def test_predict(self, data):
raw_model = VW()
raw_model.fit(data.x, data.y)
model = VWRegressor()
model.fit(data.x, data.y)
assert np.allclose(raw_model.predict(data.x), model.predict(data.x))
# ensure model can make multiple calls to predict
assert np.allclose(raw_model.predict(data.x), model.predict(data.x))
def test_predict(self, data):
raw_model = VW()
raw_model.fit(data.x, data.y)
model = VWRegressor()
model.fit(data.x, data.y)
assert np.allclose(raw_model.predict(data.x), model.predict(data.x))
# ensure model can make multiple calls to predict
assert np.allclose(raw_model.predict(data.x), model.predict(data.x))
def test_repr():
model = VW()
expected = "VW('convert_labels:False', 'quiet:True', 'sgd:False')"
assert expected == model.__repr__()
model = VWClassifier()
expected = "VWClassifier('convert_labels:False', "\
"'loss_function:logistic', 'quiet:True', 'sgd:False')"
assert expected == model.__repr__()
model = VWRegressor()
expected = "VWRegressor('convert_labels:False', 'quiet:True', 'sgd:False')"
assert expected == model.__repr__()
model = VW(convert_to_vw=False,
oaa=3,
loss_function='logistic',
probabilities=True)
expected = "VW('convert_labels:False', 'loss_function:logistic', "\
"'oaa:3', 'probabilities:True', 'quiet:True', 'sgd:False')"
assert expected == model.__repr__()
def test_sgd_param():
model1 = VWRegressor(sgd=True)
model2 = VWClassifier(sgd=True)
assert model1.get_params()['sgd'] == True
assert model2.get_params()['sgd'] == True
def test_save_load(tmp_path):
train_file = str(tmp_path / "train.model")
X = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [1, 2, 3, 4]
model_before = VWRegressor(l=100)
model_before.fit(X, y)
before_saving = model_before.predict(X)
model_before.save(train_file)
model_after = VWRegressor(l=100)
model_after.load(train_file)
after_loading = model_after.predict(X)
assert all([a == b for a, b in zip(before_saving, after_loading)])
def test_init(self):
assert isinstance(VWRegressor(), VWRegressor)
from vowpalwabbit.sklearn_vw import VWRegressor
X = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [1, 2, 3, 4]
model = VWRegressor(l=100)
model.fit(X, y)
print(model.predict(X))
model.save("/tmp/train.model")
model2 = VWRegressor(l=100)
model2.load("/tmp/train.model")
print(model2.predict(X))