def wait(self, futures):
if self.in_worker:
secede()
results = self.client.gather(list(futures))
if self.in_worker:
rejoin()
return [r.get() for r in results]
def generateCoeffs(coeffs):
c = get_client()
futures = [c.submit(coeff.generate) for coeff in coeffs]
secede()
c.gather(futures)
rejoin()
return coeffs
def retrieval_context(self):
"""Override ParallelBackendBase.retrieval_context to avoid deadlocks.
This removes thread from the worker's thread pool (using 'secede').
Seceding avoids deadlock in nested parallelism settings.
"""
# See 'joblib.Parallel.__call__' and 'joblib.Parallel.retrieve' for how
# this is used.
if hasattr(thread_state, 'execution_state'):
# we are in a worker. Secede to avoid deadlock.
secede()
yield
if hasattr(thread_state, 'execution_state'):
rejoin()
def run_experiment(self):
"""
Run the experiment. Including all scenarios and replications.
"""
dask_client = get_client(timeout=600)
# print("dask client: {}".format(dask_client))
# try:
secede()
print("secede: {}".format(self.name))
# seceded = True
# except ValueError:
# seceded = False
futures = []
total_runs = len(self.scenarios) * self.num_replications
for scenario in self.scenarios:
scenario_name, configuration = scenario
# replications_done = self.find_replications_done(scenario_name, configuration)
replications_done = 0
# random number of steps for this scenario
total_steps = random.randint(300, 700)
for run_id in range(replications_done, self.num_replications):
future = dask_client.submit(run_model, self.name, scenario_name, run_id, total_runs, total_steps,
self.scratch_path)
futures.append(future)
loggers_info = dask_client.gather(futures)
# if seceded:
print("rejoin: {}".format(self.name))
rejoin()
# gather all the output dbs into a single db
out_db_filepath = Logger.gather_databases(self.name, loggers_info)
dest_file_path_name = os.path.join(OUTPUT_DIR, os.path.split(out_db_filepath)[1])
if not os.path.exists(dest_file_path_name):
shutil.move(out_db_filepath, dest_file_path_name)
return dest_file_path_name
def generate(self):
def generateConstCoeff(c, dofs, order):
# Generate the coefficient
c.generate()
tmp = c.components
# Constant part of the coefficient?
if len(c.derivs) == 0:
return tmp
# Contract the indices from the phi expansions
for d in c.derivs:
dphis = dPhis(dofs, d)
a = tuple(range(self.order, self.order + d + 1))
b = tuple(range(d + 1))
tmp = np.tensordot(tmp, dphis, axes=(a, b))
# Ignore zeros
return tmp
c = get_client()
futures = [
c.submit(generateConstCoeff, coeff, self.parametrization.dofs,
self.order) for coeff in self.constCoeffs
]
secede()
c.gather(futures)
rejoin()
# Add them together
if len(futures) == 0:
return
self.components = futures[0].result()
for i in range(1, len(futures)):
self.components = self.components + futures[i].result()
def run_test_with_timeout(
test_config: TestConfig,
incoming_state: dict,
hostnames: List[str],
duration: int = 15,
) -> dict:
"""
Calls run_test with a timeout and signals run_test to end gracefully if timeout has completed
Args:
test_config: Config of test to run
incoming_state: Initial state to run actions/asserts in
hostnames: List of runner hostnames
duration: Optional timeout to run test within (I suppose this is to make it convenient to call in runners)
Returns:
New state after running actions and asserts
"""
if duration is None or duration < 0:
return run_test(test_config, incoming_state, hostnames)
# NOTE: Use a dask cluster scheduler?
client = get_client()
# Used to prevent system deadlock since we are spawning 2 threads
secede()
# NOTE: may improve way of doing this
timeout_signal_name = f"keep-going-{str(uuid.uuid4())}"
keep_going = Variable(timeout_signal_name)
keep_going.set(True)
run_test_task: Future = client.submit(
run_test,
test_config=test_config,
incoming_state=incoming_state,
hostnames=hostnames,
timeout_signal_name=timeout_signal_name,
)
LOGGER.debug("Test duration config: %d seconds", duration)
def distributed_timeout():
# If a timeout from a previous test did not complete, it will keep running (it cannot be canceled)
# However, if it keeps running, it can end another test early
# This means it needs to receive a signal to return
end_time = datetime.now() + timedelta(seconds=duration)
while datetime.now() <= end_time and keep_going.get():
time.sleep(test_config.get("secondsBetweenCycles", 1))
timeout_task: Future = client.submit(distributed_timeout)
# Wait for either test or timeout to finish
# Return test result if it finishes first
# End test if timeout finishes first and return state
start = datetime.now()
wait([run_test_task, timeout_task], return_when="FIRST_COMPLETED")
end = datetime.now()
rejoin()
LOGGER.debug("Test %s took %d seconds", test_config["name"], (end - start).seconds)
if run_test_task.done():
keep_going.set(False)
return run_test_task.result()
elif timeout_task.done():
LOGGER.debug("test task: %s", run_test_task)
LOGGER.debug("timeout task: %s", timeout_task)
LOGGER.info("Test %s timed out", test_config["name"])
