Repository: freeman-lab/spark-ml-streaming Branch: master Commit: 92ada1dbfdc2 Files: 17 Total size: 28.1 KB Directory structure: gitextract_2wsty9xh/ ├── .gitignore ├── LICENSE ├── README.md ├── python/ │ ├── MANIFEST.in │ ├── bin/ │ │ └── streaming-kmeans │ ├── mlstreaming/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── kmeans.py │ │ ├── lib/ │ │ │ ├── __init__.py │ │ │ └── spark-ml-streaming_2.10-0.1.0.jar │ │ └── util.py │ ├── requirements.txt │ └── setup.py └── scala/ ├── build.sbt ├── project/ │ └── build.properties └── src/ └── main/ └── scala/ └── spark/ └── mlstreaming/ ├── KMeans.scala └── Utils.scala ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ scala/target scala/project/target scala/project/project scala/.idea *.pyc .idea dist spark_ml_streaming.egg-info ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2015 Jeremy Freeman Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ [![Join the chat at https://gitter.im/freeman-lab/spark-ml-streaming](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/freeman-lab/spark-ml-streaming?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) ## Visualize streaming machine learning in Spark ![two-dimensional-demo](https://github.com/freeman-lab/spark-streaming-demos/blob/master/animations/databricks-blog-post/4-five-clusters.gif) ![one-dimensional-demo](https://github.com/freeman-lab/spark-streaming-demos/blob/master/animations/databricks-blog-post/6-half-life-5p0.gif) ### About This Python app generates data, analyzes it in Spark Streaming, and visualizes the results with Lightning. The analyses use streaming machine learning algorithms included with Spark as of version 1.2. The demos are designed for local use, but the same algorithms can run at scale on a cluster with millions of records. ### How to use To run these demos, you need: * A working installation of [Spark](http://spark.apache.org/downloads.html) * A running [Lightning](http://lightning-viz.org) server * An installation of Python with standard scientific computing libraries (NumPy, SciPy, ScikitLearn) With those three things in place, install using: pip install spark-ml-streaming Then set `SPARK_HOME` to your Spark installation, and start an executable: streaming-kmeans -l Where `lightning_host` is the address of your Lightning server. After it starts, your browser will open, and you should see data appear shortly. Try running with different settings, for example, to run a 1-d version with 4 clusters and a half-life of 10 points: streaming-kmeans -p -l -nc 4 -nd 1 -hl 10 -tu points Where `temporary_path` is where data will be written / read, if not specified the current tmp directory will be used (See Python [tempfile.gettempdir()](https://docs.python.org/2/library/tempfile.html)) 2D data will make a scatter plot and 1D data will make a line plot. You can set this with -nd. To see all options type: streaming-kmeans -h ### Build The demo relies on a Scala package included pre-built inside `python/mlstreaming/lib`. To rebuild it, use sbt: cd scala sbt package ================================================ FILE: python/MANIFEST.in ================================================ include *.txt include mlstreaming/lib/*.jar ================================================ FILE: python/bin/streaming-kmeans ================================================ #!/usr/bin/env python import time import subprocess import argparse import os import tempfile from lightning import Lightning from mlstreaming import StreamingDemo from mlstreaming.util import findspark, findjar, baseargs def main(): # parse arguments parser = argparse.ArgumentParser(description='Streaming KMeans demo.') parser = baseargs(parser) parser.add_argument('-nc', '--ncenters', type=int, default=3, required=False, help='Number of cluster centers') parser.add_argument('-nd', '--ndims', type=int, default=2, required=False, help='Number of dimensions') parser.add_argument('-rs', '--randomseed', type=int, default=None, required=False, help='Random seed') parser.add_argument('-sd', '--std', type=float, default=0.3, required=False, help='Standard deviation of points') parser.add_argument('-up', '--update', type=str, choices=('jump', 'drift', 'none'), default='drift', required=False, help='Update