Issue #93 constant handling

fanova/visualizer.py

@@ -94,9 +94,12 @@ def generate_pairwise_marginal(self, param_list, resolution=20):
         grid_fanova, grid_orig = [], []
 
         for p in params:
-            if isinstance(p, (CategoricalHyperparameter)):
+            if isinstance(p, CategoricalHyperparameter):
                 grid_orig.append(p.choices)
                 grid_fanova.append(np.arange(len(p.choices)))
+            elif isinstance(p, Constant):
+                grid_orig.append((p.value,))
+                grid_fanova.append(np.arange(1))
             else:
                 if p.log:
                     base = np.e  # assuming ConfigSpace uses the natural logarithm
@@ -108,14 +111,16 @@ def generate_pairwise_marginal(self, param_list, resolution=20):
                 grid_orig.append(grid)
                 grid_fanova.append(grid)
 
-        # Turn into arrays
-        param_indices = np.array(param_indices).squeeze()
-        grid_fanova = np.array(grid_fanova).squeeze()
+        # Turn into arrays, squeeze all but the first two dimensions (to avoid squeezing away the dimension for Constants)
+        param_indices = np.array(param_indices)
+        param_indices = param_indices.reshape([s for i, s in enumerate(param_indices.shape) if i in [0, 1] or s != 1])
+        grid_fanova = np.array(grid_fanova)
+        grid_fanova = grid_fanova.reshape([s for i, s in enumerate(grid_fanova.shape) if i in [0, 1] or s != 1])
 
         # The swap-parameter is here because this was how I found this code and without understanding of the
         #   in-detail implementation of fANOVA I assume there is a reason for the first element of the fanova
         #   method marginal_mean_variance_for_values to expect the element with less elements as the first parameter
-        swap = len(grid_fanova[1] > len(grid_fanova[0]))
+        swap = len(grid_fanova[1]) > len(grid_fanova[0])
 
         # Populating the result
         zz = np.zeros((len(grid_fanova[0]), len(grid_fanova[1])))
@@ -154,8 +159,8 @@ def plot_pairwise_marginal(self, param_list, resolution=20, show=False, three_d=
 
         params, param_names, param_indices = self._get_parameter(param_list)
 
-        first_is_cat = isinstance(params[0], CategoricalHyperparameter)
-        second_is_cat = isinstance(params[1], CategoricalHyperparameter)
+        first_is_cat = isinstance(params[0], (CategoricalHyperparameter, Constant))
+        second_is_cat = isinstance(params[1], (CategoricalHyperparameter, Constant))
 
         plt.close()
         fig = plt.figure()
@@ -181,7 +186,7 @@ def plot_pairwise_marginal(self, param_list, resolution=20, show=False, three_d=
 
                 for i, cat in enumerate(choices[0]):
                     if params[1].log:
-                        plt.semilogx(choices[1], zz[i],label='%s' % str(cat))
+                        plt.semilogx(choices[1], zz[i], label='%s' % str(cat))
                     else:
                         plt.plot(choices[1], zz[i], label='%s' % str(cat))
 
@@ -315,7 +320,7 @@ def plot_marginal(self, param, resolution=100, log_scale=None, show=True, incumb
             except AttributeError:
                 labels = str(param)
                 categorical_size = 1
-            indices = np.arange(1, categorical_size+1, 1)
+            indices = np.arange(1, categorical_size + 1, 1)
             mean, std = self.generate_marginal(param_idx)
             min_y = mean[0]
             max_y = mean[0]
@@ -408,7 +413,7 @@ def create_most_important_pairwise_marginal_plots(self, params=None, n=20, three
         for param1, param2 in most_important_pairwise_marginals:
             params, param_names, param_indices = self._get_parameter([param1, param2])
             param_names_str = re.sub('[!,@#\'\n$\[\]]', '', str(param_names))
-            outfile_name = os.path.join(self.directory, str(param_names_str).replace(" ","_") + ".png")
+            outfile_name = os.path.join(self.directory, str(param_names_str).replace(" ", "_") + ".png")
             self.logger.info("creating %s" % outfile_name)
             self.plot_pairwise_marginal((param1, param2), show=False, three_d=three_d, resolution=resolution)
             plt.savefig(outfile_name)