plot_interactive_mapper_graph¶
-
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.- Parameters
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 passingnumpy.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.
None
is equivalent to passing color_data.If an object implementing
transform
orfit_transform
, or a callable, it is applied to color_data to generate the features of interest.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 arrayy
determined by color_data and color_features. Lety
haven
columns (note: 1d feature arrays are converted to column vectors). Then, for a node representing a listI
of row indices, there will ben
colors, each computed asnode_color_statistic(y[I, i])
fori
between0
andn
.None
is equivalent to passingnumpy.mean
.layout (None, str or callable, optional, default:
"kamada-kawai"
) – Layout algorithm for the graph. Can be any accepted value for thelayout
parameter in thelayout
method ofigraph.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
) – IfTrue
, 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 notNone
, number of significant figures to which to round node summary statistics. IfNone
, 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 theupdate_traces
andupdate_layout
methods ofplotly.graph_objects.Figure
.
- Returns
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
References
- 1
igraph.Graph.layout documentation.