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Removed titles from plots

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This commit is contained in:
Deepthi Pathare
2021-06-19 11:13:22 +02:00
parent 215acd1538
commit 065a3e2ce0
1 changed files with 0 additions and 7 deletions
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7
voting_lib/voting_analysis.py
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@@ -40,7 +40,6 @@ def predict(model, data, grid_h, grid_w, party_colors, comparison_data=pd.DataFr
# Plot hit map # Plot hit map
plot_hits(prediction, grid_w, grid_h) plot_hits(prediction, grid_w, grid_h)
plt.title("Hitmap")
# converting to x and y coordinates # converting to x and y coordinates
ys, xs = np.unravel_index(np.argmax(prediction, axis=1), (grid_h, grid_w)) ys, xs = np.unravel_index(np.argmax(prediction, axis=1), (grid_h, grid_w))
@@ -48,7 +47,6 @@ def predict(model, data, grid_h, grid_w, party_colors, comparison_data=pd.DataFr
# plotting mps # plotting mps
party_affiliation = data[:,1] party_affiliation = data[:,1]
plot_mps(data[:,0], xs, ys, party_affiliation, party_colors, randomize_positions=True) plot_mps(data[:,0], xs, ys, party_affiliation, party_colors, randomize_positions=True)
plt.title("Members of Parliament")
plt.show() plt.show()
# calculating party positions based on mps # calculating party positions based on mps
@@ -66,28 +64,23 @@ def predict(model, data, grid_h, grid_w, party_colors, comparison_data=pd.DataFr
# plotting parties # plotting parties
plot_parties(party_pos, party_colors, 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 # plotting party distances in output space
part_distance_out = calc_party_distances(party_pos) part_distance_out = calc_party_distances(party_pos)
plot_party_distances(part_distance_out) plot_party_distances(part_distance_out)
plt.title('Party Distances')
plt.show() plt.show()
if not comparison_data.empty: if not comparison_data.empty:
plot_parties(comparison_data, party_colors, 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.ylabel("libertarian - authoritarian")
plt.xlabel("left < economic > right") plt.xlabel("left < economic > right")
comparison_data_dist = calc_party_distances(comparison_data) comparison_data_dist = calc_party_distances(comparison_data)
plot_party_distances(comparison_data_dist) plot_party_distances(comparison_data_dist)
plt.title("Political Compass Party Distances")
err = remove_NaN_rows_columns(normalize_df(part_distance_out) - normalize_df(comparison_data_dist)) err = remove_NaN_rows_columns(normalize_df(part_distance_out) - normalize_df(comparison_data_dist))
err = err * err err = err * err
plot_party_distances(err) plot_party_distances(err)
plt.title(f'Normalized Distance Squared Error, with MSE={np.nanmean(err.to_numpy()):.2f}')
plt.show() plt.show()
def iter_neighbours(weights, hexagon=False): def iter_neighbours(weights, hexagon=False):