def test_complexes(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5")
data = loader.load_data("use_complexes")
assert len(data) == 3
assert len(data[0]) == 2
assert len(data[0][0]) == 3 # Z, RC, PC
assert len(data[0][1]) == 1 # TS
def test_custom_split(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5",
pre_load=False,
splitting="custom")
train, val, test = loader.load_data(shuffle=False)
assert test is None
assert len(val[0][0]) == 7
def test_classification_data(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False)
data = loader.load_data(output_type="classifier")
assert len(data) == 3
assert len(data[0][0]) == 2
assert len(data[0][1]) == 1
assert len(data[0][1][0].shape) == 1
def test_load_ts_data(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5")
data = loader.load_data()
assert len(data) == 3
assert len(data[0]) == 2
assert len(data[0][0]) == 3 # Z, R, P
assert len(data[0][1]) == 1 # TS
def test_serving_both(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False)
data = loader.load_data(output_type="both")
assert len(data) == 3
assert len(data[0][0]) == 3
assert len(data[0][1]) == 2
assert data[0][1][0].shape[1:] == (loader.num_points, 3)
assert len(data[0][1][1].shape) == 1
def test_siamese_data(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False)
data = loader.load_data(output_type="siamese")
assert len(data) == 3
assert data[0][0][0].shape[1:] == (
2,
loader.num_points,
)
assert data[0][0][1].shape[1:] == (2, loader.num_points, 3)
assert len(data[0][1]) == 1
def test_distance_matrix(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False)
data = loader.load_data(output_type="both",
output_distance_matrix=True)
assert len(data) == 3
assert len(data[0][0]) == 3
assert len(data[0][1]) == 2
assert data[0][1][0].shape[1:] == (loader.num_points,
loader.num_points)
assert len(data[0][1][1].shape) == 1
from tfn.tools.loaders import TSLoader
import numpy as np
def get_atomic_histogram_data(z):
z = np.where(z == 0, np.nan, z)
z = np.where(z == 1, np.nan, z)
return [np.count_nonzero(z == i) for i in range(36)]
loader = TSLoader(path="/home/riley/dev/python/data/ts.hdf5",
splitting=None,
map_points=False)
x, _ = loader.load_data(remove_noise=True, shuffle=False)[0]
z, *_ = x
print(f"Size data: {np.count_nonzero(np.where(z == 1, 0, z), axis=1)}")
print(f"Type data: {get_atomic_histogram_data(z)}")
def test_energy_serving(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False)
data = loader.load_data(output_type="energies")
assert len(data) == 3
assert len(data[0][0]) == 3
assert len(data[0][1][0].shape) == 1
def test_remove_noise(self):
loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", splitting=None)
data = loader.load_data(remove_noise=True)
assert len(data[0][0][0]) == 55