def evaluate(self, true_splits, D_estimated):
"""
@param true_splits: the set of all full splits implied by the true tree
@param D_estimated: the estimated distance matrix
"""
self.true_splits = true_splits
BuildTreeTopology.get_splits(D_estimated, self.split_function, BuildTreeTopology.update_using_laplacian, self.on_label_split)
def evaluate(self, true_splits, D_estimated, atteson, use_nj, use_modified_nj, use_all_spectral, use_one_spectral):
"""
@param true_splits: the set of all full splits implied by the true tree
@param D_estimated: the estimated distance matrix
@param atteson: True iff the distance matrix is Atteson
"""
# initialize the errors
nj_error = None
modified_nj_error = None
all_spectral_error = None
one_spectral_error = None
if use_nj:
nj_splits = BuildTreeTopology.get_splits(D_estimated, BuildTreeTopology.split_nj, BuildTreeTopology.update_nj)
nj_error = Xtree.splits_to_rf_distance(nj_splits, true_splits)
if use_modified_nj:
modified_nj_splits = BuildTreeTopology.get_splits(D_estimated, BuildTreeTopology.split_nj, BuildTreeTopology.update_using_laplacian)
modified_nj_error = Xtree.splits_to_rf_distance(modified_nj_splits, true_splits)
if use_all_spectral:
splitter = BuildTreeTopology.split_using_eigenvector_with_nj_fallback
updater = BuildTreeTopology.update_using_laplacian
all_spectral_splits = BuildTreeTopology.get_splits(D_estimated, splitter, updater)
all_spectral_error = Xtree.splits_to_rf_distance(all_spectral_splits, true_splits)
if use_one_spectral:
splitter = SplitFunctor(len(D_estimated))
updater = UpdateFunctor(len(D_estimated))
one_spectral_splits = BuildTreeTopology.get_splits(D_estimated, splitter, updater)
one_spectral_error = Xtree.splits_to_rf_distance(one_spectral_splits, true_splits)
# add the data point
self.scatter_points.append(ScatterPoint(atteson, nj_error, modified_nj_error, all_spectral_error, one_spectral_error))
def get_eigendecomposition_report(D):
"""
@param D: a distance matrix
@return: a multi-line string
"""
out = StringIO()
# get some intermediate matrices and vectors
L = Euclid.edm_to_laplacian(D)
laplacian_fiedler = BuildTreeTopology.laplacian_to_fiedler(L)
distance_fiedler = BuildTreeTopology.edm_to_fiedler(D)
eigensplit = BuildTreeTopology.eigenvector_to_split(laplacian_fiedler)
# report the two eigenvalue lists that should be the same
HDH = MatrixUtil.double_centered(D)
HSH = -0.5 * HDH
w_distance, vt_distance = np.linalg.eigh(HSH)
print >> out, 'the laplacian-derived and distance-derived eigenvalues:'
w_laplacian, vt_laplacian = np.linalg.eigh(L)
for a, b in zip(sorted(w_laplacian), sorted(w_distance)):
print >> out, a, '\t', b
print >> out
# report the two fiedler vectors that should be the same
print >> out, 'the laplacian-derived and distance-derived fiedler vectors:'
for a, b in zip(laplacian_fiedler, distance_fiedler):
print >> out, a, '\t', b
return out.getvalue().strip()
def __call__(self, D):
"""
@param D: the distance matrix
@return: a set of two index sets defining a split of the indices
"""
if len(D) < self.large_matrix_size:
return BuildTreeTopology.split_nj(D)
else:
return BuildTreeTopology.split_using_eigenvector_with_nj_fallback(D)
def __call__(self, D, index_set):
"""
@param D: the distance matrix
@param index_set: the subset of indices that will be removed from the updated distance matrix
@return: an updated distance matrix
"""
if len(D) < self.large_matrix_size:
return BuildTreeTopology.update_nj(D, index_set)
else:
return BuildTreeTopology.update_using_laplacian(D, index_set)
def do_it_right(D):
"""
Do neighbor joining correctly.
@param D: distance matrix
@return: a sequence of splits
"""
# use neighbor joining to build the tree, saving the splits in the order they are made
split_saver = SplitSaver()
BuildTreeTopology.get_splits(D, BuildTreeTopology.split_nj, BuildTreeTopology.update_nj, split_saver)
return split_saver.splits
def _do_analysis(self, use_generalized_nj):
"""
Do some splits of the tree.
