[MNT] Fix spellings using codespell and typos (#6799)

- used `typos --locale en --write-changes .` (**automated**) - used `codespell --write-changes .` (**automated**) - changed `monotonous` to `monotonic`) (**manual**) -> fixes #6775
sktime · Jul 20, 2024 · 06427ff · 06427ff
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diff --git a/docs/source/Makefile b/docs/source/Makefile
@@ -20,7 +20,7 @@ clean:
 	@echo "Deleted directory $(BUILDDIR)."
 
 # $(O) is meant as a shortcut for custom options.
-# i.e to log stderr into a seprate file:
+# i.e to log stderr into a separate file:
 # make build O="--no-color 2> build_warnings.log"
 html:
 #	./symlink_examples.sh

diff --git a/docs/source/changelog.rst b/docs/source/changelog.rst
@@ -7837,7 +7837,7 @@ Refactored
 *  [ENH] renamed ``fit-in-transform`` and ``fit-in-predict`` to ``fit_is_empty`` (:pr:`2250`) :user:`fkiraly`
 *  [ENH] refactoring `test_all_classifiers` to test class architecture (:pr:`2257`) :user:`fkiraly`
 *  [ENH] test parameter refactor: all classifiers (:pr:`2288`) :user:`MatthewMiddlehurst`
-*  [ENH] test paraneter refactor: ``Arsenal`` (:pr:`2273`) :user:`dionysisbacchus`
+*  [ENH] test parameter refactor: ``Arsenal`` (:pr:`2273`) :user:`dionysisbacchus`
 *  [ENH] test parameter refactor: ``RocketClassifier`` (:pr:`2166`) :user:`dionysisbacchus`
 *  [ENH] test parameter refactor: ``TimeSeriesForestClassifier`` (:pr:`2277`) :user:`lielleravid`
 *  [ENH] ``FeatureUnion`` refactor - moved to ``transformations``, tags, dunder method (:pr:`2231`) :user:`fkiraly`

diff --git a/docs/source/get_involved/code_of_conduct.rst b/docs/source/get_involved/code_of_conduct.rst
@@ -86,7 +86,7 @@ genetic information, sexual orientation, disability status, physical appearance,
 body size, citizenship, nationality, national origin, ethnic or social origin, pregnancy,
 familial status, family background, veteran status, trade union membership,
 religion or belief (or lack thereof), membership of a national minority, property, age,
-socio-economic status, neurotypicality or -atypicality, education, and experience level.
+socioeconomic status, neurotypicality or -atypicality, education, and experience level.
 
 Everyone who participates in the sktime project activities is required
 to conform to this Code of Conduct. This Code of Conduct applies to all

diff --git a/examples/01c_forecasting_hierarchical_global.ipynb b/examples/01c_forecasting_hierarchical_global.ipynb
@@ -99,7 +99,7 @@
     "\n",
     "In the `\"pd.DataFrame\"` mtype, time series are represented by an in-memory container `obj: pandas.DataFrame` as follows.\n",
     "\n",
-    "* structure convention: `obj.index` must be monotonous, and one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex`.\n",
+    "* structure convention: `obj.index` must be monotonic, and one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex`.\n",
     "* variables: columns of `obj` correspond to different variables\n",
     "* variable names: column names `obj.columns`\n",
     "* time points: rows of `obj` correspond to different, distinct time points\n",
@@ -270,7 +270,7 @@
     "\n",
     "In the `\"pd-multiindex\"` mtype, time series panels are represented by an in-memory container `obj: pandas.DataFrame` as follows.\n",
     "\n",
-    "* structure convention: `obj.index` must be a pair multi-index of type `(RangeIndex, t)`, where `t` is one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex` and monotonous.\n",
+    "* structure convention: `obj.index` must be a pair multi-index of type `(RangeIndex, t)`, where `t` is one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex` and monotonic.\n",
     "* instances: rows with the same `\"instances\"` index correspond to the same instance; rows with different `\"instances\"` index correspond to different instances.\n",
     "* instance index: the first element of pairs in `obj.index` is interpreted as an instance index. \n",
     "* variables: columns of `obj` correspond to different variables\n",
@@ -407,7 +407,7 @@
    "source": [
     "#### Hierarchical time series - `Hierarchical` scitype, `\"pd_multiindex_hier\"` mtype\n",
     "\n",
-    "* structure convention: `obj.index` must be a 3 or more level multi-index of type `(RangeIndex, ..., RangeIndex, t)`, where `t` is one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex` and monotonous. We call the last index the \"time-like\" index\n",
+    "* structure convention: `obj.index` must be a 3 or more level multi-index of type `(RangeIndex, ..., RangeIndex, t)`, where `t` is one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex` and monotonic. We call the last index the \"time-like\" index\n",
     "* hierarchy level: rows with the same non-time-like indices correspond to the same hierarchy unit; rows with different non-time-like index combination correspond to different hierarchy unit.\n",
     "* hierarchy: the non-time-like indices in `obj.index` are interpreted as a hierarchy identifying index. \n",
     "* variables: columns of `obj` correspond to different variables\n",

