error.txt

OutOfData                                 Traceback (most recent call last)
<ipython-input-3-d4cacf0e18b0> in <module>()
     18 m = JubaModel()
     19 m = m.load_binary(open("/tmp/127.0.0.1_29179_classifier_iris.jubatus", "rb"))
---> 20 m = m.transform("nearest_neighbor")
     21 # Stop the classifier.
     22 classifier.stop()

/home/udagawa/work/jubakit/jubakit/jubakit/model.py in transform(self, service)
    261     else:
    262       raise UnsupportedTransformationError(t)
--> 263     return trans.transform(service)
    264 
    265   class ModelPart(object):

/home/udagawa/work/jubakit/jubakit/jubakit/model.py in transform(self, service)
    474     if service == 'nearest_neighbor' and self._is_method('nearest_neighbor', 'NN'):
    475       (rm, wm) = self._unpack_generic()
--> 476       return self._get_converted_model(service, 1, [self._extract_nn(rm), wm], self._get_backend_config())
    477     return super(ClassifierTransformer, self).transform(service)
    478 

/home/udagawa/work/jubakit/jubakit/jubakit/model.py in _extract_nn(self, rm)
    480     nn = BytesIO()
    481     unp = msgpack.Unpacker(rm)
--> 482     assert unp.read_array_header() == 2   # classifier_->pack(pk)
    483     unp.unpack(nn.write)                  #  +- nearest_neighbor_engine_->pack(pk)
    484     unp.skip()                            #  +- labels_.pack(pk)

msgpack/_unpacker.pyx in msgpack._unpacker.Unpacker.read_array_header (msgpack/_unpacker.cpp:5241)()

msgpack/_unpacker.pyx in msgpack._unpacker.Unpacker._unpack (msgpack/_unpacker.cpp:4464)()

OutOfData: No more data to unpack.

transform.py

from __future__ import absolute_import, division, print_function, unicode_literals
from jubakit.classifier import Classifier, Schema, Dataset, Config
from jubakit.model import JubaModel
import sklearn.datasets

iris = sklearn.datasets.load_iris()
dataset = Dataset.from_array(iris.data, iris.target, iris.feature_names, iris.target_names)

dataset = dataset.shuffle()

cfg = Config(method="NN")
classifier = Classifier.run(cfg)
for (idx, label) in classifier.train(dataset):
  # You can peek the datum being trained.
  print("Train: {0}".format(dataset[idx]))
print("Saving model file...")
classifier.save('iris')
m = JubaModel()
m = m.load_binary(open("/tmp/127.0.0.1_29179_classifier_iris.jubatus", "rb"))
m = m.transform("nearest_neighbor")
# Stop the classifier.
classifier.stop()