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_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)