def test_tag_propagation():
x = Collection.from_array([1, 2, 3], tags=Tags({quantity: 'acceleration', body_part: 'arm_left'}))
y = Collection.from_array([1, 2, 3], tags=Tags({quantity: 'acceleration', body_part: 'arm_right'}))
z = Collection.from_array([1, 2, 3], tags=Tags({quantity: 'position', body_part: 'leg_right'}))
assert quantity in Ratio().transform(x, y).tags
assert Ratio().transform(x, y).tags.quantity == 'acceleration'
assert body_part in Ratio().transform(x, y).tags
assert Ratio().transform(x, y).tags.body_part == 'arm'
assert body_part in Ratio().transform(y, z).tags
assert Ratio().transform(y, z).tags.body_part == 'right'
assert quantity not in Ratio().transform(x, z).tags
assert body_part not in Ratio().transform(x, z).tags
def test_entropy(x):
x = Collection.from_array(np.abs(x.values) + 1) # ensure that values > 0
result = Entropy().transform(x)
for i, a in series(x):
actual = result.values[i]
expected = stats.entropy(a)
np.testing.assert_almost_equal(actual, expected)
def test_sample_entropy(x):
x = Collection.from_array([1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5])
result = SampleEntropy().transform(x)
actual = result.values
expected = 0.55961579
# Computed using Physionet sample entropy implementation:
# https://physionet.org/content/sampen/1.0.0/
np.testing.assert_almost_equal(actual, expected)
def test_tags():
x = Collection.from_array([1, 2, 3])
x.tags.add(quantity, 'acceleration')
assert x.tags[quantity] == 'acceleration'
assert x.tags['quantity'] == 'acceleration'
assert x.tags.quantity == 'acceleration'
x.tags.remove(quantity)
assert x.tags[quantity] is None
assert x.tags['quantity'] is None
def test_energy_ratio():
x = Collection.from_array([1, 2, 3, 4, 5, 6, 7, 8])
chunks = [[1, 2, 3], [4, 5, 6], [7, 8]]
result = EnergyRatio(chunks=3).transform(x).values
assert result.shape == (1, 1, 3)
total = np.sum(np.square([1, 2, 3, 4, 5, 6, 7, 8]))
np.testing.assert_almost_equal(result[0, 0, 0], np.sum(np.square(chunks[0])) / total)
np.testing.assert_almost_equal(result[0, 0, 1], np.sum(np.square(chunks[1])) / total)
np.testing.assert_almost_equal(result[0, 0, 2], np.sum(np.square(chunks[2])) / total)
def test_create_collection_attributes():
df = pd.DataFrame({
'id': [0, 1, 2],
'x1': [1, 2, 3],
'x2': [2, 3, 4],
})
x = Collection(df)
assert x.shape == (3, 1, 2)
assert x.type == Type.WINDOWS
np.testing.assert_equal(x.id, [0, 1, 2])
np.testing.assert_equal(x.dims, ['x1', 'x2'])
def test_to_dataframe_attributes():
collection = Collection.from_array([
[[10, 20, 30]],
[[40, 50, 60]],
], dims=['x', 'y', 'z'])
print(collection.id)
df = collection.to_dataframe()
np.testing.assert_equal(df.columns.values, ['id', 'x', 'y', 'z'])
np.testing.assert_equal(df.values, [
[0, 10, 20, 30],
[1, 40, 50, 60],
])
def test_create_collection_series_variable_length():
df = pd.DataFrame({
'id': [0, 0, 0, 1, 1],
'x1': [1, 2, 3, 1, 2],
'x2': [2, 2, 2, 4, 4],
'time': [0, 1, 2, 0, 1],
})
x = Collection(df)
assert x.shape == (2, (3, 2), 2)
assert x.type == Type.WINDOWS
np.testing.assert_equal(x.id, [0, 1])
np.testing.assert_equal(x.time, [[0, 1, 2], [0, 1]])
np.testing.assert_equal(x.dims, ['x1', 'x2'])
def brownian(N=10, t=100, d=2, random_state=None, **kwargs):
"""
Generate Brownian motion data.
The implementation is based on the SciPy Cookbook. [1]_
Parameters
----------
N : int, optional
Number of windows. Default: 10
t : int, optional
Number of time stamps in each window. Default: 100
d : int, optional
Number of dimensions. Default: 2
random_state : int, optional
Random state initialization.
**kwargs
Keyword arguments to pass to the :class:`~tsfuse.data.Collection` constructor.
