def test_trivial_crossvalidation(self):
for i in range(1, 10):
m_a = DenseMatrix(np.mat(np.random.random((i + 1,4))))
m_b = DenseMatrix(np.mat(np.random.random((i + 1,4))))
tmp_a = m_a.mat.copy()
tmp_b = m_b.mat.copy()
learner = RidgeRegressionLearner(param_range=[3], intercept=False)
solution = learner.train(m_a, m_b)
learner2 = RidgeRegressionLearner(param = 3, intercept=False)
solution2 = learner2.train(m_a, m_b)
np.testing.assert_array_equal(tmp_a, m_a.mat)
np.testing.assert_array_equal(tmp_b, m_b.mat)
np.testing.assert_array_equal(solution.mat, solution2.mat)
learner = RidgeRegressionLearner(param_range=[3], intercept=False)
solution = learner.train(m_a, m_b)
np.testing.assert_array_equal(tmp_a, m_a.mat)
np.testing.assert_array_equal(tmp_b, m_b.mat)
np.testing.assert_array_equal(solution.mat, solution2.mat)
learner = RidgeRegressionLearner(param_range=[0], intercept=False)
solution = learner.train(m_a, m_b)
learner2 = LstsqRegressionLearner(intercept=False)
solution2 = learner2.train(m_a, m_b)
np.testing.assert_array_almost_equal(solution.mat, solution2.mat, 3)
def test_crossvalidation(self):
a = DenseMatrix(np.matrix([[1, 1],[2, 3],[4, 6]]))
b = DenseMatrix(np.matrix([[12, 15, 18],[21, 27, 33],[35, 46, 57]]))
res = DenseMatrix(np.matrix([[1, 2, 3],[4, 5, 6],[7, 8, 9]]))
learner = RidgeRegressionLearner(intercept=True, param_range=[0])
learner2 = LstsqRegressionLearner(intercept=False)
res1 = learner2.train(a, b)
res2 = learner.train(a, b)
np.testing.assert_array_almost_equal(res2.mat[:-1,:], res[0:2,:].mat, 6)
np.testing.assert_array_almost_equal(res2.mat[-1,:], res[2:3,:].mat, 6)
new_a = padd_matrix(a, 1)
self.assertGreater(((a * res1) - b).norm(), ((new_a * res2) - b).norm())
def test_crossvalidation(self):
a = DenseMatrix(np.matrix([[1, 1], [2, 3], [4, 6]]))
b = DenseMatrix(np.matrix([[12, 15, 18], [21, 27, 33], [35, 46, 57]]))
res = DenseMatrix(np.matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
learner = RidgeRegressionLearner(intercept=True, param_range=[0])
learner2 = LstsqRegressionLearner(intercept=False)
res1 = learner2.train(a, b)
res2 = learner.train(a, b)
np.testing.assert_array_almost_equal(res2.mat[:-1, :], res[0:2, :].mat,
6)
np.testing.assert_array_almost_equal(res2.mat[-1, :], res[2:3, :].mat,
6)
new_a = padd_matrix(a, 1)
self.assertGreater(((a * res1) - b).norm(),
((new_a * res2) - b).norm())
def test_space_train_dense(self):
test_cases = [
([("a", "b", "a_b")], self.space4, self.space5),
([("a", "b", "a_b")], self.space4, self.space6),
([("a", "b", "a_b"), ("a", "b", "a_a")], self.space4, self.space7),
]
learners = [
RidgeRegressionLearner(intercept=False,
crossvalidation=False,
param=0),
LstsqRegressionLearner(intercept=False),
LstsqRegressionLearner(intercept=True)
]
for in_data, arg_space, phrase_space in test_cases:
for learner_ in learners:
comp_model = FullAdditive(learner=learner_)
comp_model.train(in_data, arg_space, phrase_space)
comp_space = comp_model.compose(in_data, arg_space)
np.testing.assert_array_almost_equal(
comp_space.cooccurrence_matrix.mat,
phrase_space.cooccurrence_matrix.mat, 10)
self.assertListEqual(comp_space.id2column,
phrase_space.id2column)
self.assertDictEqual(comp_space.column2id,
phrase_space.column2id)
self.assertListEqual(comp_space.id2row, phrase_space.id2row)
