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Release 1.15.1 (#1144)
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20 changes: 15 additions & 5 deletions CHANGELOG.md
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Expand Up @@ -7,17 +7,27 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

## Unreleased
### Added

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### Changed

- Impose specific order of columns on return value of TSDataset.to_flatten ([#1095](https://github.com/tinkoff-ai/etna/pull/1095))
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### Fixed
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## [1.15.1] - 2023-03-02
### Changed
- Impose specific order of columns on return value of `TSDataset.to_flatten` ([#1095](https://github.com/tinkoff-ai/etna/pull/1095))
### Fixed
- Fix bug in `GaleShapleyFeatureSelectionTransform` with wrong number of remaining features ([#1110](https://github.com/tinkoff-ai/etna/pull/1110))
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## [1.15.0] - 2023-01-31
### Added
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149 changes: 77 additions & 72 deletions pyproject.toml
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[tool.poetry]
name = "etna"
version = "1.15.0"
version = "1.15.1"
repository = "https://github.com/tinkoff-ai/etna"
readme = "README.md"
description = "ETNA is the first python open source framework of Tinkoff.ru AI Center. It is designed to make working with time series simple, productive, and fun."
Expand Down Expand Up @@ -187,77 +187,82 @@ line_length = 120
[tool.pytest.ini_options]
minversion = "6.0"
doctest_optionflags = "NORMALIZE_WHITESPACE IGNORE_EXCEPTION_DETAIL NUMBER"
filterwarnings = [
"error",
"ignore: Torchmetrics v0.9 introduced a new argument class property called `full_state_update` that",
"ignore: TSDataset freq can't be inferred",
"ignore: test_size, test_start and test_end cannot be",
"ignore: You probably set wrong freq. Discovered freq in you data is None",
"ignore: Some regressors don't have enough values in segment",
"ignore: Segments contains NaNs in the last timestamps.",
"ignore: pandas.util.testing is deprecated. Use the functions in the public API",
"ignore: Call to deprecated class CatBoostModelPerSegment.", # OK
"ignore: Call to deprecated class CatBoostModelMultiSegment.", # OK
"ignore: Attribute 'loss' is an instance of `nn.Module` and is already",
"ignore: Columns from feature_to_use which are out of dataframe columns will",
"ignore: Comparison of Timestamp with datetime.date is deprecated in order to",
"ignore: CountryHoliday is deprecated, use country_holidays instead.",
"ignore: Exogenous or target data contains None! It will be dropped",
"ignore: is less than n_segments. Algo will filter data",
"ignore: Given top_k=30 is bigger than n_features=20. Transform will not filter",
"ignore: Implicitly cleaning up ",
"ignore: Maximum Likelihood optimization failed to converge. Check mle_retvals",
"ignore: Mean of empty slice",
"ignore: No frequency information was provided, so inferred frequency D will",
"ignore: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.",
"ignore: Slicing a positional slice with .loc is not supported",
"ignore: Some of external objects in input parameters could be not",
"ignore: The 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be",
"ignore: The default dtype for empty Series will be 'object' instead",
"ignore: This model does not work with exogenous features and regressors.",
"ignore: Transformation will be applied inplace, out_column param will be ignored",
"ignore: You defined a `validation_step` but have no `val_dataloader`. Skipping val",
"ignore: You probably set wrong freq. Discovered freq in you data",
"ignore: _SeasonalMovingAverageModel does not work with any exogenous series or features.",
"ignore: `np.object` is a deprecated alias for the builtin `object`. To",
"ignore: divide by zero encountered in log",
"ignore: inplace is deprecated and will be removed in a future",
"ignore: invalid value encountered in double_scalars",
"ignore: Arrays of bytes/strings is being converted to decimal numbers if",
"ignore: Attribute 'logging_metrics' is an instance of `nn.Module` and is already",
"ignore: Exogenous data contains columns with category type! It will be",
"ignore: Features {'unknown'} are not found and will be dropped!",
"ignore: SARIMAX model does not work with exogenous features",
"ignore: Series.dt.weekofyear and Series.dt.week have been deprecated",
"ignore: The dataloader, train_dataloader, does not have many workers which may",
"ignore: Creating a tensor from a list of numpy.ndarrays",
"ignore: Trying to infer the `batch_size` from an ambiguous collection",
"ignore: ReduceLROnPlateau conditioned on metric val_loss which is not available but strict",
"ignore: Checkpoint directory",
"ignore: Objective did not converge. You might want to increase the number",
"ignore: distutils Version classes are deprecated.",
"ignore: invalid escape sequence",
"ignore::pandas.core.common.SettingWithCopyWarning",
"ignore: You haven't set all parameters inside class __init__ method.* 'box_cox_bounds'",
"ignore: You haven't set all parameters inside class __init__ method.* 'use_box_cox'",
"ignore: You haven't set all parameters inside class __init__ method.* 'use_trend'",
"ignore: You haven't set all parameters inside class __init__ method.* 'use_damped_trend'",
"ignore: You haven't set all parameters inside class __init__ method.* 'seasonal_periods'",
"ignore: You haven't set all parameters inside class __init__ method.* 'show_warnings'",
"ignore: You haven't set all parameters inside class __init__ method.* 'n_jobs'",
"ignore: You haven't set all parameters inside class __init__ method.* 'multiprocessing_start_method'",
"ignore: You haven't set all parameters inside class __init__ method.* 'context'",
"ignore: You haven't set all parameters inside class __init__ method.* 'use_arma_errors'",
"ignore: New behaviour in v1.1.5",
"ignore: The 'check_less_precise' keyword in testing",
"ignore: Feature names only support names that are all strings",
"ignore: Given top_k=.* is less than n_segments. Algo will filter data without Gale-Shapley run.",
"ignore: Call to deprecated create function", # protobuf warning
"ignore: Dynamic prediction specified to begin during out-of-sample forecasting period, and so has no effect.",
"ignore: `tsfresh` is not available, to install it, run `pip install tsfresh==0.19.0 && pip install protobuf==3.20.1`",
"ignore::pytorch_lightning.utilities.rank_zero.LightningDeprecationWarning",
"ignore: The default method 'yw' can produce PACF values outside",
]

