def test_equality(self):
b1 = Bayes([0.5, 0.2, 0.3])
b2 = Bayes([5, 2, 3])
b3 = Bayes([5, 2, 5])
self.assertEqual(b1, b2)
self.assertNotEqual(b1, b3)
self.assertNotEqual(b2, b3)
def test_extract_events_odds(self):
instances = {'spam': ["buy viagra", "buy cialis"] * 100 + ["meeting love"],
'genuine': ["meeting tomorrow", "buy milk"] * 100}
odds = Bayes.extract_events_odds(instances)
b = Bayes({'spam': 0.9, 'genuine': 0.1})
b.update_from_events('buy coffee for meeting'.split(), odds)
self.assertEqual(b.most_likely(0.8), 'genuine')
def test_opposite(self):
b = Bayes([0.2, 0.8])
opposite = b.opposite()
self.assertEqual(opposite[0] / opposite[1], b[1] / b[0])
b = Bayes([0.2, 0.4, 0.4])
opposite = b.opposite()
self.assertEqual(opposite[0] / opposite[1], b[1] / b[0])
self.assertEqual(opposite[1] / opposite[2], b[2] / b[1])
self.assertEqual(opposite[0] / opposite[2], b[2] / b[0])
def test_operators(self):
b = Bayes([5, 2, 3])
b *= (2, 2, 1)
b /= (2, 2, 1)
self.assertEqual(b, [5, 2, 3])
self.assertEqual(Bayes([.5, .5]) * (.9, .1), [0.45, 0.05])
self.assertEqual(Bayes([.5, .5]) / (.9, .1), [5 / 9, 5])
self.assertEqual(Bayes([.5, .5]) * {'0': 0.9, '1': 0.1}, [0.45, 0.05])
self.assertEqual(
Bayes([.5, .5]) * [('0', 0.9), ('1', 0.1)], [0.45, 0.05])
def test_update_from_tests(self):
b = Bayes([1, 1])
b.update_from_tests([True], [0.9, 0.1])
self.assertEqual(b, [0.45, 0.05])
b = Bayes([1, 1])
b.update_from_tests([True, True, True, False], [0.5, 2])
self.assertEqual(b, [0.5**2, 2**2])
def test_update(self):
b = Bayes([1, 2])
b.update((2, 1))
self.assertEqual(b, [1, 1])
b.update((2, 1))
self.assertEqual(b, [2, 1])
b.update((2, 0))
self.assertEqual(b, [1, 0])
def test_update(self):
b = Bayes([1, 2])
b.update((2, 1))
self.assertEqual(b, [1, 1])
b.update((2, 1))
self.assertEqual(b, [2, 1])
b.update((2, 0))
self.assertEqual(b, [1, 0])
def test_list_constructor(self):
self.assertEqual(Bayes([]), [])
self.assertEqual(Bayes(()), [])
self.assertEqual(Bayes(range(5)), [0, 1, 2, 3, 4])
self.assertEqual(Bayes({'a': 10, 'b': 50}), [10, 50])
self.assertEqual(Bayes([10, 10, 20]), [10, 10, 20])
self.assertEqual(Bayes([('a', 10), ('b', 50)]), [10, 50])
with self.assertRaises(ValueError):
b = Bayes([('a', 10), ('b', 50), ('a', 15)])
def test_get_odds(self):
b = Bayes({'a': 10, 'b': 50})
self.assertEqual(b['a'], 10)
self.assertEqual(b['b'], 50)
self.assertEqual(b[0], 10)
self.assertEqual(b[1], 50)
with self.assertRaises(IndexError):
b[2]
with self.assertRaises(ValueError):
b['c']
def test_normalized(self):
self.assertEqual(Bayes([]).normalized(), [])
self.assertEqual(Bayes([2]).normalized(), [1])
self.assertEqual(Bayes([9, 1]).normalized(), [0.9, 0.1])
self.assertEqual(Bayes([2, 4, 4]).normalized(), [0.2, 0.4, 0.4])
self.assertEqual(Bayes([2, 0]).normalized(), [1.0, 0])
self.assertEqual(Bayes([0, 0]).normalized(), [0.0, 0])
def test_update_from_tests(self):
b = Bayes([1, 1])
b.update_from_tests([True], [0.9, 0.1])
self.assertEqual(b, [0.45, 0.05])
b = Bayes([1, 1])
b.update_from_tests([True, True, True, False], [0.5, 2])
self.assertEqual(b, [0.5 ** 2, 2 ** 2])
def test_opposite(self):
b = Bayes([0.2, 0.8])
opposite = b.opposite()
self.assertEqual(opposite[0] / opposite[1], b[1] / b[0])
b = Bayes([0.2, 0.4, 0.4])
opposite = b.opposite()
self.assertEqual(opposite[0] / opposite[1], b[1] / b[0])
self.assertEqual(opposite[1] / opposite[2], b[2] / b[1])
self.assertEqual(opposite[0] / opposite[2], b[2] / b[0])
def test_extract_events_odds(self):
instances = {
'spam': ["buy viagra", "buy cialis"] * 100 + ["meeting love"],
'genuine': ["meeting tomorrow", "buy milk"] * 100
}
odds = Bayes.extract_events_odds(instances)
b = Bayes({'spam': 0.9, 'genuine': 0.1})
b.update_from_events('buy coffee for meeting'.split(), odds)
self.assertEqual(b.most_likely(0.8), 'genuine')
