def test_validate_weights(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 sample weight vector that is too short.
with self.assertRaises(ValueError):
sw = np.ones(shape=(49,))
enet._validate_weights(X, y, weights=sw)
# Invalid use:
# Passing in a weight vector that matches the wrong dimenstion of X.
with self.assertRaises(ValueError):
sw = np.ones(shape=(10,))
enet._validate_weights(X, y, weights=sw)
# Invalid use:
# Passing in a weight vector containing a negative entry.
with self.assertRaises(ValueError):
sw = np.ones(shape=(50,))
sw[25] = -1
enet._validate_weights(X, y, weights=sw)
# Valid Use:
# Weight vector of the correct dimension with all non-negative
# entries.
sw = np.ones(shape=(50,))
enet._validate_weights(X, y, weights=sw)
def test_validate_weights(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 sample weight vector that is too short.
with self.assertRaises(ValueError):
sw = np.ones(shape=(49, ))
enet._validate_weights(X, y, weights=sw)
# Invalid use:
# Passing in a weight vector that matches the wrong dimenstion of X.
with self.assertRaises(ValueError):
sw = np.ones(shape=(10, ))
enet._validate_weights(X, y, weights=sw)
# Invalid use:
# Passing in a weight vector containing a negative entry.
with self.assertRaises(ValueError):
sw = np.ones(shape=(50, ))
sw[25] = -1
enet._validate_weights(X, y, weights=sw)
# Valid Use:
# Weight vector of the correct dimension with all non-negative
# entries.
sw = np.ones(shape=(50, ))
enet._validate_weights(X, y, weights=sw)
