def loadmodel(self, nameprefix):
""" Load a trained model from files.
Given the prefix of the file paths, load the model from files with name given by the prefix
followed by "_classlabels.txt", ".json" and ".h5". For shorttext>=0.4.0, a file with
extension "_config.json" would also be used.
If this has not been run, or a model was not trained by :func:`~train`,
a `ModelNotTrainedException` will be raised while performing prediction or saving the model.
:param nameprefix: prefix of the file path
:return: None
:type nameprefix: str
"""
self.model = kerasio.load_model(nameprefix)
labelfile = open(nameprefix + '_classlabels.txt', 'r')
self.classlabels = labelfile.readlines()
labelfile.close()
self.classlabels = [s.strip() for s in self.classlabels]
# check if _config.json exists.
# This file does not exist if the model was created with shorttext<0.4.0
if os.path.exists(nameprefix + '_config.json'):
config = json.load(open(nameprefix + '_config.json', 'r'))
# these fields are present for release >= 1.0.0
if 'maxlen' in config:
self.maxlen = config['maxlen']
else:
self.maxlen = 15
if 'vecsize' in config:
self.vecsize = config['vecsize']
else:
self.vecsize = self.wvmodel.vector_size
if self.vecsize != self.wvmodel.vector_size:
warnings.warn(
'Record vector size (%i) is not the same as that of the given word-embedding model (%i)! '
% (self.vecsize, self.wvmodel.vector_size) +
'Setting the vector size to be %i, but there may be run time error!'
% (self.wvmodel.vector_size), RuntimeWarning)
self.vecsize = self.wvmodel.vector_size
else:
self.maxlen = 15
self.vecsize = self.wvmodel.vector_size
warnings.warn('Model files from old versions.')
self.with_gensim = False
self.trained = True
def loadmodel(self, nameprefix):
""" Load the model with the given prefix.
Load the model with the given prefix of their paths. Note that the intermediate
classifiers are not loaded, and users are required to load them separately.
:param nameprefix: prefix of the model files
:return: None
:type nameprefix: str
"""
stackedmodeldict = pickle.load(open(nameprefix+'_stackedlogistics.pkl', 'rb'))
self.register_classlabels(stackedmodeldict['classlabels'])
self.classifier2idx = stackedmodeldict['classifiers']
self.idx2classifier = {val: key for key, val in self.classifier2idx.items()}
self.model = kerasio.load_model(nameprefix+'_stackedlogistics')
self.trained = True
def loadmodel(self, nameprefix):
""" Load a trained model from files.
Given the prefix of the file paths, load the model from files with name given by the prefix
followed by "_classlabels.txt", ".json", and ".h5".
If this has not been run, or a model was not trained by :func:`~train`,
a `ModelNotTrainedException` will be raised while performing prediction and saving the model.
:param nameprefix: prefix of the file path
:return: None
:type nameprefix: str
"""
self.model = kerasio.load_model(nameprefix)
labelfile = open(nameprefix+'_classlabels.txt', 'r')
self.classlabels = labelfile.readlines()
labelfile.close()
self.classlabels = map(lambda s: s.strip(), self.classlabels)
self.trained = True
def loadmodel(self, prefix):
""" Load the model.
:param prefix: prefix of the model path
:return: None
:type prefix: str
"""
self.dictionary = Dictionary.load(prefix + '_vocabs.gensimdict')
parameters = json.load(open(prefix + '_config.json', 'r'))
self.operation = parameters['operation']
self.alph = parameters['alph']
self.specialsignals = parameters['special_signals']
self.binarizer = SCRNNBinarizer(self.alph, self.specialsignals)
self.concatcharvec_encoder = SpellingToConcatCharVecEncoder(self.alph)
self.batchsize = parameters['batchsize']
self.nb_hiddenunits = parameters['nb_hiddenunits']
self.onehotencoder = OneHotEncoder()
self.onehotencoder.fit(
np.arange(len(self.dictionary)).reshape((len(self.dictionary), 1)))
self.model = kerasio.load_model(prefix)
self.trained = True
def loadmodel(self, nameprefix):
""" Load a trained model from files.
Given the prefix of the file paths, load the model from files with name given by the prefix
followed by "_classlabels.txt", ".json" and ".h5". For shorttext>=0.4.0, a file with
extension "_config.json" would also be used.
If this has not been run, or a model was not trained by :func:`~train`,
a `ModelNotTrainedException` will be raised while performing prediction or saving the model.
:param nameprefix: prefix of the file path
:return: None
:type nameprefix: str
"""
self.model = kerasio.load_model(nameprefix)
labelfile = open(nameprefix + '_classlabels.txt', 'r')
self.classlabels = labelfile.readlines()
labelfile.close()
self.classlabels = map(lambda s: s.strip(), self.classlabels)
# check if _config.json exists.
# This file does not exist if the model was created with shorttext<0.4.0
self.with_gensim = False
self.trained = True
def loadmodel(self, nameprefix):
""" Load a trained model from files.
Given the prefix of the file paths, load the model from files with name given by the prefix
followed by "_classlabels.txt", ".json", ".h5", "_labelidx.pkl", and "_dictionary.dict".
If this has not been run, or a model was not trained by :func:`~train`,
a `ModelNotTrainedException` will be raised while performing prediction or saving the model.
:param nameprefix: prefix of the file path
:return: None
:type nameprefix: str
"""
self.model = kerasio.load_model(nameprefix)
self.dictionary = Dictionary.load(nameprefix+'_dictionary.dict')
labelfile = open(nameprefix+'_classlabels.txt', 'r')
self.classlabels = [s.strip() for s in labelfile.readlines()]
labelfile.close()
self.labels2idx = pickle.load(open(nameprefix+'_labelidx.pkl', 'rb'))
self.trained = True