6481d27425
Put all public API in __all__, also put them all in pyspark.__init__.py, then we can got all the documents for public API by `pydoc pyspark`. It also can be used by other programs (such as Sphinx or Epydoc) to generate only documents for public APIs. Author: Davies Liu <davies.liu@gmail.com> Closes #2205 from davies/public and squashes the following commits: c6c5567 [Davies Liu] fix message f7b35be [Davies Liu] put SchemeRDD, Row in pyspark.sql module 7e3016a [Davies Liu] add __all__ in mllib 6281b48 [Davies Liu] fix doc for SchemaRDD 6caab21 [Davies Liu] add public interfaces into pyspark.__init__.py
112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from numpy import array, dot
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from math import sqrt
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from pyspark import SparkContext
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from pyspark.mllib._common import \
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_get_unmangled_rdd, _get_unmangled_double_vector_rdd, _squared_distance, \
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_serialize_double_matrix, _deserialize_double_matrix, \
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_serialize_double_vector, _deserialize_double_vector, \
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_get_initial_weights, _serialize_rating, _regression_train_wrapper
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from pyspark.mllib.linalg import SparseVector
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__all__ = ['KMeansModel', 'KMeans']
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class KMeansModel(object):
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"""A clustering model derived from the k-means method.
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>>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
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>>> model = KMeans.train(
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... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
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>>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
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True
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>>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
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True
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>>> model = KMeans.train(sc.parallelize(data), 2)
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>>> sparse_data = [
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... SparseVector(3, {1: 1.0}),
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... SparseVector(3, {1: 1.1}),
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... SparseVector(3, {2: 1.0}),
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... SparseVector(3, {2: 1.1})
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... ]
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>>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||")
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>>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.]))
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True
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>>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1]))
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True
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>>> model.predict(sparse_data[0]) == model.predict(sparse_data[1])
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True
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>>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
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True
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>>> type(model.clusterCenters)
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<type 'list'>
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"""
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def __init__(self, centers):
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self.centers = centers
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@property
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def clusterCenters(self):
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"""Get the cluster centers, represented as a list of NumPy arrays."""
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return self.centers
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def predict(self, x):
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"""Find the cluster to which x belongs in this model."""
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best = 0
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best_distance = float("inf")
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for i in range(0, len(self.centers)):
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distance = _squared_distance(x, self.centers[i])
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if distance < best_distance:
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best = i
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best_distance = distance
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return best
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class KMeans(object):
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@classmethod
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def train(cls, data, k, maxIterations=100, runs=1, initializationMode="k-means||"):
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"""Train a k-means clustering model."""
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sc = data.context
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dataBytes = _get_unmangled_double_vector_rdd(data)
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ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(
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dataBytes._jrdd, k, maxIterations, runs, initializationMode)
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if len(ans) != 1:
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raise RuntimeError("JVM call result had unexpected length")
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elif type(ans[0]) != bytearray:
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raise RuntimeError("JVM call result had first element of type "
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+ type(ans[0]) + " which is not bytearray")
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matrix = _deserialize_double_matrix(ans[0])
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return KMeansModel([row for row in matrix])
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def _test():
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import doctest
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globs = globals().copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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