def test_fit_predict(self): X = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]]) clustering = KMeans(k=5, embedded=False) self.assertTrue(not clustering.fitted) y_pred = clustering.fit_predict(X) self.assertTrue(clustering.fitted) self.assertEqual(len(y_pred), X.shape[0])
def test_fit(self):
X = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]])
clustering = KMeans(k=10, embedded=False)
self.assertRaises(RuntimeWarning, clustering.fit(X))
clustering.stop()
clustering = KMeans(k=5, embedded=False)
self.assertTrue(not clustering.fitted)
clustering.fit(X)
self.assertTrue(clustering.fitted)
def test_init(self):
clustering = KMeans(embedded=False)
self.assertEqual(2, clustering.k)
self.assertEqual('simple', clustering.compressor_method)
self.assertEqual(100, clustering.bucket_size)
self.assertEqual(100, clustering.compressed_bucket_size)
self.assertEqual(10, clustering.bicriteria_base_size)
self.assertEqual(2, clustering.bucket_length)
self.assertEqual(0.0, clustering.forgetting_factor)
self.assertEqual(0.5, clustering.forgetting_threshold)
self.assertEqual(0, clustering.seed)
self.asssrtTrue(not clustering.embedded)
self.assertEqual('euclidean', clustering.distance)
clustering.stop()
def test_predict(self): X = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]]) clustering = KMeans(k=5, embedded=False) self.assertRaises(RuntimeError, clustering.predict(X)) clustering.fit(X) y_pred = clustering.predict(X) self.assertEqual(len(y_pred), X.shape[0])
========================================
This is a simple example that illustrates Clustering service usage.
"""
from sklearn.datasets import make_blobs
from jubakit.wrapper.clustering import KMeans, GMM, DBSCAN
# make blob dataset using sklearn API.
X, y = make_blobs(n_samples=200, centers=3, n_features=2, random_state=42)
# launch clustering instance
clusterings = [
KMeans(k=3, bucket_size=200, embedded=False),
GMM(k=3, bucket_size=200, embedded=False),
DBSCAN(eps=2.0, bucket_size=200, embedded=False)
]
for clustering in clusterings:
# fit and predict
y_pred = clustering.fit_predict(X)
# print result
labels = set(y_pred)
label_counts = {}
for label in labels:
label_counts[label] = y_pred.count(label)
print('{0}: {1}'.format(clustering.__class__.__name__, label_counts))
# stop clustering service
clustering.stop()
def test_make_compressor_parameter(self):
clustering = KMeans(compressor_method='simple', embedded=False)
compressor_parameter = {'bucket_size': 100}
self.assertEqual(compressor_parameter,
clustering._make_compressor_parameter('simple'))
clustering.stop()
clustering = KMeans(compressor_method='compressive', embedded=False)
compressor_parameter = {
'bucket_size': 100,
'compressed_bucket_size': 100,
'bicriteria_base_size': 10,
'bucket_length': 2,
'forgetting_factor': 0.0,
'forgetting_threshold': 0.5,
'seed': 0
}
self.assertEqual(compressor_parameter
clustering._make_compressor_parameter('compressive'))
clustering.stop()
def test_method(self):
clustering = KMeans(embedded=False)
self.assertEqual('kmeans', clustering._method())
clustering.stop()
def test_embedded(self): clustering = KMeans(embedded=True)
def test_simple(self): clustering = KMeans(embedded=False) clustering.stop()