gtda.mapper.plot_interactive_mapper_graph(pipeline, data, color_data=None, color_features=None, node_color_statistic=None, layout='kamada_kawai', layout_dim=2, clone_pipeline=True, n_sig_figs=3, node_scale=12, plotly_params=None)[source]

As of version 0.5.0, we recommend using the object-oriented interface provided by :class:`MapperInteractivePlotter` instead of this function.

Plot Mapper graphs in a Jupyter session, with interactivity on pipeline parameters.

Extends plot_static_mapper_graph by providing functionality to interactively update parameters from the cover, clustering and graph construction steps defined in pipeline.

  • pipeline (MapperPipeline object) – Mapper pipeline to act on to data.

  • data (array-like of shape (n_samples, n_features)) – Data used to generate the Mapper graph. Can be a pandas dataframe.

  • color_data (array-like of length n_samples, or None, optional, default: None) – Data to be used to construct node colors in the Mapper graph (according to color_features and node_color_statistic). Must have the same length as data. None is the same as passing numpy.arange(len(data)).

  • color_features (object or None, optional, default: None) –

    Specifies one or more feature of interest from color_data to be used, together with node_color_statistic, to determine node colors.

    1. None is equivalent to passing color_data.

    2. If an object implementing transform or fit_transform, or a callable, it is applied to color_data to generate the features of interest.

    3. If an index or string, or list of indices/strings, it is equivalent to selecting a column or subset of columns from color_data.

  • node_color_statistic (None or callable, optional, default: None) – If a callable, node colors will be computed as summary statistics from the feature array y determined by color_data and color_features. Let y have n columns (note: 1d feature arrays are converted to column vectors). Then, for a node representing a list I of row indices, there will be n colors, each computed as node_color_statistic(y[I, i]) for i between 0 and n. None is equivalent to passing numpy.mean.

  • layout (None, str or callable, optional, default: "kamada-kawai") – Layout algorithm for the graph. Can be any accepted value for the layout parameter in the layout method of igraph.Graph 1.

  • layout_dim (int, default: 2) – The number of dimensions for the layout. Can be 2 or 3.

  • clone_pipeline (bool, optional, default: True) – If True, the input pipeline is cloned before computing the Mapper graph to prevent unexpected side effects from in-place parameter updates.

  • n_sig_figs (int or None, optional, default: 3) – If not None, number of significant figures to which to round node summary statistics. If None, no rounding is performed.

  • node_scale (int or float, optional, default: 12) – Sets the scale factor used to determine the rendered size of the nodes. Increase for larger nodes. Implements a formula in the Plotly documentation.

  • plotly_params (dict or None, optional, default: None) – Custom parameters to configure the plotly figure. Allowed keys are "node_trace", "edge_trace" and "layout", and the corresponding values should be dictionaries containing keyword arguments as would be fed to the update_traces and update_layout methods of plotly.graph_objects.Figure.


box – A box containing the following widgets: parameters of the clustering algorithm, parameters for the covering scheme, a Mapper graph arising from those parameters, a validation box, and logs.

Return type

ipywidgets.VBox object

See also

MapperInteractivePlotter, plot_static_mapper_graph, gtda.mapper.pipeline.make_mapper_pipeline



igraph.Graph.layout documentation.