2013-12-25 00:08:05 -05:00
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#
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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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2015-04-16 19:20:57 -04:00
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import sys
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import array as pyarray
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2015-11-02 16:42:16 -05:00
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import warnings
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2015-04-16 19:20:57 -04:00
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if sys.version > '3':
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xrange = range
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2015-07-22 20:22:12 -04:00
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basestring = str
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2015-04-16 19:20:57 -04:00
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2015-06-19 15:23:15 -04:00
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from math import exp, log
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from numpy import array, random, tile
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2015-02-03 02:04:55 -05:00
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2015-06-29 01:38:04 -04:00
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from collections import namedtuple
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2015-10-27 00:28:18 -04:00
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from pyspark import SparkContext, since
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2015-06-19 15:23:15 -04:00
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from pyspark.rdd import RDD, ignore_unicode_prefix
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2015-06-29 01:38:04 -04:00
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from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, callJavaFunc, _py2java, _java2py
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2015-06-19 15:23:15 -04:00
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from pyspark.mllib.linalg import SparseVector, _convert_to_vector, DenseVector
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2015-07-15 02:27:42 -04:00
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from pyspark.mllib.regression import LabeledPoint
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2015-02-03 02:04:55 -05:00
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from pyspark.mllib.stat.distribution import MultivariateGaussian
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2015-06-29 01:38:04 -04:00
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from pyspark.mllib.util import Saveable, Loader, inherit_doc, JavaLoader, JavaSaveable
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2015-06-19 15:23:15 -04:00
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from pyspark.streaming import DStream
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[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
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2015-06-19 15:23:15 -04:00
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__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture',
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2015-06-29 01:38:04 -04:00
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'PowerIterationClusteringModel', 'PowerIterationClustering',
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2015-07-15 02:27:42 -04:00
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'StreamingKMeans', 'StreamingKMeansModel',
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'LDA', 'LDAModel']
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2014-09-03 14:49:45 -04:00
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2013-12-25 00:08:05 -05:00
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2015-03-17 15:14:40 -04:00
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@inherit_doc
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class KMeansModel(Saveable, Loader):
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2014-08-06 15:58:24 -04:00
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2013-12-25 00:08:05 -05:00
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"""A clustering model derived from the k-means method.
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2015-02-03 02:04:55 -05:00
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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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2014-05-25 20:15:01 -04:00
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>>> model = KMeans.train(
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2015-05-05 10:57:39 -04:00
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... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random",
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... seed=50, initializationSteps=5, epsilon=1e-4)
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[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
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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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2015-05-05 10:57:39 -04:00
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>>> model.k
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2
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>>> model.computeCost(sc.parallelize(data))
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2.0000000000000004
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[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
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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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2015-05-05 10:57:39 -04:00
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>>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||",
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... seed=50, initializationSteps=5, epsilon=1e-4)
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[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
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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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2013-12-25 00:08:05 -05:00
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True
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[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
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>>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
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2013-12-25 00:08:05 -05:00
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True
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2015-04-16 19:20:57 -04:00
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>>> isinstance(model.clusterCenters, list)
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True
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2015-03-17 15:14:40 -04:00
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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> model.save(sc, path)
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>>> sameModel = KMeansModel.load(sc, path)
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>>> sameModel.predict(sparse_data[0]) == model.predict(sparse_data[0])
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True
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2015-06-22 14:53:11 -04:00
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>>> from shutil import rmtree
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2015-03-17 15:14:40 -04:00
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>>> try:
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2015-06-22 14:53:11 -04:00
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... rmtree(path)
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2015-03-17 15:14:40 -04:00
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... except OSError:
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... pass
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2015-10-07 18:04:53 -04:00
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>>> data = array([-383.1,-382.9, 28.7,31.2, 366.2,367.3]).reshape(3, 2)
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>>> model = KMeans.train(sc.parallelize(data), 3, maxIterations=0,
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... initialModel = KMeansModel([(-1000.0,-1000.0),(5.0,5.0),(1000.0,1000.0)]))
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>>> model.clusterCenters
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[array([-1000., -1000.]), array([ 5., 5.]), array([ 1000., 1000.])]
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2015-10-27 00:28:18 -04:00
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.. versionadded:: 0.9.0
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2013-12-25 00:08:05 -05:00
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"""
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2014-08-06 15:58:24 -04:00
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[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
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def __init__(self, centers):
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self.centers = centers
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@property
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2015-10-27 00:28:18 -04:00
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@since('1.0.0')
|
[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
|
|
|
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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2013-12-25 00:08:05 -05:00
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2015-05-05 10:57:39 -04:00
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|
@property
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2015-10-27 00:28:18 -04:00
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@since('1.4.0')
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2015-05-05 10:57:39 -04:00
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def k(self):
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"""Total number of clusters."""
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return len(self.centers)
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2015-10-27 00:28:18 -04:00
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@since('0.9.0')
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2013-12-25 00:08:05 -05:00
|
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def predict(self, x):
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|
|
|
"""Find the cluster to which x belongs in this model."""
|
|
|
|
best = 0
|
[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
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best_distance = float("inf")
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2015-06-19 15:23:15 -04:00
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if isinstance(x, RDD):
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return x.map(self.predict)
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2014-09-19 18:01:11 -04:00
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x = _convert_to_vector(x)
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for i in xrange(len(self.centers)):
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distance = x.squared_distance(self.centers[i])
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2013-12-25 00:08:05 -05:00
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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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2015-10-27 00:28:18 -04:00
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@since('1.4.0')
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2015-05-05 10:57:39 -04:00
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def computeCost(self, rdd):
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"""
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Return the K-means cost (sum of squared distances of points to
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|
|
|
their nearest center) for this model on the given data.
|
|
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"""
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|
cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector),
|
|
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[_convert_to_vector(c) for c in self.centers])
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return cost
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|
|
|
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.4.0')
|
2015-03-17 15:14:40 -04:00
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def save(self, sc, path):
|
2015-10-27 00:28:18 -04:00
|
|
|
"""
|
|
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|
Save this model to the given path.