behavior') parser.add_argument('-a', '--autoopen', type=bool, choices=(True, False), default=True, required=False, help='Whether to automatically open Lightning session on a browser') args = parser.parse_args() # basic setup sparkhome = findspark() jar = findjar() # set up lightning if args.lightning: lgn = Lightning(args.lightning) lgn.create_session('streaming-kmeans') if (args.autoopen): lgn.session.open() else: lgn = None # set temp path path = args.path if not path or path == '': path = tempfile.gettempdir() tmpdir = os.path.join(path, 'streamkmeans') # setup the demo s = StreamingDemo.make('kmeans', npoints=args.npoints, nbatches=args.nbatches) s.setup(tmpdir, overwrite=args.overwrite) s.params(ncenters=args.ncenters, ndims=args.ndims, std=args.std, seed=args.randomseed, update=args.update) # setup the spark job sparkSubmit = sparkhome + "/bin/spark-submit" sparkArgs = ["--class", "spark.mlstreaming.KMeans", jar] demoArgs = [s.datain, s.dataout, str(args.batchtime), str(args.ncenters), str(args.ndims), str(args.halflife), str(args.timeunit)] cmd = [sparkSubmit] + sparkArgs + demoArgs try: # start the spark job p = subprocess.Popen(cmd) # wait for spark streaming to start up time.sleep(4) # start the demo s.run(lgn) finally: p.kill() if __name__ == "__main__": main() ================================================ FILE: python/mlstreaming/__init__.py ================================================ from mlstreaming.base import StreamingDemo __version__ = '0.1.0' ================================================ FILE: python/mlstreaming/base.py ================================================ import os import shutil class StreamingDemo(object): def __init__(self, npoints=50, nbatches=5): self.npoints = npoints self.nbatches = nbatches def params(*args, **kwargs): """ Get analysis-specific parameters """ raise NotImplementedError def run(*args, **kwargs): """ Run the streaming demo """ raise NotImplementedError @staticmethod def make(demoname, *args, **kwargs): """ Create a streaming demo """ from mlstreaming.kmeans import StreamingKMeans DEMOS = { 'kmeans': StreamingKMeans } return DEMOS[demoname](*args, **kwargs) def setup(self, path, overwrite=False): """ Setup paths for a streaming demo where temporary data will be read / written""" if os.path.isdir(path): if overwrite: shutil.rmtree(path) os.mkdir(path) else: raise Exception('Base directory %s already exists and overwrite is set to False' % path) else: os.mkdir(path) datain = os.path.join(path, 'input') dataout = os.path.join(path, 'output') if os.path.isdir(datain): shutil.rmtree(datain) if os.path.isdir(dataout): shutil.rmtree(dataout) os.mkdir(datain) os.mkdir(dataout) self.datain = datain self.dataout = dataout return self def writepoints(self, pts, i): """ Write data points in a form that can be read by MLlib's vector parser """ f = file(os.path.join(self.datain, 'batch%g.txt' % i), 'w') s = map(lambda p: ",".join(str(p).split()).replace('[,', '[').replace(',]', ']'), pts) tmp = "\n".join(s) f.write(tmp) f.close() ================================================ FILE: python/mlstreaming/kmeans.py ================================================ import time from numpy import asarray, array, vstack, hstack, size, random, argsort, ones, argmin, sin, cos, pi from scipy.spatial.distance import cdist from sklearn.datasets import make_blobs from mlstreaming.base import StreamingDemo from mlstreaming.util import loadrecent class StreamingKMeans(StreamingDemo): def params(self, ncenters=3, ndims=2, std=0.2, seed=None, update='drift', interval=15, transition=None): """ Set up parameters for a streaming kmeans algorithm demo. Parameters ---------- ncenters : int, or array-like (ncenters, ndims) Number of clusters as an integer, or an array of starting cluster centers. If given as an integer, cluster centers will be determined randomly. ndims : int Number of dimensions std : scalar Cluster standard deviation """ random.seed(seed) if size(ncenters) == 1: centers = random.randn(ncenters, ndims) * 2 else: centers = asarray(ncenters) ncenters = centers.shape[0] self.centers = centers self.ncenters = ncenters self.ndims = ndims self.std = std self.update = update self.interval = interval self.transition = transition