diff --git a/CHANGELOG.md b/CHANGELOG.md index 854e628fd..e5a23c23e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,17 +7,27 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## Unreleased ### Added - -- - +- +- +- +- ### Changed - -- Impose specific order of columns on return value of TSDataset.to_flatten ([#1095](https://github.com/tinkoff-ai/etna/pull/1095)) - +- +- +- ### Fixed +- +- +- +- +## [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)) -- ## [1.15.0] - 2023-01-31 ### Added diff --git a/pyproject.toml b/pyproject.toml index 3c1cc2462..dcab867e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [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." @@ -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",