first prototype for detecting invalid features

ethz-asl · Nov 21, 2021 · 29775ea · 29775ea
1 parent 0dca4d3
commit 29775ea
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diff --git a/Tools/parametric_model/src/optimizers/linear_regressor.py b/Tools/parametric_model/src/optimizers/linear_regressor.py
@@ -54,6 +54,7 @@ def estimate_parameters(self, X, y):
         # Estimate parameters c such that X * c = y
         self.X = X
         self.y = y
+        self.check_features()
         self.reg.fit(self.X, self.y)
         self.estimation_completed = True
 

diff --git a/Tools/parametric_model/src/optimizers/optimizer_base_template.py b/Tools/parametric_model/src/optimizers/optimizer_base_template.py
@@ -38,6 +38,8 @@
 
 from abc import ABC, abstractmethod
 from typing import Dict, List
+import warnings
+import numpy as np
 
 
 class ParametersNotEstimatedError(Exception):
@@ -67,6 +69,15 @@ def check_estimation_completed(self):
         else:
             raise self.parametersNotEstimatedError
 
+    def check_features(self):
+        for i in range(self.X.shape[1]):
+            if np.count_nonzero(self.X[:,i]) == 0:
+                warnings.warn("Feature detected that is only zero. " + \
+            "Parameter {} is probably wrong.".format(self.param_name_list[i]),
+            RuntimeWarning
+            )
+        return
+
     @abstractmethod
     def estimate_parameters(self) -> None:
         pass

diff --git a/Tools/parametric_model/src/optimizers/qp_optimizer.py b/Tools/parametric_model/src/optimizers/qp_optimizer.py
@@ -158,6 +158,7 @@ def estimate_parameters(self, X, y):
         # remove fixed coefficients from problem formulation
         self.X = X
         self.y = y
+        self.check_features()
         self.X_reduced, self.y_reduced = self.remove_fixed_coef_features(X, y)
         self.y = y
         c = cvxpy.Variable(self.n_opt_coef)