@param use_generalized_nj: True if we use an old method of outgrouping
"""
# define the distance matrix
D = np.array(self.pruned_tree.get_distance_matrix(self.pruned_names))
# get the primary split of the criterion matrix
L = Euclid.edm_to_laplacian(D)
v = BuildTreeTopology.laplacian_to_fiedler(L)
eigensplit = BuildTreeTopology.eigenvector_to_split(v)
# assert that the first split cleanly separates the bacteria from the rest
left_indices, right_indices = eigensplit
left_domains = self._get_domains([self.pruned_names[x] for x in left_indices])
right_domains = self._get_domains([self.pruned_names[x] for x in right_indices])
if ('bacteria' in left_domains) and ('bacteria' in right_domains):
raise HandlingError('bacteria were not defined by the first split')
# now we have enough info to define the first supplementary csv file
self.first_split_object = SupplementarySpreadsheetObject(self.pruned_names, L, v)
# define the bacteria indices vs the non-bacteria indices for the second split
if 'bacteria' in left_domains:
bacteria_indices = left_indices
non_bacteria_indices = right_indices
elif 'bacteria' in right_domains:
bacteria_indices = right_indices
non_bacteria_indices = left_indices
# get the secondary split of interest
if use_generalized_nj:
D_secondary = BuildTreeTopology.update_generalized_nj(D, bacteria_indices)
L_secondary = Euclid.edm_to_laplacian(D_secondary)
else:
L_secondary = SchurAlgebra.mmerge(L, bacteria_indices)
full_label_sets = [set([i]) for i in range(len(self.pruned_names))]
next_label_sets = SchurAlgebra.vmerge(full_label_sets, bacteria_indices)
v_secondary = BuildTreeTopology.laplacian_to_fiedler(L_secondary)
eigensplit_secondary = BuildTreeTopology.eigenvector_to_split(v_secondary)
left_subindices, right_subindices = eigensplit_secondary
pruned_names_secondary = []
for label_set in next_label_sets:
if len(label_set) == 1:
label = list(label_set)[0]
pruned_names_secondary.append(self.pruned_names[label])
else:
pruned_names_secondary.append('all-bacteria')
# assert that the second split cleanly separates the eukaryota from the rest
left_subdomains = self._get_domains([pruned_names_secondary[x] for x in left_subindices])
right_subdomains = self._get_domains([pruned_names_secondary[x] for x in right_subindices])
if ('eukaryota' in left_subdomains) and ('eukaryota' in right_subdomains):
raise HandlingError('eukaryota were not defined by the second split')
# now we have enough info to define the second supplementary csv file
self.second_split_object = SupplementarySpreadsheetObject(pruned_names_secondary, L_secondary, v_secondary)
def get_verbose_summary(self):
"""
@return: a multiline string
"""
# begin the response
out = StringIO()
# show the number of taxa in various domains
print >> out, self._get_name_summary()
print >> out
# show the pruned full tree
formatted_tree_string = NewickIO.get_narrow_newick_string(self.pruned_tree, 120)
print >> out, 'this is the tree that represents all clades but for which redundant nodes have been pruned:'
print >> out, formatted_tree_string
print >> out
# split the distance matrix
D = np.array(self.pruned_tree.get_distance_matrix(self.pruned_names))
L = Euclid.edm_to_laplacian(D)
v = BuildTreeTopology.laplacian_to_fiedler(L)
eigensplit = BuildTreeTopology.eigenvector_to_split(v)
# report the eigendecomposition
print >> out, get_eigendecomposition_report(D)
print >> out
# report the clade intersections of sides of the split
side_names = [set(self.pruned_names[i] for i in side) for side in eigensplit]
print >> out, 'domains represented by each side of the primary split:'
print >> out, 'the left side has:\t', ', '.join(self._get_domains(side_names[0]))
print >> out, 'the right side has:\t', ', '.join(self._get_domains(side_names[1]))
print >> out
# prepare to do the secondary splits
left_indices, right_indices = eigensplit
full_label_sets = [set([i]) for i in range(len(self.pruned_names))]
# do the secondary splits
for index_selection, index_complement in ((left_indices, right_indices), (right_indices, left_indices)):
L_secondary = SchurAlgebra.mmerge(L, index_complement)
next_label_sets = SchurAlgebra.vmerge(full_label_sets, index_complement)
v = BuildTreeTopology.laplacian_to_fiedler(L_secondary)
left_subindices, right_subindices = BuildTreeTopology.eigenvector_to_split(v)
left_sublabels = set()
for i in left_subindices:
left_sublabels.update(next_label_sets[i])
right_sublabels = set()
for i in right_subindices:
right_sublabels.update(next_label_sets[i])
left_subnames = set(self.pruned_names[i] for i in left_sublabels)
right_subnames = set(self.pruned_names[i] for i in right_sublabels)
print >> out, 'domains represented by a subsplit:'
print >> out, 'the left side has:\t', ', '.join(self._get_domains(left_subnames))
print >> out, 'the right side has:\t', ', '.join(self._get_domains(right_subnames))
print >> out
# return the multiline string
return out.getvalue().strip()
def process(ntaxa, nseconds, branch_length_sampler):
"""
@param ntaxa: the number of taxa in the sampled trees