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|
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"""
|
2015-04-16 19:20:57 -04:00
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java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers])
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2015-03-17 15:14:40 -04:00
|
|
|
java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers)
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|
java_model.save(sc._jsc.sc(), path)
|
|
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@classmethod
|
2015-10-27 00:28:18 -04:00
|
|
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@since('1.4.0')
|
2015-03-17 15:14:40 -04:00
|
|
|
def load(cls, sc, path):
|
2015-10-27 00:28:18 -04:00
|
|
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"""
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|
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|
Load a model from the given path.
|
|
|
|
"""
|
2015-03-17 15:14:40 -04:00
|
|
|
java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel.load(sc._jsc.sc(), path)
|
|
|
|
return KMeansModel(_java2py(sc, java_model.clusterCenters()))
|
|
|
|
|
[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
|
|
|
|
2013-12-25 00:08:05 -05:00
|
|
|
class KMeans(object):
|
2015-10-27 00:28:18 -04:00
|
|
|
"""
|
|
|
|
.. versionadded:: 0.9.0
|
|
|
|
"""
|
2014-08-06 15:58:24 -04:00
|
|
|
|
2013-12-25 00:08:05 -05:00
|
|
|
@classmethod
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('0.9.0')
|
2015-05-05 10:57:39 -04:00
|
|
|
def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
|
2015-10-07 18:04:53 -04:00
|
|
|
seed=None, initializationSteps=5, epsilon=1e-4, initialModel=None):
|
2013-12-25 00:08:05 -05:00
|
|
|
"""Train a k-means clustering model."""
|
2015-11-02 16:42:16 -05:00
|
|
|
if runs != 1:
|
|
|
|
warnings.warn(
|
2016-01-08 12:47:44 -05:00
|
|
|
"Support for runs is deprecated in 1.6.0. This param will have no effect in 2.0.0.")
|
2015-10-07 18:04:53 -04:00
|
|
|
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]
|
2014-11-21 18:02:31 -05:00
|
|
|
model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
|
2015-10-07 18:04:53 -04:00
|
|
|
runs, initializationMode, seed, initializationSteps, epsilon,
|
|
|
|
clusterInitialModel)
|
2014-10-31 01:25:18 -04:00
|
|
|
centers = callJavaFunc(rdd.context, model.clusterCenters)
|
2014-09-19 18:01:11 -04:00
|
|
|
return KMeansModel([c.toArray() for c in centers])
|
[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
|
|
|
|
2013-12-25 00:08:05 -05:00
|
|
|
|
2015-07-28 18:00:25 -04:00
|
|
|
@inherit_doc
|
|
|
|
class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader):
|
|
|
|
|
|
|
|
"""
|
|
|
|
.. note:: Experimental
|
2015-02-03 02:04:55 -05:00
|
|
|
|
2015-07-28 18:00:25 -04:00
|
|
|
A clustering model derived from the Gaussian Mixture Model method.
|
2015-02-03 02:04:55 -05:00
|
|
|
|
2015-05-15 03:18:39 -04:00
|
|
|
>>> from pyspark.mllib.linalg import Vectors, DenseMatrix
|
2015-07-28 18:00:25 -04:00
|
|
|
>>> from numpy.testing import assert_equal
|
|
|
|
>>> from shutil import rmtree
|
|
|
|
>>> import os, tempfile
|
|
|
|
|
2015-02-03 02:04:55 -05:00
|
|
|
>>> clusterdata_1 = sc.parallelize(array([-0.1,-0.05,-0.01,-0.1,
|
|
|
|
... 0.9,0.8,0.75,0.935,
|
2016-01-11 17:43:25 -05:00
|
|
|
... -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2), 2)
|
2015-02-03 02:04:55 -05:00
|
|
|
>>> model = GaussianMixture.train(clusterdata_1, 3, convergenceTol=0.0001,
|
|
|
|
... maxIterations=50, seed=10)
|
|
|
|
>>> labels = model.predict(clusterdata_1).collect()
|
|
|
|
>>> labels[0]==labels[1]
|
|
|
|
False
|
|
|
|
>>> labels[1]==labels[2]
|
2016-01-11 17:43:25 -05:00
|
|
|
False
|
2015-02-03 02:04:55 -05:00
|
|
|
>>> labels[4]==labels[5]
|
|
|
|
True
|
2016-01-11 17:43:25 -05:00
|
|
|
>>> model.predict([-0.1,-0.05])
|
|
|
|
0
|
|
|
|
>>> softPredicted = model.predictSoft([-0.1,-0.05])
|
|
|
|
>>> abs(softPredicted[0] - 1.0) < 0.001
|
|
|
|
True
|
|
|
|
>>> abs(softPredicted[1] - 0.0) < 0.001
|
|
|
|
True
|
