# Path: shorttext/utils/classification_exceptions.py
# class ModelNotTrainedException(Exception):
#     def __init__(self):
#         self.message = 'Model not trained.'
# 
# Path: shorttext/utils/wordembed.py
# def shorttext_to_avgvec(shorttext, wvmodel):
#     """ Convert the short text into an averaged embedded vector representation.
# 
#     Given a short sentence, it converts all the tokens into embedded vectors according to
#     the given word-embedding model, sums
#     them up, and normalize the resulting vector. It returns the resulting vector
#     that represents this short sentence.
# 
#     :param shorttext: a short sentence
#     :param wvmodel: word-embedding model
#     :return: an embedded vector that represents the short sentence
#     :type shorttext: str
#     :type wvmodel: gensim.models.keyedvectors.KeyedVectors
#     :rtype: numpy.ndarray
#     """
#     vec = np.sum([wvmodel[token] for token in tokenize(shorttext) if token in wvmodel], axis=0)
# 
#     # normalize
#     norm = np.linalg.norm(vec)
#     if norm != 0:
#         vec /= norm
# 
#     return vec
# 
# 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
from collections import defaultdict
from scipy.spatial.distance import cosine
from shorttext.utils.classification_exceptions import ModelNotTrainedException
from shorttext.utils import shorttext_to_avgvec
from shorttext.utils.compactmodel_io import CompactIOMachine




class SumEmbeddedVecClassifier(CompactIOMachine):
    """
    This is a supervised classification algorithm for short text categorization.
    Each class label has a few short sentences, where each token is converted
    to an embedded vector, given by a pre-trained word-embedding model (e.g., Google Word2Vec model).
    They are then summed up and normalized to a unit vector for that particular class labels.
    To perform prediction, the input short sentences is converted to a unit vector
    in the same way. The similarity score is calculated by the cosine similarity.

    A pre-trained Google Word2Vec model can be downloaded `here
    <https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit>`_.
    """

    def __init__(self, wvmodel, vecsize=None, simfcn=lambda u, v: 1-cosine(u, v)):
        """ Initialize the classifier.

        :param wvmodel: Word2Vec model
        :param vecsize: length of the embedded vectors in the model (Default: None, directly extracted from word-embedding model)
        :param simfcn: similarity function (Default: cosine similarity)
        :type wvmodel: gensim.models.keyedvectors.KeyedVectors
        :type vecsize: int
        :type simfcn: function
        """

# Path: shorttext/utils/classification_exceptions.py
# class ModelNotTrainedException(Exception):
#     def __init__(self):
#         self.message = 'Model not trained.'
# 
# Path: shorttext/utils/wordembed.py
# def shorttext_to_avgvec(shorttext, wvmodel):
#     """ Convert the short text into an averaged embedded vector representation.
# 
#     Given a short sentence, it converts all the tokens into embedded vectors according to
#     the given word-embedding model, sums
#     them up, and normalize the resulting vector. It returns the resulting vector
#     that represents this short sentence.
# 
#     :param shorttext: a short sentence
#     :param wvmodel: word-embedding model
#     :return: an embedded vector that represents the short sentence
#     :type shorttext: str
#     :type wvmodel: gensim.models.keyedvectors.KeyedVectors
#     :rtype: numpy.ndarray
#     """
#     vec = np.sum([wvmodel[token] for token in tokenize(shorttext) if token in wvmodel], axis=0)
# 
#     # normalize
#     norm = np.linalg.norm(vec)
#     if norm != 0:
#         vec /= norm
# 
#     return vec
# 
# 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/SumEmbedVecClassification.py
import pickle
import numpy as np
from collections import defaultdict
from scipy.spatial.distance import cosine
from shorttext.utils.classification_exceptions import ModelNotTrainedException
from shorttext.utils import shorttext_to_avgvec
from shorttext.utils.compactmodel_io import CompactIOMachine





class SumEmbeddedVecClassifier(CompactIOMachine):
    """
    This is a supervised classification algorithm for short text categorization.
    Each class label has a few short sentences, where each token is converted
    to an embedded vector, given by a pre-trained word-embedding model (e.g., Google Word2Vec model).
    They are then summed up and normalized to a unit vector for that particular class labels.
    To perform prediction, the input short sentences is converted to a unit vector
    in the same way. The similarity score is calculated by the cosine similarity.

    A pre-trained Google Word2Vec model can be downloaded `here
    <https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit>`_.
    """

    def __init__(self, wvmodel, vecsize=None, simfcn=lambda u, v: 1-cosine(u, v)):
        """ Initialize the classifier.

        :param wvmodel: Word2Vec model
        :param vecsize: length of the embedded vectors in the model (Default: None, directly extracted from word-embedding model)
        :param simfcn: similarity function (Default: cosine similarity)
        :type wvmodel: gensim.models.keyedvectors.KeyedVectors
        :type vecsize: int
        :type simfcn: function
        """

        CompactIOMachine.__init__(self, {'classifier': 'sumvec'}, 'sumvec', ['_embedvecdict.pkl'])