def test_hyper_plane_generator(test_path):
stream = HyperplaneGenerator(random_state=112,
n_features=10,
n_drift_features=2,
mag_change=0.6,
noise_percentage=0.28,
sigma_percentage=0.1)
stream.prepare_for_use()
n_features = 10
assert stream.n_remaining_samples() == -1
expected_names = []
for i in range(n_features):
expected_names.append("att_num_" + str(i))
assert stream.feature_names == expected_names
assert stream.target_values == [0, 1]
assert stream.target_names == ["target_0"]
assert stream.n_features == n_features
assert stream.n_cat_features == 0
assert stream.n_targets == 1
assert stream.get_data_info(
) == 'Hyperplane Generator - 1 targets, 2 classes, 10 features'
assert stream.has_more_samples() is True
assert stream.is_restartable() is True
# Load test data corresponding to first 10 instances
test_file = os.path.join(test_path, 'hyper_plane_stream.npz')
data = np.load(test_file)
X_expected = data['X']
y_expected = data['y']
X, y = stream.next_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
X, y = stream.last_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
stream.restart()
X, y = stream.next_sample(10)
assert np.alltrue(X == X_expected)
assert np.alltrue(y == y_expected)
assert stream.n_targets == np.array(y).ndim
assert stream.n_features == X.shape[1]
def test_hyper_plane_generator(test_path):
stream = HyperplaneGenerator(random_state=112,
n_features=10,
n_drift_features=2,
mag_change=0.6,
noise_percentage=0.28,
sigma_percentage=0.1)
stream.prepare_for_use()
n_features = 10
assert stream.n_remaining_samples() == -1
expected_names = []
for i in range(n_features):
expected_names.append("att_num_" + str(i))
assert stream.feature_names == expected_names
assert stream.target_values == [0, 1]
assert stream.target_names == ["target_0"]
assert stream.n_features == n_features
assert stream.n_cat_features == 0
assert stream.n_targets == 1
assert stream.get_data_info(
) == 'Hyperplane Generator - 1 target(s), 2 classes, 10 features'
assert stream.has_more_samples() is True
assert stream.is_restartable() is True
# Load test data corresponding to first 10 instances
test_file = os.path.join(test_path, 'hyper_plane_stream.npz')
data = np.load(test_file)
X_expected = data['X']
y_expected = data['y']
X, y = stream.next_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
X, y = stream.last_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
stream.restart()
X, y = stream.next_sample(10)
assert np.alltrue(X == X_expected)
assert np.alltrue(y == y_expected)
assert stream.n_targets == np.array(y).ndim
assert stream.n_features == X.shape[1]
assert 'stream' == stream._estimator_type
expected_info = "HyperplaneGenerator(mag_change=0.6, n_drift_features=2, n_features=10,\n" \
" noise_percentage=0.28, random_state=112,\n" \
" sigma_percentage=0.1)"
assert stream.get_info() == expected_info
### test calculation of sum of weights, and sum of weights+features
batch_size = 10
n_features = 2
stream = HyperplaneGenerator(random_state=112,
n_features=n_features,
n_drift_features=2,
mag_change=0.6,
noise_percentage=0.0,
sigma_percentage=0.1)
stream.prepare_for_use()
# check features and weights
X, y = stream.next_sample(batch_size)
weights = stream._weights
w = np.array([0.9750571288732851, 1.2403046199226442])
data = np.array([[0.950016579405167, 0.07567720470206152],
[0.8327457625618593, 0.054805740282408255],
[0.8853514580727667, 0.7223465108072455],
[0.9811992777207516, 0.34341985076716164],
[0.39464258869483526, 0.004944924811720708],
[0.9558068694855607, 0.8206093713145775],
[0.378544457805313, 0.7847636149698817],
[0.5460739378008381, 0.1622260202888307],
[0.04500817232778065, 0.33218775732993966],
[0.8392114852107733, 0.7093616146129875]])
assert np.alltrue(weights == w)
assert np.alltrue(X == data)
# check labels
labels = np.zeros([1, batch_size])
sum_weights = np.sum(weights)
for i in range(batch_size):
if weights[0] * data[i, 0] + weights[1] * data[i,
1] >= 0.5 * sum_weights:
labels[0, i] = 1
assert np.alltrue(y == labels)