_id stringlengths 98 184 | text stringlengths 91 10.9k |
|---|---|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/state.py#L107-L120 | def unfinished(cls):
"""
"""
return [
cls.NONE,
cls.SCHEDULED,
cls.QUEUED,
cls.RUNNING,
cls.SHUTDOWN,
cls.UP_FOR_RETRY,
cls.UP_FOR_RESCHEDULE
] |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/internal/distribution_tensor_coercible.py#L71-L128 | def _value(self, dtype=None, name=None, as_ref=False): # pylint: disable=g-doc-args
"""
"""
# pylint: disable=protected-access
if as_ref:
raise NotImplementedError(
'Cannot convert a `Distribution` to a reference '
'(e.g., `tf.Variable`).')
if self._concrete_value is None:
if self._c... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hdfs_hook.py#L57-L98 | def get_conn(self):
"""
"""
# When using HAClient, proxy_user must be the same, so is ok to always
# take the first.
effective_user = self.proxy_user
autoconfig = self.autoconfig
use_sasl = configuration.conf.get('core', 'security') == 'kerberos'
... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_lambda_hook.py#L53-L68 | def invoke_lambda(self, payload):
"""
"""
awslambda_conn = self.get_conn()
response = awslambda_conn.invoke(
FunctionName=self.function_name,
InvocationType=self.invocation_type,
LogType=self.log_type,
Payload=payload,
... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/nelder_mead.py#L602-L606 | def _replace_at_index(x, index, replacement):
""""""
x_new = tf.concat([x[:index], tf.expand_dims(replacement, axis=0),
x[(index + 1):]], axis=0)
return x_new |
https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/datasets/sbu.py#L87-L110 | def download(self):
""""""
import tarfile
if self._check_integrity():
print('Files already downloaded and verified')
return
download_url(self.url, self.root, self.filename, self.md5_checksum)
# Extract file
with tarfile.open(os.path.join(self.ro... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/s3_task_handler.py#L146-L170 | def s3_write(self, log, remote_log_location, append=True):
"""
"""
if append and self.s3_log_exists(remote_log_location):
old_log = self.s3_read(remote_log_location)
log = '\n'.join([old_log, log]) if old_log else log
try:
self.hook.load_stri... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273 | def one_step(self, current_state, previous_kernel_results):
"""
"""
with tf.compat.v1.name_scope(
name=mcmc_util.make_name(self.name, 'transformed_kernel', 'one_step'),
values=[previous_kernel_results]):
transformed_next_state, kernel_results = self._inner_kernel.one_step(
pr... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/opsgenie_alert_operator.py#L126-L131 | def execute(self, context):
"""
"""
self.hook = OpsgenieAlertHook(self.opsgenie_conn_id)
self.hook.execute(self._build_opsgenie_payload()) |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/hmc.py#L1052-L1145 | def _compute_log_acceptance_correction(current_momentums,
proposed_momentums,
independent_chain_ndims,
name=None):
"""
"""
with tf.compat.v1.name_scope(
name, 'compute_log_acceptance_correcti... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_vision_hook.py#L298-L307 | def delete_product(self, location, product_id, project_id=None, retry=None, timeout=None, metadata=None):
"""
"""
client = self.get_conn()
name = ProductSearchClient.product_path(project_id, location, product_id)
self.log.info('Deleting ProductSet: %s', name)
cli... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L426-L545 | def bracket(value_and_gradients_function,
search_interval,
f_lim,
max_iterations,
expansion_param=5.0):
"""
"""
already_stopped = search_interval.failed | search_interval.converged
# If the slope at right end point is positive, step B1 in [2], then the given
# ... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/mysql_to_gcs.py#L290-L310 | def _get_col_type_dict(self):
"""
"""
schema = []
if isinstance(self.schema, string_types):
schema = json.loads(self.schema)
elif isinstance(self.schema, list):
schema = self.schema
elif self.schema is not None:
self.log.warn('... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/distribution_layer.py#L754-L768 | def _eval_all_one_hot(fn, dist, name=None):
""""""
with tf.compat.v1.name_scope(name, 'eval_all_one_hot'):
event_size = dist.event_shape_tensor()[-1]
batch_ndims = tf.size(input=dist.batch_shape_tensor())