@param nseconds: allow this many seconds to run or None to run forever
@param branch_length_sampler: a functor that returns a branch length and has a string cast
@return: a multi-line string that summarizes the results
"""
start_time = time.time()
# initialize some state that will be tracked over the entire run
degenerate_count = 0
invalid_split_count = 0
valid_split_count = 0
spectral_error_count = 0
atteson_error_count = 0
counterexample_D = None
counterexample_tree = None
# do a bunch of reconstructions from sampled distance matrices
try:
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
break
# sample the tree topology and get its set of implied full label splits
tree = TreeSampler.sample_agglomerated_tree(ntaxa)
true_splits = tree.get_nontrivial_splits()
# sample the branch lengths
for branch in tree.get_branches():
branch.length = branch_length_sampler()
# sample the atteson distance matrix
D = sample_atteson_distance_matrix(tree)
# assert that the atteson condition is true
if not BuildTreeTopology.is_atteson(tree, D):
atteson_error_count += 1
else:
try:
# see if the eigensplit is in the set of true splits
eigensplit = BuildTreeTopology.split_using_eigenvector(D)
if eigensplit in true_splits:
valid_split_count += 1
else:
invalid_split_count += 1
counterexample_D = D
counterexample_tree = tree
break
except BuildTreeTopology.DegenerateSplitException, e:
degenerate_count += 1
except BuildTreeTopology.InvalidSpectralSplitException, e:
spectral_error_count += 1
def get_response_content(fs):
out = StringIO()
# get the tree
tree = NewickIO.parse(fs.tree, FelTree.NewickTree)
# assert that each node is named
for node in tree.preorder():
if not node.name:
raise HandlingError('each node in the tree must have a name')
# get the function that converts a matrix to a string
if fs.plain_matrix:
m_to_string = MatrixUtil.m_to_string
elif fs.latex_matrix:
m_to_string = latexutil.m_to_latex_string
# print the results for the split of the full tree
print >> out, get_full_tree_message(tree, m_to_string)
print >> out
# get the alphabetically ordered names of the tips
ordered_tip_names = list(sorted(tip.get_name() for tip in tree.gen_tips()))
# get the corresponding ordered ids
tip_name_to_id = dict((tip.get_name(), id(tip)) for tip in tree.gen_tips())
ordered_tip_ids = [tip_name_to_id[name] for name in ordered_tip_names]
# get the distance matrix defined by the tips of the tree
D = np.array(tree.get_partial_distance_matrix(ordered_tip_ids))
L = Euclid.edm_to_laplacian(D)
#print >> out, 'the Laplacian obtained from the full tree by Schur complementation:'
#print >> out, MatrixUtil.m_to_string(L)
#print >> out
print >> out, 'the Schur complement in the Laplacian of the full tree scaled by', fs.scaling_factor
print >> out, m_to_string(fs.scaling_factor * L)
print >> out
#L_merged = SchurAlgebra.mmerge(L, set([3,4,5]))
#print >> out, 'the merged Laplacian:'
#print >> out, MatrixUtil.m_to_string(L_merged)
#print >> out
# get the Fiedler cut of the Schur Laplacian
v = BuildTreeTopology.laplacian_to_fiedler(L)
eigensplit = BuildTreeTopology.eigenvector_to_split(v)
print >> out, 'the Fiedler split of the Schur complement of the full tree:'
for name, value in zip(ordered_tip_names, v):
print >> out, name, ':', value
print >> out
# get the Fiedler cuts of Schur complements of child trees
print >> out, get_child_messages(L, eigensplit, ordered_tip_names, m_to_string, fs.scaling_factor)
print >> out
# get the Fiedler cuts of Schur complements of subtrees
print >> out, get_subtree_messages(D, eigensplit, ordered_tip_names)
# return the response
return out.getvalue()
def get_child_messages(L, eigensplit, ordered_tip_names, m_to_string, scaling_factor):
"""
@param L: the laplacian corresponding to tips of the tree
@param eigensplit: the split induced by the fiedler vector
@param ordered_tip_names: names of the tips of the tree conformant to v and L
@param m_to_string: a function that converts a matrix to a string
@param scaling_factor: show the Laplacian scaled by this factor
@return: a multi-line string
"""
out = StringIO()
n = len(L)
ordered_label_sets = [set([i]) for i in range(n)]
all_labels = set(range(n))
for i, child in enumerate(eigensplit):
complement = all_labels - child
L_child = SchurAlgebra.mmerge(L, complement)
print >> out, 'the Schur complement in the Laplacian of child tree', i+1, 'scaled by', scaling_factor
print >> out, m_to_string(scaling_factor * L_child)
print >> out
child_label_sets = SchurAlgebra.vmerge(ordered_label_sets, complement)
v_child = BuildTreeTopology.laplacian_to_fiedler(L_child)
print >> out, 'the Fiedler split of the Schur complement in the Laplacian of child tree', i+1
for label_set, value in zip(child_label_sets, v_child):
s = label_set_to_string(label_set, ordered_tip_names)
print >> out, s, ':', value
print >> out
return out.getvalue().strip()
def split_function(self, D):
"""
Split the distance matrix using signs of an eigenvector of -HDH/2.
If a degenerate split is found then a DegenerateSplitException is raised.
