Rename DFATOOL_DTREE_* to DFATOOL_RMT_*

6 files changed, 21 insertions, 21 deletions

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 1718a4f..149653d 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -55,11 +55,11 @@ make_model:
   script:
   - mkdir -p models
   - wget -q https://ess.cs.uos.de/.private/dfatool/multipass.json.xz https://ess.cs.uos.de/.private/dfatool/multipass.kconfig
-  - DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS=0 bin/analyze-kconfig.py multipass.kconfig multipass.json.xz --export-webconf models/multipass-rmt.json
+  - DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS=0 bin/analyze-kconfig.py multipass.kconfig multipass.json.xz --export-webconf models/multipass-rmt.json
   - wget -q https://ess.cs.uos.de/.private/dfatool/x264.json.xz https://ess.cs.uos.de/.private/dfatool/x264.kconfig https://ess.cs.uos.de/.private/dfatool/x264.nfpkeys.json
   - mv x264.nfpkeys.json nfpkeys.json
   - DFATOOL_MODEL=cart DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1 bin/analyze-kconfig.py x264.kconfig x264.json.xz --export-webconf models/x264-cart.json
-  - DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS=0 bin/analyze-kconfig.py x264.kconfig x264.json.xz --export-webconf models/x264-rmt.json
+  - DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS=0 bin/analyze-kconfig.py x264.kconfig x264.json.xz --export-webconf models/x264-rmt.json
   artifacts:
     paths:
     - models
diff --git a/README.md b/README.md
index b99a54e..51c202c 100644
--- a/README.md
+++ b/README.md
@@ -112,8 +112,8 @@ The following variables may be set to alter the behaviour of dfatool components.
 | `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, lmt, **rmt**, xgb | Modeling method. See below for method-specific configuration options. |
-| `DFATOOL_DTREE_ENABLED` | 0, **1** | Use decision trees in get\_fitted |
-| `DFATOOL_DTREE_FUNCTION_LEAVES` | 0, **1** | Use functions (fitted via linear regression) in decision tree leaves when modeling numeric parameters with at least three distinct values. If 0, integer parameters are treated as enums instead. |
+| `DFATOOL_RMT_ENABLED` | 0, **1** | Use decision trees in get\_fitted |
+| `DFATOOL_RMT_FUNCTION_LEAVES` | 0, **1** | Use functions (fitted via linear regression) in decision tree leaves when modeling numeric parameters with at least three distinct values. If 0, integer parameters are treated as enums instead. |
 | `DFATOOL_CART_MAX_DEPTH` | **0** .. *n* | maximum depth for sklearn CART. Default (0): unlimited. |
 | `DFATOOL_LMT_MAX_DEPTH` | **5** .. 20 | Maximum depth for LMT. |
 | `DFATOOL_LMT_MIN_SAMPLES_SPLIT` | 0.0 .. 1.0, **6** .. *n* | Minimum samples required to still perform an LMT split. A value below 1.0 sets the specified ratio of the total number of training samples as minimum. |
@@ -135,10 +135,10 @@ The following variables may be set to alter the behaviour of dfatool components.
 | `DFATOOL_KCONF_IGNORE_STRING` | 0, **1** | Ignore string configuration options. These often hold compiler paths and other not really helpful information. |
 | `DFATOOL_FIT_LINEAR_ONLY` | **0**, 1 | Only consider linear functions (a + bx) in regression analysis. Useful for comparison with Linear Model Trees / M5. |
 | `DFATOOL_REGRESSION_SAFE_FUNCTIONS` | **0**, 1 | Use safe functions only (e.g. 1/x returnning 1 for x==0) |
-| `DFATOOL_DTREE_NONBINARY_NODES` | 0, **1** | Enable non-binary nodes (i.e., nodes with more than two children corresponding to enum variables) in decision trees |
-| `DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS` | **0**, 1 | Ignore parameters deemed irrelevant by stddev heuristic during regression tree generation. Use with caution. |
+| `DFATOOL_RMT_NONBINARY_NODES` | 0, **1** | Enable non-binary nodes (i.e., nodes with more than two children corresponding to enum variables) in decision trees |
+| `DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS` | **0**, 1 | Ignore parameters deemed irrelevant by stddev heuristic during regression tree generation. Use with caution. |
 | `DFATOOL_PARAM_RELEVANCE_THRESHOLD` | 0 .. **0.5** .. 1 | Threshold for relevant parameter detection: parameter *i* is relevant if mean standard deviation (data partitioned by all parameters) / mean standard deviation (data partition by all parameters but *i*) is less than threshold |
-| `DFATOOL_DTREE_LOSS_IGNORE_SCALAR` | **0**, 1 | Ignore scalar parameters when computing the loss for split node candidates. Instead of computing the loss of a single partition for each `x_i == j`, compute the loss of partitions for `x_i == j` in which non-scalar parameters vary and scalar parameters are constant. This way, scalar parameters do not affect the decision about which non-scalar parameter to use for splitting. |
+| `DFATOOL_RMT_LOSS_IGNORE_SCALAR` | **0**, 1 | Ignore scalar parameters when computing the loss for split node candidates. Instead of computing the loss of a single partition for each `x_i == j`, compute the loss of partitions for `x_i == j` in which non-scalar parameters vary and scalar parameters are constant. This way, scalar parameters do not affect the decision about which non-scalar parameter to use for splitting. |
 | `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR` | **0**, 1 | Some models (e.g. FOL, sklearn CART, XGBoost) do not support categorical parameters. Ignore them (0) or convert them to scalar indexes (1). Conversion uses lexical order. |
 | `DFATOOL_FOL_SECOND_ORDER` | **0**, 1 | Add second-order components (interaction of feature pairs) to first-order linear function. |
 
