class TestRedisBayesPrideAndPrejudice(unittest.TestCase):
def setUp(self):
self.classifier = RedisBayesWorkClassifier(
name="Pride and Prejudice",
datastore=TEST_REDIS)
self.labels = [False, # b1629290
False, # b2006265
True, # b1276421
True, # b1313597
True, # b1329008
True, # b1143215
True, # b1177629
False, # b1224585
True, # b1009861
True, # b1605863
True, # b1685471
False, # b1724262
False, # b1724269
False, # b1285281
False, # b1629141
False, # b1636638
False, # b1636639
False, # b1285281
True, # b2033293
False, # b1724269
False, # b1724262
False, # b1629141
False, # b1238652
False, # b1433978
False, # b1628582
False, # b1022859
False, # b1773251
False, # b2089950
False, # b1146944
False] # b1675906
self.classifier.load_training_marc(
os.path.join('ColoradoCollege',
'pride-and-prejudice.mrc'),
self.labels)
def test_init(self):
self.assert_(self.classifier is not None)
self.assertEquals(len(self.labels), 30)
def test_classify_(self):
false_tokens = ["infinite jest david foster wallace",
"jane austen sense sensibility"]
true_tokens = ["pride prejudice jane austen",
"pride prejudice jane austen 1775 1817"]
for tokens in false_tokens:
print("{0} is {1}".format(tokens,
self.classifier.__classify__(tokens)))
self.assert_(not self.classifier.__classify__(tokens))
for tokens in true_tokens:
self.assert_(self.classifier.__classify__(tokens))
def tearDown(self):
TEST_REDIS.flushdb()
class TestRedisBayesMobyDick(unittest.TestCase):
def setUp(self):
self.classifier = RedisBayesWorkClassifier(
name="Moby Dick",
datastore=TEST_REDIS)
self.labels = [False, False, False, True, True, False, True, False,
True, False, True, False, False, False, True, True, True,
True, True, True, False, False]
self.classifier.load_training_marc(os.path.join('ColoradoCollege',
'moby-dick.mrc'),
self.labels)
def test_init(self):
self.assert_(self.classifier is not None)
self.assertEquals(len(self.labels), 22)
def test_classify(self):
false_tokens = ["pride prejudice jane austen",
"infinite jest david foster wallace",
"jane eyre emily bronte"]
true_tokens = ["moby dick hermin melville 1841 1891",
"moby dick herman melville"]
for tokens in false_tokens:
self.assert_(not self.classifier.__classify__(tokens))
for tokens in true_tokens:
self.assert_(self.classifier.__classify__(tokens))
def tearDown(self):
TEST_REDIS.flushdb()
def setUp(self):
self.classifier = RedisBayesWorkClassifier(
name="Moby Dick",
datastore=TEST_REDIS)
self.labels = [False, False, False, True, True, False, True, False,
True, False, True, False, False, False, True, True, True,
True, True, True, False, False]
self.classifier.load_training_marc(os.path.join('ColoradoCollege',
'moby-dick.mrc'),
self.labels)
class TestRedisBayesPrideAndPrejudice(unittest.TestCase):
def setUp(self):
self.classifier = RedisBayesWorkClassifier(name="Pride and Prejudice", datastore=TEST_REDIS)
self.labels = [
False, # b1629290
False, # b2006265
True, # b1276421
True, # b1313597
True, # b1329008
True, # b1143215
True, # b1177629
False, # b1224585
True, # b1009861
True, # b1605863
True, # b1685471
False, # b1724262
False, # b1724269
False, # b1285281
False, # b1629141
False, # b1636638
False, # b1636639
False, # b1285281
True, # b2033293
False, # b1724269
False, # b1724262
False, # b1629141
False, # b1238652
False, # b1433978
False, # b1628582
False, # b1022859
False, # b1773251
False, # b2089950
False, # b1146944
False,
] # b1675906
