unit8co · gdevos010 · Sep 23, 2024 · Sep 28, 2024 · Sep 29, 2024 · Sep 29, 2024
@@ -21,7 +21,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.5
+    rev: v0.6.8
     hooks:
       # try to fix what is possible
       - id: ruff

@@ -55,6 +55,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
   - Updated the Ray Tune Hyperparameter Optimization example in the [user guide](https://unit8co.github.io/darts/userguide/hyperparameter_optimization.html) to work with the latest `ray` versions (`>=2.31.0`). [#2459](https://github.com/unit8co/darts/pull/2459) by [He Weilin](https://github.com/cnhwl).
   - Indicate that `multi_models=False` induce a lags shift for each step in `output_chunk_length` in `RegressionModel` and `LinearRegressionModel`. [#2511](https://github.com/unit8co/darts/pull/2511) by [Antoine Madrona](https://github.com/madtoinou).
   - Added reference to `timeseries_generation.datetime_attribute_timeseries` in `TimeSeries.add_datetime_attribute` (0-indexing of encoding is enforced). [#2511](https://github.com/unit8co/darts/pull/2511) by [Antoine Madrona](https://github.com/madtoinou).
+  - Update ruff to v6.8. Also fixed many typos [#2548](https://github.com/unit8co/darts/pull/2548) by [Greg DeVosNouri](https://github.com/gdevos010)
 
 **Fixed**
 
@@ -265,11 +266,11 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 **Fixed**
 
 - Fixed a bug when using `RegressionModel` with `lags=None`, some `lags_*covariates`, and the covariates starting after or at the same time as the first predictable time step; the lags were not extracted from the correct indices. [#2176](https://github.com/unit8co/darts/pull/2176) by [Dennis Bader](https://github.com/dennisbader).
-- Fixed a bug when calling `window_transform` on a `TimeSeries` with a hierarchy. The hierarchy is now only preseved for single transformations applied to all components, or removed otherwise. [#2207](https://github.com/unit8co/darts/pull/2207) by [Antoine Madrona](https://github.com/madtoinou).
+- Fixed a bug when calling `window_transform` on a `TimeSeries` with a hierarchy. The hierarchy is now only preserved for single transformations applied to all components, or removed otherwise. [#2207](https://github.com/unit8co/darts/pull/2207) by [Antoine Madrona](https://github.com/madtoinou).
 - 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_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 `gridsearch()` with `use_fitted_values=True`, where the model was not propely instantiated for sanity checks. [#2222](https://github.com/unit8co/darts/pull/2222) by [Antoine Madrona](https://github.com/madtoinou).
+- Fixed a bug in `gridsearch()` with `use_fitted_values=True`, where the model was not properly instantiated for sanity checks. [#2222](https://github.com/unit8co/darts/pull/2222) by [Antoine Madrona](https://github.com/madtoinou).
 - Fixed a bug in `TimeSeries.append/prepend_values()`, where the component names and the hierarchy were dropped. [#2237](https://github.com/unit8co/darts/pull/2237) by [Antoine Madrona](https://github.com/madtoinou).
 - Fixed a bug in `get_multioutput_estimator()`, where the index of the estimator was incorrectly calculated. [#2246](https://github.com/unit8co/darts/pull/2246) by [Antoine Madrona](https://github.com/madtoinou).
 - 🔴 Fixed a bug in `datetime_attribute_timeseries()`, where 1-indexed attributes were not properly handled. Also, 0-indexing is now enforced for all the generated encodings. [#2242](https://github.com/unit8co/darts/pull/2242) by [Antoine Madrona](https://github.com/madtoinou).

@@ -165,7 +165,7 @@ def fit_detect(
         Returns
         -------
         Union[TimeSeries, Sequence[TimeSeries]]
-            Binary prediciton (1 if considered as an anomaly, 0 if not)
+            Binary prediction (1 if considered as an anomaly, 0 if not)
         """
         self.fit(series)
         return self.detect(series, name="series")

@@ -666,7 +666,7 @@ def _plot_series(series, ax_id, linewidth, label_name, **kwargs):
     series
         The series to plot.
     ax_id
-        The axis the series will be ploted on.
+        The axis the series will be plotted on.
     linewidth
         Thickness of the line.
     label_name

