optparser.add_option("-c", "--classifyExamples", default=None, dest="classifyExamples", help="Example File", metavar="FILE") optparser.add_option("--classIds", default=None, dest="classIds", help="Class ids", metavar="FILE") optparser.add_option("-m", "--model", default=None, dest="model", help="path to model file") #optparser.add_option("-w", "--work", default=None, dest="work", help="Working directory for intermediate and debug files") optparser.add_option("-o", "--output", default=None, dest="output", help="Output directory or file") optparser.add_option("-r", "--remote", default=None, dest="remote", help="Remote connection") #optparser.add_option("-c", "--classifier", default="SVMMultiClassClassifier", dest="classifier", help="Classifier Class") optparser.add_option("-p", "--parameters", default=None, dest="parameters", help="Parameters for the classifier") #optparser.add_option("-d", "--ids", default=None, dest="ids", help="") #optparser.add_option("--filterIds", default=None, dest="filterIds", help="") optparser.add_option("--install", default=None, dest="install", help="Install directory (or DEFAULT)") optparser.add_option("--installFromSource", default=False, action="store_true", dest="installFromSource", help="") (options, args) = optparser.parse_args() assert options.action in ["TRAIN", "CLASSIFY", "OPTIMIZE"] classifier = ScikitClassifier(Connection.getConnection(options.remote)) if options.action == "TRAIN": import time trained = classifier.train(options.examples, options.output, options.parameters, options.classifyExamples) status = trained.getStatus() while status not in ["FINISHED", "FAILED"]: print >> sys.stderr, "Training classifier, status =", status time.sleep(10) status = trained.getStatus() print >> sys.stderr, "Training finished, status =", status if trained.getStatus() == "FINISHED": trained.downloadPredictions() trained.downloadModel() elif options.action == "CLASSIFY": classified = classifier.classify(options.examples, options.output, options.model, True) if classified.getStatus() == "FINISHED":
if "," in options.install: destDir, downloadDir = options.install.split(",") else: destDir = options.install install(destDir, downloadDir, False, options.installFromSource) sys.exit() # elif options.filterIds != None: # assert options.model != None # classifier = SVMMultiClassClassifier() # filteredIds = classifier.filterIds(options.filterIds, options.model, verbose=True) # if options.output != None: # filteredIds.write(options.output) else: assert options.action in ["TRAIN", "CLASSIFY", "OPTIMIZE"] classifier = SVMMultiClassClassifier( Connection.getConnection(options.remote)) if options.action == "TRAIN": import time trained = classifier.train(options.examples, options.output, options.parameters, options.classifyExamples) status = trained.getStatus() while status not in ["FINISHED", "FAILED"]: print >> sys.stderr, "Training classifier, status =", status time.sleep(10) status = trained.getStatus() print >> sys.stderr, "Training finished, status =", status if trained.getStatus() == "FINISHED": trained.downloadPredictions() trained.downloadModel() elif options.action == "CLASSIFY":
if options.install != "DEFAULT": if "," in options.install: destDir, downloadDir = options.install.split(",") else: destDir = options.install install(destDir, downloadDir, False, options.installFromSource) sys.exit() # elif options.filterIds != None: # assert options.model != None # classifier = SVMMultiClassClassifier() # filteredIds = classifier.filterIds(options.filterIds, options.model, verbose=True) # if options.output != None: # filteredIds.write(options.output) else: assert options.action in ["TRAIN", "CLASSIFY", "OPTIMIZE"] classifier = SVMMultiClassClassifier(Connection.getConnection(options.remote)) if options.action == "TRAIN": import time trained = classifier.train(options.examples, options.output, options.parameters, options.classifyExamples) status = trained.getStatus() while status not in ["FINISHED", "FAILED"]: print >> sys.stderr, "Training classifier, status =", status time.sleep(10) status = trained.getStatus() print >> sys.stderr, "Training finished, status =", status if trained.getStatus() == "FINISHED": trained.downloadPredictions() trained.downloadModel() elif options.action == "CLASSIFY": classified = classifier.classify(options.examples, options.output, options.model, True) if classified.getStatus() == "FINISHED":
optparser.add_option("-c", "--classifyExamples", default=None, dest="classifyExamples", help="Example File", metavar="FILE") optparser.add_option("--classIds", default=None, dest="classIds", help="Class ids", metavar="FILE") optparser.add_option("-m", "--model", default=None, dest="model", help="path to model file") #optparser.add_option("-w", "--work", default=None, dest="work", help="Working directory for intermediate and debug files") optparser.add_option("-o", "--output", default=None, dest="output", help="Output directory or file") optparser.add_option("-r", "--remote", default=None, dest="remote", help="Remote connection") #optparser.add_option("-c", "--classifier", default="SVMMultiClassClassifier", dest="classifier", help="Classifier Class") optparser.add_option("-p", "--parameters", default=None, dest="parameters", help="Parameters for the classifier") #optparser.add_option("-d", "--ids", default=None, dest="ids", help="") #optparser.add_option("--filterIds", default=None, dest="filterIds", help="") optparser.add_option("--install", default=None, dest="install", help="Install directory (or DEFAULT)") optparser.add_option("--installFromSource", default=False, action="store_true", dest="installFromSource", help="") (options, args) = optparser.parse_args() assert options.action in ["TRAIN", "CLASSIFY", "OPTIMIZE"] classifier = SKLearnSVM(Connection.getConnection(options.remote)) if options.action == "TRAIN": import time trained = classifier.train(options.examples, options.output, options.parameters, options.classifyExamples) status = trained.getStatus() while status not in ["FINISHED", "FAILED"]: print >> sys.stderr, "Training classifier, status =", status time.sleep(10) status = trained.getStatus() print >> sys.stderr, "Training finished, status =", status if trained.getStatus() == "FINISHED": trained.downloadPredictions() trained.downloadModel() elif options.action == "CLASSIFY": classified = classifier.classify(options.examples, options.output, options.model, True) if classified.getStatus() == "FINISHED":