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Fix/ts prepend (#2237)
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* fix: append/prepend correctul retain components names and hierarchy

* updated changelog

* fix: revert unecessary change

* update changelog

---------

Co-authored-by: dennisbader <dennis.bader@gmx.ch>
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madtoinou and dennisbader committed Feb 24, 2024
1 parent 6fbb670 commit bf51476
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3 changes: 2 additions & 1 deletion CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,8 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
**Fixed**
- Fixed a bug in probabilistic `LinearRegressionModel.fit()`, where the `model` attribute was not pointing to all underlying estimators. [#2205](https://github.com/unit8co/darts/pull/2205) by [Antoine Madrona](https://github.com/madtoinou).
- Raise an error in `RegressionEsembleModel` when the `regression_model` was created with `multi_models=False` (not supported). [#2205](https://github.com/unit8co/darts/pull/2205) by [Antoine Madrona](https://github.com/madtoinou).
- Fixed a bug in `coefficient_of_variaton()` with `intersect=True`, where the coefficient was not computed on the intersection. [#2202](https://github.com/unit8co/darts/pull/2202) by [Antoine Madrona](https://github.com/madtoinou).
- Fixed a bug in `coefficient_of_variation()` with `intersect=True`, where the coefficient was not computed on the intersection. [#2202](https://github.com/unit8co/darts/pull/2202) by [Antoine Madrona](https://github.com/madtoinou).
- Fixed a bug in `TimeSeries.append/prepend_values()`, where the components names and the hierarchy were dropped. [#2237](https://github.com/unit8co/darts/pull/2237) by [Antoine Madrona](https://github.com/madtoinou).

### For developers of the library:
- Updated pre-commit hooks to the latest version using `pre-commit autoupdate`.
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55 changes: 33 additions & 22 deletions darts/tests/test_timeseries.py
Original file line number Diff line number Diff line change
Expand Up @@ -627,9 +627,11 @@ def helper_test_shift(test_case, test_series: TimeSeries):
def helper_test_append(test_case, test_series: TimeSeries):
# reconstruct series
seriesA, seriesB = test_series.split_after(pd.Timestamp("20130106"))
assert seriesA.append(seriesB) == test_series
assert seriesA.append(seriesB).freq == test_series.freq
assert test_series.time_index.equals(seriesA.append(seriesB).time_index)
appended = seriesA.append(seriesB)
assert appended == test_series
assert appended.freq == test_series.freq
assert test_series.time_index.equals(appended.time_index)
assert appended.components.equals(seriesA.components)

# Creating a gap is not allowed
seriesC = test_series.drop_before(pd.Timestamp("20130108"))
Expand All @@ -648,23 +650,26 @@ def helper_test_append_values(test_case, test_series: TimeSeries):
# reconstruct series
seriesA, seriesB = test_series.split_after(pd.Timestamp("20130106"))
arrayB = seriesB.all_values()
assert seriesA.append_values(arrayB) == test_series
assert test_series.time_index.equals(seriesA.append_values(arrayB).time_index)
appended = seriesA.append_values(arrayB)
assert appended == test_series
assert test_series.time_index.equals(appended.time_index)

# arrayB shape shouldn't affect append_values output:
squeezed_arrayB = arrayB.squeeze()
assert seriesA.append_values(squeezed_arrayB) == test_series
assert test_series.time_index.equals(
seriesA.append_values(squeezed_arrayB).time_index
)
appended_sq = seriesA.append_values(squeezed_arrayB)
assert appended_sq == test_series
assert test_series.time_index.equals(appended_sq.time_index)
assert appended_sq.components.equals(seriesA.components)

@staticmethod
def helper_test_prepend(test_case, test_series: TimeSeries):
# reconstruct series
seriesA, seriesB = test_series.split_after(pd.Timestamp("20130106"))
assert seriesB.prepend(seriesA) == test_series
assert seriesB.prepend(seriesA).freq == test_series.freq
assert test_series.time_index.equals(seriesB.prepend(seriesA).time_index)
prepended = seriesB.prepend(seriesA)
assert prepended == test_series
assert prepended.freq == test_series.freq
assert test_series.time_index.equals(prepended.time_index)
assert prepended.components.equals(seriesB.components)

