def test_regression():
"""NOTE: subsequent calls may flip the direction of eigenvectors
(mulitply by -1), so we can only compare absolute values.
This was not a problem for svds.. investigate if we can get
deterministic behavior back.
"""
X, y = datasets.make_cubic(random_state=123)
for n_dir in range(1, X.shape[1]):
sir = SlicedInverseRegression(n_directions=n_dir)
# take shape is correct
X_sir = sir.fit(X, y).transform(X)
np.testing.assert_equal(X_sir.shape[1], n_dir)
# should match fit_transform
X_sir2 = sir.fit_transform(X, y)
np.testing.assert_allclose(np.abs(X_sir), np.abs(X_sir2))
# call transform again and check if things are okay
X_sir = sir.transform(X)
X_sir2 = sir.fit_transform(X, y)
np.testing.assert_allclose(np.abs(X_sir), np.abs(X_sir2))
# there is one true angle it should fine
true_beta = (1 / np.sqrt(2)) * np.hstack((np.ones(2), np.zeros(8)))
angle = np.dot(true_beta, sir.directions_[0, :])
np.testing.assert_allclose(np.abs(angle), 1, rtol=1e-1)
def test_errors_alpha_out_of_bounds():
X, y = datasets.make_cubic(random_state=123)
sir = SlicedInverseRegression(alpha=10)
with pytest.raises(ValueError):
sir.fit(X, y)
def test_cubic():
X, y = datasets.make_cubic(random_state=123)
save = SlicedAverageVarianceEstimation().fit(X, y)
true_beta = (1 / np.sqrt(2)) * np.hstack((np.ones(2), np.zeros(8)))
angle = np.dot(true_beta, save.directions_[0, :])
np.testing.assert_allclose(np.abs(angle), 1, rtol=1e-1)
def test_sparse_not_supported():
X, y = datasets.make_cubic(random_state=123)
X = sparse.csr_matrix(X)
with pytest.raises(TypeError):
SlicedAverageVarianceEstimation().fit(X, y)
def test_n_directions_none():
X, y = datasets.make_cubic(random_state=123)
sir = SlicedAverageVarianceEstimation(n_directions=None).fit(X, y)
np.testing.assert_equal(sir.n_directions_, X.shape[1])
"""
=========================
Sliced Inverse Regression
=========================
An example plot of :class:`sliced.sir.SlicedInverseRegression`
"""
import numpy as np
import matplotlib.pyplot as plt
from sliced import SlicedInverseRegression
from sliced import datasets
X, y = datasets.make_cubic(random_state=123)
sir = SlicedInverseRegression()
X_sir = sir.fit_transform(X, y)
# estimate of the first dimension reducing directions
beta1_hat = sir.directions_[0, :]
# plot data projected onto the first direction
plt.scatter(X_sir[:, 0], y, c=y, cmap='viridis', linewidth=0.5, edgecolor='k')
plt.xlabel("$X\hat{\\beta_1}$")
plt.ylabel("y")
# annotation showing the direction found
beta_text = "$\\beta_1$ = " + "{0}".format([0.707, 0.707])
plt.annotate(beta_text, xy=(-2, 6.5))
beta1_hat_text = "$\hat{\\beta_1}$ = " + "{0}".format(
np.round(beta1_hat, 3).tolist()[:2])
def test_sparse_not_supported():
X, y = datasets.make_cubic(random_state=123)
X = sparse.csr_matrix(X)
with pytest.raises(TypeError):
SlicedInverseRegression().fit(X, y)
def test_n_directions_none():
X, y = datasets.make_cubic(random_state=123)
sir = SlicedInverseRegression(n_directions=None).fit(X, y)
np.testing.assert_equal(sir.n_directions_, X.shape[1])