacov is False, then this is the time series. If iasacov is true
then this is interpreted as autocovariance starting with lag 0
nlags : integer
largest lag to include in recursion or order of the autoregressive
... | statsmodels/tsa/stattools.py | levinson_durbin | josef-pkt/statsmodels | python | def levinson_durbin(s, nlags=10, isacov=False):
'Levinson-Durbin recursion for autoregressive processes\n\n Parameters\n ----------\n s : array_like\n If isacov is False, then this is the time series. If iasacov is true\n then this is interpreted as autocovariance starting with lag 0\n nla... |
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