@param D: the distance matrix
@return: a set of two index sets defining a split of the indices
"""
try:
# get the matrix whose eigendecomposition is of interest
HSH = Euclid.edm_to_dccov(D)
# get the eigendecomposition
eigenvalues, V_T = np.linalg.eigh(HSH)
eigenvectors = V_T.T.tolist()
# save the eigenvalues for reporting
self.eigenvalues = eigenvalues
# get the eigenvector of interest
w, v = max(zip(eigenvalues, eigenvectors))
# get the indices with positive eigenvector valuations
n = len(D)
positive = frozenset(i for i, x in enumerate(v) if x > 0)
nonpositive = frozenset(set(range(n)) - positive)
# check for a degenerate split
for index_set in (positive, nonpositive):
assert len(index_set) > 0
for index_set in (positive, nonpositive):
if len(index_set) == 1:
index, = index_set
raise BuildTreeTopology.DegenerateSplitException(index)
return frozenset((positive, nonpositive))
except BuildTreeTopology.DegenerateSplitException, e:
self.eigenvalues = None
return BuildTreeTopology.split_nj(D)
def get_response_content(fs):
# read the matrix
D = fs.matrix
# read the ordered labels
ordered_labels = Util.get_stripped_lines(StringIO(fs.labels))
# validate the input
if len(D) != len(ordered_labels):
raise HandlingError('the number of taxon labels should match the number of rows in the distance matrix')
# get the split and update methods
if fs.option_a:
split_function = BuildTreeTopology.split_nj
update_function = BuildTreeTopology.update_nj
elif fs.option_b:
split_function = BuildTreeTopology.split_nj
update_function = BuildTreeTopology.update_using_laplacian
elif fs.option_c:
split_function = BuildTreeTopology.split_using_eigenvector_with_nj_fallback
update_function = BuildTreeTopology.update_using_laplacian
elif fs.option_d:
split_function = BuildTreeTopology.split_using_eigenvector
update_function = BuildTreeTopology.update_using_laplacian
# get the splits
index_splits = BuildTreeTopology.get_splits(D, split_function, update_function)
# start to prepare the reponse
out = StringIO()
for index_split in index_splits:
taxon_split = [[ordered_labels[i] for i in group] for group in index_split]
print >> out, split_to_string(taxon_split)
# write the response
return out.getvalue()
def get_full_tree_message(tree, m_to_string):
"""
In this function we find the Fiedler split of the full tree.
@param tree: each node in this tree must have a name
@param m_to_string: a function that converts a matrix to a string
@return: a message about the split of the tips of the tree induced by the fiedler vector
"""
out = StringIO()
# get the alphabetically ordered names
ordered_names = list(sorted(node.get_name() for node in tree.preorder()))
# get the corresponding ordered ids
name_to_id = dict((node.get_name(), id(node)) for node in tree.preorder())
ordered_ids = [name_to_id[name] for name in ordered_names]
# get the full weighted adjacency matrix
A = np.array(tree.get_affinity_matrix(ordered_ids))
print >> out, 'the weighted reciprocal adjacency matrix of the full tree:'
print >> out, m_to_string(get_reciprocal_matrix(A))
print >> out
# get the full Laplacian matrix
L = Euclid.adjacency_to_laplacian(A)
# get the fiedler split
v = BuildTreeTopology.laplacian_to_fiedler(L)
print >> out, 'the Fiedler split of the full tree:'
for name, value in zip(ordered_names, v):
print >> out, name, ':', value
return out.getvalue().strip()
def get_response_content(fs):
out = StringIO()
# try to make some graphs
unconnected_count = 0
invalid_split_count = 0
valid_split_count = 0
for graph_index in range(fs.ngraphs):
G = erdos_renyi(fs.nvertices, fs.pedge)
if is_connected(G):
# add interesting edge weights
add_exponential_weights(G)
# turn the adjacency matrix into a laplacian matrix
L = Euclid.adjacency_to_laplacian(G)
for v in range(fs.nvertices):
small_index_to_big_index = {}
for i_small, i_big in enumerate([i for i in range(fs.nvertices) if i != v]):
small_index_to_big_index[i_small] = i_big
# take the schur complement with respect to the given vertex
L_reduced = get_single_element_schur_complement(L, v)
assert len(L_reduced) == len(L) - 1
# get the loadings of the vertices of the reduced graph
if fs.fiedler_cut:
Y_reduced = BuildTreeTopology.laplacian_to_fiedler(L_reduced)
elif fs.random_cut:
Y_reduced = get_random_vector(L_reduced)
assert len(Y_reduced) == len(L_reduced)
# expand the fiedler vector with positive and negative valuations for the removed vertex
found_valid_split = False
for augmented_loading in (-1.0, 1.0):
# get the augmented split vector for this assignment of the removed vertex
Y_full = [0]*len(G)
for i_reduced, loading in enumerate(Y_reduced):
i_big = small_index_to_big_index[i_reduced]
Y_full[i_big] = loading
Y_full[v] = augmented_loading
assert len(Y_full) == len(G)
# get the two graphs defined by the split
subgraph_a, subgraph_b = list(gen_subgraphs(G, Y_full))
# if the subgraphs are both connected then the split is valid
if is_connected(subgraph_a) and is_connected(subgraph_b):
found_valid_split = True
# if a valid split was not found then show the matrix
if found_valid_split:
valid_split_count += 1
else:
print >> out, 'Found a matrix that was split incompatibly by a cut of its schur complement!'
