def test_iris_empty_test(self):
data = load_data(
"iris",
train_size=150,
)
assert len(data.train_data) == 150
assert len(data.train_target) == 150
assert len(data.test_data) == 0
assert len(data.test_target) == 0
assert data.train_target.shape[1] == 16
assert data.test_target.shape[1] == 16
def test_circles_basic(self):
data = load_data("circles",
train_size=150,
test_size=50,
target_length=2)
assert len(data.train_data) == 150
assert len(data.train_target) == 150
assert len(data.test_data) == 50
assert len(data.test_target) == 50
assert len(data.train_data[0]) == 2
assert len(data.train_target[0]) == 2
assert len(data.test_data[0]) == 2
assert len(data.test_target[0]) == 2
def test_iris_basic(self):
num_classes = 3
data = load_data(
"iris",
train_size=120,
test_size=30,
target_length=num_classes, # num of classes
)
assert len(data.train_data) == 120
assert len(data.train_target) == 120
assert len(data.test_data) == 30
assert len(data.test_target) == 30
assert len(data.train_data[0]) == 4
assert len(data.train_target[0]) == num_classes
assert len(data.test_data[0]) == 4
assert len(data.test_target[0]) == num_classes
def test_mnist_basic(self):
classes = (6, 7, 8)
data = load_data(
"mnist",
wires=5,
classes=classes,
train_size=150,
test_size=50,
target_length=len(classes),
)
assert len(data.train_data) == 150
assert len(data.train_target) == 150
assert len(data.test_data) == 50
assert len(data.test_target) == 50
assert len(data.train_data[0]) == 5
assert len(data.train_target[0]) == len(classes)
assert len(data.test_data[0]) == 5
assert len(data.test_target[0]) == len(classes)
exclude=("data", "target"),
)
seed = train_params.pop("seed", 1337)
np.random.seed(seed)
random.seed(seed)
data_params = {
"wires": train_params["wires"],
"classes": [6, 9],
"train_size": 120,
"test_size": 100,
"shuffle": True,
"target_length": len(train_params.get("interpret", (0, ))),
}
try:
data = load_data(train_params.pop("dataset"), **data_params)
except ValueError:
data = DataSet(None, None, None, None)
testing = train_params.pop("testing", False)
result = train(
**train_params,
data=data.train_data,
target=data.train_target,
validation_data=data.validation_data,
validation_target=data.validation_target,
)
if testing:
test(
result["__circuit"],
masked_circuit=result["__masked_circuit"],
rotations=result["__rotations"],