# 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/gensim_corpora.py
# def generate_gensim_corpora(classdict, preprocess_and_tokenize=tokenize):
# def save_corpus(dictionary, corpus, prefix):
# def load_corpus(prefix):
# def update_corpus_labels(dictionary, corpus, newclassdict, preprocess_and_tokenize=tokenize):
# def tokens_to_fracdict(tokens):
# 
# Path: shorttext/utils/classification_exceptions.py
# class ModelNotTrainedException(Exception):
# class AlgorithmNotExistException(Exception):
# class WordEmbeddingModelNotExistException(Exception):
# class UnequalArrayLengthsException(Exception):
# class NotImplementedException(Exception):
# class IncorrectClassificationModelFileException(Exception):
# class OperationNotDefinedException(Exception):
#     def __init__(self):
#     def __init__(self, algoname):
#     def __init__(self, path):
#     def __init__(self, arr1, arr2):
#     def __init__(self):
#     def __init__(self, expectedname, actualname):
#     def __init__(self, opname):
# 
# 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 shorttext.utils.kerasmodel_io as kerasio
from scipy.sparse import dok_matrix
from gensim.corpora import Dictionary
from keras.models import Sequential
from keras.layers import Dense
from keras.regularizers import l2
from shorttext.utils import tokenize
from shorttext.utils import gensim_corpora as gc
from shorttext.utils import classification_exceptions as e
from shorttext.utils.compactmodel_io import CompactIOMachine
    :type nb_outputs: int
    :type l2reg: float
    :type bias_l2reg: float
    :type optimizer: str
    :rtype: keras.model.Sequential
    """
    kmodel = Sequential()
    kmodel.add(Dense(units=nb_outputs,
                     activation='softmax',
                     input_shape=(nb_features,),
                     kernel_regularizer=l2(l2reg),
                     bias_regularizer=l2(bias_l2reg))
               )
    kmodel.compile(loss='categorical_crossentropy', optimizer=optimizer)
    return kmodel


class MaxEntClassifier(CompactIOMachine):
    """
    This is a classifier that implements the principle of maximum entropy.

    Reference:
    * Adam L. Berger, Stephen A. Della Pietra, Vincent J. Della Pietra, "A Maximum Entropy Approach to Natural Language Processing," *Computational Linguistics* 22(1): 39-72 (1996).
    """
    def __init__(self, preprocessor=lambda s: s.lower()):
        """ Initializer.

        :param preprocessor: text preprocessor
        :type preprocessor: function
        """

# 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/gensim_corpora.py
# def generate_gensim_corpora(classdict, preprocess_and_tokenize=tokenize):
# def save_corpus(dictionary, corpus, prefix):
# def load_corpus(prefix):
# def update_corpus_labels(dictionary, corpus, newclassdict, preprocess_and_tokenize=tokenize):
# def tokens_to_fracdict(tokens):
# 
# Path: shorttext/utils/classification_exceptions.py
# class ModelNotTrainedException(Exception):
# class AlgorithmNotExistException(Exception):
# class WordEmbeddingModelNotExistException(Exception):
# class UnequalArrayLengthsException(Exception):
# class NotImplementedException(Exception):
# class IncorrectClassificationModelFileException(Exception):
# class OperationNotDefinedException(Exception):
#     def __init__(self):
#     def __init__(self, algoname):
#     def __init__(self, path):
#     def __init__(self, arr1, arr2):
#     def __init__(self):
#     def __init__(self, expectedname, actualname):
#     def __init__(self, opname):
# 
# 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/bow/maxent/MaxEntClassification.py
import pickle
import shorttext.utils.kerasmodel_io as kerasio
from scipy.sparse import dok_matrix
from gensim.corpora import Dictionary
from keras.models import Sequential
from keras.layers import Dense
from keras.regularizers import l2
from shorttext.utils import tokenize
from shorttext.utils import gensim_corpora as gc
from shorttext.utils import classification_exceptions as e
from shorttext.utils.compactmodel_io import CompactIOMachine

    :type nb_outputs: int
    :type l2reg: float
    :type bias_l2reg: float
    :type optimizer: str
    :rtype: keras.model.Sequential
    """
    kmodel = Sequential()
    kmodel.add(Dense(units=nb_outputs,
                     activation='softmax',
                     input_shape=(nb_features,),
                     kernel_regularizer=l2(l2reg),
                     bias_regularizer=l2(bias_l2reg))
               )
    kmodel.compile(loss='categorical_crossentropy', optimizer=optimizer)
    return kmodel


class MaxEntClassifier(CompactIOMachine):
    """
    This is a classifier that implements the principle of maximum entropy.

    Reference:
    * Adam L. Berger, Stephen A. Della Pietra, Vincent J. Della Pietra, "A Maximum Entropy Approach to Natural Language Processing," *Computational Linguistics* 22(1): 39-72 (1996).
    """
    def __init__(self, preprocessor=lambda s: s.lower()):
        """ Initializer.

        :param preprocessor: text preprocessor
        :type preprocessor: function
        """

        CompactIOMachine.__init__(self,