def _run_method(method, X, y, train, test):
"""
Train the `method` on X[train] and test on X[test].
"""
clf = run_delayed(method)
start = time()
clf.fit(X[train], y[train])
accs = clf.score(X[test], y[test])
wall = time() - start
return accs, wall
job_batchsize = int(J / n_jobs)
a = args.j * job_batchsize
b = a + job_batchsize if (args.j < n_jobs-1) else J
# Get cross-validation folds
cv = LeaveOneOut(n) if (args.k == 0) else KFold(n, args.k)
if not args.a:
# Setup list of jobs
jobs = iter(delayed(run_method)(method, X, y, train, test, force=args.f)
for method, (train, test) in itertools.product(methods, cv))
# Run only jobs in batch
for job in itertools.islice(jobs, a, b):
run_delayed(job)
else: # Aggregate results
accs = np.empty(M * args.k)
wall = np.empty(M * args.k)
accs.fill(np.nan)
wall.fill(np.nan)
# Fetch data
jobs = iter(delayed(run_method)(method, X, y, train, test, load=True)
for method, (train, test) in itertools.product(methods, cv))
for i, job in enumerate(jobs):
accs[i], wall[i] = run_delayed(job)
accs.resize((M, args.k))
wall.resize((M, args.k))