# Creating a gap is not allowed
seriesC = test_series.drop_before(pd.Timestamp("20130108"))
Expand All @@ -683,15 +688,17 @@ def helper_test_prepend_values(test_case, test_series: TimeSeries):
# reconstruct series
seriesA, seriesB = test_series.split_after(pd.Timestamp("20130106"))
arrayA = seriesA.data_array().values
assert seriesB.prepend_values(arrayA) == test_series
assert test_series.time_index.equals(seriesB.prepend_values(arrayA).time_index)
prepended = seriesB.prepend_values(arrayA)
assert prepended == test_series
assert test_series.time_index.equals(prepended.time_index)
assert prepended.components.equals(test_series.components)

# arrayB shape shouldn't affect append_values output:
squeezed_arrayA = arrayA.squeeze()
assert seriesB.prepend_values(squeezed_arrayA) == test_series
assert test_series.time_index.equals(
seriesB.prepend_values(squeezed_arrayA).time_index
)
prepended_sq = seriesB.prepend_values(squeezed_arrayA)
assert prepended_sq == test_series
assert test_series.time_index.equals(prepended_sq.time_index)
assert prepended_sq.components.equals(test_series.components)

def test_slice(self):
TestTimeSeries.helper_test_slice(self, self.series1)
Expand All @@ -711,15 +718,16 @@ def test_shift(self):
def test_append(self):
TestTimeSeries.helper_test_append(self, self.series1)
# Check `append` deals with `RangeIndex` series correctly:
series_1 = linear_timeseries(start=1, length=5, freq=2)
series_2 = linear_timeseries(start=11, length=2, freq=2)
series_1 = linear_timeseries(start=1, length=5, freq=2, column_name="A")
series_2 = linear_timeseries(start=11, length=2, freq=2, column_name="B")
appended = series_1.append(series_2)
expected_vals = np.concatenate(
[series_1.all_values(), series_2.all_values()], axis=0
)
expected_idx = pd.RangeIndex(start=1, stop=15, step=2)
assert np.allclose(appended.all_values(), expected_vals)
assert appended.time_index.equals(expected_idx)
assert appended.components.equals(series_1.components)

def test_append_values(self):
TestTimeSeries.helper_test_append_values(self, self.series1)
Expand All @@ -732,19 +740,21 @@ def test_append_values(self):
expected_idx = pd.RangeIndex(start=1, stop=15, step=2)
assert np.allclose(appended.all_values(), expected_vals)
assert appended.time_index.equals(expected_idx)
assert appended.components.equals(series.components)

def test_prepend(self):
TestTimeSeries.helper_test_prepend(self, self.series1)
# Check `prepend` deals with `RangeIndex` series correctly:
series_1 = linear_timeseries(start=1, length=5, freq=2)
series_2 = linear_timeseries(start=11, length=2, freq=2)
series_1 = linear_timeseries(start=1, length=5, freq=2, column_name="A")
series_2 = linear_timeseries(start=11, length=2, freq=2, column_name="B")
prepended = series_2.prepend(series_1)
expected_vals = np.concatenate(
[series_1.all_values(), series_2.all_values()], axis=0
)
expected_idx = pd.RangeIndex(start=1, stop=15, step=2)
assert np.allclose(prepended.all_values(), expected_vals)
assert prepended.time_index.equals(expected_idx)
assert prepended.components.equals(series_1.components)

def test_prepend_values(self):
TestTimeSeries.helper_test_prepend_values(self, self.series1)
Expand All @@ -757,6 +767,7 @@ def test_prepend_values(self):
expected_idx = pd.RangeIndex(start=-3, stop=11, step=2)
assert np.allclose(prepended.all_values(), expected_vals)
assert prepended.time_index.equals(expected_idx)
assert prepended.components.equals(series.components)

def test_with_values(self):
vals = np.random.rand(5, 10, 3)
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4 changes: 3 additions & 1 deletion darts/timeseries.py
Original file line number Diff line number Diff line change
Expand Up @@ -2744,7 +2744,7 @@ def append(self, other: Self) -> Self:
)
raise_if_not(
other.freq == self.freq,
"Appended TimeSeries must have the same frequency as the current one",
"Both series must have the same frequency.",
logger,
)
raise_if_not(
Expand Down Expand Up @@ -2875,6 +2875,8 @@ def prepend_values(self, values: np.ndarray) -> Self:
times=idx,
fill_missing_dates=False,
static_covariates=self.static_covariates,
columns=self.columns,
hierarchy=self.hierarchy,
)
)

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