# TODO: Uncomment after some solution in https://github.com/pytest-dev/pytest/issues/10773
#filterwarnings = [
# "error",
# "ignore: Torchmetrics v0.9 introduced a new argument class property called `full_state_update` that",
# "ignore: TSDataset freq can't be inferred",
# "ignore: test_size, test_start and test_end cannot be",
# "ignore: You probably set wrong freq. Discovered freq in you data is None",
# "ignore: Some regressors don't have enough values in segment",
# "ignore: Segments contains NaNs in the last timestamps.",
# "ignore: pandas.util.testing is deprecated. Use the functions in the public API",
# "ignore: Call to deprecated class CatBoostModelPerSegment.", # OK
# "ignore: Call to deprecated class CatBoostModelMultiSegment.", # OK
# "ignore: Attribute 'loss' is an instance of `nn.Module` and is already",
# "ignore: Columns from feature_to_use which are out of dataframe columns will",
# "ignore: Comparison of Timestamp with datetime.date is deprecated in order to",
# "ignore: CountryHoliday is deprecated, use country_holidays instead.",
# "ignore: Exogenous or target data contains None! It will be dropped",
# "ignore: is less than n_segments. Algo will filter data",
# "ignore: Given top_k=30 is bigger than n_features=20. Transform will not filter",
# "ignore: Implicitly cleaning up ",
# "ignore: Maximum Likelihood optimization failed to converge. Check mle_retvals",
# "ignore: Mean of empty slice",
# "ignore: No frequency information was provided, so inferred frequency D will",
# "ignore: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.",
# "ignore: Slicing a positional slice with .loc is not supported",
# "ignore: Some of external objects in input parameters could be not",
# "ignore: The 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be",
# "ignore: The default dtype for empty Series will be 'object' instead",
# "ignore: This model does not work with exogenous features and regressors.",
# "ignore: Transformation will be applied inplace, out_column param will be ignored",
# "ignore: You defined a `validation_step` but have no `val_dataloader`. Skipping val",
# "ignore: You probably set wrong freq. Discovered freq in you data",
# "ignore: SeasonalMovingAverageModel does not work with any exogenous series or features.",
# "ignore: MovingAverageModel does not work with any exogenous series or features.",
# "ignore: NaiveModel does not work with any exogenous series or features.",
# "ignore: `np.object` is a deprecated alias for the builtin `object`. To",
# "ignore: divide by zero encountered in log",
# "ignore: inplace is deprecated and will be removed in a future",
# "ignore: invalid value encountered in double_scalars",
# "ignore: Arrays of bytes/strings is being converted to decimal numbers if",
# "ignore: Attribute 'logging_metrics' is an instance of `nn.Module` and is already",
# "ignore: Exogenous data contains columns with category type! It will be",
# "ignore: Features {'unknown'} are not found and will be dropped!",
# "ignore: SARIMAX model does not work with exogenous features",
# "ignore: Series.dt.weekofyear and Series.dt.week have been deprecated",
# "ignore: The dataloader, train_dataloader, does not have many workers which may",
# "ignore: Creating a tensor from a list of numpy.ndarrays",
# "ignore: Trying to infer the `batch_size` from an ambiguous collection",
# "ignore: ReduceLROnPlateau conditioned on metric val_loss which is not available but strict",
# "ignore: Checkpoint directory",
# "ignore: Objective did not converge. You might want to increase the number",
# "ignore: distutils Version classes are deprecated.",
# "ignore: invalid escape sequence",
# "ignore::pandas.core.common.SettingWithCopyWarning",
# "ignore: You haven't set all parameters inside class __init__ method.* 'box_cox_bounds'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'use_box_cox'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'use_trend'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'use_damped_trend'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'seasonal_periods'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'show_warnings'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'n_jobs'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'multiprocessing_start_method'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'context'",
# "ignore: You haven't set all parameters inside class __init__ method.* 'use_arma_errors'",
# "ignore: New behaviour in v1.1.5",
# "ignore: The 'check_less_precise' keyword in testing",
# "ignore: Feature names only support names that are all strings",
# "ignore: Given top_k=.* is less than n_segments. Algo will filter data without Gale-Shapley run.",
# "ignore: Call to deprecated create function", # protobuf warning
# "ignore: Dynamic prediction specified to begin during out-of-sample forecasting period, and so has no effect.",
# "ignore: `tsfresh` is not available, to install it, run `pip install tsfresh==0.19.0 && pip install protobuf==3.20.1`",
# "ignore::pytorch_lightning.utilities.rank_zero.LightningDeprecationWarning",
# "ignore: The default method 'yw' can produce PACF values outside",
# "ignore: All-NaN slice encountered",
#]
markers = [
"smoke",
"long_1",
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