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[SPARK-12631][PYSPARK][DOC] PySpark clustering parameter desc to consistent format

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Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent.  This is for the clustering module.

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #10610 from BryanCutler/param-desc-consistent-cluster-SPARK-12631.
This commit is contained in:
Bryan Cutler 2016-02-02 10:50:22 -08:00 committed by Xiangrui Meng
parent b93830126c
commit cba1d6b659
6 changed files with 228 additions and 103 deletions
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12
mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
View file

@ -45,10 +45,10 @@ import org.apache.spark.util.Utils
* This is due to high-dimensional data (a) making it difficult to cluster at all (based
* on statistical/theoretical arguments) and (b) numerical issues with Gaussian distributions.
*
* @param k The number of independent Gaussians in the mixture model
* @param convergenceTol The maximum change in log-likelihood at which convergence
* is considered to have occurred.
* @param maxIterations The maximum number of iterations to perform
* @param k Number of independent Gaussians in the mixture model.
* @param convergenceTol Maximum change in log-likelihood at which convergence
* is considered to have occurred.
* @param maxIterations Maximum number of iterations allowed.
*/
@Since("1.3.0")
class GaussianMixture private (
@ -108,7 +108,7 @@ class GaussianMixture private (
def getK: Int = k
/**
* Set the maximum number of iterations to run. Default: 100
* Set the maximum number of iterations allowed. Default: 100
*/
@Since("1.3.0")
def setMaxIterations(maxIterations: Int): this.type = {
@ -117,7 +117,7 @@ class GaussianMixture private (
}
/**
* Return the maximum number of iterations to run
* Return the maximum number of iterations allowed
*/
@Since("1.3.0")
def getMaxIterations: Int = maxIterations

31
mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
View file

@ -70,13 +70,13 @@ class KMeans private (
}
/**
* Maximum number of iterations to run.
* Maximum number of iterations allowed.
*/
@Since("1.4.0")
def getMaxIterations: Int = maxIterations
/**
* Set maximum number of iterations to run. Default: 20.
* Set maximum number of iterations allowed. Default: 20.
*/
@Since("0.8.0")
def setMaxIterations(maxIterations: Int): this.type = {
@ -482,12 +482,15 @@ object KMeans {
/**
* Trains a k-means model using the given set of parameters.
*
* @param data training points stored as `RDD[Vector]`
* @param k number of clusters
* @param maxIterations max number of iterations
* @param runs number of parallel runs, defaults to 1. The best model is returned.
* @param initializationMode initialization model, either "random" or "k-means||" (default).
* @param seed random seed value for cluster initialization
* @param data Training points as an `RDD` of `Vector` types.
* @param k Number of clusters to create.
* @param maxIterations Maximum number of iterations allowed.
* @param runs Number of runs to execute in parallel. The best model according to the cost
* function will be returned. (default: 1)
* @param initializationMode The initialization algorithm. This can either be "random" or
* "k-means||". (default: "k-means||")
* @param seed Random seed for cluster initialization. Default is to generate seed based
* on system time.
*/
@Since("1.3.0")
def train(
@ -508,11 +511,13 @@ object KMeans {
/**
* Trains a k-means model using the given set of parameters.
*
* @param data training points stored as `RDD[Vector]`
* @param k number of clusters
* @param maxIterations max number of iterations
* @param runs number of parallel runs, defaults to 1. The best model is returned.
* @param initializationMode initialization model, either "random" or "k-means||" (default).
* @param data Training points as an `RDD` of `Vector` types.
* @param k Number of clusters to create.
* @param maxIterations Maximum number of iterations allowed.
* @param runs Number of runs to execute in parallel. The best model according to the cost
* function will be returned. (default: 1)
* @param initializationMode The initialization algorithm. This can either be "random" or
* "k-means||". (default: "k-means||")
*/
@Since("0.8.0")
def train(

