Variables
	file = Path(__file__).resolve()

	parent

	root

str	dir_path = "../datasets/tables/megallen/amazon-google-structured"

str	path_tableA = "/".join([dir_path, "table_a.csv"])

str	path_tableB = "/".join([dir_path, "table_b.csv"])

str	path_gold = "/".join([dir_path, "gold.csv"])

str	path_rule = "test/tmp/rules_amazon_google_structured1.txt"

str	path_range = "test/tmp/ranges_amazon_google_structured.txt"

str	path_tree = "test/tmp/trees_amazon_google_structured.txt"

str	path_rf = "test/tmp/rf_amazon_google_structured.joblib"

	gold_graph = nx.Graph()

	tableA = read_csv_table(path_tableA)

	tableB = read_csv_table(path_tableB)

	gold = read_csv_golds(path_gold, gold_graph)

	attr_types_ltable = au.get_attr_types(tableA)

	attr_types_rtable = au.get_attr_types(tableB)

	sample_strategy

	blocking_attr

	cluster_tau

	sample_tau

	step2_tau

	num_data

	random_forest

	trigraph

	model_path

	tree_path

	range_path

	num_tree

	sample_size

	ground_truth_label

	True

	training_strategy

	inmemory

	at_ltable

	at_rtable

	rule_path

	move_strategy

	additional_rule_path

	None

	optimal_rule_path

	blocking_attr_type

	blocking_top_k

	table_size

	is_join_topk

	is_idf_weighted

Variable Documentation

◆ additional_rule_path

test_block_all.additional_rule_path

◆ at_ltable

test_block_all.at_ltable

◆ at_rtable

test_block_all.at_rtable

◆ attr_types_ltable

test_block_all.attr_types_ltable = au.get_attr_types(tableA)

◆ attr_types_rtable

test_block_all.attr_types_rtable = au.get_attr_types(tableB)

◆ blocking_attr

test_block_all.blocking_attr

◆ blocking_attr_type

test_block_all.blocking_attr_type

◆ blocking_top_k

test_block_all.blocking_top_k

◆ cluster_tau

test_block_all.cluster_tau

◆ dir_path

str test_block_all.dir_path = "../datasets/tables/megallen/amazon-google-structured"

◆ file

test_block_all.file = Path(__file__).resolve()

◆ gold

test_block_all.gold = read_csv_golds(path_gold, gold_graph)

◆ gold_graph

test_block_all.gold_graph = nx.Graph()

◆ ground_truth_label

test_block_all.ground_truth_label

◆ inmemory

test_block_all.inmemory

◆ is_idf_weighted

test_block_all.is_idf_weighted

◆ is_join_topk

test_block_all.is_join_topk

◆ model_path

test_block_all.model_path

◆ move_strategy

test_block_all.move_strategy

◆ None

test_block_all.None

◆ num_data

test_block_all.num_data

◆ num_tree

test_block_all.num_tree

◆ optimal_rule_path

test_block_all.optimal_rule_path

◆ parent

test_block_all.parent

◆ path_gold

test_block_all.path_gold = "/".join([dir_path, "gold.csv"])

◆ path_range

str test_block_all.path_range = "test/tmp/ranges_amazon_google_structured.txt"

◆ path_rf

test_block_all.path_rf = "test/tmp/rf_amazon_google_structured.joblib"

◆ path_rule

test_block_all.path_rule = "test/tmp/rules_amazon_google_structured1.txt"

◆ path_tableA

test_block_all.path_tableA = "/".join([dir_path, "table_a.csv"])

◆ path_tableB

test_block_all.path_tableB = "/".join([dir_path, "table_b.csv"])

◆ path_tree

test_block_all.path_tree = "test/tmp/trees_amazon_google_structured.txt"

◆ random_forest

test_block_all.random_forest

◆ range_path

test_block_all.range_path

◆ root

test_block_all.root

◆ rule_path

test_block_all.rule_path

◆ sample_size

test_block_all.sample_size

◆ sample_strategy

test_block_all.sample_strategy

◆ sample_tau

test_block_all.sample_tau

◆ step2_tau

test_block_all.step2_tau

◆ table_size

test_block_all.table_size

◆ tableA

test_block_all.tableA = read_csv_table(path_tableA)

◆ tableB

test_block_all.tableB = read_csv_table(path_tableB)

◆ training_strategy

test_block_all.training_strategy

◆ tree_path

test_block_all.tree_path

◆ trigraph

test_block_all.trigraph

◆ True

test_block_all.True

Variables