diff options
| author | Birte Kristina Friesel <birte.friesel@uos.de> | 2024-02-21 11:46:06 +0100 |
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| committer | Birte Kristina Friesel <birte.friesel@uos.de> | 2024-02-21 11:46:06 +0100 |
| commit | 5d83f255f05c3b74df0ace1f70b260959b392eca (patch) | |
| tree | 19722b4889911cd3893990531fd62732f937e719 /doc | |
| parent | c3dbe93034bdeff9dba534d29b04daa527d70241 (diff) | |
update documentation and examples
Diffstat (limited to 'doc')
| -rw-r--r-- | doc/analysis-nfp.md | 2 | ||||
| -rw-r--r-- | doc/modeling-method.md | 26 |
2 files changed, 16 insertions, 12 deletions
diff --git a/doc/analysis-nfp.md b/doc/analysis-nfp.md index 877ac2a..5221c55 100644 --- a/doc/analysis-nfp.md +++ b/doc/analysis-nfp.md @@ -8,7 +8,7 @@ Classification and Regression Trees (CART) are capable of generating accurate mo 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_CATEGORICAL_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 . +DFATOOL_MODEL=cart DFATOOL_PARAM_CATEGORICAL_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. diff --git a/doc/modeling-method.md b/doc/modeling-method.md index e4865d9..58fe03b 100644 --- a/doc/modeling-method.md +++ b/doc/modeling-method.md @@ -1,27 +1,22 @@ # Modeling Method Selection +Set `DFATOOL_MODEL` to an appropriate value, e.g. `DFATOOL_MODEL=cart`. + ## CART (Regression Trees) -Enable these with `DFATOOL_DTREE_SKLEARN_CART=1` and `--force-tree`. +sklearn CART ("Decision Tree Regression") algorithm. Uses binary nodes and supports splits on scalar variables. ### Related Options * `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1` converts categorical parameters (which are not supported by CART) to numeric ones. -## XGB (Gradient-Boosted Forests / eXtreme Gradient boosting) +## DECART (Regression Trees) -Enable these with `DFATOOL_USE_XGBOOST=1` and `--force-tree`. -You should also specify `DFATOOL_XGB_N_ESTIMATORS`, `DFATOOL_XGB_MAX_DEPTH`, and possibly `OMP_NUM_THREADS`. - -### Related Options - -* `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1` converts categorical parameters (which are not supported by XGB) to numeric ones. -* Anything prefixed with `DFATOOL_XGB_`. +sklearn CART ("Decision Tree Regression") algorithm. Ignores scalar parameters, thus emulating the DECART algorithm. ## LMT (Linear Model Trees) -Enable these with `DFATOOL_DTREE_LMT=1` and `--force-tree`. -They always use a maximum depth of 20. +[Linear Model Tree](https://github.com/cerlymarco/linear-tree) algorithm. Uses binary nodes and linear functions. ### Related Options @@ -51,6 +46,15 @@ All of these are valid regression model trees. * `DFATOOL_ULS_SKIP_CODEPENDENT_CHECK=1` * `DFATOOL_REGRESSION_SAFE_FUNCTIONS=1` +## XGB (Gradient-Boosted Forests / eXtreme Gradient boosting) + +You should also specify `DFATOOL_XGB_N_ESTIMATORS`, `DFATOOL_XGB_MAX_DEPTH`, and possibly `OMP_NUM_THREADS`. + +### Related Options + +* `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1` converts categorical parameters (which are not supported by XGB) to numeric ones. +* Anything prefixed with `DFATOOL_XGB_`. + ## Least-Squares Regression If dfatool determines that there is no need for a tree structure, or if `DFATOOL_DTREE_ENABLED=0` has beenset, it will go straight to least-squares regression. |