113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
|
|
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
|
|
#
|
|
# License: BSD Style.
|
|
|
|
import numpy as np
|
|
from scipy import sparse
|
|
|
|
from sklearn.utils.testing import assert_array_almost_equal
|
|
from sklearn.utils.testing import assert_equal
|
|
|
|
from sklearn.linear_model.base import LinearRegression
|
|
from sklearn.utils import check_random_state
|
|
from sklearn.datasets.samples_generator import make_sparse_uncorrelated
|
|
from sklearn.datasets.samples_generator import make_regression
|
|
|
|
|
|
def test_linear_regression():
|
|
"""
|
|
Test LinearRegression on a simple dataset.
|
|
"""
|
|
# a simple dataset
|
|
X = [[1], [2]]
|
|
Y = [1, 2]
|
|
|
|
clf = LinearRegression()
|
|
clf.fit(X, Y)
|
|
|
|
assert_array_almost_equal(clf.coef_, [1])
|
|
assert_array_almost_equal(clf.intercept_, [0])
|
|
assert_array_almost_equal(clf.predict(X), [1, 2])
|
|
|
|
# test it also for degenerate input
|
|
X = [[1]]
|
|
Y = [0]
|
|
|
|
clf = LinearRegression()
|
|
clf.fit(X, Y)
|
|
assert_array_almost_equal(clf.coef_, [0])
|
|
assert_array_almost_equal(clf.intercept_, [0])
|
|
assert_array_almost_equal(clf.predict(X), [0])
|
|
|
|
|
|
def test_fit_intercept():
|
|
"""
|
|
Test assertions on betas shape.
|
|
"""
|
|
X2 = np.array([[0.38349978, 0.61650022],
|
|
[0.58853682, 0.41146318]])
|
|
X3 = np.array([[0.27677969, 0.70693172, 0.01628859],
|
|
[0.08385139, 0.20692515, 0.70922346]])
|
|
y = np.array([1, 1])
|
|
|
|
lr2_without_intercept = LinearRegression(fit_intercept=False).fit(X2, y)
|
|
lr2_with_intercept = LinearRegression(fit_intercept=True).fit(X2, y)
|
|
|
|
lr3_without_intercept = LinearRegression(fit_intercept=False).fit(X3, y)
|
|
lr3_with_intercept = LinearRegression(fit_intercept=True).fit(X3, y)
|
|
|
|
assert_equal(lr2_with_intercept.coef_.shape,
|
|
lr2_without_intercept.coef_.shape)
|
|
assert_equal(lr3_with_intercept.coef_.shape,
|
|
lr3_without_intercept.coef_.shape)
|
|
assert_equal(lr2_without_intercept.coef_.ndim,
|
|
lr3_without_intercept.coef_.ndim)
|
|
|
|
|
|
def test_linear_regression_sparse(random_state=0):
|
|
"Test that linear regression also works with sparse data"
|
|
random_state = check_random_state(random_state)
|
|
n = 100
|
|
X = sparse.eye(n, n)
|
|
beta = random_state.rand(n)
|
|
y = X * beta[:, np.newaxis]
|
|
|
|
ols = LinearRegression()
|
|
ols.fit(X, y.ravel())
|
|
assert_array_almost_equal(beta, ols.coef_ + ols.intercept_)
|
|
assert_array_almost_equal(ols.residues_, 0)
|
|
|
|
|
|
def test_linear_regression_multiple_outcome(random_state=0):
|
|
"Test multiple-outcome linear regressions"
|
|
X, y = make_regression(random_state=random_state)
|
|
|
|
Y = np.vstack((y, y)).T
|
|
n_features = X.shape[1]
|
|
|
|
clf = LinearRegression(fit_intercept=True)
|
|
clf.fit((X), Y)
|
|
assert_equal(clf.coef_.shape, (2, n_features))
|
|
Y_pred = clf.predict(X)
|
|
clf.fit(X, y)
|
|
y_pred = clf.predict(X)
|
|
assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3)
|
|
|
|
|
|
def test_linear_regression_sparse_multiple_outcome(random_state=0):
|
|
"Test multiple-outcome linear regressions with sparse data"
|
|
random_state = check_random_state(random_state)
|
|
X, y = make_sparse_uncorrelated(random_state=random_state)
|
|
X = sparse.coo_matrix(X)
|
|
Y = np.vstack((y, y)).T
|
|
n_features = X.shape[1]
|
|
|
|
ols = LinearRegression()
|
|
ols.fit(X, Y)
|
|
assert_equal(ols.coef_.shape, (2, n_features))
|
|
Y_pred = ols.predict(X)
|
|
ols.fit(X, y.ravel())
|
|
y_pred = ols.predict(X)
|
|
assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3)
|