maml.sampling package

Package implementing sampling methods.

maml.sampling.clustering module

Clustering methods.

class maml.sampling.clustering.BirchClustering(n: int = 1, threshold_init=0.5, **kwargs)

Bases: BaseEstimator, TransformerMixin

sklearn_auto_wrap_output_keys( = {‘transform’_ )

fit(X, y=None)

transform(PCAfeatures)

maml.sampling.direct module

maml.sampling.pca module

class maml.sampling.pca.PrincipalComponentAnalysis(weighting_PCs=True)

Bases: BaseEstimator, TransformerMixin

Wrap around PCA in scikit-learn to support weighting PCs.

sklearn_auto_wrap_output_keys( = {‘transform’_ )

fit(normalized_features)

transform(normalized_features)

maml.sampling.stratified_sampling module

Implementation of stratefied sampling approaches.

class maml.sampling.stratified_sampling.SelectKFromClusters(k: int = 1, allow_duplicate=False)

Bases: BaseEstimator, TransformerMixin

Wrapper around selection of K data from each cluster.

sklearn_auto_wrap_output_keys( = {‘transform’_ )

fit(X, y=None)

Fit the model.

• Parameters

  • X – Input features

  • y – Target.

transform(clustering_data: dict)

Perform clustering.

• Parameters clustering_data – Data to cluster.