Replace RMT_ENABLED=0 with MODEL=uls

3 files changed, 6 insertions, 6 deletions

diff --git a/README.md b/README.md
index b83fc21..fcc8eba 100644
--- a/README.md
+++ b/README.md
@@ -112,9 +112,8 @@ The following variables may be set to alter the behaviour of dfatool components.
 | `DFATOOL_KCONF_WITH_CHOICE_NODES` | 0, **1** | Treat kconfig choices (e.g. "choice Model → MobileNet / ResNet / Inception") as enum parameters. If enabled, the corresponding boolean kconfig variables (e.g. "Model\_MobileNet") are not converted to parameters. If disabled, all (and only) boolean kconfig variables are treated as parameters. Mostly relevant for analyze-kconfig, eval-kconfig |
 | `DFATOOL_COMPENSATE_DRIFT` | **0**, 1 | Perform drift compensation for loaders without sync input (e.g. EnergyTrace or Keysight) |
 | `DFATOOL_DRIFT_COMPENSATION_PENALTY` | 0 .. 100 (default: majority vote over several penalties) | Specify penalty for ruptures.py PELT changepoint petection |
-| `DFATOOL_MODEL` | cart, decart, fol, lgbm, lmt, **rmt**, symreg, xgb | Modeling method. See below for method-specific configuration options. |
+| `DFATOOL_MODEL` | cart, decart, fol, lgbm, lmt, **rmt**, symreg, uls, xgb | Modeling method. See below for method-specific configuration options. |
 | `DFATOOL_RMT_SUBMODEL` | cart, fol, static, symreg, **uls** | Modeling method for RMT leaf functions. |
-| `DFATOOL_RMT_ENABLED` | 0, **1** | Use decision trees in get\_fitted |
 | `DFATOOL_CART_MAX_DEPTH` | **0** .. *n* | maximum depth for sklearn CART. Default (0): unlimited. |
 | `DFATOOL_LGBM_BOOSTER` | **gbdt**, dart, rf | Boosting type. |
 | `DFATOOL_LGBM_N_ESTIMATORS` | .., **100**, .. | Number of estimators. |
diff --git a/doc/modeling-method.md b/doc/modeling-method.md
index bd0a15d..98d8fcf 100644
--- a/doc/modeling-method.md
+++ b/doc/modeling-method.md
@@ -60,7 +60,7 @@ You should also specify `DFATOOL_XGB_N_ESTIMATORS`, `DFATOOL_XGB_MAX_DEPTH`, and
 
 ## Least-Squares Regression
 
-If dfatool determines that there is no need for a tree structure, or if `DFATOOL_RMT_ENABLED=0` has beenset, it will go straight to least-squares regression.
+If dfatool determines that there is no need for a tree structure, or if `DFATOOL_MODEL=uls`, it will go straight to least-squares regression.
 By default, it still utilizes the RMT/ULS algorithms to find and fit a suitable function template.
 If needed, `--function-override` can be used to set a function template manually.
 For instance, in order to specify that NMC DPU allocation latency is a function of the number of DPUs (and nothing else), ue `--function-override 'NMC reconfiguration:latency_dpu_alloc_us:regression_arg(0) + regression_arg(1) * parameter(n_dpus)'`
diff --git a/lib/model.py b/lib/model.py
index 5770218..2452af7 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -300,7 +300,7 @@ class AnalyticModel:
 
         model_type = os.getenv("DFATOOL_MODEL", "rmt")
 
-        if model_type != "rmt":
+        if model_type != "rmt" and model_type != "uls":
             for name in self.names:
                 for attr in self.by_name[name]["attributes"]:
                     if model_type == "cart":
@@ -319,7 +319,7 @@ class AnalyticModel:
                         self.attr_by_name[name][attr].build_xgb()
                     else:
                         logger.error(f"build_fitted: unknown model type: {model_type}")
-        elif self.force_tree:
+        elif model_type == "rmt" and self.force_tree:
             for name in self.names:
                 for attr in self.by_name[name]["attributes"]:
                     if (
@@ -337,8 +337,9 @@ class AnalyticModel:
                         threshold=threshold,
                     )
         else:
+            # model_type == "rmt" and not self.force_tree or model_type == "uls"
             paramfit = ParamFit()
-            tree_allowed = bool(int(os.getenv("DFATOOL_RMT_ENABLED", "1")))
+            tree_allowed = model_type == "rmt"
             tree_required = dict()
 
             for name in self.names: