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_lad_equals_quantiles(seed, alpha):
# Make sure quantile loss with alpha = .5 is equivalent to LAD
lad = LeastAbsoluteError()
ql = QuantileLossFunction(alpha=alpha)
n_samples = 50
rng = np.random.RandomState(seed)
raw_predictions = rng.normal(size=(n_samples))
y_true = rng.normal(size=(n_samples))
lad_loss = lad(y_true, raw_predictions)
ql_loss = ql(y_true, raw_predictions)
if alpha == 0.5:
assert lad_loss == approx(2 * ql_loss)
weights = np.linspace(0, 1, n_samples)**2
lad_weighted_loss = lad(y_true, raw_predictions, sample_weight=weights)
ql_weighted_loss = ql(y_true, raw_predictions, sample_weight=weights)
if alpha == 0.5:
assert lad_weighted_loss == approx(2 * ql_weighted_loss)
pbl_weighted_loss = mean_pinball_loss(y_true,
raw_predictions,
sample_weight=weights,
alpha=alpha)
assert pbl_weighted_loss == approx(ql_weighted_loss)
def test_quantile_loss_function():
# Non regression test for the QuantileLossFunction object
# There was a sign problem when evaluating the function
# for negative values of 'ytrue - ypred'
x = np.asarray([-1.0, 0.0, 1.0])
y_found = QuantileLossFunction(1, 0.9)(x, np.zeros_like(x))
y_expected = np.asarray([0.1, 0.0, 0.9]).mean()
np.testing.assert_allclose(y_found, y_expected)
def test_lad_equals_quantile_50(seed):
# Make sure quantile loss with alpha = .5 is equivalent to LAD
lad = LeastAbsoluteError(n_classes=1)
ql = QuantileLossFunction(n_classes=1, alpha=0.5)
n_samples = 50
rng = np.random.RandomState(seed)
raw_predictions = rng.normal(size=(n_samples))
y_true = rng.normal(size=(n_samples))
lad_loss = lad(y_true, raw_predictions)
ql_loss = ql(y_true, raw_predictions)
assert_almost_equal(lad_loss, 2 * ql_loss)
weights = np.linspace(0, 1, n_samples)**2
lad_weighted_loss = lad(y_true, raw_predictions, sample_weight=weights)
ql_weighted_loss = ql(y_true, raw_predictions, sample_weight=weights)
assert_almost_equal(lad_weighted_loss, 2 * ql_weighted_loss)
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))