# Reshape `eye(d)` to: `[d] + [1]*batch_ndims + [d]`.
x = tf.reshape(
tf.eye(event_size... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/interceptor.py#L96-L172 | def get_next_interceptor():
"""
"""
try:
interceptor = _interceptor_stack.stack.pop()
yield interceptor
finally:
_interceptor_stack.stack.append(interceptor) |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_fileshare_hook.py#L64-L81 | def check_for_file(self, share_name, directory_name, file_name, **kwargs):
"""
"""
return self.connection.exists(share_name, directory_name,
file_name, **kwargs) |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/bfgs_utils.py#L309-L322 | def _check_convergence(current_position,
next_position,
current_objective,
next_objective,
next_gradient,
grad_tolerance,
f_relative_tolerance,
x_tolerance):
... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/auth/backends/password_auth.py#L107-L132 | def authenticate(session, username, password):
"""
"""
if not username or not password:
raise AuthenticationError()
user = session.query(PasswordUser).filter(
PasswordUser.username == username).first()
if not user:
raise AuthenticationError()
if not user.authentic... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1729-L1732 | def _propagate_cov(cov, linop, dist):
""""""
# For linop A and input cov P, returns `A P A' + dist.cov()`
return linop.matmul(linop.matmul(cov), adjoint_arg=True) + dist.covariance() |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/hmc.py#L556-L564 | def bootstrap_results(self, init_state):
""""""
kernel_results = self._impl.bootstrap_results(init_state)
if self.step_size_update_fn is not None:
step_size_assign = self.step_size_update_fn(self.step_size, None) # pylint: disable=not-callable
kernel_results = kernel_results._replace(
... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L190-L210 | def build_fake_data(num_examples=10):
""""""
class Dummy(object):
pass
num_examples = 10
mnist_data = Dummy()
mnist_data.train = Dummy()
mnist_data.train.images = np.float32(np.random.randn(
num_examples, *IMAGE_SHAPE))
mnist_data.train.labels = np.int32(np.random.permutation(
np.arange(... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L48-L60 | def check_for_bucket(self, bucket_name):
"""
"""
try:
self.get_conn().head_bucket(Bucket=bucket_name)
return True
except ClientError as e:
self.log.info(e.response["Error"]["Message"])
return False |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/disentangled_vae.py#L846-L866 | def sample_static_prior(self, samples, batch_size, fixed=False):
"""
"""
dist = self.static_prior()
if fixed: # in either case, shape is (samples, batch, latent)
sample = dist.sample((samples, 1)) + tf.zeros([batch_size, 1])
else:
sample = dist.sample((samples, batch_size))
return s... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/deep_exponential_family.py#L178-L231 | def load_nips2011_papers(path):
"""
"""
path = os.path.expanduser(path)
filename = "NIPS_1987-2015.csv"
filepath = os.path.join(path, filename)
if not os.path.exists(filepath):
url = ("https://archive.ics.uci.edu/ml/machine-learning-databases/"
"00371/NIPS_1987-2015.csv")
if not tf.io.gfi... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1705-L1779 | def insert_all(self, project_id, dataset_id, table_id,
rows, ignore_unknown_values=False,
skip_invalid_rows=False, fail_on_error=False):
"""
"""
dataset_project_id = project_id if project_id else self.project_id
body = {
"rows"... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/text_messages_hmc.py#L64-L153 | def benchmark_text_messages_hmc(
num_results=int(3e3),
num_burnin_steps=int(3e3),
num_leapfrog_steps=3):
""""""
if not tf.executing_eagerly():
tf.compat.v1.reset_default_graph()