Returns
-------
generated : Collection
References
----------
.. [1] https://scipy-cookbook.readthedocs.io/items/BrownianMotion.html
"""
if random_state is not None:
np.random.seed(random_state)
def generate(n, delta=0.25, dt=0.1, initial=0.0):
x = np.empty(n)
x[0] = initial
for k in range(1, n):
x[k] = x[k - 1] + norm.rvs(scale=delta**2 * dt)
return x
values = np.empty((N, t, d), dtype=float)
for i in range(N):
for j in range(d):
values[i, :, j] = generate(t)
return Collection.from_array(values, **kwargs)
def test_to_dataframe_time_series_variable_length():
collection = Collection.from_array([
[[10, 20, 30],
[11, 21, 31],
[12, 22, 33],],
[[40, 50, 60],
[41, 51, 61],],
], time=[
[0, 1, 2],
[0, 1],
], dims=['x', 'y', 'z'])
df = collection.to_dataframe()
np.testing.assert_equal(df.columns.values, ['id', 'time', 'x', 'y', 'z'])
np.testing.assert_equal(df.values, [
[0, 0, 10, 20, 30],
[0, 1, 11, 21, 31],
[0, 2, 12, 22, 33],
[1, 0, 40, 50, 60],
[1, 1, 41, 51, 61],
])
def test_has_duplicate_empty():
x = Collection.from_array([np.nan])
actual = HasDuplicate().transform(x).values
np.testing.assert_equal(actual, False)
def test_create_collection_windows_variable_length():
x = Collection.from_array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[2, 3, 4], [5, 6, 7]]])
assert x.shape == (2, (3, 2), 3)
assert x.type == Type.WINDOWS
def test_roots():
x = Collection.from_array([-1, 0, 1])
actual = Roots().transform(x).values
np.testing.assert_almost_equal(actual.flatten(), [1, -1])
def test_create_collection_windows():
x = Collection.from_array([[[1, 2, 3], [4, 5, 6]], [[2, 3, 4], [5, 6, 7]]])
assert x.shape == (2, 2, 3)
assert x.type == Type.WINDOWS
def test_number_of_peaks_support_1():
x = Collection.from_array([1, 2, 1, 2, 3, 2, 3])
actual = NumberPeaks(support=1).transform(x).values
np.testing.assert_equal(actual, 2)
def test_arg_max():
x = Collection.from_array([1, 2, 3, 4, 3, 2, 1])
actual = ArgMax().transform(x).values
np.testing.assert_equal(actual, 3)
def test_longest_strike_above_mean_three():
x = Collection.from_array([0, 0, 0, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 0])
actual = LongestStrikeAboveMean().transform(x).values
np.testing.assert_equal(actual, 3)
def test_number_crossings_one():
x = Collection.from_array([0, 2, 3, 4])
actual = NumberCrossings().transform(x).values
np.testing.assert_equal(actual, 1)
def test_has_duplicate_max_false():
x = Collection.from_array([1, 1, 2, 3])
actual = HasDuplicateMax().transform(x).values
np.testing.assert_equal(actual, False)
def test_has_duplicate_min_false():
x = Collection.from_array([2, 3, 4, 4])
actual = HasDuplicateMin().transform(x).values
np.testing.assert_equal(actual, False)
def test_value_count():
x = Collection.from_array([1, 1, 2, 3])
actual = ValueCount(value=1).transform(x).values
np.testing.assert_equal(actual, 2)
def test_has_duplicate_true():
x = Collection.from_array([1, 2, 3, 3])
actual = HasDuplicate().transform(x).values
np.testing.assert_equal(actual, True)
def test_longest_strike_above_mean_zero():
x = Collection.from_array([1, 1, 1])
actual = LongestStrikeAboveMean().transform(x).values
np.testing.assert_equal(actual, 0)
def test_number_of_unique_values_1_rel():
x = Collection.from_array([2, 2, 2, 2])
actual = NumberUniqueValues(rel=True).transform(x).values
np.testing.assert_equal(actual, 0.25)
def test_min():
x = Collection.from_array([4, 3, 2, 1, 2, 3, 4])
actual = Min().transform(x).values
np.testing.assert_equal(actual, 1)
def test_number_of_unique_values_0_abs():
x = Collection.from_array([np.nan])
actual = NumberUniqueValues(rel=False).transform(x).values
np.testing.assert_equal(actual, np.nan)
def test_angle_perpendicular():
p1 = Collection.from_array([1, 0])
p2 = Collection.from_array([0, 0])
p3 = Collection.from_array([0, 2])
actual = Angle().transform(p1, p2, p3, ignore_preconditions=True).values
np.testing.assert_almost_equal(np.mod(actual, np.pi), np.pi / 2)
def test_sum_of_reoccurring_data_points_0():
x = Collection.from_array([1, 2, 3, 4])
actual = SumReoccurringDataPoints().transform(x).values
np.testing.assert_equal(actual, 0)
def test_number_of_peaks_support_2_zero():
x = Collection.from_array([3, 2, 1, 0])
actual = NumberPeaks(support=2).transform(x).values
np.testing.assert_equal(actual, 0)
def test_sum_of_reoccurring_values():
x = Collection.from_array([1, 1, 2, 3, 3, 4])
actual = SumReoccurringValues().transform(x).values
np.testing.assert_equal(actual, 4)
