def test_cross_validation(shutdown_only):
register_ray()
iris = load_iris()
clf = SVC(kernel="linear", C=1, random_state=0)
with joblib.parallel_backend("ray", n_jobs=5):
accuracy = cross_val_score(clf, iris.data, iris.target, cv=5)
assert len(accuracy) == 5
for result in accuracy:
assert result > 0.95
def test_svm_multiple_nodes(ray_start_cluster_2_nodes):
digits = load_digits()
param_space = {
"C": np.logspace(-6, 6, 30),
"gamma": np.logspace(-8, 8, 30),
"tol": np.logspace(-4, -1, 30),
"class_weight": [None, "balanced"],
}
model = SVC(kernel="rbf")
search = RandomizedSearchCV(model,
param_space,
cv=5,
n_iter=100,
verbose=10)
register_ray()
with joblib.parallel_backend("ray"):
search.fit(digits.data, digits.target)
assert ray.is_initialized()
def test_sklearn_benchmarks(ray_start_cluster_2_nodes):
ESTIMATORS = {
"CART":
DecisionTreeClassifier(),
"ExtraTrees":
ExtraTreesClassifier(n_estimators=10),
"RandomForest":
RandomForestClassifier(),
"Nystroem-SVM":
make_pipeline(Nystroem(gamma=0.015, n_components=1000),
LinearSVC(C=1)),
"SampledRBF-SVM":
make_pipeline(RBFSampler(gamma=0.015, n_components=1000),
LinearSVC(C=1)),
"LogisticRegression-SAG":
LogisticRegression(solver="sag", tol=1e-1, C=1e4),
"LogisticRegression-SAGA":
LogisticRegression(solver="saga", tol=1e-1, C=1e4),
"MultilayerPerceptron":
MLPClassifier(hidden_layer_sizes=(32, 32),
max_iter=100,
alpha=1e-4,
solver="sgd",
learning_rate_init=0.2,
momentum=0.9,
verbose=1,
tol=1e-2,
random_state=1),
"MLP-adam":
MLPClassifier(hidden_layer_sizes=(32, 32),
max_iter=100,
alpha=1e-4,
solver="adam",
learning_rate_init=0.001,
verbose=1,
tol=1e-2,
random_state=1)
}
# Load dataset.
print("Loading dataset...")
data = fetch_openml("mnist_784")
X = check_array(data["data"], dtype=np.float32, order="C")
y = data["target"]
# Normalize features.
X = X / 255
# Create train-test split.
print("Creating train-test split...")
n_train = 6000
X_train = X[:n_train]
y_train = y[:n_train]
register_ray()
train_time = {}
random_seed = 0
# Use two workers per classifier.
num_jobs = 2
with joblib.parallel_backend("ray"):
for name in sorted(ESTIMATORS.keys()):
print("Training %s ... " % name, end="")
estimator = ESTIMATORS[name]
estimator_params = estimator.get_params()
estimator.set_params(
**{
p: random_seed
for p in estimator_params if p.endswith("random_state")
})
if "n_jobs" in estimator_params:
estimator.set_params(n_jobs=num_jobs)
time_start = time()
estimator.fit(X_train, y_train)
train_time[name] = time() - time_start
print("training", name, "took", train_time[name], "seconds")
def test_ray_backend(shutdown_only):
register_ray()
from ray.experimental.joblib.ray_backend import RayBackend
with joblib.parallel_backend("ray"):
assert type(joblib.parallel.get_active_backend()[0]) == RayBackend
def test_register_ray():
register_ray()
assert "ray" in joblib.parallel.BACKENDS
assert not ray.is_initialized()