All warning as errors pytest

pyproject.toml

@@ -177,6 +177,76 @@ 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.",
+]
+markers = [
+    "smoke",
+    "long"
+]
 
 [tool.mypy]
 ignore_missing_imports = true