[DOCS] Example usage in docstring

[FBruzzesi]

As discussed in #586, although most of the library features are documented in the user guide, the best way to showcase how to access and use each class/function would be a minimal example usage in the docstrings. And this is currently covered only for a subset of the library features.

Here the list of all the remaining classes that would benefit from it:

• preprocessing.outlier_remover.OutlierRemover (solved in Added example to docstring of outlier_remover #639 )
• meta.outlier_classifier.OutlierClassifier (solved in Example for preprocessing.dictmapper.DictMapper and meta.outlier_classifier.OutlierClassifier #646)
• preprocessing.dictmapper.DictMapper (solved in Example for preprocessing.dictmapper.DictMapper and meta.outlier_classifier.OutlierClassifier #646)
• preprocessing.pandastransformers.PandasTypeSelector (solved in Docstrings API examples #648)
• preprocessing.projections.InformationFilter (solved in Docstrings API examples #648)
• preprocessing.repeatingbasis.RepeatingBasisFunction (solved in Docstrings API examples #648)
• preprocessing.formulaictransformer.FormulaicTransformer (solved in Docstrings API examples #648)
• preprocessing.identitytransformer.IdentityTransformer (solved in Docstrings API examples #648)
• linear_model.LowessRegression #650
• linear_model.ProbWeightRegression (solved in first docstrings for linear model #691)
• linear_model.DeadZoneRegressor (solved in first docstrings for linear model #691)
• linear_model.DemographicParityClassifier (solved in first docstrings for linear model #691)
• linear_model.EqualOpportunityClassifier (solved in first docstrings for linear model #691)
• decomposition.pca_reconstruction.PCAOutlierDetection #652
• decomposition.umap_reconstruction.UMAPOutlierDetection #653
• mixture.bayesian_gmm_classifier.BayesianGMMClassifier
• mixture.bayesian_gmm_detector.BayesianGMMOutlierDetector
• mixture.gmm_classifier.GMMClassifier
• mixture.gmm_detector.GMMOutlierDetector
• model_selection.TimeGapSplit
• model_selection.GroupTimeSeriesSplit
• model_selection.KlusterFoldValidation
• naive_bayes.GaussianMixtureNB
• naive_bayes.BayesianGaussianMixtureNB
• neighbors.BayesianKernelDensityClassifier
• meta.confusion_balancer.ConfusionBalancer
• meta.estimator_transformer.EstimatorTransformer
• meta.grouped_predictor.GroupedPredictor
• meta.grouped_transformer.GroupedTransformer
• meta.regression_outlier_detector.RegressionOutlierDetector
• meta.subjective_classifier.SubjectiveClassifier
• meta.thresholder.Thresholder
• preprocessing.intervalencoder.IntervalEncoder

As an instance of such minimal example you can refer to QuantileRegression docstring section, which renders as in its API section.

If possible try to add one unique example covering the relevant features and methods in the top level docstring of the class.

[likeajumprope]

I am going to work on some of those today (Johanna). The first one has definitively documentation

[anopsy]

I'm gonna do preprocessing.outlier_remover.OutlierRemover

[anopsy]

I'm going to work on DictMapper

[anopsy]

Hi hi! I'm going to add a usage example to sklego.meta.outlier_classifier.OutlierClassifier.

I have a question though, it requires both X, y in fit, otherwise it raises a ValueError. I understand that y is required here (classifier/metrics etc) but I kind of thought that when the fit signature says fit(X, y=None) y=None implies that y is optional. But in this case it's required. Just wondering

[koaning]

I have a question though, it requires both X, y in fit, otherwise it raises a ValueError. I kind of thought that when the fit signature says fit(X, y=None) y=None implies that y is optional. But in this case it's required. Just wondering

This might deserve a ticket of its own. My first reaction is that most outlier models don't require a y ... can't remember why the ValueError would be there.

[anopsy]

According to the docs, the intention was to morph this outlier model into a classifier, thus making the use of metrics possible.

[anopsy]

I'll work this week on usage examples for the following three classes
preprocessing.pandastransformers.PandasTypeSelector
preprocessing.projections.InformationFilter
preprocessing.repeatingbasis.RepeatingBasisFunction

[anopsy]

I'll work on these:
preprocessing.formulaictransformer.FormulaicTransformer
preprocessing.identitytransformer.IdentityTransformer
preprocessing.intervalencoder.IntervalEncoder

I also noticed that some examples start with "Examples" and some with "Example" and it has an influence on how the example is shown in documenattion I'll change "Examples" -> Example" so it gets the nice frame

[FBruzzesi]

Does anyone know if there is a way in a PR description to tag an issue and reference a particular task only? Or make some sort of partial fix reference? Otherwise it will automagically close the issue on merge.

I know it's possible to click on a task to create a separated issue, but maybe that's a bit too much