@@ -54,14 +54,14 @@ def __init__(
             Whether to remove the NaNs from the start and the end of the transformed series.
         drop_static_covariates
             If set to `True`, the statics covariates of the input series won't be transferred to the output.
-            This migth be useful for multivariate series with component-specific static covariates.
+            This might be useful for multivariate series with component-specific static covariates.
         name
             A specific name for the scaler
         n_jobs
             The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is
-            passed as input to a method, parallelising operations regarding different ``TimeSeries``. Defaults to `1`
+            passed as input to a method, parallelizing operations regarding different ``TimeSeries``. Defaults to `1`
             (sequential). Setting the parameter to `-1` means using all the available processors.
-            Note: for a small amount of data, the parallelisation overhead could end up increasing the total
+            Note: for a small amount of data, the parallelization overhead could end up increasing the total
             required amount of time.
         verbose
             Optionally, whether to print operations progress

@@ -260,9 +260,9 @@ def _create_component_masks(
         """
         Returns a boolean array indicating which components of the UNTRANSFORMED
         `stat_covs` are numerical and a boolean array indicating which components
-        of the UNTRANSFORMED `stat_covs` are categoical.
+        of the UNTRANSFORMED `stat_covs` are categorical.
 
-        It's important to recognise that these masks only apply to the UNTRANSFORMED
+        It's important to recognize that these masks only apply to the UNTRANSFORMED
         static covariates since some transformations can generate multiple new components
         from a single component (e.g. one-hot encoding).
         """
@@ -327,9 +327,9 @@ def _create_inv_component_masks(
         """
         Returns a boolean array indicating which components of the TRANSFORMED
         `stat_covs` are numerical and a boolean array indicating which components
-        of the TRANSFORMED `stat_covs` are categoical.
+        of the TRANSFORMED `stat_covs` are categorical.
 
-        It's important to recognise that these masks only apply to the UNTRANSFORMED
+        It's important to recognize that these masks only apply to the UNTRANSFORMED
         static covariates since some transformations can generate multiple new components
         from a single component (e.g. one-hot encoding).
         """

@@ -398,7 +398,7 @@ def summary_plot(
         Returns
         -------
         shaps_
-            A nested dictionary {horizon : {component : shap.Explaination}} containing the raw Explanations for all
+            A nested dictionary {horizon : {component : shap.Explanation}} containing the raw Explanations for all
             the horizons and components.
         """
 

@@ -3,7 +3,7 @@
 -------
 
 For deterministic forecasts (point predictions with `num_samples == 1`), probabilistic forecasts (`num_samples > 1`),
-and quantile forecasts. For probablistic and quantile forecasts, use parameter `q` to define the quantile(s) to
+and quantile forecasts. For probabilistic and quantile forecasts, use parameter `q` to define the quantile(s) to
 compute the deterministic metrics on:
 
     - Aggregated over time:

@@ -287,14 +287,14 @@ def wrapper_multivariate_support(*args, **kwargs) -> METRIC_OUTPUT_TYPE:
                 raise_log(
                     ValueError(
                         "`q` must be of tuple of `(np.ndarray, Optional[pd.Index])` "
-                        "where the (quantile values, optioanl quantile component names). "
+                        "where the (quantile values, optional quantile component names). "
                         f"Received `q={q}`."
                     ),
                     logger=logger,
                 )
             q, q_comp_names = q
             if not pred_series.is_stochastic:
-                # quantile component names are required if the predictions are not stochastic (as for stocahstic
+                # quantile component names are required if the predictions are not stochastic (as for stochastic
                 # predictions, the quantiles can be retrieved from the sample dimension for each component)
                 if q_comp_names is None:
                     q_comp_names = pd.Index(
@@ -611,7 +611,7 @@ def _get_error_scale(
     """Computes the error scale based on a naive seasonal forecasts on `insample` values with seasonality `m`."""
     if not isinstance(m, int):
         raise_log(
-            ValueError(f"Seasonality `m` must be of type `int`, recevied `m={m}`"),
+            ValueError(f"Seasonality `m` must be of type `int`, received `m={m}`"),
             logger=logger,
         )
 

@@ -257,7 +257,7 @@ def __init__(
         hidden_fc_sizes
             Sizes of hidden layers connecting the last hidden layer of the RNN module to the output, if any.
         dropout
-            Fraction of neurons afected by Dropout.
+            Fraction of neurons affected by Dropout.
         activation
             The name of a torch.nn activation function to be applied between the layers of the fully connected network.
             Default: "ReLU".

