if not node.is_leaf():
children = node.get_children()
if outgroup in children:
children.remove(outgroup)
left_child, right_child = children
op_list = [
node.id, bg.BEAGLE_OP_NONE, bg.BEAGLE_OP_NONE, left_child.id,
left_child.id, right_child.id, right_child.id
]
op = bg.make_operation(op_list)
bg.BeagleOperationArray_setitem(operations, op_index, op)
op_index += 1
nodeIndices = bg.make_intarray(node_list)
dervIndices = bg.make_intarray(derv_list)
edgeLengths = bg.make_doublearray(edge_list)
for node in tree.traverse("preorder"):
if not node.is_root():
parent = node.up
if not parent.is_root():
sibling = node.get_sisters()[0]
op_list = [
node.id + n_edges, bg.BEAGLE_OP_NONE, bg.BEAGLE_OP_NONE,
parent.id + n_edges, parent.id, sibling.id, sibling.id
]
op = bg.make_operation(op_list)
bg.BeagleOperationArray_setitem(operations, op_index, op)
op_index += 1
else:
children = parent.get_children()
def loglikelihood_beagle_evaluate(instance,
tree,
tip_name_to_address,
id_attr="id",
leaf_attr="name",
scaling=False):
n_taxa = len(tree)
# n_patterns = len(val)
# n_states = len(model.pi)
n_internals = n_taxa - 2
n_transition_probs = 2 * n_taxa - 3
n_scale_buffers = 0
if scaling:
n_scale_buffers = n_internals + 1
outgroup = tree.children[0]
refresh_ids(tree, attr=id_attr)
# Sanity check(s)
assert set(getattr(node, id_attr)
for node in tree.get_leaves()) == set(range(len(tree)))
assert set(getattr(node, id_attr)
for node in tree.traverse()) == set(range(2 * len(tree) - 2))
# a list of indices and edge lengths
# create a list of partial likelihood update operations
node_list = []
edge_list = []
operations = bg.new_BeagleOperationArray(n_internals)
op_index = 0
for node in reversed(list(tree.traverse("levelorder"))):
if not node.is_root():
node_list.append(getattr(node, id_attr))
edge_list.append(node.dist)
if not node.is_leaf():
children = node.get_children()
if outgroup in children:
children.remove(outgroup)
left_child, right_child = children
scaling_index = bg.BEAGLE_OP_NONE
if scaling:
scaling_index = op_index + 1
node_address = getattr(node, id_attr)
if left_child.is_leaf():
left_child_address = tip_name_to_address[getattr(
left_child, leaf_attr)]
else:
left_child_address = getattr(left_child, id_attr)
if right_child.is_leaf():
right_child_address = tip_name_to_address[getattr(
right_child, leaf_attr)]
else:
right_child_address = getattr(right_child, id_attr)
op_list = [
node_address, scaling_index, bg.BEAGLE_OP_NONE,
left_child_address, left_child_address, right_child_address,
right_child_address
]
op = bg.make_operation(op_list)
bg.BeagleOperationArray_setitem(operations, op_index, op)
op_index += 1
nodeIndices = bg.make_intarray(node_list)
edgeLengths = bg.make_doublearray(edge_list)
# tell BEAGLE to populate the transition matrices for the above edge lengths
bg.beagleUpdateTransitionMatrices(
instance, # instance
0, # eigenIndex
nodeIndices, # probabilityIndices
None, # firstDerivativeIndices
None, # secondDerivativeIndices
edgeLengths, # edgeLengths
len(node_list)) # count
# this invokes all the math to carry out the likelihood calculation
cumulative_scale_index = bg.BEAGLE_OP_NONE
if scaling:
cumulative_scale_index = 0
bg.beagleResetScaleFactors(instance, cumulative_scale_index)
bg.beagleUpdatePartials(
instance, # instance
operations, # eigenIndex
n_internals, # operationCount
cumulative_scale_index) # cumulative scale index
logLp = bg.new_doublep()
categoryWeightIndex = bg.make_intarray([0])
stateFrequencyIndex = bg.make_intarray([0])