diff --git a/extension_templates/transformer.py b/extension_templates/transformer.py
@@ -217,7 +217,7 @@ class MyTransformer(BaseTransformer):
         #
         # skip-inverse-transform = is inverse-transform skipped when called?
         "skip-inverse-transform": False,
-        # if False, capability:inverse_transform tag behaviour is as per devault
+        # if False, capability:inverse_transform tag behaviour is as per default
         # if True, inverse_transform is the identity transform and raises no exception
         #   this is useful for transformers where inverse_transform
         #   may be called but should behave as the identity, e.g., imputers

diff --git a/pyproject.toml b/pyproject.toml
@@ -376,7 +376,7 @@ ignore=[
   "C414", # Unnecessary `list` call within `sorted()`
   "S301", # pickle and modules that wrap it can be unsafe
   "C416", # Unnecessary list comprehension - rewrite as a generator
-  "S310", # Audit URL open for permited schemes
+  "S310", # Audit URL open for permitted schemes
   "S202", # Uses of `tarfile.extractall()`
   "S307", # Use of possibly insecure function
   "C417", # Unnecessary `map` usage (rewrite using a generator expression)
@@ -386,7 +386,7 @@ ignore=[
   "S105", # Possible hardcoded password
   "PT018", # Checks for assertions that combine multiple independent condition
   "S602", # sub process call with shell=True unsafe
-  "C419", # Unnecessary list comprehension, some are flaged yet are not
+  "C419", # Unnecessary list comprehension, some are flagged yet are not
   "C409", # Unnecessary `list` literal passed to `tuple()` (rewrite as a `tuple` literal)
   "S113", # Probable use of httpx call withour timeout
 ]

diff --git a/sktime/annotation/base/_base.py b/sktime/annotation/base/_base.py
@@ -37,7 +37,7 @@ class BaseSeriesAnnotator(BaseEstimator):
     task : str {"segmentation", "change_point_detection", "anomaly_detection"}
         The main annotation task:
         * If ``segmentation``, the annotator divides timeseries into discrete chunks
-        based on certain criteria. The same label can be applied at mulitple
+        based on certain criteria. The same label can be applied at multiple
         disconnected regions of the timeseries.
         * If ``change_point_detection``, the annotator finds points where the
         statistical properties of the timeseries change significantly.
@@ -537,7 +537,7 @@ def _sparse_segments_to_dense(y_sparse, index):
         """
         if y_sparse.index.is_overlapping:
             raise NotImplementedError(
-                "Cannot convert overlapping segments to a dense formet yet."
+                "Cannot convert overlapping segments to a dense format yet."
             )
 
         interval_indexes = y_sparse.index.get_indexer(index)

diff --git a/sktime/annotation/hmm_learn/gaussian.py b/sktime/annotation/hmm_learn/gaussian.py
@@ -17,7 +17,7 @@ class GaussianHMM(BaseHMMLearn):
     ----------
     n_components : int
         Number of states
-    covariance_type : {"sperical", "diag", "full", "tied"}, optional
+    covariance_type : {"spherical", "diag", "full", "tied"}, optional
         The type of covariance parameters to use:
         * "spherical" --- each state uses a single variance value that
             applies to all features.

diff --git a/sktime/annotation/hmm_learn/gmm.py b/sktime/annotation/hmm_learn/gmm.py
@@ -19,7 +19,7 @@ class GMMHMM(BaseHMMLearn):
         Number of states in the model.
     n_mix : int
         Number of states in the GMM.
-    covariance_type : {"sperical", "diag", "full", "tied"}, optional
+    covariance_type : {"spherical", "diag", "full", "tied"}, optional
         The type of covariance parameters to use:
         * "spherical" --- each state uses a single variance value that
             applies to all features.

