| Name | gurobi-modelanalyzer JSON |
| Version |
2.1.0
JSON |
| download |
| home_page | None |
| Summary | Model analysis tools for explaining ill-conditioning and analyzing solutions. |
| upload_time | 2024-11-12 13:27:33 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
optimization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Gurobi Model Analyzer
Gurobi Model Analyzer is an
[open-source](https://gurobi-modelanalyzer.readthedocs.io/en/stable/license.html) 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.
# Documentation
The latest user manual is available on
[readthedocs](https://gurobi-modelanalyzer.readthedocs.io/en/latest/).
# Contact us
For questions related to using Gurobi Model Analyzer, please use
[Gurobi's Forum](https://support.gurobi.com/hc/en-us/community/topics/10373864542609-GitHub-Projects).
For reporting bugs, issues, and feature requests please open an issue.
If you encounter issues with Gurobi or gurobipy please contact
[Gurobi Support](https://support.gurobi.com/hc/en-us).
# Installation
## Dependencies
- Python >= 3.9
- [`numpy`](https://pypi.org/project/numpy/) >= 1.21.5 (although earlier
versions compatible with python 3.7 will probably work).
- [questionary](https://pypi.org/project/questionary/) for the (optional) interactive version
## Pip installation
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.
## Example usage
### Using the explainer functions
```python
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.
### Using the solution checker
Testing a suboptimal solution
```python
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
```python
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}")
```
# Getting a Gurobi License
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:
* https://www.gurobi.com/academia/academic-program-and-licenses/
For a commercial evaluation, you can
[request an evaluation license](https://www.gurobi.com/free-trial/?utm_source=internal&utm_medium=documentation&utm_campaign=fy21_pipinstall_eval_pypipointer&utm_content=c_na&utm_term=pypi).
Other useful resources to get started:
* https://www.gurobi.com/documentation/
* https://support.gurobi.com/hc/en-us/community/topics/
# Development
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](CONTRIBUTING.md) for more information.
## Submitting a Pull Request
Before opening a Pull Request, have a look at the full
[Contributing page](CONTRIBUTING.md) to make sure your code complies with
our guidelines.
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"description": "# Gurobi Model Analyzer\n\nGurobi Model Analyzer is an\n[open-source](https://gurobi-modelanalyzer.readthedocs.io/en/stable/license.html) python package that provides\ndetailed analysis of model solutions and model characteristics.\nIt consists of a results_analyzer module that calculates\nexplanations of ill-conditioned basis matrices and a solcheck module that analysizes a given solution.\n\n\n# Documentation\n\nThe latest user manual is available on\n[readthedocs](https://gurobi-modelanalyzer.readthedocs.io/en/latest/).\n\n\n# Contact us\n\nFor questions related to using Gurobi Model Analyzer, please use\n[Gurobi's Forum](https://support.gurobi.com/hc/en-us/community/topics/10373864542609-GitHub-Projects).\n\nFor reporting bugs, issues, and feature requests please open an issue.\n\nIf you encounter issues with Gurobi or gurobipy please contact\n[Gurobi Support](https://support.gurobi.com/hc/en-us).\n\n\n# Installation\n\n## Dependencies\n\n- Python >= 3.9\n- [`numpy`](https://pypi.org/project/numpy/) >= 1.21.5 (although earlier\n versions compatible with python 3.7 will probably work).\n- [questionary](https://pypi.org/project/questionary/) for the (optional) interactive version\n\n## Pip installation\n\nThe easiest way to install gurobi-modelanalyzer is using pip in a\nvirtual environment:\n\n```\n(.venv) pip install gurobi-modelanalyzer\n```\n\nThis will also install the numpy and gurobipy dependencies. One of the\nadvanced functions makes use of matplotlib; if you haven't already installed\nthat and plan to use this function (matrix_bitmap), you can either install\nthe matplotlib package directly, or install it with the gurobi-modelanalyzer\npackage via \"pip install gurobi-modelanalyzer matplotlib\".\n\nPlease note that gurobipy is commercial software and requires a\nlicense. When installed via pip or conda, gurobipy ships with a free\nlicense for testing and can only solve models of limited size.\n\n\n## Example usage\n### Using the explainer functions\n\n```python\nimport gurobipy as gp\nimport gurobi_modelanalyzer as gma\n\nmodel = gp.read(\"myillconditionedmodel.mps\")\nmodel.optimize()\ngma.kappa_explain(model)\n\n# row-based explanation (default)\ngma.kappa_explain(model, expltype=\"ROWS\")\n\n# column-based explanation\ngma.kappa_explain(model, expltype=\"COLS\")\n\n# angle-based explanation (only looks for pairs of rows or columns\n# that cause ill-conditioning.\ngma.angle_explain(model)\n```\n\nUse `help(gma.kappa_explain)` or `help(gma.angle_explain)` for information\non more advanced usage.\n\n### Using the solution checker\n\nTesting a suboptimal solution\n\n```python\nimport gurobipy as gp\nimport gurobi_modelanalyzer as gma\n\nm = gp.read(\"examples/data/afiro.mps\")\n\nsol = {m.getVarByName(\"X01\"): 78, m.getVarByName(\"X22\"): 495}\nsc = gma.SolCheck(m)\n\nsc.test_sol(sol)\nprint(f\"Solution Status: {sc.Status}\")\nsc.optimize()\nfor v in sol.keys():\n print(f\"{v.VarName}: Fixed value: {sol[v]}, Computed value: {v.X}\")\n```\n\nTesting an infeasible solution\n\n```python\nm = gp.read(\"examples/data/misc07.mps\")\n\nsol = {m.getVarByName(\"COL260\"): 2400.5}\nsc = gma.sol_check(m)\n\nsc.test_sol(sol)\n\nprint(f\"Solution Status: {sc.Status}\")\nsc.inf_repair()\nfor c in m.getConstrs():\n if abs(c._Violation) > 0.0001:\n print(f\"{c.ConstrName}: RHS: {c.RHS}, Violation: {c._Violation}\")\n```\n\n\n# Getting a Gurobi License\nAlternatively to the bundled limited license, there are licenses that can handle models of all sizes.\n\nAs a student or staff member of an academic institution, you qualify for a free, full-product license.\nFor more information, see:\n\n* https://www.gurobi.com/academia/academic-program-and-licenses/\n\nFor a commercial evaluation, you can\n[request an evaluation license](https://www.gurobi.com/free-trial/?utm_source=internal&utm_medium=documentation&utm_campaign=fy21_pipinstall_eval_pypipointer&utm_content=c_na&utm_term=pypi).\n\nOther useful resources to get started:\n* https://www.gurobi.com/documentation/\n* https://support.gurobi.com/hc/en-us/community/topics/\n\n\n# Development\nWe value any level of experience in using Gurobi Model Analyzer and would like to encourage you to\ncontribute directly to this project. Please see the [Contributing Guide](CONTRIBUTING.md) for more information.\n\n\n## Submitting a Pull Request\nBefore opening a Pull Request, have a look at the full\n[Contributing page](CONTRIBUTING.md) to make sure your code complies with\nour guidelines.\n",
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