# Build a static, pretend dataset.
count_data = tf.cast(
tf.concat(
[tfd.Poisson(rate=15.).sample(43... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/transformed_distribution.py#L415-L430 | def _finish_log_prob_for_one_fiber(self, y, x, ildj, event_ndims,
**distribution_kwargs):
""""""
x = self._maybe_rotate_dims(x, rotate_right=True)
log_prob = self.distribution.log_prob(x, **distribution_kwargs)
if self._is_maybe_event_override:
log_prob = tf.re... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L95-L122 | def get_pandas_df(self, sql, parameters=None, dialect=None):
"""
"""
private_key = self._get_field('key_path', None) or self._get_field('keyfile_dict', None)
if dialect is None:
dialect = 'legacy' if self.use_legacy_sql else 'standard'
return read_gbq(sql,
... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L165-L183 | def create_version(self, project_id, model_name, version_spec):
"""
"""
parent_name = 'projects/{}/models/{}'.format(project_id, model_name)
create_request = self._mlengine.projects().models().versions().create(
parent=parent_name, body=version_spec)
response... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L474-L485 | def buildcontent(self):
"""
"""
self.buildcontainer()
# if the subclass has a method buildjs this method will be
# called instead of the method defined here
# when this subclass method is entered it does call
# the method buildjschart defined here
self.bui... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/internal/util.py#L255-L293 | def _maybe_expand_trailing_dim(observed_time_series_tensor):
"""
"""
with tf.compat.v1.name_scope(
'maybe_expand_trailing_dim', values=[observed_time_series_tensor]):
if (observed_time_series_tensor.shape.ndims is not None and
tf.compat.dimension_value(
observed_time_series_tensor.s... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L111-L129 | def _append_label(cluster_proto, key, val):
"""
"""
val = val.replace('.', '-').replace('+', '-')
cluster_proto.resource_labels.update({key: val})
return cluster_proto |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/gcs_task_handler.py#L166-L177 | def parse_gcs_url(gsurl):
"""
"""
parsed_url = urlparse(gsurl)
if not parsed_url.netloc:
raise AirflowException('Please provide a bucket name')
else:
bucket = parsed_url.netloc
blob = parsed_url.path.strip('/')
return bucke... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/no_u_turn_sampler/nuts.py#L457-L466 | def _embed_no_none_gradient_check(value_and_gradients_fn):
""""""
@functools.wraps(value_and_gradients_fn)
def func_wrapped(*args, **kwargs):
"""Wrapped function which checks for None gradients."""
value, grads = value_and_gradients_fn(*args, **kwargs)
if any(grad is None for grad in grads):
rai... |
https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L255-L340 | def pad(img, padding, fill=0, padding_mode='constant'):
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if not isinstance(padding, (numbers.Number, tuple)):
raise TypeError('Got inappropriate padding arg')
if not isinstance(fill, ... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/dense_variational.py#L193-L213 | def compute_output_shape(self, input_shape):
"""
"""
input_shape = tf.TensorShape(input_shape)
input_shape = input_shape.with_rank_at_least(2)
if tf.compat.dimension_value(input_shape[-1]) is None:
raise ValueError(
'The innermost dimension of `input_shape` must be defined, '
... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/glm/family.py#L174-L179 | def _name_scope(self, name=None, default_name=None, values=None):
""""""
with tf.compat.v1.name_scope(self.name):
with tf.compat.v1.name_scope(
name, default_name, values=values or []) as scope:
yield scope |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/diagnostic.py#L35-L143 | def effective_sample_size(states,
filter_threshold=0.,
filter_beyond_lag=None,
name=None):
"""
"""
states_was_list = _is_list_like(states)
