ehrapy.plot.ncp

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

Plot the factors from a Non-negative CP decomposition.

Produces one row of three panels per component, laid out as rank × 3 panels in a fixed three-column grid:

Panel 1 — Temporal profile (line chart)

The normalised temporal factor c_r for component r, plotted against the relative time axis. Each value shows how the collective influence of this component rises or falls at that time point. A rising curve indicates a condition that worsens (or becomes more prevalent) over time; a peaked curve suggests a transient event; a flat curve indicates a time-independent pattern.

Panel 2 — Top variables (horizontal bar chart)

The n_top clinical variables with the highest normalised loading b_r for component r, sorted by loading magnitude. These are the variables that best characterise the component — i.e. the diseases, measurements, or features that tend to co-occur in the patient sub-group captured by this component.

Panel 3 — Sample loadings (histogram)

Distribution of the patient-level loading a_r across all observations. A spike near zero with a heavy right tail means the component is selective — only a sub-group of patients expresses it. A broad, roughly uniform distribution means the component is diffuse — relevant to most patients to varying degrees.

All three factor vectors are normalised to [0, 1] before plotting so that components with different absolute scales are visually comparable.

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 ehrdata as ed, ehrapy as ep
>>> edata = ed.dt.ehrdata_blobs(n_variables=8, n_centers=3, n_observations=30, base_timepoints=12)
>>> ep.tl.ncp(edata, layer="tem_data", rank=3, sigmoid_transform=True)
>>> ep.pl.ncp(edata, n_top=5)