limen.log
Turn finished experiment runs into analyzable tables for prediction quality, confusion behavior, backtests, and parameter diagnostics.
Canonical docs
What this package owns
Owns post-run analysis and reconstruction of per-round results. Does not own experiment execution, model training, or artifact persistence during the run itself.
Key entry points
| Entry point | Use case | Notes |
|---|---|---|
Log | Main post-run analysis object | Import from limen.log.log |
permutation_prediction_performance | Aligned predictions, actuals, and price columns for one round | Available as a Log method |
permutation_confusion_metrics | One-round classification diagnostics | Available as a Log method |
experiment_confusion_metrics | One table across all permutations | Available as a Log method and re-exported helper |
experiment_backtest_results | Snapshot backtests across all rounds | Uses limen.backtest downstream |
experiment_parameter_correlation | Inspect which parameters move with a chosen metric | Supports sweep interpretation |
Adjacent modules
limen.experimentcreates the run state thatLoganalyzes.limen.backtestpowers the backtest layer thatexperiment_backtest_results()calls.limen.cohortcommonly consumes confusion-metrics output downstream.
Quick orientation
log/
├── log.py # Log class and test-window reconstruction
├── _permutation_prediction_performance.py
├── _permutation_confusion_metrics.py
├── _experiment_confusion_metrics.py
├── _experiment_backtest_results.py
├── _experiment_parameter_correlation.py
└── _read_from_file.py
Things to know
Log(file_path='my_experiment.csv')supports CSV-log cleanup but does not expose every method that a live UEL-backedLogcan support.- Manifest-aware logs reconstruct the test window with the same bar-formation logic used during the run.
- The package root re-exports helper functions, but the
Logclass itself lives inlimen.log.log. Logbridges raw experiment output and higher-level selection decisions.