|
|
|
>>> abs(softPredicted[2] - 0.0) < 0.001
|
|
|
|
True
|
2015-07-28 18:00:25 -04:00
|
|
|
|
|
|
|
>>> path = tempfile.mkdtemp()
|
|
|
|
>>> model.save(sc, path)
|
|
|
|
>>> sameModel = GaussianMixtureModel.load(sc, path)
|
|
|
|
>>> assert_equal(model.weights, sameModel.weights)
|
|
|
|
>>> mus, sigmas = list(
|
|
|
|
... zip(*[(g.mu, g.sigma) for g in model.gaussians]))
|
|
|
|
>>> sameMus, sameSigmas = list(
|
|
|
|
... zip(*[(g.mu, g.sigma) for g in sameModel.gaussians]))
|
|
|
|
>>> mus == sameMus
|
|
|
|
True
|
|
|
|
>>> sigmas == sameSigmas
|
|
|
|
True
|
|
|
|
>>> from shutil import rmtree
|
|
|
|
>>> try:
|
|
|
|
... rmtree(path)
|
|
|
|
... except OSError:
|
|
|
|
... pass
|
|
|
|
|
2015-05-15 03:18:39 -04:00
|
|
|
>>> data = array([-5.1971, -2.5359, -3.8220,
|
|
|
|
... -5.2211, -5.0602, 4.7118,
|
|
|
|
... 6.8989, 3.4592, 4.6322,
|
|
|
|
... 5.7048, 4.6567, 5.5026,
|
|
|
|
... 4.5605, 5.2043, 6.2734])
|
|
|
|
>>> clusterdata_2 = sc.parallelize(data.reshape(5,3))
|
2015-02-03 02:04:55 -05:00
|
|
|
>>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001,
|
2015-04-16 19:20:57 -04:00
|
|
|
... maxIterations=150, seed=10)
|
2015-02-03 02:04:55 -05:00
|
|
|
>>> labels = model.predict(clusterdata_2).collect()
|
2015-10-28 02:07:37 -04:00
|
|
|
>>> labels[0]==labels[1]
|
2015-02-03 02:04:55 -05:00
|
|
|
True
|
2015-10-28 02:07:37 -04:00
|
|
|
>>> labels[2]==labels[3]==labels[4]
|
2015-02-03 02:04:55 -05:00
|
|
|
True
|
2015-10-27 00:28:18 -04:00
|
|
|
|
|
|
|
.. versionadded:: 1.3.0
|
2015-02-03 02:04:55 -05:00
|
|
|
"""
|
|
|
|
|
2015-05-15 03:18:39 -04:00
|
|
|
@property
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.4.0')
|
2015-05-15 03:18:39 -04:00
|
|
|
def weights(self):
|
|
|
|
"""
|
|
|
|
Weights for each Gaussian distribution in the mixture, where weights[i] is
|
|
|
|
the weight for Gaussian i, and weights.sum == 1.
|
|
|
|
"""
|
2015-07-28 18:00:25 -04:00
|
|
|
return array(self.call("weights"))
|
2015-05-15 03:18:39 -04:00
|
|
|
|
|
|
|
@property
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.4.0')
|
2015-05-15 03:18:39 -04:00
|
|
|
def gaussians(self):
|
|
|
|
"""
|
|
|
|
Array of MultivariateGaussian where gaussians[i] represents
|
|
|
|
the Multivariate Gaussian (Normal) Distribution for Gaussian i.
|
|
|
|
"""
|
2015-07-28 18:00:25 -04:00
|
|
|
return [
|
|
|
|
MultivariateGaussian(gaussian[0], gaussian[1])
|
2015-11-10 19:42:28 -05:00
|
|
|
for gaussian in self.call("gaussians")]
|
2015-05-15 03:18:39 -04:00
|
|
|
|
|
|
|
@property
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.4.0')
|
2015-05-15 03:18:39 -04:00
|
|
|
def k(self):
|
|
|
|
"""Number of gaussians in mixture."""
|
2015-07-28 18:00:25 -04:00
|
|
|
return len(self.weights)
|
2015-02-03 02:04:55 -05:00
|
|
|
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.3.0')
|
2015-02-03 02:04:55 -05:00
|
|
|
def predict(self, x):
|
|
|
|
"""
|
2016-01-11 17:43:25 -05:00
|
|
|
Find the cluster to which the point 'x' or each point in RDD 'x'
|
|
|
|
has maximum membership in this model.
|
2015-02-03 02:04:55 -05:00
|
|
|
|
2016-01-11 17:43:25 -05:00
|
|
|
:param x: vector or RDD of vector represents data points.
|
|
|
|
:return: cluster label or RDD of cluster labels.
|
2015-02-03 02:04:55 -05:00
|
|
|
"""
|
|
|
|
if isinstance(x, RDD):
|
|
|
|
cluster_labels = self.predictSoft(x).map(lambda z: z.index(max(z)))
|
|
|
|
return cluster_labels
|
2015-05-15 13:43:18 -04:00
|
|
|
else:
|
2016-01-11 17:43:25 -05:00
|
|
|
z = self.predictSoft(x)
|
|
|
|
return z.argmax()
|
2015-02-03 02:04:55 -05:00
|
|
|
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.3.0')
|
2015-02-03 02:04:55 -05:00
|
|
|
def predictSoft(self, x):
|
|
|
|
"""
|
2016-01-11 17:43:25 -05:00
|
|
|
Find the membership of point 'x' or each point in RDD 'x' to all mixture components.
|
2015-02-03 02:04:55 -05:00
|
|
|
|
2016-01-11 17:43:25 -05:00
|
|
|
:param x: vector or RDD of vector represents data points.
|
|
|
|
:return: the membership value to all mixture components for vector 'x'
|
|
|
|
or each vector in RDD 'x'.