# Convert all args to lists.
if not states_was_list:
states = [states]
filter_beyond_... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/disentangled_vae.py#L787-L844 | def reconstruct(self, inputs, samples=1, sample_static=False,
sample_dynamic=False, swap_static=False, swap_dynamic=False,
fix_static=False, fix_dynamic=False):
"""
"""
batch_size = tf.shape(input=inputs)[-5]
length = len(tf.unstack(inputs, axis=-4)) # hack for graph... |
https://github.com/asciimoo/searx/blob/a84caa22cf947e973c10aa968d35fb2bdda6d048/searx/poolrequests.py#L90-L128 | def request(method, url, **kwargs):
""""""
time_before_request = time()
# session start
session = SessionSinglePool()
# proxies
kwargs['proxies'] = settings['outgoing'].get('proxies') or None
# timeout
if 'timeout' in kwargs:
timeout = kwargs['timeout']
else:
timeo... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/models/bayesian_resnet.py#L25-L92 | def bayesian_resnet(input_shape,
num_classes=10,
kernel_posterior_scale_mean=-9.0,
kernel_posterior_scale_stddev=0.1,
kernel_posterior_scale_constraint=0.2):
"""
"""
filters = [64, 128, 256, 512]
kernels = [3, 3, 3, 3]
strides = ... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/nelder_mead.py#L832-L844 | def _resolve_parameters(dim,
reflection,
expansion,
contraction,
shrinkage,
dtype):
""""""
dim = tf.cast(dim, dtype=dtype)
reflection = 1. if reflection is None else reflection
expansion = (1.... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/glm/fisher_scoring.py#L517-L620 | def prepare_args(model_matrix,
response,
model_coefficients,
predicted_linear_response,
offset,
name=None):
"""
"""
graph_deps = [model_matrix, response, model_coefficients,
predicted_linear_response, offset]
wi... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/lkj.py#L47-L56 | def _uniform_unit_norm(dimension, shape, dtype, seed):
""""""
# This works because the Gaussian distribution is spherically symmetric.
# raw shape: shape + [dimension]
raw = normal.Normal(
loc=dtype_util.as_numpy_dtype(dtype)(0),
scale=dtype_util.as_numpy_dtype(dtype)(1)).sample(
tf.concat... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/forecast.py#L172-L362 | def forecast(model,
observed_time_series,
parameter_samples,
num_steps_forecast):
"""
"""
with tf.compat.v1.name_scope(
'forecast',
values=[observed_time_series, parameter_samples, num_steps_forecast]):
[
observed_time_series,
mask
] = s... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L447-L475 | def insert_bucket_acl(self, bucket_name, entity, role, user_project=None):
"""
"""
self.log.info('Creating a new ACL entry in bucket: %s', bucket_name)
client = self.get_conn()
bucket = client.bucket(bucket_name=bucket_name)
bucket.acl.reload()
bucket.acl... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagrun.py#L242-L329 | def update_state(self, session=None):
"""
"""
dag = self.get_dag()
tis = self.get_task_instances(session=session)
self.log.debug("Updating state for %s considering %s task(s)", self, len(tis))
for ti in list(tis):
# skip in db?
if ti.st... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/__init__.py#L28-L46 | def ds_add(ds, days):
"""
"""
ds = datetime.strptime(ds, '%Y-%m-%d')
if days:
ds = ds + timedelta(days)
return ds.isoformat()[:10] |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/utils.py#L79-L185 | def generate_pages(current_page, num_of_pages,
search=None, showPaused=None, window=7):
"""
"""
void_link = 'javascript:void(0)'
first_node = Markup("""<li class="paginate_button {disabled}" id="dags_first">
<a href="{href_link}" aria-controls="dags" data-dt-idx="0" tabindex... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1097-L1145 | def latents_to_observations(self, latent_means, latent_covs):
"""
"""
with tf.name_scope("latents_to_observations"):
pushforward_latents_step = build_pushforward_latents_step(
self.get_observation_matrix_for_timestep,
self.get_observation_noise_for_timestep)
latent_means =... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/nest_util.py#L98-L113 | def _nested_convert_to_tensor(struct, dtype=None, name=None):
""""""
if dtype is not None or not tf.nest.is_nested(struct):
return tf.convert_to_tensor(struct, dtype=dtype)
if _maybe_convertible_to_tensor(struct):
try:
