From c9769dd98fd9bfafee7d378d2abffabf942c883a Mon Sep 17 00:00:00 2001
From: Andreas Mueller <amueller@nyu.edu>
Date: Sun, 1 Nov 2015 22:13:54 -0500
Subject: [PATCH] COSMIT don't use the deprecated residuals property of ols
---
sklearn/linear_model/tests/test_base.py | 11 +++++------
1 file changed, 5 insertions(+), 6 deletions(-)
diff --git a/sklearn/linear_model/tests/test_base.py b/sklearn/linear_model/tests/test_base.py
index 1e44c3d18b..f4a43a96f5 100644
--- a/sklearn/linear_model/tests/test_base.py
+++ b/sklearn/linear_model/tests/test_base.py
@@ -12,7 +12,6 @@ from sklearn.utils.testing import assert_equal
from sklearn.linear_model.base import LinearRegression
from sklearn.linear_model.base import center_data, sparse_center_data, _rescale_data
from sklearn.utils import check_random_state
-from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_greater
from sklearn.datasets.samples_generator import make_sparse_uncorrelated
from sklearn.datasets.samples_generator import make_regression
@@ -114,7 +113,7 @@ def test_fit_intercept():
def test_linear_regression_sparse(random_state=0):
- "Test that linear regression also works with sparse data"
+ # Test that linear regression also works with sparse data
random_state = check_random_state(random_state)
for i in range(10):
n = 100
@@ -125,11 +124,12 @@ def test_linear_regression_sparse(random_state=0):
ols = LinearRegression()
ols.fit(X, y.ravel())
assert_array_almost_equal(beta, ols.coef_ + ols.intercept_)
- assert_array_almost_equal(ols.residues_, 0)
+
+ assert_array_almost_equal(ols.predict(X) - y.ravel(), 0)
def test_linear_regression_multiple_outcome(random_state=0):
- "Test multiple-outcome linear regressions"
+ # Test multiple-outcome linear regressions
X, y = make_regression(random_state=random_state)
Y = np.vstack((y, y)).T
@@ -145,7 +145,7 @@ def test_linear_regression_multiple_outcome(random_state=0):
def test_linear_regression_sparse_multiple_outcome(random_state=0):
- "Test multiple-outcome linear regressions with sparse data"
+ # 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)
@@ -321,4 +321,3 @@ def test_rescale_data():
rescaled_y2 = y * np.sqrt(sample_weight)
assert_array_almost_equal(rescaled_X, rescaled_X2)
assert_array_almost_equal(rescaled_y, rescaled_y2)
-
--
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