self.assertDictEqual(comp_space.row2id, phrase_space.row2id)
self.assertEqual(comp_model._has_intercept,
learner_._intercept)
#load argument and phrase space
arg_space = io_utils.load("./data/out/ex10.pkl")
phrase_space = io_utils.load("data/out/PHRASE_SS.ex10.pkl")
print("\nDefault regression:")
my_comp = LexicalFunction()
print(type(my_comp.regression_learner).__name__)
my_comp.train(train_data, arg_space, phrase_space)
#print its parameters
print("Lexical function space:")
print(my_comp.function_space.id2row)
cooc_mat = my_comp.function_space.cooccurrence_matrix
cooc_mat.reshape(my_comp.function_space.element_shape)
print(cooc_mat)
print("\nRidge Regression with lambda = 2")
rr_learner = RidgeRegressionLearner(param=2,
intercept=False,
crossvalidation=False)
my_comp = LexicalFunction(learner=rr_learner)
my_comp.train(train_data, arg_space, phrase_space)
#print its parameters
print("Lexical function space:")
print(my_comp.function_space.id2row)
cooc_mat = my_comp.function_space.cooccurrence_matrix
cooc_mat.reshape(my_comp.function_space.element_shape)
print(cooc_mat)
print "Applying SVD..."
space = space.apply(Svd(100))
print "Creating peripheral space.."
per_space = PeripheralSpace.build(space,
data=data_path + "per.raw.SV.sm",
cols=data_path + "per.raw.SV.cols",
format="sm")
#reading in train data
train_data_file = data_path + "ML08_SV_train.txt"
train_data = io_utils.read_tuple_list(train_data_file, fields=[0, 1, 2])
print "Training Lexical Function composition model..."
comp_model = LexicalFunction(learner=RidgeRegressionLearner(param=2))
comp_model.train(train_data, space, per_space)
print "Composing phrases..."
test_phrases_file = data_path + "ML08nvs_test.txt"
test_phrases = io_utils.read_tuple_list(test_phrases_file, fields=[0, 1, 2])
composed_space = comp_model.compose(test_phrases, space)
print "Reading similarity test data..."
test_similarity_file = data_path + "ML08data_new.txt"
test_pairs = io_utils.read_tuple_list(test_similarity_file, fields=[0, 1])
gold = io_utils.read_list(test_similarity_file, field=2)
print "Computing similarity with lexical function..."
pred = composed_space.get_sims(test_pairs, CosSimilarity())
def test_trivial_crossvalidation(self):
for i in range(1, 10):
m_a = DenseMatrix(np.mat(np.random.random((i + 1, 4))))
m_b = DenseMatrix(np.mat(np.random.random((i + 1, 4))))
tmp_a = m_a.mat.copy()
tmp_b = m_b.mat.copy()
learner = RidgeRegressionLearner(param_range=[3], intercept=False)
solution = learner.train(m_a, m_b)
learner2 = RidgeRegressionLearner(param=3, intercept=False)
solution2 = learner2.train(m_a, m_b)
np.testing.assert_array_equal(tmp_a, m_a.mat)
np.testing.assert_array_equal(tmp_b, m_b.mat)
np.testing.assert_array_equal(solution.mat, solution2.mat)
learner = RidgeRegressionLearner(param_range=[3], intercept=False)
solution = learner.train(m_a, m_b)
np.testing.assert_array_equal(tmp_a, m_a.mat)
np.testing.assert_array_equal(tmp_b, m_b.mat)
np.testing.assert_array_equal(solution.mat, solution2.mat)
learner = RidgeRegressionLearner(param_range=[0], intercept=False)
solution = learner.train(m_a, m_b)
learner2 = LstsqRegressionLearner(intercept=False)
solution2 = learner2.train(m_a, m_b)
np.testing.assert_array_almost_equal(solution.mat, solution2.mat,
3)