# Try converting the structure wholesale.
return tf.convert_to_tensor(valu... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1577-L1619 | def build_kalman_cov_step(get_transition_matrix_for_timestep,
get_transition_noise_for_timestep,
get_observation_matrix_for_timestep,
get_observation_noise_for_timestep):
"""
"""
def cov_step(previous_covs, t):
"""Single step of pr... |
https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L468-L499 | def five_crop(img, size):
"""
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
w, h = img.size
crop_h, crop_w = size
if crop_w > w or crop_h > h:
raise ValueError... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/replica_exchange_mc.py#L569-L575 | def _get_field(kernel_results, field_name):
""""""
if hasattr(kernel_results, field_name):
return getattr(kernel_results, field_name)
if hasattr(kernel_results, 'accepted_results'):
return getattr(kernel_results.accepted_results, field_name)
raise TypeError('Cannot extract %s from %s' % (field_name, ker... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/vi/csiszar_divergence.py#L550-L585 | def jeffreys(logu, name=None):
"""
"""
with tf.compat.v1.name_scope(name, "jeffreys", [logu]):
logu = tf.convert_to_tensor(value=logu, name="logu")
return 0.5 * tf.math.expm1(logu) * logu |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagrun.py#L115-L160 | def find(dag_id=None, run_id=None, execution_date=None,
state=None, external_trigger=None, no_backfills=False,
session=None):
"""
"""
DR = DagRun
qry = session.query(DR)
if dag_id:
qry = qry.filter(DR.dag_id == dag_id)
if ru... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L96-L109 | def batch_shape_tensor(self):
"""
"""
batch_shape = tf.constant([], dtype=tf.int32)
for param in self.parameters:
batch_shape = tf.broadcast_dynamic_shape(
batch_shape, param.prior.batch_shape_tensor())
return batch_shape |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/nelder_mead.py#L847-L880 | def _evaluate_objective_multiple(objective_function, arg_batch,
batch_evaluate_objective):
"""
"""
n_points = tf.shape(input=arg_batch)[0]
if batch_evaluate_objective:
return objective_function(arg_batch), n_points
return tf.map_fn(objective_function, arg_batch), n_points |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/distribution_layer.py#L586-L594 | def params_size(event_size, num_components, name=None):
""""""
with tf.compat.v1.name_scope(
name, 'CategoricalMixtureOfOneHotCategorical_params_size',
[event_size, num_components]):
return MixtureSameFamily.params_size(
num_components,
OneHotCategorical.params_size(eve... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/models/bayesian_resnet.py#L95-L121 | def _resnet_block(x, filters, kernel, stride, kernel_posterior_fn):
""""""
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
if stride != 1 or filters != x.shape[1]:
shortcut = _projection_shortcut(x, filters, stride, kernel_posterior_fn)
else:
shortcut = x
x = ... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/half_cauchy.py#L36-L63 | def check_arg_in_support(f):
"""
"""
@functools.wraps(f)
def _check_arg_and_apply_f(*args, **kwargs):
dist = args[0]
x = args[1]
with tf.control_dependencies([
assert_util.assert_greater_equal(
x, dist.loc, message="x is not in the support of the distribution")
] if dist.vali... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/vi/csiszar_divergence.py#L620-L661 | def modified_gan(logu, self_normalized=False, name=None):
"""
"""
with tf.compat.v1.name_scope(name, "chi_square", [logu]):
logu = tf.convert_to_tensor(value=logu, name="logu")
y = tf.nn.softplus(logu) - logu
if self_normalized:
y += 0.5 * tf.math.expm1(logu)
return y |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/quantiles.py#L805-L817 | def _insert_back_keep_dims(x, axis):
"""
"""
for i in sorted(axis):
x = tf.expand_dims(x, axis=i)
return x |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/glm/family.py#L137-L161 | def log_prob(self, response, predicted_linear_response, name=None):
