def test_shuffled_edges():
d = copy.deepcopy(d_original)
original_dwell = json.loads(arbplf_dwell(json.dumps(d)))
d = copy.deepcopy(d_original)
original_ll = json.loads(arbplf_ll(json.dumps(d)))
d = copy.deepcopy(d_original)
d['site_reduction'] = {'aggregation' : 'sum'}
original_em_update = json.loads(arbplf_em_update(json.dumps(d)))
iter_count = 10
for i in range(iter_count):
d_shuffled, perm = _shuffle_edges(d_original)
# the ll output does not have an edge column
d = copy.deepcopy(d_shuffled)
ll = json.loads(arbplf_ll(json.dumps(d)))
assert_equal(ll, original_ll)
d = copy.deepcopy(d_shuffled)
dwell = json.loads(arbplf_dwell(json.dumps(d)))
dwell_prime = _perm_output_edges(dwell, perm)
dwell_prime['data'].sort()
assert_equal(dwell_prime, original_dwell)
d = copy.deepcopy(d_shuffled)
d['site_reduction'] = {'aggregation' : 'sum'}
em_update = json.loads(arbplf_em_update(json.dumps(d)))
em_update_prime = _perm_output_edges(em_update, perm)
em_update_prime['data'].sort()
assert_equal(em_update_prime, original_em_update)
def test_shuffled_nodes():
d = copy.deepcopy(d_original)
original_dwell = json.loads(arbplf_dwell(json.dumps(d)))
d = copy.deepcopy(d_original)
original_ll = json.loads(arbplf_ll(json.dumps(d)))
d = copy.deepcopy(d_original)
d['site_reduction'] = {'aggregation' : 'sum'}
original_em_update = json.loads(arbplf_em_update(json.dumps(d)))
iter_count = 10
for i in range(iter_count):
d_shuffled = _shuffle_nodes(d_original)
d = copy.deepcopy(d_shuffled)
dwell = json.loads(arbplf_dwell(json.dumps(d)))
assert_equal(dwell, original_dwell)
d = copy.deepcopy(d_shuffled)
ll = json.loads(arbplf_ll(json.dumps(d)))
assert_equal(ll, original_ll)
d = copy.deepcopy(d_shuffled)
d['site_reduction'] = {'aggregation' : 'sum'}
em_update = json.loads(arbplf_em_update(json.dumps(d)))
assert_equal(em_update, original_em_update)
def mydwell(d):
"""
Provides a dict -> pandas.DataFrame wrapper of the pure JSON arbplf_dwell.
"""
s = arbplf_dwell(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 mydwell(d):
"""
Provides a dict -> pandas.DataFrame wrapper of the pure JSON arbplf_dwell.
"""
s = arbplf_dwell(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
return df
def test_dwell():
d = copy.deepcopy(D)
s = arbplf_dwell(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
actual = df.pivot('edge', 'state', 'value').values
# compute the desired closed form solution
u = np.cumsum([0] + rates)
a, b = u[:-1], u[1:]
v = exprel(b - a) * exp(-b)
desired = np.vstack([v, 1-v]).T
# compare actual and desired result
assert_allclose(actual, desired)
def test_truncated_dwell():
d = copy.deepcopy(D)
d['model_and_data']['probability_array'][0][-1] = [0, 1]
s = arbplf_dwell(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
actual = df.pivot('edge', 'state', 'value').values
# compute the desired closed form solution
u = np.cumsum([0] + rates)
a, b = u[:-1], u[1:]
T = u[-1]
# this way is not robust when a == b.
def F(x):
return -(exp(T - x) + x) / expm1(T)
v = (F(b) - F(a)) / (b - a)
desired = np.vstack([v, 1-v]).T
# this way is better
v = (exprel(b-a)*exp(T-b) - 1) / expm1(T)
desired = np.vstack([v, 1-v]).T
# compare actual and desired result
assert_allclose(actual, desired)
def mydwell(d):
s = arbplf_dwell(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
return df
def mydwell(d):
s = arbplf_dwell(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
return df
