def mymarginal(d):
"""
Provides a dict -> pandas.DataFrame wrapper of the JSON arbplf_marginal.
"""
s = arbplf_marginal(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
return df
def mymarginal(d):
"""
Provides a dict -> pandas.DataFrame wrapper of the JSON arbplf_marginal.
"""
s = arbplf_marginal(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
return df
def test_heterogeneous_edge_rates():
# try changing one of the edge rate coefficients
d = {
"model_and_data": {
"edges": [[0, 1], [1, 2]],
"edge_rate_coefficients": [1, 2],
"rate_matrix": [[0, 1], [0, 0]],
"probability_array": [[[1, 0], [1, 1], [1, 0]]]
},
"site_reduction": {
"aggregation": "only"
}
}
actual_marginal = json.loads(arbplf_marginal(json.dumps(d)))
assert_equal(actual_marginal, desired_marginal)
g = copy.deepcopy(d)
g['trans_reduction'] = dict(selection=[[0, 1], [1, 0]])
actual_trans = json.loads(arbplf_trans(json.dumps(g)))
assert_equal(actual_trans, desired_trans)
actual_ll = json.loads(arbplf_ll(json.dumps(d)))
desired_ll = {"columns": ["value"], "data": [[-3.0]]}
assert_equal(actual_ll, desired_ll)
actual_em_update = json.loads(arbplf_em_update(json.dumps(d)))
assert_equal(actual_em_update, desired_em_update)
actual_dwell = json.loads(arbplf_dwell(json.dumps(d)))
assert_equal(actual_dwell, desired_dwell)
def test_edges_are_not_preordered():
# Try switching the order of the edges in the input
# and increasing the birth rate in the rate matrix.
d = {
"model_and_data": {
"edges": [[1, 2], [0, 1]],
"edge_rate_coefficients": [1, 2],
"rate_matrix": [[0, 2], [0, 0]],
"probability_array": [[[1, 0], [1, 1], [1, 0]]]
},
"site_reduction": {
"aggregation": "only"
}
}
actual_marginal = json.loads(arbplf_marginal(json.dumps(d)))
assert_equal(actual_marginal, desired_marginal)
g = copy.deepcopy(d)
g['trans_reduction'] = dict(selection=[[0, 1], [1, 0]])
actual_trans = json.loads(arbplf_trans(json.dumps(g)))
assert_equal(actual_trans, desired_trans)
actual_ll = json.loads(arbplf_ll(json.dumps(d)))
desired_ll = {"columns": ["value"], "data": [[-6.0]]}
assert_equal(actual_ll, desired_ll)
actual_em_update = json.loads(arbplf_em_update(json.dumps(d)))
assert_equal(actual_em_update, desired_em_update)
actual_dwell = json.loads(arbplf_dwell(json.dumps(d)))
assert_equal(actual_dwell, desired_dwell)
def test_marginal():
d = copy.deepcopy(D)
s = arbplf_marginal(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
actual = df.pivot('node', 'state', 'value').values
# compute the desired closed form solution
u = np.cumsum([0] + rates)
desired = np.vstack([np.exp(-u), -np.expm1(-u)]).T
# compare actual and desired result
assert_allclose(actual, desired)
def test_truncated_marginal():
d = copy.deepcopy(D)
d['model_and_data']['probability_array'][0][-1] = [0, 1]
s = arbplf_marginal(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
actual = df.pivot('node', 'state', 'value').values
# compute the desired closed form solution
u = np.cumsum([0] + rates)
T = u[-1]
v = (exp(-T) - exp(-u)) / expm1(-T)
desired = np.vstack([v, 1-v]).T
# compare actual and desired result
assert_allclose(actual, desired)