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introduce party color mapping to make colors more consistent

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This commit is contained in:
Hannes Kuchelmeister
2021-06-12 11:30:28 +02:00
parent f48c5839db
commit 215acd1538
4 changed files with 41 additions and 10 deletions
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22
voting_lib/voting_analysis.py
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@@ -8,6 +8,7 @@ import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
import re
from matplotlib.colors import ListedColormap
def train_model(X, grid_h, grid_w, radius, step, ep):
@@ -27,7 +28,7 @@ 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, comparison_data=pd.DataFrame()):
def predict(model, data, grid_h, grid_w, party_colors, comparison_data=pd.DataFrame()):
# default tight layout
plt.rcParams["figure.autolayout"] = True
@@ -46,7 +47,7 @@ def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
# plotting mps
party_affiliation = data[:,1]
plot_mps(data[:,0], xs, ys, party_affiliation, randomize_positions=True)
plot_mps(data[:,0], xs, ys, party_affiliation, party_colors, randomize_positions=True)
plt.title("Members of Parliament")
plt.show()
@@ -64,7 +65,7 @@ def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
plt.colorbar()
# plotting parties
plot_parties(party_pos, randomize_positions=False, new_plot=False)
plot_parties(party_pos, party_colors, randomize_positions=False, new_plot=False)
plt.title('Node distance plot with parties')
# plotting party distances in output space
@@ -74,7 +75,7 @@ def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
plt.show()
if not comparison_data.empty:
plot_parties(comparison_data, randomize_positions=False, new_plot=True)
plot_parties(comparison_data, party_colors, randomize_positions=False, new_plot=True)
plt.title("Political Compass")
plt.ylabel("libertarian - authoritarian")
plt.xlabel("left < economic > right")
@@ -173,7 +174,7 @@ def plot_hoverscatter(x, y, categories, hover_labels, colors, cmap = plt.cm.RdYl
fig.canvas.mpl_connect("motion_notify_event", hover)
def plot_mps(names, xs, ys, party_affiliation, randomize_positions=True):
def plot_mps(names, xs, ys, party_affiliation, party_colors, randomize_positions=True):
# converting parties to numeric format
party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
@@ -186,7 +187,9 @@ def plot_mps(names, xs, ys, party_affiliation, randomize_positions=True):
ys_disp = ys
parties = party_index_mapping[party_ids]
plot_hoverscatter(xs_disp, ys_disp, party_index_mapping, names + " (" + parties + ")", party_ids)
colormap = ListedColormap(list(map(lambda x: party_colors[x], party_index_mapping)))
plot_hoverscatter(xs_disp, ys_disp, party_index_mapping, names + " (" + parties + ")", party_ids, cmap=colormap)
def calc_party_pos(members_of_parliament, party_affiliation):
party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
@@ -203,9 +206,12 @@ def calc_party_pos(members_of_parliament, party_affiliation):
return pd.DataFrame(data=party_pos, index=party_index_mapping)
def plot_parties(parties, randomize_positions=False, new_plot=True):
def plot_parties(parties, party_colors, randomize_positions=False, new_plot=True):
party_index_mapping = parties.index
colors = list(map(lambda x: party_colors[x], party_index_mapping))
if new_plot:
plt.figure()
@@ -219,7 +225,7 @@ def plot_parties(parties, randomize_positions=False, new_plot=True):
for i, party in enumerate(party_index_mapping):
print("Party ", party, " x = ", xs_disp[i], "y = ", ys_disp[i])
plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2)
plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2, c=colors[i], edgecolors='black')
plt.legend(title='Parties', bbox_to_anchor=(1.3, 1), loc='upper left')