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 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 main():
np.random.seed(123475)
# sample a random rate matrix
state_count = 3
edge_count = 3
node_count = edge_count + 1
#Q = sample_rate_matrix(state_count)
Q = sample_reversible_rate_matrix(state_count)
p = equilibrium(Q)
expected_rate = -p.dot(np.diag(Q))
print('expected rate:', expected_rate)
Q = Q / expected_rate
np.fill_diagonal(Q, 0)
# use ad hoc data
probability_array = [[[1, 1, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0]],
[[1, 1, 1], [0, 1, 0], [1, 0, 0], [1, 0, 0]],
[[1, 1, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]]]
site_weights = [.7, .2, .1]
edges = [[0, 1], [0, 2], [0, 3]]
coefficients = [.01, .01, .01]
d = {
"model_and_data": {
"edges": edges,
"edge_rate_coefficients": coefficients,
"rate_matrix": Q.tolist(),
"probability_array": probability_array
},
"site_reduction": {
"aggregation": site_weights
}
}
print(d)
for i in range(100):
s = arbplf_em_update(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
y = df.value.values.tolist()
d['model_and_data']['edge_rate_coefficients'] = y
print('coefficients updated by EM:', y)
s = arbplf_newton_refine(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
y = df.value.values.tolist()
print('coefficients updated by newton refinement:', y)
d['trans_reduction'] = {
'selection': [[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]],
'aggregation': 'sum'
}
d['model_and_data']['edge_rate_coefficients'] = y
s = arbplf_trans(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
y = df.value.values.tolist()
print('conditionally expected transition counts:', y)
def main():
np.random.seed(123475)
# sample a random rate matrix
state_count = 3
edge_count = 3
node_count = edge_count + 1
# Q = sample_rate_matrix(state_count)
Q = sample_reversible_rate_matrix(state_count)
p = equilibrium(Q)
expected_rate = -p.dot(np.diag(Q))
print("expected rate:", expected_rate)
Q = Q / expected_rate
np.fill_diagonal(Q, 0)
# use ad hoc data
probability_array = [
[[1, 1, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0]],
[[1, 1, 1], [0, 1, 0], [1, 0, 0], [1, 0, 0]],
[[1, 1, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]],
]
site_weights = [0.7, 0.2, 0.1]
edges = [[0, 1], [0, 2], [0, 3]]
coefficients = [0.01, 0.01, 0.01]
d = {
"model_and_data": {
"edges": edges,
"edge_rate_coefficients": coefficients,
"rate_matrix": Q.tolist(),
"probability_array": probability_array,
},
"site_reduction": {"aggregation": site_weights},
}
print(d)
for i in range(100):
s = arbplf_em_update(json.dumps(d))
df = pd.read_json(StringIO(s), orient="split", precise_float=True)
y = df.value.values.tolist()
d["model_and_data"]["edge_rate_coefficients"] = y
print("coefficients updated by EM:", y)
s = arbplf_newton_refine(json.dumps(d))
df = pd.read_json(StringIO(s), orient="split", precise_float=True)
y = df.value.values.tolist()
print("coefficients updated by newton refinement:", y)
d["trans_reduction"] = {"selection": [[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]], "aggregation": "sum"}
d["model_and_data"]["edge_rate_coefficients"] = y
s = arbplf_trans(json.dumps(d))
df = pd.read_json(StringIO(s), orient="split", precise_float=True)
y = df.value.values.tolist()
print("conditionally expected transition counts:", y)