def demo():
""" _test_stream_speed
This demo tests the sample generation speed of the file stream.
"""
# Setup the stream
# stream = FileStream("../datasets/covtype.csv", -1, 1)
stream = RandomRBFGeneratorDrift()
stream.prepare_for_use()
# Test with RandomTreeGenerator
# opt_list = [['-c', '2'], ['-o', '0'], ['-u', '5'], ['-v', '4']]
# stream = RandomTreeGenerator(opt_list)
# stream.prepare_for_use()
# Setup the evaluator
evaluator = EvaluateStreamGenerationSpeed(100000, float("inf"), None, 5)
# Evaluate
evaluator.eval(stream)
def demo():
""" _test_streams
This demo tests if the streams are correctly generating samples.
:return:
"""
opt = FileOption('FILE', 'OPT_NAME', '../datasets/covtype.csv', 'csv',
False)
stream = FileStream(opt, -1, 1)
stream.prepare_for_use()
rbf_drift = RandomRBFGeneratorDrift(change_speed=41.00,
num_centroids=50,
model_seed=32523423,
instance_seed=5435,
num_classes=2,
num_att=10,
num_drift_centroids=50)
rbf_drift.prepare_for_use()
sea = SEAGenerator()
print('1 instance:\n')
X, y = stream.next_instance()
print(X)
print(y)
X, y = sea.next_instance()
print(X)
print(y)
print('\n\n10 instances:\n')
X, y = stream.next_instance(10)
print(X)
print(y)
X, y = sea.next_instance(10)
print(X)
print(y)
def test_random_rbf_generator_drift(test_path):
stream = RandomRBFGeneratorDrift(model_seed=99,
instance_seed=50,
num_classes=4,
num_att=10,
num_centroids=50,
change_speed=0.87,
num_drift_centroids=50)
stream.prepare_for_use()
assert stream.estimated_remaining_instances() == -1
expected_header = [
'att_num_0', 'att_num_1', 'att_num_2', 'att_num_3', 'att_num_4',
'att_num_5', 'att_num_6', 'att_num_7', 'att_num_8', 'att_num_9'
]
assert stream.get_attributes_header() == expected_header
expected_classes = [0, 1, 2, 3]
assert stream.get_classes() == expected_classes
assert stream.get_classes_header() == ['class']
assert stream.get_num_attributes() == 10
assert stream.get_num_nominal_attributes() == 0
assert stream.get_num_numerical_attributes() == 10
assert stream.get_num_targets() == 4
assert stream.get_num_values_per_nominal_attribute() == 0
assert stream.get_plot_name(
) == 'Random RBF Generator with drift - 4 class labels'
assert stream.has_more_instances() is True
assert stream.is_restartable() is True
# Load test data corresponding to first 10 instances
test_file = os.path.join(test_path, 'random_rbf_drift_stream.npz')
data = np.load(test_file)
X_expected = data['X']
y_expected = data['y']
X, y = stream.next_instance()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
X, y = stream.get_last_instance()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
stream.restart()
X, y = stream.next_instance(10)
assert np.alltrue(X == X_expected)
assert np.alltrue(y == y_expected)
def test_random_rbf_generator_drift(test_path):
stream = RandomRBFGeneratorDrift(model_random_state=99,
sample_random_state=50,
n_classes=4,
n_features=10,
n_centroids=50,
change_speed=0.87,
num_drift_centroids=50)
stream.prepare_for_use()
assert stream.n_remaining_samples() == -1
expected_names = [
'att_num_0', 'att_num_1', 'att_num_2', 'att_num_3', 'att_num_4',
'att_num_5', 'att_num_6', 'att_num_7', 'att_num_8', 'att_num_9'
]
assert stream.feature_names == expected_names
expected_targets = [0, 1, 2, 3]
assert stream.target_values == expected_targets
assert stream.target_names == ['target_0']
assert stream.n_features == 10
assert stream.n_cat_features == 0
assert stream.n_num_features == 10
assert stream.n_targets == 1
assert stream.get_data_info(
) == 'Random RBF Generator with drift - 1 targets, 4 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, 'random_rbf_drift_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]