cumulativeScaleIndex = bg.make_intarray([cumulative_scale_index])
indexFocalParent = bg.make_intarray([getattr(tree, id_attr)])
indexFocalChild = bg.make_intarray([getattr(outgroup, id_attr)])
bg.beagleCalculateEdgeLogLikelihoods(
instance, # instance number
indexFocalParent, # indices of parent partialsBuffers
indexFocalChild, # indices of child partialsBuffers
indexFocalChild, # transition probability matrices for this edge
None, # first derivative matrices
None, # second derivative matrices
categoryWeightIndex, # weights to apply to each partialsBuffer
stateFrequencyIndex, # state frequencies for each partialsBuffer
cumulativeScaleIndex, # scaleBuffers containing accumulated factors
1, # Number of partialsBuffer
logLp, # destination for log likelihood
None, # destination for first derivative
None # destination for second derivative
)
logL = bg.doublep_value(logLp)
return logL
def loglikelihood_beagle(tree,
seqs,
model=JC,
id_attr=None,
leaf_attr=None,
scaling=False):
if id_attr is None:
id_attr = "id"
if leaf_attr is None:
leaf_attr = "name"
tree = tree.copy()
seq_dict = convert_to_dict_lists(seqs)
val = next(iter(seq_dict.values()))
n_taxa = len(seq_dict)
n_patterns = len(val)
n_states = len(model.pi)
n_internals = n_taxa - 2
n_transition_probs = 2 * n_taxa - 3
n_scale_buffers = 0
if scaling:
n_scale_buffers = n_internals + 1
outgroup = next(iter(tree.get_leaves()))
reroot(tree, outgroup)
refresh_ids(tree, attr=id_attr)
# Sanity check(s)
assert set(getattr(node, id_attr)
for node in tree.get_leaves()) == set(range(len(tree)))
assert set(getattr(node, id_attr)
for node in tree.traverse()) == set(range(2 * len(tree) - 2))
# Instantiate Beagle
requirementFlags = 0
if scaling:
requirementFlags |= bg.BEAGLE_FLAG_SCALING_MANUAL
returnInfo = bg.BeagleInstanceDetails()
instance = bg.beagleCreateInstance(
n_taxa, # tips
n_internals, # partials
n_taxa, # sequences
n_states, # states
n_patterns, # patterns
1, # models
n_transition_probs, # transition matrices
1, # rate categories
n_scale_buffers, # scale buffers
None, # resource restrictions
0, # length of resource list
0, # preferred flags
requirementFlags, # required flags
returnInfo # output details
)
assert instance >= 0
# Set tip states block
for node in tree.get_leaves():
states = bg.make_intarray(seq_dict[getattr(node, leaf_attr)])
# states = bg.createStates(seqs[getattr(node, leaf_attr)], dna_ids)
bg.beagleSetTipStates(instance, getattr(node, id_attr), states)
patternWeights = bg.createPatternWeights([1] * n_patterns)
bg.beagleSetPatternWeights(instance, patternWeights)
# create array of state background frequencies
freqs = bg.createPatternWeights(model.pi)
# freqs = bg.createPatternWeights([0.25] * 4)
bg.beagleSetStateFrequencies(instance, 0, freqs)
# create an array containing site category weights and rates
weights = bg.createPatternWeights([1.0])
rates = bg.createPatternWeights([1.0])
bg.beagleSetCategoryWeights(instance, 0, weights)
bg.beagleSetCategoryRates(instance, rates)
# set the Eigen decomposition
eigvec = bg.createPatternWeights(model.U.ravel())
invvec = bg.createPatternWeights(model.U_inv.ravel())
eigval = bg.createPatternWeights(model.D)
# eigvec = bg.createPatternWeights([1.0, 2.0, 0.0, 0.5,
# 1.0, -2.0, 0.5, 0.0,
# 1.0, 2.0, 0.0, -0.5,
# 1.0, -2.0, -0.5, 0.0])
# invvec = bg.createPatternWeights([0.25, 0.25, 0.25, 0.25,
# 0.125, -0.125, 0.125, -0.125,
# 0.0, 1.0, 0.0, -1.0,
# 1.0, 0.0, -1.0, 0.0])
# eigval = bg.createPatternWeights([0.0, -1.3333333333333333, -1.3333333333333333, -1.3333333333333333])
bg.beagleSetEigenDecomposition(instance, 0, eigvec, invvec, eigval)