13
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
View file

@ -61,14 +61,13 @@ class LDA private (
ldaOptimizer = new EMLDAOptimizer)
/**
* Number of topics to infer. I.e., the number of soft cluster centers.
*
* Number of topics to infer, i.e., the number of soft cluster centers.
*/
@Since("1.3.0")
def getK: Int = k
/**
* Number of topics to infer. I.e., the number of soft cluster centers.
* Set the number of topics to infer, i.e., the number of soft cluster centers.
* (default = 10)
*/
@Since("1.3.0")
@ -222,13 +221,13 @@ class LDA private (
def setBeta(beta: Double): this.type = setTopicConcentration(beta)
/**
* Maximum number of iterations for learning.
* Maximum number of iterations allowed.
*/
@Since("1.3.0")
def getMaxIterations: Int = maxIterations
/**
* Maximum number of iterations for learning.
* Set the maximum number of iterations allowed.
* (default = 20)
*/
@Since("1.3.0")
@ -238,13 +237,13 @@ class LDA private (
}
/**
* Random seed
* Random seed for cluster initialization.
*/
@Since("1.3.0")
def getSeed: Long = seed
/**
* Random seed
* Set the random seed for cluster initialization.
*/
@Since("1.3.0")
def setSeed(seed: Long): this.type = {

4
mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
View file

@ -111,7 +111,9 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
*
* @param k Number of clusters.
* @param maxIterations Maximum number of iterations of the PIC algorithm.
* @param initMode Initialization mode.
* @param initMode Set the initialization mode. This can be either "random" to use a random vector
* as vertex properties, or "degree" to use normalized sum similarities.
* Default: random.
*
* @see [[http://en.wikipedia.org/wiki/Spectral_clustering Spectral clustering (Wikipedia)]]
*/

6
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
View file

@ -183,7 +183,7 @@ class StreamingKMeans @Since("1.2.0") (
}
/**
* Set the decay factor directly (for forgetful algorithms).
* Set the forgetfulness of the previous centroids.
*/
@Since("1.2.0")
def setDecayFactor(a: Double): this.type = {
@ -192,7 +192,9 @@ class StreamingKMeans @Since("1.2.0") (
}
/**
* Set the half life and time unit ("batches" or "points") for forgetful algorithms.
* Set the half life and time unit ("batches" or "points"). If points, then the decay factor
* is raised to the power of number of new points and if batches, then decay factor will be
* used as is.
*/
@Since("1.2.0")
def setHalfLife(halfLife: Double, timeUnit: String): this.type = {

