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[DOC] fix incorrect references of classes in getting started page #2762

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[DOC] fix incorrect references of classes in getting started page #2762

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3af7610
fix: incorrect references of classes in getting started page
inclinedadarsh Apr 24, 2025
712e1fb
fix: typo in BaseSimilaritySearch
inclinedadarsh Apr 24, 2025
ae703f4
Merge branch 'main' into getting-started-typo-fix
inclinedadarsh Apr 25, 2025
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10 changes: 5 additions & 5 deletions docs/getting_started.md
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Original file line number Diff line number Diff line change
Expand Up @@ -314,7 +314,7 @@ tasks related to time series similarity search. The estimators can be used stand
or as parts of pipelines, while the functions give you the tools to build your own
estimators that would rely on similarity search at some point.

The estimators are inheriting from the [BaseSimiliaritySearch](similarity_search.base.BaseSimiliaritySearch)
The estimators are inheriting from the [BaseSimilaritySearch](similarity_search.base.BaseSimilaritySearch)
class accepts as inputs 3D time series (n_cases, n_channels, n_timepoints) for the
fit method. Univariate and single series can still be used, but will need to be reshaped
to this format.
Expand Down Expand Up @@ -356,7 +356,7 @@ and those that transform a collection.

### Transformers for Single Time Series

Transformers inheriting from the [BaseSeriesTransformer](transformations.base.BaseSeriesTransformer)
Transformers inheriting from the [BaseSeriesTransformer](transformations.series.base.BaseSeriesTransformer)
in the `aeon.transformations.series` package transform a single (possibly multivariate)
time series into a different time series or a feature vector. More info to follow.

Expand Down Expand Up @@ -385,7 +385,7 @@ Most time series classification and regression algorithms are based on some form
transformation into an alternative feature space. For example, we might extract some
summary time series features from each series, and fit a traditional classifier or
regressor on these features. For example, we could use
[Catch22](transformations.collection.feauture_based), which calculates 22 summary
[Catch22](transformations.collection.feature_based.Catch22), which calculates 22 summary
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good spot with typo, but it should just be transformations.collection.feature_based, it is declared in the init.

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Similar to the padder case, when I just do transformations.collection.feature_based (without .Catch22), it tries to find a local section in the getting started page with the id of transformations.collection.feature_based. But it works perfectly fine when I add .Catch22 in the end (locally tested)

image

I have tested this locally, and transformations.collection.feature_based.Catch22 works perfectly fine.

statistics for each series.

```{code-block} python
Expand All @@ -403,7 +403,7 @@ statistics for each series.
```

There are also series-to-series transformations, such as the
[Padder](transformations.collection) to lengthen
[Padder](transformations.collection.Padder) to lengthen
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I think transformations.collection is correct, Padder is declared in the init and there is no file Padder, the class is in file _pad

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image

Weird enough, it tries to find a section with transformations.collection in the getting started page when I don't add .Padder in the end.

Also, I built it locally with transformations.collection.Padder and it works perfectly fine.

Here's the page it tries to find when I put transformations.collection.Padder in the link:
image

I have tested this locally, and it works perfectly fine.

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I have also tested it locally it is working fine with transformations.collection.Padder, I think it requires the class name.

series and process unequal length collections.

```{code-block} python
Expand Down Expand Up @@ -438,7 +438,7 @@ For machine learning tasks such as classification, regression and clustering, th
`scikit-learn` `make_pipeline` functionality can be used if the transformer outputs
a valid input type.

The following example uses the [Catch22](transformations.collection.catch22.Catch22)
The following example uses the [Catch22](transformations.collection.feature_based.Catch22)
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I think just transformations.collection.feature_based is sufficient.

feature extraction transformer and a random forest classifier to classify.

```{code-block} python
Expand Down