13hannes11/UU_NCML_Project
1
0
Fork 0
mirror of https://github.com/13hannes11/UU_NCML_Project.git synced 2024-09-03 20:50:59 +02:00
Code Issues Projects Releases Wiki Activity

fixed positioning bug in display_mps causing the centroid to shift

Browse Source
This commit is contained in:
Hannes Kuchelmeister
2021-05-11 19:13:01 +02:00
parent 0f74c92fa0
commit 69759ccd3b
1 changed files with 24 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
voting_lib/voting_analysis.py
View File

@@ -39,14 +39,14 @@ def predict(model, data, grid_h, grid_w):
# plotting mps
party_affiliation = data[:,1]
xs_disp, ys_disp = plot_mps(data[:,0], xs, ys, party_affiliation)
plot_mps(data[:,0], xs, ys, party_affiliation, randomize_positions=True)
plt.show()
# calculating party positions based on mps
party_pos = calc_party_pos(np.column_stack((xs_disp, ys_disp)), party_affiliation)
party_pos = calc_party_pos(np.column_stack((xs, ys)), party_affiliation)
# plotting parties
plot_parties(party_pos)
plot_parties(party_pos, randomize_positions=False, new_plot=True)
plt.show()
# plotting party distances in output space
@@ -149,16 +149,20 @@ def plot_hoverscatter(x, y, labels, colors, cmap = plt.cm.RdYlGn):
fig.canvas.mpl_connect("motion_notify_event", hover)
#plt.show()
def plot_mps(names, xs, ys, party_affiliation):
def plot_mps(names, xs, ys, party_affiliation, randomize_positions=True):
# converting parties to numeric format
party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
# add random offset to show points that are in the same location
xs_disp = xs + np.random.rand(xs.shape[0])
ys_disp = ys + np.random.rand(ys.shape[0])
if randomize_positions:
xs_disp = xs + np.random.rand(xs.shape[0]) - 0.5
ys_disp = ys + np.random.rand(ys.shape[0]) - 0.5
else:
xs_disp = xs
ys_disp = ys
parties = party_index_mapping[party_ids]
plot_hoverscatter(xs_disp, ys_disp, names + " (" + parties + ")", party_ids)
return xs_disp, ys_disp
def calc_party_pos(members_of_parliament, party_affiliation):
party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
@@ -174,18 +178,27 @@ def calc_party_pos(members_of_parliament, party_affiliation):
party_pos /= party_count
return pd.DataFrame(data=party_pos, index=party_index_mapping)
def plot_parties(parties):
def plot_parties(parties, randomize_positions=False, new_plot=True):
cmap = plt.cm.RdYlGn
party_index_mapping = parties.index
plt.figure()
if new_plot:
plt.figure()
party_colors=np.array(range(len(party_index_mapping)))
plt.scatter(parties[0].to_numpy() , parties[1].to_numpy(), c=party_colors, cmap=cmap)
if randomize_positions:
xs_disp = parties[0].to_numpy() + np.random.rand(parties.shape[0]) - 0.5
ys_disp = parties[0].to_numpy() + np.random.rand(parties.shape[0]) - 0.5
else:
xs_disp = parties[0].to_numpy()
ys_disp = parties[1].to_numpy()
plt.scatter(xs_disp , ys_disp, c=party_colors, cmap=cmap)
# plotting labels
offset = 0.01
for x,y, party in zip(parties[0], parties[1], party_index_mapping):
for x,y, party in zip(xs_disp, ys_disp, party_index_mapping):
plt.text(x + offset, y + offset, party)
def calc_party_distances(parties):
distances = np.zeros((parties.shape[0], parties.shape[0]))