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fenago
87 lines
3.1 KiB
Python
87 lines
3.1 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.testing as np_testing
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import numpy as np
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from sklearn.model_selection import train_test_split
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import statsmodels.formula.api as smf
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class Test(unittest.TestCase):
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def setUp(self):
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import Activity2_02
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self.exercises = Activity2_02
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self.rawBostonData = pd.read_csv('../Dataset/Boston.csv')
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self.rawBostonData = self.rawBostonData.dropna()
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self.rawBostonData = self.rawBostonData.drop_duplicates()
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self.renamedBostonData = self.rawBostonData.rename(columns={'CRIM': 'crimeRatePerCapita',
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' ZN ': 'landOver25K_sqft',
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'INDUS ': 'non-retailLandProptn',
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'CHAS': 'riverDummy',
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'NOX': 'nitrixOxide_pp10m',
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'RM': 'AvgNo.RoomsPerDwelling',
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'AGE': 'ProptnOwnerOccupied',
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'DIS': 'weightedDist',
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'RAD': 'radialHighwaysAccess',
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'TAX': 'propTaxRate_per10K',
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'PTRATIO': 'pupilTeacherRatio',
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'LSTAT': 'pctLowerStatus',
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'MEDV': 'medianValue_Ks'})
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self.X = self.renamedBostonData.drop('crimeRatePerCapita', axis=1)
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self.y = self.renamedBostonData[['crimeRatePerCapita']]
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self.seed = 10
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self.test_data_size = 0.3
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self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.X, self.y, test_size=self.test_data_size, random_state=self.seed)
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self.train_data = pd.concat([self.X_train, self.y_train], axis=1)
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self.test_data = pd.concat([self.X_test, self.y_test], axis=1)
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self.multiLogLinMod = smf.ols(
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formula='np.log(crimeRatePerCapita) ~ (pctLowerStatus + radialHighwaysAccess + medianValue_Ks + nitrixOxide_pp10m)**2', \
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data=self.train_data)
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self.multiLogLinModResult = self.multiLogLinMod.fit()
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def test_rawBostonData(self):
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pd_testing.assert_frame_equal(self.exercises.rawBostonData, self.rawBostonData)
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def test_renamedBostonData(self):
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pd_testing.assert_frame_equal(self.exercises.renamedBostonData, self.renamedBostonData)
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def test_X(self):
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np_testing.assert_array_equal(self.exercises.X , self.X)
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def test_y(self):
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np_testing.assert_array_equal(self.exercises.y , self.y)
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def test_seed(self):
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self.assertEqual(self.exercises.seed, self.seed)
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def test_test_data_size(self):
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self.assertEqual(self.exercises.test_data_size, self.test_data_size)
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def test_X_train(self):
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np_testing.assert_array_equal(self.exercises.X_train , self.X_train)
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def test_X_test(self):
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np_testing.assert_array_equal(self.exercises.X_test, self.X_test)
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def test_y_train(self):
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np_testing.assert_array_equal(self.exercises.y_train , self.y_train)
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def test_y_test(self):
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np_testing.assert_array_equal(self.exercises.y_test , self.y_test)
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def test_train_data(self):
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pd_testing.assert_frame_equal(self.exercises.train_data, self.train_data)
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def test_test_data(self):
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pd_testing.assert_frame_equal(self.exercises.test_data, self.test_data)
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def test_params(self):
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np_testing.assert_array_equal(self.exercises.multiLogLinModResult.params, self.multiLogLinModResult.params)
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if __name__ == '__main__':
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unittest.main()
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