def test_simple_train_compose_intercept(self):
#TODO test a1_car twice in the phrase list
train_data = [("a1", "car", "a1_car"),
("a1", "man", "a1_man"),
]
#model with train and then compose
learner_ = LstsqRegressionLearner(intercept=True)
model = LexicalFunction(learner=learner_)
model.train(train_data, self.n_space, self.an_space)
new_space = model.function_space
np.testing.assert_array_almost_equal(new_space.cooccurrence_matrix.mat,
np.mat([[0.66666667, 0.33333333,
-0.33333333, 0.33333333,
0.66666667, 0.33333333]]),
7)
self.assertTupleEqual(new_space.element_shape, (2, 3))
self.assertListEqual(new_space.id2row, ["a1"])
self.assertListEqual(new_space.id2column, [])
comp_space = model.compose(train_data, self.n_space)
np.testing.assert_array_almost_equal(comp_space.cooccurrence_matrix.mat,
self.an_space.cooccurrence_matrix.mat, 10
)
self.assertListEqual(comp_space.id2row, ["a1_car", "a1_man"])
self.assertListEqual(comp_space.id2column, self.ft)
#new model, without training
model2 = LexicalFunction(function_space=new_space, intercept=True)
comp_space = model2.compose(train_data, self.n_space)
self.assertListEqual(comp_space.id2row, ["a1_car", "a1_man"])
self.assertListEqual(comp_space.id2column, [])
np.testing.assert_array_almost_equal(comp_space.cooccurrence_matrix.mat,
self.n_space.cooccurrence_matrix.mat,
8)
#recursive application
comp_space2 = model2.compose([("a1", "a1_car", "a1_a1_car"),
("a1", "a1_man", "a1_a1_man")],
comp_space)
self.assertListEqual(comp_space2.id2row, ["a1_a1_car", "a1_a1_man"])
self.assertListEqual(comp_space.id2column, [])
np.testing.assert_array_almost_equal(comp_space2.cooccurrence_matrix.mat,
self.n_space.cooccurrence_matrix.mat,
8)
self.assertEqual(comp_space.element_shape, (2,))
self.assertEqual(comp_space2.element_shape, (2,))
def test_train_intercept(self):
a1_mat = DenseMatrix(np.mat([[3, 4], [5, 6]]))
a2_mat = DenseMatrix(np.mat([[1, 2], [3, 4]]))
train_data = [("a1", "man", "a1_man"),
("a2", "car", "a2_car"),
("a1", "boy", "a1_boy"),
("a2", "boy", "a2_boy")
]
n_mat = DenseMatrix(np.mat([[13, 21], [3, 4], [5, 6]]))
n_space = Space(n_mat, ["man", "car", "boy"], self.ft)
an1_mat = (a1_mat * n_mat.transpose()).transpose()
an2_mat = (a2_mat * n_mat.transpose()).transpose()
an_mat = an1_mat.vstack(an2_mat)
an_space = Space(an_mat, ["a1_man", "a1_car", "a1_boy", "a2_man", "a2_car", "a2_boy"], self.ft)
#test train
model = LexicalFunction(learner=LstsqRegressionLearner(intercept=True))
model.train(train_data, n_space, an_space)
a_space = model.function_space
a1_mat.reshape((1, 4))
#np.testing.assert_array_almost_equal(a1_mat.mat,
# a_space.cooccurrence_matrix.mat[0])
a2_mat.reshape((1, 4))
#np.testing.assert_array_almost_equal(a2_mat.mat,
# a_space.cooccurrence_matrix.mat[1])
self.assertListEqual(a_space.id2row, ["a1", "a2"])
self.assertTupleEqual(a_space.element_shape, (2, 3))
#test compose
a1_mat = DenseMatrix(np.mat([[3, 4, 5, 6]]))
a2_mat = DenseMatrix(np.mat([[1, 2, 3, 4]]))
a_mat = a_space.cooccurrence_matrix
a_space = Space(a_mat, ["a1", "a2"], [], element_shape=(2, 3))
model = LexicalFunction(function_space=a_space, intercept=True)
comp_space = model.compose(train_data, n_space)
self.assertListEqual(comp_space.id2row, ["a1_man", "a2_car", "a1_boy", "a2_boy"])
self.assertListEqual(comp_space.id2column, [])
self.assertEqual(comp_space.element_shape, (2,))
np.testing.assert_array_almost_equal(comp_space.cooccurrence_matrix.mat,
an_mat[[0, 4, 2, 5]].mat, 8)
def test_min_samples1(self):
#TODO test a1_car twice in the phrase list
train_data = [("bla3", "man", "a1_car"),
("a1", "car", "a1_car"),
("bla2", "man", "a1_car"),
("a1", "man", "a1_man"),
("bla1", "man", "a1_car")
]
#model with train and then compose
learner_ = LstsqRegressionLearner(intercept=True)
model = LexicalFunction(learner=learner_, min_samples=2)
model.train(train_data, self.n_space, self.an_space)
new_space = model.function_space
np.testing.assert_array_almost_equal(new_space.cooccurrence_matrix.mat,
