mirror of
https://github.com/13hannes11/UU_NCML_Project.git
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Restrucuted source files
This commit is contained in:
Deepthi Pathare
@@ -1,317 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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import numpy as np
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import pandas as pd
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from itertools import product
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from neupy import algorithms
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# Loading German Parliament Votes
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def load_german_data():
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title_file = "filename_to_titles.csv"
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vote_counter = -1
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data = pd.DataFrame()
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name_column = 'Bezeichnung'
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party_column = 'Fraktion/Gruppe'
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vote_column_to_title = {}
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voting_features = ['ja', 'nein', 'Enthaltung', 'ungültig']
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for dirname, _, filenames in os.walk('./de/csv'):
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for filename in filenames:
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if filename != title_file:
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vote_counter += 1
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df = pd.read_csv(os.path.join(dirname, filename))
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# Give each voting behaviour type an identifier from 0 to len(voting_features) - 1
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for i, feature in enumerate(voting_features):
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df[feature] *= i
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vote_column_name = f'vote_{vote_counter}'
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# Map column name of vote to filename -> allows retrieving what the vote was about
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vote_column_to_title[vote_column_name] = filename
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# add feature for the vote
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df[vote_column_name] = df[voting_features].sum(axis=1)
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if data.empty:
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# if first file that is loaded set data equal to data from first file
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data = df[[name_column, party_column, vote_column_name]]
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else:
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# merge data with already loaded data
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data = data.merge(df[[name_column, vote_column_name]], on=name_column)
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print(vote_column_to_title)
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print(data)
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return data
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# Loading UK Parliament Votes
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def load_uk_data():
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# Preprocess data
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vote_counter = -1
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data = pd.DataFrame()
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name_column = 'Member'
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party_column = 'Party'
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vote_column = 'Vote'
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column_to_filename = {}
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voting_features = {'Aye':0, 'Teller - Ayes':0, 'No':1, 'Teller - Noes':1, 'No Vote Recorded':2}
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for dirname, _, filenames in os.walk('./uk/csv'):
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for filename in filenames:
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vote_counter += 1
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# Read title rows
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# sep is set to new line so it never splits up the title cells
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title_df = pd.read_csv(os.path.join(dirname, filename), sep='\n',nrows=(3),skip_blank_lines=True,header=None)
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# Read data rows
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df = pd.read_csv(os.path.join(dirname, filename),skiprows=(10))
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# Give each voting behaviour type an identifier from 0 to len(voting_features) - 1
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df[vote_column].replace(voting_features, inplace=True)
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#Replace the vote column name
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vote_column_name = f'vote_{vote_counter}'
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df=df.rename(columns={vote_column:vote_column_name})