diff --git a/sktime/base/_base.py b/sktime/base/_base.py
@@ -200,7 +200,7 @@ def _handle_numpy2_softdeps(self):
           are in NOT_NP2_COMPATIBLE, this is a hard-coded
           list of soft dependencies that are not numpy 2 compatible
         * if any are found, adds a numpy<2.0 soft dependency to the list,
-          and sets it as a dynamic overide of the python_dependencies tag
+          and sets it as a dynamic override of the python_dependencies tag
         """
         from packaging.requirements import Requirement
 

diff --git a/sktime/benchmarking/_lib_mini_kotsu/__init__.py b/sktime/benchmarking/_lib_mini_kotsu/__init__.py
@@ -7,7 +7,7 @@
 
 The module is currently undergoing a refactor with the following targets:
 
-* remove kotsu as a soft dependeny. The package is no longer maintained and
+* remove kotsu as a soft dependency. The package is no longer maintained and
   a dependency liability for sktime, maintainers are non-responsive.
 * replace kotsu with sktime's own benchmarking module, with an API that is closer
   to sklearn's estimator API (e.g., no separation of class and params).

diff --git a/sktime/benchmarking/_lib_mini_kotsu/registration.py b/sktime/benchmarking/_lib_mini_kotsu/registration.py
@@ -125,7 +125,7 @@ def make(self, id: str, **kwargs) -> Entity:
             raise KeyError(f"No registered entity with ID {id}")
 
     def all(self):
-        """Return all the entitys in the registry."""
+        """Return all the entities in the registry."""
         return self.entity_specs.values()
 
     def register(

diff --git a/sktime/classification/dictionary_based/_boss_pyts.py b/sktime/classification/dictionary_based/_boss_pyts.py
@@ -66,8 +66,8 @@ class BOSSVSClassifierPyts(_PytsAdapter, BaseClassifier):
         alphabet are used.
 
     numerosity_reduction : bool (default = True)
-        If True, delete sample-wise all but one occurence of back to back
-        identical occurences of the same words.
+        If True, delete sample-wise all but one occurrence of back to back
+        identical occurrences of the same words.
 
     use_idf : bool (default = True)
         Enable inverse-document-frequency reweighting.

diff --git a/sktime/datasets/_readers_writers/utils.py b/sktime/datasets/_readers_writers/utils.py
@@ -299,6 +299,6 @@ def get_path(path: Union[str, pathlib.Path], suffix: str) -> str:
     if not p_.suffix:
         # Checks if a file with the same name exists
         if not os.path.exists(resolved_path):
-            # adds the specified extention to the path
+            # adds the specified extension to the path
             resolved_path += suffix
     return resolved_path
diff --git a/sktime/datatypes/_series_as_panel/_convert.py b/sktime/datatypes/_series_as_panel/_convert.py
@@ -25,7 +25,7 @@ def convert_Series_to_Panel(obj, store=None, return_to_mtype=False):
     """Convert series to a single-series panel.
 
     Adds a dummy dimension to the series.
-    For pd.Series or DataFrame, this results in a list of DataFram (dim added is list).
+    For pd.Series or DataFrame, this results in a list of DataFrame (dim added is list).
     For numpy array, this results in a third dimension being added.
 
     Assumes input is conformant with one of the three Series mtypes.

diff --git a/sktime/dists_kernels/dtw/_dtw_sktime.py b/sktime/dists_kernels/dtw/_dtw_sktime.py
@@ -36,7 +36,7 @@ class DtwDist(BasePairwiseTransformerPanel):
     Note that the more flexible options above may be less performant.
 
     The algorithms are also available as alignment estimators
-    ``sktime.alignmnent.dtw_numba``, producing alignments aka alignment paths.
+    ``sktime.alignment.dtw_numba``, producing alignments aka alignment paths.
 
     DTW was originally proposed in [1]_, DTW computes the distance between two
     time series by considering their alignments during the calculation.

diff --git a/sktime/forecasting/autots.py b/sktime/forecasting/autots.py
@@ -72,7 +72,7 @@ class AutoTS(BaseForecaster):
         The initial template to use for the forecast.
         'Random' - randomly generates starting template, 'General' uses template
         included in package, 'General+Random' - both of previous.
-        Also can be overriden with import_template()
+        Also can be overridden with import_template()
     random_seed (int):
         The random seed for reproducibility.
         Random seed allows (slightly) more consistent results.
@@ -101,7 +101,7 @@ class AutoTS(BaseForecaster):
     drop_most_recent (int):
         Option to drop n most recent data points. Useful, say, for monthly sales
         data where the current (unfinished) month is included. occurs after any
-        aggregration is applied, so will be whatever is specified by frequency,
+        aggregation is applied, so will be whatever is specified by frequency,
         will drop n frequencies
     drop_data_older_than_periods (int):
         The threshold for dropping old data points.

