# Path: shorttext/utils/classification_exceptions.py
# class ModelNotTrainedException(Exception):
#     def __init__(self):
#         self.message = 'Model not trained.'
# 
# Path: shorttext/utils/textpreprocessing.py
# def preprocess_text(text, pipeline):
# def text_preprocessor(pipeline):
# def oldschool_standard_text_preprocessor(stopwordsfile):
# def standard_text_preprocessor_1():
# def standard_text_preprocessor_2():
# 
# 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 numpy as np
import shorttext.utils.kerasmodel_io as kerasio
from shorttext.utils.classification_exceptions import ModelNotTrainedException
from shorttext.utils.textpreprocessing import tokenize
from shorttext.utils.compactmodel_io import CompactIOMachine
        them up, and normalize the resulting vector. It returns the resulting vector
        that represents this short sentence.

        :param shorttext: a short sentence
        :return: an embedded vector that represents the short sentence
        :type shorttext: str
        :rtype: numpy.ndarray
        """
        vec = np.sum([self.wvmodel[token] for token in tokenize(shorttext) if token in self.wvmodel])
        norm = np.linalg.norm(vec)
        if norm != 0:
            vec /= np.linalg.norm(vec)
        return vec

    def score(self, shorttext):
        """ Calculate the scores for all the class labels for the given short sentence.

        Given a short sentence, calculate the classification scores for all class labels,
        returned as a dictionary with key being the class labels, and values being the scores.
        If the short sentence is empty, or if other numerical errors occur, the score will be `numpy.nan`.

        If neither :func:`~train` nor :func:`~loadmodel` was run, it will raise `ModelNotTrainedException`.

        :param shorttext: a short sentence
        :return: a dictionary with keys being the class labels, and values being the corresponding classification scores
        :type shorttext: str
        :rtype: dict
        :raise: ModelNotTrainedException
        """
        if not self.trained:

# Path: shorttext/utils/classification_exceptions.py
# class ModelNotTrainedException(Exception):
#     def __init__(self):
#         self.message = 'Model not trained.'
# 
# Path: shorttext/utils/textpreprocessing.py
# def preprocess_text(text, pipeline):
# def text_preprocessor(pipeline):
# def oldschool_standard_text_preprocessor(stopwordsfile):
# def standard_text_preprocessor_1():
# def standard_text_preprocessor_2():
# 
# 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/classifiers/embed/sumvec/VarNNSumEmbedVecClassification.py
import numpy as np
import shorttext.utils.kerasmodel_io as kerasio
from shorttext.utils.classification_exceptions import ModelNotTrainedException
from shorttext.utils.textpreprocessing import tokenize
from shorttext.utils.compactmodel_io import CompactIOMachine

        them up, and normalize the resulting vector. It returns the resulting vector
        that represents this short sentence.

        :param shorttext: a short sentence
        :return: an embedded vector that represents the short sentence
        :type shorttext: str
        :rtype: numpy.ndarray
        """
        vec = np.sum([self.wvmodel[token] for token in tokenize(shorttext) if token in self.wvmodel])
        norm = np.linalg.norm(vec)
        if norm != 0:
            vec /= np.linalg.norm(vec)
        return vec

    def score(self, shorttext):
        """ Calculate the scores for all the class labels for the given short sentence.

        Given a short sentence, calculate the classification scores for all class labels,
        returned as a dictionary with key being the class labels, and values being the scores.
        If the short sentence is empty, or if other numerical errors occur, the score will be `numpy.nan`.

        If neither :func:`~train` nor :func:`~loadmodel` was run, it will raise `ModelNotTrainedException`.

        :param shorttext: a short sentence
        :return: a dictionary with keys being the class labels, and values being the corresponding classification scores
        :type shorttext: str
        :rtype: dict
        :raise: ModelNotTrainedException
        """
        if not self.trained:

            raise ModelNotTrainedException()