return self def run(self, lgn=None): viz = None closestpoint = lambda centers, p: argmin(cdist(centers, array([p]))) centers = self.centers modeltime = 0 model = [] # loop over batches for i in range(1, self.nbatches): print('generating batch %g' % i) # drift means the points will slowly drift by adding noise to the position if self.update == 'drift': centers += random.randn(centers.shape[0], centers.shape[1]) * 0.15 # jump means every 15 batches the points will shift if self.update == 'jump': if i % self.interval == 0: if self.transition: centers = asarray(self.transition) else: base = random.rand(self.ncenters * self.ndims) * 1 + 2 delta = asarray([-d if random.rand() > 0.5 else d for d in base])\ .reshape(self.ncenters, self.ndims) centers = centers + delta # generate the points by sampling from the clusters and write to disk npoints = self.npoints pts, labels = make_blobs(npoints, self.ndims, centers, cluster_std=self.std) self.writepoints(pts, i) time.sleep(1) # get the latest model (after waiting) model, modeltime = loadrecent(self.dataout + '/*-model.txt', modeltime, model) # plot an update (if we got a valid model) if len(model) == self.ncenters: clrs = labels order = argsort(labels) clrs = clrs[order] pts = pts[order] s = ones(self.npoints) * 10 if self.ndims == 1: pts = vstack((pts, model[:,None])) else: pts = vstack((pts, model)) clrs = hstack((clrs, ones(self.ncenters) * 5)) s = hstack((s, ones(self.ncenters) * 10)) # wait a few iterations before plotting if (lgn is not None) & (i > 5): # scatter plot for two dimensions if self.ndims == 2: if viz is None: viz = lgn.scatterstreaming(pts[:, 0], pts[:, 1], label=clrs, size=s) else: viz.append(pts[:, 0], pts[:, 1], label=clrs, size=s) # line plot for one dimension elif self.ndims == 1: if viz is None: viz = lgn.linestreaming(pts, label=clrs, size=s/2) else: viz.append(pts, label=clrs, size=s/2) else: raise Exception('Plotting only supported with 1 or 2 dimensions') ================================================ FILE: python/mlstreaming/lib/__init__.py ================================================ ================================================ FILE: python/mlstreaming/util.py ================================================ import os import glob from numpy import loadtxt def findspark(): sparkhome = os.getenv("SPARK_HOME") if sparkhome is None: raise Exception("The environment variable SPARK_HOME must be set to the Spark installation directory") else: return sparkhome def findjar(): calldir = os.path.dirname(os.path.realpath(__file__)) jardir = os.path.join(calldir, 'lib', '*.jar') jar = glob.glob(jardir) if len(jar) == 0 or not os.path.exists(jar[0]): raise Exception("Cannot find jar, looking at %s" % jar) else: return jar[0] def loadrecent(filename, oldtime, oldoutput): try: fname = max(glob.iglob(filename), key=os.path.getctime) except: print('No file found') return [], oldtime newtime = os.path.getctime(fname) if not (newtime > oldtime): print('File is not new') return oldoutput, oldtime try: f = open(fname) if os.fstat(f.fileno()).st_size == 0: print('File is empty') return [], oldtime except: print('Cannot load file') return [], oldtime prediction = loadtxt(fname, delimiter=',') return prediction, newtime def baseargs(parser): parser.add_argument('-p', '--path', type=str, default=None, required=False, help='Temporary location to store outputs') parser.add_argument('-o', '--overwrite', type=bool, choices=(True, False), default=True, required=False, help='Whether to overwrite the temporary location if it already exists') parser.add_argument('-lgn', '--lightning', type=str, default=None, required=False, help='Lightning server for visualization') parser.add_argument('-bt', '--batchtime', type=int, default=1, required=False, help='Frequency of updates') parser.add_argument('-np', '--npoints', type=int, default=50, required=False, help='Number of data points per batch') parser.add_argument('-nb', '--nbatches', type=int, default=40, required=False, help='Number of batches') parser.add_argument('-hl', '--halflife', type=float, default=1, required=False, help='Half life for streaming updates') parser.add_argument('-tu', '--timeunit', type=str, default='batches', choices=('batches', 'points'), required=False, help='Time unit for streaming updates') return parser ================================================ FILE: python/requirements.txt ================================================ argparse numpy scipy scikit-learn lightning-python ================================================ FILE: python/setup.py ================================================ #!