|
2015-02-03 02:04:55 -05:00
|
|
|
"""
|
|
|
|
if isinstance(x, RDD):
|
2015-07-28 18:00:25 -04:00
|
|
|
means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians])
|
2015-02-03 02:04:55 -05:00
|
|
|
membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector),
|
2015-07-28 18:00:25 -04:00
|
|
|
_convert_to_vector(self.weights), means, sigmas)
|
2015-04-16 19:20:57 -04:00
|
|
|
return membership_matrix.map(lambda x: pyarray.array('d', x))
|
2015-05-15 13:43:18 -04:00
|
|
|
else:
|
2016-01-11 17:43:25 -05:00
|
|
|
return self.call("predictSoft", _convert_to_vector(x)).toArray()
|
2015-02-03 02:04:55 -05:00
|
|
|
|
2015-07-28 18:00:25 -04:00
|
|
|
@classmethod
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-07-28 18:00:25 -04:00
|
|
|
def load(cls, sc, path):
|
|
|
|
"""Load the GaussianMixtureModel from disk.
|
|
|
|
|
|
|
|
:param sc: SparkContext
|
|
|
|
:param path: str, path to where the model is stored.
|
|
|
|
"""
|
|
|
|
model = cls._load_java(sc, path)
|
|
|
|
wrapper = sc._jvm.GaussianMixtureModelWrapper(model)
|
|
|
|
return cls(wrapper)
|
|
|
|
|
2015-02-03 02:04:55 -05:00
|
|
|
|
|
|
|
class GaussianMixture(object):
|
|
|
|
"""
|
2015-07-28 18:00:25 -04:00
|
|
|
.. note:: Experimental
|
|
|
|
|
2015-02-20 05:31:32 -05:00
|
|
|
Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.
|
2015-02-03 02:04:55 -05:00
|
|
|
|
|
|
|
:param data: RDD of data points
|
|
|
|
:param k: Number of components
|
|
|
|
:param convergenceTol: Threshold value to check the convergence criteria. Defaults to 1e-3
|
|
|
|
:param maxIterations: Number of iterations. Default to 100
|
|
|
|
:param seed: Random Seed
|
2015-05-15 03:18:39 -04:00
|
|
|
:param initialModel: GaussianMixtureModel for initializing learning
|
2015-10-27 00:28:18 -04:00
|
|
|
|
|
|
|
.. versionadded:: 1.3.0
|
2015-02-03 02:04:55 -05:00
|
|
|
"""
|
|
|
|
@classmethod
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.3.0')
|
2015-05-15 03:18:39 -04:00
|
|
|
def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
|
2015-02-03 02:04:55 -05:00
|
|
|
"""Train a Gaussian Mixture clustering model."""
|
2015-05-15 03:18:39 -04:00
|
|
|
initialModelWeights = None
|
|
|
|
initialModelMu = None
|
|
|
|
initialModelSigma = None
|
|
|
|
if initialModel is not None:
|
|
|
|
if initialModel.k != k:
|
|
|
|
raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s"
|
|
|
|
% (initialModel.k, k))
|
2016-01-07 13:32:56 -05:00
|
|
|
initialModelWeights = list(initialModel.weights)
|
2015-05-15 03:18:39 -04:00
|
|
|
initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
|
|
|
|
initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
|
2015-07-28 18:00:25 -04:00
|
|
|
java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector),
|
|
|
|
k, convergenceTol, maxIterations, seed,
|
|
|
|
initialModelWeights, initialModelMu, initialModelSigma)
|
|
|
|
return GaussianMixtureModel(java_model)
|
2015-02-03 02:04:55 -05:00
|
|
|
|
|
|
|
|
2015-06-29 01:38:04 -04:00
|
|
|
class PowerIterationClusteringModel(JavaModelWrapper, JavaSaveable, JavaLoader):
|
|
|
|
|
|
|
|
"""
|
|
|
|
.. note:: Experimental
|
|
|
|
|
|
|
|
Model produced by [[PowerIterationClustering]].
|
|
|
|
|
2015-07-06 19:15:12 -04:00
|
|
|
>>> data = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (1, 3, 1.0),
|
|
|
|
... (2, 3, 1.0), (3, 4, 0.1), (4, 5, 1.0), (4, 15, 1.0), (5, 6, 1.0),
|
|
|
|
... (6, 7, 1.0), (7, 8, 1.0), (8, 9, 1.0), (9, 10, 1.0), (10, 11, 1.0),
|
|
|
|
... (11, 12, 1.0), (12, 13, 1.0), (13, 14, 1.0), (14, 15, 1.0)]
|
2015-06-29 01:38:04 -04:00
|
|
|
>>> rdd = sc.parallelize(data, 2)
|
|
|
|
>>> model = PowerIterationClustering.train(rdd, 2, 100)
|
|
|
|
>>> model.k
|
|
|
|
2
|
2015-07-06 19:15:12 -04:00
|
|
|
>>> result = sorted(model.assignments().collect(), key=lambda x: x.id)
|
|
|
|
>>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster
|
|
|
|
True
|
|
|
|
>>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster
|
|
|
|
True
|
2015-06-29 01:38:04 -04:00
|
|
|
>>> import os, tempfile
|
|
|
|
>>> path = tempfile.mkdtemp()
|
|
|
|
>>> model.save(sc, path)
|
|
|
|
>>> sameModel = PowerIterationClusteringModel.load(sc, path)
|
|
|
|
>>> sameModel.k
|
|
|
|
2
|
2015-07-06 19:15:12 -04:00
|
|
|
>>> result = sorted(model.assignments().collect(), key=lambda x: x.id)
|
|
|
|
>>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster
|
|
|
|
True
|
|
|
|
>>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster
|
|
|
|
True
|
2015-06-29 01:38:04 -04:00
|
|
|
>>> from shutil import rmtree
|
|
|
|
>>> try:
|
|
|
|
... rmtree(path)
|
|
|
|
... except OSError:
|
|
|
|
... pass
|
2015-10-27 00:28:18 -04:00
|
|
|
|
|
|
|
.. versionadded:: 1.5.0
|
2015-06-29 01:38:04 -04:00
|
|
|
"""
|
|
|
|
|
|
|
|
@property
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-06-29 01:38:04 -04:00
|
|
|
def k(self):
|
|
|
|
"""
|
|
|
|
Returns the number of clusters.
