def test_semantic_types(self, data_set, test_sizes):
logging.info("Testing semantic types.")
rank_score_map = defaultdict(lambda: defaultdict(lambda: 0))
count_map = defaultdict(lambda: defaultdict(lambda: 0))
index_config = {'name': data_set}
source_map = self.dataset_map[data_set]
double_name_list = list(source_map.values()) * 2
file_write.write("Dataset: " + data_set + "\n")
for size in test_sizes:
start_time = time.time()
for idx, source_name in enumerate(list(source_map.keys())):
train_names = [
source.index_name
for source in double_name_list[idx + 1:idx + size + 1]
]
train_examples_map = searcher.search_types_data(
index_config, train_names)
source = source_map[source_name]
for column in source.column_map.values():
if column.semantic_type:
textual_train_map = searcher.search_similar_text_data(
index_config, column.value_text, train_names)
semantic_types = column.predict_type(
train_examples_map, textual_train_map,
self.random_forest)
logging.debug(
" semantic types: {}".format(semantic_types))
for threshold in [0.01]:
found = False
rank = 1
rank_score = 0
for prediction in semantic_types:
if column.semantic_type in prediction[1]:
if prediction[0] > threshold and prediction[
0] != 0:
rank_score = 1.0 / (rank)
found = True
break
if prediction[0] != 0:
rank += len(prediction[1])
if not found and semantic_types[0][0] < threshold:
rank_score = 1
file_write.write(column.name + "\t" +
column.semantic_type + "\t" +
str(semantic_types) + "\n")
file_write.write(str(rank_score) + "\n")
rank_score_map[size][threshold] += rank_score
count_map[size][threshold] += 1
running_time = time.time() - start_time
for threshold in [0.01]:
file_write.write("Size: " + str(size) + " F-measure: " +
str(rank_score_map[size][threshold] * 1.0 /
count_map[size][threshold]) + " Time: " +
str(running_time) + " Count: " +
str(count_map[size][threshold]) + "\n")
def test_semantic_types(self, data_set, test_sizes):
rank_score_map = defaultdict(lambda: defaultdict(lambda: 0))
count_map = defaultdict(lambda: defaultdict(lambda: 0))
index_config = {'name': data_set}
source_map = self.dataset_map[data_set]
double_name_list = source_map.values() * 2
file_write.write("Dataset: " + data_set + "\n")
for size in test_sizes:
start_time = time.time()
for idx, source_name in enumerate(source_map.keys()):
train_names = [source.index_name for source in double_name_list[idx + 1: idx + size + 1]]
train_examples_map = searcher.search_types_data(index_config, train_names)
source = source_map[source_name]
for column in source.column_map.values():
if column.semantic_type:
textual_train_map = searcher.search_similar_text_data(index_config, column.value_text,
train_names)
semantic_types = column.predict_type(train_examples_map, textual_train_map, self.random_forest)
for threshold in [0.0]:
found = False
rank = 1
rank_score = 0
for prediction in semantic_types[:1]:
if column.semantic_type in prediction[1]:
if prediction[0] > threshold and prediction[0] != 0:
rank_score = 1.0 / (rank)
found = True
break
if prediction[0] != 0:
rank += len(prediction[1])
if not found and semantic_types[0][0] < threshold:
rank_score = 1
# file_write.write(
# column.name + "\t" + column.semantic_type + "\t" + str(semantic_types) + "\n")
file_write.write(str(rank_score) + "\n")
rank_score_map[size][threshold] += rank_score
count_map[size][threshold] += 1
running_time = time.time() - start_time
for threshold in [0.0]:
file_write.write(
"Size: " + str(size) + " F-measure: " + str(
rank_score_map[size][threshold] * 1.0 / count_map[size][threshold]) + " Time: " + str(
running_time) + " Count: " + str(count_map[size][threshold]) + "\n")
def test_semantic_types_from_2_sets(self, train_set, test_set):
self.read_class_type_from_csv("data/datasets/%s/classes.csv" %
test_set)
print(self.file_class_map.keys())
rank_score_map = defaultdict(lambda: 0)
count_map = defaultdict(lambda: 0)
source_result_map = {}
train_index_config = {'name': train_set}
for idx, source_name in enumerate(self.dataset_map[test_set]):
if source_name not in self.file_class_map:
continue
train_examples_map = searcher.search_types_data(
train_index_config, [self.file_class_map[source_name]])
source = self.dataset_map[test_set][source_name]
column_result_map = {}
for column in source.column_map.values():
if not column.semantic_type or not column.value_list or "ontology" not in column.semantic_type:
continue
textual_train_map = searcher.search_similar_text_data(
train_index_config, column.value_text,
[self.file_class_map[source_name]])
semantic_types = column.predict_type(train_examples_map,
textual_train_map,
self.random_forest)
print(column.name)
file_write.write(column.name + "\t" + column.semantic_type +
"\t" + str(semantic_types) + "\n")
for threshold in [0.1, 0.15, 0.2, 0.25, 0.5]:
rank = 0
found = False
rank_score = 0
for prediction in semantic_types:
if column.semantic_type in prediction[1]:
if prediction[0][1] >= threshold:
rank_score = 1.0 / (rank + 1)
found = True
if not found and prediction[0][0] != 0:
