d33d3c61ae
Author: Reynold Xin <rxin@apache.org> Closes #871 from rxin/mllib-pep8 and squashes the following commits: 848416f [Reynold Xin] Fixed a typo in the previous cleanup (c -> sc). a8db4cd [Reynold Xin] Fix PEP8 violations in Python mllib.
107 lines
4 KiB
Python
107 lines
4 KiB
Python
#
|
|
# Licensed to the Apache Software Foundation (ASF) under one or more
|
|
# contributor license agreements. See the NOTICE file distributed with
|
|
# this work for additional information regarding copyright ownership.
|
|
# The ASF licenses this file to You under the Apache License, Version 2.0
|
|
# (the "License"); you may not use this file except in compliance with
|
|
# the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
from numpy import array, dot
|
|
from math import sqrt
|
|
from pyspark import SparkContext
|
|
from pyspark.mllib._common import \
|
|
_get_unmangled_rdd, _get_unmangled_double_vector_rdd, _squared_distance, \
|
|
_serialize_double_matrix, _deserialize_double_matrix, \
|
|
_serialize_double_vector, _deserialize_double_vector, \
|
|
_get_initial_weights, _serialize_rating, _regression_train_wrapper
|
|
from pyspark.mllib.linalg import SparseVector
|
|
|
|
|
|
class KMeansModel(object):
|
|
"""A clustering model derived from the k-means method.
|
|
|
|
>>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
|
|
>>> model = KMeans.train(
|
|
... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
|
|
>>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
|
|
True
|
|
>>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
|
|
True
|
|
>>> model = KMeans.train(sc.parallelize(data), 2)
|
|
>>> sparse_data = [
|
|
... SparseVector(3, {1: 1.0}),
|
|
... SparseVector(3, {1: 1.1}),
|
|
... SparseVector(3, {2: 1.0}),
|
|
... SparseVector(3, {2: 1.1})
|
|
... ]
|
|
>>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||")
|
|
>>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.]))
|
|
True
|
|
>>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1]))
|
|
True
|
|
>>> model.predict(sparse_data[0]) == model.predict(sparse_data[1])
|
|
True
|
|
>>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
|
|
True
|
|
>>> type(model.clusterCenters)
|
|
<type 'list'>
|
|
"""
|
|
def __init__(self, centers):
|
|
self.centers = centers
|
|
|
|
@property
|
|
def clusterCenters(self):
|
|
"""Get the cluster centers, represented as a list of NumPy arrays."""
|
|
return self.centers
|
|
|
|
def predict(self, x):
|
|
"""Find the cluster to which x belongs in this model."""
|
|
best = 0
|
|
best_distance = float("inf")
|
|
for i in range(0, len(self.centers)):
|
|
distance = _squared_distance(x, self.centers[i])
|
|
if distance < best_distance:
|
|
best = i
|
|
best_distance = distance
|
|
return best
|
|
|
|
|
|
class KMeans(object):
|
|
@classmethod
|
|
def train(cls, data, k, maxIterations=100, runs=1, initializationMode="k-means||"):
|
|
"""Train a k-means clustering model."""
|
|
sc = data.context
|
|
dataBytes = _get_unmangled_double_vector_rdd(data)
|
|
ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(
|
|
dataBytes._jrdd, k, maxIterations, runs, initializationMode)
|
|
if len(ans) != 1:
|
|
raise RuntimeError("JVM call result had unexpected length")
|
|
elif type(ans[0]) != bytearray:
|
|
raise RuntimeError("JVM call result had first element of type "
|
|
+ type(ans[0]) + " which is not bytearray")
|
|
matrix = _deserialize_double_matrix(ans[0])
|
|
return KMeansModel([row for row in matrix])
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
globs = globals().copy()
|
|
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
|
globs['sc'].stop()
|
|
if failure_count:
|
|
exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|