def train(
cls,
rdd,
k,
maxIterations=100,
runs=1,
initializationMode="k-means||",
seed=None,
initializationSteps=5,
epsilon=1e-4,
initialModel=None,
):
"""Train a k-means clustering model."""
clusterInitialModel = []
if initialModel is not None:
if not isinstance(initialModel, KMeansModel):
raise Exception(
"initialModel is of " + str(type(initialModel)) + ". It needs " "to be of <type 'KMeansModel'>"
)
clusterInitialModel = [_convert_to_vector(c) for c in initialModel.clusterCenters]
model = callMLlibFunc(
"trainKMeansModel",
rdd.map(_convert_to_vector),
k,
maxIterations,
runs,
initializationMode,
seed,
initializationSteps,
epsilon,
clusterInitialModel,
)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
def train(cls,
rdd,
k,
maxIterations=100,
runs=1,
initializationMode="k-means||",
seed=None,
initializationSteps=5,
epsilon=1e-4,
initialModel=None):
"""Train a k-means clustering model."""
if runs != 1:
warnings.warn(
"Support for runs is deprecated in 1.6.0. This param will have no effect in 1.7.0."
)
clusterInitialModel = []
if initialModel is not None:
if not isinstance(initialModel, KMeansModel):
raise Exception("initialModel is of " +
str(type(initialModel)) + ". It needs "
"to be of <type 'KMeansModel'>")
clusterInitialModel = [
_convert_to_vector(c) for c in initialModel.clusterCenters
]
model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector),
k, maxIterations, runs, initializationMode, seed,
initializationSteps, epsilon,
clusterInitialModel)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
def train(
cls,
rdd,
k,
maxIterations=100,
runs=1,
initializationMode="k-means||",
seed=None,
initializationSteps=5,
epsilon=1e-4,
):
"""Train a k-means clustering model."""
model = callMLlibFunc(
"trainKMeansModel",
rdd.map(_convert_to_vector),
k,
maxIterations,
runs,
initializationMode,
seed,
initializationSteps,
epsilon,
)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
seed=None, initializationSteps=5, epsilon=1e-4):
"""Train a k-means clustering model."""
model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
runs, initializationMode, seed, initializationSteps, epsilon)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
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 train(cls,
rdd,
k,
maxIterations=100,
initializationMode="k-means||",
seed=None,
initializationSteps=2,
epsilon=1e-4,
initialModel=None):
"""
Train a k-means clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of clusters to create.
:param maxIterations:
Maximum number of iterations allowed.
(default: 100)
:param initializationMode:
The initialization algorithm. This can be either "random" or
"k-means||".
(default: "k-means||")
:param seed:
Random seed value for cluster initialization. Set as None to
generate seed based on system time.
(default: None)
:param initializationSteps:
Number of steps for the k-means|| initialization mode.
This is an advanced setting -- the default of 2 is almost
always enough.
(default: 2)
:param epsilon:
Distance threshold within which a center will be considered to
have converged. If all centers move less than this Euclidean
distance, iterations are stopped.
(default: 1e-4)
:param initialModel:
Initial cluster centers can be provided as a KMeansModel object
rather than using the random or k-means|| initializationModel.
(default: None)
"""
clusterInitialModel = []
if initialModel is not None:
if not isinstance(initialModel, KMeansModel):
raise Exception("initialModel is of " +
str(type(initialModel)) + ". It needs "
"to be of <type 'KMeansModel'>")
clusterInitialModel = [
_convert_to_vector(c) for c in initialModel.clusterCenters
]
model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector),
k, maxIterations, initializationMode, seed,
initializationSteps, epsilon,
clusterInitialModel)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
seed=None, initializationSteps=5, epsilon=1e-4, initialModel=None):
"""
Train a k-means clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of clusters to create.
:param maxIterations:
Maximum number of iterations allowed.
(default: 100)
:param runs:
Number of runs to execute in parallel. The best model according
to the cost function will be returned (deprecated in 1.6.0).
(default: 1)
:param initializationMode:
The initialization algorithm. This can be either "random" or
"k-means||".
(default: "k-means||")
:param seed:
Random seed value for cluster initialization. Set as None to
generate seed based on system time.
(default: None)
:param initializationSteps:
Number of steps for the k-means|| initialization mode.
This is an advanced setting -- the default of 5 is almost
always enough.
(default: 5)
:param epsilon:
Distance threshold within which a center will be considered to
have converged. If all centers move less than this Euclidean
distance, iterations are stopped.
(default: 1e-4)
:param initialModel:
Initial cluster centers can be provided as a KMeansModel object
rather than using the random or k-means|| initializationModel.
