def test_multi_grid(self):
size = 2**5 - 1 # test odd size
freq = 1 / size
input1 = np.cos(np.arange(size) * 2 * np.pi * freq)
input2 = np.sin(np.arange(size) * 2 * np.pi *
freq) + 0.1 * np.random.random(size=size)
series1 = _series_from_values(input1)
series2 = _series_from_values(input2)
exact_distance = dtw.dtw(series1, series2,
multi_grid_radius=-1).distance()
approx_distance = dtw.dtw(series1, series2,
multi_grid_radius=1).distance()
self.assertAlmostEqual(exact_distance, approx_distance, 3)
def test_sakoe_chiba_window(self):
window = 2
alignment = dtw.dtw(self.series1,
self.series2,
window=dtw.SakoeChiba(window_size=2))
path = alignment.path()
for i, j in path:
self.assertGreaterEqual(window, abs(i - j))
def test_multivariate(self):
n = 2
values1 = np.repeat(self.series1.univariate_values(), n)
values2 = np.repeat(self.series2.univariate_values(), n)
values1 = values1.reshape((-1, n))
values2 = values2.reshape((-1, n))
multi_series1 = TimeSeries.from_values(values1)
multi_series2 = TimeSeries.from_values(values2)
radius = 2
alignment_uni = dtw.dtw(self.series1,
self.series2,
multi_grid_radius=radius)
alignment_multi = dtw.dtw(multi_series1,
multi_series2,
multi_grid_radius=radius)
self.assertTrue(np.all(alignment_uni.path() == alignment_multi.path()))
def test_itakura_window(self):
n = 6
m = 5
slope = 1.5
window = dtw.Itakura(max_slope=slope)
window.init_size(n, m)
cells = list(window)
self.assertEqual(
cells,
[
(1, 1),
(1, 2),
(2, 1),
(2, 2),
(2, 3),
(3, 1),
(3, 2),
(3, 3),
(3, 4),
(4, 2),
(4, 3),
(4, 4),
(5, 2),
(5, 3),
(5, 4),
(5, 5),
(6, 4),
(6, 5),
],
)
sizes = [(10, 43), (543, 45), (34, 11)]
for n, m in sizes:
slope = m / n + 1
series1 = tg.sine_timeseries(length=n,
value_frequency=1 / n,
value_phase=0)
series2 = tg.sine_timeseries(length=m,
value_frequency=1 / m,
value_phase=np.pi / 4)
dist = dtw.dtw(series1, series2,
window=dtw.Itakura(slope)).mean_distance()
self.assertGreater(1, dist)
def test_shift(self):
input1 = [
1,
1,
1,
1,
1.2,
1.4,
1.2,
1,
1,
1,
1,
1,
1,
1.2,
1.4,
1.6,
1.8,
1.6,
1.4,
1.2,
1,
1,
]
input2 = [1] + input1[:-1]
expected_path = ([(0, 0)] + list(
(i - 1, i) for i in range(1, len(input1))) +
[(len(input1) - 1, len(input2) - 1)])
series1 = _series_from_values(input1)
series2 = _series_from_values(input2)
exact_alignment = dtw.dtw(series1, series2, multi_grid_radius=-1)
self.assertEqual(
exact_alignment.distance(),
0,
"Minimum cost between two shifted series should be 0",
)
self.assertTrue(np.array_equal(exact_alignment.path(), expected_path),
"Incorrect path")
def test_warp(self):
# Support different time dimension names
xa1 = self.series1.data_array().rename({"time": "time1"})
xa2 = self.series2.data_array().rename({"time": "time2"})
static_covs = pd.DataFrame([[0.0, 1.0]], columns=["st1", "st2"])
series1 = TimeSeries.from_xarray(xa1).with_static_covariates(
static_covs)
series2 = TimeSeries.from_xarray(xa2).with_static_covariates(
static_covs)
alignment = dtw.dtw(series1, series2)
warped1, warped2 = alignment.warped()
self.assertAlmostEqual(alignment.mean_distance(),
mae(warped1, warped2))
assert warped1.static_covariates.equals(series1.static_covariates)
assert warped2.static_covariates.equals(series2.static_covariates)
"""
def _dtw_multigrid():
dtw.dtw(series1, series2, multi_grid_radius=1).distance()
def _dtw_exact():
dtw.dtw(series1, series2, multi_grid_radius=-1).distance()
def test_plot(self):
align = dtw.dtw(self.series2, self.series1)
align.plot()
align.plot_alignment()
def test_nans(self):
with self.assertRaises(ValueError):
series1 = _series_from_values([np.nan, 0, 1, 2, 3])
series2 = _series_from_values([0, 1, 2, 3, 4])
dtw.dtw(series1, series2)
def dtw_metric(
actual_series: Union[TimeSeries, Sequence[TimeSeries]],
pred_series: Union[TimeSeries, Sequence[TimeSeries]],
metric: Callable[
[
Union[TimeSeries, Sequence[TimeSeries]],
Union[TimeSeries, Sequence[TimeSeries]],
],
Union[float, np.ndarray],
] = mae,
*,
reduction: Callable[[np.ndarray], float] = np.mean,
inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x,
n_jobs: int = 1,
verbose: bool = False,
**kwargs
) -> float:
"""
Applies Dynamic Time Warping to actual_series and pred_series before passing it into the metric.
Enables comparison between series of different lengths, phases and time indices.
Defaults to using mae as a metric.
See darts.dataprocessing.dtw.dtw for more supported parameters.
Parameters
----------
actual_series
The (sequence of) actual series.
pred_series
The (sequence of) predicted series.
metric
The selected metric with signature '[[TimeSeries, TimeSeries], float]' to use. Default: `mae`.
reduction
Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate
the metrics of different components in case of multivariate ``TimeSeries`` instances.
inter_reduction
Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``.
This function can be used to aggregate the metrics of different series in case the metric is evaluated on a
``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair
of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the
pairwise metrics.
n_jobs
The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is
passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1`
(sequential). Setting the parameter to `-1` means using all the available processors.
verbose
Optionally, whether to print operations progress
Returns
-------
float
Result of calling metric(warped_series1, warped_series2)
"""
alignment = dtw.dtw(actual_series, pred_series, **kwargs)
if metric == mae and "distance" not in kwargs:
return alignment.mean_distance()
warped_actual_series, warped_pred_series = alignment.warped()
return metric(warped_actual_series, warped_pred_series)
