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

python -m pip install gurobi-logtools

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

SeeCHANGELOG for added, removed or fixed functionality.

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 providedgurobi-logtools.ipynb notebook with theexample data set to get started.Additionally, there is aGurobi TechTalk demonstrating how to use it (YouTube):

1. parse log files:

   importgurobi_logtoolsasgltresults=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 aJupyter 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 thePlotly Python library - of course, any other plotting library of your choice can be used to work with these DataFrames.

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

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 nicenames912-MIPFocus1-Presolve1-TimeLimit600-glass4-0.log912-MIPFocus1-Presolve1-TimeLimit600-glass4-1.log912-MIPFocus1-Presolve1-TimeLimit600-glass4-2.log912-MIPFocus2-Presolve1-TimeLimit600-glass4-0.log912-MIPFocus2-Presolve1-TimeLimit600-glass4-1.log912-MIPFocus2-Presolve1-TimeLimit600-glass4-2.log912-Presolve1-TimeLimit600-glass4-0.log912-Presolve1-TimeLimit600-glass4-1.log912-Presolve1-TimeLimit600-glass4-2.log