def test_simple_load(self): #trained = io_utils.load(self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl") #new_space = trained.function_space ac.main([ "apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "--load_model", self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "--output_format", "dm" ]) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.LexicalFunction.an_train_data.txt.dm", format="dm") sp2 = Space.build(data=self.dir_ + "AN_mat.dm", format="dm") self._test_equal_spaces_dense(sp1, sp2)
def test_simple_load(self): #trained = io_utils.load(self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl") #new_space = trained.function_space ac.main(["apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "--load_model", self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "--output_format", "dm" ] ) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.LexicalFunction.an_train_data.txt.dm", format="dm") sp2 = Space.build(data=self.dir_ + "AN_mat.dm", format="dm") self._test_equal_spaces_dense(sp1, sp2)
def test_simple_define(self): #trained = io_utils.load(self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl") #new_space = trained.function_space #compose with lexical function ac.main([ "apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "--load_model", self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "--output_format", "dm" ]) sp2 = Space.build(data=self.dir_ + "COMPOSED_SS.LexicalFunction.an_train_data.txt.dm", format="dm") #compose with weighted addition ac.main([ "apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "weighted_add", "--alpha", "0.5", "--beta", "0.5", "-a", self.dir_ + "CORE_SS.A_mat.pkl" + "," + self.dir_ + "CORE_SS.N_mat.pkl", "--output_format", "dm" ]) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.WeightedAdditive.an_train_data.txt.dm", format="dm") sp3 = io_utils.load( self.dir_ + "COMPOSED_SS.WeightedAdditive.an_train_data.txt.pkl") np.testing.assert_array_equal(sp1.cooccurrence_matrix.mat, np.mat([[3, 4], [4, 5]])) self._test_equal_spaces_structs(sp1, sp2) sp1.to_sparse() sp3.to_sparse() self._test_equal_spaces_sparse(sp1, sp3) #the two output format have to contain identical data sp1.to_dense() sp3.to_dense() self._test_equal_spaces_dense(sp1, sp3) #compose with dilation ac.main([ "apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "dilation", "--lambda", "1", "-a", self.dir_ + "CORE_SS.A_mat.pkl" + "," + self.dir_ + "CORE_SS.N_mat.pkl", "--output_format", "dm" ]) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.Dilation.an_train_data.txt.dm", format="dm") n_space = io_utils.load(self.dir_ + "CORE_SS.N_mat.pkl") sp1.to_dense() n_space.to_dense() np.testing.assert_array_almost_equal( sp1.cooccurrence_matrix.mat, n_space.cooccurrence_matrix.mat * 25) self._test_equal_spaces_structs(sp1, sp2) #compose with dilation, change the order of the arguments ac.main([ "apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "na_train_data.txt", "-o", self.dir_, "-m", "dilation", "--lambda", "1", "-a", self.dir_ + "CORE_SS.N_mat.pkl" + "," + self.dir_ + "CORE_SS.A_mat.pkl", "--output_format", "dm" ]) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.Dilation.na_train_data.txt.dm", format="dm") sp1.to_dense() np.testing.assert_array_almost_equal(sp1.cooccurrence_matrix.mat, np.mat([[75, 100], [183, 244]]), 5) self._test_equal_spaces_structs(sp1, sp2) #compose with multiplicative ac.main([ "apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "aan_train_data.txt", "-o", self.dir_, "-m", "mult", "-a", self.dir_ + "CORE_SS.A_mat.pkl" + "," + self.dir_ + "COMPOSED_SS.Dilation.an_train_data.txt.pkl", "--output_format", "dm" ]) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.Multiplicative.aan_train_data.txt.dm", format="dm")
def test_simple_define(self): #trained = io_utils.load(self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl") #new_space = trained.function_space #compose with lexical function ac.main(["apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "--load_model", self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl", "-a", self.dir_ + "CORE_SS.N_mat.pkl", "--output_format", "dm" ] ) sp2 = Space.build(data=self.dir_ + "COMPOSED_SS.LexicalFunction.an_train_data.txt.dm", format="dm") #compose with weighted addition ac.main(["apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "weighted_add", "--alpha", "0.5", "--beta", "0.5", "-a", self.dir_ + "CORE_SS.A_mat.pkl"+ "," + self.dir_ + "CORE_SS.N_mat.pkl", "--output_format", "dm" ] ) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.WeightedAdditive.an_train_data.txt.dm", format="dm") sp3 = io_utils.load(self.dir_ + "COMPOSED_SS.WeightedAdditive.an_train_data.txt.pkl") np.testing.assert_array_equal(sp1.cooccurrence_matrix.mat, np.mat([[3,4],[4,5]])) self._test_equal_spaces_structs(sp1, sp2) sp1.to_sparse() sp3.to_sparse() self._test_equal_spaces_sparse(sp1, sp3) #the two output format have to contain identical data sp1.to_dense() sp3.to_dense() self._test_equal_spaces_dense(sp1, sp3) #compose with dilation ac.main(["apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m", "dilation", "--lambda", "1", "-a", self.dir_ + "CORE_SS.A_mat.pkl"+ "," + self.dir_ + "CORE_SS.N_mat.pkl", "--output_format", "dm" ] ) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.Dilation.an_train_data.txt.dm", format="dm") n_space = io_utils.load(self.dir_ + "CORE_SS.N_mat.pkl") sp1.to_dense() n_space.to_dense() np.testing.assert_array_almost_equal(sp1.cooccurrence_matrix.mat, n_space.cooccurrence_matrix.mat*25) self._test_equal_spaces_structs(sp1, sp2) #compose with dilation, change the order of the arguments ac.main(["apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "na_train_data.txt", "-o", self.dir_, "-m", "dilation", "--lambda", "1", "-a", self.dir_ + "CORE_SS.N_mat.pkl" + "," + self.dir_ + "CORE_SS.A_mat.pkl", "--output_format", "dm" ] ) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.Dilation.na_train_data.txt.dm", format="dm") sp1.to_dense() np.testing.assert_array_almost_equal(sp1.cooccurrence_matrix.mat, np.mat([[75,100],[183,244]]),5) self._test_equal_spaces_structs(sp1, sp2) #compose with multiplicative ac.main(["apply_composition.py", "-l", self.dir_ + "log1.txt", "-i", self.dir_ + "aan_train_data.txt", "-o", self.dir_, "-m", "mult", "-a", self.dir_ + "CORE_SS.A_mat.pkl"+ "," + self.dir_ + "COMPOSED_SS.Dilation.an_train_data.txt.pkl", "--output_format", "dm" ] ) sp1 = Space.build(data=self.dir_ + "COMPOSED_SS.Multiplicative.aan_train_data.txt.dm", format="dm")