diff --git a/doc/modeling-method.md b/doc/modeling-method.md
index 7a4c635..057f7ee 100644
--- a/doc/modeling-method.md
+++ b/doc/modeling-method.md
@@ -43,8 +43,8 @@ All of these are valid regression model trees.
 ### Related Options
 
 * `--force-tree` builds a tree structure even if dfatool's heuristic indicates that no non-integer parameter affects the modeled performance attribute.
-* `DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS=0` disables the relevant parameter detection heuristic when building the tree structure. By default, irrelevant parameters cannot end up as decision nodes.
-* `DFATOOL_FIT_LINEAR_ONLY=1` makes RMT behave more like LMT by only considering linear functions in leaf nodes.
+* `DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS=0` disables the relevant parameter detection heuristic when building the tree structure. By default, irrelevant parameters cannot end up as decision nodes.
+* `DFATOOL_SUBMODEL=fol` makes RMT only consider linear functions (a + bx) in regression analysis. Useful for comparison with LMT / M5.
 * `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1`
 * `DFATOOL_ULS_SKIP_CODEPENDENT_CHECK=1`
 * `DFATOOL_REGRESSION_SAFE_FUNCTIONS=1`
@@ -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_DTREE_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_RMT_ENABLED=0` has beenset, 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/examples/explore-and-model-static b/examples/explore-and-model-static
index 74c6359..b951b1d 100755
--- a/examples/explore-and-model-static
+++ b/examples/explore-and-model-static
@@ -12,13 +12,13 @@ set -ex
 ../bin/explore-kconfig.py --log-level debug --random 10 --with-neighbourhood ../examples/kconfig-static
 
 # Each of the following lines generates a single model
-DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS=0 DFATOOL_KCONF_WITH_CHOICE_NODES=0 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-rmt-b.json --export-raw-predictions ../models/example-static-rmt-b-eval.json ../examples/kconfig-static/Kconfig .
-DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS=0 DFATOOL_KCONF_WITH_CHOICE_NODES=1 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-rmt-nb.json --export-raw-predictions ../models/example-static-rmt-nb-eval.json ../examples/kconfig-static/Kconfig .
+DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS=0 DFATOOL_KCONF_WITH_CHOICE_NODES=0 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-rmt-b.json --export-raw-predictions ../models/example-static-rmt-b-eval.json ../examples/kconfig-static/Kconfig .
+DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS=0 DFATOOL_KCONF_WITH_CHOICE_NODES=1 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-rmt-nb.json --export-raw-predictions ../models/example-static-rmt-nb-eval.json ../examples/kconfig-static/Kconfig .
 
