make SymbolicRegressionFunction inherit from SKLearnRegressionFunction

4 files changed, 39 insertions, 113 deletions

diff --git a/README.md b/README.md
index ff27a81..a60562a 100644
--- a/README.md
+++ b/README.md
@@ -111,7 +111,7 @@ 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, lmt, **rmt**, xgb | Modeling method. See below for method-specific configuration options. |
+| `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_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. |
diff --git a/lib/functions.py b/lib/functions.py
index 13897bb..07d3e2f 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -923,7 +923,7 @@ class LMTFunction(SKLearnRegressionFunction):
         except np.linalg.LinAlgError as e:
             logger.error(f"LMT generation failed: {e}")
             self.fit_success = False
-            return
+            return self
         logger.debug("Fitted LMT")
 
         self.regressor = lmt
@@ -1175,107 +1175,29 @@ class XGBoostFunction(SKLearnRegressionFunction):
         }
 
 
-class SymbolicRegressionFunction(ModelFunction):
-    def __init__(self, value, parameters, num_args=0, **kwargs):
-        super().__init__(value, **kwargs)
-        self.parameter_names = parameters
-        self._num_args = num_args
-        self.fit_success = False
-
+class SymbolicRegressionFunction(SKLearnRegressionFunction):
     def fit(self, param_values, data, ignore_param_indexes=None):
-        self.categorical_to_scalar = bool(
-            int(os.getenv("DFATOOL_PARAM_CATEGORICAL_TO_SCALAR", "0"))
-        )
-        fit_parameters, categorical_to_index, ignore_index = param_to_ndarray(
+        fit_parameters, self.categorical_to_index, self.ignore_index = param_to_ndarray(
             param_values,
             with_nan=False,
             categorical_to_scalar=self.categorical_to_scalar,
             ignore_indexes=ignore_param_indexes,
         )
-        self.categorical_to_index = categorical_to_index
-        self.ignore_index = ignore_index
 
         if fit_parameters.shape[1] == 0:
             logger.debug(
                 f"Cannot use Symbolic Regression due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape is {fit_parameters.shape}"
             )
-            return
+            self.fit_success = False
+            return self
 
         from dfatool.gplearn.genetic import SymbolicRegressor
 
         self.regressor = SymbolicRegressor()
         self.regressor.fit(fit_parameters, data)
+        self._build_feature_names()
         self.fit_success = True
-
-    # TODO inherit from SKLearnRegressionFunction, making this obsolete.
-    # requires SKLearnRegressionFunction to provide .fit and refactoring
-    # build_tree to move .fit into SKLearnRegressionFunction descendants.
-    def is_predictable(self, param_list=None):
-        """
-        Return whether the model function can be evaluated on the given parameter values.
-
-        For a StaticFunction, this is always the case (i.e., this function always returns true).
-        """
-        return True
-
-    # TODO inherit from SKLearnRegressionFunction, making this obsolete.
-    # requires SKLearnRegressionFunction to provide .fit and refactoring
-    # build_tree to move .fit into SKLearnRegressionFunction descendants.
-    def eval(self, param_list=None):
-        """
-        Evaluate model function with specified param/arg values.
-
-        Far a Staticfunction, this is just the static value
-
-        """
-        if param_list is None:
-            return self.value
-        actual_param_list = list()
-        for i, param in enumerate(param_list):
-            if not self.ignore_index[i]:
-                if i in self.categorical_to_index:
-                    try:
-                        actual_param_list.append(self.categorical_to_index[i][param])
-                    except KeyError:
-                        # param was not part of training data. substitute an unused scalar.
-                        # Note that all param values which were not part of training data map to the same scalar this way.
-                        # This should be harmless.
-                        actual_param_list.append(
-                            max(self.categorical_to_index[i].values()) + 1
-                        )
-                else:
-                    actual_param_list.append(int(param))
-        predictions = self.regressor.predict(np.array([actual_param_list]))
-        if predictions.shape == (1,):
-            return predictions[0]
-        return predictions
-
-    # TODO inherit from SKLearnRegressionFunction, making this obsolete.
-    # requires SKLearnRegressionFunction to provide .fit and refactoring
-    # build_tree to move .fit into SKLearnRegressionFunction descendants.
-    def eval_arr(self, params):
-        actual_params = list()
-        for param_tuple in params:
-            actual_param_list = list()
-            for i, param in enumerate(param_tuple):
-                if not self.ignore_index[i]:
-                    if i in self.categorical_to_index:
-                        try:
-                            actual_param_list.append(
-                                self.categorical_to_index[i][param]
-                            )
-                        except KeyError:
-                            # param was not part of training data. substitute an unused scalar.
-                            # Note that all param values which were not part of training data map to the same scalar this way.
-                            # This should be harmless.
-                            actual_param_list.append(
-                                max(self.categorical_to_index[i].values()) + 1
-                            )
-                    else:
-                        actual_param_list.append(int(param))
-            actual_params.append(actual_param_list)
-        predictions = self.regressor.predict(np.array(actual_params))
-        return predictions
+        return self
 
