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fenago
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import unittest
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import import_ipynb
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import pandas as pd
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import numpy as np
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import pandas.testing as pd_testing
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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 StandardScaler
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class Test(unittest.TestCase):
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def setUp(self):
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import Activity5_1
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self.exercises = Activity5_1
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self.file_url = '../DataSet/german.data-numeric'
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self.df = pd.read_csv(self.file_url, header=None, sep='\s\s+', prefix='X')
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self.X = self.df[['X3', 'X9']]
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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.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, random_state=8).fit(self.X_scaled)
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self.inertia.append(self.kmeans.inertia_)
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self.clusters['inertia'] = self.inertia
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self.clusters_number = 5
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self.kmeans = KMeans(random_state=1, n_clusters=self.clusters_number, init='k-means++', n_init=50, max_iter=1000)
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self.kmeans.fit(self.X_scaled)
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self.df['cluster'] = 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_scaled(self):
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np_testing.assert_array_equal(self.exercises.X_scaled, self.X_scaled)
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def test_clusters(self):
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pd_testing.assert_frame_equal(self.exercises.clusters, self.clusters)
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def test_inertia(self):
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np_testing.assert_array_equal(self.exercises.inertia, self.inertia)
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def test_clusters_number(self):
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self.assertEqual(self.exercises.clusters_number, self.clusters_number)
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if __name__ == '__main__':
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unittest.main()
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