diff options
Diffstat (limited to 'bin/eval-kconfig.py')
| -rwxr-xr-x | bin/eval-kconfig.py | 10 |
1 files changed, 8 insertions, 2 deletions
diff --git a/bin/eval-kconfig.py b/bin/eval-kconfig.py index 1f44b9e..7f48b52 100755 --- a/bin/eval-kconfig.py +++ b/bin/eval-kconfig.py @@ -10,6 +10,8 @@ import logging import os import sys +import numpy as np + from dfatool import kconfig, validation from dfatool.loader import KConfigAttributes from dfatool.model import KConfigModel @@ -32,6 +34,9 @@ def main(): parser.add_argument( "--attribute", choices=["rom", "ram"], default="rom", help="Model attribute" ) + parser.add_argument( + "--with-choice-node", action="store_true", help="Use non-binary Choice Nodes" + ) parser.add_argument("kconfig_path", type=str, help="Path to Kconfig file") parser.add_argument( "experiment_root", type=str, help="Experiment results directory" @@ -53,14 +58,15 @@ def main(): measures = list() for training_set, validation_set in partition_pairs: model = KConfigModel.from_benchmark(data, args.attribute, indices=training_set) + model.with_choice_node = args.with_choice_node model.build_tree() measures.append(model.assess_benchmark(data, indices=validation_set)) aggregate = dict() for measure in measures[0].keys(): - aggregate[measure] = np.mean(map(lambda m: m[measure], measures)) + aggregate[measure] = np.mean(list(map(lambda m: m[measure], measures))) aggregate["unpredictable_count"] = np.sum( - map(lambda m: m["unpredictable_count"], measures) + list(map(lambda m: m["unpredictable_count"], measures)) ) print("10-fold Cross Validation:") |