def __init__(self, text_converter=None, model=None):
if text_converter is not None and isinstance(text_converter, GroceryTextConverter):
self.text_converter = text_converter
else:
self.text_converter = GroceryTextConverter()
self.svm_model = model
self._hashcode = str(uuid.uuid4())
def load(self, model_name):
try:
with open(model_name + '/id', 'r') as fin:
self._hashcode = fin.readline().strip()
except IOError:
raise ValueError("The given model is invalid.")
self.text_converter = GroceryTextConverter().load(model_name + '/converter')
self.svm_model = LearnerModel(model_name + '/learner')
class GroceryTextModel(object):
def __init__(self, text_converter=None, model=None):
if isinstance(text_converter, GroceryTextConverter):
self.text_converter = text_converter
self.svm_model = model
self._hashcode = str(uuid.uuid4())
def __str__(self):
return 'TextModel instance ({0}, {1})'.format(self.text_converter, self.svm_model)
def get_labels(self):
return [self.text_converter.get_class_name(k) for k in self.svm_model.get_labels()]
def load(self, model_name):
try:
with open(model_name + '/id', 'r') as fin:
self._hashcode = fin.readline().strip()
except IOError:
raise ValueError("The given model is invalid.")
self.text_converter = GroceryTextConverter().load(model_name + '/converter')
self.svm_model = LearnerModel(model_name + '/learner')
def save(self, model_name, force=False):
if self.svm_model is None:
raise Exception('This model can not be saved because svm model is not given.')
if os.path.exists(model_name) and force:
shutil.rmtree(model_name)
try:
os.mkdir(model_name)
except OSError as e:
raise OSError(e, 'Please use force option to overwrite the existing files.')
self.text_converter.save(model_name + '/converter')
self.svm_model.save(model_name + '/learner', force)
with open(model_name + '/id', 'w') as fout:
fout.write(self._hashcode)
def predict_text(self, text):
if self.svm_model is None:
raise Exception('This model is not usable because svm model is not given')
# process unicode type
if isinstance(text, unicode):
text = text.encode('utf-8')
if not isinstance(text, str):
raise TypeError('The argument should be plain text')
text = self.text_converter.to_svm(text)
y, dec = predict_one(text, self.svm_model)
y = self.text_converter.get_class_name(int(y))
labels = [self.text_converter.get_class_name(k) for k in
self.svm_model.label[:self.svm_model.nr_class]]
return GroceryPredictResult(predicted_y=y, dec_values=dec[:self.svm_model.nr_class], labels=labels)
class GroceryTextModel(object):
def __init__(self, text_converter=None, model=None):
if isinstance(text_converter, GroceryTextConverter):
self.text_converter = text_converter
self.svm_model = model
self._hashcode = str(uuid.uuid4())
def __str__(self):
return 'TextModel instance ({0}, {1})'.format(self.text_converter, self.svm_model)
def get_labels(self):
return [self.text_converter.get_class_name(k) for k in self.svm_model.get_labels()]
def load(self, model_name):
try:
with open(model_name + '/id', 'r') as fin:
self._hashcode = fin.readline().strip()
except IOError:
raise ValueError("The given model is invalid.")
self.text_converter = GroceryTextConverter().load(model_name + '/converter')
self.svm_model = LearnerModel(model_name + '/learner')
def save(self, model_name, force=False):
if self.svm_model is None:
raise Exception('This model can not be saved because svm model is not given.')
if os.path.exists(model_name) and force:
shutil.rmtree(model_name)
try:
os.mkdir(model_name)
except OSError as e:
raise OSError(e, 'Please use force option to overwrite the existing files.')
self.text_converter.save(model_name + '/converter')
self.svm_model.save(model_name + '/learner', force)
with open(model_name + '/id', 'w') as fout:
fout.write(self._hashcode)
def predict_text(self, text):
if self.svm_model is None:
raise Exception('This model is not usable because svm model is not given')
# process unicode type
if isinstance(text, unicode):
text = text.encode('utf-8')
if not isinstance(text, str):
raise TypeError('The argument should be plain text')
text = self.text_converter.to_svm(text)
y, dec = predict_one(text, self.svm_model)
y = self.text_converter.get_class_name(int(y))
labels = [self.text_converter.get_class_name(k) for k in
self.svm_model.label[:self.svm_model.nr_class]]
return GroceryPredictResult(predicted_y=y, dec_values=dec[:self.svm_model.nr_class], labels=labels)
def __init__(self, text_converter=None, model=None):
if text_converter is not None and isinstance(text_converter, GroceryTextConverter):
self.text_converter = text_converter
else:
self.text_converter = GroceryTextConverter()
self.svm_model = model
self._hashcode = str(uuid.uuid4())
def load(self, model_name):
try:
with open(model_name + '/id', 'r') as fin:
self._hashcode = fin.readline().strip()
except IOError:
raise ValueError("The given model is invalid.")
self.text_converter = GroceryTextConverter().load(model_name + '/converter')
self.svm_model = LearnerModel(model_name + '/learner')