def contrib_score_grad(trained_model):
K.clear_session()
fpath = trained_model / 'imp-score.grad.fixture.h5'
bpnet_contrib(str(trained_model),
str(fpath),
method='grad',
overwrite=True)
return fpath
def test_contrib_regions(tmp_path, method, trained_model, regions):
"""Test different scenarios regarding subsetting
"""
K.clear_session()
bpnet_contrib(str(trained_model),
str(tmp_path / 'imp-score.h5'),
method=method,
regions=str(regions))
def test_contrib_bias_model(tmp_path, method, trained_model_w_bias):
"""Test whether we can compute differnet contribution scores
"""
K.clear_session()
fpath = tmp_path / 'imp-score.h5'
bpnet_contrib(str(trained_model_w_bias), str(fpath), method=method)
cf = ContribFile(fpath)
assert cf.get_contrib()['Task1'].shape[-1] == 4
def contrib_score_grad_null(trained_model):
K.clear_session()
fpath = trained_model / 'imp-score.grad.null.fixture.h5'
bpnet_contrib(str(trained_model),
str(fpath),
method='grad',
shuffle_seq=True,
max_regions=16,
overwrite=True)
return fpath
def test_contrib_dataspec_fasta_file(tmp_path, method, trained_model,
dataspec_bias, fasta_file):
"""Test different scenarios regarding subsetting
"""
K.clear_session()
with pytest.raises(ValueError):
bpnet_contrib(str(trained_model),
str(tmp_path / 'imp-score.h5'),
method=method,
fasta_file=str(fasta_file),
dataspec=str(dataspec_bias))