265
python/pyspark/mllib/clustering.py
View file

@ -88,8 +88,11 @@ class BisectingKMeansModel(JavaModelWrapper):
Find the cluster that each of the points belongs to in this
model.
:param x: the point (or RDD of points) to determine
compute the clusters for.
:param x:
A data point (or RDD of points) to determine cluster index.
:return:
Predicted cluster index or an RDD of predicted cluster indices
if the input is an RDD.
"""
if isinstance(x, RDD):
vecs = x.map(_convert_to_vector)
@ -105,7 +108,8 @@ class BisectingKMeansModel(JavaModelWrapper):
points to their nearest center) for this model on the given
data. If provided with an RDD of points returns the sum.
:param point: the point or RDD of points to compute the cost(s).
:param point:
A data point (or RDD of points) to compute the cost(s).
"""
if isinstance(x, RDD):
vecs = x.map(_convert_to_vector)
@ -143,17 +147,23 @@ class BisectingKMeans(object):
"""
Runs the bisecting k-means algorithm return the model.
:param rdd: input RDD to be trained on
:param k: The desired number of leaf clusters (default: 4).
The actual number could be smaller if there are no divisible
leaf clusters.
:param maxIterations: the max number of k-means iterations to
split clusters (default: 20)
:param minDivisibleClusterSize: the minimum number of points
(if >= 1.0) or the minimum proportion of points (if < 1.0)
of a divisible cluster (default: 1)
:param seed: a random seed (default: -1888008604 from
classOf[BisectingKMeans].getName.##)
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
The desired number of leaf clusters. The actual number could
be smaller if there are no divisible leaf clusters.
(default: 4)
:param maxIterations:
Maximum number of iterations allowed to split clusters.
(default: 20)
:param minDivisibleClusterSize:
Minimum number of points (if >= 1.0) or the minimum proportion
of points (if < 1.0) of a divisible cluster.
(default: 1)
:param seed:
Random seed value for cluster initialization.
(default: -1888008604 from classOf[BisectingKMeans].getName.##)
"""
java_model = callMLlibFunc(
"trainBisectingKMeans", rdd.map(_convert_to_vector),
@ -239,8 +249,11 @@ class KMeansModel(Saveable, Loader):
Find the cluster that each of the points belongs to in this
model.
:param x: the point (or RDD of points) to determine
compute the clusters for.
:param x:
A data point (or RDD of points) to determine cluster index.
:return:
Predicted cluster index or an RDD of predicted cluster indices
if the input is an RDD.
"""
best = 0
best_distance = float("inf")
@ -262,7 +275,8 @@ class KMeansModel(Saveable, Loader):
their nearest center) for this model on the given
data.
:param point: the RDD of points to compute the cost on.
:param rdd:
The RDD of points to compute the cost on.
"""
cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector),
[_convert_to_vector(c) for c in self.centers])
@ -296,7 +310,44 @@ class KMeans(object):
@since('0.9.0')
def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
seed=None, initializationSteps=5, epsilon=1e-4, initialModel=None):
"""Train a k-means clustering model."""
"""
Train a k-means clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of clusters to create.
:param maxIterations:
Maximum number of iterations allowed.
(default: 100)
:param runs:
Number of runs to execute in parallel. The best model according
to the cost function will be returned (deprecated in 1.6.0).
(default: 1)
:param initializationMode:
The initialization algorithm. This can be either "random" or
"k-means||".
(default: "k-means||")
:param seed:
Random seed value for cluster initialization. Set as None to
generate seed based on system time.
(default: None)
:param initializationSteps:
Number of steps for the k-means|| initialization mode.
This is an advanced setting -- the default of 5 is almost
always enough.
(default: 5)
:param epsilon:
Distance threshold within which a center will be considered to
have converged. If all centers move less than this Euclidean
distance, iterations are stopped.
(default: 1e-4)
:param initialModel:
Initial cluster centers can be provided as a KMeansModel object
rather than using the random or k-means|| initializationModel.
(default: None)
"""
if runs != 1:
warnings.warn(
"Support for runs is deprecated in 1.6.0. This param will have no effect in 2.0.0.")
@ -415,8 +466,11 @@ class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader):
Find the cluster to which the point 'x' or each point in RDD 'x'
has maximum membership in this model.
:param x: vector or RDD of vector represents data points.
:return: cluster label or RDD of cluster labels.
:param x:
A feature vector or an RDD of vectors representing data points.
:return:
Predicted cluster label or an RDD of predicted cluster labels
if the input is an RDD.
"""
if isinstance(x, RDD):
cluster_labels = self.predictSoft(x).map(lambda z: z.index(max(z)))
@ -430,9 +484,11 @@ class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader):
"""
Find the membership of point 'x' or each point in RDD 'x' to all mixture components.
: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'.
:param x:
A feature vector or an RDD of vectors representing data points.
:return:
The membership value to all mixture components for vector 'x'
or each vector in RDD 'x'.
"""
if isinstance(x, RDD):
means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians])
@ -447,8 +503,10 @@ class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader):
def load(cls, sc, path):
