class LexfunModel(Model):
lexfun = None
def __init__(self, space, learner='LeastSquares', intercept=True, param=None):
# super(LexfunModel, self).__init__(space)
Model.__init__(self, space)
if learner == 'Ridge':
# If param==None, generalized CV will be performed within standard param range
learner = RidgeRegressionLearner(intercept=intercept, param=param)
elif learner == 'LeastSquares':
learner = LstsqRegressionLearner()
else:
raise NameError("No such learner: %s" % learner)
self.lexfun = LexicalFunction(learner=learner)
def fit(self, train_pairs, verbose=False):
if len(train_pairs) == 0:
raise NameError('Error: Train set is empty')
else:
if verbose:
print 'fit: Fitting a lexfun model on %d pairs' % (len(train_pairs))
# LexicalFunction class is designed to be run on a dataset with different function words (==patterns).
# We use a dummy function word here.
train_pairs_ext = [('dummy', base, derived) for (base, derived) in train_pairs]
self.lexfun.train(train_pairs_ext, self.space, self.space)
def predict(self, base, verbose=False):
if self.lexfun is None:
raise NameError('Error: Model has not yet been trained')
composed_space = self.lexfun.compose([('dummy', base, 'derived')], self.space)
return composed_space.get_row('derived')
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 get_vector(self, df):
# 3. use the trained models to compose new SVO sentences
# 3.1 use the V model to create new VO combinations
data = (str(df[1]), str(df[2]), str(df[1:]))
# ("take/V", "place/N", "take/V_place/N")
vo_composed_space = self.v_model.compose([data], self.n_space)
# todo how do we get VO vectors? these are (100x100)+100 dimensional (intercept).
# todo do we allow document features of different dimensionality
# vo_composed_space.cooccurrence_matrix.mat
# 3.2 the new VO combinations will be used as functions:
# load the new VO combinations obtained through composition into
# a new composition model
expanded_vo_model = LexicalFunction(function_space=vo_composed_space,
intercept=self.v_model._has_intercept)
# 3.3 use the new VO combinations by composing them with subject nouns
# in order to obtain new SVO sentences
data = (str(df[1:]), str(df[0]), str(df))
svo_composed_space = expanded_vo_model.compose([data], self.n_space)
# print the composed spaces:
# logging.info("SVO composed space:")
# logging.info(svo_composed_space.id2row)
# logging.info(svo_composed_space.cooccurrence_matrix)
# get vectors out. these are 100-dimensional
return svo_composed_space.cooccurrence_matrix.mat
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_)
model._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 train_one_space(core_space, per_space, func_pos, number_of_lambdas):
param_range = np.logspace(-1,1,number_of_lambdas)
training_list = get_training_list(per_space, 1, func_pos)
per_space = per_space.apply(RowNormalization())
composition_model = LexicalFunction(
learner=RidgeRegressionLearner(param_range=param_range,
intercept=False))
composition_model.train(training_list, core_space, per_space)
return composition_model.function_space
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._MIN_SAMPLES = 1
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)
model2._MIN_SAMPLES = 1
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_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()
model._MIN_SAMPLES = 5
self.assertRaises(ValueError, model.train, train_data, self.n_space, self.an_space)
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 predict_using_TENSOR ( compound, TENSOR_matrix, unigram_space ) :
adj = compound.split('_')[0]
noun = compound.split('_')[1]
composed_space_1 = TENSOR_matrix.compose([("tens_adj", adj, "predicted_ADJ_"+adj)], unigram_space )
# eg ( "tens_adj", "good", "predicted_ADJ_good")
#tens_adj -> Tensor matrix , good -> unigram, predicted_ADJ_good -> to compute ( using tens_adj * good )
#print composed_space_1.id2row
expanded_model = LexicalFunction(function_space=composed_space_1,
intercept=TENSOR_matrix._has_intercept)
composed_space_2 = expanded_model.compose([("predicted_ADJ_"+adj, noun, compound)], unigram_space )
# eg ( "predicted_ADJ_good", "boy" , "good_boy" )
#predicted_ADJ_good -> ADJ_good matrix computed above, boy -> unigram, good_boy -> to compute ( predicted_ADJ_good * boy )
return composed_space_2
def __init__(self, space, learner='LeastSquares', intercept=True, param=None):
# super(LexfunModel, self).__init__(space)
Model.__init__(self, space)
if learner == 'Ridge':
# If param==None, generalized CV will be performed within standard param range
learner = RidgeRegressionLearner(intercept=intercept, param=param)
