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")