"""Load the GaussianMixtureModel from disk.
:param sc: SparkContext
:param path: str, path to where the model is stored.
:param sc:
SparkContext.
:param path:
Path to where the model is stored.
"""
model = cls._load_java(sc, path)
wrapper = sc._jvm.GaussianMixtureModelWrapper(model)
@ -461,19 +519,35 @@ class GaussianMixture(object):
Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.
: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
:param initialModel: GaussianMixtureModel for initializing learning
.. versionadded:: 1.3.0
"""
@classmethod
@since('1.3.0')
def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
"""Train a Gaussian Mixture clustering model."""
"""
Train a Gaussian Mixture clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of independent Gaussians in the mixture model.
:param convergenceTol:
Maximum change in log-likelihood at which convergence is
considered to have occurred.
(default: 1e-3)
:param maxIterations:
Maximum number of iterations allowed.
(default: 100)
:param seed:
Random seed for initial Gaussian distribution. Set as None to
generate seed based on system time.
(default: None)
:param initialModel:
Initial GMM starting point, bypassing the random
initialization.
(default: None)
"""
initialModelWeights = None
initialModelMu = None
initialModelSigma = None
@ -574,18 +648,24 @@ class PowerIterationClustering(object):
@since('1.5.0')
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.
:param rdd:
An RDD of (i, j, s\ :sub:`ij`\) tuples representing the
affinity matrix, which is the matrix A in the PIC paper. The
similarity s\ :sub:`ij`\ must be nonnegative. This is a symmetric
matrix and hence s\ :sub:`ij`\ = s\ :sub:`ji`\ For any (i, j) with
nonzero similarity, there should be either (i, j, s\ :sub:`ij`\) or
(j, i, s\ :sub:`ji`\) in the input. Tuples with i = j are ignored,
because it is assumed s\ :sub:`ij`\ = 0.0.
:param k:
Number of clusters.
:param maxIterations:
Maximum number of iterations of the PIC algorithm.
(default: 100)
:param initMode:
Initialization mode. This can be either "random" to use
a random vector as vertex properties, or "degree" to use
normalized sum similarities.
(default: "random")
"""
model = callMLlibFunc("trainPowerIterationClusteringModel",
rdd.map(_convert_to_vector), int(k), int(maxIterations), initMode)
@ -625,8 +705,10 @@ class StreamingKMeansModel(KMeansModel):
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.
: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]
@ -673,11 +755,14 @@ class StreamingKMeansModel(KMeansModel):
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.
:param data:
RDD with new data for the model update.
: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,
then decay factor will be used as is.
"""
if not isinstance(data, RDD):
raise TypeError("Data should be of an RDD, got %s." % type(data))
@ -704,10 +789,17 @@ class StreamingKMeans(object):
More details on how the centroids are updated are provided under the
docs of StreamingKMeansModel.
:param k: int, number of clusters
:param decayFactor: float, forgetfulness of the previous centroids.
:param timeUnit: can be "batches" or "points". If points, then the
decayfactor is raised to the power of no. of new points.
:param k:
Number of clusters.
(default: 2)
:param decayFactor:
Forgetfulness of the previous centroids.
(default: 1.0)
: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, then
decay factor will be used as is.
(default: "batches")
.. versionadded:: 1.5.0
"""
@ -870,11 +962,13 @@ class LDAModel(JavaModelWrapper, JavaSaveable, Loader):
WARNING: If vocabSize and k are large, this can return a large object!
: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.
: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.
"""
if maxTermsPerTopic is None:
topics = self.call("describeTopics")
@ -887,8 +981,10 @@ class LDAModel(JavaModelWrapper, JavaSaveable, Loader):
def load(cls, sc, path):
"""Load the LDAModel from disk.
:param sc: SparkContext
:param path: str, path to where the model is stored.
:param sc:
SparkContext.
:param path:
Path to where the model is stored.
"""
if not isinstance(sc, SparkContext):
raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
@ -909,17 +1005,38 @@ class LDA(object):
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".
:param rdd:
RDD of documents, which are tuples of document IDs and term
(word) count vectors. The term count vectors are "bags of
words" with a fixed-size vocabulary (where the vocabulary size
is the length of the vector). Document IDs must be unique
and >= 0.
:param k:
Number of topics to infer, i.e., the number of soft cluster
centers.
(default: 10)
:param maxIterations:
Maximum number of iterations allowed.
(default: 20)
:param docConcentration:
Concentration parameter (commonly named "alpha") for the prior
placed on documents' distributions over topics ("theta").
(default: -1.0)
:param topicConcentration:
Concentration parameter (commonly named "beta" or "eta") for
the prior placed on topics' distributions over terms.
(default: -1.0)
:param seed:
Random seed for cluster initialization. Set as None to generate
seed based on system time.
(default: None)
:param checkpointInterval:
Period (in iterations) between checkpoints.
(default: 10)
:param optimizer:
LDAOptimizer used to perform the actual calculation. Currently
"em", "online" are supported.
(default: "em")
"""
model = callMLlibFunc("trainLDAModel", rdd, k, maxIterations,
docConcentration, topicConcentration, seed,