|
|
|
|
"""
|
|
|
|
return self.call("k")
|
|
|
|
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-06-29 01:38:04 -04:00
|
|
|
def assignments(self):
|
|
|
|
"""
|
|
|
|
Returns the cluster assignments of this model.
|
|
|
|
"""
|
|
|
|
return self.call("getAssignments").map(
|
|
|
|
lambda x: (PowerIterationClustering.Assignment(*x)))
|
|
|
|
|
|
|
|
@classmethod
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-06-29 01:38:04 -04:00
|
|
|
def load(cls, sc, path):
|
2015-10-27 00:28:18 -04:00
|
|
|
"""
|
|
|
|
Load a model from the given path.
|
|
|
|
"""
|
2015-06-29 01:38:04 -04:00
|
|
|
model = cls._load_java(sc, path)
|
|
|
|
wrapper = sc._jvm.PowerIterationClusteringModelWrapper(model)
|
|
|
|
return PowerIterationClusteringModel(wrapper)
|
|
|
|
|
|
|
|
|
|
|
|
class PowerIterationClustering(object):
|
|
|
|
"""
|
|
|
|
.. note:: Experimental
|
|
|
|
|
|
|
|
Power Iteration Clustering (PIC), a scalable graph clustering algorithm
|
|
|
|
developed by [[http://www.icml2010.org/papers/387.pdf Lin and Cohen]].
|
|
|
|
From the abstract: PIC finds a very low-dimensional embedding of a
|
|
|
|
dataset using truncated power iteration on a normalized pair-wise
|
|
|
|
similarity matrix of the data.
|
2015-10-27 00:28:18 -04:00
|
|
|
|
|
|
|
.. versionadded:: 1.5.0
|
2015-06-29 01:38:04 -04:00
|
|
|
"""
|
|
|
|
|
|
|
|
@classmethod
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-06-29 01:38:04 -04:00
|
|
|
def train(cls, rdd, k, maxIterations=100, initMode="random"):
|
|
|
|
"""
|
|
|
|
:param rdd: an RDD of (i, j, s,,ij,,) tuples representing the
|
|
|
|
affinity matrix, which is the matrix A in the PIC paper.
|
|
|
|
The similarity s,,ij,, must be nonnegative.
|
|
|
|
This is a symmetric matrix and hence s,,ij,, = s,,ji,,.
|
|
|
|
For any (i, j) with nonzero similarity, there should be
|
|
|
|
either (i, j, s,,ij,,) or (j, i, s,,ji,,) in the input.
|
|
|
|
Tuples with i = j are ignored, because we assume
|
|
|
|
s,,ij,, = 0.0.
|
|
|
|
:param k: Number of clusters.
|
|
|
|
:param maxIterations: Maximum number of iterations of the
|
|
|
|
PIC algorithm.
|
|
|
|
:param initMode: Initialization mode.
|
|
|
|
"""
|
|
|
|
model = callMLlibFunc("trainPowerIterationClusteringModel",
|
|
|
|
rdd.map(_convert_to_vector), int(k), int(maxIterations), initMode)
|
|
|
|
return PowerIterationClusteringModel(model)
|
|
|
|
|
|
|
|
class Assignment(namedtuple("Assignment", ["id", "cluster"])):
|
|
|
|
"""
|
|
|
|
Represents an (id, cluster) tuple.
|
2015-10-27 00:28:18 -04:00
|
|
|
|
|
|
|
.. versionadded:: 1.5.0
|
2015-06-29 01:38:04 -04:00
|
|
|
"""
|
|
|
|
|
|
|
|
|
2015-06-19 15:23:15 -04:00
|
|
|
class StreamingKMeansModel(KMeansModel):
|
|
|
|
"""
|
|
|
|
.. note:: Experimental
|
|
|
|
|
|
|
|
Clustering model which can perform an online update of the centroids.
|
|
|
|
|
|
|
|
The update formula for each centroid is given by
|
|
|
|
|
|
|
|
* c_t+1 = ((c_t * n_t * a) + (x_t * m_t)) / (n_t + m_t)
|
|
|
|
* n_t+1 = n_t * a + m_t
|
|
|
|
|
|
|
|
where
|
|
|
|
|
|
|
|
* c_t: Centroid at the n_th iteration.
|
|
|
|
* n_t: Number of samples (or) weights associated with the centroid
|
|
|
|
at the n_th iteration.
|
|
|
|
* x_t: Centroid of the new data closest to c_t.
|
|
|
|
* m_t: Number of samples (or) weights of the new data closest to c_t
|
|
|
|
* c_t+1: New centroid.
|
|
|
|
* n_t+1: New number of weights.
|
|
|
|
* a: Decay Factor, which gives the forgetfulness.
|
|
|
|
|
|
|
|
Note that if a is set to 1, it is the weighted mean of the previous
|
|
|
|
and new data. If it set to zero, the old centroids are completely
|
|
|
|
forgotten.
