diff options
| -rwxr-xr-x | bin/eval-kconfig.py | 10 | ||||
| -rw-r--r-- | lib/model.py | 11 |
2 files changed, 15 insertions, 6 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:") diff --git a/lib/model.py b/lib/model.py index f422204..a75033c 100644 --- a/lib/model.py +++ b/lib/model.py @@ -1317,6 +1317,7 @@ class KConfigModel: self.choices = kconfig_benchmark.choice_names self.symbol = kconfig_benchmark.symbol self.choice = kconfig_benchmark.choice + self.with_choice_node = True self.max_loss = 10 if callable(attribute): self.attribute = "custom" @@ -1356,9 +1357,6 @@ class KConfigModel: return np.sum((model_value - values) ** 2, dtype=np.float64) def build_tree(self): - # without ChoiceNode: - # self.model = self._build_tree(self.symbols, list(), self.data, 0) - standalone_symbols = list( filter( lambda sym: self.symbol[sym].choice is None @@ -1369,7 +1367,12 @@ class KConfigModel: tree_choices = list( filter(lambda choice: not self.choice[choice].is_optional, self.choices) ) - self.model = self._build_tree(standalone_symbols, tree_choices, self.data, 0) + if self.with_choice_node: + self.model = self._build_tree( + standalone_symbols, tree_choices, self.data, 0 + ) + else: + self.model = self._build_tree(self.symbols, list(), self.data, 0) def value_for_config(self, kconf): return self.model.model(kconf) |