"""
"""
with self._name_scope(
name, 'log_prob', [response, predicted_linear_response]):
dtype = dtype_util.common_dtype([response, predicted_linear_response])
response = tf.convert_to_tensor(
value=response, ... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L363-L371 | def buildhtml(self):
"""
"""
self.buildcontent()
self.content = self.htmlcontent
self.htmlcontent = self.template_page_nvd3.render(chart=self) |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/quantiles.py#L626-L723 | def quantiles(x,
num_quantiles,
axis=None,
interpolation=None,
keep_dims=False,
validate_args=False,
name=None):
"""
"""
with tf.compat.v1.name_scope(
name, 'quantiles', values=[x, num_quantiles, axis]):
x = tf.convert_... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L39-L47 | def val_where(cond, tval, fval):
""""""
if isinstance(tval, tf.Tensor):
return tf.where(cond, tval, fval)
elif isinstance(tval, tuple):
cls = type(tval)
return cls(*(val_where(cond, t, f) for t, f in zip(tval, fval)))
else:
raise Exception(TypeError) |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L602-L638 | def variance(x, sample_axis=0, keepdims=False, name=None):
"""
"""
with tf.compat.v1.name_scope(name, 'variance', values=[x, sample_axis]):
return covariance(
x, y=None, sample_axis=sample_axis, event_axis=None, keepdims=keepdims) |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L161-L187 | def build_input_pipeline(mnist_data, batch_size, heldout_size):
""""""
# Build an iterator over training batches.
training_dataset = tf.data.Dataset.from_tensor_slices(
(mnist_data.train.images, np.int32(mnist_data.train.labels)))
training_batches = training_dataset.shuffle(
50000, reshuffle_each_i... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/program_transformations.py#L226-L246 | def _get_function_inputs(f, src_kwargs):
"""
"""
if hasattr(f, "_func"): # functions returned by tf.make_template
f = f._func # pylint: disable=protected-access
try: # getargspec was deprecated in Python 3.6
argspec = inspect.getfullargspec(f)
except AttributeError:
argspec = inspect.getargspe... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_bigtable_hook.py#L199-L214 | def delete_table(self, instance_id, table_id, project_id=None):
"""
"""
table = self.get_instance(instance_id=instance_id, project_id=project_id).table(table_id=table_id)
table.delete() |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L552-L599 | def stddev(x, sample_axis=0, keepdims=False, name=None):
"""
"""
with tf.compat.v1.name_scope(name, 'stddev', values=[x, sample_axis]):
return tf.sqrt(variance(x, sample_axis=sample_axis, keepdims=keepdims)) |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_firehose_hook.py#L44-L56 | def put_records(self, records):
"""
"""
firehose_conn = self.get_conn()
response = firehose_conn.put_record_batch(
DeliveryStreamName=self.delivery_stream,
Records=records
)
return response |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/generated_random_variables.py#L148-L175 | def _make_random_variable(distribution_cls):
""""""
@interceptable
@functools.wraps(distribution_cls, assigned=('__module__', '__name__'))
@docstring_util.expand_docstring(
cls=distribution_cls.__name__,
doc=inspect.cleandoc(distribution_cls.__init__.__doc__ or ''))
def func(*args, **kwargs):
... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L132-L139 | def name(dtype):
""""""
dtype = tf.as_dtype(dtype)
if hasattr(dtype, 'name'):
return dtype.name
if hasattr(dtype, '__name__'):
return dtype.__name__
return str(dtype) |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/quantiles.py#L726-L787 | def _get_static_ndims(x,
expect_static=False,
expect_ndims=None,
expect_ndims_no_more_than=None,
expect_ndims_at_least=None):
"""
"""
ndims = x.shape.ndims
if ndims is None:
shape_const = tf.get_static_value(tf.shape(inp... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sqoop_hook.py#L202-L233 | def import_table(self, table, target_dir=None, append=False, file_type="text",
