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":
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":