mirror of
https://github.com/13hannes11/UU_NCML_Project.git
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263 lines
9.1 KiB
Python
263 lines
9.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import numpy as np
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from neupy import algorithms
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from itertools import product
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sn
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import re
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from matplotlib.colors import ListedColormap
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def train_model(X, grid_h, grid_w, radius, step, ep):
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inp = X.shape[1] # No of features (bills)
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# Create SOFM
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sofmnet = algorithms.SOFM(
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n_inputs=inp,
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step=step,
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show_epoch=100,
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shuffle_data=True,
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verbose=True,
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learning_radius=radius,
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features_grid=(grid_h, grid_w)
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)
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sofmnet.train(X, epochs=ep)
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return sofmnet
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def predict(model, data, grid_h, grid_w, party_colors, comparison_data=pd.DataFrame()):
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# default tight layout
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plt.rcParams["figure.autolayout"] = True
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X = data[:,2:]
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# predicting mp positions
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prediction = model.predict(X)
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print(f'prediction: {prediction}')
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# Plot hit map
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plot_hits(prediction, grid_w, grid_h)
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plt.title("Hitmap")
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# converting to x and y coordinates
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ys, xs = np.unravel_index(np.argmax(prediction, axis=1), (grid_h, grid_w))
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# plotting mps
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party_affiliation = data[:,1]
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plot_mps(data[:,0], xs, ys, party_affiliation, party_colors, randomize_positions=True)
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plt.title("Members of Parliament")
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plt.show()
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# calculating party positions based on mps
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party_pos = calc_party_pos(np.column_stack((xs, ys)), party_affiliation)
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print(party_pos)
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# Plot node distnaces
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plt.figure()
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weight = model.weight.reshape((model.n_inputs, grid_h, grid_w))
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heatmap = compute_heatmap(weight, grid_h, grid_w)
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plt.imshow(heatmap, cmap ='Blues', interpolation='nearest',zorder=1, alpha=0.5)
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plt.axis('off')
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plt.colorbar()
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# plotting parties
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plot_parties(party_pos, party_colors, randomize_positions=False, new_plot=False)
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plt.title('Node distance plot with parties')
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# plotting party distances in output space
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part_distance_out = calc_party_distances(party_pos)
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plot_party_distances(part_distance_out)
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plt.title('Party Distances')
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plt.show()
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if not comparison_data.empty:
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plot_parties(comparison_data, party_colors, randomize_positions=False, new_plot=True)
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plt.title("Political Compass")
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plt.ylabel("libertarian - authoritarian")
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plt.xlabel("left < economic > right")
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comparison_data_dist = calc_party_distances(comparison_data)
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plot_party_distances(comparison_data_dist)
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plt.title("Political Compass Party Distances")
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err = remove_NaN_rows_columns(normalize_df(part_distance_out) - normalize_df(comparison_data_dist))
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err = err * err
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plot_party_distances(err)
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plt.title(f'Normalized Distance Squared Error, with MSE={np.nanmean(err.to_numpy()):.2f}')
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plt.show()
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def iter_neighbours(weights, hexagon=False):
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_, grid_height, grid_width = weights.shape
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hexagon_even_actions = ((-1, 0), (0, -1), (1, 0), (0, 1), (1, 1), (-1, 1))
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hexagon_odd_actions = ((-1, 0), (0, -1), (1, 0), (0, 1), (-1, -1), (1, -1))
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rectangle_actions = ((-1, 0), (0, -1), (1, 0), (0, 1))
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for neuron_x, neuron_y in product(range(grid_height), range(grid_width)):
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neighbours = []
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if hexagon and neuron_x % 2 == 1:
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actions = hexagon_even_actions
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elif hexagon:
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actions = hexagon_odd_actions
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else:
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actions = rectangle_actions
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for shift_x, shift_y in actions:
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neigbour_x = neuron_x + shift_x
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neigbour_y = neuron_y + shift_y
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if 0 <= neigbour_x < grid_height and 0 <= neigbour_y < grid_width:
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neighbours.append((neigbour_x, neigbour_y))
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yield (neuron_x, neuron_y), neighbours
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def compute_heatmap(weight, grid_height, grid_width):
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heatmap = np.zeros((grid_height, grid_width))
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for (neuron_x, neuron_y), neighbours in iter_neighbours(weight):
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total_distance = 0
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for (neigbour_x, neigbour_y) in neighbours:
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neuron_vec = weight[:, neuron_x, neuron_y]
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neigbour_vec = weight[:, neigbour_x, neigbour_y]
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distance = np.linalg.norm(neuron_vec - neigbour_vec)
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total_distance += distance
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avg_distance = total_distance / len(neighbours)
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heatmap[neuron_x, neuron_y] = avg_distance
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return heatmap
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def plot_hoverscatter(x, y, categories, hover_labels, colors, cmap = plt.cm.RdYlGn):
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fig, ax = plt.subplots()
