cpsat-logutils 0.0.4

Utils for parsing the logs of OR-Tools' CP-SAT solver.

cpsat-logutils

Utilities to parse and work with the logs of OR‑Tools CP‑SAT.

This library extracts key information from CP‑SAT logs (solutions, bounds, presolve stats, subsolver activity, search progress, conflicts, etc.) and exposes them in structured Python objects you can analyze or visualize.

Installation

pip install cpsat-logutils

Quickstart

1) Enable CP‑SAT logging in your solver

Enable detailed CP‑SAT log output and capture it programmatically.

from ortools.sat.python import cp_model

model = cp_model.CpModel()
# ... build your model ...

solver = cp_model.CpSolver()
solver.parameters.log_search_progress = True  # Show detailed search log

log_lines: list[str] = []
solver.log_callback = log_lines.append  # Capture logs in a list

status = solver.Solve(model)
raw_log = "\n".join(log_lines)

2) Parse the log with cpsat-logutils

Below is a step-by-step parsing workflow. For each block, explore its block-specific methods in the blocks/ directory, and adapt the calls shown here to your needs.

a) Instantiate the parser

Create the parser instance from the raw log string.

from cpsat_logutils import LogParser

parser = LogParser(raw_log)

b) Retrieve solver-level info

Get high-level solver metadata such as version, number of workers, and parameters.

from cpsat_logutils.blocks import SolverBlock

if solver_block := parser.get_block_of_type_or_none(SolverBlock):
    print("CP-SAT version:", solver_block.get_version())
    print("Workers:", solver_block.get_number_of_workers())
    print("Parameters:", solver_block.get_parameters())

c) Inspect model statistics

Display the number of variables and constraints before and after presolve.

from cpsat_logutils.blocks import InitialModelBlock, PresolvedModelBlock

if initial := parser.get_block_of_type_or_none(InitialModelBlock):
    print(
        "Initial model:",
        initial.get_num_variables(),
        "vars,",
        initial.get_num_constraints(),
        "constraints",
    )

if presolved := parser.get_block_of_type_or_none(PresolvedModelBlock):
    print(
        "Presolved model:",
        presolved.get_num_variables(),
        "vars,",
        presolved.get_num_constraints(),
        "constraints",
    )

d) Check presolve outcome

Determine whether the problem was solved during presolve.

from cpsat_logutils.blocks import PresolveSummaryBlock

solved_by_presolve = False
if ps := parser.get_block_of_type_or_none(PresolveSummaryBlock):
    solved_by_presolve = ps.is_solved_by_presolve()
    print("Solved by presolve:", solved_by_presolve)

e) Explore search progress and stats

If presolve did not solve the problem, inspect search progress events, task timing, search statistics, and objective bounds.

from cpsat_logutils.blocks import (
    SearchProgressBlock,
    SearchStatsBlock,
    TaskTimingBlock,
    ObjectiveBoundsBlock,
)

if not solved_by_presolve:
    if sp := parser.get_block_of_type_or_none(SearchProgressBlock):
        print("Presolve time (s):", sp.get_presolve_time())
        print(sp.get_events())  # list of events, see BoundEvent, ObjEvent, ModelEvent

    if tt := parser.get_block_of_type_or_none(TaskTimingBlock):
        print(tt.to_pandas().head())

    if ss := parser.get_block_of_type_or_none(SearchStatsBlock):
        print(ss.to_pandas().head())

    if ob := parser.get_block_of_type_or_none(ObjectiveBoundsBlock):
        print(ob.to_pandas().head())

f) Get final solver response

Access the solver’s final response, including status and objective value.

from cpsat_logutils.blocks import ResponseBlock

if resp := parser.get_block_of_type_or_none(ResponseBlock):
    print(resp.to_dict())

3) Save or visualize

cpsat-logutils focuses on parsing and structuring; you can:

• export DataFrames to CSV/JSON for dashboards,
• plot progress/bounds over time with matplotlib/plotly,
• feed the output into your own analyzers.

If you prefer a ready‑made GUI, see the CP‑SAT Log Analyzer below.

Examples

• Minimal example logs live in example_logs/ of this repo.
• See the test suite in tests/ for end‑to‑end parsing and assertions.

FAQ

Which CP‑SAT versions are supported? The parser targets the log format used by recent OR‑Tools releases (9.8+). If a newer CP‑SAT version changes the log format and something breaks, please open an issue with a sample CP‑SAT output log.

Can I parse logs from other languages (C++/Java/C#)? Yes. As long as you enable log_search_progress and collect the textual CP‑SAT log, the content is the same. Save it to a text file or feed the string to the parser.

Do I need to redirect stdout? No. In Python you can capture logs via solver.log_callback = my_fn to avoid duplicates.

Related resources

• CP‑SAT (official docs) — overview & tutorials: https://developers.google.com/optimization/cp/cp_solver
• The CP‑SAT Primer — in‑depth guide by the same author: https://d-krupke.github.io/cpsat-primer/00_intro.html
• CP‑SAT Log Analyzer (GUI) — Streamlit app to explore logs interactively: https://cpsat-log-analyzer.streamlit.app/ • source: https://github.com/d-krupke/CP-SAT-Log-Analyzer

Contributing

Issues and PRs are welcome! If you hit a parsing edge case, please attach a sample CP‑SAT output log that reproduces it.

When submitting a merge request, please ensure:

• All tests pass locally: pytest
• Code style and linting pass: use pre-commit with the provided configuration (.pre-commit-config.yaml). Run pre-commit run --all-files before submitting.

Refer to the CI workflow for the full test and lint configuration.

Version History

• v0.0.3: Extended __all__ import