def test_multinomial_loss():
# test if the multinomial loss and gradient computations are consistent
X, y = iris.data, iris.target.astype(np.float64)
n_samples, n_features = X.shape
n_classes = len(np.unique(y))
rng = check_random_state(42)
weights = rng.randn(n_features, n_classes)
intercept = rng.randn(n_classes)
sample_weights = rng.randn(n_samples)
np.abs(sample_weights, sample_weights)
# compute loss and gradient like in multinomial SAG
dataset, _ = make_dataset(X, y, sample_weights, random_state=42)
loss_1, grad_1 = _multinomial_grad_loss_all_samples(
dataset, weights, intercept, n_samples, n_features, n_classes)
# compute loss and gradient like in multinomial LogisticRegression
loss = LinearModelLoss(
base_loss=HalfMultinomialLoss(n_classes=n_classes),
fit_intercept=True,
)
weights_intercept = np.vstack((weights, intercept)).T
loss_2, grad_2 = loss.loss_gradient(weights_intercept,
X,
y,
l2_reg_strength=0.0,
sample_weight=sample_weights)
grad_2 = grad_2[:, :-1].T
# comparison
assert_array_almost_equal(grad_1, grad_2)
assert_almost_equal(loss_1, loss_2)
def test_multinomial_loss():
# test if the multinomial loss and gradient computations are consistent
X, y = iris.data, iris.target.astype(np.float64)
n_samples, n_features = X.shape
n_classes = len(np.unique(y))
rng = check_random_state(42)
weights = rng.randn(n_features, n_classes)
intercept = rng.randn(n_classes)
sample_weights = rng.randn(n_samples)
np.abs(sample_weights, sample_weights)
# compute loss and gradient like in multinomial SAG
dataset, _ = make_dataset(X, y, sample_weights, random_state=42)
loss_1, grad_1 = _multinomial_grad_loss_all_samples(
dataset, weights, intercept, n_samples, n_features, n_classes)
# compute loss and gradient like in multinomial LogisticRegression
lbin = LabelBinarizer()
Y_bin = lbin.fit_transform(y)
weights_intercept = np.vstack((weights, intercept)).T.ravel()
loss_2, grad_2, _ = _multinomial_loss_grad(weights_intercept, X, Y_bin,
0.0, sample_weights)
grad_2 = grad_2.reshape(n_classes, -1)
grad_2 = grad_2[:, :-1].T
# comparison
assert_array_almost_equal(grad_1, grad_2)
assert_almost_equal(loss_1, loss_2)
def test_fused_types_make_dataset():
iris = load_iris()
X_32 = iris.data.astype(np.float32)
y_32 = iris.target.astype(np.float32)
X_csr_32 = sparse.csr_matrix(X_32)
sample_weight_32 = np.arange(y_32.size, dtype=np.float32)
X_64 = iris.data.astype(np.float64)
y_64 = iris.target.astype(np.float64)
X_csr_64 = sparse.csr_matrix(X_64)
sample_weight_64 = np.arange(y_64.size, dtype=np.float64)
# array
dataset_32, _ = make_dataset(X_32, y_32, sample_weight_32)
dataset_64, _ = make_dataset(X_64, y_64, sample_weight_64)
xi_32, yi_32, _, _ = dataset_32._next_py()
xi_64, yi_64, _, _ = dataset_64._next_py()
xi_data_32, _, _ = xi_32
xi_data_64, _, _ = xi_64
assert xi_data_32.dtype == np.float32
assert xi_data_64.dtype == np.float64
assert_allclose(yi_64, yi_32, rtol=rtol)
# csr
datasetcsr_32, _ = make_dataset(X_csr_32, y_32, sample_weight_32)
datasetcsr_64, _ = make_dataset(X_csr_64, y_64, sample_weight_64)
xicsr_32, yicsr_32, _, _ = datasetcsr_32._next_py()
xicsr_64, yicsr_64, _, _ = datasetcsr_64._next_py()
xicsr_data_32, _, _ = xicsr_32
xicsr_data_64, _, _ = xicsr_64
assert xicsr_data_32.dtype == np.float32
assert xicsr_data_64.dtype == np.float64
assert_allclose(xicsr_data_64, xicsr_data_32, rtol=rtol)
assert_allclose(yicsr_64, yicsr_32, rtol=rtol)
assert_array_equal(xi_data_32, xicsr_data_32)
assert_array_equal(xi_data_64, xicsr_data_64)
assert_array_equal(yi_32, yicsr_32)
assert_array_equal(yi_64, yicsr_64)