diff --git a/sktime/forecasting/base/adapters/_neuralforecast.py b/sktime/forecasting/base/adapters/_neuralforecast.py
@@ -269,8 +269,8 @@ def _fit(
         """
         # A. freq is given {use this}
         # B. freq is auto
-        #     B1. freq is infered from fh {use this}
-        #     B2. freq is not infered from fh
+        #     B1. freq is inferred from fh {use this}
+        #     B2. freq is not inferred from fh
         #         B2.1. y is date-like {raise exception}
         #         B2.2. y is not date-like
         #             B2.2.1 equispaced integers {use diff in time}
@@ -291,7 +291,7 @@ def _fit(
 
         if self.freq != "auto":  # A: freq is given as non-auto
             self._freq = self.freq
-        elif fh.freq:  # B1: freq is infered from fh
+        elif fh.freq:  # B1: freq is inferred from fh
             self._freq = fh.freq
         elif isinstance(y.index, pandas.DatetimeIndex):  # B2.1: y is date-like
             raise ValueError(

diff --git a/sktime/forecasting/base/adapters/_pytorchforecasting.py b/sktime/forecasting/base/adapters/_pytorchforecasting.py
@@ -32,7 +32,7 @@ class _PytorchForecastingAdapter(_BaseGlobalForecaster):
         parameters to initialize `TimeSeriesDataSet` [1]_ from `pandas.DataFrame`
         max_prediction_length will be overwrite according to fh
         time_idx, target, group_ids, time_varying_known_reals, time_varying_unknown_reals
-        will be infered from data, so you do not have to pass them
+        will be inferred from data, so you do not have to pass them
     train_to_dataloader_params : Dict[str, Any] (default=None)
         parameters to be passed for `TimeSeriesDataSet.to_dataloader()`
         by default {"train": True}

diff --git a/sktime/forecasting/compose/_pipeline.py b/sktime/forecasting/compose/_pipeline.py
@@ -1507,7 +1507,7 @@ def _check_unknown_exog(
         # either columns explicitly specified through the `columns` argument
         # or all columns in the `X` argument passed in `fit` call are future-unknown
         if self.columns is None or len(self.columns) == 0:
-            # `self._X` is guranteed to exist and be a DataFrame at this point
+            # `self._X` is guaranteed to exist and be a DataFrame at this point
             # ensured by `self.X_was_None_` check in `_get_forecaster_X_prediction`
             unknown_columns = self._X.columns
         else:

diff --git a/sktime/forecasting/compose/_reduce.py b/sktime/forecasting/compose/_reduce.py
@@ -669,7 +669,7 @@ def pool_preds(y_preds):
             return y_pred
 
         def _coerce_to_numpy(y_pred):
-            """Coerce predictions to numpy array, assumes pd.DataFram or numpy."""
+            """Coerce predictions to numpy array, assumes pd.DataFrame or numpy."""
             if isinstance(y_pred, pd.DataFrame):
                 return y_pred.values
             else:

diff --git a/sktime/forecasting/compose/tests/test_transformer_select_forecaster.py b/sktime/forecasting/compose/tests/test_transformer_select_forecaster.py
@@ -207,7 +207,7 @@ def test_get_params(
         fallback_forecaster=fallback_forecaster,
     )
 
-    # Fit the forecaster and then fetch the generated paramaters
+    # Fit the forecaster and then fetch the generated parameters
     forecaster.fit(y=series_generator(), fh=horizon)
     forecaster_params = forecaster.get_params()
 

diff --git a/sktime/forecasting/enbpi.py b/sktime/forecasting/enbpi.py
@@ -18,7 +18,7 @@ class EnbPIForecaster(BaseForecaster):
     """
     Ensemble Bootstrap Prediction Interval Forecaster.
 
-    The forecaster combines sktime forecasters, with tsbootstrap bootsrappers
+    The forecaster combines sktime forecasters, with tsbootstrap bootstrappers
     and the EnbPI algorithm [1] implemented in fortuna using the
     tutorial from this blogpost [2].
 