/usr/bin/env python from setuptools import setup import mlstreaming setup( name='spark-ml-streaming', version=str(mlstreaming.__version__), description='A Python library for visualizing streaming machine learning in Spark', author='Jeremy Freeman', author_email='the.freeman.lab@gmail.com', url='https://github.com/freeman-lab/spark-ml-streaming', packages=['mlstreaming', 'mlstreaming.lib' ], scripts = ['bin/streaming-kmeans'], package_data = {'mlstreaming.lib': ['spark-ml-streaming_2.10-' + str(mlstreaming.__version__) + '.jar']}, install_requires=open('requirements.txt').read().split() ) ================================================ FILE: scala/build.sbt ================================================ name := "spark-ml-streaming" version := "0.1.0" scalaVersion := "2.10.3" ivyXML := libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "1.0.4" libraryDependencies += "org.apache.spark" %% "spark-core" % "1.2.0" libraryDependencies += "org.apache.spark" %% "spark-streaming" % "1.2.0" libraryDependencies += "org.apache.spark" % "spark-mllib_2.10" % "1.2.0" libraryDependencies += "org.scalatest" % "scalatest_2.10" % "2.0" % "test" libraryDependencies += "io.spray" %% "spray-json" % "1.2.5" libraryDependencies += "org.jblas" % "jblas" % "1.2.3" resolvers += "spray" at "http://repo.spray.io/" resolvers ++= Seq( "Akka Repository" at "http://repo.akka.io/releases/", "Spray Repository" at "http://repo.spray.cc/") ================================================ FILE: scala/project/build.properties ================================================ sbt.version=0.12.4 ================================================ FILE: scala/src/main/scala/spark/mlstreaming/KMeans.scala ================================================ package spark.mlstreaming import java.util.Calendar import org.apache.spark.SparkConf import org.apache.spark.mllib.clustering.StreamingKMeans import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.streaming.{Seconds, StreamingContext} /** * Demo of streaming k-means with Spark Streaming. * Reads data from one directory, and prints models and predictions to another. * * Run via Python executable in the top-level project directory * */ object KMeans { def main(args: Array[String]) { if (args.length != 7) { System.err.println( "Usage: KMeans " + " ") System.exit(1) } val (inputDir, outputDir, batchDuration, numClusters, numDimensions, halfLife, timeUnit) = (args(0), args(1), args(2).toLong, args(3).toInt, args(4).toInt, args(5).toFloat, args(6)) val conf = new SparkConf().setMaster("local").setAppName("KMeansDemo") val ssc = new StreamingContext(conf, Seconds(batchDuration)) val trainingData = ssc.textFileStream(inputDir).map(Vectors.parse) val model = new StreamingKMeans() .setK(numClusters) .setHalfLife(halfLife, timeUnit) .setRandomCenters(numDimensions, 0.0) model.trainOn(trainingData) val predictions = model.predictOn(trainingData) predictions.foreachRDD { rdd => val modelString = model.latestModel().clusterCenters .map(c => c.toString.slice(1, c.toString.length-1)).mkString("\n") val predictString = rdd.map(p => p.toString).collect().mkString("\n") val dateString = Calendar.getInstance().getTime.toString.replace(" ", "-").replace(":", "-") Utils.printToFile(outputDir, dateString + "-model", modelString) Utils.printToFile(outputDir, dateString + "-predictions", predictString) } ssc.start() ssc.awaitTermination() } } ================================================ FILE: scala/src/main/scala/spark/mlstreaming/Utils.scala ================================================ package spark.mlstreaming import java.io.{FileWriter, BufferedWriter, File} /** * Utilities for streaming demos */ object Utils { def printToFile(pathName: String, fileName: String, contents: String) = { val file = new File(pathName + "/" + fileName + ".txt") val bw = new BufferedWriter(new FileWriter(file)) bw.write(contents) bw.close() } }