add political compass for germany including plotting

voting_lib/voting_analysis.py

@@ -26,7 +26,7 @@ def train_model(X, grid_h, grid_w, radius, step, ep):
     sofmnet.train(X, epochs=ep)
     return sofmnet
 
-def predict(model, data, grid_h, grid_w):
+def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
 
     X = data[:,2:]
     
@@ -45,6 +45,8 @@ def predict(model, data, grid_h, grid_w):
     # calculating party positions based on mps
     party_pos = calc_party_pos(np.column_stack((xs, ys)), party_affiliation)
 
+    print(party_pos)
+
     # Plot node distnaces
     plt.figure()
     weight = model.weight.reshape((model.n_inputs, grid_h, grid_w))
@@ -66,6 +68,22 @@ def predict(model, data, grid_h, grid_w):
     plot_party_distances(part_distance_out)
     plt.show()
 
+    if not comparison_data.empty:
+       plot_parties(comparison_data, randomize_positions=False, new_plot=True)
+       plt.title("political compass")
+       plt.ylabel("libertarian - authoritarian")
+       plt.xlabel("left < economic > right")
+       plt.show()
+       comparison_data_dist = calc_party_distances(comparison_data)
+       plot_party_distances(comparison_data_dist)
+       plt.show()
+       err = normalize_df(part_distance_out) - normalize_df(comparison_data_dist)
+       err = err * err
+       plot_party_distances(err)
+       plt.title(f'distance squared error, with mse={str(np.nanmean(err.to_numpy())):.2}')
+       plt.show()
+
+
     #  # plotting party distances in input space (TODO discard)
     # party_pos_out = calc_party_pos(X, party_affiliation)
     # part_distance_in = calc_party_distances(party_pos_out)
@@ -204,8 +222,7 @@ def plot_parties(parties, randomize_positions=False, new_plot=True):
         print("Party ", party, " x = ", xs_disp[i], "y = ", ys_disp[i])
         plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2)
         
-    plt.legend(title='Parties',bbox_to_anchor=(1.3, 1), loc='upper left')
-    
+    plt.legend(title='Parties')    
 def calc_party_distances(parties):
     distances = np.zeros((parties.shape[0], parties.shape[0]))
     for i, (_, left_party) in enumerate(parties.iterrows()):
@@ -220,3 +237,10 @@ def plot_party_distances(distances):
     ax = plt.gca()
     ax.tick_params(axis="x", bottom=False, top=True, labelbottom=False, labeltop=True)
     sn.heatmap(distances, cmap='Oranges', annot=True)
+
+
+def normalize_df(dataframe):
+    df = dataframe.copy(deep=True)
+    df = df - np.min(df.to_numpy())
+    df = df / np.max(df.to_numpy()) 
+    return df