 DFATOOL_MODEL=cart DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1 DFATOOL_KCONF_WITH_CHOICE_NODES=0 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-cart-b.json --export-raw-predictions ../models/example-static-cart-b-eval.json ../examples/kconfig-static/Kconfig .
 DFATOOL_MODEL=cart DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1 DFATOOL_KCONF_WITH_CHOICE_NODES=1 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-cart-nb.json --export-raw-predictions ../models/example-static-cart-nb-eval.json ../examples/kconfig-static/Kconfig .
 
-DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS=0 DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1 DFATOOL_FIT_FOL=1 DFATOOL_KCONF_WITH_CHOICE_NODES=0 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-fol-b.json --export-raw-predictions ../models/example-static-fol-b-eval.json ../examples/kconfig-static/Kconfig .
-DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS=0 DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1 DFATOOL_FIT_FOL=1 DFATOOL_KCONF_WITH_CHOICE_NODES=1 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-fol-nb.json --export-raw-predictions ../models/example-static-fol-nb-eval.json ../examples/kconfig-static/Kconfig .
+DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS=0 DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1 DFATOOL_FIT_FOL=1 DFATOOL_KCONF_WITH_CHOICE_NODES=0 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-fol-b.json --export-raw-predictions ../models/example-static-fol-b-eval.json ../examples/kconfig-static/Kconfig .
+DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS=0 DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1 DFATOOL_FIT_FOL=1 DFATOOL_KCONF_WITH_CHOICE_NODES=1 ../bin/analyze-kconfig.py --export-webconf ../models/example-static-fol-nb.json --export-raw-predictions ../models/example-static-fol-nb-eval.json ../examples/kconfig-static/Kconfig .
 
 cp ../examples/kconfig-static/Kconfig ../models/example-static.kconfig
diff --git a/lib/model.py b/lib/model.py
index 715ccb1..3d3e45d 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -331,7 +331,7 @@ class AnalyticModel:
                     )
         else:
             paramfit = ParamFit()
-            tree_allowed = bool(int(os.getenv("DFATOOL_DTREE_ENABLED", "1")))
+            tree_allowed = bool(int(os.getenv("DFATOOL_RMT_ENABLED", "1")))
             use_symreg = bool(int(os.getenv("DFATOOL_FIT_SYMREG", "0")))
             tree_required = dict()
 
diff --git a/lib/parameters.py b/lib/parameters.py
index e4499ce..390420e 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -832,7 +832,7 @@ class ModelAttribute:
 
     def build_symreg_model(self):
         ignore_irrelevant = bool(
-            int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "0"))
+            int(os.getenv("DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS", "0"))
         )
         ignore_param_indexes = list()
         if ignore_irrelevant:
@@ -936,7 +936,7 @@ class ModelAttribute:
 
     def build_fol(self):
         ignore_irrelevant = bool(
-            int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "0"))
+            int(os.getenv("DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS", "0"))
         )
         ignore_param_indexes = list()
         if ignore_irrelevant:
@@ -1028,21 +1028,21 @@ class ModelAttribute:
 
         if with_function_leaves is None:
             with_function_leaves = bool(
-                int(os.getenv("DFATOOL_DTREE_FUNCTION_LEAVES", "1"))
+                int(os.getenv("DFATOOL_RMT_FUNCTION_LEAVES", "1"))
             )
         if with_nonbinary_nodes is None:
             with_nonbinary_nodes = bool(
-                int(os.getenv("DFATOOL_DTREE_NONBINARY_NODES", "1"))
+                int(os.getenv("DFATOOL_RMT_NONBINARY_NODES", "1"))
             )
         if with_gplearn_symreg is None:
             with_gplearn_symreg = bool(int(os.getenv("DFATOOL_USE_SYMREG", "0")))
         if ignore_irrelevant_parameters is None:
             ignore_irrelevant_parameters = bool(
-                int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "0"))
+                int(os.getenv("DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS", "0"))
             )
         if loss_ignore_scalar is None:
             loss_ignore_scalar = bool(
-                int(os.getenv("DFATOOL_DTREE_LOSS_IGNORE_SCALAR", "0"))
+                int(os.getenv("DFATOOL_RMT_LOSS_IGNORE_SCALAR", "0"))
             )
 
         if loss_ignore_scalar and not with_function_leaves: