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fenago
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import unittest
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import import_ipynb
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import pandas as pd
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import pandas.testing as pd_testing
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from sklearn.cluster import KMeans
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class Test(unittest.TestCase):
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def setUp(self):
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import Exercise5_3
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self.exercises = Exercise5_3
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self.file_url = '../DataSet/taxstats2015.csv'
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self.df = pd.read_csv(self.file_url, usecols=['Postcode', 'Average total business income', 'Average total business expenses'])
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self.X = self.df[['Average total business income', 'Average total business expenses']]
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self.clusters = pd.DataFrame()
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self.inertia = []
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self.clusters['cluster_range'] = range(1, 15)
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for k in self.clusters['cluster_range']:
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self.kmeans = KMeans(n_clusters=k).fit(self.X)
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self.inertia.append(self.kmeans.inertia_)
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self.clusters['inertia'] = self.inertia
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self.optim_cluster = 4
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self.kmeans = KMeans(random_state=42, n_clusters=self.optim_cluster)
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self.kmeans.fit(self.X)
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self.df['cluster2'] = self.kmeans.predict(self.X)
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def test_file_url(self):
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self.assertEqual(self.exercises.file_url, self.file_url)
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def test_df(self):
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pd_testing.assert_frame_equal(self.exercises.df, self.df)
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def test_clusters(self):
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self.assertCountEqual(self.exercises.clusters, self.clusters)
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if __name__ == '__main__':
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unittest.main()
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