columns=None, split_by=None, where=None, direct=False,
driver=None, extra_import_options=None):
"""
"""
cmd = self._import_cmd(target_dir, append, file_type, split_by... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/sprites_dataset.py#L140-L149 | def create_character(skin, hair, top, pants):
""""""
dtype = skin.dtype
hair_mask = tf.cast(hair[..., -1:] <= 0, dtype)
top_mask = tf.cast(top[..., -1:] <= 0, dtype)
pants_mask = tf.cast(pants[..., -1:] <= 0, dtype)
char = (skin * hair_mask) + hair
char = (char * top_mask) + top
char = (char * pants_mas... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/util.py#L440-L474 | def enable_store_parameters_in_results(kernel):
"""
"""
kernel_stack = []
while hasattr(kernel, 'parameters') and 'inner_kernel' in kernel.parameters:
kernel_stack.append(kernel)
kernel = kernel.parameters['inner_kernel']
def _recreate_kernel(kernel, parameters):
new_parameters = kernel.parameter... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1526-L1666 | def _execute_helper(self):
"""
"""
self.executor.start()
self.log.info("Resetting orphaned tasks for active dag runs")
self.reset_state_for_orphaned_tasks()
# Start after resetting orphaned tasks to avoid stressing out DB.
self.processor_agent.start()
... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/views.py#L2492-L2500 | def get_count_query(self):
"""
"""
return (
super().get_count_query()
.filter(models.DagModel.is_active)
.filter(~models.DagModel.is_subdag)
) |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/lbfgs.py#L277-L366 | def _get_search_direction(state):
"""
"""
# The number of correction pairs that have been collected so far.
num_elements = tf.minimum(
state.num_iterations,
distribution_util.prefer_static_shape(state.position_deltas)[0])
def _two_loop_algorithm():
"""L-BFGS two-loop algorithm."""
# Corre... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/logistic_regression.py#L89-L131 | def visualize_decision(features, labels, true_w_b, candidate_w_bs, fname):
"""
"""
fig = figure.Figure(figsize=(6, 6))
canvas = backend_agg.FigureCanvasAgg(fig)
ax = fig.add_subplot(1, 1, 1)
ax.scatter(features[:, 0], features[:, 1],
c=np.float32(labels[:, 0]),
cmap=cm.get_cmap("bi... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/wasb_hook.py#L137-L151 | def read_file(self, container_name, blob_name, **kwargs):
"""
"""
return self.connection.get_blob_to_text(container_name,
blob_name,
**kwargs).content |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L572-L639 | def move_dimension(x, source_idx, dest_idx):
"""
"""
ndims = prefer_static_rank(x)
dtype = dtype_util.common_dtype([source_idx, dest_idx],
preferred_dtype=tf.int32)
source_idx = tf.convert_to_tensor(value=source_idx, dtype=dtype)
dest_idx = tf.convert_to_tensor(value=dest_i... |
https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/datasets/utils.py#L93-L112 | def list_dir(root, prefix=False):
"""
"""
root = os.path.expanduser(root)
directories = list(
filter(
lambda p: os.path.isdir(os.path.join(root, p)),
os.listdir(root)
)
)
if prefix is True:
directories = [os.path.join(root, d) for d in directories... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/tensorshape_util.py#L119-L141 | def constant_value_as_shape(tensor): # pylint: disable=invalid-name
"""
"""
shape = tf.get_static_value(tensor)
if shape is not None:
return [None if dim == -1 else dim for dim in shape]
return tensor_util.constant_value_as_shape(tensor) |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_glue_catalog_hook.py#L139-L152 | def get_table_location(self, database_name, table_name):
"""
"""
table = self.get_table(database_name, table_name)
return table['StorageDescriptor']['Location'] |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/interpolation.py#L893-L927 | def _make_expand_x_fn_for_batch_interpolation(y_ref, axis):
""""""
