# this is the entrance of the auto-ER procedure
from md_discovery.script.md_discover import md_discover


ltable_path = '/home/w/PycharmProjects/py_entitymatching/py_entitymatching/datasets/end-to-end/Amazon-GoogleProducts/Amazon.csv'
rtable_path = '/home/w/PycharmProjects/py_entitymatching/py_entitymatching/datasets/end-to-end/Amazon-GoogleProducts/GoogleProducts.csv'
mapping_path = '/home/w/PycharmProjects/py_entitymatching/py_entitymatching/datasets/end-to-end/Amazon-GoogleProducts/Amzon_GoogleProducts_perfectMapping.csv'
mapping_lid = 'idAmazon'  # mapping表中左表id名
mapping_rid = 'idGoogleBase'  # mapping表中右表id名
ltable_id = 'id'  # 左表id字段名称
rtable_id = 'id'  # 右表id字段名称
target_attr = 'id'  # 进行md挖掘时的目标字段
lr_attrs_map = {'title': 'name'}  # 如果两个表中存在对应字段名称不一样的情况，将名称加入列表便于调整一致
similarity_threshold = 0.7
confidence_threshold = 0.8
interpretability_weight = 0.3

def run(l_table_path, r_table_path, mapping_path):
    # while The termination condition is not met:
    while True:
    #     er()
        md_discover()
    #     hpo()
    return


if __name__ == '__main__':
    # todo 距离度量用户可设置?
    print(ltable_path)