|
|
|
|
|
|
|
|
:param clusterCenters: Initial cluster centers.
|
|
|
|
:param clusterWeights: List of weights assigned to each cluster.
|
|
|
|
|
|
|
|
>>> initCenters = [[0.0, 0.0], [1.0, 1.0]]
|
|
|
|
>>> initWeights = [1.0, 1.0]
|
|
|
|
>>> stkm = StreamingKMeansModel(initCenters, initWeights)
|
|
|
|
>>> data = sc.parallelize([[-0.1, -0.1], [0.1, 0.1],
|
|
|
|
... [0.9, 0.9], [1.1, 1.1]])
|
|
|
|
>>> stkm = stkm.update(data, 1.0, u"batches")
|
|
|
|
>>> stkm.centers
|
|
|
|
array([[ 0., 0.],
|
|
|
|
[ 1., 1.]])
|
|
|
|
>>> stkm.predict([-0.1, -0.1])
|
|
|
|
0
|
|
|
|
>>> stkm.predict([0.9, 0.9])
|
|
|
|
1
|
|
|
|
>>> stkm.clusterWeights
|
|
|
|
[3.0, 3.0]
|
|
|
|
>>> decayFactor = 0.0
|
|
|
|
>>> data = sc.parallelize([DenseVector([1.5, 1.5]), DenseVector([0.2, 0.2])])
|
|
|
|
>>> stkm = stkm.update(data, 0.0, u"batches")
|
|
|
|
>>> stkm.centers
|
|
|
|
array([[ 0.2, 0.2],
|
|
|
|
[ 1.5, 1.5]])
|
|
|
|
>>> stkm.clusterWeights
|
|
|
|
[1.0, 1.0]
|
|
|
|
>>> stkm.predict([0.2, 0.2])
|
|
|
|
0
|
|
|
|
>>> stkm.predict([1.5, 1.5])
|
|
|
|
1
|
2015-10-27 00:28:18 -04:00
|
|
|
|
|
|
|
.. versionadded:: 1.5.0
|
2015-06-19 15:23:15 -04:00
|
|
|
"""
|
|
|
|
def __init__(self, clusterCenters, clusterWeights):
|
|
|
|
super(StreamingKMeansModel, self).__init__(centers=clusterCenters)
|
|
|
|
self._clusterWeights = list(clusterWeights)
|
|
|
|
|
|
|
|
@property
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-06-19 15:23:15 -04:00
|
|
|
def clusterWeights(self):
|
|
|
|
"""Return the cluster weights."""
|
|
|
|
return self._clusterWeights
|
|
|
|
|
|
|
|
@ignore_unicode_prefix
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-06-19 15:23:15 -04:00
|
|
|
def update(self, data, decayFactor, timeUnit):
|
|
|
|
"""Update the centroids, according to data
|
|
|
|
|
|
|
|
:param data: Should be a RDD that represents the new data.
|
|
|
|
:param decayFactor: forgetfulness of the previous centroids.
|
|
|
|
:param timeUnit: Can be "batches" or "points". If points, then the
|
|
|
|
decay factor is raised to the power of number of new
|
|
|
|
points and if batches, it is used as it is.
|
|
|
|
"""
|
|
|
|
if not isinstance(data, RDD):
|
|
|
|
raise TypeError("Data should be of an RDD, got %s." % type(data))
|
|
|
|
data = data.map(_convert_to_vector)
|
|
|
|
decayFactor = float(decayFactor)
|
|
|
|
if timeUnit not in ["batches", "points"]:
|
|
|
|
raise ValueError(
|
|
|
|
"timeUnit should be 'batches' or 'points', got %s." % timeUnit)
|
|
|
|
vectorCenters = [_convert_to_vector(center) for center in self.centers]
|
|
|
|
updatedModel = callMLlibFunc(
|
|
|
|
"updateStreamingKMeansModel", vectorCenters, self._clusterWeights,
|
|
|
|
data, decayFactor, timeUnit)
|
|
|
|
self.centers = array(updatedModel[0])
|
|
|
|
self._clusterWeights = list(updatedModel[1])
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
|
|
class StreamingKMeans(object):
|
|
|
|
"""
|
|
|
|
.. note:: Experimental
|
|
|
|
|
|
|
|
Provides methods to set k, decayFactor, timeUnit to configure the
|
|
|
|
KMeans algorithm for fitting and predicting on incoming dstreams.
|
|
|
|
More details on how the centroids are updated are provided under the
|
|
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docs of StreamingKMeansModel.
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:param k: int, number of clusters
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:param decayFactor: float, forgetfulness of the previous centroids.
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:param timeUnit: can be "batches" or "points". If points, then the
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decayfactor is raised to the power of no. of new points.
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2015-10-27 00:28:18 -04:00
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.. versionadded:: 1.5.0
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2015-06-19 15:23:15 -04:00
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"""
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def __init__(self, k=2, decayFactor=1.0, timeUnit="batches"):
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self._k = k
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self._decayFactor = decayFactor
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if timeUnit not in ["batches", "points"]:
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raise ValueError(
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"timeUnit should be 'batches' or 'points', got %s." % timeUnit)
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self._timeUnit = timeUnit
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self._model = None
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2015-10-27 00:28:18 -04:00
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@since('1.5.0')
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2015-06-19 15:23:15 -04:00
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def latestModel(self):
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"""Return the latest model"""
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return self._model
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def _validate(self, dstream):
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if self._model is None:
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raise ValueError(
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"Initial centers should be set either by setInitialCenters "
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"or setRandomCenters.")
