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# this is the entrance of the auto-ER procedure
from md_discovery . script . md_discover import md_discover
# todo: magellan ER模块读入初始化配置或hpo配置
# todo: 模块间的自动化调用
# 入口到ER/HPO到ER
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： （ ）
# 7. 距离度量方式 ？
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
print ( ltable_path )
