def test_exponential_loss():
"""Check that we compute the negative gradient of the exponential loss.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/9666
"""
loss = ExponentialLoss(n_classes=2)
y_true = np.array([0])
y_pred = np.array([0])
# we expect to have loss = exp(0) = 1
assert loss(y_true, y_pred) == pytest.approx(1)
# we expect to have negative gradient = -1 * (1 * exp(0)) = -1
assert_allclose(loss.negative_gradient(y_true, y_pred), -1)
def test_init_raw_predictions_shapes():
# Make sure get_init_raw_predictions returns float64 arrays with shape
# (n_samples, K) where K is 1 for binary classification and regression, and
# K = n_classes for multiclass classification
rng = np.random.RandomState(0)
n_samples = 100
X = rng.normal(size=(n_samples, 5))
y = rng.normal(size=n_samples)
for loss in (LeastSquaresError(n_classes=1),
LeastAbsoluteError(n_classes=1),
QuantileLossFunction(n_classes=1),
HuberLossFunction(n_classes=1)):
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, 1)
assert raw_predictions.dtype == np.float64
y = rng.randint(0, 2, size=n_samples)
for loss in (BinomialDeviance(n_classes=2), ExponentialLoss(n_classes=2)):
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, 1)
assert raw_predictions.dtype == np.float64
for n_classes in range(3, 5):
y = rng.randint(0, n_classes, size=n_samples)
loss = MultinomialDeviance(n_classes=n_classes)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, n_classes)
assert raw_predictions.dtype == np.float64
def test_init_raw_predictions_values():
# Make sure the get_init_raw_predictions() returns the expected values for
# each loss.
rng = np.random.RandomState(0)
n_samples = 100
X = rng.normal(size=(n_samples, 5))
y = rng.normal(size=n_samples)
# Least squares loss
loss = LeastSquaresError(n_classes=1)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
# Make sure baseline prediction is the mean of all targets
assert_almost_equal(raw_predictions, y.mean())
# Least absolute and huber loss
for Loss in (LeastAbsoluteError, HuberLossFunction):
loss = Loss(n_classes=1)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
# Make sure baseline prediction is the median of all targets
assert_almost_equal(raw_predictions, np.median(y))
# Quantile loss
for alpha in (.1, .5, .9):
loss = QuantileLossFunction(n_classes=1, alpha=alpha)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
# Make sure baseline prediction is the alpha-quantile of all targets
assert_almost_equal(raw_predictions, np.percentile(y, alpha * 100))
y = rng.randint(0, 2, size=n_samples)
# Binomial deviance
loss = BinomialDeviance(n_classes=2)
init_estimator = loss.init_estimator().fit(X, y)
# Make sure baseline prediction is equal to link_function(p), where p
# is the proba of the positive class. We want predict_proba() to return p,
# and by definition
# p = inverse_link_function(raw_prediction) = sigmoid(raw_prediction)
# So we want raw_prediction = link_function(p) = log(p / (1 - p))
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
p = y.mean()
assert_almost_equal(raw_predictions, np.log(p / (1 - p)))
# Exponential loss
loss = ExponentialLoss(n_classes=2)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
p = y.mean()
assert_almost_equal(raw_predictions, .5 * np.log(p / (1 - p)))
# Multinomial deviance loss
for n_classes in range(3, 5):
y = rng.randint(0, n_classes, size=n_samples)
loss = MultinomialDeviance(n_classes=n_classes)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
for k in range(n_classes):
p = (y == k).mean()
assert_almost_equal(raw_predictions[:, k], np.log(p))