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if not isinstance(dstream, DStream):
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raise TypeError(
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"Expected dstream to be of type DStream, "
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"got type %s" % type(dstream))
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2015-10-27 00:28:18 -04:00
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@since('1.5.0')
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2015-06-19 15:23:15 -04:00
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def setK(self, k):
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"""Set number of clusters."""
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self._k = k
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return self
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2015-10-27 00:28:18 -04:00
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@since('1.5.0')
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2015-06-19 15:23:15 -04:00
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def setDecayFactor(self, decayFactor):
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"""Set decay factor."""
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self._decayFactor = decayFactor
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return self
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2015-10-27 00:28:18 -04:00
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@since('1.5.0')
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2015-06-19 15:23:15 -04:00
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def setHalfLife(self, halfLife, timeUnit):
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"""
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Set number of batches after which the centroids of that
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particular batch has half the weightage.
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"""
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self._timeUnit = timeUnit
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self._decayFactor = exp(log(0.5) / halfLife)
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return self
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2015-10-27 00:28:18 -04:00
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@since('1.5.0')
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2015-06-19 15:23:15 -04:00
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def setInitialCenters(self, centers, weights):
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"""
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Set initial centers. Should be set before calling trainOn.
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"""
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self._model = StreamingKMeansModel(centers, weights)
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return self
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2015-10-27 00:28:18 -04:00
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@since('1.5.0')
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2015-06-19 15:23:15 -04:00
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def setRandomCenters(self, dim, weight, seed):
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"""
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Set the initial centres to be random samples from
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a gaussian population with constant weights.
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"""
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rng = random.RandomState(seed)
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clusterCenters = rng.randn(self._k, dim)
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clusterWeights = tile(weight, self._k)
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self._model = StreamingKMeansModel(clusterCenters, clusterWeights)
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return self
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2015-10-27 00:28:18 -04:00
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@since('1.5.0')
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2015-06-19 15:23:15 -04:00
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def trainOn(self, dstream):
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"""Train the model on the incoming dstream."""
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self._validate(dstream)
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def update(rdd):
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self._model.update(rdd, self._decayFactor, self._timeUnit)
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dstream.foreachRDD(update)
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2015-10-27 00:28:18 -04:00
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@since('1.5.0')
|
2015-06-19 15:23:15 -04:00
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def predictOn(self, dstream):
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|
"""
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Make predictions on a dstream.
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|
Returns a transformed dstream object
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|
"""
|
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|
self._validate(dstream)
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return dstream.map(lambda x: self._model.predict(x))
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|
2015-10-27 00:28:18 -04:00
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|
@since('1.5.0')
|
2015-06-19 15:23:15 -04:00
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|
def predictOnValues(self, dstream):
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|
"""
|
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|
Make predictions on a keyed dstream.
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|
Returns a transformed dstream object.
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|
"""
|
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|
self._validate(dstream)
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|
return dstream.mapValues(lambda x: self._model.predict(x))
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|
2015-11-07 01:56:29 -05:00
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class LDAModel(JavaModelWrapper, JavaSaveable, Loader):
|
2015-07-15 02:27:42 -04:00
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|
""" A clustering model derived from the LDA method.
|
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|
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
|
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|
Terminology
|
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|
- "word" = "term": an element of the vocabulary
|
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|
|
- "token": instance of a term appearing in a document
|
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|
|
- "topic": multinomial distribution over words representing some concept
|
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|
|
References:
|
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|
|
- Original LDA paper (journal version):
|
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|
|
Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
|
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|
|
|
|
|
|
>>> from pyspark.mllib.linalg import Vectors
|
2015-07-22 20:22:12 -04:00
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|
>>> from numpy.testing import assert_almost_equal, assert_equal
|
2015-07-15 02:27:42 -04:00
|
|
|
>>> data = [
|
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|
|
... [1, Vectors.dense([0.0, 1.0])],
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|
|
... [2, SparseVector(2, {0: 1.0})],
|
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|
|
... ]
|
|
|
|
>>> rdd = sc.parallelize(data)
|
2015-11-07 01:56:29 -05:00
|
|
|
>>> model = LDA.train(rdd, k=2, seed=1)
|
2015-07-15 02:27:42 -04:00
|
|
|
>>> model.vocabSize()
|
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|
|
2
|
2015-11-07 01:56:29 -05:00
|
|
|
>>> model.describeTopics()
|
|
|
|
[([1, 0], [0.5..., 0.49...]), ([0, 1], [0.5..., 0.49...])]
|
|
|
|
>>> model.describeTopics(1)
|
|
|
|
[([1], [0.5...]), ([0], [0.5...])]
|
|
|
|
|
2015-07-15 02:27:42 -04:00
|
|
|
>>> topics = model.topicsMatrix()
|
|
|
|
>>> topics_expect = array([[0.5, 0.5], [0.5, 0.5]])
|
|
|
|
>>> assert_almost_equal(topics, topics_expect, 1)
|
2015-07-22 20:22:12 -04:00
|
|
|
|
|
|
|
>>> import os, tempfile
|
|
|
|
>>> from shutil import rmtree
|
|
|
|
>>> path = tempfile.mkdtemp()
|
|
|
|
>>> model.save(sc, path)
|
|
|
|
>>> sameModel = LDAModel.load(sc, path)
|
|
|
|
>>> assert_equal(sameModel.topicsMatrix(), model.topicsMatrix())
|
|
|
|
>>> sameModel.vocabSize() == model.vocabSize()
|
|
|
|
True
|
|
|
|
>>> try:
|
|
|
|
... rmtree(path)
|
|
|
|
... except OSError:
|
|
|
|
... pass
|
2015-10-27 00:28:18 -04:00
|
|
|
|
|
|
|
.. versionadded:: 1.5.0
|
2015-07-15 02:27:42 -04:00
|
|
|
"""
|
|
|
|
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-07-15 02:27:42 -04:00
|
|
|
def topicsMatrix(self):
|
|
|
|
"""Inferred topics, where each topic is represented by a distribution over terms."""
