def test_remove_all_keywords(self):
burst = Burst.run(Config())
keyword_dataset = self._make_stub_keyword_dataset()
for idx, result in burst.add_keyword(keyword_dataset):
pass
self.assertEqual(burst.remove_all_keywords(), True)
burst.stop()
def test_remove_all_keywords(self):
burst = Burst.run(Config())
keyword_dataset = self._make_stub_keyword_dataset()
for idx, result in burst.add_keyword(keyword_dataset):
pass
self.assertEqual(burst.remove_all_keywords(), True)
burst.stop()
def test_get_results(self):
burst = Burst.run(Config())
burst.get_result('keyword')
burst.stop()
def test_add_documents(self):
burst = Burst.run(Config())
document_dataset = self._make_stub_document_dataset()
for idx, result in burst.add_documents(document_dataset):
self.assertEqual(result, 1)
burst.stop()
def test_add_keyword(self):
burst = Burst.run(Config())
keyword_dataset = self._make_stub_keyword_dataset()
for idx, result in burst.add_keyword(keyword_dataset):
self.assertEqual(result, True)
burst.stop()
def test_embedded(self): burst = Burst.run(Config(), embedded=True) burst.stop()
def test_embedded(self):
burst = Burst.run(Config(), embedded=True)
burst.stop()
def test_get_all_bursted_results_at(self):
burst = Burst.run(Config())
self.assertRaises(
ValueError, burst.get_all_bursted_results_at, 'hoge')
burst.get_all_bursted_results_at(10)
burst.stop()
def test_get_all_keywords(self):
burst = Burst.run(Config())
burst.get_all_keywords()
burst.stop()
def test_get_all_bursted_results_at(self):
burst = Burst.run(Config())
self.assertRaises(ValueError, burst.get_all_bursted_results_at, 'hoge')
burst.get_all_bursted_results_at(10)
burst.stop()
def test_get_all_bursted_results(self):
burst = Burst.run(Config())
burst.get_all_bursted_results()
burst.stop()
def test_get_result_at(self):
burst = Burst.run(Config())
self.assertRaises(ValueError, burst.get_result_at, 'keyword', 'hoge')
burst.get_result_at('keyword', 10)
burst.stop()
def test_get_results(self):
burst = Burst.run(Config())
burst.get_result('keyword')
burst.stop()
def test_add_documents(self):
burst = Burst.run(Config())
document_dataset = self._make_stub_document_dataset()
for idx, result in burst.add_documents(document_dataset):
self.assertEqual(result, 1)
burst.stop()
def test_add_keyword(self):
burst = Burst.run(Config())
keyword_dataset = self._make_stub_keyword_dataset()
for idx, result in burst.add_keyword(keyword_dataset):
self.assertEqual(result, True)
burst.stop()
def test_get_result_at(self):
burst = Burst.run(Config())
self.assertRaises(
ValueError, burst.get_result_at, 'keyword', 'hoge')
burst.get_result_at('keyword', 10)
burst.stop()
def test_get_all_bursted_results(self): burst = Burst.run(Config()) burst.get_all_bursted_results() burst.stop()
from jubakit.burst import DocumentSchema, DocumentDataset
from jubakit.burst import Burst, Config
from jubakit.loader.csv import CSVLoader
keyword_loader = CSVLoader('burst_keywords.csv')
keyword_schema = KeywordSchema({
'keyword': KeywordSchema.KEYWORD,
'scaling': KeywordSchema.SCALING,
'gamma': KeywordSchema.GAMMA
})
keyword_dataset = KeywordDataset(keyword_loader, keyword_schema)
document_loader = CSVLoader('burst_documents.csv')
document_schema = DocumentSchema({
'position': DocumentSchema.POSITION,
'text': DocumentSchema.TEXT
})
document_dataset = DocumentDataset(document_loader, document_schema)
burst = Burst.run(Config())
for _ in burst.add_keyword(keyword_dataset):
pass
for _ in burst.add_documents(document_dataset):
pass
for result in burst.get_result('burst').batches:
print(result)
burst.stop()
def test_get_all_keywords(self): burst = Burst.run(Config()) burst.get_all_keywords() burst.stop()
def test_simple(self): burst = Burst() burst.stop()
from jubakit.burst import KeywordSchema, KeywordDataset
from jubakit.burst import DocumentSchema, DocumentDataset
from jubakit.burst import Burst, Config
from jubakit.loader.csv import CSVLoader
keyword_loader = CSVLoader('burst_keywords.csv')
keyword_schema = KeywordSchema({
'keyword': KeywordSchema.KEYWORD,
'scaling': KeywordSchema.SCALING,
'gamma': KeywordSchema.GAMMA
})
keyword_dataset = KeywordDataset(keyword_loader, keyword_schema)
document_loader = CSVLoader('burst_documents.csv')
document_schema = DocumentSchema({
'position': DocumentSchema.POSITION,
'text': DocumentSchema.TEXT
})
document_dataset = DocumentDataset(document_loader, document_schema)
burst = Burst.run(Config())
for _ in burst.add_keyword(keyword_dataset): pass
for _ in burst.add_documents(document_dataset): pass
for result in burst.get_result('burst').batches:
print(result)
burst.stop()
def test_simple(self):
burst = Burst()
burst.stop()