print >> out, 'matrix:'
print >> out, MatrixUtil.m_to_string(G)
print >> out, 'index that was removed:', v
invalid_split_count += 1
else:
unconnected_count += 1
# show the number of connected and of unconnected graphs
print >> out, 'this many random graphs were connected:', fs.ngraphs - unconnected_count
print >> out, 'this many random graphs were not connected:', unconnected_count
print >> out, 'this many splits were valid:', valid_split_count
print >> out, 'this many splits were invalid:', invalid_split_count
# return the result
return out.getvalue()
def do_search(self, nseconds, sampling_function):
"""
@param nseconds: allowed search time or None
@param sampling_function: a function that samples a branch length
@return: True if a tree was found that met the criteria
"""
if not self.is_initialized():
raise RuntimeError("the search was not sufficiently initialized")
true_splits = self.tree.get_nontrivial_splits()
start_time = time.time()
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
return False
# assign new sampled branch lengths
for branch in self.tree.get_branches():
branch.length = sampling_function()
# get the distance matrix so we can use a library function to get the split
D = np.array(self.tree.get_distance_matrix())
ntips = len(D)
# get the Laplacian matrix of the full tree and the corresponding Fiedler split of the leaves
if self.force_difference or self.informative_full_split:
A_aug = np.array(self.tree.get_weighted_adjacency_matrix(self.id_to_index))
L_aug = Euclid.adjacency_to_laplacian(A_aug)
v_aug = BuildTreeTopology.laplacian_to_fiedler(L_aug)
left_aug, right_aug = BuildTreeTopology.eigenvector_to_split(v_aug)
left = [x for x in left_aug if x in range(ntips)]
right = [x for x in right_aug if x in range(ntips)]
leaf_eigensplit_aug = BuildTreeTopology.make_split(left, right)
if self.force_difference:
if leaf_eigensplit_aug == self.desired_primary_split:
self.aug_split_collision_count += 1
continue
if self.informative_full_split:
if min(len(s) for s in leaf_eigensplit_aug) < 2:
self.aug_split_degenerate_count += 1
continue
# get the eigensplit
try:
eigensplit = BuildTreeTopology.split_using_eigenvector(D)
except BuildTreeTopology.DegenerateSplitException, e:
self.degenerate_primary_split_count += 1
continue
except BuildTreeTopology.InvalidSpectralSplitException, e:
self.error_primary_split_count += 1
continue
def get_response_content(fs):
# read the matrix
D = np.array(fs.matrix)
n = len(D)
# read the ordered labels
ordered_labels = Util.get_stripped_lines(StringIO(fs.labels))
selected_labels = Util.get_stripped_lines(StringIO(fs.selection))
# validate the input
if n != len(ordered_labels):
raise HandlingError("the number of taxon labels should match the number of rows in the distance matrix")
# get the two sets of indices
index_set_A = set(i for i, label in enumerate(ordered_labels) if label in selected_labels)
index_set_B = set(range(n)) - index_set_A
# get internal values related to the split
R, alpha, beta, gamma = get_R_alpha_beta_gamma(D, index_set_B)
# get the two new distance matrices
D_A = BuildTreeTopology.update_generalized_nj(D, index_set_B)
D_B = BuildTreeTopology.update_generalized_nj(D, index_set_A)
# get the names associated with the indices of the new distance matrices
all_names = [set([name]) for name in ordered_labels]
D_A_names = [set_to_string(x) for x in SchurAlgebra.vmerge(all_names, index_set_B)]
D_B_names = [set_to_string(x) for x in SchurAlgebra.vmerge(all_names, index_set_A)]
# show the results
out = StringIO()
print >> out, "alpha:", alpha
print >> out, "beta:", beta
print >> out, "gamma:", gamma
print >> out
print >> out, "new distance matrix corresponding to the selected names:"
print >> out, MatrixUtil.m_to_string(D_A)
print >> out
print >> out, "ordered labels corresponding to this matrix:"
for name in D_A_names:
print >> out, name
print >> out
print >> out, "new distance matrix corresponding to the non-selected names:"
print >> out, MatrixUtil.m_to_string(D_B)
print >> out
print >> out, "ordered labels corresponding to this matrix:"
for name in D_B_names:
print >> out, name
# return the response
return out.getvalue()
def evaluate(self, true_splits, D_estimated):
"""
@param true_splits: a set of full splits that defines the true tree topology
@param D_estimated: an estimated distance matrix conformant to the split labels
@return: 1 if success, 0 if failure
"""
estimated_splits = BuildTreeTopology.get_splits(D_estimated, self.split_function, self.update_function)
if estimated_splits == true_splits:
return 1
else:
return 0
def process(ntaxa, length, nseconds, builders, branch_length_sampler):
"""
@param ntaxa: the number of taxa in the sampled trees
@param length: the length of sequences used to sample the distance matrix
@param nseconds: allow this many seconds to run