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ANNOTATION_DISTANCE = 5
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TRANSPARENCY = 0.8
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scatterplot = plt.scatter(x,y,c=colors, s=5, cmap=cmap)
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handles, labels = scatterplot.legend_elements(prop="colors", alpha=0.6)
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print(labels[0])
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cat = list(map(lambda l: categories[int(re.sub(r'([^\d]+)', "", l))], labels))
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legend = ax.legend(handles, cat, bbox_to_anchor=(1.3, 1), loc='upper left')
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annot = ax.annotate("", xy=(0,0),
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xytext=(ANNOTATION_DISTANCE, ANNOTATION_DISTANCE),
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textcoords="offset points",
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bbox=dict(boxstyle="Square"))
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annot.set_visible(False)
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def update_annot(ind):
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index = ind["ind"][0]
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pos = scatterplot.get_offsets()[index]
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annot.xy = pos
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text = f'{hover_labels[index]}'
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annot.set_text(text)
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annot.get_bbox_patch().set_alpha(TRANSPARENCY)
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def hover(event):
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vis = annot.get_visible()
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if event.inaxes == ax:
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cont, ind = scatterplot.contains(event)
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if cont:
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update_annot(ind)
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annot.set_visible(True)
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fig.canvas.draw_idle()
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else:
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if vis:
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annot.set_visible(False)
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fig.canvas.draw_idle()
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fig.canvas.mpl_connect("motion_notify_event", hover)
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def plot_mps(names, xs, ys, party_affiliation, party_colors, randomize_positions=True):
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# converting parties to numeric format
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party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
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# add random offset to show points that are in the same location
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if randomize_positions:
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xs_disp = xs + np.random.rand(xs.shape[0]) - 0.5
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ys_disp = ys + np.random.rand(ys.shape[0]) - 0.5
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else:
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xs_disp = xs
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ys_disp = ys
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parties = party_index_mapping[party_ids]
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colormap = ListedColormap(list(map(lambda x: party_colors[x], party_index_mapping)))
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plot_hoverscatter(xs_disp, ys_disp, party_index_mapping, names + " (" + parties + ")", party_ids, cmap=colormap)
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def calc_party_pos(members_of_parliament, party_affiliation):
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party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
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party_pos = np.zeros((party_index_mapping.shape[0], members_of_parliament.shape[1]))
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party_count = np.zeros((party_index_mapping.shape[0], members_of_parliament.shape[1]))
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for i, mp in enumerate(members_of_parliament):
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party_index = party_ids[i]
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party_pos[party_index] = party_pos[party_index] + mp
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party_count[party_index] += 1
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party_pos /= party_count
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return pd.DataFrame(data=party_pos, index=party_index_mapping)
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def plot_parties(parties, party_colors, randomize_positions=False, new_plot=True):
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party_index_mapping = parties.index
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colors = list(map(lambda x: party_colors[x], party_index_mapping))
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if new_plot:
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plt.figure()
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if randomize_positions:
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xs_disp = parties[0].to_numpy() + np.random.rand(parties.shape[0]) - 0.5
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ys_disp = parties[0].to_numpy() + np.random.rand(parties.shape[0]) - 0.5
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else:
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xs_disp = parties[0].to_numpy()
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ys_disp = parties[1].to_numpy()
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for i, party in enumerate(party_index_mapping):
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print("Party ", party, " x = ", xs_disp[i], "y = ", ys_disp[i])
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plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2, c=colors[i], edgecolors='black')
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plt.legend(title='Parties', bbox_to_anchor=(1.3, 1), loc='upper left')
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def calc_party_distances(parties):
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distances = np.zeros((parties.shape[0], parties.shape[0]))
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for i, (_, left_party) in enumerate(parties.iterrows()):
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for j, (_, top_party) in enumerate(parties.iterrows()):
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distances[i,j] = np.linalg.norm(left_party.to_numpy() - top_party.to_numpy())
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party_index_mapping = parties.index
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return pd.DataFrame(data=distances, index=party_index_mapping, columns=party_index_mapping)
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def plot_party_distances(distances):
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plt.figure()
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ax = plt.gca()
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ax.tick_params(axis="x", bottom=False, top=True, labelbottom=False, labeltop=True)
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sn.heatmap(distances, cmap='Oranges', annot=True)
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def plot_hits(prediction, grid_w, grid_h):
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hits = (prediction.sum(axis=0)).reshape(grid_w, grid_h)
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plt.figure()
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sn.heatmap(hits, annot=True, xticklabels=False, yticklabels=False, cbar=False)
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def normalize_df(dataframe):
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df = dataframe.copy(deep=True)
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df = df - np.min(df.to_numpy())
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df = df / np.max(df.to_numpy())
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return df
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def remove_NaN_rows_columns(dataframe):
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df = dataframe.copy(deep=True)
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df = df.dropna(axis=0, how='all', thresh=None, subset=None, inplace=False)
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df = df.dropna(axis=1, how='all', thresh=None, subset=None, inplace=False)
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return df
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