def test_empty_constructor(self):
with self.assertRaises(ValueError):
b = Bayes()
def test_update_from_events(self):
b = Bayes([1, 1])
b.update_from_events(['a', 'a', 'a'], {'a': (0.5, 2)})
self.assertEqual(b, [0.5 ** 3, 2 ** 3])
def test_is_likely(self):
b = Bayes({'a': 9, 'b': 1})
self.assertTrue(b.is_likely('a'))
self.assertTrue(b.is_likely('a', 0.89))
self.assertFalse(b.is_likely('a', 0.91))
def test_conversions(self):
b = Bayes({'a': 9, 'b': 1, 'c': 0})
self.assertEqual(b, b.normalized())
self.assertEqual(b.normalized()['a'], 0.9)
self.assertEqual(b.opposite().opposite(), b)
def test_conversions(self):
b = Bayes({'a': 9, 'b': 1, 'c': 0})
self.assertEqual(b, b.normalized())
self.assertEqual(b.normalized()['a'], 0.9)
self.assertEqual(b.opposite().opposite(), b)
def test_is_likely(self):
b = Bayes({'a': 9, 'b': 1})
self.assertTrue(b.is_likely('a'))
self.assertTrue(b.is_likely('a', 0.89))
self.assertFalse(b.is_likely('a', 0.91))
def test_most_likely(self):
b = Bayes({'a': 9, 'b': 1})
self.assertEqual(b.most_likely(), 'a')
self.assertEqual(b.most_likely(0), 'a')
self.assertEqual(b.most_likely(0.89), 'a')
self.assertIsNone(b.most_likely(0.91))
def test_set_odds(self):
b = Bayes((10, 20, 30))
b[0] = 50
b[1] = 40
b[2] = 30
self.assertEqual(b, [50, 40, 30])
def test_update_from_events(self):
b = Bayes([1, 1])
b.update_from_events(['a', 'a', 'a'], {'a': (0.5, 2)})
self.assertEqual(b, [0.5**3, 2**3])
def test_most_likely(self):
b = Bayes({'a': 9, 'b': 1})
self.assertEqual(b.most_likely(), 'a')
self.assertEqual(b.most_likely(0), 'a')
self.assertEqual(b.most_likely(0.89), 'a')
self.assertIsNone(b.most_likely(0.91))
# Classifies "unknown_file" as either a Python or Java file, considering
# you have directories with examples of each language.
#print classify_file("unknown_file", ["java_files", "python_files"])
# Classifies every file under "folder" as either a Python or Java file,
# considering you have subdirectories with examples of each language.
#print classify_folder("folder")
print('')
print(' == Low Level Functions == ')
print(' -- Classic Cancer Test Problem --')
# 1% chance of having cancer.
b = Bayes([('not cancer', 0.99), ('cancer', 0.01)])
# Test positive, 9.6% false positives and 80% true positives
b.update((9.6, 80))
print(b)
print('Most likely:', b.most_likely())
print('')
print(' -- Spam Filter With Existing Model --')
# Database with number of sightings of each words in (genuine, spam)
# emails.
words_odds = {'buy': (5, 100), 'viagra': (1, 1000), 'meeting': (15, 2)}
# Emails to be analyzed.
emails = [
"let's schedule a meeting for tomorrow", # 100% genuine (meeting)
"buy some viagra", # 100% spam (buy, viagra)
def b():
return Bayes((0.99, 0.01), labels=['not cancer', 'cancer'])
# Classifies "unknown_file" as either a Python or Java file, considering
# you have directories with examples of each language.
#print classify_file("unknown_file", ["java_files", "python_files"])
# Classifies every file under "folder" as either a Python or Java file,
# considering you have subdirectories with examples of each language.
#print classify_folder("folder")
print('')
print(' == Low Level Functions == ')
print(' -- Classic Cancer Test Problem --')
# 1% chance of having cancer.
b = Bayes([('not cancer', 0.99), ('cancer', 0.01)])
# Test positive, 9.6% false positives and 80% true positives
b.update((9.6, 80))
print(b)
print('Most likely:', b.most_likely())
print('')
print(' -- Spam Filter With Existing Model --')
# Database with number of sightings of each words in (genuine, spam)
# emails.
words_odds = {'buy': (5, 100), 'viagra': (1, 1000), 'meeting': (15, 2)}
# Emails to be analyzed.
emails = [
"let's schedule a meeting for tomorrow", # 100% genuine (meeting)
"buy some viagra", # 100% spam (buy, viagra)