def test_core_computation():
inbag_ex = np.array([[1., 2., 0., 1.],
[1., 0., 2., 0.],
[1., 1., 1., 2.]])
X_train_ex = np.array([[3, 3],
[6, 4],
[6, 6]])
X_test_ex = np.vstack([np.array([[5, 2],
[5, 5]]) for _ in range(1000)])
pred_centered_ex = np.vstack([np.array([[-20, -20, 10, 30],
[-20, 30, -20, 10]])
for _ in range(1000)])
n_trees = 4
our_vij = fci._core_computation(X_train_ex, X_test_ex, inbag_ex,
pred_centered_ex, n_trees)
r_vij = np.concatenate([np.array([112.5, 387.5]) for _ in range(1000)])
npt.assert_almost_equal(our_vij, r_vij)
for mc, ml in zip([True, False], [.01, None]):
our_vij = fci._core_computation(X_train_ex, X_test_ex, inbag_ex,
pred_centered_ex, n_trees,
memory_constrained=True,
memory_limit=.01,
test_mode=True)
npt.assert_almost_equal(our_vij, r_vij)
def test_core_computation():
inbag_ex = np.array([[1., 2., 0., 1.], [1., 0., 2., 0.], [1., 1., 1., 2.]])
X_train_ex = np.array([[3, 3], [6, 4], [6, 6]])
X_test_ex = np.vstack([np.array([[5, 2], [5, 5]]) for _ in range(1000)])
pred_centered_ex = np.vstack([
np.array([[-20, -20, 10, 30], [-20, 30, -20, 10]]) for _ in range(1000)
])
n_trees = 4
our_vij = fci._core_computation(X_train_ex, X_test_ex, inbag_ex,
pred_centered_ex, n_trees)
r_vij = np.concatenate([np.array([112.5, 387.5]) for _ in range(1000)])
npt.assert_almost_equal(our_vij, r_vij)
our_vij = fci._core_computation(X_train_ex,
X_test_ex,
inbag_ex,
pred_centered_ex,
n_trees,
memory_constrained=True,
memory_limit=.01,
test_mode=True)
npt.assert_almost_equal(our_vij, r_vij)
def test_core_computation():
inbag_ex = np.array([[1., 2., 0., 1.], [1., 0., 2., 0.], [1., 1., 1., 2.]])
X_train_ex = np.array([[3, 3], [6, 4], [6, 6]])
X_test_ex = np.array([[5, 2], [5, 5]])
pred_centered_ex = np.array([[-20, -20, 10, 30], [-20, 30, -20, 10]])
n_trees = 4
our_vij = fci._core_computation(X_train_ex, X_test_ex, inbag_ex,
pred_centered_ex, n_trees)
r_vij = np.array([112.5, 387.5])
npt.assert_almost_equal(our_vij, r_vij)
def test_bias_correction():
inbag_ex = np.array([[1., 2., 0., 1.], [1., 0., 2., 0.], [1., 1., 1., 2.]])
X_train_ex = np.array([[3, 3], [6, 4], [6, 6]])
X_test_ex = np.array([[5, 2], [5, 5]])
pred_centered_ex = np.array([[-20, -20, 10, 30], [-20, 30, -20, 10]])
n_trees = 4
our_vij = fci._core_computation(X_train_ex, X_test_ex, inbag_ex,
pred_centered_ex, n_trees)
our_vij_unbiased = fci._bias_correction(our_vij, inbag_ex,
pred_centered_ex, n_trees)
r_unbiased_vij = np.array([-42.1875, 232.8125])
npt.assert_almost_equal(our_vij_unbiased, r_unbiased_vij)
def test_bias_correction():
inbag_ex = np.array([[1., 2., 0., 1.],
[1., 0., 2., 0.],
[1., 1., 1., 2.]])
X_train_ex = np.array([[3, 3],
[6, 4],
[6, 6]])
X_test_ex = np.array([[5, 2],
[5, 5]])
pred_centered_ex = np.array([[-20, -20, 10, 30], [-20, 30, -20, 10]])
n_trees = 4
our_vij = fci._core_computation(X_train_ex, X_test_ex, inbag_ex,
pred_centered_ex, n_trees)
our_vij_unbiased = fci._bias_correction(our_vij, inbag_ex,
pred_centered_ex,
n_trees)
r_unbiased_vij = np.array([-42.1875, 232.8125])
npt.assert_almost_equal(our_vij_unbiased, r_unbiased_vij)