def test_basic():
nr_class = 3
model = AveragedPerceptron(((1, ), (2, ), (3, ), (4, ), (5, )))
instances = [(1, {1: 1, 3: -5}), (2, {2: 4, 3: 5})]
for clas, feats in instances:
eg = Example(nr_class)
eg.features = feats
model(eg)
eg.costs = [i != clas for i in range(nr_class)]
model.update(eg)
eg = Example(nr_class)
eg.features = {1: 2, 2: 1}
model(eg)
assert eg.guess == 2
eg = Example(nr_class)
eg.features = {0: 2, 2: 1}
model(eg)
assert eg.scores[1] == 0
eg = Example(nr_class)
eg.features = {1: 2, 2: 1}
model(eg)
assert eg.scores[2] > 0
eg = Example(nr_class)
eg.features = {1: 2, 1: 1}
model(eg)
assert eg.scores[1] > 0
eg = Example(nr_class)
eg.features = {0: 3, 3: 1}
model(eg)
assert eg.scores[1] < 0
eg = Example(nr_class)
eg.features = {0: 3, 3: 1}
model(eg)
assert eg.scores[2] > 0
def model(instances):
templates = []
for batch in instances:
for _, feats in batch:
for key in feats:
templates.append((key, ))
templates = tuple(set(templates))
model = AveragedPerceptron(templates)
for batch in instances:
model.time += 1
for clas, feats in batch:
for key, value in feats.items():
model.update_weight(key, clas, value)
return model
def test_dump_load(model):
loc = tempfile.mkstemp()[1]
model.end_training()
model.dump(loc)
string = open(loc, 'rb').read()
assert string
new_model = AveragedPerceptron([(1, ), (2, ), (3, ), (4, )])
nr_class = 5
assert get_scores(nr_class, model, {1: 1, 3: 1, 4: 1}) != \
get_scores(nr_class, new_model, {1:1, 3:1, 4:1})
assert get_scores(nr_class, model, {2:1, 5:1}) != \
get_scores(nr_class, new_model, {2:1, 5:1})
assert get_scores(nr_class, model, {2:1, 3:1, 4:1}) != \
get_scores(nr_class, new_model, {2:1, 3:1, 4:1})
new_model.load(loc)
assert get_scores(nr_class, model, {1:1, 3:1, 4:1}) == \
get_scores(nr_class, new_model, {1:1, 3:1, 4:1})
assert get_scores(nr_class, model, {2:1, 5:1}) == \
get_scores(nr_class, new_model, {2:1, 5:1})
assert get_scores(nr_class, model, {2:1, 3:1, 4:1}) == \
get_scores(nr_class, new_model, {2:1, 3:1, 4:1})
def __init__(self, nlp, nr_class):
self.nlp = nlp
self.nr_class = nr_class
self._eg = Example(nr_class=nr_class)
self._model = AveragedPerceptron([])
