def setUp(self): self.dir_ = data_dir + "pipelines_test_resources/" #use as a conversion tool, creates the files we want bcs.main([ "build_core_space.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "N_mat", "-o", self.dir_, "--input_format", "dm" ]) bcs.main([ "build_core_space.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "AN_mat", "-o", self.dir_, "--input_format", "dm" ]) bcs.main([ "build_core_space.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "A_mat", "-o", self.dir_, "--input_format", "dm" ]) tc.main([ "train_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "lexical_func", "-p", self.dir_ + "CORE_SS.AN_mat.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "-r", "lstsq", "--intercept", "False", "--export_params", "True" ])
def test_simple_lstsq_inter(self): tc.main(["train_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "lexical_func", "-p", self.dir_ + "CORE_SS.AN_mat.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "-r", "lstsq", "--intercept", "True", "--export_params", "True", ]) trained = io_utils.load(self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl") new_space = trained.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, ["big"]) self.assertListEqual(new_space.id2column, []) a_space = Space.build(data=self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.params.dm", format="dm") self._test_equal_spaces_dense(a_space, new_space)
def test_simple_lstsq_no_inter(self): tc.main([ "train_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "lexical_func", "-p", self.dir_ + "CORE_SS.AN_mat.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "-r", "lstsq", "--intercept", "False", "--export_params", "True" ]) trained = io_utils.load( self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl") new_space = trained.function_space np.testing.assert_array_almost_equal(new_space.cooccurrence_matrix.mat, np.mat([1, 0, 0, 1]), 10) self.assertTupleEqual(new_space.element_shape, (2, 2)) self.assertListEqual(new_space.id2row, ["big"]) self.assertListEqual(new_space.id2column, []) a_space = Space.build( data=self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.params.dm", format="dm") self._test_equal_spaces_dense(a_space, new_space) tc.main([ "train_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "lexical_func", "-p", self.dir_ + "CORE_SS.AN_mat.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "-r", "ridge", "--lambda", "0", "--crossvalidation", "False", "--intercept", "False", "--export_params", "True" ]) trained = io_utils.load( self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl") new_space2 = trained.function_space np.testing.assert_array_almost_equal( new_space2.cooccurrence_matrix.mat, np.mat([1, 0, 0, 1]), 10) self.assertTupleEqual(new_space2.element_shape, (2, 2)) self.assertListEqual(new_space2.id2row, ["big"]) self.assertListEqual(new_space2.id2column, []) a_space = Space.build( data=self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.params.dm", format="dm") self._test_equal_spaces_dense(a_space, new_space2)
def setUp(self): self.dir_ = data_dir + "pipelines_test_resources/" #use as a conversion tool, creates the files we want bcs.main(["build_core_space.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "N_mat", "-o", self.dir_, "--input_format", "dm" ]) bcs.main(["build_core_space.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "AN_mat", "-o", self.dir_, "--input_format", "dm" ]) bcs.main(["build_core_space.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "A_mat", "-o", self.dir_, "--input_format", "dm" ]) tc.main(["train_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "lexical_func", "-p", self.dir_ + "CORE_SS.AN_mat.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "-r", "lstsq", "--intercept", "False", "--export_params", "True" ])