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Updated party colors & modified training params

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Deepthi Pathare
2021-06-25 15:24:09 +02:00
parent 065a3e2ce0
commit 7107b5547a
4 changed files with 15 additions and 16 deletions
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7
de_analysis.py
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@@ -13,12 +13,12 @@ grid_h = 11 # Grid height
grid_w = 11 # Grid width grid_w = 11 # Grid width
radius = 2 # Neighbour radius radius = 2 # Neighbour radius
step = 0.5 # Learning step step = 0.5 # Learning step
ep = 300 # No of epochs ep = 500 # No of epochs
# Load data # Load data
dataset = ld.load_german_data() dataset = ld.load_german_data()
period_to_compass_year = {17:2005, 18:2013, 19:2017} period_to_compass_year = {17:2009, 18:2013, 19:2017}
for period, df in dataset.items(): for period, df in dataset.items():
@@ -31,5 +31,4 @@ for period, df in dataset.items():
model = va.train_model(X, grid_h, grid_w, radius, step, ep) model = va.train_model(X, grid_h, grid_w, radius, step, ep)
# Predict and visualize output # Predict and visualize output
va.predict(model, data, grid_h, grid_w, de_name_color, pc.get_compass_parties(year=period_to_compass_year[period], country='de')) va.predict(model, data, grid_h, grid_w, de_name_color, pc.get_compass_parties(year=period_to_compass_year[period], country='de'))

11
uk_analysis.py
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@@ -11,15 +11,12 @@ import numpy as np
import pandas as pd import pandas as pd
import os import os
# Train model # Train model
grid_h = 30 # Grid height grid_h = 13 # Grid height
grid_w = 30 # Grid width grid_w = 13 # Grid width
radius = 3 # Neighbour radius radius = 2 # Neighbour radius
step = 0.5 step = 0.5
ep = 1 # No of epochs ep = 300 # No of epochs
period_to_compass_year = {'2015_uk':2015, '2017_uk':2017, '2019_uk':2019} period_to_compass_year = {'2015_uk':2015, '2017_uk':2017, '2019_uk':2019}
main_directory = 'uk/csv' main_directory = 'uk/csv'

10
voting_lib/party_colors.py
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@@ -1,13 +1,13 @@
de_name_color = { de_name_color = {
'AfD': 'blue', 'AfD': 'blue',
'BÜ90/GR': 'green', 'BÜ90/GR': 'green',
'CDU/CSU': 'black', 'CDU/CSU': 'orange',
'DIE LINKE.': 'purple', 'DIE LINKE.': 'purple',
'FDP': 'yellow', 'DIE LINKE': 'purple',
'FDP': 'magenta',
'SPD': 'red', 'SPD': 'red',
} }
uk_name_color = { uk_name_color = {
'Conservative': 'blue', 'Conservative': 'blue',
'Democratic Unionist Party': 'salmon', 'Democratic Unionist Party': 'salmon',
@@ -15,9 +15,11 @@ uk_name_color = {
'Labour': 'red', 'Labour': 'red',
'Liberal Democrat': 'darkorange', 'Liberal Democrat': 'darkorange',
'Plaid Cymru': 'darkgreen', 'Plaid Cymru': 'darkgreen',
'Scottish National Party': 'yellow', 'Scottish National Party': 'magenta',
'Sinn Féin': 'yellowgreen', 'Sinn Féin': 'yellowgreen',
'Social Democratic & Labour Party': 'cyan', 'Social Democratic & Labour Party': 'cyan',
'UK Independence Party': 'purple', 'UK Independence Party': 'purple',
'Ulster Unionist Party': 'lightskyblue', 'Ulster Unionist Party': 'lightskyblue',
'Alba Party': 'black',
'Alliance': 'olive',
} }

3
voting_lib/voting_analysis.py
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@@ -81,6 +81,7 @@ def predict(model, data, grid_h, grid_w, party_colors, comparison_data=pd.DataFr
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'MSE={np.nanmean(err.to_numpy()):.2f}')
plt.show() plt.show()
def iter_neighbours(weights, hexagon=False): def iter_neighbours(weights, hexagon=False):
@@ -218,7 +219,7 @@ def plot_parties(parties, party_colors, randomize_positions=False, new_plot=True
for i, party in enumerate(party_index_mapping): for i, party in enumerate(party_index_mapping):
print("Party ", party, " x = ", xs_disp[i], "y = ", ys_disp[i]) print("Party ", party, " x = ", xs_disp[i], "y = ", ys_disp[i])
plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2, c=colors[i], edgecolors='black') plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2, c=colors[i], edgecolors='None')
plt.legend(title='Parties', bbox_to_anchor=(1.3, 1), loc='upper left') plt.legend(title='Parties', bbox_to_anchor=(1.3, 1), loc='upper left')