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# Map column name of vote to title -> allows retrieving what the vote was about
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column_to_filename[vote_column_name] = title_df.iat[2,0]
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if data.empty:
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# if first file that is loaded set data equal to data from first file
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data = df[[name_column, party_column, vote_column_name]]
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else:
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# merge data with already loaded data
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data = data.merge(df[[name_column, vote_column_name]], on=name_column)
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print(column_to_filename)
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print(data)
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return data
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# Heatmap
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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, 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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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'{labels[index]}'
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annot.set_text(text)
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annot.get_bbox_patch().set_facecolor(cmap(colors[index]))
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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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#plt.show()
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def plot_mps(names, xs, ys, party_affiliation):
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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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ys_disp = ys + np.random.rand(ys.shape[0])
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xs_disp = xs + np.random.rand(xs.shape[0])
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parties = party_index_mapping[party_ids]
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plot_hoverscatter(xs_disp, ys_disp, data[:,0] + " (" + parties + ")", party_ids)
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def plot_parties(xs, ys, party_affiliation):
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cmap = plt.cm.RdYlGn
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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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# calculate average position of party
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party_count = np.zeros(party_index_mapping.shape[0])
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party_xs = np.zeros(party_index_mapping.shape[0])
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party_ys = np.zeros(party_index_mapping.shape[0])
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for x, y, party_id in zip(xs, ys, party_ids):
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party_xs[party_id] += x
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party_ys[party_id] += y
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party_count[party_id] += 1
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party_xs /= party_count
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party_ys /= party_count
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plt.figure()
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party_colors=np.array(range(len(party_index_mapping)))
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plt.scatter(party_xs, party_ys, c=party_colors, cmap=cmap)
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# plotting labels
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offset = 0.01
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for x,y, party in zip(party_xs, party_ys, party_index_mapping):
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plt.text(x + offset, y + offset, party)
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#Simple SOFM for German
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plt.style.use('ggplot')
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# Load data
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data = load_german_data().to_numpy()
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X = data[:,2:]
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print(X)
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inp = X.shape[1] # No of features (bill count)
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h = 10 # Grid height
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w = 10 # Grid width
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rad = 2 # Neighbour radius
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ep = 300 # No of epochs
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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=0.5,
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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=rad,
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features_grid=(h,w),
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)
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sofmnet.train(X, epochs=ep)
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#Visualizing Output
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plt.figure()
