def test_method_param(self):
self.assertTrue(
Config(method='lof')['parameter']['ignore_kth_same_point'])
self.assertEqual('inverted_index_euclid',
Config(method='lof')['parameter']['method'])
self.assertEqual('euclid_lsh',
Config(method='light_lof')['parameter']['method'])
def setUp(self): self._service = Anomaly.run(Config())
def test_default(self):
config = Config.default()
self.assertEqual('lof', config['method'])
def test_methods(self): config = Config() self.assertTrue(isinstance(config.methods(), list))
# Define a function to determine if the record is positive or negative.
# In this example we treat `Iris-virginica` as an "anomaly" record.
def is_positive(x):
return x == 'Iris-virginica'
# Create a Dataset.
dataset = Dataset(loader, schema)
# Extract the negative (non-anomaly) dataset.
dataset_neg = dataset.convert(
lambda data: filter(lambda x: not is_positive(x['Species']), data))
# Create an Anomaly Service.
cfg = Config(parameter={'nearest_neighbor_num': 3})
anomaly = Anomaly.run(cfg)
# Update the anomaly model using negative dataset.
for (idx, row_id, flag, score) in anomaly.add(dataset_neg):
pass
# Calculate LOF scores for the full dataset.
# It is expected that `Iris-virginica` records get higher scores than others.
y_true = []
y_score = []
for (idx, row_id, flag, score) in anomaly.calc_score(dataset):
y_true.append(is_positive(flag))
y_score.append(score)
print('Score ({0}): {1}'.format(flag, score))
def test_default(self):
config = Config.default()
self.assertEqual('lof', config['method'])
def test_methods(self):
config = Config()
self.assertTrue(isinstance(config.methods(), list))
def test_simple(self):
config = Config()
self.assertEqual('lof', config['method'])
def test_embedded(self):
anomaly = Anomaly.run(Config(), embedded=True)