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