def test_validate_excl_preds(self):
X = np.random.random(size=(50,10))
w = np.random.random(size=(10,))
y = np.dot(X, w)
enet = ElasticNet(alpha=.5)
# Invalid use
# Passing in a excl_preds array that is to long.
with self.assertRaises(ValueError):
excl_preds = np.ones(shape=(12,))
enet._validate_excl_preds(X, y, excl_preds=excl_preds)
# Invalid use
# Alltempt to exclude a predictor out of range, i.e. that does
# not exist.
with self.assertRaises(ValueError):
excl_preds = np.ones(shape=(11,))
excl_preds[0] = 1
excl_preds[5] = 10
enet._validate_excl_preds(X, y, excl_preds=excl_preds)
# Valid use
# Exclude some in range predictors.
excl_preds = np.array([1, 2, 4, 6, 8])
enet._validate_excl_preds(X, y, excl_preds=excl_preds)
def test_validate_excl_preds(self):
X = np.random.random(size=(50, 10))
w = np.random.random(size=(10, ))
y = np.dot(X, w)
enet = ElasticNet(alpha=.5)
# Invalid use
# Passing in a excl_preds array that is to long.
with self.assertRaises(ValueError):
excl_preds = np.ones(shape=(12, ))
enet._validate_excl_preds(X, y, excl_preds=excl_preds)
# Invalid use
# Alltempt to exclude a predictor out of range, i.e. that does
# not exist.
with self.assertRaises(ValueError):
excl_preds = np.ones(shape=(11, ))
excl_preds[0] = 1
excl_preds[5] = 10
enet._validate_excl_preds(X, y, excl_preds=excl_preds)
# Valid use
# Exclude some in range predictors.
excl_preds = np.array([1, 2, 4, 6, 8])
enet._validate_excl_preds(X, y, excl_preds=excl_preds)
