def test_update_n_sensors(data, n_sensors):
x, y, l1_penalty = data
model = SSPOC(l1_penalty=l1_penalty).fit(x, y, quiet=True)
model.update_sensors(n_sensors=n_sensors, quiet=True)
assert len(model.selected_sensors) == n_sensors
assert model.n_sensors == n_sensors
def test_large_threshold(data):
x, y, l1_penalty = data
model = SSPOC(l1_penalty=l1_penalty).fit(x, y, quiet=True)
model.update_sensors(threshold=10, quiet=True)
assert len(model.selected_sensors) == 0
assert model.n_sensors == 0
def test_update_threshold(data):
x, y, l1_penalty = data
model = SSPOC(threshold=0.01, l1_penalty=l1_penalty).fit(x, y, quiet=True)
nnz = len(model.selected_sensors)
# Larger threshold should result in fewer sensors
model.update_sensors(threshold=1, quiet=True)
assert len(model.selected_sensors) < nnz
def test_dummy_predict(data):
x, y, l1_penalty = data
model = SSPOC(l1_penalty=l1_penalty).fit(x, y, quiet=True)
model.update_sensors(n_sensors=0, xy=(x, y), quiet=True)
assert model.n_sensors == 0
# Test that model can still make predictions, albeit random ones
# when it has no sensors to work with
y_pred = model.predict(x)
assert len(y_pred) == len(y)
def test_not_fitted(data_binary_classification):
x, y, _ = data_binary_classification
model = SSPOC()
# Shouldn't be able to call any of these methods before fitting
with pytest.raises(NotFittedError):
model.predict(x)
with pytest.raises(NotFittedError):
model.update_sensors(n_sensors=5)
with pytest.raises(NotFittedError):
model.selected_sensors
Gamma = lambda x: np.dot(x, f_gamma)
beta = Gamma(X).squeeze()
dz = beta > 0
#y = H(x)
X_tr, X_te, dz_tr, dz_te = train_test_split(X, dz)
model = SSPOC()
model.fit(X_tr, dz_tr)
plt.figure()
plt.plot(np.sort(np.abs(model.sensor_coef_)), 'o')
plt.title('Coefficient magnitudes')
model.update_sensors(n_sensors=2, xy=(X_tr, dz_tr))
print('Portion of sensors used:', len(model.selected_sensors) / 10)
print('Selected sensors:', model.selected_sensors)
plt.figure()
plt.plot(f_gamma)
plt.stem(model.selected_sensors, np.ones_like(model.selected_sensors))
accuracy = metrics.accuracy_score(
dz_te, model.predict(X_te[:, model.selected_sensors]))
print(accuracy)
#%%
def plot_sensor_locations(sensors, ax=None):
img = np.zeros(64)
img[sensors] = 16
def test_bad_update_sensors_input(data_binary_classification):
x, y, _ = data_binary_classification
model = SSPOC().fit(x, y, quiet=True)
with pytest.raises(ValueError):
model.update_sensors()
