def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||"):
"""Train a k-means clustering model."""
# cache serialized data to avoid objects over head in JVM
jcached = _to_java_object_rdd(rdd.map(_convert_to_vector), cache=True)
model = callMLlibFunc("trainKMeansModel", jcached, k, maxIterations, runs,
initializationMode)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
first = data.first()
if not isinstance(first, LabeledPoint):
raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first)
initial_weights = initial_weights or [0.0] * len(data.first().features)
weights, intercept = train_func(_to_java_object_rdd(data, cache=True),
_convert_to_vector(initial_weights))
return modelClass(weights, intercept)
def _prepare(cls, ratings):
assert isinstance(ratings, RDD), "ratings should be RDD"
first = ratings.first()
if not isinstance(first, Rating):
if isinstance(first, (tuple, list)):
ratings = ratings.map(lambda x: Rating(*x))
else:
raise ValueError("rating should be RDD of Rating or tuple/list")
return _to_java_object_rdd(ratings, True)
def _prepare(cls, ratings):
assert isinstance(ratings, RDD), "ratings should be RDD"
first = ratings.first()
if not isinstance(first, Rating):
if isinstance(first, (tuple, list)):
ratings = ratings.map(lambda x: Rating(*x))
else:
raise ValueError(
"rating should be RDD of Rating or tuple/list")
return _to_java_object_rdd(ratings, True)
def test_to_java_object_rdd(self): # SPARK-6660
data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0L)
self.assertEqual(_to_java_object_rdd(data).count(), 10)
def test_to_java_object_rdd(self): # SPARK-6660
data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0)
self.assertEqual(_to_java_object_rdd(data).count(), 10)
def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
initial_weights = initial_weights or [0.0] * len(data.first().features)
weights, intercept = train_func(_to_java_object_rdd(data, cache=True),
_convert_to_vector(initial_weights))
return modelClass(weights, intercept)
def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
initial_weights = initial_weights or [0.0] * len(data.first().features)
weights, intercept = train_func(_to_java_object_rdd(data, cache=True),
_convert_to_vector(initial_weights))
return modelClass(weights, intercept)
