def test_loadsave(self):
x = Classifier(CONFIG)
x.train([
LabeledDatum('Y', Datum({'x': 'y'})),
LabeledDatum('N', Datum({'x': 'n'})),
])
path = '/tmp/127.0.0.1_0_classifier_hoge.jubatus'
def _remove_model():
try:
os.remove(path)
except Exception:
pass
_remove_model()
try:
self.assertEqual({'127.0.0.1': 0}, x.save('hoge'))
self.assertTrue(os.path.isfile(path))
x = Classifier(CONFIG)
self.assertTrue(x.load('hoge'))
y = x.classify([
Datum({'x': 'y'}),
Datum({'x': 'n'})
])
self.assertEqual(['Y', 'N'], [list(sorted(
z, key=lambda x:x.score, reverse=True))[0].label for z in y])
finally:
_remove_model()
def test_loadsave(self):
x = Classifier(CONFIG)
x.train([
LabeledDatum('Y', Datum({'x': 'y'})),
LabeledDatum('N', Datum({'x': 'n'})),
])
path = '/tmp/127.0.0.1_0_classifier_hoge.jubatus'
def _remove_model():
try:
os.remove(path)
except Exception:
pass
_remove_model()
try:
self.assertEqual(
{'127.0.0.1_0': '/tmp/127.0.0.1_0_classifier_hoge.jubatus'},
x.save('hoge'))
self.assertTrue(os.path.isfile(path))
x = Classifier(CONFIG)
self.assertTrue(x.load('hoge'))
y = x.classify([Datum({'x': 'y'}), Datum({'x': 'n'})])
self.assertEqual(['Y', 'N'], [
list(sorted(z, key=lambda x: x.score, reverse=True))[0].label
for z in y
])
finally:
_remove_model()
def test_types(self):
x = Classifier(CONFIG)
x.train([
LabeledDatum('Y', Datum({'x': 'y'})),
LabeledDatum('N', Datum({'x': 'n'})),
])
y = x.classify([Datum({'x': 'y'}), Datum({'x': 'n'})])
self.assertTrue(isinstance(y[0][0], EstimateResult))
self.assertEqual(['Y', 'N'], [
list(sorted(z, key=lambda x: x.score, reverse=True))[0].label
for z in y
])
def test_types(self):
x = Classifier(CONFIG)
x.train([
LabeledDatum('Y', Datum({'x': 'y'})),
LabeledDatum('N', Datum({'x': 'n'})),
])
y = x.classify([
Datum({'x': 'y'}),
Datum({'x': 'n'})
])
self.assertTrue(isinstance(y[0][0], EstimateResult))
self.assertEqual(['Y', 'N'], [list(sorted(
z, key=lambda x:x.score, reverse=True))[0].label for z in y])
def test_num(self):
x = Classifier(CONFIG)
self.assertEqual(
2, x.train([
('Y', Datum({'x': 1})),
('N', Datum({'x': -1})),
]))
def _test_classify(x):
y = x.classify([Datum({'x': 1}), Datum({'x': -1})])
self.assertEqual(['Y', 'N'], [
list(sorted(z, key=lambda x: x.score, reverse=True))[0].label
for z in y
])
self.assertEqual(x.get_labels(), {'N': 1, 'Y': 1})
_test_classify(x)
model = x.save_bytes()
x.clear()
self.assertEqual({}, x.get_labels())
x.set_label('Y')
x.set_label('N')
self.assertEqual({'N': 0, 'Y': 0}, x.get_labels())
x.delete_label(u'Y')
self.assertEqual({'N': 0}, x.get_labels())
x = Classifier(CONFIG)
x.load_bytes(model)
_test_classify(x)
self.assertEqual(CONFIG, json.loads(x.get_config()))
def test_num(self):
x = Classifier(CONFIG)
self.assertEqual(2, x.train([
('Y', Datum({'x': 1})),
('N', Datum({'x': -1})),
]))
def _test_classify(x):
y = x.classify([
Datum({'x': 1}),
Datum({'x': -1})
])
self.assertEqual(['Y', 'N'], [list(sorted(
z, key=lambda x:x.score, reverse=True))[0].label for z in y])
self.assertEqual(x.get_labels(), {'N': 1, 'Y': 1})
_test_classify(x)
model = x.save_bytes()
x.clear()
self.assertEqual({}, x.get_labels())
x.set_label('Y')
x.set_label('N')
self.assertEqual({'N': 0, 'Y': 0}, x.get_labels())
x.delete_label(u'Y')
self.assertEqual({'N': 0}, x.get_labels())
x = Classifier(CONFIG)
x.load_bytes(model)
_test_classify(x)
self.assertEqual(CONFIG, json.loads(x.get_config()))
def test_str(self):
x = Classifier(CONFIG)
self.assertEqual(
2, x.train([
('Y', Datum({'x': 'y'})),
('N', Datum({'x': 'n'})),
]))
y = x.classify([Datum({'x': 'y'}), Datum({'x': 'n'})])
self.assertEqual(['Y', 'N'], [
list(sorted(z, key=lambda x: x.score, reverse=True))[0].label
for z in y
])
def test_str(self):
x = Classifier(CONFIG)
self.assertEqual(2, x.train([
('Y', Datum({'x': 'y'})),
('N', Datum({'x': 'n'})),
]))
y = x.classify([
Datum({'x': 'y'}),
Datum({'x': 'n'})
])
self.assertEqual(['Y', 'N'], [list(sorted(
z, key=lambda x:x.score, reverse=True))[0].label for z in y])
def test_num(self):
x = Classifier(CONFIG)
self.assertEqual(
2, x.train([
('Y', Datum({'x': 1})),
('N', Datum({'x': -1})),
]))
def _test_classify(x):
y = x.classify([Datum({'x': 1}), Datum({'x': -1})])
self.assertEqual(['Y', 'N'], [
list(sorted(z, key=lambda x: x.score, reverse=True))[0].label
for z in y
])
self.assertEqual(x.get_labels(), {'N': 1, 'Y': 1})
_test_classify(x)
model = x.save_bytes()
self.assertTrue(x.clear())
self.assertEqual({}, x.get_labels())
x.set_label('Y')
x.set_label('N')
self.assertEqual({'N': 0, 'Y': 0}, x.get_labels())
x.delete_label(u'Y')
self.assertEqual({'N': 0}, x.get_labels())
x = Classifier(CONFIG)
x.load_bytes(model)
_test_classify(x)
self.assertEqual(CONFIG, json.loads(x.get_config()))
if sys.version_info[0] == 3:
x = pickle.loads(pickle.dumps(x))
_test_classify(x)
self.assertEqual(CONFIG, json.loads(x.get_config()))
st = x.get_status()
self.assertTrue(isinstance(st, dict))
self.assertEqual(len(st), 1)
self.assertEqual(list(st.keys())[0], 'embedded')
self.assertTrue(isinstance(st['embedded'], dict))
