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| author | Daniel Friesel <daniel.friesel@uos.de> | 2022-01-05 15:23:51 +0100 |
|---|---|---|
| committer | Daniel Friesel <daniel.friesel@uos.de> | 2022-01-05 15:23:51 +0100 |
| commit | 2c5bcd77f2c952cc5269ca3e4b6e0a7323ebd085 (patch) | |
| tree | 93da4dc33c77855445e6aa21f45c12b1803861fa /bin/analyze-kconfig.py | |
| parent | d9aee2a314ae6d3fc0216893a4ccfd8bb66ffa9c (diff) | |
cross validation: return intermediate models used for XV
These are interesting for statistics, e.g. to determine the average dtree size
Diffstat (limited to 'bin/analyze-kconfig.py')
| -rwxr-xr-x | bin/analyze-kconfig.py | 18 |
1 files changed, 10 insertions, 8 deletions
diff --git a/bin/analyze-kconfig.py b/bin/analyze-kconfig.py index bd9cccb..048c8c9 100755 --- a/bin/analyze-kconfig.py +++ b/bin/analyze-kconfig.py @@ -237,19 +237,21 @@ def main(): fit_duration = time.time() - fit_start_time if xv_method == "montecarlo": - static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) - analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) + static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count) + analytic_quality, _ = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) if lut_model: - lut_quality = xv.montecarlo( + 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.kfold(lambda m: m.get_fitted()[0], xv_count) + static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count) + analytic_quality, _ = xv.kfold(lambda m: m.get_fitted()[0], xv_count) if lut_model: - lut_quality = xv.kfold(lambda m: m.get_param_lut(fallback=True), xv_count) + lut_quality, _ = xv.kfold( + lambda m: m.get_param_lut(fallback=True), xv_count + ) else: lut_quality = None else: @@ -315,9 +317,9 @@ def main(): json.dump(json_model, f, sort_keys=True, cls=dfatool.utils.NpEncoder) if xv_method == "montecarlo": - static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) + static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count) elif xv_method == "kfold": - static_quality = xv.kfold(lambda m: m.get_static(), xv_count) + static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count) else: static_quality = model.assess(static_model) |