analyze-kconfig: add cross-validation support

1 files changed, 30 insertions, 8 deletions

diff --git a/bin/analyze-kconfig.py b/bin/analyze-kconfig.py
index ec2f324..004e691 100755
--- a/bin/analyze-kconfig.py
+++ b/bin/analyze-kconfig.py
@@ -18,6 +18,7 @@ import numpy as np
 import dfatool.utils
 from dfatool.loader import KConfigAttributes
 from dfatool.model import AnalyticModel
+from dfatool.validation import CrossValidator
 
 
 def main():
@@ -66,6 +67,12 @@ def main():
         help="Restrict model generation to N random samples",
         metavar="N",
     )
+    parser.add_argument(
+        "--cross-validate",
+        type=str,
+        help="Report modul accuracy via Cross-Validation",
+        metavar="METHOD:COUNT",
+    )
     parser.add_argument("kconfig_path", type=str, help="Path to Kconfig file")
     parser.add_argument(
         "model",
@@ -113,18 +120,33 @@ def main():
         observations = None
 
         model = AnalyticModel(
-            by_name, parameter_names, compute_stats=not args.force_tree
+            by_name,
+            parameter_names,
+            compute_stats=not args.force_tree,
+            force_tree=args.force_tree,
         )
 
-        if args.force_tree:
-            for name in model.names:
-                for attr in model.by_name[name]["attributes"]:
-                    # TODO specify correct threshold
-                    model.build_dtree(name, attr, 0)
-            model.fit_done = True
+        if args.cross_validate:
+            xv_method, xv_count = args.cross_validate.split(":")
+            xv_count = int(xv_count)
+            xv = CrossValidator(
+                AnalyticModel,
+                by_name,
+                parameter_names,
+                compute_stats=not args.force_tree,
+                force_tree=args.force_tree,
+            )
+        else:
+            xv_method = None
 
         param_model, param_info = model.get_fitted()
-        analytic_quality = model.assess(param_model)
+
+        if xv_method == "montecarlo":
+            analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count)
+        elif xv_method == "kfold":
+            analytic_quality = xv.kfold(lambda m: m.get_fitted()[0], xv_count)
+        else:
+            analytic_quality = model.assess(param_model)
 
         print("Model Error on Training Data:")
         for name in model.names: