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fenago
45 lines
1.6 KiB
Python
45 lines
1.6 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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import numpy as np
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import numpy.testing as np_testing
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from sklearn.cluster import KMeans
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.preprocessing import StandardScaler
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class Test(unittest.TestCase):
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def setUp(self):
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import Exercise5_5
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self.exercises = Exercise5_5
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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.min_max_scaler = MinMaxScaler()
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self.min_max_scaler.fit(self.X)
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self.X_min_max = self.min_max_scaler.transform(self.X)
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self.kmeans = KMeans(random_state=1, n_clusters=4, init='k-means++', n_init=5)
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self.kmeans.fit(self.X_min_max)
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self.df['cluster8'] = self.kmeans.predict(self.X_min_max)
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self.standard_scaler = StandardScaler()
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self.X_scaled = self.standard_scaler.fit_transform(self.X)
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self.kmeans = KMeans(random_state=1, n_clusters=4, init='k-means++', n_init=5)
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self.kmeans.fit(self.X_scaled)
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self.df['cluster9'] = self.kmeans.predict(self.X_scaled)
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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_X_min_max(self):
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np_testing.assert_array_equal(self.exercises.X_min_max, self.X_min_max)
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def test_X_scaled(self):
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np_testing.assert_array_equal(self.exercises.X_scaled, self.X_scaled)
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if __name__ == '__main__':
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unittest.main()
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