@@ -3155,7 +3155,7 @@ def _verify_passed_predict_covariates(self, future_covariates):
 
     @property
     def _supress_generate_predict_encoding(self) -> bool:
-        """Controls wether encodings should be generated in :func:`FutureCovariatesLocalForecastingModel.predict()``"""
+        """Controls whether encodings should be generated in :func:`FutureCovariatesLocalForecastingModel.predict()``"""
         return False
 
     @property

@@ -115,7 +115,7 @@ def __init__(
         To add a new naive model:
         - subclass from `_GlobalNaiveModel` with implementation of private method `_create_model` that creates an
             object of:
-        - subclass from `_GlobalNaiveModule` with implemention of private method `_forward`
+        - subclass from `_GlobalNaiveModule` with implementation of private method `_forward`
 
         .. note::
             - Model checkpointing with `save_checkpoints=True`, and checkpoint loading with `load_from_checkpoint()`

@@ -251,7 +251,7 @@ def fit(
 
                 self._model_container[quantile] = self.model
 
-            # replace the last trained QuantileRegressor with the dictionnary of Regressors.
+            # replace the last trained QuantileRegressor with the dictionary of Regressors.
             self.model = self._model_container
 
             return self

@@ -536,7 +536,7 @@ def _produce_predict_output(self, x: Tuple) -> torch.Tensor:
     def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         # we must save the dtype for correct parameter precision at loading time
         checkpoint["model_dtype"] = self.dtype
-        # we must save the shape of the input to be able to instanciate the model without calling fit_from_dataset
+        # we must save the shape of the input to be able to instantiate the model without calling fit_from_dataset
         checkpoint["train_sample_shape"] = self.train_sample_shape
         # we must save the loss to properly restore it when resuming training
         checkpoint["loss_fn"] = self.criterion

@@ -111,7 +111,7 @@ def encode_year(idx):
                 }
             ..
         cap
-            Parameter specifiying the maximum carrying capacity when predicting with logistic growth.
+            Parameter specifying the maximum carrying capacity when predicting with logistic growth.
             Mandatory when `growth = 'logistic'`, otherwise ignored.
             See <https://facebook.github.io/prophet/docs/saturating_forecasts.html> for more information
             on logistic forecasts.
@@ -121,7 +121,7 @@ def encode_year(idx):
             - a function taking a DatetimeIndex or RangeIndex and returning a corresponding a Sequence of numbers,
             where each number indicates the carrying capacity at this index.
         floor
-            Parameter specifiying the minimum carrying capacity when predicting logistic growth.
+            Parameter specifying the minimum carrying capacity when predicting logistic growth.
             Optional when `growth = 'logistic'` (defaults to 0), otherwise ignored.
             See <https://facebook.github.io/prophet/docs/saturating_forecasts.html> for more information
             on logistic forecasts.

@@ -458,12 +458,12 @@ def ensemble(
         ensembled = [
             self.regression_model.predict(
                 n=len(prediction),
-                series=serie,
+                series=series,
                 future_covariates=prediction,
                 num_samples=num_samples,
                 predict_likelihood_parameters=predict_likelihood_parameters,
             )
-            for serie, prediction in zip(series, predictions)
+            for series, prediction in zip(series, predictions)
         ]
         return seq2series(ensembled) if is_single_series else ensembled
 

@@ -369,7 +369,7 @@ def _generate_lags(
                     raise_log(
                         ValueError(
                             f"`{lags_name}` - `{comp_name}`: must be either a {supported_types}. "
-                            f"Gived : {type(comp_lags)}."
+                            f"Given : {type(comp_lags)}."
                         ),
                         logger,
                     )

@@ -318,7 +318,7 @@ def __init__(
         n_rnn_layers
             The number of recurrent layers.
         dropout
-            Fraction of neurons afected by Dropout.
+            Fraction of neurons affected by Dropout.
         training_length
             The length of both input (target and covariates) and output (target) time series used during
             training. Must have a larger value than `input_chunk_length`, because otherwise during training

@@ -292,7 +292,7 @@ def test_aggregator_predict_wrong_inputs(self, config):
 
     @pytest.mark.parametrize("config", list_NonFittableAggregator)
     def test_NonFittableAggregator_predict(self, config):
-        """Check that predict() works as intented"""
+        """Check that predict() works as intended"""
         aggregator_cls, cls_kwargs, _ = config
         aggregator = aggregator_cls(**cls_kwargs)
 

@@ -1262,7 +1262,7 @@ def test_PyODScorer(self):
         with pytest.raises(ValueError):
             PyODScorer(model=KNN(), window=0)
 
-        # diff_fn paramter
+        # diff_fn parameter
         # must be None, 'diff' or 'abs_diff'
         with pytest.raises(ValueError):
             PyODScorer(model=KNN(), diff_fn="random")
@@ -1531,7 +1531,7 @@ def helper_evaluate_nll_scorer(
             np.array(deterministic_values).reshape(2, 2, -1)
         )
 