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weight = sofmnet.weight.reshape((sofmnet.n_inputs, h, w))
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heatmap = compute_heatmap(weight, h, w)
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plt.imshow(heatmap, cmap='Greys_r', interpolation='nearest')
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plt.axis('off')
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plt.colorbar()
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plt.show()
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# predicting mp positions
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prediction = sofmnet.predict(X)
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print(f'prediction: {prediction}')
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# converting to x and y coordinates
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ys, xs = np.unravel_index(np.argmax(prediction, axis=1), (h, w))
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# plotting mps
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plot_mps(data[:,0], xs, ys, data[:,1])
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plt.show()
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# plotting parties
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plot_parties(xs, ys, data[:,1])
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plt.show()
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#Simple SOFM for UK
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plt.style.use('ggplot')
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# Load data
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data = load_uk_data().to_numpy()
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X = data[:,2:]
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print(X)
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inp = X.shape[1] # No of features (bill count)
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h = 30 # Grid height
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w = 30 # Grid width
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rad = 3 # Neighbour radius
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ep = 100 # No of epochs
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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=0.5,
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show_epoch=20,
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shuffle_data=True,
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verbose=True,
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learning_radius=rad,
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features_grid=(h,w),
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)
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sofmnet.train(X, epochs=ep)
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#Visualizing Output
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'''
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fig = plt.figure()
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ax = plt.axes(projection='3d')
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ax.scatter3D(*sofmnet.weight, label='SOFM Weights')
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ax.scatter3D(*X.T, label='Input');
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ax.set_xlabel('vote_0')
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ax.set_ylabel('vote_1')
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ax.set_zlabel('vote_2')
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ax.legend()
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plt.show()
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'''
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# predicting mp positions
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prediction = sofmnet.predict(X)
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print(f'prediction: {prediction}')
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# converting to x and y coordinates
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ys, xs = np.unravel_index(np.argmax(prediction, axis=1), (h, w))
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# plotting mps
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plot_mps(data[:,0], xs, ys, data[:,1])
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plt.show()
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# plotting parties
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plot_parties(xs, ys, data[:,1])
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plt.show()
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21
german_analysis.py
Executable file
21
german_analysis.py
Executable file
@@ -0,0 +1,21 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import load_data as ld
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import voting_analysis as va
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# Load data
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data = ld.load_german_data().to_numpy()
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X = data[:,2:]
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# Train model
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grid_h = 10 # Grid height
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grid_w = 10 # Grid width
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radius = 2 # Neighbour radius
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step = 0.5
