def test_multilabel(self):
x = Classifier(CONFIG)
tdata = np.array([
[0, 1, 0],
[0, 0, 1],
[1, 0, 0],
], dtype='f8')
ttargets = np.array([1, 2, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
], dtype='f8'))
self.assertTrue(isinstance(y, np.ndarray))
self.assertEqual(y.dtype, np.int32)
self.assertTrue(np.equal(y, np.array([0, 1, 2])).all())
y = x.decision_function(np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
], dtype='f8'))
self.assertTrue(isinstance(y, np.ndarray))
self.assertEqual((3, 3), y.shape)
self.assertTrue(y[0][0] > y[0][1] and y[0][0] > y[0][2])
self.assertTrue(y[1][1] > y[1][0] and y[1][1] > y[1][2])
self.assertTrue(y[2][2] > y[2][0] and y[2][2] > y[2][1])
def test_load_config_from_file(self):
with tempfile.NamedTemporaryFile() as fd:
fd.write(json.dumps(CONFIG).encode('utf8'))
fd.flush()
x = Classifier(fd.name)
self.assertEqual(CONFIG, json.loads(x.get_config()))
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_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_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 linear_classifier(method='AROW', regularization_weight=1.0):
""" 線形分類器を起動する """
cfg = copy.deepcopy(CONFIG)
cfg['method'] = method
if method not in ('perceptron', 'PA'): # perceptron, PA 以外はパラメータが必要
cfg['parameter']['regularization_weight'] = regularization_weight
return Classifier(cfg)
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_issue_30(self):
path = '/tmp/127.0.0.1_0_classifier_foo.jubatus'
open(path, 'wb').close()
try:
Classifier({'method': 'perceptron', 'converter': {}}).load('foo')
except Exception:
pass
finally:
os.remove(path)
def test_multilabel(self):
x = Classifier(CONFIG)
tdata = np.array([
[0, 1, 0],
[0, 0, 1],
[1, 0, 0],
], dtype='f8')
ttargets = np.array([1, 2, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
], dtype='f8'))
self.assertTrue(isinstance(y, np.ndarray))
self.assertEqual(y.dtype, np.int32)
self.assertTrue(np.equal(y, np.array([0, 1, 2])).all())
y = x.decision_function(
np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
], dtype='f8'))
self.assertTrue(isinstance(y, np.ndarray))
self.assertEqual((3, 3), y.shape)
self.assertTrue(y[0][0] > y[0][1] and y[0][0] > y[0][2])
self.assertTrue(y[1][1] > y[1][0] and y[1][1] > y[1][2])
self.assertTrue(y[2][2] > y[2][0] and y[2][2] > y[2][1])
def test_sparse(self):
from scipy.sparse import csr_matrix
x = Classifier(CONFIG)
tdata = csr_matrix(np.array([
[1, 0, 1],
[0, 1, 1],
], dtype='f8'))
ttargets = np.array([1, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(
csr_matrix(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8')))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
y = x.decision_function(
csr_matrix(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8')))
self.assertEqual(2, len(y))
self.assertTrue(not isinstance(y[0], (list, tuple, np.ndarray)))
self.assertTrue(y[0] > 0)
self.assertTrue(y[1] < 0)
def test_numpy(self):
x = Classifier(CONFIG)
tdata = np.array([
[1, 0, 1],
[0, 1, 1],
], dtype='f8')
ttargets = np.array([1, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
y = x.decision_function(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(2, len(y))
self.assertTrue(not isinstance(y[0], (list, tuple, np.ndarray)))
self.assertTrue(y[0] > 0)
self.assertTrue(y[1] < 0)
def test_sparse(self):
from scipy.sparse import csr_matrix
x = Classifier(CONFIG)
tdata = csr_matrix(np.array([
[1, 0, 1],
[0, 1, 1],
], dtype='f8'))
ttargets = np.array([1, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(csr_matrix(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8')))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
y = x.decision_function(csr_matrix(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8')))
self.assertEqual(2, len(y))
self.assertTrue(not isinstance(y[0], (list, tuple, np.ndarray)))
self.assertTrue(y[0] > 0)
self.assertTrue(y[1] < 0)
def test_numpy(self):
x = Classifier(CONFIG)
tdata = np.array([
[1, 0, 1],
[0, 1, 1],
], dtype='f8')
ttargets = np.array([1, 0])
x.partial_fit(tdata, ttargets)
y = x.predict(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(1, y[0])
self.assertEqual(0, y[1])
y = x.decision_function(np.array([
[1, 0, 0],
[0, 1, 0],
], dtype='f8'))
self.assertEqual(2, len(y))
self.assertTrue(not isinstance(y[0], (list, tuple, np.ndarray)))
self.assertTrue(y[0] > 0)
self.assertTrue(y[1] < 0)
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))
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()))