diff --git a/sktime/forecasting/hf_transformers_forecaster.py b/sktime/forecasting/hf_transformers_forecaster.py
@@ -207,7 +207,7 @@ def _fit(self, y, X, fh):
             )
         else:
             raise ValueError(
-                "The model type is not inferrable from the config."
+                "The model type is not inferable from the config."
                 "Thus, the model cannot be loaded."
             )
         # Load model with the updated config

diff --git a/sktime/forecasting/pytorchforecasting.py b/sktime/forecasting/pytorchforecasting.py
@@ -24,7 +24,7 @@ class PytorchForecastingTFT(_PytorchForecastingAdapter):
         parameters to initialize `TimeSeriesDataSet` [2]_ from `pandas.DataFrame`
         max_prediction_length will be overwrite according to fh
         time_idx, target, group_ids, time_varying_known_reals, time_varying_unknown_reals
-        will be infered from data, so you do not have to pass them
+        will be inferred from data, so you do not have to pass them
     train_to_dataloader_params : Dict[str, Any] (default=None)
         parameters to be passed for `TimeSeriesDataSet.to_dataloader()`
         by default {"train": True}
@@ -216,7 +216,7 @@ class PytorchForecastingNBeats(_PytorchForecastingAdapter):
         parameters to initialize `TimeSeriesDataSet` [2]_ from `pandas.DataFrame`
         max_prediction_length will be overwrite according to fh
         time_idx, target, group_ids, time_varying_known_reals, time_varying_unknown_reals
-        will be infered from data, so you do not have to pass them
+        will be inferred from data, so you do not have to pass them
     train_to_dataloader_params : Dict[str, Any] (default=None)
         parameters to be passed for `TimeSeriesDataSet.to_dataloader()`
         by default {"train": True}

diff --git a/sktime/forecasting/tests/test_reconcile.py b/sktime/forecasting/tests/test_reconcile.py
@@ -41,7 +41,7 @@ def test_reconciler_fit_predict(method, flatten, no_levels):
 
     Raises
     ------
-    This test asserts that the output of ReconcilerForecaster is actually hierarhical
+    This test asserts that the output of ReconcilerForecaster is actually hierarchical
     in that the predictions sum together appropriately. It also tests the index
     and columns of the fitted s and g matrix from each method and finally tests
     if the method works for both named and unnamed indexes

diff --git a/sktime/libs/pykalman/sqrt/bierman.py b/sktime/libs/pykalman/sqrt/bierman.py
@@ -1,4 +1,4 @@
-"""Bierman's verion of the Kalman Filter.
+"""Bierman's version of the Kalman Filter.
 
 =====================================
 Inference for Linear-Gaussian Systems

diff --git a/sktime/libs/pykalman/tests/test_standard.py b/sktime/libs/pykalman/tests/test_standard.py
@@ -30,7 +30,7 @@ def data():
 )
 class TestKalmanFilter:
     """All of the actual tests to check against an implementation of the usual
-    Kalman Filter. Abstract so that sister implementations can re-use these
+    Kalman Filter. Abstract so that sister implementations can reuse these
     tests.
     """
 

diff --git a/sktime/performance_metrics/forecasting/probabilistic/_classes.py b/sktime/performance_metrics/forecasting/probabilistic/_classes.py
@@ -989,7 +989,7 @@ class AUCalibration(_BaseDistrForecastingMetric):
     Computes the unsigned area between the calibration curve and the diagonal.
 
     The calibration curve is the cumulative curve of the sample of
-    predictive cumulative distibution functions evaluated at the true values.
+    predictive cumulative distribution functions evaluated at the true values.
 
     Mathematically, let :math:`d_1, \dots, d_N` be the predictive distributions,
     let :math:`y_1, \dots, y_N` be the true values, and let :math:`F_i` be the

diff --git a/sktime/registry/_tags.py b/sktime/registry/_tags.py
@@ -479,7 +479,7 @@ class capability__feature_importance(_BaseTag):
 
     If the tag is ``True``, the estimator can produce feature importances.
 
-    Feature importances are queriable by the fitted parameter interface
+    Feature importances are queryable by the fitted parameter interface
     via ``get_fitted_params``, after calling ``fit`` of the respective estimator.
 
     If the tag is ``False``, the estimator does not produce feature importances.
@@ -544,7 +544,7 @@ class capability__train_estimate(_BaseTag):
     produce and store an estimate of their own statistical performance,
     e.g., via out-of-bag estimates, or cross-validation.
 
-    Training performance estimates are queriable by the fitted parameter interface
+    Training performance estimates are queryable by the fitted parameter interface
     via ``get_fitted_params``, after calling ``fit`` of the respective estimator.
 
     If the tag is ``False``, the estimator does not produce
@@ -616,7 +616,7 @@ class capability__exogeneous(_BaseTag):
     that can be used to improve forecasting accuracy.
 
     If the forecaster uses exogeneous data (``ignore-exogeneous-X=False``),
-    the ``X`` parmameter in ``fit``, ``predict``, and other methods
+    the ``X`` parameter in ``fit``, ``predict``, and other methods
     can be used to pass exogeneous data to the forecaster.
 
     If the ``X-y-must-have-same-index`` tag is ``True``,