MMMPlotSuite.sensitivity_analysis#

MMMPlotSuite.sensitivity_analysis(data=None, hdi_prob=0.94, aggregation=None, backend=None)[source]#

Plot sensitivity analysis results showing response to input changes.

Visualizes how model outputs (e.g., channel contributions) change as inputs (e.g., channel spend) are varied. Shows mean response line and HDI bands across sweep values.

Parameters:

dataxr.DataArray or xr.Dataset, optional

Sensitivity analysis data with required dimensions: - ‘sample’: MCMC samples - ‘sweep’: Sweep values (e.g., multipliers)

If Dataset, should contain ‘x’ variable. If None, uses self.idata.sensitivity_analysis. This parameter allows: - Testing with mock sensitivity analysis results - Plotting external sweep results - Comparing different sensitivity analyses

hdi_probfloat, default 0.94

HDI probability mass (between 0 and 1).

aggregationdict, optional

Aggregations to apply before plotting. Keys: “sum”, “mean”, or “median” Values: tuple of dimension names

Example: {"sum": ("channel",)} sums over channels before plotting.

backendstr | None, optional

Backend to use for plotting. If None, uses global backend configuration.

Returns:

PlotCollection

arviz_plots PlotCollection object containing the plot.

Use .show() to display or .save("filename") to save. Unlike the legacy suite which returned (Figure, Axes) or Axes, this provides a unified interface across all backends.

Raises:

ValueError: If no sensitivity analysis data found in self.idata and no data provided.

See also

uplift_curve: Plot uplift percentages (derived from sensitivity analysis)
marginal_curve: Plot marginal effects (derived from sensitivity analysis)
LegacyMMMPlotSuite.sensitivity_analysis: Legacy matplotlib-only implementation

Notes

Breaking changes from legacy implementation:

Returns PlotCollection instead of (Figure, Axes) or Axes
Lost ax, subplot_kwargs, plot_kwargs parameters (use backend methods)
Cleaner implementation without monkey-patching
Data parameter for explicit data passing (no side effects on self.idata)

Examples

Run sweep and plot results:

from pymc_marketing.mmm.sensitivity_analysis import SensitivityAnalysis

# Run sensitivity sweep
sweeps = np.linspace(0.5, 1.5, 11)
sa = SensitivityAnalysis(mmm.model, mmm.idata)
results = sa.run_sweep(
    var_input="channel_data",
    sweep_values=sweeps,
    var_names="channel_contribution",
    sweep_type="multiplicative",
    extend_idata=True,  # Store in idata
)

# Plot stored results
pc = mmm.plot.sensitivity_analysis(hdi_prob=0.9)
pc.show()

Aggregate over channels:

pc = mmm.plot.sensitivity_analysis(
    hdi_prob=0.9, aggregation={"sum": ("channel",)}
)
pc.show()

Use different backend:

pc = mmm.plot.sensitivity_analysis(backend="plotly")
pc.show()

Provide explicit data:

external_results = sa.run_sweep(...)  # Not stored in idata
pc = mmm.plot.sensitivity_analysis(data=external_results)
pc.show()