 
 # first-order linear function (no feature interaction)
@@ -1356,7 +1278,7 @@ class FOLFunction(ModelFunction):
             )
         except ValueError as err:
             logger.warning(f"Fit failed: {err} (function: {self.model_function})")
-            return
+            return self
         if res.status > 0:
             self.model_args = res.x
             self.fit_success = True
@@ -1364,6 +1286,7 @@ class FOLFunction(ModelFunction):
             logger.warning(
                 f"Fit failed: {res.message} (function: {self.model_function})"
             )
+        return self
 
     def is_predictable(self, param_list=None):
         """
diff --git a/lib/model.py b/lib/model.py
index 26c4646..b92a50c 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -308,6 +308,8 @@ class AnalyticModel:
                         self.attr_by_name[name][attr].build_fol()
                     elif model_type == "lmt":
                         self.attr_by_name[name][attr].build_lmt()
+                    elif model_type == "symreg":
+                        self.attr_by_name[name][attr].build_symreg()
                     elif model_type == "xgb":
                         self.attr_by_name[name][attr].build_xgb()
                     else:
@@ -332,7 +334,6 @@ class AnalyticModel:
         else:
             paramfit = ParamFit()
             tree_allowed = bool(int(os.getenv("DFATOOL_RMT_ENABLED", "1")))
-            use_symreg = bool(int(os.getenv("DFATOOL_FIT_SYMREG", "0")))
             tree_required = dict()
 
             for name in self.names:
@@ -340,8 +341,6 @@ class AnalyticModel:
                 for attr in self.attr_by_name[name].keys():
                     if self.attr_by_name[name][attr].function_override is not None:
                         self.attr_by_name[name][attr].fit_override_function()
-                    elif use_symreg:
-                        self.attr_by_name[name][attr].build_symreg_model()
                     elif self.attr_by_name[name][
                         attr
                     ].all_relevant_parameters_are_none_or_numeric():
diff --git a/lib/parameters.py b/lib/parameters.py
index 1ae4e4c..bafc2a5 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -830,29 +830,6 @@ class ModelAttribute:
                     return False
         return True
 
-    def build_symreg_model(self):
-        ignore_irrelevant = bool(
-            int(os.getenv("DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS", "0"))
-        )
-        ignore_param_indexes = list()
-        if ignore_irrelevant:
-            for param_index, param in enumerate(self.param_names):
-                if not self.stats.depends_on_param(param):
-                    ignore_param_indexes.append(param_index)
-        x = df.SymbolicRegressionFunction(
-            self.median,
-            self.param_names,
-            n_samples=self.data.shape[0],
-            num_args=self.arg_count,
-        )
-        x.fit(self.param_values, self.data, ignore_param_indexes=ignore_param_indexes)
-        if x.fit_success:
-            self.model_function = x
-        else:
-            logger.debug(
-                f"Symbolic Regression model generation for {self.name} {self.attr} failed."
-            )
-
     def fit_override_function(self):
         function_str = self.function_override
         x = df.AnalyticFunction(
@@ -986,6 +963,33 @@ class ModelAttribute:
             )
             return False
 
+    def build_symreg(self):
+        ignore_irrelevant = bool(
+            int(os.getenv("DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS", "0"))
+        )
+        ignore_param_indexes = list()
+        if ignore_irrelevant:
+            for param_index, param in enumerate(self.param_names):
+                if not self.stats.depends_on_param(param):
+                    ignore_param_indexes.append(param_index)
+        x = df.SymbolicRegressionFunction(
+            np.mean(self.data),
+            n_samples=self.data.shape[0],
+            param_names=self.param_names,
+            arg_count=self.arg_count,
+        ).fit(self.param_values, self.data, ignore_param_indexes=ignore_param_indexes)
+        if x.fit_success:
+            self.model_function = x
+            return True
+        else:
+            logger.debug(
+                f"Symbolic Regression model generation for {self.name} {self.attr} failed."
+            )
+            self.model_function = df.StaticFunction(
+                np.mean(self.data), n_samples=len(self.data)
+            )
+            return False
+
     def build_xgb(self):
         mf = df.XGBoostFunction(
             np.mean(self.data),