diff options
| -rw-r--r-- | README.md | 3 | ||||
| -rw-r--r-- | doc/modeling-method.md | 2 | ||||
| -rw-r--r-- | lib/functions.py | 2 | ||||
| -rw-r--r-- | lib/parameters.py | 9 |
4 files changed, 8 insertions, 8 deletions
@@ -112,9 +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**, symreg, xgb | Modeling method. See below for method-specific configuration options. | -| `DFATOOL_SUBMODEL` | fol, **uls** | Modeling method for RMT leaf functions. | +| `DFATOOL_RMT_SUBMODEL` | fol, static, **uls** | Modeling method for RMT leaf functions. | | `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. | diff --git a/doc/modeling-method.md b/doc/modeling-method.md index 057f7ee..bd0a15d 100644 --- a/doc/modeling-method.md +++ b/doc/modeling-method.md @@ -44,7 +44,7 @@ All of these are valid regression model trees. * `--force-tree` builds a tree structure even if dfatool's heuristic indicates that no non-integer parameter affects the modeled performance attribute. * `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_RMT_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` diff --git a/lib/functions.py b/lib/functions.py index 07f1823..c6ea283 100644 --- a/lib/functions.py +++ b/lib/functions.py @@ -1844,7 +1844,7 @@ class analytic: repr_str="β₀ + β₁ * safe_sqrt(x)", ) - if os.getenv("DFATOOL_SUBMODEL", "uls") == "fol": + if os.getenv("DFATOOL_RMT_SUBMODEL", "uls") == "fol": functions = {"linear": functions["linear"]} return functions diff --git a/lib/parameters.py b/lib/parameters.py index bafc2a5..acc77d4 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -598,7 +598,7 @@ class ModelAttribute: # There must be at least 3 distinct data values (≠ None) if an analytic model # is to be fitted. For 2 (or fewer) values, decision trees are better. - # Exceptions such as DFATOOL_SUBMODEL=fol (2 values sufficient) + # Exceptions such as DFATOOL_RMT_SUBMODEL=fol (2 values sufficient) # can be handled via DFATOOL_ULS_MIN_DISTINCT_VALUES self.min_values_for_analytic_model = int( os.getenv("DFATOOL_ULS_MIN_DISTINCT_VALUES", "3") @@ -1031,9 +1031,10 @@ class ModelAttribute: """ if with_function_leaves is None: - with_function_leaves = bool( - int(os.getenv("DFATOOL_RMT_FUNCTION_LEAVES", "1")) - ) + if os.getenv("DFATOOL_RMT_SUBMODEL", "uls") == "static": + with_function_leaves = False + else: + with_function_leaves = True if with_nonbinary_nodes is None: with_nonbinary_nodes = bool( int(os.getenv("DFATOOL_RMT_NONBINARY_NODES", "1")) |