# This expansion is to help x broadcast with `y`, the output.
# In the batch case, the output shape is going to be
# Broadcast(y_ref.shape[:axis], x.shape[:-1]) +
# x.shape[-1:] + y_ref.shape[axis+1:]
# Recall we made axis non-negative... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/differential_evolution.py#L674-L709 | def _get_mutants(population,
population_size,
mixing_indices,
differential_weight):
"""
"""
mixing_indices = tf.reshape(mixing_indices, [-1])
weights = tf.stack([1.0, differential_weight, -differential_weight])
def _mutant_part(population_part):
donors = ... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/task_runner/cgroup_task_runner.py#L197-L210 | def _get_cgroup_names():
"""
"""
with open("/proc/self/cgroup") as f:
lines = f.readlines()
d = {}
for line in lines:
line_split = line.rstrip().split(":")
subsystem = line_split[1]
group_name = line_spl... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/prefer_static.py#L120-L151 | def cond(pred, true_fn=None, false_fn=None, name=None):
"""
"""
if not callable(true_fn):
raise TypeError('`true_fn` must be callable.')
if not callable(false_fn):
raise TypeError('`false_fn` must be callable.')
pred_value = _get_static_predicate(pred)
if pred_value is not None:
if pred_value:
... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/sprites_dataset.py#L113-L118 | def read_image(filepath):
""""""
im_bytes = tf.io.read_file(filepath)
im = tf.image.decode_image(im_bytes, channels=CHANNELS)
im = tf.image.convert_image_dtype(im, tf.float32)
return im |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/positive_semidefinite_kernels/internal/util.py#L68-L86 | def sum_rightmost_ndims_preserving_shape(x, ndims):
"""
"""
x = tf.convert_to_tensor(value=x)
if x.shape.ndims is not None:
axes = tf.range(x.shape.ndims - ndims, x.shape.ndims)
else:
axes = tf.range(tf.rank(x) - ndims, tf.rank(x))
return tf.reduce_sum(input_tensor=x, axis=axes) |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/bfgs_utils.py#L47-L91 | def get_initial_state_args(value_and_gradients_function,
initial_position,
grad_tolerance,
control_inputs=None):
"""
"""
if control_inputs:
with tf.control_dependencies(control_inputs):
f0, df0 = value_and_gradients_functio... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L920-L928 | def _print_stat(self):
"""
"""
if ((timezone.utcnow() - self.last_stat_print_time).total_seconds() >
self.print_stats_interval):
if len(self._file_paths) > 0:
self._log_file_processing_stats(self._file_paths)
self.last_stat_print_t... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L246-L266 | def get_accessible_dag_ids(self, username=None):
"""
"""
if not username:
username = g.user
if username.is_anonymous or 'Public' in username.roles:
# return an empty set if the role is public
return set()
roles = {role.name for role ... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/slice_sampler_utils.py#L26-L87 | def _left_doubling_increments(batch_shape, max_doublings, step_size, seed=None,
name=None):
"""
"""
with tf.compat.v1.name_scope(name, 'left_doubling_increments',
[batch_shape, max_doublings, step_size]):
step_size = tf.convert_to_tensor(value=step... |
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L342-L417 | def _launch_process(result_queue,
file_path,
pickle_dags,
dag_id_white_list,
thread_name,
zombies):
"""
"""
def helper():
# This helper runs in the newly c... |
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/prefer_static.py#L154-L179 | def case(pred_fn_pairs, default=None, exclusive=False, name='smart_case'):
"""
"""
return control_flow_ops._case_helper( # pylint: disable=protected-access
cond, pred_fn_pairs, default, exclusive, name, allow_python_preds=True) |
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