vis_rf_path.py File Reference

Namespaces
namespace	vis_rf_path

Variables
	vis_rf_path.file = Path(__file__).resolve()
	vis_rf_path.parent
	vis_rf_path.root
str	vis_rf_path.dir_path = "../datasets/tables/megallen/amazon-google-structured"
str	vis_rf_path.path_tableA = "/".join([dir_path, "table_a.csv"])
str	vis_rf_path.path_tableB = "/".join([dir_path, "table_b.csv"])
str	vis_rf_path.path_gold = "/".join([dir_path, "gold.csv"])
str	vis_rf_path.path_rule = "simjoin_entitymatching/blocker/rules/rules_amazon_google_structured_1.txt"
str	vis_rf_path.path_range = "simjoin_entitymatching/matcher/model/ranges/ranges_amazon_google_structured_1.txt"
str	vis_rf_path.path_tree = "simjoin_entitymatching/matcher/model/trees/trees_amazon_google_structured_1.txt"
str	vis_rf_path.path_rf = "simjoin_entitymatching/matcher/model/rf_amazon_google_structured_1.joblib"
	vis_rf_path.gold_graph = nx.Graph()
	vis_rf_path.tableA = read_csv_table(path_tableA)
	vis_rf_path.tableB = read_csv_table(path_tableB)
	vis_rf_path.gold = read_csv_golds(path_gold, gold_graph)
dict	vis_rf_path.map_A = {tableA.loc[ridx, "id"] : ridx for ridx in list(tableA.index)}
dict	vis_rf_path.map_B = {tableB.loc[ridx, "id"] : ridx for ridx in list(tableB.index)}
	vis_rf_path.attr_types_ltable = au.get_attr_types(tableA)
	vis_rf_path.attr_types_rtable = au.get_attr_types(tableB)
	vis_rf_path.rf = randf.RandomForest()
	vis_rf_path.graph
	vis_rf_path.at_ltable
	vis_rf_path.at_rtable
	vis_rf_path.wrtie_fea_names
	vis_rf_path.blk_res_cand
	vis_rf_path.H
	vis_rf_path.false_neg = pd.read_csv("test/debug/false_neg.csv")
	vis_rf_path.lid = int(row["ltable_id"])
	vis_rf_path.rid = int(row["rtable_id"])
	vis_rf_path.exclude_attrs