def test_continous_generator():
eligible_cells = ['K562', 'HepG2', 'H1', 'A549', 'HeLa-S3']
eligible_targets = ['DNase', 'CTCF', 'RAD21', 'LARP7']
dataset = EpitomeDataset(targets=eligible_targets, cells=eligible_cells)
test_celltypes = ['K562']
eligible_cells.remove(test_celltypes[0])
radii = [1, 10]
# fake data for DNase
similarity_matrix = np.ones(dataset.get_data(Dataset.TRAIN).shape[1])
results = load_data(dataset.get_data(Dataset.TRAIN),
test_celltypes,
eligible_cells,
dataset.matrix,
dataset.targetmap,
dataset.cellmap,
radii,
mode=Dataset.RUNTIME,
continuous=True,
similarity_matrix=similarity_matrix,
similarity_targets='DNase',
return_feature_names=True,
indices=np.arange(0, 10))()
li_results = list(results)
feature_names = li_results[0][1][0]
# make sure we don't have the agreement features
assert (len(li_results[0][0][0]) == 20)
assert (len(list(filter(lambda x: '_agree' in x, feature_names))) == 0)
def test_generator_only_H3(self):
# generate consistent data
data = np.zeros(self.train_shape)
data[::2] = 1 # every 2nd row is 1s
eligible_cells = ['K562', 'HepG2', 'H1', 'HeLa-S3']
eligible_targets = ['H3K27ac', 'CTCF']
matrix, cellmap, targetmap = self.getFeatureData(
eligible_targets, eligible_cells, similarity_targets=['H3K27ac'])
assert (len(list(targetmap)) == 2) # should not have added DNase
label_cell_types = ['K562']
eligible_cells.remove(label_cell_types[0])
results = load_data(data, ['K562'],
eligible_cells,
matrix,
targetmap,
cellmap,
radii=[1, 3],
mode=Dataset.VALID,
similarity_targets=['H3K27ac'],
indices=np.arange(0, 2),
return_feature_names=True)()
li_results = list(results)
labels = li_results[0][1][0]
assert (labels[0] == 'HepG2_H3K27ac')
def test_generator_dnase_array():
# should not fail if similarity_targets are just for DNase and is a single array.
# https://github.com/YosefLab/epitome/issues/4
eligible_cells = ['K562', 'HepG2', 'H1', 'A549', 'HeLa-S3']
eligible_targets = ['DNase', 'CTCF', 'RAD21', 'LARP7']
dataset = EpitomeDataset(targets=eligible_targets, cells=eligible_cells)
test_celltypes = ['K562']
eligible_cells.remove(test_celltypes[0])
radii = [1, 10]
# fake data for DNase
similarity_matrix = np.ones(dataset.get_data(Dataset.TRAIN).shape[1])
results = load_data(dataset.get_data(Dataset.TRAIN),
test_celltypes,
eligible_cells,
dataset.matrix,
dataset.targetmap,
dataset.cellmap,
radii,
mode=Dataset.RUNTIME,
similarity_matrix=similarity_matrix,
similarity_targets='DNase',
return_feature_names=True,
indices=np.arange(0, 10))()
li_results = list(results)
# if we reach here, an error was not thrown :)
assert (len(li_results) == 10)
assert (len(li_results[0][0][0]) == 28)
feature_names = li_results[0][1][0]
assert (len(list(filter(lambda x: '_agree' in x,
feature_names))) == len(radii) *
len(eligible_cells))
def test_generator_no_dnase(self):
# generate consistent data
data = np.zeros(self.train_shape)
data[::2] = 1 # every 2nd row is 1s
eligible_cells = ['K562', 'HepG2', 'H1', 'A549', 'HeLa-S3']
eligible_targets = ['DNase', 'CTCF']
matrix, cellmap, targetmap = self.getFeatureData(
eligible_targets, eligible_cells)
label_cell_types = ['K562']
eligible_cells.remove(label_cell_types[0])
results = load_data(
data,
['K562'],
eligible_cells,
matrix,
targetmap,
cellmap,
radii=[], # no dnase
mode=Dataset.VALID,
indices=np.arange(0, 10))()
li_results = list(results)
# this element is a positive
pos_position = 6
print(li_results[pos_position][-2])
assert (np.all(li_results[pos_position][-2] == 1))
def test_generator_multiple_sim():
eligible_cells = ['K562', 'HepG2', 'H1', 'A549', 'HeLa-S3']
eligible_targets = ['DNase', 'CTCF', 'RAD21']
dataset = EpitomeDataset(targets=eligible_targets, cells=eligible_cells)
label_cell_types = ['K562']
eligible_cells.remove(label_cell_types[0])
similarity_matrix = np.ones([2, dataset.get_data(Dataset.TRAIN).shape[1]])
radii = [1, 10]
results = load_data(dataset.get_data(Dataset.TRAIN), ['K562'],
eligible_cells,
dataset.matrix,
dataset.targetmap,
dataset.cellmap,
radii=radii,
mode=Dataset.RUNTIME,
similarity_matrix=similarity_matrix,
similarity_targets=['DNase', 'CTCF'],
indices=np.arange(0, 10))()
li_results = list(results)
# length should include eligible targets and 2* radius for pos and agreement
# for each of the 2 similarity targets
assert (len(li_results[0][0]) == len(eligible_cells) *
(len(eligible_targets) + len(radii) * 4))
def test_generator_sparse_data():
eligible_cells = ['K562', 'HepG2', 'H1', 'A549', 'HeLa-S3']
eligible_targets = ['DNase', 'CTCF', 'RAD21', 'LARP7']
dataset = EpitomeDataset(targets=eligible_targets,
cells=eligible_cells,
min_cells_per_target=1,
min_targets_per_cell=1)
label_cell_types = ['HepG2']
eligible_cells.remove(label_cell_types[0])
results = list(
load_data(dataset.get_data(Dataset.TRAIN),
label_cell_types,
eligible_cells,
dataset.matrix,
dataset.targetmap,
dataset.cellmap,
radii=[],
mode=Dataset.VALID,
return_feature_names=True,
indices=np.arange(0, 10))())
# get first features
features = results[0][0]
# get labels
labels = results[0][1]
# all cell types but K562 are missing LARP7 data
assert (len(features[0]) == len(eligible_cells) * len(eligible_targets) -
3)
# make sure mask is masking out LARP7 for HepG2
assert (np.all(features[-1] == [1., 0., 1.]))
# make sure first label cell is not the test cell K562
assert (labels[-2][0] == 'lbl_HepG2_RAD21')
assert (labels[-2][1] == 'lbl_HepG2_LARP7')
assert (labels[-2][2] == 'lbl_HepG2_CTCF')
def test_generator_radius():
eligible_cells = ['K562', 'HepG2', 'H1', 'A549', 'HeLa-S3']
eligible_targets = ['DNase', 'CTCF', 'RAD21']
dataset = EpitomeDataset(targets=eligible_targets, cells=eligible_cells)
label_cell_types = ['K562']
eligible_cells.remove(label_cell_types[0])
radii = [1, 10]
results = load_data(dataset.get_data(Dataset.TRAIN), ['K562'],
eligible_cells,
dataset.matrix,
dataset.targetmap,
dataset.cellmap,
radii=radii,
mode=Dataset.VALID,
indices=np.arange(0, 10))()
li_results = list(results)
# length should include eligible targets and 2* radius for pos and agreement
assert (len(li_results[0][0]) == len(eligible_cells) *
(len(eligible_targets) + len(radii) * 2))
