# Source code for nlp_architect.models.cross_doc_coref.sieves_config

    # ******************************************************************************
    # Copyright 2017-2018 Intel Corporation
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #     http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ******************************************************************************
    from typing import List, Tuple

    from nlp_architect.data.cdc_resources.relations.relation_types_enums import RelationType

    class SievesConfiguration(object):
        def __init__(self):
            """Cross document co-reference event and entity evaluation configuration settings"""

            self.__sieves_order = None
            self.__run_evaluation = False

        @property
        def sieves_order(self):
            """
            Sieve definition and Sieve running order

            Tuple[SieveType, RelationType, Threshold(float)] - define sieves to run, were

            Strict- Merge clusters only in case all mentions has current relation between them,
            Relax- Merge clusters in case (matched mentions) / len(cluster_1.mentions)) >= thresh,
            Very_Relax- Merge clusters in case (matched mentions) / (all possible pairs) >= thresh

            RelationType represent the type of sieve to run.

            """
            return self.__sieves_order

        @sieves_order.setter
        def sieves_order(self, sieves_order: List[Tuple[RelationType, float]]):
            self.__sieves_order = sieves_order

        @property
        def run_evaluation(self):
            """Should run evaluation (True/False)"""
            return self.__run_evaluation

        @run_evaluation.setter
        def run_evaluation(self, run_evaluation: bool):
            self.__run_evaluation = run_evaluation

    class EventSievesConfiguration(SievesConfiguration):
        def __init__(self):
            super(EventSievesConfiguration, self).__init__()

            self.run_evaluation = True

            self.sieves_order = [
                (RelationType.SAME_HEAD_LEMMA, 1.0),
                (RelationType.EXACT_STRING, 1.0),
                (RelationType.WIKIPEDIA_DISAMBIGUATION, 0.1),
                (RelationType.WORD_EMBEDDING_MATCH, 0.7),
                (RelationType.WIKIPEDIA_REDIRECT_LINK, 0.1),
                (RelationType.FUZZY_HEAD_FIT, 0.5),
                (RelationType.FUZZY_FIT, 1.0),
                (RelationType.WITHIN_DOC_COREF, 1.0),
                (RelationType.WIKIPEDIA_TITLE_PARENTHESIS, 0.1),
                (RelationType.WIKIPEDIA_BE_COMP, 0.1),
                (RelationType.WIKIPEDIA_CATEGORY, 0.1),
                (RelationType.VERBOCEAN_MATCH, 0.1),
                (RelationType.WORDNET_DERIVATIONALLY, 1.0),
            ]

    class EntitySievesConfiguration(SievesConfiguration):
        def __init__(self):
            super(EntitySievesConfiguration, self).__init__()

            self.run_evaluation = True

            self.sieves_order = [
                (RelationType.SAME_HEAD_LEMMA, 1.0),
                (RelationType.EXACT_STRING, 1.0),
                (RelationType.FUZZY_FIT, 1.0),
                (RelationType.WIKIPEDIA_REDIRECT_LINK, 0.1),
                (RelationType.WIKIPEDIA_DISAMBIGUATION, 0.1),
                (RelationType.WORD_EMBEDDING_MATCH, 0.7),
                (RelationType.WORDNET_PARTIAL_SYNSET_MATCH, 0.1),
                (RelationType.FUZZY_HEAD_FIT, 0.5),
                (RelationType.WIKIPEDIA_CATEGORY, 0.1),
                (RelationType.WITHIN_DOC_COREF, 1.0),
                (RelationType.WIKIPEDIA_BE_COMP, 0.1),
                (RelationType.WIKIPEDIA_TITLE_PARENTHESIS, 0.1),
                (RelationType.WORDNET_SAME_SYNSET, 1.0),
                (RelationType.REFERENT_DICT, 0.5),
            ]