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ep = 300 # No of epochs
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model = va.train_model(X, grid_h, grid_w, radius, step, ep)
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# Predict and visualize output
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va.predict(model, data, grid_h, grid_w)
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100
load_data.py
Executable file
100
load_data.py
Executable file
@@ -0,0 +1,100 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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import pandas as pd
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import numpy as np
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def load_german_data():
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"""
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Load German Parliament data
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return : Data with columns [Member, Party, vote_0, vote_1 etc]
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"""
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title_file = "filename_to_titles.csv"
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vote_counter = -1
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data = pd.DataFrame()
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name_column_g = 'Bezeichnung'
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party_column_g = 'Fraktion/Gruppe'
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name_column = 'Member'
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party_column = 'Party'
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vote_column_to_title = {}
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voting_features = ['ja', 'nein', 'Enthaltung', 'ungültig']
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for dirname, _, filenames in os.walk('./de/csv'):
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for filename in filenames:
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if filename != title_file:
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vote_counter += 1
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df = pd.read_csv(os.path.join(dirname, filename))
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# Give each voting behaviour type an identifier from 0 to len(voting_features) - 1
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for i, feature in enumerate(voting_features):
|
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df[feature] *= i
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vote_column_name = f'vote_{vote_counter}'
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# Map column name of vote to filename -> allows retrieving what the vote was about
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vote_column_to_title[vote_column_name] = filename
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# add feature for the vote
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df[vote_column_name] = df[voting_features].sum(axis=1)
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df=df.rename(columns={name_column_g:name_column,party_column_g:party_column})
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if data.empty:
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# if first file that is loaded set data equal to data from first file
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data = df[[name_column, party_column, vote_column_name]]
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else:
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# merge data with already loaded data
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data = data.merge(df[[name_column, vote_column_name]], on=name_column)
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print(data)
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return data
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def load_uk_data():
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"""
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Load German Parliament data
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return : Data with columns [Member, Party, vote_0, vote_1 etc]
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"""
|
||||
# Preprocess data
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vote_counter = -1
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data = pd.DataFrame()
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||||
name_column = 'Member'
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party_column = 'Party'
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vote_column = 'Vote'
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column_to_filename = {}
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voting_features = {'Aye':0, 'Teller - Ayes':0, 'No':1, 'Teller - Noes':1, 'No Vote Recorded':2}
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for dirname, _, filenames in os.walk('./uk/csv'):
|
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for filename in filenames:
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vote_counter += 1
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||||
# Read title rows
|
||||
# sep is set to new line so it never splits up the title cells
|
||||
title_df = pd.read_csv(os.path.join(dirname, filename), sep='\n',nrows=(3),skip_blank_lines=True,header=None)
|
||||
|
||||
# Read data rows
|
||||
df = pd.read_csv(os.path.join(dirname, filename),skiprows=(10))
