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# 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.
# coding=utf-8
"""Tests for NaiveBayes module."""
import copy
import unittest
from Bio import NaiveBayes
# Importing NaiveBayes will itself raise MissingPythonDependencyError
# if NumPy is unavailable.
import numpy
try:
hash(numpy.float64(123.456))
except TypeError:
# Due to a bug in NumPy 1.12.1, this is unhashable under
# PyPy3.5 v5.7 beta - it has been fixed in NumPy
from Bio import MissingPythonDependencyError
raise MissingPythonDependencyError(
"Please update NumPy if you want to use Bio.NaiveBayes "
"(under this version numpy.float64 is unhashable)."
) from None
del numpy
class CarTest(unittest.TestCase):
def test_car_data(self):
"""Simple example using car data."""
# Car data from example 'Naive Bayes Classifier example'
# by Eric Meisner November 22, 2003
# http://www.inf.u-szeged.hu/~ormandi/teaching/mi2/02-naiveBayes-example.pdf
xcar = [
["Red", "Sports", "Domestic"],
["Red", "Sports", "Domestic"],
["Red", "Sports", "Domestic"],
["Yellow", "Sports", "Domestic"],
["Yellow", "Sports", "Imported"],
["Yellow", "SUV", "Imported"],
["Yellow", "SUV", "Imported"],
["Yellow", "SUV", "Domestic"],
["Red", "SUV", "Imported"],
["Red", "Sports", "Imported"],
]
ycar = [
"Yes",
"No",
"Yes",
"No",
"Yes",
"No",
"Yes",
"No",
"No",
"Yes",
]
carmodel = NaiveBayes.train(xcar, ycar)
self.assertEqual(
"Yes", NaiveBayes.classify(carmodel, ["Red", "Sports", "Domestic"])
)
self.assertEqual(
"No", NaiveBayes.classify(carmodel, ["Red", "SUV", "Domestic"])
)
class NaiveBayesTest(unittest.TestCase):
def setUp(self):
# Using example from https://en.wikipedia.org/wiki/Naive_Bayes_classifier
# height (feet), weight (lbs), foot size (inches)
self.xs = [
[6, 180, 12],
[5.92, 190, 11],
[5.58, 170, 12],
[5.92, 165, 10],
[5, 100, 6],
[5.5, 150, 8],
[5.42, 130, 7],
[5.75, 150, 9],
]
self.ys = [
"male",
"male",
"male",
"male",
"female",
"female",
"female",
"female",
]
self.model = NaiveBayes.train(self.xs, self.ys)
self.test = [6, 130, 8]
def test_train_function_no_training_set(self):
self.assertRaises(ValueError, NaiveBayes.train, [], self.ys)
def test_train_function_input_lengths(self):
ys = copy.copy(self.ys)
ys.pop()
self.assertRaises(ValueError, NaiveBayes.train, self.xs, ys)
def test_train_function_uneven_dimension_of_training_set(self):
xs = copy.copy(self.xs)
xs[0] = [1]
self.assertRaises(ValueError, NaiveBayes.train, xs, self.ys)
def test_train_function_with_priors(self):
model = NaiveBayes.train(self.xs, self.ys, priors={"male": 0.1, "female": 0.9})
result = NaiveBayes.calculate(model, self.test, scale=True)
expected = -692.0
self.assertEqual(expected, round(result["male"]))
def test_classify_function(self):
expected = "female"
result = NaiveBayes.classify(self.model, self.test)
self.assertEqual(expected, result)
def test_calculate_function_wrong_dimensionality(self):
xs = self.xs[0]
xs.append(100)
self.assertRaises(ValueError, NaiveBayes.calculate, self.model, xs)
def test_calculate_function_with_scale(self):
result = NaiveBayes.calculate(self.model, self.test, scale=True)
expected = -689.0
self.assertEqual(expected, round(result["male"]))
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
unittest.main(testRunner=runner)
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