(default: None)
"""
if runs != 1:
warnings.warn(
"Support for runs is deprecated in 1.6.0. This param will have no effect in 2.0.0.")
clusterInitialModel = []
if initialModel is not None:
if not isinstance(initialModel, KMeansModel):
raise Exception("initialModel is of "+str(type(initialModel))+". It needs "
"to be of <type 'KMeansModel'>")
clusterInitialModel = [_convert_to_vector(c) for c in initialModel.clusterCenters]
model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
runs, initializationMode, seed, initializationSteps, epsilon,
clusterInitialModel)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
def callBigDlFunc(bigdl_type, name, *args):
""" Call API in PythonBigDL """
sc = SparkContext.getOrCreate()
if bigdl_type == "float":
api = getattr(
sc._jvm.com.intel.analytics.bigdl.python.api.PythonBigDL.ofFloat(),
name)
elif bigdl_type == "double":
api = getattr(
sc._jvm.com.intel.analytics.bigdl.python.api.PythonBigDL.ofDouble(
), name)
else:
raise Exception("Not supported bigdl_type: %s" % bigdl_type)
return callJavaFunc(sc, api, *args)
def callBigDlFunc(bigdl_type, name, *args):
""" Call API in PythonBigDL """
sc = SparkContext.getOrCreate()
if bigdl_type == "float":
api = getattr(
sc._jvm.com.intel.analytics.bigdl.python.api.PythonBigDL.ofFloat(),
name)
elif bigdl_type == "double":
api = getattr(
sc._jvm.com.intel.analytics.bigdl.python.api.PythonBigDL.ofDouble(),
name)
else:
raise Exception("Not supported bigdl_type: %s" % bigdl_type)
return callJavaFunc(sc, api, *args)
def callBigDlFunc(bigdl_type, name, *args):
""" Call API in PythonBigDL """
gateway = _get_gateway()
error = Exception("Cannot find function: %s" % name)
for jinvoker in JavaCreator.instance(bigdl_type, gateway).value:
# hasattr(jinvoker, name) always return true here,
# so you need to invoke the method to check if it exist or not
try:
api = getattr(jinvoker, name)
result = callJavaFunc(api, *args)
except Exception as e:
error = e
if "does not exist" not in str(e):
raise e
else:
return result
raise error
def callBigDlFunc(bigdl_type, name, *args):
""" Call API in PythonBigDL """
jinstance = JavaCreator.instance(bigdl_type=bigdl_type).value
sc = get_spark_context()
api = getattr(jinstance, name)
return callJavaFunc(sc, api, *args)
def train(
cls,
rdd,
k,
maxIterations=100,
initializationMode="k-means||",
seed=None,
initializationSteps=2,
epsilon=1e-4,
initialModel=None,
distanceMeasure="euclidean",
):
"""
Train a k-means clustering model.
.. versionadded:: 0.9.0
Parameters
----------
rdd : ::py:class:`pyspark.RDD`
Training points as an `RDD` of :py:class:`pyspark.mllib.linalg.Vector`
or convertible sequence types.
k : int
Number of clusters to create.
maxIterations : int, optional
Maximum number of iterations allowed.
(default: 100)
initializationMode : str, optional
The initialization algorithm. This can be either "random" or
"k-means||".
(default: "k-means||")
seed : int, optional
Random seed value for cluster initialization. Set as None to
generate seed based on system time.
(default: None)
initializationSteps :
Number of steps for the k-means|| initialization mode.
This is an advanced setting -- the default of 2 is almost
always enough.
(default: 2)
epsilon : float, optional
Distance threshold within which a center will be considered to
have converged. If all centers move less than this Euclidean
distance, iterations are stopped.
(default: 1e-4)
initialModel : :py:class:`KMeansModel`, optional
Initial cluster centers can be provided as a KMeansModel object
rather than using the random or k-means|| initializationModel.
(default: None)
distanceMeasure : str, optional
The distance measure used by the k-means algorithm.
(default: "euclidean")
"""
clusterInitialModel = []
if initialModel is not None:
if not isinstance(initialModel, KMeansModel):
raise TypeError("initialModel is of " +
str(type(initialModel)) + ". It needs "
"to be of <type 'KMeansModel'>")
clusterInitialModel = [
_convert_to_vector(c) for c in initialModel.clusterCenters
]
model = callMLlibFunc(
"trainKMeansModel",
rdd.map(_convert_to_vector),
k,
maxIterations,
initializationMode,
seed,
initializationSteps,
epsilon,
clusterInitialModel,
distanceMeasure,
)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
