def benchmark1(output_dir="_prof_group_sparse_covariance"):
"""Run group_sparse_covariance on a simple case, for benchmarking."""
parameters = {'n_tasks': 40, 'n_var': 30, 'density': 0.15,
'alpha': .01, 'tol': 1e-4, 'max_iter': 50}
_, _, gt = create_signals(parameters,
output_dir=output_dir)
_, est_precs = utils.timeit(group_sparse_covariance)(
gt["signals"], parameters['alpha'], max_iter=parameters['max_iter'],
tol=parameters['tol'], verbose=1, debug=False)
# Check that output doesn't change between invocations.
utils.cache_array(est_precs, os.path.join(output_dir,
"benchmark1_est_precs.npy"),
decimal=4)
def benchmark2(output_dir="_prof_group_sparse_covariance"):
"""Run GroupSparseCovarianceCV on a simple case, for benchmarking."""
parameters = {'n_tasks': 40, 'n_var': 10, 'density': 0.15,
'alphas': 4, 'tol': 1e-4, 'max_iter': 50}
parameters["tol_cv"] = parameters["tol"]
parameters["max_iter_cv"] = parameters["max_iter"]
_, _, gt = create_signals(parameters,
output_dir=output_dir)
gsc = GroupSparseCovarianceCV(alphas=parameters['alphas'],
max_iter=parameters['max_iter'],
tol=parameters['tol'],
max_iter_cv=parameters['max_iter_cv'],
tol_cv=parameters['tol_cv'],
verbose=1, debug=False,
early_stopping=True)
utils.timeit(gsc.fit)(gt["signals"])
print(gsc.alpha_)
utils.cache_array(gsc.precisions_,
os.path.join(output_dir,
"est_precs_cv_{n_var:d}.npy".format(**parameters)),
decimal=3)