# a list of indices and edge lengths
# create a list of partial likelihood update operations
node_list = []
edge_list = []
operations = bg.new_BeagleOperationArray(n_internals)
op_index = 0
for node in reversed(list(tree.traverse("levelorder"))):
if node is not outgroup:
# print(f"Node is {getattr(node, id_attr)}")
if node.is_root():
# print(f"Adding outgroup {getattr(outgroup, id_attr)}")
node_list.append(getattr(outgroup, id_attr))
edge_list.append(outgroup.dist)
else:
# print(f"Adding node {getattr(node, id_attr)}")
node_list.append(getattr(node, id_attr))
edge_list.append(node.dist)
if not node.is_leaf():
children = node.get_children()
if outgroup in children:
children.remove(outgroup)
left_child, right_child = children
scaling_index = bg.BEAGLE_OP_NONE
if scaling:
scaling_index = op_index + 1
op_list = [
getattr(node, id_attr), scaling_index, bg.BEAGLE_OP_NONE,
getattr(left_child, id_attr),
getattr(left_child, id_attr),
getattr(right_child, id_attr),
getattr(right_child, id_attr)
]
# print(f"Adding operation {op_list}")
op = bg.make_operation(op_list)
bg.BeagleOperationArray_setitem(operations, op_index, op)
op_index += 1
nodeIndices = bg.make_intarray(node_list)
edgeLengths = bg.make_doublearray(edge_list)
# tell BEAGLE to populate the transition matrices for the above edge lengths
bg.beagleUpdateTransitionMatrices(
instance, # instance
0, # eigenIndex
nodeIndices, # probabilityIndices
None, # firstDerivativeIndices
None, # secondDerivativeIndices
edgeLengths, # edgeLengths
len(node_list)) # count
# this invokes all the math to carry out the likelihood calculation
cumulative_scale_index = bg.BEAGLE_OP_NONE
if scaling:
cumulative_scale_index = 0
bg.beagleResetScaleFactors(instance, cumulative_scale_index)
bg.beagleUpdatePartials(
instance, # instance
operations, # eigenIndex
n_internals, # operationCount
cumulative_scale_index) # cumulative scale index
logLp = bg.new_doublep()
categoryWeightIndex = bg.make_intarray([0])
stateFrequencyIndex = bg.make_intarray([0])
cumulativeScaleIndex = bg.make_intarray([cumulative_scale_index])
indexFocalParent = bg.make_intarray([getattr(tree, id_attr)])
indexFocalChild = bg.make_intarray([getattr(outgroup, id_attr)])
bg.beagleCalculateEdgeLogLikelihoods(
instance, # instance number
indexFocalParent, # indices of parent partialsBuffers
indexFocalChild, # indices of child partialsBuffers
indexFocalChild, # transition probability matrices for this edge
None, # first derivative matrices
None, # second derivative matrices
categoryWeightIndex, # weights to apply to each partialsBuffer
stateFrequencyIndex, # state frequencies for each partialsBuffer
cumulativeScaleIndex, # scaleBuffers containing accumulated factors
1, # Number of partialsBuffer
logLp, # destination for log likelihood
None, # destination for first derivative
None # destination for second derivative
)
logL = bg.doublep_value(logLp)
return logL
def gradient_loglikelihood_beagle(tree,
seqs,
model=JC,
id_attr=None,
leaf_attr=None,
scaling=False):
"""Calculate branch length gradient of a tree and sequences.
:param tree: Ete3 Tree object representing the tree topology and branch lengths.
:param seqs: Array-like or dictionary of lists of sequence characters or integers.
:param model: Substitution model in namedtuple "Model". Default: Jukes-Cantor.
:param id_attr: Attribute/feature of each node that uniquely identifies it.
