Gurobi Model Analyzer is an open-source python package that provides detailed analysis of model solutions and model characteristics. It consists of a results_analyzer module that calculates explanations of ill-conditioned basis matrices and a solcheck module that analysizes a given solution.
The latest user manual is available on readthedocs.
For questions related to using Gurobi Model Analyzer, please use Gurobi's Forum.
For reporting bugs, issues, and feature requests please open an issue.
If you encounter issues with Gurobi or gurobipy please contact Gurobi Support.
- Python >= 3.9
numpy>= 1.21.5 (although earlier versions compatible with python 3.7 will probably work).- questionary for the (optional) interactive version
The easiest way to install gurobi-modelanalyzer is using pip in a virtual environment:
(.venv) pip install gurobi-modelanalyzer
This will also install the numpy and gurobipy dependencies. One of the advanced functions makes use of matplotlib; if you haven't already installed that and plan to use this function (matrix_bitmap), you can either install the matplotlib package directly, or install it with the gurobi-modelanalyzer package via "pip install gurobi-modelanalyzer matplotlib".
Please note that gurobipy is commercial software and requires a license. When installed via pip or conda, gurobipy ships with a free license for testing and can only solve models of limited size.
import gurobipy as gp
import gurobi_modelanalyzer as gma
model = gp.read("myillconditionedmodel.mps")
model.optimize()
gma.kappa_explain(model)
# row-based explanation (default)
gma.kappa_explain(model, expltype="ROWS")
# column-based explanation
gma.kappa_explain(model, expltype="COLS")
# angle-based explanation (only looks for pairs of rows or columns
# that cause ill-conditioning.
gma.angle_explain(model)Use help(gma.kappa_explain) or help(gma.angle_explain) for information
on more advanced usage.
Testing a suboptimal solution
import gurobipy as gp
import gurobi_modelanalyzer as gma
m = gp.read("examples/data/afiro.mps")
sol = {m.getVarByName("X01"): 78, m.getVarByName("X22"): 495}
sc = gma.SolCheck(m)
sc.test_sol(sol)
print(f"Solution Status: {sc.Status}")
sc.optimize()
for v in sol.keys():
print(f"{v.VarName}: Fixed value: {sol[v]}, Computed value: {v.X}")Testing an infeasible solution
m = gp.read("examples/data/misc07.mps")
sol = {m.getVarByName("COL260"): 2400.5}
sc = gma.sol_check(m)
sc.test_sol(sol)
print(f"Solution Status: {sc.Status}")
sc.inf_repair()
for c in m.getConstrs():
if abs(c._Violation) > 0.0001:
print(f"{c.ConstrName}: RHS: {c.RHS}, Violation: {c._Violation}")Alternatively to the bundled limited license, there are licenses that can handle models of all sizes.
As a student or staff member of an academic institution, you qualify for a free, full-product license. For more information, see:
For a commercial evaluation, you can request an evaluation license.
Other useful resources to get started:
We value any level of experience in using Gurobi Model Analyzer and would like to encourage you to contribute directly to this project. Please see the Contributing Guide for more information.
Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines.