v0.0.11

Author: bartzbeielstein

src/spotPython/build/kriging.py

@@ -333,7 +333,7 @@ def fit(self, nat_X, nat_y):
         self.nat_to_cod_init()
         if self.n_theta > self.k:
             self.n_theta = self.k
-            logger.warning("Warning: More theta values than dimensions. `n_theta` set to `k`.")
+            logger.warning("More theta values than dimensions. `n_theta` set to `k`.")
         self.theta = zeros(self.n_theta)
         # TODO: Currently not used:
         self.x0_theta = ones((self.n_theta,)) * self.n / (100 * self.k)

src/spotPython/spot/spot.py

@@ -321,7 +321,7 @@ def run(self):
             # Condition: select only X= that have min distance
             # to existing solutions
             X0, X0_ind = selectNew(A=X0, X=self.X, tolerance=self.tolerance_x)
-            logger.debug("XO values are new: %s", X0_ind)
+            logger.debug("XO values are new: %s %s", X0_ind, X0)
             # 1. There are X0 that fullfil the condition.
             # Note: The number of new X0 can be smaller than self.n_points!
             if X0.shape[0] > 0:
@@ -605,12 +605,15 @@ def plot_contour(self, i=0, j=1, min_z=None, max_z=None, show=True):
         pylab.show()
 
     def print_importance(self):
-        theta = np.power(10, self.surrogate.theta)
-        print("Importance relative to the most important parameter:")
-        imp = 100 * theta / np.max(theta)
-        if self.var_name is None:
-            for i in range(len(imp)):
-                print("x", i, ": ", imp[i])
+        if self.surrogate.n_theta > 1:
+            theta = np.power(10, self.surrogate.theta)
+            print("Importance relative to the most important parameter:")
+            imp = 100 * theta / np.max(theta)
+            if self.var_name is None:
+                for i in range(len(imp)):
+                    print("x", i, ": ", imp[i])
+            else:
+                for i in range(len(imp)):
+                    print(self.var_name[i] + ": ", imp[i])
         else:
-            for i in range(len(imp)):
-                print(self.var_name[i] + ": ", imp[i])
+            print("Importantance requires more than one theta values (n_theta>1).")