np.mat([[0.66666667, 0.33333333,
-0.33333333, 0.33333333,
0.66666667, 0.33333333]]),
7)
self.assertTupleEqual(new_space.element_shape, (2, 3))
self.assertListEqual(new_space.id2row, ["a1"])
self.assertListEqual(new_space.id2column, [])
def test_min_samples2(self):
train_data = [("a1", "man", "bla"),
("a1", "car", "a1_car"),
("a1", "man", "bla"),
("a1", "man", "a1_man"),
("a1", "bla", "a1_man"),
("a1", "man", "bla")
]
model = LexicalFunction(min_samples=5)
self.assertRaises(ValueError, model.train, train_data, self.n_space, self.an_space)
def test_lexical_function(self):
self.m12 = DenseMatrix(np.mat([[3, 1], [9, 2]]))
self.m22 = DenseMatrix(np.mat([[4, 3], [2, 1]]))
self.ph2 = DenseMatrix(np.mat([[18, 11], [24, 7]]))
self.row = ["a", "b"]
self.ft = ["f1", "f2"]
self.space1 = Space(DenseMatrix(self.m12), self.row, self.ft)
self.space2 = Space(DenseMatrix(self.ph2), ["a_a", "a_b"], self.ft)
m = LexicalFunction()
m._MIN_SAMPLES = 1
self.assertRaises(IllegalStateError, m.export, self.prefix + ".lf1")
m.train([("a", "b", "a_b"), ("a", "a", "a_a")], self.space1,
self.space2)
m.export(self.prefix + ".lf2")
#ex16.py
#-------
from composes.utils import io_utils
from composes.composition.lexical_function import LexicalFunction
from composes.similarity.cos import CosSimilarity
#training data
#trying to learn a "good" function
train_data = [("good_function", "car", "good_car"),
("good_function", "book", "good_book")]
#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")
#train a lexical function model on the data
my_comp = LexicalFunction()
my_comp.train(train_data, arg_space, phrase_space)
#print its parameters
print "\nLexical 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
#similarity within the learned functional space
print "\nSimilarity between good and good in the function space:"
print my_comp.function_space.get_sim("good_function", "good_function",
CosSimilarity())
#load N and SVO spaces
n_space = Space.build(data="./data/in/ex19-n.sm",
cols="./data/in/ex19-n.cols",
format="sm")
svo_space = Space.build(data="./data/in/ex19-svo.sm",
cols="./data/in/ex19-svo.cols",
format="sm")
print "\nInput SVO training space:"
print svo_space.id2row
print svo_space.cooccurrence_matrix
#1. train a model to learn VO functions on train data: VO N -> SVO
print "\nStep 1 training"
vo_model = LexicalFunction(learner=LstsqRegressionLearner())
vo_model.train(train_vo_data, n_space, svo_space)
#2. train a model to learn V functions on train data: V N -> VO
# where VO space: function space learned in step 1
print "\nStep 2 training"
vo_space = vo_model.function_space
v_model = LexicalFunction(learner=LstsqRegressionLearner())
v_model.train(train_v_data, n_space, vo_space)
#print the learned model
print "\n3D Verb space"
print v_model.function_space.id2row
print v_model.function_space.cooccurrence_matrix
#3. use the trained models to compose new SVO sentences
#-------
from composes.utils import io_utils
from composes.composition.lexical_function import LexicalFunction
from composes.utils.regression_learner import RidgeRegressionLearner
#training data
#trying to learn a "good" function
train_data = [("good_function", "car", "good_car"),
("good_function", "book", "good_book")]
#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)
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_3d(self):
# setting up
v_mat = DenseMatrix(np.mat([[0, 0, 1, 1, 2, 2, 3, 3], #hate
[0, 1, 2, 4, 5, 6, 8, 9]])) #love
vo11_mat = DenseMatrix(np.mat([[0, 11], [22, 33]])) #hate boy
vo12_mat = DenseMatrix(np.mat([[0, 7], [14, 21]])) #hate man
vo21_mat = DenseMatrix(np.mat([[6, 34], [61, 94]])) #love boy
vo22_mat = DenseMatrix(np.mat([[2, 10], [17, 26]])) #love car
train_vo_data = [("hate_boy", "man", "man_hate_boy"),
("hate_man", "man", "man_hate_man"),
("hate_boy", "boy", "boy_hate_boy"),
("hate_man", "boy", "boy_hate_man"),
("love_car", "boy", "boy_love_car"),
("love_boy", "man", "man_love_boy"),
("love_boy", "boy", "boy_love_boy"),
("love_car", "man", "man_love_car")
]