elif learner == 'LeastSquares':
learner = LstsqRegressionLearner()
else:
raise NameError("No such learner: %s" % learner)
self.lexfun = LexicalFunction(learner=learner)
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 learn_ADJ_matrices ( ) :
bigram_space = load_space(args.function[2])
train_data=[]
adj_list = extract_adj(bigram_space)
for bigram in bigram_space.id2row :
pair = bigram.split('_')
if( pair[0] in adj_list ) :
train_data.append(("ADJ"+"_"+pair[0], pair[1], bigram))
# eg ( "ADJ_good", boy, good_boy ) , where "ADJ_good" -> matrix to learn, boy -> unigram , good_boy -> bigram
my_comp=LexicalFunction()
my_comp.train(train_data, unigram_space, bigram_space)
#unigram_space -> for "boy" , bigram_space -> for "good_boy"
save_space(my_comp, "ADJ_matrices", "matrices")
def compose_space_TENSOR ( ) :
bigram_space = load_space(args.function[2])
TENSOR_matrix = load_space(args.function[3])
predicted_ADJs = []
predicted_bigrams = []
adj_list = extract_adj(bigram_space)
for adj in adj_list :
predicted_ADJs.append(("tens_adj", adj, "predicted_ADJ_"+adj) )
# eg ( "tens_adj", "good", "predicted_ADJ_good")
#tens_adj -> Tensor matrix , good -> unigram, predicted_ADJ_good -> to compute ( using tens_adj * good )
# Obtain the ADJ matrices using => TENSOR * adj
composed_space_1 = TENSOR_matrix.compose(predicted_ADJs, unigram_space )
expanded_model = LexicalFunction(function_space=composed_space_1,
intercept=TENSOR_matrix._has_intercept)
for bigram in bigram_space.id2row :
adj = bigram.split('_')[0]
noun = bigram.split('_')[1]
if( not adj in adj_list or noun not in unigram_space.id2row ) :
continue
predicted_bigrams.append(("predicted_ADJ_"+adj, noun, "predicted_"+bigram) )
# eg ( "predicted_ADJ_good", "boy" , "predict_good_boy" )
#predicted_ADJ_good -> ADJ_good matrix computed above, boy -> unigram, predicted_good_boy -> to compute (predicted_ADJ_good * boy )
# Predicted composition = predicted_ADJ * noun ( where predicted_ADJ = TENSOR * adj )
composed_space_2 = expanded_model.compose(predicted_bigrams, unigram_space )
print "Number of elements in the space : ", len(composed_space_2.id2row)
save_space(composed_space_2, "composed_space_TENSOR" , "composed_space")
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_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")
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")
def learn_TENSOR_matrix ( ) :
bigram_space = load_space(args.function[2])
my_comp_list = []
id2row_list = []
adj_list = extract_adj(bigram_space)
for adj in adj_list :
train_data=[]
for bigram in bigram_space.id2row :
pair = bigram.split('_')
if( not pair[0] == adj ) :
continue
train_data.append(("ADJ"+"_"+adj, pair[1], bigram))
# eg ( "ADJ_good", "boy", "good_boy"), where "ADJ_good" -> matrix to learn, boy -> unigram , good_boy -> bigram
my_comp=LexicalFunction() # 1)
#Learn ADJ matrix for each adjective
my_comp.train(train_data, unigram_space, bigram_space)
my_comp_list.append(my_comp.function_space.cooccurrence_matrix)
id2row_list.append(my_comp.function_space.id2row)
my_mat_id2row=id2row_list.pop()
my_mat_space=Space(my_comp_list.pop(),my_mat_id2row,[])
#Create a new space using the ADJ matrices created
for i in range(len(id2row_list)):
my_mat_id2row.extend(id2row_list[i])
my_mat_space=Space(my_mat_space.cooccurrence_matrix.vstack(my_comp_list[i]),my_mat_id2row,[])
my_mat_space._element_shape = my_comp.function_space.element_shape
#Use the ADJ matrices space to learn the tensor matrix
train_data=[('tens_adj',adj,"ADJ"+"_"+adj) for adj in adj_list]
# eg ( "tens_adj", good, ADJ_good )
#where "tens_adj" -> tensor matrix to learn, good -> unigram , ADJ_good -> adjective matrix learnt by 'my_comp' in 1)
my_tens_adj=LexicalFunction()
my_tens_adj.train(train_data, unigram_space, my_mat_space)
# unigram_space -> for "good" , my_mat_space -> for "ADJ_good"
save_space(my_tens_adj, "TENSOR_matrix", "matrices")
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._MIN_SAMPLES = 1
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._MIN_SAMPLES = 1
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._MIN_SAMPLES = 1
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._MIN_SAMPLES = 1
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)
model2._MIN_SAMPLES = 1
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, [])
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, [])
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)
#ex18.py
#-------
from composes.utils import io_utils
from composes.composition.lexical_function import LexicalFunction
#training data
