# Path: citation_extractor/io/iob.py
# def count_tokens(instances):
#     """Short summary.
# 
#     :param type instances: Description of parameter `instances`.
#     :return: Description of returned object.
#     :rtype: type
# 
#     """
#     return sum([1 for instance in instances for token in instance])
# 
# def file_to_instances(inp_file):
#     """Reads a IOB file a converts it into a list of instances.
# 
#     :param type inp_file: Path to the input IOB file.
#     :return: A list of tuples, where tuple[0] is the token and tuple[1]
#         contains its assigned label.
#     :rtype: list
# 
#     """
#     f = codecs.open(inp_file,"r","utf-8")
#     inp_text = f.read()
#     f.close()
#     out=[]
#     comment=re.compile(r'#.*?')
#     for i in inp_text.split("\n\n"):
#         inst=[]
#         for j in i.split("\n"):
#             if(not comment.match(j)):
#                 temp = tuple(j.split("\t"))
#                 if(len(temp)>1):
#                     inst.append(temp)
#         if(inst and len(inst)>1):
#             out.append(inst)
#     return out
# 
# def instance_to_string(instance):
#     """Converts a feature dictionary into a string representation.
# 
#     :param type instance: Description of parameter `instance`.
#     :return: Description of returned object.
#     :rtype: type
# 
#     """
#     def token_to_string(tok_dict):
#         return [tok_dict[k] for k in sorted(tok_dict.keys())]
# 
#     return [
#         "\t".join(token_to_string(feature_set))
#         for feature_set in instance
#     ]


import json
import logging
import os
import pickle
import re
import string
import types
import citation_extractor
        import re
    import glob
    import codecs
            import codecs
        import codecs
        import codecs
    import doctest
from sklearn_crfsuite import CRF
from stop_words import safe_get_stop_words
from citation_extractor.crfpp_wrap import *
from citation_extractor.io.iob import (count_tokens, file_to_instances,
                                       instance_to_string)
        from nltk.classify.scikitlearn import SklearnClassifier
        from sklearn.ensemble import AdaBoostClassifier
        from sklearn.naive_bayes import GaussianNB
        from sklearn.ensemble import RandomForestClassifier
        from citation_extractor.Utils.FastDict import LookupDictionary
                labels = [token[2] for token in instance]
                tokens = [token[0] for token in instance]
                sentence_feats = []

                for n, token in enumerate(tokens):
                    dict_features = fe.get_features(
                            [token],
                            labels=labels,
                            outp_label=False,
                            legacy_features=pos_tags
                    )[0]
                    sentence_feats.append([dict_features, labels[n]])

                feature_sets.append(self.apply_feature_template(sentence_feats, out_label=True))

            X_train = [
                        [token[0] for token in instance]
                        for instance in feature_sets
                    ]
            y_train = [
                        [token[1] for token in instance]
                        for instance in feature_sets
                    ]
            self.classifier._clf = self.classifier._clf.fit(X_train, y_train)
            return

        else:
            feature_sets = []

            logger.info("Training with %i instances " % len(iob_data))

# Path: citation_extractor/io/iob.py
# def count_tokens(instances):
#     """Short summary.
# 
#     :param type instances: Description of parameter `instances`.
#     :return: Description of returned object.
#     :rtype: type
# 
#     """
#     return sum([1 for instance in instances for token in instance])
# 
# def file_to_instances(inp_file):
#     """Reads a IOB file a converts it into a list of instances.
# 
#     :param type inp_file: Path to the input IOB file.
#     :return: A list of tuples, where tuple[0] is the token and tuple[1]
#         contains its assigned label.
#     :rtype: list
# 
#     """
#     f = codecs.open(inp_file,"r","utf-8")
#     inp_text = f.read()
#     f.close()
#     out=[]
#     comment=re.compile(r'#.*?')
#     for i in inp_text.split("\n\n"):
#         inst=[]
#         for j in i.split("\n"):
#             if(not comment.match(j)):
#                 temp = tuple(j.split("\t"))
#                 if(len(temp)>1):
#                     inst.append(temp)
#         if(inst and len(inst)>1):
#             out.append(inst)
#     return out
# 
# def instance_to_string(instance):
#     """Converts a feature dictionary into a string representation.
# 
#     :param type instance: Description of parameter `instance`.
#     :return: Description of returned object.
#     :rtype: type
# 
#     """
#     def token_to_string(tok_dict):
#         return [tok_dict[k] for k in sorted(tok_dict.keys())]
# 
#     return [
#         "\t".join(token_to_string(feature_set))
#         for feature_set in instance
#     ]


# Path: citation_extractor/core.py
import json
import logging
import os
import pickle
import re
import string
import types
import citation_extractor
        import re
    import glob
    import codecs
            import codecs
        import codecs
        import codecs
    import doctest
from sklearn_crfsuite import CRF
from stop_words import safe_get_stop_words
from citation_extractor.crfpp_wrap import *
from citation_extractor.io.iob import (count_tokens, file_to_instances,
                                       instance_to_string)
        from nltk.classify.scikitlearn import SklearnClassifier
        from sklearn.ensemble import AdaBoostClassifier
        from sklearn.naive_bayes import GaussianNB
        from sklearn.ensemble import RandomForestClassifier
        from citation_extractor.Utils.FastDict import LookupDictionary

                labels = [token[2] for token in instance]
                tokens = [token[0] for token in instance]
                sentence_feats = []

                for n, token in enumerate(tokens):
                    dict_features = fe.get_features(
                            [token],
                            labels=labels,
                            outp_label=False,
                            legacy_features=pos_tags
                    )[0]
                    sentence_feats.append([dict_features, labels[n]])

                feature_sets.append(self.apply_feature_template(sentence_feats, out_label=True))

            X_train = [
                        [token[0] for token in instance]
                        for instance in feature_sets
                    ]
            y_train = [
                        [token[1] for token in instance]
                        for instance in feature_sets
                    ]
            self.classifier._clf = self.classifier._clf.fit(X_train, y_train)
            return

        else:
            feature_sets = []

            logger.info("Training with %i instances " % len(iob_data))

            logger.info("Training with %i tokens" % count_tokens(iob_data))