# if do not find a phrase
# what to do?
train_v_data = [("love", "boy", "love_boy"),
("hate", "man", "hate_man"),
("hate", "boy", "hate_boy"),
("love", "car", "love_car")]
sentences = ["man_hate_boy", "car_hate_boy", "boy_hate_boy",
"man_hate_man", "car_hate_man", "boy_hate_man",
"man_love_boy", "car_love_boy", "boy_love_boy",
"man_love_car", "car_love_car", "boy_love_car"]
n_mat = DenseMatrix(np.mat([[3, 4], [1, 2], [5, 6]]))
n_space = Space(n_mat, ["man", "car", "boy"], self.ft)
s1_mat = (vo11_mat * n_mat.transpose()).transpose()
s2_mat = (vo12_mat * n_mat.transpose()).transpose()
s3_mat = (vo21_mat * n_mat.transpose()).transpose()
s4_mat = (vo22_mat * n_mat.transpose()).transpose()
s_mat = vo11_mat.nary_vstack([s1_mat, s2_mat, s3_mat, s4_mat])
s_space = Space(s_mat, sentences, self.ft)
#test train 2d
model = LexicalFunction(learner=LstsqRegressionLearner(intercept=False))
model.train(train_vo_data, n_space, s_space)
vo_space = model.function_space
self.assertListEqual(vo_space.id2row, ["hate_boy", "hate_man", "love_boy", "love_car"])
self.assertTupleEqual(vo_space.element_shape, (2, 2))
vo11_mat.reshape((1, 4))
np.testing.assert_array_almost_equal(vo11_mat.mat,
vo_space.cooccurrence_matrix.mat[0])
vo12_mat.reshape((1, 4))
np.testing.assert_array_almost_equal(vo12_mat.mat,
vo_space.cooccurrence_matrix.mat[1])
vo21_mat.reshape((1, 4))
np.testing.assert_array_almost_equal(vo21_mat.mat,
vo_space.cooccurrence_matrix.mat[2])
vo22_mat.reshape((1, 4))
np.testing.assert_array_almost_equal(vo22_mat.mat,
vo_space.cooccurrence_matrix.mat[3])
# test train 3d
model2 = LexicalFunction(learner=LstsqRegressionLearner(intercept=False))
model2.train(train_v_data, n_space, vo_space)
v_space = model2.function_space
np.testing.assert_array_almost_equal(v_mat.mat,
v_space.cooccurrence_matrix.mat)
self.assertListEqual(v_space.id2row, ["hate", "love"])
self.assertTupleEqual(v_space.element_shape, (2, 2, 2))
# test compose 3d
vo_space2 = model2.compose(train_v_data, n_space)
id2row1 = list(vo_space.id2row)
id2row2 = list(vo_space2.id2row)
id2row2.sort()
self.assertListEqual(id2row1, id2row2)
row_list = vo_space.id2row
vo_rows1 = vo_space.get_rows(row_list)
vo_rows2 = vo_space2.get_rows(row_list)
np.testing.assert_array_almost_equal(vo_rows1.mat, vo_rows2.mat, 7)
self.assertTupleEqual(vo_space.element_shape, vo_space2.element_shape)
def test_simple_3d_intercept(self):
train_data1 = [("drive_car", "I", "I_drive_car"),
("read_man", "You", "You_read_man"),
("read_man", "I", "I_read_man"),
("drive_car", "You", "You_drive_car"),
("drive_man", "You", "You_drive_man"),
("drive_man", "I", "I_drive_man")
]
train_data2 = [("drive", "car", "drive_car"),
("drive", "man", "drive_man"),
]
n_mat = DenseMatrix(np.mat([[1, 2], [3, 4], [5, 6], [7, 8]]))
svo_mat = DenseMatrix(np.mat([[1, 2], [3, 4], [1, 2], [3, 4], [3, 4], [1, 2]]))
