zen_mapper¶
Submodules¶
Classes¶
A cover comprised of equally sized rectangular elements |
Functions¶
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Wraps a scikit-learn clusterer for use with zen-mapper. |
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Converts a zen-mapper komplex to a networkx graph |
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A precomputed cover |
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Constructs a simplicial complex representation of the data. |
Package Contents¶
- zen_mapper.sk_learn(base_clusterer: C) zen_mapper.types.Clusterer[C]¶
Wraps a scikit-learn clusterer for use with zen-mapper.
This function acts as an adapter, allowing scikit-learn’s clustering algorithms to be integrated into the zen-mapper pipeline. Note: any datapoints which are considered noise by the base clusterer are ignored.
- Parameters:
base_clusterer (C) – An instance of a scikit-learn compatible clustering algorithm. This object should have a fit_predict method and a labels_ attribute after fitting, which is standard for scikit-learn clusterers.
- Returns:
An object conforming to the zen-mapper Clusterer protocol, which wraps the provided clusterer. This allows zen-mapper to use the scikit-learn clusterer’s fit_predict methods within its pipeline. A copy of the fitted base clusterer is also returned as metadata allowing for inspection of the fitted model (e.g., centroids, parameters, dendrograms) after the mapper pipeline.
- Return type:
Clusterer[C]
- zen_mapper.to_networkx(komplex: zen_mapper.types.Komplex)¶
Converts a zen-mapper komplex to a networkx graph
This function takes a Komplex object, which represents a simplicial complex, and converts it into a networkx.Graph object. The vertices of the Komplex become the nodes in the networkx graph, and the 1-simplices (edges) of the Komplex become the edges in the networkx graph.
- Parameters:
komplex (Komplex) – The Komplex object to convert. This object is expected to have a vertices attribute and support indexing for its simplices (e.g., komplex[1] for 1-simplices).
- Returns:
A networkx.Graph object representing the 0- and 1-dimensional structure of the input Komplex.
- Return type:
networkx.Graph
- Raises:
ImportError – If the networkx library is not installed.
- class zen_mapper.Width_Balanced_Cover(n_elements: numpy.typing.ArrayLike, percent_overlap: float)¶
A cover comprised of equally sized rectangular elements
- Parameters:
n_elements (ArrayLike) – the number of covering elements along each dimension. If the data is dimension d this results in d^n covering elements.
percent_overlap (float) – a number between 0 and 1 representing the ammount of overlap between adjacent covering elements.
- Raises:
Value Error – if n_elements < 1
Value Error – if percent_overlap is not in (0,1)
- n_elements¶
- percent_overlap¶
- __call__(data)¶
- zen_mapper.precomputed_cover(cover: zen_mapper.types.Cover) zen_mapper.types.CoverScheme¶
A precomputed cover
- Parameters:
cover (Cover) – the precomputed cover to use
- zen_mapper.rectangular_cover(centers, widths, data, tol=1e-09)¶
- zen_mapper.mapper(data: numpy.ndarray, projection: numpy.ndarray, cover_scheme: zen_mapper.types.CoverScheme, clusterer: zen_mapper.types.Clusterer[M], dim: int | None, min_intersection: int = 1) zen_mapper.types.MapperResult[M]¶
Constructs a simplicial complex representation of the data.
- Parameters:
data (np.ndarray)
projection (np.ndarray) – The output of the lens/filter function on the data. Must have the same number of elements as data.
cover_scheme (CoverScheme) – For cover generation. Should be a callable object that takes a numpy array and returns a list of list(indices).
clusterer (Clusterer) – A callable object that takes in a dataset and returns an iterator of numpy arrays which contain indices for clustered points.
dim (int) – The highest dimension of the mapper complex to compute.
min_intersection (int) – The minimum intersection required between clusters to make a simplex.
- Returns:
An object containing: - nodes: List of clusters where each cluster is a list of data indices. - nerve: A complete list of simplices. - cover: List of list(indices) corresponding to elements of the cover.
- Return type:
