def load_binary_clf_tracks() -> typing.List[Track]:
"""Return binary classification tracks."""
return [
Track(
name="Phishing",
dataset=datasets.Phishing(),
metric=metrics.Accuracy() + metrics.F1(),
),
Track(
name="Bananas",
dataset=datasets.Bananas(),
metric=metrics.Accuracy() + metrics.F1(),
),
]
def test_perceptron_sklearn_coherence():
"""Checks that the sklearn and river implementations produce the same results."""
ss = preprocessing.StandardScaler()
cr = lm.Perceptron()
sk = sklm.Perceptron()
for x, y in datasets.Bananas():
x = ss.learn_one(x).transform_one(x)
cr.learn_one(x, y)
sk.partial_fit([list(x.values())], [y], classes=[False, True])
for i, w in enumerate(cr.weights.values()):
assert math.isclose(w, sk.coef_[0][i])
assert math.isclose(cr.intercept, sk.intercept_[0])
def test_log_reg_sklearn_coherence(river_params, sklearn_params):
"""Checks that the sklearn and river implementations produce the same results."""
ss = preprocessing.StandardScaler()
rv = lm.LogisticRegression(**river_params)
sk = sklm.SGDClassifier(**sklearn_params)
for x, y in datasets.Bananas().take(100):
x = ss.learn_one(x).transform_one(x)
rv.learn_one(x, y)
sk.partial_fit([list(x.values())], [y], classes=[False, True])
for i, w in enumerate(rv.weights.values()):
assert math.isclose(w, sk.coef_[0][i])
assert math.isclose(rv.intercept, sk.intercept_[0])
def test_log_reg_sklearn_coherence():
"""Checks that the sklearn and river implementations produce the same results."""
ss = preprocessing.StandardScaler()
cr = lm.LogisticRegression(optimizer=optim.SGD(.01))
sk = sklm.SGDClassifier(learning_rate='constant',
eta0=.01,
alpha=.0,
loss='log')
for x, y in datasets.Bananas():
x = ss.learn_one(x).transform_one(x)
cr.learn_one(x, y)
sk.partial_fit([list(x.values())], [y], classes=[False, True])
for i, w in enumerate(cr.weights.values()):
assert math.isclose(w, sk.coef_[0][i])
assert math.isclose(cr.intercept, sk.intercept_[0])
p = {j: random.gauss(0, 1) for j in keys}
norm = utils.math.norm(p, order=2)
for j in p:
p[j] /= norm
yield p
@pytest.mark.parametrize(
'lm, dataset',
[
pytest.param(lm(
optimizer=copy.deepcopy(optimizer), initializer=initializer, l2=0),
dataset,
id=f'{lm.__name__} - {optimizer} - {initializer}')
for lm, dataset in [(lm.LinearRegression, datasets.TrumpApproval()
), (lm.LogisticRegression, datasets.Bananas())]
for optimizer, initializer in itertools.product(
[
optim.AdaBound(),
optim.AdaDelta(),
optim.AdaGrad(),
optim.AdaMax(),
optim.Adam(),
optim.AMSGrad(),
# TODO: check momentum optimizers
# optim.Momentum(),
# optim.NesterovMomentum(),
optim.RMSProp(),
optim.SGD()
],
[