n_space = Space(n_mat, ["I", "You", "man", "car"], [])
svo_space = Space(svo_mat, ["I_drive_car", "You_read_man",
"I_read_man", "You_drive_car",
"You_drive_man", "I_drive_man"], ["f1", "f2"])
#test first stage train
model = LexicalFunction(learner=LstsqRegressionLearner(intercept=True))
model.train(train_data1, n_space, svo_space)
vo_space = model.function_space
np.testing.assert_array_almost_equal(vo_space.cooccurrence_matrix.mat,
np.mat([[0.6666, 0.3333, -0.3333,
0.3333, 0.6666, 0.3333],
[0.6666, 0.3333, -0.3333,
0.3333, 0.6666, 0.3333],
[0.6666, 0.3333, -0.3333,
0.3333, 0.6666, 0.3333]]),
4)
self.assertTupleEqual(vo_space.element_shape, (2, 3))
self.assertListEqual(vo_space.id2row, ["drive_car", "drive_man", "read_man"])
self.assertListEqual(vo_space.id2column, [])
#test first stage compose
comp_space = model.compose([train_data1[0]], n_space)
np.testing.assert_array_almost_equal(comp_space.cooccurrence_matrix.mat,
np.mat([[1, 2]]), 8)
self.assertTupleEqual(comp_space.element_shape, (2,))
self.assertListEqual(comp_space.id2row, ["I_drive_car"])
self.assertListEqual(comp_space.id2column, ["f1", "f2"])
#test second stage train
model = LexicalFunction(learner=LstsqRegressionLearner(intercept=True))
model.train(train_data2, n_space, vo_space)
v_space = model.function_space
np.testing.assert_array_almost_equal(v_space.cooccurrence_matrix.mat,
np.mat([[-0.2222, 0.2222, 0.4444,
-0.1111, 0.1111, 0.2222,
0.1111, -0.1111, -0.2222,
-0.1111, 0.1111, 0.2222,
-0.2222, 0.2222, 0.4444,
-0.1111, 0.1111, 0.2222]]),
4)
self.assertTupleEqual(v_space.element_shape, (2, 3, 3))
self.assertListEqual(v_space.id2row, ["drive"])
self.assertListEqual(v_space.id2column, [])
#test compose1
comp_space = model.compose([train_data2[0]], n_space)
np.testing.assert_array_almost_equal(comp_space.cooccurrence_matrix.mat,
np.mat([[0.6666, 0.3333, -0.3333,
0.3333, 0.6666, 0.3333]]), 4)
self.assertTupleEqual(comp_space.element_shape, (2, 3))
self.assertListEqual(comp_space.id2row, ["drive_car"])
self.assertListEqual(comp_space.id2column, [])
#test compose2
model2 = LexicalFunction(function_space=comp_space, intercept=True)
comp_space2 = model2.compose([train_data1[0]], n_space)
np.testing.assert_array_almost_equal(comp_space2.cooccurrence_matrix.mat,
np.mat([[1, 2]]), 8)
self.assertTupleEqual(comp_space2.element_shape, (2,))
self.assertListEqual(comp_space2.id2row, ["I_drive_car"])
self.assertListEqual(comp_space2.id2column, [])
#recursive application, write a wrapper around it!!!
comp_space2 = model2.compose([("drive_car", "I", "I_drive_car")], n_space)
np.testing.assert_array_almost_equal(comp_space2.cooccurrence_matrix.mat,
np.mat([[1, 2]]), 8)
self.assertTupleEqual(comp_space2.element_shape, (2,))
self.assertListEqual(comp_space2.id2row, ["I_drive_car"])
self.assertListEqual(comp_space2.id2column, [])