:param leaf_attr: Attribute/feature of each leaf that uniquely identifies it,
and uniquely identifies the sequence data row/entry in seqs.
:return: float representing the derivative of the log-likelihood of the tree,
given the sequence data and model.
"""
if id_attr is None:
id_attr = "id"
if leaf_attr is None:
leaf_attr = "name"
tree = tree.copy()
seq_dict = convert_to_dict_lists(seqs)
val = next(iter(seq_dict.values()))
n_taxa = len(seq_dict)
n_patterns = len(val)
n_states = len(model.pi)
n_internals = n_taxa - 2
n_edges = 2 * n_taxa - 3
n_partials = n_internals + n_edges
n_transition_probs = n_edges
n_derivatives = n_edges
n_matrices = n_transition_probs + n_derivatives
if scaling:
print("Scaling not currently supported.")
scaling = False
n_scale_buffers = 0
if scaling:
n_scale_buffers = n_internals + 1
outgroup = tree.children[0]
refresh_ids(tree, attr=id_attr)
# Sanity check(s)
assert set(getattr(node, id_attr)
for node in tree.get_leaves()) == set(range(len(tree)))
assert set(getattr(node, id_attr)
for node in tree.traverse()) == set(range(2 * len(tree) - 2))
# Instantiate Beagle
requirementFlags = 0
if scaling:
requirementFlags |= bg.BEAGLE_FLAG_SCALING_MANUAL
returnInfo = bg.BeagleInstanceDetails()
instance = bg.beagleCreateInstance(
n_taxa, # tips
n_partials, # partials
n_taxa, # sequences
n_states, # states
n_patterns, # patterns
1, # models
n_matrices, # transition matrices
1, # rate categories
n_scale_buffers, # scale buffers
None, # resource restrictions
0, # length of resource list
0, # preferred flags
requirementFlags, # required flags
returnInfo # output details
)
assert instance >= 0
# Set tip states block
for node in tree.get_leaves():
states = bg.make_intarray(seq_dict[getattr(node, leaf_attr)])
bg.beagleSetTipStates(instance, getattr(node, id_attr), states)
patternWeights = bg.createPatternWeights([1] * n_patterns)
bg.beagleSetPatternWeights(instance, patternWeights)
# create array of state background frequencies
freqs = bg.createPatternWeights(model.pi)
# freqs = bg.createPatternWeights([0.25] * 4)
bg.beagleSetStateFrequencies(instance, 0, freqs)
# create an array containing site category weights and rates
weights = bg.createPatternWeights([1.0])
rates = bg.createPatternWeights([1.0])
bg.beagleSetCategoryWeights(instance, 0, weights)
bg.beagleSetCategoryRates(instance, rates)
# set the Eigen decomposition
eigvec = bg.createPatternWeights(model.U.ravel())
invvec = bg.createPatternWeights(model.U_inv.ravel())
eigval = bg.createPatternWeights(model.D)
# eigvec = bg.createPatternWeights([1.0, 2.0, 0.0, 0.5,
# 1.0, -2.0, 0.5, 0.0,
# 1.0, 2.0, 0.0, -0.5,
# 1.0, -2.0, -0.5, 0.0])
# invvec = bg.createPatternWeights([0.25, 0.25, 0.25, 0.25,
# 0.125, -0.125, 0.125, -0.125,
# 0.0, 1.0, 0.0, -1.0,
# 1.0, 0.0, -1.0, 0.0])
# eigval = bg.createPatternWeights([0.0, -1.3333333333333333, -1.3333333333333333, -1.3333333333333333])
bg.beagleSetEigenDecomposition(instance, 0, eigvec, invvec, eigval)
# a list of indices and edge lengths
# create a list of partial likelihood update operations
node_list = []
derv_list = []
edge_list = []
operations = bg.new_BeagleOperationArray(n_internals + n_edges)
op_index = 0
for node in tree.traverse("postorder"):
if not node.is_root():
node_list.append(getattr(node, id_attr))
derv_list.append(getattr(node, id_attr) +
n_edges) # derivative indices
edge_list.append(node.dist)
if not node.is_leaf():
children = node.get_children()
