Description:
gurobilogtools 3.1.0
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
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.
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
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
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