Fit and evaluate mobility results
Use fitting APIs to summarize an observed distribution, then use evaluation APIs to compare observations, simulations, or alternative preprocessing runs.
from fastmob.measures.fitting import fit_values_to_truncated_powerlaw
from fastmob.measures.evaluation import wasserstein_distance
fit = fit_values_to_truncated_powerlaw(jump_values)
distance = wasserstein_distance(observed_values, simulated_values)
Choose the comparison at the same level as the question:
| Compare | Use |
|---|---|
| Scalar predictions | mse, rmse, r_squared |
| Ranked or continuous distributions | wasserstein_distance, divergence metrics |
| Origin-destination flows | common_part_of_commuters, common_part_of_links |
| Spatiotemporal visitation distributions | stvd_emd |
For dataframe-level grouped comparisons, use the typed dataframe
compare_to() method rather than constructing a manual join. See the
fitting reference and
evaluation reference for assumptions,
output fields, and optional dependencies.