Merge pull request #24 from ogrisel/fix-nan-1d-regularized-covariance

FIX regularized covariance on 1D data
scikit-learn · Feb 13, 2015 · 021bf74 · 021bf74
2 parents 8ef0b9a + 9f50699
commit 021bf74
Show file tree

Hide file tree

Showing 2 changed files with 14 additions and 9 deletions.
diff --git a/sklearn/covariance/shrunk_covariance_.py b/sklearn/covariance/shrunk_covariance_.py
@@ -228,8 +228,7 @@ def ledoit_wolf_shrinkage(X, assume_centered=False, block_size=1000):
     # get final beta as the min between beta and delta
     beta = min(beta, delta)
     # finally get shrinkage
-    shrinkage = beta / delta
-
+    shrinkage = 0 if beta == 0 else beta / delta
     return shrinkage
 
 
@@ -461,7 +460,7 @@ def oas(X, assume_centered=False):
     num = alpha + mu ** 2
     den = (n_samples + 1.) * (alpha - (mu ** 2) / n_features)
 
-    shrinkage = min(num / den, 1.)
+    shrinkage = 1. if den == 0 else min(num / den, 1.)
     shrunk_cov = (1. - shrinkage) * emp_cov
     shrunk_cov.flat[::n_features + 1] += shrinkage * mu
 

diff --git a/sklearn/covariance/tests/test_covariance.py b/sklearn/covariance/tests/test_covariance.py
@@ -61,6 +61,8 @@ def test_covariance():
     X_1sample = np.arange(5)
     cov = EmpiricalCovariance()
     assert_warns(UserWarning, cov.fit, X_1sample)
+    assert_array_almost_equal(cov.covariance_,
+                              np.zeros(shape=(5, 5), dtype=np.float64))
 
     # test integer type
     X_integer = np.asarray([[0, 1], [1, 0]])
@@ -181,9 +183,11 @@ def test_ledoit_wolf():
 
     # test with one sample
     # FIXME I don't know what this test does
-    #X_1sample = np.arange(5)
-    #lw = LedoitWolf()
-    #assert_warns(UserWarning, lw.fit, X_1sample)
+    X_1sample = np.arange(5)
+    lw = LedoitWolf()
+    assert_warns(UserWarning, lw.fit, X_1sample)
+    assert_array_almost_equal(lw.covariance_,
+                              np.zeros(shape=(5, 5), dtype=np.float64))
 
     # test shrinkage coeff on a simple data set (without saving precision)
     lw = LedoitWolf(store_precision=False)
@@ -253,9 +257,11 @@ def test_oas():
 
     # test with one sample
     # FIXME I don't know what this test does
-    #X_1sample = np.arange(5)
-    #oa = OAS()
-    #assert_warns(UserWarning, oa.fit, X_1sample)
+    X_1sample = np.arange(5)
+    oa = OAS()
+    assert_warns(UserWarning, oa.fit, X_1sample)
+    assert_array_almost_equal(oa.covariance_,
+                              np.zeros(shape=(5, 5), dtype=np.float64))
 
     # test shrinkage coeff on a simple data set (without saving precision)
     oa = OAS(store_precision=False)