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)