|
||||
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||||
# Give each voting behaviour type an identifier from 0 to len(voting_features) - 1
|
||||
df[vote_column].replace(voting_features, inplace=True)
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||||
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||||
#Replace the vote column name
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vote_column_name = f'vote_{vote_counter}'
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||||
df=df.rename(columns={vote_column:vote_column_name})
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||||
# Map column name of vote to title -> allows retrieving what the vote was about
|
||||
column_to_filename[vote_column_name] = title_df.iat[2,0]
|
||||
|
||||
if data.empty:
|
||||
# if first file that is loaded set data equal to data from first file
|
||||
data = df[[name_column, party_column, vote_column_name]]
|
||||
else:
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||||
# merge data with already loaded data
|
||||
data = data.merge(df[[name_column, vote_column_name]], on=name_column)
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||||
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||||
print(data)
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return data
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@@ -1,50 +0,0 @@
|
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import pandas as pd
|
||||
import os
|
||||
|
||||
title_file = "filename_to_titles.csv"
|
||||
|
||||
# Preprocess data
|
||||
vote_counter = -1
|
||||
data = pd.DataFrame()
|
||||
|
||||
name_column = 'Bezeichnung'
|
||||
|
||||
# Load filename to
|
||||
filename_to_title_map = {}
|
||||
|
||||
with open(os.path.join("./de/csv/", title_file), 'r') as file:
|
||||
for line in file.readlines():
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||||
filename, title = line.split(';', 2)
|
||||
filename_to_title_map[filename] = title
|
||||
|
||||
|
||||
|
||||
vote_column_to_title = {}
|
||||
|
||||
voting_features = ['ja', 'nein', 'Enthaltung', 'ungültig']
|
||||
for dirname, _, filenames in os.walk('./de/csv'):
|
||||
for filename in filenames:
|
||||
if filename != title_file:
|
||||
vote_counter += 1
|
||||
print(os.path.join(dirname, filename))
|
||||
df = pd.read_csv(os.path.join(dirname, filename))
|
||||
|
||||
# Give each voting behaviour type an identifier from 0 to len(voting_features) - 1
|
||||
for i, feature in enumerate(voting_features):
|
||||
df[feature] *= i
|
||||
vote_column_name = f'vote_{vote_counter}'
|
||||
# Map column name of vote to filename -> allows retrieving what the vote was about
|
||||
vote_column_to_title[vote_column_name] = filename_to_title_map[filename]
|
||||
|
||||
# add feature for the vote
|
||||
df[vote_column_name] = df[voting_features].sum(axis=1)
|
||||
|
||||
if data.empty:
|
||||
# if first file that is loaded set data equal to data from first file
|
||||
data = df[[name_column, vote_column_name]]
|
||||
else:
|
||||
# merge data with already loaded data
|
||||
data = data.merge(df[[name_column, vote_column_name]], on=name_column)
|
||||
|
||||
print(vote_column_to_title)
|
||||
print(data)
|
||||
@@ -1,43 +0,0 @@
|
||||
import pandas as pd
|
||||
import os
|
||||
|
||||
# Preprocess data
|
||||
vote_counter = -1
|
||||
data = pd.DataFrame()
|
||||
|
||||
name_column = 'Member'
|
||||
vote_column = 'Vote'
|
||||
|
||||
vote_column_to_title = {}
|
||||
|
||||
voting_features = {'Aye':0, 'No':1, 'No Vote Recorded':2}
|
||||
for dirname, _, filenames in os.walk('./uk/csv'):
|
||||
for filename in filenames:
|
||||
vote_counter += 1
|
||||
print(os.path.join(dirname, filename))
|
||||
|
||||
# Read title rows
|
||||
title_df = pd.read_csv(os.path.join(dirname, filename),nrows=(3),skip_blank_lines=True,header=None)
|
||||
|
||||
# Read data rows
|
||||
df = pd.read_csv(os.path.join(dirname, filename),skiprows=(10))
|
||||
|
||||
# Give each voting behaviour type an identifier from 0 to len(voting_features) - 1
|
||||
df[vote_column].replace(voting_features, inplace=True)
|
||||
|
||||
#Replace the vote column name
|
||||
vote_column_name = f'vote_{vote_counter}'
|
||||
df=df.rename(columns={vote_column:vote_column_name})
|
||||
|
||||
# Map column name of vote to title -> allows retrieving what the vote was about
|
||||
vote_column_to_title[vote_column_name] = title_df.iat[2,0]
|
||||
|
||||
if data.empty:
|
||||
# if first file that is loaded set data equal to data from first file
|
||||
data = df[[name_column, vote_column_name]]
|
||||
else:
|
||||
# merge data with already loaded data
|
||||
data = data.merge(df[[name_column, vote_column_name]], on=name_column)
|
||||
|
||||
print(vote_column_to_title)
|
||||
print(data)
|
||||
21
uk_analysis.py
Executable file
21
uk_analysis.py
Executable file
@@ -0,0 +1,21 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import load_data as ld
|
||||
import voting_analysis as va
|
||||
|
||||
# Load data
|
||||
data = ld.load_uk_data().to_numpy()
|
||||
X = data[:,2:]
|
||||
|
||||
# Train model
|
||||
grid_h = 30 # Grid height
|
||||
grid_w = 30 # Grid width
|
||||
radius = 3 # Neighbour radius
|
||||
step = 0.5
|
||||
ep = 100 # No of epochs
|
||||
|
||||
model = va.train_model(X, grid_h, grid_w, radius, step, ep)
|
||||
|
||||
# Predict and visualize output
|
||||
va.predict(model, data, grid_h, grid_w)
|
||||
168
voting_analysis.py
Normal file
168
voting_analysis.py
Normal file