|
|
|
|
return self.call("topicsMatrix").toArray()
|
|
|
|
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-07-15 02:27:42 -04:00
|
|
|
def vocabSize(self):
|
|
|
|
"""Vocabulary size (number of terms or terms in the vocabulary)"""
|
|
|
|
return self.call("vocabSize")
|
|
|
|
|
2015-11-07 01:56:29 -05:00
|
|
|
@since('1.6.0')
|
|
|
|
def describeTopics(self, maxTermsPerTopic=None):
|
|
|
|
"""Return the topics described by weighted terms.
|
2015-07-22 20:22:12 -04:00
|
|
|
|
2015-11-07 01:56:29 -05:00
|
|
|
WARNING: If vocabSize and k are large, this can return a large object!
|
2015-11-09 19:25:29 -05:00
|
|
|
|
|
|
|
:param maxTermsPerTopic: Maximum number of terms to collect for each topic.
|
|
|
|
(default: vocabulary size)
|
|
|
|
:return: Array over topics. Each topic is represented as a pair of matching arrays:
|
|
|
|
(term indices, term weights in topic).
|
|
|
|
Each topic's terms are sorted in order of decreasing weight.
|
2015-07-22 20:22:12 -04:00
|
|
|
"""
|
2015-11-07 01:56:29 -05:00
|
|
|
if maxTermsPerTopic is None:
|
|
|
|
topics = self.call("describeTopics")
|
|
|
|
else:
|
|
|
|
topics = self.call("describeTopics", maxTermsPerTopic)
|
|
|
|
return topics
|
2015-07-22 20:22:12 -04:00
|
|
|
|
|
|
|
@classmethod
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-07-22 20:22:12 -04:00
|
|
|
def load(cls, sc, path):
|
|
|
|
"""Load the LDAModel from disk.
|
|
|
|
|
|
|
|
:param sc: SparkContext
|
|
|
|
:param path: str, path to where the model is stored.
|
|
|
|
"""
|
|
|
|
if not isinstance(sc, SparkContext):
|
|
|
|
raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
|
|
|
|
if not isinstance(path, basestring):
|
|
|
|
raise TypeError("path should be a basestring, got type %s" % type(path))
|
2015-11-07 01:56:29 -05:00
|
|
|
model = callMLlibFunc("loadLDAModel", sc, path)
|
|
|
|
return LDAModel(model)
|
2015-07-22 20:22:12 -04:00
|
|
|
|
2015-07-15 02:27:42 -04:00
|
|
|
|
|
|
|
class LDA(object):
|
2015-10-27 00:28:18 -04:00
|
|
|
"""
|
|
|
|
.. versionadded:: 1.5.0
|
|
|
|
"""
|
2015-07-15 02:27:42 -04:00
|
|
|
|
|
|
|
@classmethod
|
2015-10-27 00:28:18 -04:00
|
|
|
@since('1.5.0')
|
2015-07-15 02:27:42 -04:00
|
|
|
def train(cls, rdd, k=10, maxIterations=20, docConcentration=-1.0,
|
|
|
|
topicConcentration=-1.0, seed=None, checkpointInterval=10, optimizer="em"):
|
|
|
|
"""Train a LDA model.
|
|
|
|
|
|
|
|
:param rdd: RDD of data points
|
|
|
|
:param k: Number of clusters you want
|
|
|
|
:param maxIterations: Number of iterations. Default to 20
|
|
|
|
:param docConcentration: Concentration parameter (commonly named "alpha")
|
|
|
|
for the prior placed on documents' distributions over topics ("theta").
|
|
|
|
:param topicConcentration: Concentration parameter (commonly named "beta" or "eta")
|
|
|
|
for the prior placed on topics' distributions over terms.
|
|
|
|
:param seed: Random Seed
|
|
|
|
:param checkpointInterval: Period (in iterations) between checkpoints.
|
|
|
|
:param optimizer: LDAOptimizer used to perform the actual calculation.
|
|
|
|
Currently "em", "online" are supported. Default to "em".
|
|
|
|
"""
|
|
|
|
model = callMLlibFunc("trainLDAModel", rdd, k, maxIterations,
|
|
|
|
docConcentration, topicConcentration, seed,
|
|
|
|
checkpointInterval, optimizer)
|
|
|
|
return LDAModel(model)
|
|
|
|
|
|
|
|
|
2013-12-25 00:08:05 -05:00
|
|
|
def _test():
|
|
|
|
import doctest
|
2015-06-29 01:38:04 -04:00
|
|
|
import pyspark.mllib.clustering
|
|
|
|
globs = pyspark.mllib.clustering.__dict__.copy()
|
2013-12-25 00:08:05 -05:00
|
|
|
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
|
2014-05-25 20:15:01 -04:00
|
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
2013-12-25 00:08:05 -05:00
|
|
|
globs['sc'].stop()
|
|
|
|
if failure_count:
|
|
|
|
exit(-1)
|
|
|
|
|
[WIP] SPARK-1430: Support sparse data in Python MLlib
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes #341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
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|
|
|
2013-12-25 00:08:05 -05:00
|
|
|
if __name__ == "__main__":
|
|
|
|
_test()
|