move fit_parameters check to helper function

1 files changed, 26 insertions, 60 deletions

diff --git a/lib/functions.py b/lib/functions.py
index 88ecb76..47f2983 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -615,6 +615,13 @@ class SKLearnRegressionFunction(ModelFunction):
         )
         self.fit_success = None
 
+    def _check_fit_param(self, fit_parameters, name, step):
+        if fit_parameters.shape[1] == 0:
+            logger.warning(f"Cannot generate {name}: {step} removed all parameters")
+            self.fit_success = False
+            return False
+        return True
+
     def _preprocess_parameters(self, fit_parameters, data):
         if dfatool_preproc_relevance_method == "mi":
             return self._preprocess_parameters_mi(fit_parameters, data)
@@ -785,20 +792,12 @@ class CARTFunction(SKLearnRegressionFunction):
                 )
             )
 
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot generate CART due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape after param_to_ndarray is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(fit_parameters, "CART", "param_to_ndarray"):
             return self
 
         fit_parameters = self._preprocess_parameters(fit_parameters, data)
 
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot generate CART due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape after pre-processing is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(fit_parameters, "CART", "preprocessing"):
             return self
 
         logger.debug("Fitting sklearn CART ...")
@@ -962,20 +961,13 @@ class LMTFunction(SKLearnRegressionFunction):
             with_nan=False,
             categorical_to_scalar=self.categorical_to_scalar,
         )
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot generate LMT due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+
+        if not self._check_fit_param(fit_parameters, "LMT", "param_to_ndarray"):
             return self
 
         fit_parameters = self._preprocess_parameters(fit_parameters, data)
 
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot generate LMT due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape after pre-processing is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(fit_parameters, "LMT", "preprocessing"):
             return self
 
         logger.debug("Fitting LMT ...")
@@ -1106,20 +1098,13 @@ class LightGBMFunction(SKLearnRegressionFunction):
             with_nan=False,
             categorical_to_scalar=self.categorical_to_scalar,
         )
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot run LightGBM due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+
+        if not self._check_fit_param(fit_parameters, "LightGBM", "param_to_ndarray"):
             return self
 
         fit_parameters = self._preprocess_parameters(fit_parameters, data)
 
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot generate LightGBM due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape after pre-processing is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(fit_parameters, "LightGBM", "preprocessing"):
             return self
 
         import dfatool.lightgbm as lightgbm
@@ -1295,20 +1280,13 @@ class XGBoostFunction(SKLearnRegressionFunction):
             with_nan=False,
             categorical_to_scalar=self.categorical_to_scalar,
         )
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot run XGBoost due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+
+        if not self._check_fit_param(fit_parameters, "XGBoost", "param_to_ndarray"):
             return self
 
         fit_parameters = self._preprocess_parameters(fit_parameters, data)
 
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot run XGBoost due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape after pre-processing is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(fit_parameters, "XGBoost", "preprocessing"):
             return self
 
         import xgboost
@@ -1495,20 +1473,16 @@ class SymbolicRegressionFunction(SKLearnRegressionFunction):
             ignore_indexes=ignore_param_indexes,
         )
 
-        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}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(
+            fit_parameters, "Symbolic Regression", "param_to_ndarray"
+        ):
             return self
 
         fit_parameters = self._preprocess_parameters(fit_parameters, data)
 
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot use Symbolic Regression due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape after pre-processing is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(
+            fit_parameters, "Symbolic Regression", "preprocessing"
+        ):
             return self
 
         from dfatool.gplearn.genetic import SymbolicRegressor
@@ -1570,20 +1544,12 @@ class FOLFunction(ModelFunction):
             ignore_indexes=ignore_param_indexes,
         )
 
-        if fit_parameters.shape[1] == 0:
-            logger.debug(
-                f"Cannot run FOL due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(fit_parameters, "FOL", "param_to_ndarray"):
             return self
 
         fit_parameters = self._preprocess_parameters(fit_parameters, data)
 
-        if fit_parameters.shape[1] == 0:
-            logger.warning(
-                f"Cannot run FOL due to lack of parameters: parameter shape is {np.array(param_values).shape}, fit_parameter shape after pre-processing is {fit_parameters.shape}"
-            )
-            self.fit_success = False
+        if not self._check_fit_param(fit_parameters, "FOL", "preprocessing"):
             return self
 
         fit_parameters = fit_parameters.swapaxes(0, 1)