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130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
# Copyright 2004-2008 by Michiel de Hoon. All rights reserved.
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# This code is part of the Biopython distribution and governed by its
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# license. Please see the LICENSE file that should have been included
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# as part of this package.
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# See the Biopython Tutorial for an explanation of the biological
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# background of these tests.
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"""Tests for LogisticRegression module."""
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import copy
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import unittest
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import warnings
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try:
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import numpy as np
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from numpy import linalg # missing in PyPy's micronumpy
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except ImportError:
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from Bio import MissingExternalDependencyError
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raise MissingExternalDependencyError(
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"Install NumPy if you want to use Bio.LogisticRegression."
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) from None
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from Bio import BiopythonDeprecationWarning
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", category=BiopythonDeprecationWarning)
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from Bio import LogisticRegression
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xs = [
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[-53, -200.78],
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[117, -267.14],
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[57, -163.47],
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[16, -190.30],
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[11, -220.94],
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[85, -193.94],
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[16, -182.71],
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[15, -180.41],
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[-26, -181.73],
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[58, -259.87],
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[126, -414.53],
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[191, -249.57],
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[113, -265.28],
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[145, -312.99],
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[154, -213.83],
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[147, -380.85],
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[93, -291.13],
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]
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ys = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
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def show_progress(iteration, loglikelihood):
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"""No action callback function, used when training the model."""
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class TestLogisticRegression(unittest.TestCase):
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def test_xs_and_ys_input_parameter_lengths(self):
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modified_xs = copy.copy(xs)
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modified_xs.pop()
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self.assertRaises(ValueError, LogisticRegression.train, modified_xs, ys)
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def test_ys_input_class_assignments(self):
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modified_ys = copy.copy(ys)
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modified_ys.pop()
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modified_ys.append(2)
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self.assertRaises(ValueError, LogisticRegression.train, xs, modified_ys)
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def test_dimensionality_of_input_xs(self):
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modified_xs = copy.copy(xs)
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modified_xs[0] = []
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self.assertRaises(ValueError, LogisticRegression.train, modified_xs, ys)
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def test_calculate_model(self):
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model = LogisticRegression.train(xs, ys)
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beta = model.beta
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self.assertAlmostEqual(beta[0], 8.9830, places=4)
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self.assertAlmostEqual(beta[1], -0.0360, places=4)
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self.assertAlmostEqual(beta[2], 0.0218, places=4)
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def test_calculate_model_with_update_callback(self):
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model = LogisticRegression.train(xs, ys, update_fn=show_progress)
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beta = model.beta
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self.assertAlmostEqual(beta[0], 8.9830, places=4)
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def test_classify(self):
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model = LogisticRegression.train(xs, ys)
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result = LogisticRegression.classify(model, [6, -173.143442352])
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self.assertEqual(result, 1)
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result = LogisticRegression.classify(model, [309, -271.005880394])
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self.assertEqual(result, 0)
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def test_calculate_probability(self):
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model = LogisticRegression.train(xs, ys)
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q, p = LogisticRegression.calculate(model, [6, -173.143442352])
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self.assertAlmostEqual(p, 0.993242, places=6)
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self.assertAlmostEqual(q, 0.006758, places=6)
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q, p = LogisticRegression.calculate(model, [309, -271.005880394])
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self.assertAlmostEqual(p, 0.000321, places=6)
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self.assertAlmostEqual(q, 0.999679, places=6)
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def test_model_accuracy(self):
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correct = 0
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model = LogisticRegression.train(xs, ys)
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predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
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for i in range(len(predictions)):
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prediction = LogisticRegression.classify(model, xs[i])
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self.assertEqual(prediction, predictions[i])
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if prediction == ys[i]:
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correct += 1
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self.assertEqual(correct, 16)
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def test_leave_one_out(self):
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correct = 0
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predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0]
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for i in range(len(predictions)):
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model = LogisticRegression.train(xs[:i] + xs[i + 1 :], ys[:i] + ys[i + 1 :])
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prediction = LogisticRegression.classify(model, xs[i])
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self.assertEqual(prediction, predictions[i])
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if prediction == ys[i]:
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correct += 1
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self.assertEqual(correct, 15)
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if __name__ == "__main__":
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runner = unittest.TextTestRunner(verbosity=2)
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unittest.main(testRunner=runner)
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