# NOTE: add timed out to summary?
keep_going.set(False)
return run_test_task.result()
def coclustering(Z,
nclusters_row,
nclusters_col,
errobj,
niters,
epsilon,
col_clusters_init=None,
row_clusters_init=None,
run_on_worker=False):
"""
Run the co-clustering, Dask implementation
:param Z: m x n data matrix
:param nclusters_row: num row clusters
:param nclusters_col: number of column clusters
:param errobj: convergence threshold for the objective function
:param niters: maximum number of iterations
:param epsilon: numerical parameter, avoids zero arguments in log
:param row_clusters_init: initial row cluster assignment
:param col_clusters_init: initial column cluster assignment
:param run_on_worker: whether the function is submitted to a Dask worker
:return: has converged, number of iterations performed. final row and
column clustering, error value
"""
client = get_client()
Z = da.array(Z) if not isinstance(Z, da.Array) else Z
[m, n] = Z.shape
row_chunks, col_chunks = Z.chunksize
row_clusters = da.array(row_clusters_init) \
if row_clusters_init is not None \
else _initialize_clusters(m, nclusters_row, chunks=row_chunks)
col_clusters = da.array(col_clusters_init) \
if col_clusters_init is not None \
else _initialize_clusters(n, nclusters_col, chunks=col_chunks)
R = _setup_cluster_matrix(nclusters_row, row_clusters)
C = _setup_cluster_matrix(nclusters_col, col_clusters)
e, old_e = 2 * errobj, 0
s = 0
converged = False
Gavg = Z.mean()
while (not converged) & (s < niters):
logger.debug(f'Iteration # {s} ..')
# Calculate cluster based averages
# nel_clusters is a matrix with the number of elements per co-cluster
# originally computed as: da.dot(da.dot(R.T, da.ones((m, n))), C)
nel_row_clusters = da.bincount(row_clusters, minlength=nclusters_row)
nel_col_clusters = da.bincount(col_clusters, minlength=nclusters_col)
logger.debug('num of populated clusters: row {}, col {}'.format(
da.sum(nel_row_clusters > 0).compute(),
da.sum(nel_col_clusters > 0).compute()))
nel_clusters = da.outer(nel_row_clusters, nel_col_clusters)
CoCavg = (da.matmul(da.matmul(R.T, Z), C) + Gavg * epsilon) / \
(nel_clusters + epsilon)
# Calculate distance based on row approximation
d_row = _distance(Z, da.matmul(C, CoCavg.T), epsilon)
# Assign to best row cluster
row_clusters = da.argmin(d_row, axis=1)
R = _setup_cluster_matrix(nclusters_row, row_clusters)
# Calculate distance based on column approximation
d_col = _distance(Z.T, da.matmul(R, CoCavg), epsilon)
# Assign to best column cluster
col_clusters = da.argmin(d_col, axis=1)
C = _setup_cluster_matrix(nclusters_col, col_clusters)
# Error value (actually just the column components really)
old_e = e
minvals = da.min(d_col, axis=1)
# power 1 divergence, power 2 euclidean
e = da.sum(da.power(minvals, 1))
row_clusters, R, col_clusters, C, e = client.persist(
[row_clusters, R, col_clusters, C, e])
if run_on_worker:
# this is workaround for e.compute() for a function that runs
# on a worker with multiple threads
# https://github.com/dask/distributed/issues/3827
e = client.compute(e)
secede()
e = e.result()
rejoin()
else:
e = e.compute()
logger.debug(f'Error = {e:+.15e}, dE = {e - old_e:+.15e}')
converged = abs(e - old_e) < errobj
s = s + 1
if converged:
logger.debug(f'Coclustering converged in {s} iterations')
else:
logger.debug(f'Coclustering not converged in {s} iterations')
return converged, s, row_clusters, col_clusters, e
def calc_cv_per_model(nr_particles, model_weights, N_BOOTSTR, test_w,
transitions, test_X):
"""
Calculate the Coefficient of Variation.
Parameters
----------
nr_particles: int
Number of particles to estimate the CV for
model_weights: np.ndarray
array of model weights
N_BOOTSTR: int
Nr of bootstrapped KDEs to take to estimate the CV
test_w: List[np.ndarray]
test_w[m] are the weights of the test points test_X[m] of model m
transitions: List[Transition]
List of transitions
test_X: List[np.ndarray]
test_X[m] are the test points with weights test_w[m]
client: Client to execute on
Returns
-------
cv, variations_at_X: float, List[np.ndarray]
* cv is the mean variation
* variations_at_X are the variations at the test_X
"""
test_transitions = copy.deepcopy(transitions)
# how many particles to draw for each model
n_per_model = np.random.multinomial(nr_particles, model_weights)
# N_BOOTSTR times, train test_transitions on n_per_model points, and
# calculate the weights associated with test_X, for each model
logger.debug("Start CV")
futures = []
for _ in range(N_BOOTSTR):
futures.append(
weights(n_per_model, transitions, test_transitions, test_X))
logger.debug("Gathering futures")
secede()
chunked = client.gather(futures)
rejoin()
bootstr_w_at_test_X = [
np.concatenate(chunk) for bs in chunked for chunk in bs
]
del chunked
per_model_w = [np.asarray(arr) for arr in zip(*bootstr_w_at_test_X)]
# calculate the cv of the bootstrapped weights for each model
variations_at_X = [st.variation(ws, axis=0) for ws in per_model_w]
# normalize by number of samples per model
model_weighted_variations_at_X = [
var * n / n_per_model.sum()
for var, n in zip(variations_at_X, n_per_model)
]
# weight cvs by the point weights
point_weighted_var_at_X = [
var * w for var, w in zip(model_weighted_variations_at_X, test_w)
]
# compute an "average coefficient of variation":
# for each model, sum up the weighted cvs over the test points
# then, take the sum over all models
cv = sum(var.sum() for var in point_weighted_var_at_X)
logger.debug("CV done")
return float(cv), variations_at_X
def _rejoin_pool_dask():
from dask.distributed import rejoin
rejoin()