if outgroup in children:
children.remove(outgroup)
left_child, right_child = children
scaling_index = bg.BEAGLE_OP_NONE
if scaling:
scaling_index = op_index + 1
op_list = [
getattr(node, id_attr), scaling_index, bg.BEAGLE_OP_NONE,
getattr(left_child, id_attr),
getattr(left_child, id_attr),
getattr(right_child, id_attr),
getattr(right_child, id_attr)
]
op = bg.make_operation(op_list)
bg.BeagleOperationArray_setitem(operations, op_index, op)
op_index += 1
nodeIndices = bg.make_intarray(node_list)
dervIndices = bg.make_intarray(derv_list)
edgeLengths = bg.make_doublearray(edge_list)
for node in tree.traverse("preorder"):
if not node.is_root():
parent = node.up
if not parent.is_root():
sibling = node.get_sisters()[0]
op_list = [
getattr(node, id_attr) + n_edges, bg.BEAGLE_OP_NONE,
bg.BEAGLE_OP_NONE,
getattr(parent, id_attr) + n_edges,
getattr(parent, id_attr),
getattr(sibling, id_attr),
getattr(sibling, id_attr)
]
op = bg.make_operation(op_list)
bg.BeagleOperationArray_setitem(operations, op_index, op)
op_index += 1
else:
children = parent.get_children()
children.remove(node)
# TODO: Do I add scaling factors here?
op_list = [
getattr(node, id_attr) + n_edges, bg.BEAGLE_OP_NONE,
bg.BEAGLE_OP_NONE,
getattr(children[0], id_attr),
getattr(children[0], id_attr),
getattr(children[1], id_attr),
getattr(children[1], id_attr)
]
op = bg.make_operation(op_list)
bg.BeagleOperationArray_setitem(operations, op_index, op)
op_index += 1
# tell BEAGLE to populate the transition matrices for the above edge lengths
bg.beagleUpdateTransitionMatrices(
instance, # instance
0, # eigenIndex
nodeIndices, # probabilityIndices
dervIndices, # firstDerivativeIndices
None, # secondDerivativeIndices
edgeLengths, # edgeLengths
len(node_list)) # count
# this invokes all the math to carry out the likelihood calculation
cumulative_scale_index = bg.BEAGLE_OP_NONE
if scaling:
cumulative_scale_index = 0
bg.beagleResetScaleFactors(instance, cumulative_scale_index)
bg.beagleUpdatePartials(
instance, # instance
operations, # eigenIndex
n_internals + n_edges, # operationCount
cumulative_scale_index) # cumulative scale index
categoryWeightIndex = bg.make_intarray([0])
stateFrequencyIndex = bg.make_intarray([0])
cumulativeScaleIndex = bg.make_intarray([cumulative_scale_index])
logLp = bg.new_doublep()
dlogLp = bg.new_doublep()
result = dict()
for node in tree.traverse('preorder'):
if not node.is_root():
upper_partials_index = bg.make_intarray(
[getattr(node, id_attr) + n_edges])
node_index = bg.make_intarray([getattr(node, id_attr)])
node_deriv_index = bg.make_intarray(
[getattr(node, id_attr) + n_edges])
bg.beagleCalculateEdgeLogLikelihoods(
instance, # instance number
upper_partials_index, # indices of parent partialsBuffers
node_index, # indices of child partialsBuffers
node_index, # transition probability matrices for this edge
node_deriv_index, # first derivative matrices
None, # second derivative matrices
categoryWeightIndex, # weights to apply to each partialsBuffer
stateFrequencyIndex, # state frequencies for each partialsBuffer
cumulativeScaleIndex, # scaleBuffers containing accumulated factors
1, # Number of partialsBuffer
logLp, # destination for log likelihood
dlogLp, # destination for first derivative # derivative code
None # destination for second derivative
)
# logL = bg.doublep_value(logLp)
dlogL = bg.doublep_value(dlogLp)
result[getattr(node, id_attr)] = dlogL
return result