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)
def test_full_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 = FullAdditive() self.assertRaises(IllegalStateError, m.export,self.prefix + ".full1") m.train([("a","b","a_b"),("a","a","a_a")], self.space1, self.space2) m.export(self.prefix + ".full2")
def test_full_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 = FullAdditive() self.assertRaises(IllegalStateError, m.export, self.prefix + ".full1") m.train([("a", "b", "a_b"), ("a", "a", "a_a")], self.space1, self.space2) m.export(self.prefix + ".full2")
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)
from composes.composition.full_additive import FullAdditive #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") #load a phrase space phrase_space = io_utils.load("data/out/PHRASE_SS.ex10.pkl") print("Training phrase space") print(phrase_space.id2row) print(phrase_space.cooccurrence_matrix) #train a FullAdditive model on the data my_comp = FullAdditive() my_comp.train(train_data, arg_space, phrase_space) #print its parameters print("\nA:", my_comp._mat_a_t.transpose()) print("B:", my_comp._mat_b_t.transpose()) #use the model to compose the train data composed_space = my_comp.compose([("good", "bike", "good_bike")], arg_space) print("\nComposed space:") print(composed_space.id2row) print(composed_space.cooccurrence_matrix)
from composes.composition.full_additive import FullAdditive #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") #load a phrase space phrase_space = io_utils.load("data/out/PHRASE_SS.ex10.pkl") print "Training phrase space" print phrase_space.id2row print phrase_space.cooccurrence_matrix #train a FullAdditive model on the data my_comp = FullAdditive() my_comp.train(train_data, arg_space, phrase_space) #print its parameters print "\nA:", my_comp._mat_a_t.transpose() print "B:", my_comp._mat_b_t.transpose() #use the model to compose the train data composed_space = my_comp.compose([("good", "bike", "good_bike")], arg_space) print "\nComposed space:" print composed_space.id2row print composed_space.cooccurrence_matrix