self.classifier.load_training_marc(os.path.join("ColoradoCollege", "pride-and-prejudice.mrc"), self.labels)
def test_init(self):
self.assert_(self.classifier is not None)
self.assertEquals(len(self.labels), 30)
def test_classify_(self):
false_tokens = ["infinite jest david foster wallace", "jane austen sense sensibility"]
true_tokens = ["pride prejudice jane austen", "pride prejudice jane austen 1775 1817"]
for tokens in false_tokens:
print("{0} is {1}".format(tokens, self.classifier.__classify__(tokens)))
self.assert_(not self.classifier.__classify__(tokens))
for tokens in true_tokens:
self.assert_(self.classifier.__classify__(tokens))
def tearDown(self):
TEST_REDIS.flushdb()
def setUp(self):
self.classifier = RedisBayesWorkClassifier(name="Pride and Prejudice", datastore=TEST_REDIS)
self.labels = [
False, # b1629290
False, # b2006265
True, # b1276421
True, # b1313597
True, # b1329008
True, # b1143215
True, # b1177629
False, # b1224585
True, # b1009861
True, # b1605863
True, # b1685471
False, # b1724262
False, # b1724269
False, # b1285281
False, # b1629141
False, # b1636638
False, # b1636639
False, # b1285281
True, # b2033293
False, # b1724269
False, # b1724262
False, # b1629141
False, # b1238652
False, # b1433978
False, # b1628582
False, # b1022859
False, # b1773251
False, # b2089950
False, # b1146944
False,
] # b1675906
self.classifier.load_training_marc(os.path.join("ColoradoCollege", "pride-and-prejudice.mrc"), self.labels)
def setUp(self):
self.classifier = RedisBayesWorkClassifier(name="Moby Dick", datastore=TEST_REDIS)
self.labels = [
False,
False,
False,
True,
True,
False,
True,
False,
True,
False,
True,
False,
False,
False,
True,
True,
True,
True,
True,
True,
False,
False,
]
self.classifier.load_training_marc(os.path.join("ColoradoCollege", "moby-dick.mrc"), self.labels)
class TestRedisBayesMobyDick(unittest.TestCase):
def setUp(self):
self.classifier = RedisBayesWorkClassifier(name="Moby Dick", datastore=TEST_REDIS)
self.labels = [
False,
False,
False,
True,
True,
False,
True,
False,
True,
False,
True,
False,
False,
False,
True,
True,
True,
True,
True,
True,
False,
False,
]
self.classifier.load_training_marc(os.path.join("ColoradoCollege", "moby-dick.mrc"), self.labels)
def test_init(self):
self.assert_(self.classifier is not None)
self.assertEquals(len(self.labels), 22)
def test_classify(self):
false_tokens = ["pride prejudice jane austen", "infinite jest david foster wallace", "jane eyre emily bronte"]
true_tokens = ["moby dick hermin melville 1841 1891", "moby dick herman melville"]
for tokens in false_tokens:
self.assert_(not self.classifier.__classify__(tokens))
for tokens in true_tokens:
self.assert_(self.classifier.__classify__(tokens))
def tearDown(self):
TEST_REDIS.flushdb()
def setUp(self):
self.classifier = RedisBayesWorkClassifier(
name="Pride and Prejudice",
datastore=TEST_REDIS)
self.labels = [False, # b1629290
False, # b2006265
True, # b1276421
True, # b1313597
True, # b1329008
True, # b1143215
True, # b1177629
False, # b1224585
True, # b1009861
True, # b1605863
True, # b1685471
False, # b1724262
False, # b1724269
False, # b1285281
False, # b1629141
False, # b1636638
False, # b1636639
False, # b1285281
True, # b2033293
False, # b1724269
False, # b1724262
False, # b1629141
False, # b1238652
False, # b1433978
False, # b1628582
False, # b1022859
False, # b1773251
False, # b2089950
False, # b1146944
False] # b1675906
self.classifier.load_training_marc(
os.path.join('ColoradoCollege',
'pride-and-prejudice.mrc'),
self.labels)
