[MNT] Add codespell support (config, workflow to detect/not fix) and make it fix a "few" typos (#2653)

* Add github action to codespell main on push and PRs

* Add rudimentary codespell config

* Add pre-commit definition for codespell

* Some skips for codespell -- lots of work todo

* [DATALAD RUNCMD] run codespell throughout fixing typos automagically (but ignoring overall fail due to ambigous ones)

=== Do not change lines below ===
{
 "chain": [],
 "cmd": "codespell -w || :",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^

* codespeed notebook and testing

* incorrect typos and notebooks

* more typos

* config and fixes

* Update similarity_search.ipynb

fix notebook typos

* update skip

* temp typo and annotation workflow

* annotations

* fix typos

---------

Co-authored-by: Matthew Middlehurst <pfm15hbu@gmail.com>
Co-authored-by: Antoine Guillaume <antoine.guillaume45@gmail.com>

Author: 3 people

.github/actions/numba_cache/action.yml

@@ -62,6 +62,6 @@ runs:
         path: ${{ github.workspace }}/.numba_cache
         # Try restore using today's date
         key: numba-${{ inputs.cache_name }}-${{ inputs.runner_os }}-${{ inputs.python_version }}-${{ env.CURRENT_DATE }}
-        # If cant restore with today's date try another cache (without date)
+        # If can't restore with today's date try another cache (without date)
         restore-keys: |
           numba-${{ inputs.cache_name }}-${{ inputs.runner_os }}-${{ inputs.python_version }}-

.github/utilities/codespell_ignore_words.txt

@@ -0,0 +1,4 @@
+fpr
+mape
+recuse
+strat

.github/workflows/pr_precommit.yml

@@ -60,3 +60,16 @@ jobs:
         with:
           commit_message: Automatic `pre-commit` fixes
           commit_user_name: aeon-actions-bot[bot]
+
+  codespell-annotations:
+    runs-on: ubuntu-24.04
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Annotate locations with typos
+        uses: codespell-project/codespell-problem-matcher@v1
+
+      - name: Codespell
+        uses: codespell-project/actions-codespell@v2

.pre-commit-config.yaml

@@ -78,3 +78,11 @@ repos:
     hooks:
       - id: check-manifest
         stages: [ manual ]
+
+  - repo: https://github.com/codespell-project/codespell
+    # Configuration for codespell is in pyproject.toml
+    rev: v2.4.1
+    hooks:
+      - id: codespell
+        additional_dependencies:
+          - tomli  # for python_version < '3.11'

aeon/anomaly_detection/series/distance_based/_kmeans.py

@@ -42,7 +42,7 @@ class KMeansAD(BaseSeriesAnomalyDetector):
 
     stride : int, default=1
         The stride of the sliding window. The stride determines how many time points
-        the windows are spaced appart. A stride of 1 means that the window is moved one
+        the windows are spaced apart. A stride of 1 means that the window is moved one
         time point forward compared to the previous window. The larger the stride, the
         fewer windows are created, which leads to noisier anomaly scores.
 

aeon/anomaly_detection/series/distance_based/_merlin.py

@@ -102,26 +102,26 @@ def _predict(self, X):
 
         r = 2 * np.sqrt(self.min_length)
         distances = np.full(len(lengths), -1.0)
-        indicies = np.full(len(lengths), -1)
+        indices = np.full(len(lengths), -1)
 
-        indicies[0], distances[0] = self._find_index(X, lengths[0], r, np.multiply, 0.5)
+        indices[0], distances[0] = self._find_index(X, lengths[0], r, np.multiply, 0.5)
 
         for i in range(1, min(5, len(lengths))):
             r = distances[i - 1] * 0.99
-            indicies[i], distances[i] = self._find_index(
+            indices[i], distances[i] = self._find_index(
                 X, lengths[i], r, np.multiply, 0.99
             )
 
         for i in range(min(5, len(lengths)), len(lengths)):
             m = mean(distances[i - 5 : i])
             s = std(distances[i - 5 : i])
             r = m - 2 * s
-            indicies[i], distances[i] = self._find_index(
+            indices[i], distances[i] = self._find_index(
                 X, lengths[i], r, np.subtract, s
             )
 
         anomalies = np.zeros(X.shape[0], dtype=bool)
-        for i in indicies:
+        for i in indices:
             if i > -1:
                 anomalies[i] = True
 

aeon/anomaly_detection/series/outlier_detection/_stray.py

@@ -20,7 +20,7 @@ class STRAY(BaseSeriesAnomalyDetector):
     ability to detect clusters of outliers in multidimensional data without
     requiring a model of the typical behavior of the system. However, it suffers
     from some limitations that affect its accuracy. STRAY is an extension of
-    HDoutliers that uses extreme value theory for the anomolous threshold
+    HDoutliers that uses extreme value theory for the anomalous threshold
     calculation, to deal with data streams that exhibit non-stationary behavior.
 
     Parameters
@@ -39,7 +39,7 @@ class STRAY(BaseSeriesAnomalyDetector):
         Proportion of possible candidates for outliers. This defines the starting point
         for the bottom up searching algorithm.
     size_threshold : int, default=50
-        Sample size to calculate an emperical threshold.
+        Sample size to calculate an empirical threshold.
     outlier_tail : str {"min", "max"}, default="max"
         Direction of the outlier tail.
 

aeon/base/_estimators/compose/collection_ensemble.py

@@ -42,7 +42,7 @@ class BaseCollectionEnsemble(ComposableEstimatorMixin, BaseCollectionEstimator):
         Only used if weights is a float. The method used to generate a performance
         estimation from the training data set i.e. cross-validation.
         If None, predictions are made using that estimators fit_predict or
-            fit_predict_proba methods. These are somtimes overridden for efficient
+            fit_predict_proba methods. These are sometimes overridden for efficient
             performance evaluations, i.e. out-of-bag predictions.
         If int or sklearn object input, the parameter is passed directly to the cv
             parameter of the cross_val_predict function from sklearn.

aeon/benchmarking/results_loaders.py

@@ -201,7 +201,7 @@ def estimator_alias(name: str) -> str:
 def get_available_estimators(
     task: str = "classification", as_list: bool = False
 ) -> Union[pd.DataFrame, list]:
-    """Get a DataFrame of estimators avialable for a specific learning task.
+    """Get a DataFrame of estimators available for a specific learning task.
 
     Parameters
     ----------
@@ -251,7 +251,7 @@ def get_estimator_results(
 
     Parameters
     ----------
-    estimators : str ot list of str
+    estimators : str or list of str
         Estimator name or list of estimator names to search for. See
         get_available_estimators, aeon.benchmarking.results_loading.NAME_ALIASES or
         the directory at path for valid options.

aeon/benchmarking/stats.py

@@ -57,7 +57,7 @@ def nemenyi_test(ordered_avg_ranks, n_datasets, alpha):
     ordered_avg_ranks : np.array
         Average ranks of estimators.
     n_datasets : int
-        Mumber of datasets.
+        Number of datasets.
     alpha : float
         alpha level for Nemenyi test.