rank += len(prediction[1])
if not found:
if semantic_types[0][0][1] < threshold:
rank_score = 1
file_write.write(str(rank_score) + "\n")
rank_score_map[threshold] += rank_score
count_map[threshold] += 1
source_result_map[source_name] = column_result_map
for threshold in [0.1, 0.15, 0.2, 0.25, 0.5]:
file_write.write(" MRR: " + str(rank_score_map[threshold] * 1.0 /
count_map[threshold]) +
" Count: " + str(count_map[threshold]) + "\n")
return source_result_map
def test_semantic_types_from_2_sets(self, train_set, test_set):
# self.read_class_type_from_csv("data/datasets/%s/classes.csv" % test_set)
# print self.file_class_map.keys()
rank_score_map = defaultdict(lambda: 0)
count_map = defaultdict(lambda: 0)
source_result_map = {}
train_index_config = {'name': train_set}
train_names = [source.index_name for source in self.dataset_map[train_set].values()]
for idx, source_name in enumerate(self.dataset_map[test_set]):
# if source_name not in self.file_class_map:
# continue
train_examples_map = searcher.search_types_data(train_index_config,
train_names)
source = self.dataset_map[test_set][source_name]
self.logger.info("Test source: %s", source_name)
column_result_map = {}
for column in source.column_map.values():
# if not column.semantic_type or not column.value_list or "ontology" not in column.semantic_type:
# continue
if not column.semantic_type or not column.value_list:
continue
textual_train_map = searcher.search_similar_text_data(train_index_config, column.value_text,
train_names)
semantic_types = column.predict_type(train_examples_map, textual_train_map, self.random_forest)
column_result_map[column.name] = semantic_types
self.logger.info(" -> column: %s", column.name)
file_write.write(
column.name + "\t" + column.semantic_type + "\t" + str(semantic_types) + "\n")
for threshold in [0.0, 0.1, 0.15, 0.2, 0.25, 0.5]:
found = False
rank = 1
rank_score = 0
for prediction in semantic_types[:1]:
if column.semantic_type in prediction[1]:
if prediction[0] > threshold and prediction[0] != 0:
rank_score = 1.0 / rank
found = True
break
if prediction[0] != 0:
rank += len(prediction[1])
if not found and semantic_types[0][0] < threshold:
rank_score = 1
file_write.write(str(rank_score) + "\n")
rank_score_map[threshold] += rank_score
count_map[threshold] += 1
source_result_map[source_name] = column_result_map
for threshold in [0.0, 0.1, 0.15, 0.2, 0.25, 0.5]:
file_write.write(
" MRR: " + str(
rank_score_map[threshold] * 1.0 / count_map[threshold]) + " Count: " + str(
count_map[threshold]) + " threshold=" + str(threshold) + "\n")
return source_result_map
def test_semantic_types_from_2_sets(self, train_set, test_set):
# self.read_class_type_from_csv("data/datasets/%s/classes.csv" % test_set)
# print self.file_class_map.keys()
rank_score_map = defaultdict(lambda: 0)
count_map = defaultdict(lambda: 0)
source_result_map = {}
train_index_config = {'name': train_set}
train_names = [
source.index_name
for source in self.dataset_map[train_set].values()
]
self.logger.info("Train source: %s", train_names)
valid = True
for idx, source_name in enumerate(self.dataset_map[test_set]):
# if source_name not in self.file_class_map:
# continue
train_examples_map = searcher.search_types_data(
train_index_config, train_names)
source = self.dataset_map[test_set][source_name]
self.logger.info("Test source: %s", source_name)
column_result_map = {}
for column in source.column_map.values():
# if not column.semantic_type or not column.value_list or "ontology" not in column.semantic_type:
# continue
if not column.semantic_type or not column.value_list:
continue
textual_train_map = searcher.search_similar_text_data(
train_index_config, column.value_text, train_names)
# self.logger.info(textual_train_map)
try:
semantic_types = column.predict_type(
train_examples_map, textual_train_map,
self.random_forest)
except KeyError:
print("KEY ERROR")
valid = False
break
# if(not semantic_types):
# self.logger.info("Could not do "+column.name)
# continue
column_result_map[column.name] = semantic_types
self.logger.info(" -> column: %s", column.name)
file_write.write(column.name + "\t" + column.semantic_type +
"\t" + str(semantic_types) + "\n")
for threshold in [0.0, 0.1, 0.15, 0.2, 0.25, 0.5]:
found = False
rank = 1
rank_score = 0
for prediction in semantic_types[:1]:
if column.semantic_type in prediction[1]:
if prediction[0] > threshold and prediction[0] != 0:
rank_score = 1.0 / rank
found = True
break
if prediction[0] != 0:
rank += len(prediction[1])
if not found and semantic_types[0][0] < threshold:
rank_score = 1
file_write.write(str(rank_score) + "\n")
rank_score_map[threshold] += rank_score
count_map[threshold] += 1
source_result_map[source_name] = column_result_map
# for threshold in [0.0, 0.1, 0.15, 0.2, 0.25, 0.5]:
# file_write.write(
# " MRR: " + str(
# rank_score_map[threshold] * 1.0 / count_map[threshold]) + " Count: " + str(
# count_map[threshold]) + " threshold=" + str(threshold) + "\n")
return source_result_map