#trying to learn a "book" 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)
#apply the trained model
comp_sp1 = my_comp.compose([("good_function", "car", "good_car")], arg_space)
#apply the trained model a second time
comp_sp2 = my_comp.compose([("good_function", "good_car", "good_good_car")],
comp_sp1)
#print the composed spaces:
print "\nComposed space 1:"
print comp_sp1.id2row
print comp_sp1.cooccurrence_matrix
print "\nComposed space 2:"
def train_grefenstette_multistep_composer(all_vectors_file, root_dir):
"""
Train Grefenstette et al's multistep regression VO/SVO model
Adapted from dissect's ex19.py
:param all_vectors_file: file containing N, V, VO and SVO vectors
:param root_dir: where to write temp files and output
"""
mkdirs_if_not_exists(root_dir)
vo_composer_output_file = join(root_dir, 'vo_comp.pkl')
svo_composer_output_file = join(root_dir, 'svo_comp.pkl')
filename = basename(all_vectors_file)
noun_events_file = join(root_dir, '%s-onlyN.tmp' % filename)
# verb_events_file = join(root_dir, '%s-onlyV.tmp' % filename)
# vo_events_file = join(root_dir, '%s-onlyVO.tmp' % filename)
svo_events_file = join(root_dir, '%s-onlySVO.tmp' % filename)
# this has unigrams and observed phrases
thes = Vectors.from_tsv(all_vectors_file)
thes.to_tsv(noun_events_file,
entry_filter=lambda x: x.type == '1-GRAM' and x.tokens[0].pos == 'N')
_translate_byblo_to_dissect(noun_events_file)
# thes.to_tsv(verb_events_file,
# entry_filter=lambda x: x.type == '1-GRAM' and x.tokens[0].pos == 'V')
# _translate_byblo_to_dissect(verb_events_file)
# thes.to_tsv(vo_events_file,
# entry_filter=lambda x: x.type == 'VO')
# _translate_byblo_to_dissect(vo_events_file)
thes.to_tsv(svo_events_file,
entry_filter=lambda x: x.type == 'SVO')
_translate_byblo_to_dissect(svo_events_file)
train_vo_data, train_v_data = [], []
for phrase in thes.keys():
df = DocumentFeature.from_string(phrase)
if df.type == 'SVO':
train_vo_data.append((str(df[1:]), str(df[0]), str(df)))
if df.type == 'VO':
train_v_data.append((str(df[0]), str(df[1]), str(df)))
# logging.info('train_vo_data %r', len(train_vo_data))
# logging.info('train_v_data %r', len(train_v_data))
# load N and SVO spaces
n_space = Space.build(data=noun_events_file + '.sm',
cols=noun_events_file + '.cols',
format="sm")
svo_space = Space.build(data=svo_events_file + '.sm',
cols=svo_events_file + '.cols',
format="sm")
logging.info("Input SVO training space:")
logging.info(svo_space.id2row)
# logging.info(svo_space.cooccurrence_matrix)
# 1. train a model to learn VO functions on train data: VO N -> SVO
logging.info("Step 1 training")
vo_model = LexicalFunction(learner=RidgeRegressionLearner(), min_samples=2) # Gref et al 2013, §5 says 3
vo_model.train(train_vo_data, n_space, svo_space)
io_utils.save(vo_model, vo_composer_output_file)
# 2. train a model to learn V functions on train data: V N -> VO
# where VO space: function space learned in step 1
logging.info("Step 2 training")
vo_space = vo_model.function_space
v_model = LexicalFunction(learner=RidgeRegressionLearner(), min_samples=2)
v_model.train(train_v_data, n_space, vo_space)
io_utils.save(v_model, svo_composer_output_file)
#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
#-------
from composes.utils import io_utils
from composes.composition.lexical_function import LexicalFunction
#training data
#trying to learn a "book" 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)
#apply the trained model
comp_sp1 = my_comp.compose([("good_function", "car",
"good_car")],
arg_space)
#apply the trained model a second time
comp_sp2 = my_comp.compose([("good_function", "good_car",
"good_good_car")],
comp_sp1)
#print the composed spaces:
print "\nComposed space 1:"
pair_data = pickle.load(f)
train_data = []
vocab = set(my_space.id2row)
for tup in pair_data:
if tup[1] in vocab and tup[2] in vocab:
train_data.append(tup)
'''
try:
with open('temp_func.pkl', 'rb') as file:
print("Loading model...")
my_comp = pickle.load(file)
except FileNotFoundError:
'''
print("Training LexicalFunction...")
my_comp = LexicalFunction()
my_comp.train(train_data, my_space, my_space)
with open('temp_func.pkl', 'wb') as file:
pickle.dump(my_comp, file)
print("Building composed space...")
composed_space = my_comp.compose(train_data, my_space)
# print(composed_space.id2row)
# print(composed_space.cooccurrence_matrix)
# compute similarity between two words in the space
cos_sim = {}
for pair in train_data:
cos = my_space.get_sim(pair[1],
pair[2],
#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
#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())
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())