def test_kmeans_k1(self):
""" With k=1 always get one cluster centered on the mean"""
data_local = [array([1.0, 2.0, 6.0]), array([1.0, 3.0, 0.0]), array([1.0, 4.0, 6.0])]
data = self.sc.parallelize(zip(range(1, 4), data_local))
model = KMeans(k=1, maxiter=20, tol=0.001).train(data)
labels = model.predict(data)
assert array_equal(model.centers[0], array([1.0, 3.0, 4.0]))
assert array_equal(labels.map(lambda (_, v): v).collect(), array([0, 0, 0]))
def test_kmeans_k2(self):
""" Test k=2 also with more points"""
data, centerstrue = DataSets.make(self.sc, "kmeans",
k=2, nrecords=50, npartitions=5, seed=42, returnparams=True)
centerstrue = KMeansModel(centerstrue)
model = KMeans(k=2, maxIterations=20).fit(data)
labels = array(model.predict(data).values().collect())
labelstrue = array(centerstrue.predict(data).values().collect())
assert(array_equal(labels, labelstrue) or array_equal(labels, 1 - labelstrue))
def test_kmeans_k1(self):
""" With k=1 always get one cluster centered on the mean"""
data_local = [
array([1.0, 2.0, 6.0]),
array([1.0, 3.0, 0.0]),
array([1.0, 4.0, 6.0])]
data = Series(self.sc.parallelize(zip(range(1, 4), data_local)))
model = KMeans(k=1, maxIterations=20).fit(data)
labels = model.predict(data)
assert array_equal(model.centers[0], array([1.0, 3.0, 4.0]))
assert array_equal(labels.values().collect(), array([0, 0, 0]))
def test_kmeans_k1(self):
""" With k=1 always get one cluster centered on the mean"""
data_local = [
array([1.0, 2.0, 6.0]),
array([1.0, 3.0, 0.0]),
array([1.0, 4.0, 6.0])]
data = self.sc.parallelize(zip(range(1, 4), data_local))
model = KMeans(k=1, maxiter=20, tol=0.001).train(data)
labels = model.predict(data)
assert array_equal(model.centers[0], array([1.0, 3.0, 4.0]))
assert array_equal(labels.map(lambda (_, v): v).collect(), array([0, 0, 0]))
def test_kmeans_k1(self):
""" With k=1 always get one cluster centered on the mean"""
data_local = [
array([1.0, 2.0, 6.0]),
array([1.0, 3.0, 0.0]),
array([1.0, 4.0, 6.0])
]
data = Series(self.sc.parallelize(zip(range(1, 4), data_local)))
model = KMeans(k=1, maxIterations=20).fit(data)
labels = model.predict(data)
assert array_equal(model.centers[0], array([1.0, 3.0, 4.0]))
assert array_equal(labels.values().collect(), array([0, 0, 0]))
def test_kmeans_k2(self):
""" Test k=2 also with more points"""
data, centerstrue = DataSets.make(self.sc, "kmeans",
k=2, nrecords=50, npartitions=5, seed=42, returnparams=True)
centerstrue = KMeansModel(centerstrue)
model = KMeans(k=2, maxiter=20, tol=0.001, init="sample").train(data)
labels = array(model.predict(data).values().collect())
labelstrue = array(centerstrue.predict(data).values().collect())
print(labels)
print(labelstrue)
assert(array_equal(labels, labelstrue) or array_equal(labels, 1 - labelstrue))
def test_kmeans_k2(self):
""" Test k=2 also with more points"""
data, centerstrue = DataSets.make(
self.sc, "kmeans", k=2, nrecords=50, npartitions=5, seed=42, returnparams=True
)
centerstrue = KMeansModel(centerstrue)
model = KMeans(k=2, maxiter=20, tol=0.001, init="sample").train(data)
labels = array(model.predict(data).values().collect())
labelstrue = array(centerstrue.predict(data).values().collect())
print(labels)
print(labelstrue)
assert array_equal(labels, labelstrue) or array_equal(labels, 1 - labelstrue)
def test_KMeans_k2(self):
""" Test k=2 also with more points"""
data, centersTrue = DataSets.make(self.sc,
"kmeans",
k=2,
nrecords=50,
npartitions=5,
seed=42,
returnParams=True)
centersTrue = KMeansModel(centersTrue)
model = KMeans(k=2, maxIterations=20).fit(data)
labels = array(model.predict(data).values().collect())
labelsTrue = array(centersTrue.predict(data).values().collect())
assert (array_equal(labels, labelsTrue)
or array_equal(labels, 1 - labelsTrue))
def execute(self):
model = KMeans(self.k)
print "RESULT: ",self.parent.result
self.result = model.fit(self.parent.result)
print type