def test_fextractor_indices_with_channel_number():
ex = pipeline.FeatureExtractor(
[('rand', _RandomMultipleFeature(features_per_channel=2)),
('0', _NthSampleFeature(0))],
n_channels=2)
assert ex.channel_indices == {'0': (0, 2, 4), '1': (1, 3, 5)}
assert ex.feature_indices == {'rand': (0, 1, 2, 3), '0': (4, 5)}
def test_fextractor_indices_no_arguments_inferred():
ex = pipeline.FeatureExtractor(
[('rand', _RandomMultipleFeature(features_per_channel=2)),
('0', _NthSampleFeature(0))])
data = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
ex.process(data)
assert ex.channel_indices == {'0': (0, 2, 4), '1': (1, 3, 5)}
assert ex.feature_indices == {'rand': (0, 1, 2, 3), '0': (4, 5)}
def test_fextractor_indices_with_names():
ex = pipeline.FeatureExtractor(
[('rand', _RandomMultipleFeature(features_per_channel=2)),
('0', _NthSampleFeature(0))],
channel_names=['channel_1', 'channel_2'])
assert ex.channel_indices == {
'channel_1': (0, 2, 4),
'channel_2': (1, 3, 5)}
assert ex.feature_indices == {'rand': (0, 1, 2, 3), '0': (4, 5)}
def test_fextractor_clear():
ex = pipeline.FeatureExtractor([('0', _NthSampleFeature(0)),
('1', _NthSampleFeature(2))])
data_2ch = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
data_1ch = np.array([[0, 1, 2, 3, 4]])
assert_array_equal(np.array([0, 5, 2, 7]), ex.process(data_2ch))
ex.clear()
assert_array_equal(np.array([0, 2]), ex.process(data_1ch))
def test_fextractor_simple():
f0 = _NthSampleFeature(0)
ex = pipeline.FeatureExtractor([('0', f0),
('1', _NthSampleFeature(1))])
data = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
assert_array_equal(np.array([0, 5, 1, 6]), ex.process(data))
assert_array_equal(np.array([0, 5, 1, 6]), ex.process(data))
assert ex.feature_indices['0'] == (0, 2)
assert ex.feature_indices['1'] == (2, 4)
assert ex.named_features['0'] is f0
def test_feature_selector():
fe = pipeline.FeatureExtractor(
[('N0', _NthSampleFeature(0)),
('N2', _NthSampleFeature(2))],
n_channels=3)
fs = pipeline.FeatureSelector(
features=['N2'],
feature_indices=fe.feature_indices)
pipe = pipeline.Pipeline([fe, fs])
data = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
truth = np.array([2, 7, 12])
assert_array_equal(truth, pipe.process(data))
def test_channel_selector():
fe = pipeline.FeatureExtractor(
[('0', _NthSampleFeature(0)),
('2', _NthSampleFeature(2))],
channel_names=['channel_1', 'channel_2', 'channel_3'])
cs = pipeline.ChannelSelector(
channels=['channel_1', 'channel_3'],
channel_indices=fe.channel_indices)
pipe = pipeline.Pipeline([fe, cs])
data = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
truth = np.array([0, 10, 2, 12])
assert_array_equal(truth, pipe.process(data))
def test_fextractor_unequal_feature_sizes():
ex = pipeline.FeatureExtractor([('0', _NthSampleFeature(0)),
('1', _NthSampleFeature(2, channel=1))])
data = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
assert_array_equal(np.array([0, 5, 7]), ex.process(data))
def test_fextractor_indices_no_arguments():
ex = pipeline.FeatureExtractor(
[('rand', _RandomMultipleFeature(features_per_channel=2)),
('0', _NthSampleFeature(0))])
assert ex.channel_indices == {}
assert ex.feature_indices == {}