@param builders: tree builder objects
@param branch_length_sampler: returns a tree drawn from some distribution
@return: a multi-line string that summarizes the results
"""
start_time = time.time()
# track the number of samples that failed for various reasons
n_zero_errors = 0
n_infinite_errors = 0
n_failed_spectral_splits = 0
# define the number of attempts that fall into each of the four categories
non_atteson_results = [[0, 0], [0, 0]]
atteson_results = [[0, 0], [0, 0]]
#pachter_results = [[0, 0], [0, 0]]
# evaluate the quality of reconstructions from a bunch of different samples
try:
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
break
# sample the tree topology and get its set of implied full label splits
tree = TreeSampler.sample_agglomerated_tree(ntaxa)
true_splits = tree.get_nontrivial_splits()
# sample the branch lengths
for branch in tree.get_branches():
branch.length = branch_length_sampler()
try:
D = sample_distance_matrix(tree, length)
a, b = [
builder.evaluate(true_splits, D) for builder in builders
]
if BuildTreeTopology.is_atteson(tree, D):
atteson_results[a][b] += 1
#elif BuildTreeTopology.is_quartet_additive(tree, D) and BuildTreeTopology.is_quartet_consistent(tree, D):
#pachter_results[a][b] += 1
else:
non_atteson_results[a][b] += 1
except InfiniteDistanceError as e:
n_infinite_errors += 1
except ZeroDistanceError as e:
n_zero_errors += 1
except BuildTreeTopology.InvalidSpectralSplitException, e:
n_failed_spectral_splits += 1
except KeyboardInterrupt, e:
pass
def process(ntaxa, length, nseconds, builders, branch_length_sampler):
"""
@param ntaxa: the number of taxa in the sampled trees
@param length: the length of sequences used to sample the distance matrix
@param nseconds: allow this many seconds to run
@param builders: tree builder objects
@param branch_length_sampler: returns a tree drawn from some distribution
@return: a multi-line string that summarizes the results
"""
start_time = time.time()
# track the number of samples that failed for various reasons
n_zero_errors = 0
n_infinite_errors = 0
n_failed_spectral_splits = 0
# define the number of attempts that fall into each of the four categories
non_atteson_results = [[0, 0], [0, 0]]
atteson_results = [[0, 0], [0, 0]]
#pachter_results = [[0, 0], [0, 0]]
# evaluate the quality of reconstructions from a bunch of different samples
try:
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
break
# sample the tree topology and get its set of implied full label splits
tree = TreeSampler.sample_agglomerated_tree(ntaxa)
true_splits = tree.get_nontrivial_splits()
# sample the branch lengths
for branch in tree.get_branches():
branch.length = branch_length_sampler()
try:
D = sample_distance_matrix(tree, length)
a, b = [builder.evaluate(true_splits, D) for builder in builders]
if BuildTreeTopology.is_atteson(tree, D):
atteson_results[a][b] += 1
#elif BuildTreeTopology.is_quartet_additive(tree, D) and BuildTreeTopology.is_quartet_consistent(tree, D):
#pachter_results[a][b] += 1
else:
non_atteson_results[a][b] += 1
except InfiniteDistanceError as e:
n_infinite_errors += 1
except ZeroDistanceError as e:
n_zero_errors += 1
except BuildTreeTopology.InvalidSpectralSplitException, e:
n_failed_spectral_splits += 1
except KeyboardInterrupt, e:
pass
def process(ntaxa, length, nseconds, branch_length_sampler, use_nj,
use_modified_nj, use_all_spectral, use_one_spectral):
"""
@param ntaxa: the number of taxa in the sampled trees
@param length: the length of sequences used to sample the distance matrix
@param nseconds: allow this many seconds to run or None to run forever
@param branch_length_sampler: a functor that returns a branch length and has a string cast
@return: a multi-line string that summarizes the results
"""
start_time = time.time()
# initialize the builder object
builder = Builder()
# track the number of samples that failed for various reasons
n_zero_errors = 0
n_infinite_errors = 0
n_failed_spectral_splits = 0
# do a bunch of reconstructions of sampled distance matrices
try:
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
break
# sample the tree topology and get its set of implied full label splits
tree = TreeSampler.sample_agglomerated_tree(ntaxa)
true_splits = tree.get_nontrivial_splits()
# sample the branch lengths
for branch in tree.get_branches():
branch.length = branch_length_sampler()
try:
D = sample_distance_matrix(tree, length)
# determine whether or not the distance matrix is Atteson with respect to the tree
atteson = BuildTreeTopology.is_atteson(tree, D)
# record information about the splits
builder.evaluate(true_splits, D, atteson, use_nj,
use_modified_nj, use_all_spectral,
use_one_spectral)
except InfiniteDistanceError as e:
n_infinite_errors += 1
except ZeroDistanceError as e:
n_zero_errors += 1
