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36 lines
1.0 KiB
Python
36 lines
1.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#This code is modified to run in Kaggle
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import voting_lib.load_data as ld
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import voting_lib.voting_analysis as va
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import voting_lib.political_compass as pc
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import numpy as np
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import pandas as pd
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import os
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# Train model
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grid_h = 30 # Grid height
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grid_w = 30 # Grid width
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radius = 3 # Neighbour radius
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step = 0.5
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ep = 1 # No of epochs
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period_to_compass_year = {'2015_uk':2015, '2017_uk':2017, '2019_uk':2019}
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main_directory = 'uk/csv'
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for dirname, _, filenames in os.walk(main_directory):
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if dirname == main_directory: #to skip main directory path
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continue
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elif os.path.isdir(dirname):
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# Load data
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data = ld.load_uk_data(dirname).to_numpy()
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X = data[:,2:]
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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, pc.get_compass_parties(year=period_to_compass_year[dirname.split('/')[-1]], country='uk')) |