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| -rw-r--r-- | README.md | 6 |
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@@ -51,14 +51,16 @@ Most approaches in the literature use boolean variables. [analyze-kconfig.py](bin/analyze-kconfig.py) builds, evaluates, and exports NFP models from explore-kconfig measurements. Command-line options and environment variables determine which kind of NFP model it generates. -For example, the following command generates a CART model for busybox. +For example, when called in the benchmark data directory from the previous section, the following command generates a CART model for busybox and stores it in a kconfig-webconf-compatible format in `busybox.json`. Classification and Regression Trees (CART) are capable of generating accurate models from a relatively small amount of samples, but only annotate important features. Hence, after loading a CART model into kconfig-webconf, only a small subset of busybox features will be annotated with NFP deltas. ``` -DFATOOL_DTREE_SKLEARN_CART=1 DFATOOL_PARAM_CATEGORIAL_TO_SCALAR=1 DFATOOL_KCONF_WITH_CHOICE_NODES=0 ~/var/ess/aemr/dfatool/bin/analyze-kconfig.py --export-webconf busybox.json --force-tree ../busybox-1.35.0/Kconfig . +DFATOOL_DTREE_SKLEARN_CART=1 DFATOOL_PARAM_CATEGORIAL_TO_SCALAR=1 DFATOOL_KCONF_WITH_CHOICE_NODES=0 .../dfatool/bin/analyze-kconfig.py --export-webconf busybox.json --force-tree ../busybox-1.35.0/Kconfig . ``` +Refer to the [kconfig-webconf README](https://ess.cs.uos.de/git/software/kconfig-webconf/-/blob/master/README.md#user-content-performance-aware-configuration) for details on using the generated model. + We also have a short [video example](https://ess.cs.uos.de/static/videos/splc22-kconfig-webconf.mp4) illustrating this workflow. ## Dependencies |