kmapper.KeplerMapper¶
-
class
kmapper.
KeplerMapper
(verbose=0)[source]¶ With this class you can build topological networks from (high-dimensional) data.
Fit a projection/lens/function to a dataset and transform it. For instance “mean_of_row(x) for x in X”
Map this projection with overlapping intervals/hypercubes. Cluster the points inside the interval (Note: we cluster on the inverse image/original data to lessen projection loss). If two clusters/nodes have the same members (due to the overlap), then: connect these with an edge.
Visualize the network using HTML and D3.js.
- KM has a number of nice features, some which get forgotten.
project
: Some projections it makes sense to use a distance matrix, such as knn_distance_#. Usingdistance_matrix = <metric>
for a custom metric.fit_transform
: Applies a sequence of projections. Currently, this API is a little confusing and might be changed in the future.
-
__init__
(verbose=0)[source]¶ Constructor for KeplerMapper class.
- Parameters
verbose (int, default is 0) – Logging level. Currently 3 levels (0,1,2) are supported. For no logging, set verbose=0. For some logging, set verbose=1. For complete logging, set verbose=2.
Methods
__init__
([verbose])Constructor for KeplerMapper class.
data_from_cluster_id
(cluster_id, graph, data)Returns the original data of each cluster member for a given cluster ID
fit_transform
(X[, projection, scaler, …])Same as .project() but accepts lists for arguments so you can chain.
map
(lens[, X, clusterer, cover, nerve, …])Apply Mapper algorithm on this projection and build a simplicial complex.
project
(X[, projection, scaler, distance_matrix])Creates the projection/lens from a dataset.
visualize
(graph[, color_values, colorscale, …])Generate a visualization of the simplicial complex mapper output.