I'm using SCIP to solve MILPs in Python using PyScipOpt. After solving a problem, the solver statistics can be either 1) printed as a string using printStatistics(), or 2) saved to an external file using writeStatistics(). For example:
import pyscipopt as pso
model = pso.Model()
model.addVar(name="x", obj=1)
model.optimize()
model.printStatistics()
model.writeStatistics(filename="stats.txt")
There's a lot of information in printStatistics/writeStatistics that doesn't seem to be accessible from the Python model object directly (e.g. primal-dual integral value, data for individual branching rules or primal heuristics, etc.) It would be helpful to be able to extract the data from this output via, e.g., attributes of the model object or a dictionary.
Is there any way to access this information from the model object without having to parse the raw text/file output?
PySCIPOpt does not provide access to the statistics directly. The data for the various tables (e.g. separators, presolvers, etc.) are stored separately for every single plugin in SCIP and are sometimes not straightforward to collect.
If you are only interested in certain statistics about the general solving process, then you might want to add PySCIPOpt wrappers for a few of the simple get functions defined in scip_solvingstats.c.
Lastly, you might want to check out IPET for parsing the statistics output.