except BuildTreeTopology.InvalidSpectralSplitException, e:
n_failed_spectral_splits += 1
except KeyboardInterrupt, e:
pass
def process(ntaxa, length, nseconds, branch_length_sampler, use_nj, use_modified_nj, use_all_spectral, use_one_spectral):
"""
@param ntaxa: the number of taxa in the sampled trees
@param length: the length of sequences used to sample the distance matrix
@param nseconds: allow this many seconds to run or None to run forever
@param branch_length_sampler: a functor that returns a branch length and has a string cast
@return: a multi-line string that summarizes the results
"""
start_time = time.time()
# initialize the builder object
builder = Builder()
# track the number of samples that failed for various reasons
n_zero_errors = 0
n_infinite_errors = 0
n_failed_spectral_splits = 0
# do a bunch of reconstructions of sampled distance matrices
try:
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
break
# sample the tree topology and get its set of implied full label splits
tree = TreeSampler.sample_agglomerated_tree(ntaxa)
true_splits = tree.get_nontrivial_splits()
# sample the branch lengths
for branch in tree.get_branches():
branch.length = branch_length_sampler()
try:
D = sample_distance_matrix(tree, length)
# determine whether or not the distance matrix is Atteson with respect to the tree
atteson = BuildTreeTopology.is_atteson(tree, D)
# record information about the splits
builder.evaluate(true_splits, D, atteson, use_nj, use_modified_nj, use_all_spectral, use_one_spectral)
except InfiniteDistanceError as e:
n_infinite_errors += 1
except ZeroDistanceError as e:
n_zero_errors += 1
except BuildTreeTopology.InvalidSpectralSplitException, e:
n_failed_spectral_splits += 1
except KeyboardInterrupt, e:
pass
def get_subtree_messages(D, eigensplit, ordered_tip_names):
"""
@param D: the matrix of pairwise distances among tips of the tree
@param eigensplit: the split induced by the fiedler vector
@param ordered_tip_names: names of the tips of the tree conformant to v and D
@return: a multi-line string
"""
out = StringIO()
n = len(D)
ordered_label_sets = [set([i]) for i in range(n)]
all_labels = set(range(n))
for i, child in enumerate(eigensplit):
complement = all_labels - child
D_child = MatrixUtil.get_principal_submatrix(D, list(sorted(child)))
child_label_sets = SchurAlgebra.vdelete(ordered_label_sets, complement)
v_child = BuildTreeTopology.edm_to_fiedler(D_child)
print >> out, 'the Fiedler split of Schur complements of subtree', i + 1
for label_set, value in zip(child_label_sets, v_child):
s = label_set_to_string(label_set, ordered_tip_names)
print >> out, s, ':', value
print >> out
return out.getvalue().strip()
def get_response_content(fs):
# read the points and edges
points, edges = read_points_and_edges(fs.graph_data)
# get the width and height of the drawable area of the image
width = fs.total_width - 2 * fs.border
height = fs.total_height - 2 * fs.border
if width < 1 or height < 1:
msg = 'the image dimensions do not allow for enough drawable area'
raise HandlingError(msg)
# read the image info
show_labels = None
if fs.label_from_0:
show_labels = 0
elif fs.label_from_1:
show_labels = 1
# define the valuations which will define the node colors
if fs.color_x:
valuations = [p[0] for p in points]
elif fs.color_fiedler_weighted or fs.color_fiedler_unweighted:
if fs.color_fiedler_weighted:
X = [np.array(p) for p in points]
dists = [np.linalg.norm(X[j] - X[i]) for i, j in edges]
weights = [1.0 / d for d in dists]
else:
weights = [1.0 for e in edges]
L = edges_to_laplacian(edges, weights)
valuations = BuildTreeTopology.laplacian_to_fiedler(L)
else:
valuations = [0 for p in points]
valuations = [-v if fs.flip else v for v in valuations]
colors = valuations_to_colors(valuations)
# draw the image
ext = Form.g_imageformat_to_ext[fs.imageformat]
info = ImageInfo(fs.total_width, fs.total_height, fs.black, show_labels,
fs.border, ext)
try:
return get_image_string(points, edges, colors, info)
except CairoUtil.CairoUtilError as e:
raise HandlingError(e)
def get_response_content(fs):
# read the points and edges
points, edges = read_points_and_edges(fs.graph_data)
# get the width and height of the drawable area of the image
width = fs.total_width - 2*fs.border
height = fs.total_height - 2*fs.border
if width < 1 or height < 1:
msg = 'the image dimensions do not allow for enough drawable area'
raise HandlingError(msg)
# read the image info
show_labels = None
if fs.label_from_0:
show_labels = 0
elif fs.label_from_1:
show_labels = 1
# define the valuations which will define the node colors
if fs.color_x:
valuations = [p[0] for p in points]
elif fs.color_fiedler_weighted or fs.color_fiedler_unweighted:
if fs.color_fiedler_weighted:
X = [np.array(p) for p in points]
dists = [np.linalg.norm(X[j] - X[i]) for i, j in edges]
