MMMPlotSuite.waterfall_components_decomposition#

MMMPlotSuite.waterfall_components_decomposition(var, figsize=(14, 7), **kwargs)[source]#

Create a waterfall plot showing the decomposition of the target into its components.

This plot visualizes how different model components (channels, controls, intercept, seasonality, etc.) contribute to the overall prediction. Each component is shown as a horizontal bar with its contribution value and percentage.

Parameters:

varlist of str: List of contribution variable names from the posterior to include in the plot. Example: [“intercept_contribution_original_scale”,

“channel_contribution_original_scale”, “control_contribution_original_scale”]
original_scalebool, default True: If True, plot contributions in the original scale of the target. Typically you’ll want to use variables ending with “_original_scale”.
figsizetuple of int, default (14, 7): The size of the figure in inches (width, height).
**kwargs: Additional keyword arguments passed to matplotlib’s subplots function.

Returns:

figmatplotlib.figure.Figure: The Figure object containing the plot.
axmatplotlib.axes.Axes: The Axes object with the waterfall plot.

Raises:

ValueError: If no posterior data is found in idata. If none of the requested variables are present in idata.posterior.

Examples

Create a waterfall plot with contribution variables:

fig, ax = mmm.plot.waterfall_components_decomposition(
    var=[
        "intercept_contribution_original_scale",
        "channel_contribution_original_scale",
        "control_contribution_original_scale",
    ]
)

With custom figure size:

fig, ax = mmm.plot.waterfall_components_decomposition(
    var=["channel_contribution", "intercept_contribution"],
    original_scale=False,
    figsize=(16, 8),
)