@@ -0,0 +1,168 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import numpy as np
|
||||
from neupy import algorithms
|
||||
from itertools import product
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
def train_model(X, grid_h, grid_w, radius, step, ep):
|
||||
|
||||
inp = X.shape[1] # No of features (bills)
|
||||
|
||||
# Create SOFM
|
||||
sofmnet = algorithms.SOFM(
|
||||
n_inputs=inp,
|
||||
step=0.5,
|
||||
show_epoch=100,
|
||||
shuffle_data=True,
|
||||
verbose=True,
|
||||
learning_radius=radius,
|
||||
features_grid=(grid_h, grid_w)
|
||||
)
|
||||
|
||||
sofmnet.train(X, epochs=ep)
|
||||
return sofmnet
|
||||
|
||||
def predict(model, data, grid_h, grid_w):
|
||||
|
||||
X = data[:,2:]
|
||||
|
||||
# predicting mp positions
|
||||
prediction = model.predict(X)
|
||||
print(f'prediction: {prediction}')
|
||||
|
||||
# converting to x and y coordinates
|
||||
ys, xs = np.unravel_index(np.argmax(prediction, axis=1), (grid_h, grid_w))
|
||||
|
||||
# plotting mps
|
||||
plot_mps(data[:,0], xs, ys, data[:,1])
|
||||
plt.show()
|
||||
|
||||
# plotting parties
|
||||
plot_parties(xs, ys, data[:,1])
|
||||
plt.show()
|
||||
|
||||
# Heatmap of weights
|
||||
plt.figure()
|
||||
weight = model.weight.reshape((model.n_inputs, grid_h, grid_w))
|
||||
heatmap = compute_heatmap(weight, grid_h, grid_w)
|
||||
plt.imshow(heatmap, cmap='Greys_r', interpolation='nearest')
|
||||
plt.axis('off')
|
||||
plt.colorbar()
|
||||
plt.show()
|
||||
|
||||
def iter_neighbours(weights, hexagon=False):
|
||||
_, grid_height, grid_width = weights.shape
|
||||
|
||||
hexagon_even_actions = ((-1, 0), (0, -1), (1, 0), (0, 1), (1, 1), (-1, 1))
|
||||
hexagon_odd_actions = ((-1, 0), (0, -1), (1, 0), (0, 1), (-1, -1), (1, -1))
|
||||
rectangle_actions = ((-1, 0), (0, -1), (1, 0), (0, 1))
|
||||
|
||||
for neuron_x, neuron_y in product(range(grid_height), range(grid_width)):
|
||||
neighbours = []
|
||||
|
||||
if hexagon and neuron_x % 2 == 1:
|
||||
actions = hexagon_even_actions
|
||||
elif hexagon:
|
||||
actions = hexagon_odd_actions
|
||||
else:
|
||||
actions = rectangle_actions
|
||||
|
||||
for shift_x, shift_y in actions:
|
||||
neigbour_x = neuron_x + shift_x
|
||||
neigbour_y = neuron_y + shift_y
|
||||
|
||||
if 0 <= neigbour_x < grid_height and 0 <= neigbour_y < grid_width:
|
||||
neighbours.append((neigbour_x, neigbour_y))
|
||||
|
||||
yield (neuron_x, neuron_y), neighbours
|
||||
|
||||
def compute_heatmap(weight, grid_height, grid_width):
|
||||
heatmap = np.zeros((grid_height, grid_width))
|
||||
for (neuron_x, neuron_y), neighbours in iter_neighbours(weight):
|
||||
total_distance = 0
|
||||
|
||||
for (neigbour_x, neigbour_y) in neighbours:
|
||||
neuron_vec = weight[:, neuron_x, neuron_y]
|
||||
neigbour_vec = weight[:, neigbour_x, neigbour_y]
|
||||
|
||||
distance = np.linalg.norm(neuron_vec - neigbour_vec)
|
||||
total_distance += distance
|
||||
|
||||
avg_distance = total_distance / len(neighbours)
|
||||
heatmap[neuron_x, neuron_y] = avg_distance
|
||||
|
||||
return heatmap
|
||||
|
||||
def plot_hoverscatter(x, y, labels, colors, cmap = plt.cm.RdYlGn):
|
||||
fig,ax = plt.subplots()
|
||||
ANNOTATION_DISTANCE = 5
|
||||
TRANSPARENCY = 0.8
|
||||
scatterplot = plt.scatter(x,y,c=colors, s=5, cmap=cmap)
|
||||
|
||||
annot = ax.annotate("", xy=(0,0),
|
||||
xytext=(ANNOTATION_DISTANCE, ANNOTATION_DISTANCE),
|
||||
textcoords="offset points",
|
||||
bbox=dict(boxstyle="Square"))
|
||||
annot.set_visible(False)
|
||||
|
||||
def update_annot(ind):
|
||||
index = ind["ind"][0]
|
||||
pos = scatterplot.get_offsets()[index]
|
||||
annot.xy = pos
|
||||
text = f'{labels[index]}'
|
||||
annot.set_text(text)
|
||||
annot.get_bbox_patch().set_facecolor(cmap(colors[index]))
|
||||
annot.get_bbox_patch().set_alpha(TRANSPARENCY)
|
||||
|
||||
def hover(event):
|
||||
vis = annot.get_visible()
|
||||
if event.inaxes == ax:
|
||||
cont, ind = scatterplot.contains(event)
|
||||
if cont:
|
||||
update_annot(ind)
|
||||
annot.set_visible(True)
|
||||
fig.canvas.draw_idle()
|
||||
else:
|
||||
if vis:
|
||||
annot.set_visible(False)
|
||||
fig.canvas.draw_idle()
|
||||
|
||||
fig.canvas.mpl_connect("motion_notify_event", hover)
|
||||
#plt.show()
|
||||
|
||||
def plot_mps(names, xs, ys, party_affiliation):
|
||||
# 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
|
||||
ys_disp = ys + np.random.rand(ys.shape[0])
|
||||
xs_disp = xs + np.random.rand(xs.shape[0])
|
||||
parties = party_index_mapping[party_ids]
|
||||
plot_hoverscatter(xs_disp, ys_disp, names + " (" + parties + ")", party_ids)
|
||||
|
||||
|
||||
def plot_parties(xs, ys, party_affiliation):
|
||||
cmap = plt.cm.RdYlGn
|
||||
# converting parties to numeric format
|
||||
party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
|
||||
|
||||
# calculate average position of party
|
||||
party_count = np.zeros(party_index_mapping.shape[0])
|
||||
party_xs = np.zeros(party_index_mapping.shape[0])
|
||||
party_ys = np.zeros(party_index_mapping.shape[0])
|
||||
for x, y, party_id in zip(xs, ys, party_ids):
|
||||
party_xs[party_id] += x
|
||||
party_ys[party_id] += y
|
||||
party_count[party_id] += 1
|
||||
party_xs /= party_count
|
||||
party_ys /= party_count
|
||||
|
||||
plt.figure()
|
||||
party_colors=np.array(range(len(party_index_mapping)))
|
||||
plt.scatter(party_xs, party_ys, c=party_colors, cmap=cmap)
|
||||
# plotting labels
|
||||
offset = 0.01
|
||||
for x,y, party in zip(party_xs, party_ys, party_index_mapping):
|
||||
plt.text(x + offset, y + offset, party)
|
||||
Reference in New Issue
Block a user