gurobi-logtools

gurobi-logtools

Extract information from Gurobi log files and generate pandas DataFrames or Excel worksheets for further processing. Also includes a wrapper for out-of-the-box interactive visualizations using the plotting library Plotly.

[!NOTE] We have renamed the project to gurobi-logtools, so please also adapt the import statement accordingly:

import gurobi_logtools as glt

Installation

python -m pip install gurobi-logtools

It is recommended to prepend the pip install command with python -m to ensure that the package is installed using the correct Python version currently active in your environment.

See CHANGELOG for added, removed or fixed functionality.

Usage

First, you need a set of Gurobi log files to compare, e.g.,

• results from several model instances
• comparisons of different parameter settings
• performance variability experiments involving multiple random seed runs
• ...

You may also use the provided gurobi-logtools.ipynb notebook with the example data set to get started. Additionally, there is a Gurobi TechTalk demonstrating how to use it (YouTube):

Pandas/Plotly

1. parse log files:

   import gurobi_logtools as glt
   
   results = glt.parse(["run1/*.log", "run2/*.log"])
   summary = results.summary()
   nodelog_progress = results.progress("nodelog")
   

   Depending on your requirements, you may need to filter or modify the resulting DataFrames.

2. draw interactive charts, preferably in a Jupyter Notebook:

   • final results from the individual runs:

   glt.plot(summary, type="box")
   

   • progress charts for the individual runs:

   glt.plot(nodelog_progress, y="Gap", color="Log", type="line")
   

   • progress of the norel heuristic (note, the time recorded here is since the start of norel, and does not include presolve + read time):

   glt.plot(results.progress("norel"), x="Time", y="Incumbent", color="Log", type="line")
   

   These are just examples using the Plotly Python library - of course, any other plotting library of your choice can be used to work with these DataFrames.

Excel

Convert your log files to Excel worksheets right on the command-line:

python -m gurobi_logtools myrun.xlsx data/*.log

List all available options and how to use the command-line tool:

python -m gurobi_logtools --help

Rename log files

The command line tool can also rename log files according to the parameters set and model solved in a given run. This is useful if your log files do not have a consistent naming scheme, or if multiple runs are logged per file and you want to extract the individual runs.

For example:

python -m gurobi_logtools --write-to-dir nicenames summary.xlsx tests/assets/combined/*.log

separates logs for individual runs in the input files and writes copies to the 'nicenames' folder with a consistent naming scheme:

> ls nicenames
912-MIPFocus1-Presolve1-TimeLimit600-glass4-0.log
912-MIPFocus1-Presolve1-TimeLimit600-glass4-1.log
912-MIPFocus1-Presolve1-TimeLimit600-glass4-2.log
912-MIPFocus2-Presolve1-TimeLimit600-glass4-0.log
912-MIPFocus2-Presolve1-TimeLimit600-glass4-1.log
912-MIPFocus2-Presolve1-TimeLimit600-glass4-2.log
912-Presolve1-TimeLimit600-glass4-0.log
912-Presolve1-TimeLimit600-glass4-1.log
912-Presolve1-TimeLimit600-glass4-2.log