test_match Namespace Reference

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)
list	numeric_attr = ["price", "year"]
	schemas = list(tableA)[1:]
	attr_types_ltable = au.get_attr_types(tableA)
	attr_types_rtable = au.get_attr_types(tableB)
	random_forest
	trigraph
	blocking_attr
	model_path
	tree_path
	range_path
	num_tree
	sample_size
	ground_truth_label
	True
	training_strategy
	inmemory
	num_data
	at_ltable
	at_rtable
	is_interchangeable
	flag_consistent

Variable Documentation

at_ltable

test_match.at_ltable

at_rtable

test_match.at_rtable

attr_types_ltable

test_match.attr_types_ltable = au.get_attr_types(tableA)

attr_types_rtable

test_match.attr_types_rtable = au.get_attr_types(tableB)

blocking_attr

test_match.blocking_attr

dir_path

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

file

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

flag_consistent

test_match.flag_consistent

gold

test_match.gold = read_csv_golds(path_gold, gold_graph)

gold_graph

test_match.gold_graph = nx.Graph()

ground_truth_label

test_match.ground_truth_label

inmemory

test_match.inmemory

is_interchangeable

test_match.is_interchangeable

model_path

test_match.model_path

num_data

test_match.num_data

num_tree

test_match.num_tree

numeric_attr

test_match.numeric_attr = ["price", "year"]

parent

test_match.parent

path_gold

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

path_range

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

path_rf

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

path_rule

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

path_tableA

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

path_tableB

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

path_tree

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

random_forest

test_match.random_forest

range_path

test_match.range_path

root

test_match.root

sample_size

test_match.sample_size

schemas

list test_match.schemas = list(tableA)[1:]

tableA

test_match.tableA = read_csv_table(path_tableA)

tableB

test_match.tableB = read_csv_table(path_tableB)

training_strategy

test_match.training_strategy

tree_path

test_match.tree_path

trigraph

test_match.trigraph

True

test_match.True