diff options
Diffstat (limited to 'bin')
| -rwxr-xr-x | bin/analyze-archive.py | 3 | ||||
| -rwxr-xr-x | bin/analyze-kconfig.py | 15 |
2 files changed, 10 insertions, 8 deletions
diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py index a5706de..a725833 100755 --- a/bin/analyze-archive.py +++ b/bin/analyze-archive.py @@ -646,7 +646,6 @@ if __name__ == "__main__": PTAModel, by_name, parameters, arg_count, force_tree=args.force_tree ) xv.parameter_aware = args.parameter_aware_cross_validation - xv.export_filename = args.export_xv else: xv_method = None @@ -901,10 +900,12 @@ if __name__ == "__main__": ) if xv_method == "montecarlo": + 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": + xv.export_filename = args.export_xv analytic_quality, xv_analytic_models = xv.kfold( lambda m: m.get_fitted()[0], xv_count ) diff --git a/bin/analyze-kconfig.py b/bin/analyze-kconfig.py index 0bf6ff8..1d03839 100755 --- a/bin/analyze-kconfig.py +++ b/bin/analyze-kconfig.py @@ -214,7 +214,6 @@ def main(): max_std=max_std, ) xv.parameter_aware = args.parameter_aware_cross_validation - xv.export_filename = args.export_xv else: xv_method = None @@ -235,26 +234,28 @@ def main(): if xv_method == "montecarlo": static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count) - analytic_quality, xv_analytic_models = xv.montecarlo( - lambda m: m.get_fitted()[0], xv_count - ) if lut_model: lut_quality, _ = xv.montecarlo( lambda m: m.get_param_lut(fallback=True), xv_count ) else: lut_quality = None - elif xv_method == "kfold": - static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count) - analytic_quality, xv_analytic_models = xv.kfold( + 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) if lut_model: lut_quality, _ = xv.kfold( lambda m: m.get_param_lut(fallback=True), xv_count ) else: lut_quality = None + xv.export_filename = args.export_xv + analytic_quality, xv_analytic_models = xv.kfold( + lambda m: m.get_fitted()[0], xv_count + ) else: static_quality = model.assess(static_model) analytic_quality = model.assess(param_model) |