def test_get_all_sensors(data):
x = data
max_sensors = x.shape[1]
model = SensorSelector()
model.fit(x)
assert len(model.get_all_sensors()) == max_sensors
def test_update_n_basis_modes_unfit_basis(data_random):
data = data_random
n_basis_modes = 5
model = SensorSelector()
model.update_n_basis_modes(n_basis_modes, data)
assert model.basis_matrix_.shape[1] == n_basis_modes
def test_prefit_basis(data_random):
data = data_random
basis = Identity()
basis.fit(data)
# This data should be ignored during the fit
data_to_ignore = nan * data_random
model = SensorSelector(basis=basis)
model.fit(data_to_ignore, prefit_basis=True)
assert not any(isnan(model.get_selected_sensors()))
def test_predict_accuracy(data_vandermonde_testing):
# Polynomials up to degree 10 on [0, 1]
data, x_test = data_vandermonde_testing
model = SensorSelector()
model.fit(data, seed=1)
model.set_number_of_sensors(8)
sensors = model.get_selected_sensors()
assert sqrt(mean((x_test - model.predict(x_test[sensors]))**2)) <= 1.0e-3
# Should also work for row vectors
x_test = x_test.reshape(1, -1)
assert sqrt(mean(
(x_test - model.predict(x_test[:, sensors]))**2)) <= 1.0e-3
def test_ccqr_integration(data_random):
data = data_random
costs = zeros(data.shape[1])
costs[[1, 3, 5]] = 100
optimizer = CCQR(sensor_costs=costs)
model = SensorSelector(optimizer=optimizer).fit(data)
check_is_fitted(model)
def test_square_predict(data_random_square):
data = data_random_square
model = SensorSelector()
model.fit(data)
sensors = model.get_selected_sensors()
assert data.shape == model.predict(data[:, sensors]).shape
def test_predict(data_random):
data = data_random
n_sensors = 5
model = SensorSelector(n_sensors=n_sensors)
model.fit(data)
# Wrong size input for predict
# (should only pass data at sensor locations)
with pytest.raises(ValueError):
model.predict(data)
# Rectangular case
sensors = model.get_selected_sensors()
assert data.shape == model.predict(data[:, sensors]).shape
def test_update_n_basis_modes_refit(data_random):
data = data_random
n_basis_modes = 5
model = SensorSelector(basis=Identity(n_basis_modes=n_basis_modes))
model.fit(data)
assert model.basis_matrix_.shape[1] == n_basis_modes
model.update_n_basis_modes(n_basis_modes + 1, data)
assert model.basis_matrix_.shape[1] == n_basis_modes + 1
def test_update_n_basis_modes(data_random):
data = data_random
model = SensorSelector()
model.fit(data)
assert model.basis.n_basis_modes == data.shape[0]
assert model.basis_matrix_.shape[1] == data.shape[0]
n_basis_modes = 5
model.update_n_basis_modes(n_basis_modes)
assert model.basis.n_basis_modes == data.shape[0]
assert model.basis_matrix_.shape[1] == n_basis_modes
def test_reconstruction_error(data_vandermonde_testing):
data, x_test = data_vandermonde_testing
model = SensorSelector(n_sensors=3)
model.fit(data)
assert len(model.reconstruction_error(x_test)) == min(
model.n_sensors, data.shape[1])
sensor_range = [1, 2, 3]
assert len(model.reconstruction_error(x_test,
sensor_range=sensor_range)) == 3
def test_n_sensors(data_random):
# Check for bad inputs
with pytest.raises(ValueError):
model = SensorSelector(n_sensors=0)
with pytest.raises(ValueError):
model = SensorSelector(n_sensors=5.4)
with pytest.raises(ValueError):
model = SensorSelector(n_sensors="1")
with pytest.raises(ValueError):
model = SensorSelector(n_sensors=[1])
n_sensors = 5
x = data_random
model = SensorSelector(n_sensors=n_sensors)
model.fit(x)
assert len(model.get_selected_sensors()) == n_sensors
def test_basis_compatibility(data_vandermonde, basis):
x = data_vandermonde
model = SensorSelector(basis=basis)
model.fit(x)
check_is_fitted(model)
def test_set_number_of_sensors(data_vandermonde):
x = data_vandermonde
max_sensors = x.shape[1]
model = SensorSelector()
model.fit(x)
with pytest.raises(ValueError):
model.set_number_of_sensors(max_sensors + 1)
with pytest.raises(ValueError):
model.set_number_of_sensors(0)
with pytest.raises(ValueError):
model.set_number_of_sensors(1.5)
with pytest.raises(ValueError):
model.set_number_of_sensors("3")
model.set_number_of_sensors(15)
assert len(model.get_selected_sensors()) == 15
def test_not_fitted(data_vandermonde):
x = data_vandermonde
model = SensorSelector()
# Should not be able to call any of these methods before fitting
with pytest.raises(NotFittedError):
model.predict(x)
with pytest.raises(NotFittedError):
model.get_selected_sensors()
with pytest.raises(NotFittedError):
model.get_all_sensors()
with pytest.raises(NotFittedError):
model.set_number_of_sensors(20)
with pytest.raises(NotFittedError):
model.score(x)
with pytest.raises(NotFittedError):
model.reconstruction_error(x)
def test_sensor_selector_properties(data_random):
data = data_random
model = SensorSelector().fit(data)
assert all(model.get_all_sensors() == model.all_sensors)
assert all(model.get_selected_sensors() == model.selected_sensors)
def test_score(data_vandermonde):
data = data_vandermonde
weak_model = SensorSelector(n_sensors=3)
weak_model.fit(data)
# You must pass in data with as many features as the training set
with pytest.raises(ValueError):
weak_model.score(data[:, :5])
strong_model = SensorSelector(n_sensors=8)
strong_model.fit(data)
assert weak_model.score(data) < strong_model.score(data)
def test_update_n_basis_modes_errors(data_random):
data = data_random
n_basis_modes = 5
model = SensorSelector(basis=Identity(n_basis_modes=n_basis_modes))
model.fit(data)
with pytest.raises(ValueError):
model.update_n_basis_modes(0)
with pytest.raises(ValueError):
model.update_n_basis_modes("5")
with pytest.raises(ValueError):
model.update_n_basis_modes(data.shape[0] + 1)
# Need to pass x when increasing n_basis_modes beyond capacity
# of the original basis
with pytest.raises(ValueError):
model.update_n_basis_modes(n_basis_modes + 1)