# Path: shorttext/utils/compactmodel_io.py
# class CompactIOMachine:
#     """ Base class that implements compact model I/O.
# 
#     This is to replace the original :func:`compactio` decorator.
# 
#     """
#     def __init__(self, infodict, prefix, suffices):
#         """
# 
#         :param infodict: information about the model. Must contain the key 'classifier'.
#         :param prefix: prefix of names of the model file
#         :param suffices: suffices of the names of the model file
#         :type infodict: dict
#         :type prefix: str
#         :type suffices: list
#         """
#         self.infodict = infodict
#         self.prefix = prefix
#         self.suffices = suffices
# 
#     def savemodel(self, nameprefix):
#         """ Abstract method for `savemodel`.
# 
#         :param nameprefix: prefix of the model path
#         :type nameprefix: str
#         """
#         raise e.OperationNotDefinedException()
# 
#     def loadmodel(self, nameprefix):
#         """ Abstract method for `loadmodel`.
# 
#         :param nameprefix: prefix of the model path
#         :type nameprefix: str
#         """
#         raise e.OperationNotDefinedException()
# 
#     def save_compact_model(self, filename, *args, **kwargs):
#         """ Save the model in a compressed binary format.
# 
#         :param filename: name of the model file
#         :param args: arguments
#         :param kwargs: arguments
#         :type filename: str
#         :type args: dict
#         :type kwargs: dict
#         """
#         save_compact_model(filename, self.savemodel, self.prefix, self.suffices, self.infodict, *args, **kwargs)
# 
#     def load_compact_model(self, filename, *args, **kwargs):
#         """ Load the model in a compressed binary format.
# 
#         :param filename: name of the model file
#         :param args: arguments
#         :param kwargs: arguments
#         :type filename: str
#         :type args: dict
#         :type kwargs: dict
#         """
#         return load_compact_model(filename, self.loadmodel, self.prefix, self.infodict, *args, **kwargs)
# 
#     def get_info(self):
#         """ Getting information for the dressed machine.
# 
#         :return: dictionary of the information for the dressed machine.
#         :rtype: dict
#         """
#         return {'classifier': self.infodict['classifier'],
#                 'prefix': self.prefix,
#                 'suffices': self.suffices}


import pickle
import numpy as np
import shorttext.utils.classification_exceptions as e
import shorttext.utils.kerasmodel_io as kerasio
from abc import ABC, abstractmethod
from keras.layers import Dense, Reshape
from keras.models import Sequential
from keras.regularizers import l2
from shorttext.utils.compactmodel_io import CompactIOMachine
        :param classdict: training data
        :param args: arguments to be parsed
        :param kwargs: arguments to be parsed
        :return: None
        :type classdict: dict
        :type args: dict
        :type kwargs: dict
        :raise: NotImplementedException
        """
        raise e.NotImplementedException()

    @abstractmethod
    def score(self, shorttext, *args, **kwargs):
        """ Calculate the scores for each class labels.

        Not implemented. `NotImplemntedException` raised.

        :param shorttext: short text to be scored
        :param args: arguments to be parsed
        :param kwargs: arguments to be parsed
        :return: dictionary of scores for all class labels
        :type shorttext: str
        :type args: dict
        :type kwargs: dict
        :rtype: dict
        :raise: NotImplementedException
        """
        raise e.NotImplementedException()



# Path: shorttext/utils/compactmodel_io.py
# class CompactIOMachine:
#     """ Base class that implements compact model I/O.
# 
#     This is to replace the original :func:`compactio` decorator.
# 
#     """
#     def __init__(self, infodict, prefix, suffices):
#         """
# 
#         :param infodict: information about the model. Must contain the key 'classifier'.
#         :param prefix: prefix of names of the model file
#         :param suffices: suffices of the names of the model file
#         :type infodict: dict
#         :type prefix: str
#         :type suffices: list
#         """
#         self.infodict = infodict
#         self.prefix = prefix
#         self.suffices = suffices
# 
#     def savemodel(self, nameprefix):
#         """ Abstract method for `savemodel`.
# 
#         :param nameprefix: prefix of the model path
#         :type nameprefix: str
#         """
#         raise e.OperationNotDefinedException()
# 
#     def loadmodel(self, nameprefix):
#         """ Abstract method for `loadmodel`.
# 
#         :param nameprefix: prefix of the model path
#         :type nameprefix: str
#         """
#         raise e.OperationNotDefinedException()
# 
#     def save_compact_model(self, filename, *args, **kwargs):
#         """ Save the model in a compressed binary format.
# 
#         :param filename: name of the model file
#         :param args: arguments
#         :param kwargs: arguments
#         :type filename: str
#         :type args: dict
#         :type kwargs: dict
#         """
#         save_compact_model(filename, self.savemodel, self.prefix, self.suffices, self.infodict, *args, **kwargs)
# 
#     def load_compact_model(self, filename, *args, **kwargs):
#         """ Load the model in a compressed binary format.
# 
#         :param filename: name of the model file
#         :param args: arguments
#         :param kwargs: arguments
#         :type filename: str
#         :type args: dict
#         :type kwargs: dict
#         """
#         return load_compact_model(filename, self.loadmodel, self.prefix, self.infodict, *args, **kwargs)
# 
#     def get_info(self):
#         """ Getting information for the dressed machine.
# 
#         :return: dictionary of the information for the dressed machine.
#         :rtype: dict
#         """
#         return {'classifier': self.infodict['classifier'],
#                 'prefix': self.prefix,
#                 'suffices': self.suffices}


# Path: shorttext/stack/stacking.py
import pickle
import numpy as np
import shorttext.utils.classification_exceptions as e
import shorttext.utils.kerasmodel_io as kerasio
from abc import ABC, abstractmethod
from keras.layers import Dense, Reshape
from keras.models import Sequential
from keras.regularizers import l2
from shorttext.utils.compactmodel_io import CompactIOMachine

        :param classdict: training data
        :param args: arguments to be parsed
        :param kwargs: arguments to be parsed
        :return: None
        :type classdict: dict
        :type args: dict
        :type kwargs: dict
        :raise: NotImplementedException
        """
        raise e.NotImplementedException()

    @abstractmethod
    def score(self, shorttext, *args, **kwargs):
        """ Calculate the scores for each class labels.

        Not implemented. `NotImplemntedException` raised.

        :param shorttext: short text to be scored
        :param args: arguments to be parsed
        :param kwargs: arguments to be parsed
        :return: dictionary of scores for all class labels
        :type shorttext: str
        :type args: dict
        :type kwargs: dict
        :rtype: dict
        :raise: NotImplementedException
        """
        raise e.NotImplementedException()



class LogisticStackedGeneralization(StackedGeneralization, CompactIOMachine):