def test_weighted_additive(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 = WeightedAdditive()
m.export(self.prefix + ".add1")
m.train([("a", "a", "a_a")], self.space1, self.space2)
m.export(self.prefix + ".add2")
def create_model(model, alpha, beta, lambda_):
#TODO: IMPORTANT here: if alpha, beta of lambda are none
model_dict = {
"weighted_add": WeightedAdditive,
"dilation": Dilation,
"mult": Multiplicative
}
if not model in model_dict:
raise ValueError("Invalid model:%s" % model)
if model == "weighted_add":
model_obj = WeightedAdditive(alpha, beta)
elif model == "dilation":
model_obj = Dilation(lambda_)
else:
model_obj = Multiplicative()
return model_obj
#ex10.py
#-------
from composes.utils import io_utils
from composes.composition.weighted_additive import WeightedAdditive
#load a space
my_space = io_utils.load("./data/out/ex10.pkl")
print my_space.id2row
print my_space.cooccurrence_matrix
# instantiate a weighted additive model
my_comp = WeightedAdditive(alpha = 1, beta = 1)
# use the model to compose words in my_space
composed_space = my_comp.compose([("good", "book", "good_book"),
("good", "car", "good_car")],
my_space)
print composed_space.id2row
print composed_space.cooccurrence_matrix
#save the composed space
io_utils.save(composed_space, "data/out/PHRASE_SS.ex10.pkl")
#ex13.py
#-------
from composes.utils import io_utils
from composes.composition.weighted_additive import WeightedAdditive
#training data
train_data = [("good", "car", "good_car"), ("good", "book", "good_book")]
#load an argument space
arg_space = io_utils.load("./data/out/ex10.pkl")
print arg_space.id2row
print arg_space.cooccurrence_matrix
#load a phrase space
phrase_space = io_utils.load("data/out/PHRASE_SS.ex10.pkl")
print phrase_space.id2row
print phrase_space.cooccurrence_matrix
#train a weighted additive model on the data
my_comp = WeightedAdditive()
my_comp.train(train_data, arg_space, phrase_space)
#print its parameters
print "alpha:", my_comp.alpha
print "beta:", my_comp.beta
print "Computing similarity with lexical function..." pred = composed_space.get_sims(test_pairs, CosSimilarity()) #use this composed space to assign similarities print "Scoring lexical function..." print scoring_utils.score(gold, pred, "spearman") print "Training Full Additive composition model..." comp_model = FullAdditive(learner=RidgeRegressionLearner(param=2)) comp_model.train(train_data, space, per_space) composed_space = comp_model.compose(test_phrases, space) pred = composed_space.get_sims(test_pairs, CosSimilarity()) print scoring_utils.score(gold, pred, "spearman") print "Training Weighted Additive composition model..." comp_model = WeightedAdditive() comp_model.train(train_data, space, per_space) print "alpha, beta:", comp_model.alpha, comp_model.beta composed_space = comp_model.compose(test_phrases, space) pred = composed_space.get_sims(test_pairs, CosSimilarity()) print scoring_utils.score(gold, pred, "spearman") print "Training Dilation composition model..." comp_model = Dilation() comp_model.train(train_data, space, per_space) print "lambda:", comp_model._lambda composed_space = comp_model.compose(test_phrases, space) pred = composed_space.get_sims(test_pairs, CosSimilarity()) print scoring_utils.score(gold, pred, "spearman") print "Multiplicative composition model..."
##########################################################################
from composes.utils import io_utils
from composes.composition.weighted_additive import WeightedAdditive
from composes.similarity.cos import CosSimilarity
import sys
pkl=sys.argv[1]
base=sys.argv[2]
minus=sys.argv[3]
plus=sys.argv[4]
space = io_utils.load(pkl)
# instantiate an additive and subtractive model
add = WeightedAdditive(alpha = 1, beta = 1)
sub = WeightedAdditive(alpha = 1, beta = -1)
#print space.get_neighbours(base, 10, CosSimilarity())
print "Subtracting",minus,"from",base
composed_space = sub.compose([(base, minus, "step1")], space)
#print composed_space.get_neighbours("step1", 10, CosSimilarity(),space)
print "Adding",plus,"..."
composed_space2 = add.compose([("step1", plus, "step2")], (composed_space,space))
print composed_space2.get_neighbours("step2", 10, CosSimilarity(),space)
