diff options
Diffstat (limited to 'bin')
| -rwxr-xr-x | bin/analyze-kconfig.py | 59 |
1 files changed, 33 insertions, 26 deletions
diff --git a/bin/analyze-kconfig.py b/bin/analyze-kconfig.py index 5262b82..bf965a5 100755 --- a/bin/analyze-kconfig.py +++ b/bin/analyze-kconfig.py @@ -51,6 +51,7 @@ def write_csv(f, model, attr, precision=None): def main(): + timing = dict() parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__ ) @@ -323,7 +324,7 @@ def main(): else: max_std = None - constructor_start = time.time() + ts = time.time() model = AnalyticModel( by_name, parameter_names, @@ -332,8 +333,7 @@ def main(): compute_stats=not args.skip_param_stats, function_override=function_override, ) - constructor_duration = time.time() - constructor_start - logging.debug(f"AnalyticModel(...) took {constructor_duration : 7.1f} seconds") + timing["AnalyticModel"] = time.time() - ts if not model.names: logging.error( @@ -431,9 +431,26 @@ def main(): xv_count = None static_model = model.get_static() + + ts = time.time() + if xv_method == "montecarlo": + static_quality, _ = xv.montecarlo( + lambda m: m.get_static(), xv_count, static=True + ) + elif xv_method == "kfold": + static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count, static=True) + else: + static_quality = model.assess(static_model) + timing["assess static"] = time.time() - ts + try: + ts = time.time() lut_model = model.get_param_lut() + timing["get lut"] = time.time() - ts + + ts = time.time() lut_quality = model.assess(lut_model) + timing["assess lut"] = time.time() - ts except RuntimeError as e: if args.force_tree: # this is to be expected @@ -452,47 +469,37 @@ def main(): if args.export_csv_only: return - fit_start_time = time.time() + ts = time.time() param_model, param_info = model.get_fitted() - fit_duration = time.time() - fit_start_time - logging.debug(f"model.get_fitted(...) took {fit_duration : 7.1f} seconds") + timing["get model"] = time.time() - ts + ts = time.time() if xv_method == "montecarlo": - static_quality, _ = xv.montecarlo( - lambda m: m.get_static(), xv_count, static=True - ) xv.export_filename = args.export_xv analytic_quality, xv_analytic_models = xv.montecarlo( lambda m: m.get_fitted()[0], xv_count ) elif xv_method == "kfold": - static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count, static=True) xv.export_filename = args.export_xv analytic_quality, xv_analytic_models = xv.kfold( lambda m: m.get_fitted()[0], xv_count ) else: - assess_start = time.time() - static_quality = model.assess(static_model) - assess_duration = time.time() - assess_start - logging.debug(f"model.assess(static) took {assess_duration : 7.1f} seconds") if args.export_raw_predictions: analytic_quality, raw_results = model.assess(param_model, return_raw=True) with open(args.export_raw_predictions, "w") as f: json.dump(raw_results, f, cls=dfatool.utils.NpEncoder) else: - assess_start = time.time() analytic_quality = model.assess(param_model) - assess_duration = time.time() - assess_start - logging.debug(f"model.assess(param) took {assess_duration : 7.1f} seconds") xv_analytic_models = None - if lut_model: - assess_start = time.time() - lut_quality = model.assess(lut_model) - assess_duration = time.time() - assess_start - logging.debug(f"model.assess(lut) took {assess_duration : 7.1f} seconds") - else: - lut_quality = None + timing["assess model"] = time.time() - ts + + if lut_model: + ts = time.time() + lut_quality = model.assess(lut_model) + timing["assess lut"] = time.time() - ts + else: + lut_quality = None if "static" in args.show_model or "all" in args.show_model: print("--- static model ---") @@ -578,8 +585,8 @@ def main(): xv_models=xv_analytic_models, ) ) - dref["constructor duration"] = (constructor_duration, r"\second") - dref["regression duration"] = (fit_duration, r"\second") + for key, value in timing.items(): + dref[f"timing/{key}"] = (value, r"\second") dfatool.cli.export_dataref( args.export_dref, dref, precision=args.dref_precision ) |