def test_unaligned_regression_genes(self):
tfs = ['tf1', 'tf2', 'tf3']
targets = ['gene1', 'gene2', 'gene3']
targets1 = ['gene1', 'gene2']
targets2 = ['gene1', 'gene3']
des = [InferelatorData(pd.DataFrame(np.array([[1, 1, 3], [0, 0, 2], [0, 0, 1]]).astype(float), columns=tfs)),
InferelatorData(pd.DataFrame(np.array([[1, 1, 3], [0, 0, 2], [0, 0, 1]]).astype(float), columns=tfs))]
res = [InferelatorData(pd.DataFrame(np.array([[1, 1], [2, 2], [3, 3]]).astype(float), columns=targets1)),
InferelatorData(pd.DataFrame(np.array([[1, 1], [2, 2], [3, 3]]).astype(float), columns=targets2))]
priors = pd.DataFrame([[0, 1, 1], [1, 0, 1], [1, 0, 1]], index=targets, columns=tfs)
r = amusr_regression.AMuSR_regression(des, res, tfs=tfs, genes=targets, priors=priors, use_numba=self.use_numba)
out = [pd.DataFrame([['tf3', 'gene1', -1, 1], ['tf3', 'gene1', -1, 1]],
index=pd.MultiIndex(levels=[[0, 1], [0]], codes=[[0, 1], [0, 0]]),
columns=['regulator', 'target', 'weights', 'resc_weights']),
pd.DataFrame([['tf3', 'gene2', -1, 1]],
index=pd.MultiIndex(levels=[[0, 1], [0]], codes=[[0], [0]]),
columns=['regulator', 'target', 'weights', 'resc_weights']),
pd.DataFrame([['tf3', 'gene3', -1, 1]],
index=pd.MultiIndex(levels=[[0, 1], [0]], codes=[[1], [0]]),
columns=['regulator', 'target', 'weights', 'resc_weights'])]
regress_data = r.regress()
for i in range(len(targets)):
pdt.assert_frame_equal(pd.concat(regress_data[i]), out[i], check_dtype=False)
weights, resc_weights = r.pileup_data(regress_data)
def test_format_priors_noweight(self):
runner = amusr_regression.AMuSR_regression([pd.DataFrame()],
[pd.DataFrame()], None)
tfs = ['tf1', 'tf2']
priors = [
pd.DataFrame([[0, 1], [1, 0]],
index=['gene1', 'gene2'],
columns=tfs),
pd.DataFrame([[0, 0], [1, 0]],
index=['gene1', 'gene2'],
columns=tfs)
]
gene1_prior = amusr_regression.format_prior(priors, 'gene1', [0, 1], 1)
gene2_prior = amusr_regression.format_prior(priors, 'gene2', [0, 1], 1)
npt.assert_almost_equal(gene1_prior, np.array([[1., 1.], [1., 1.]]))
npt.assert_almost_equal(gene2_prior, np.array([[1., 1.], [1., 1.]]))
def test_amusr_regression(self):
des = [
np.array([[1, 1, 3], [0, 0, 2], [0, 0, 1]]).astype(float),
np.array([[1, 1, 3], [0, 0, 2], [0, 0, 1]]).astype(float)
]
res = [
np.array([1, 2, 3]).reshape(-1, 1).astype(float),
np.array([1, 2, 3]).reshape(-1, 1).astype(float)
]
tfs = ['tf1', 'tf2', 'tf3']
targets = ['gene1', 'gene2']
priors = [
pd.DataFrame([[0, 1, 1], [1, 0, 1]], index=targets, columns=tfs),
pd.DataFrame([[0, 0, 1], [1, 0, 1]], index=targets, columns=tfs)
]
runner = amusr_regression.AMuSR_regression(
[pd.DataFrame(des[0], columns=tfs)],
[pd.DataFrame(res[0], columns=["gene1"])], None)
gene1_prior = amusr_regression.format_prior(priors, 'gene1', [0, 1],
1.)
gene2_prior = amusr_regression.format_prior(priors, 'gene2', [0, 1],
1.)
output = []
output.append(
amusr_regression.run_regression_EBIC(des, res,
['tf1', 'tf2', 'tf3'], [0, 1],
'gene1', gene1_prior))
output.append(
amusr_regression.run_regression_EBIC(des, res,
['tf1', 'tf2', 'tf3'], [0, 1],
'gene2', gene2_prior))
out0 = pd.DataFrame(
[['tf3', 'gene1', -1, 1], ['tf3', 'gene1', -1, 1]],
index=pd.MultiIndex(levels=[[0, 1], [0]], labels=[[0, 1], [0, 0]]),
columns=['regulator', 'target', 'weights', 'resc_weights'])
out1 = pd.DataFrame(
[['tf3', 'gene2', -1, 1], ['tf3', 'gene2', -1, 1]],
index=pd.MultiIndex(levels=[[0, 1], [0]], labels=[[0, 1], [0, 0]]),
columns=['regulator', 'target', 'weights', 'resc_weights'])
pdt.assert_frame_equal(pd.concat(output[0]), out0, check_dtype=False)
pdt.assert_frame_equal(pd.concat(output[1]), out1, check_dtype=False)
def test_heuristic_c(self):
set_Cs = np.arange(0, 5, dtype=float) / 10
regress = amusr_regression.AMuSR_regression(self.workflow._task_design, self.workflow._task_response,
priors=self.workflow.priors_data, Cs=set_Cs)
def is_passed(X, Y, TFs, tasks, gene, prior, Cs=None, Ss=None, lambda_Bs=None,
lambda_Ss=None, scale_data=False, **kwargs):
npt.assert_array_equal(set_Cs, Cs)
return amusr_regression.run_regression_EBIC(X, Y, TFs, tasks, gene, prior, Cs, Ss, lambda_Bs,
lambda_Ss, scale_data)
regress.regression_function = is_passed
self.workflow.set_regression_parameters(heuristic_Cs=set_Cs)
npt.assert_array_equal(self.workflow.heuristic_Cs, set_Cs)
output = regress.run()
