| 1 | import os |
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| 2 | import unittest |
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| 3 | import numpy as np |
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| 4 | |
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| 5 | from model.predictor import * |
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| 6 | from common.constants import KEYWORDS_FILE |
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| 7 | |
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| 8 | class TestPredictionMethods(unittest.TestCase): |
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| 9 | |
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| 10 | def test_fit(self): |
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| 11 | predictor = HelpPagePredictor() |
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| 12 | |
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| 13 | self.assertRaises(AssertionError, |
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| 14 | predictor.fit, |
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| 15 | None, |
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| 16 | np.array([])) |
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| 17 | |
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| 18 | self.assertRaises(AssertionError, |
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| 19 | predictor.fit, |
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| 20 | np.array([]), |
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| 21 | None) |
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| 22 | predictor.fit(np.array([]),np.array([])) |
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| 23 | |
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| 24 | def test_predict(self): |
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| 25 | predictor = HelpPagePredictor() |
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| 26 | vector1 = normalise_vector(np.array([1, 4, 10])) |
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| 27 | vector2 = normalise_vector(np.array([2, 3, 1])) |
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| 28 | vector3 = normalise_vector(np.array([3, 9, 3])) |
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| 29 | |
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| 30 | vectors = np.array([vector1, vector2, vector3]) |
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| 31 | files = np.array(["file1", "file2", "file3"]) |
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| 32 | |
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| 33 | testvec = normalise_vector(np.array([1, 1, 1])) |
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| 34 | |
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| 35 | print("distance to 1") |
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| 36 | print(vector_distance(testvec, vector1)) |
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| 37 | print() |
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| 38 | print("distance to 2") |
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| 39 | print(vector_distance(testvec, vector2)) |
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| 40 | print() |
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| 41 | print("distance to 3") |
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| 42 | print(vector_distance(testvec, vector3)) |
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| 43 | print() |
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| 44 | |
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| 45 | predictor.fit(vectors, files) |
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| 46 | prediction = predictor.predict(np.array([testvec])) |
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| 47 | print("Prediction:") |
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| 48 | print(prediction) |
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| 49 | self.assertEqual(prediction[0], "file2") |
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| 50 | |
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| 51 | |
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| 52 | if __name__ == '__main__': |
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| 53 | unittest.main() |
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