-        # compute the NLL values witn score_from_prediction for scorer with window=1 and 2
+        # compute the NLL values with score_from_prediction for scorer with window=1 and 2
         # t -> timestamp, c -> component and w -> window used in scorer
         value_t1_c1_w1 = NLLscorer_w1.score_from_prediction(
             series[0]["0"], distribution_series[0]["0"]
@@ -1570,7 +1570,7 @@ def helper_evaluate_nll_scorer(
         )
 
         # multivariate case
-        # compute the NLL values witn score_from_prediction for scorer with window=1 and window=2
+        # compute the NLL values with score_from_prediction for scorer with window=1 and window=2
         value_t1_2_c1_2_w1 = NLLscorer_w1.score_from_prediction(
             series, distribution_series
         )
@@ -1686,7 +1686,7 @@ def test_window_greater_than_one(self, window, model, series):
         assert score_T[-1] == score_F[-1]
 
     def test_fun_window_agg(self):
-        """Verify that the anomaly score aggregation works as intented"""
+        """Verify that the anomaly score aggregation works as intended"""
         # window = 2, alternating anomaly scores
         window = 2
         scorer = KMeansScorer(window=window)

@@ -540,8 +540,8 @@ def test_include_current(self):
     )
     def test_ts_windowtransf_hierarchy(self, transforms):
         """Checking that supported transforms behave as expected:
-        - implicitely applied to all components
-        - passing explicitely all components
+        - implicitly applied to all components
+        - passing explicitly all components
         """
         ts = helper_generate_ts_hierarchy(10)
 

@@ -37,7 +37,7 @@ def test_rin(self):
             rin = RINorm(input_dim=7, affine=affine)
             x_norm = rin(x)
 
-            # expand dims to simulate probablistic forecasting
+            # expand dims to simulate probabilistic forecasting
             x_denorm = rin.inverse(x_norm.view(x_norm.shape + (1,))).squeeze(-1)
             assert torch.all(torch.isclose(x, x_denorm)).item()
 

@@ -1132,15 +1132,15 @@ def test_gridsearch_sample_weight(self, config):
         else:
             sample_weight = "linear"
 
-        paramameters = {"lags": [3], "output_chunk_length": [1]}
+        parameters = {"lags": [3], "output_chunk_length": [1]}
         start_kwargs = {"start": -1, "start_format": "position"}
         gs_kwargs = {"val_series": ts} if use_val_series else {"forecast_horizon": 1}
         gs_non_weighted = LinearRegressionModel.gridsearch(
-            paramameters, series=ts[:-1], **start_kwargs, **gs_kwargs
+            parameters, series=ts[:-1], **start_kwargs, **gs_kwargs
         )[-1]
 
         gs_weighted = LinearRegressionModel.gridsearch(
-            paramameters,
+            parameters,
             series=ts[:-1],
             sample_weight=sample_weight,
             **start_kwargs,

@@ -82,7 +82,7 @@ def test_trained_models(self):
 
         # both global trained, retrain = True
         with pytest.raises(ValueError):
-            # models need to be explicitely reset before retraining them
+            # models need to be explicitly reset before retraining them
             NaiveEnsembleModel(
                 [global_model, global_model], train_forecasting_models=True
             )

@@ -2552,7 +2552,7 @@ def test_predict_likelihood_parameters(self, model_type):
         ),
     )
     def test_probabilistic_optimized_hist_fc_regression(self, config):
-        """Tests optimized probilistic historical forecasts for regression models."""
+        """Tests optimized probabilistic historical forecasts for regression models."""
         np.random.seed(42)
         lpo, multi_models, n = config
         ocl = 2

@@ -520,7 +520,7 @@ def test_predict_likelihood_parameters_univariate_torch_models(
                     n_samples,
                 ))
 
-                # Dirichlet must be handled sligthly differently since its multivariate
+                # Dirichlet must be handled slightly differently since its multivariate
                 if isinstance(lkl, DirichletLikelihood):
                     values = torch.swapaxes(values, 1, 3)
                     values = torch.squeeze(values, 3)

@@ -121,7 +121,7 @@ def test_builtin_extended_trainer(self):
             )
             model.fit(self.series, epochs=1)
 
-        # flaot 16 not supported
+        # float 16 not supported
         with pytest.raises(ValueError):
             invalid_trainer_kwarg = {
                 "precision": "16-mixed",
-Original file line number
+Diff line change
@@ Expand Up / @@ -398,7 +398,7 @@ def summary_plot( @@
             Returns
             -------
             shaps_
-                A nested dictionary {horizon : {component : shap.Explaination}} containing the raw Explanations for all
+                A nested dictionary {horizon : {component : shap.Explanation}} containing the raw Explanations for all
                 the horizons and components.
             """
@@ Expand Down @@