ehrapy.plot.ncp

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ehrapy.plot.ncp#

ehrapy.plot.ncp(edata, *, key='ncp', n_top=12, width=380, height=280)[source]#

Plot the factors from a Non-negative CP decomposition.

For each component, three side-by-side panels are shown:

  • Temporal profile – normalised time factor, showing how the risk pattern evolves across the time axis.

  • Top variables – horizontal bar chart of the n_top variables with the highest loading in this component.

  • Sample loadings – histogram of per-observation loadings, revealing whether the component is diffuse (broad distribution) or selective (heavy right tail).

Requires ncp() to have been run first.

Parameters:

  • edata (EHRData) – Central data object containing NCP results.

  • key (str, default: 'ncp') – Key under which NCP results are stored (matches key_added in ncp()).

  • n_top (int, default: 12) – Number of top-loaded variables to display per component.

  • width (int, default: 380) – Width of each individual panel in pixels.

  • height (int, default: 280) – Height of each individual panel in pixels.

Return type:

Layout

Returns:

HoloViews Layout with rank × 3 panels arranged in three columns.

Examples

>>> import numpy as np, pandas as pd
>>> import ehrdata as ed, ehrapy as ep
>>> np.random.seed(0)
>>> tensor = np.abs(np.random.randn(30, 8, 12))
>>> edata = ed.EHRData(
...     shape=(30, 8),
...     layers={"data": tensor},
...     var=pd.DataFrame(index=[f"var_{i}" for i in range(8)]),
... )
>>> ep.tl.ncp(edata, layer="data", rank=3)
>>> ep.pl.ncp(edata, n_top=5)
../../_images/ncp.png
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