weights = [1.0 / d for d in dists]
else:
weights = [1.0 for e in edges]
L = edges_to_laplacian(edges, weights)
valuations = BuildTreeTopology.laplacian_to_fiedler(L)
else:
valuations = [0 for p in points]
valuations = [-v if fs.flip else v for v in valuations]
colors = valuations_to_colors(valuations)
# draw the image
ext = Form.g_imageformat_to_ext[fs.imageformat]
info = ImageInfo(fs.total_width, fs.total_height,
fs.black, show_labels, fs.border, ext)
try:
return get_image_string(points, edges, colors, info)
except CairoUtil.CairoUtilError as e:
raise HandlingError(e)
def get_subtree_messages(D, eigensplit, ordered_tip_names):
"""
@param D: the matrix of pairwise distances among tips of the tree
@param eigensplit: the split induced by the fiedler vector
@param ordered_tip_names: names of the tips of the tree conformant to v and D
@return: a multi-line string
"""
out = StringIO()
n = len(D)
ordered_label_sets = [set([i]) for i in range(n)]
all_labels = set(range(n))
for i, child in enumerate(eigensplit):
complement = all_labels - child
D_child = MatrixUtil.get_principal_submatrix(D, list(sorted(child)))
child_label_sets = SchurAlgebra.vdelete(ordered_label_sets, complement)
v_child = BuildTreeTopology.edm_to_fiedler(D_child)
print >> out, 'the Fiedler split of Schur complements of subtree', i+1
for label_set, value in zip(child_label_sets, v_child):
s = label_set_to_string(label_set, ordered_tip_names)
print >> out, s, ':', value
print >> out
return out.getvalue().strip()
try:
D = sample_distance_matrix(tree, sequence_length)
except InfiniteDistanceError as e:
return incr_attribute(attribute_array, 'nsamples.rejected.inf')
except ZeroDistanceError as e:
return incr_attribute(attribute_array, 'nsamples.rejected.zero')
except BuildTreeTopology.InvalidSpectralSplitException, e:
return incr_attribute(attribute_array, 'nsamples.rejected.fail')
# see if the top down reconstruction was successful
try:
splitter = BuildTreeTopology.split_using_eigenvector_with_nj_fallback
if nj_like:
updater = BuildTreeTopology.update_generalized_nj
else:
updater = BuildTreeTopology.update_using_laplacian
all_spectral_splits = BuildTreeTopology.get_splits(
D, splitter, updater)
top_down_success = (all_spectral_splits == true_splits)
except BuildTreeTopology.InvalidSpectralSplitException, e:
return incr_attribute(attribute_array, 'nsamples.rejected.fail')
# at this point the sample is accepted
incr_attribute(attribute_array, 'nsamples.accepted')
# determine whether or not the distance matrix is Atteson with respect to the tree
if BuildTreeTopology.is_atteson(tree, D):
incr_attribute(attribute_array, 'nsamples.accepted.atteson')
# see if the bottom up reconstruction was successful
nj_splits = BuildTreeTopology.get_splits(D, BuildTreeTopology.split_nj,
BuildTreeTopology.update_nj)
nj_success = (nj_splits == true_splits)
# note the joint results of the two reconstruction methods
if top_down_success and nj_success:
incr_attribute(attribute_array, 'nsuccesses.both')
def get_standard_response(fs):
"""
@param fs: a FieldStorage object containing the cgi arguments
@return: a (response_headers, response_text) pair
"""
# begin the response
out = StringIO()
# show a summary of the original data
print >> out, 'data summary before removing branches with zero length:'
print >> out, len(archaea_names), 'archaea names in the original tree'
print >> out, len(bacteria_names), 'bacteria names in the original tree'
print >> out, len(eukaryota_names), 'eukaryota names in the original tree'
print >> out, len(all_names), 'total names in the original tree'
print >> out
# get the pruned full tree
pruned_full_tree = get_pruned_tree(full_tree)
ordered_names = list(node.get_name() for node in pruned_full_tree.gen_tips())
# show a summary of the processed data
print >> out, 'data summary after removing branches with zero length:'
print >> out, len(ordered_names), 'total names in the processed non-degenerate tree'
print >> out
# draw the pruned full tree
print >> out, 'this is the tree that represents all clades but for which redundant nodes have been pruned:'
formatted_tree_string = NewickIO.get_narrow_newick_string(pruned_full_tree, 120)
print >> out, formatted_tree_string
print >> out
# split the distance matrix
D = np.array(pruned_full_tree.get_distance_matrix(ordered_names))
L = Euclid.edm_to_laplacian(D)
v = BuildTreeTopology.laplacian_to_fiedler(L)
eigensplit = BuildTreeTopology.eigenvector_to_split(v)
# report the eigendecomposition
print >> out, get_eigendecomposition_report(D)
# report the clade intersections of sides of the split
side_names = [set(ordered_names[i] for i in side) for side in eigensplit]
clade_name_pairs = ((archaea_names, 'archaea'), (bacteria_names, 'bacteria'), (eukaryota_names, 'eukaryota'))
print >> out, 'clade intersections with each side of the split:'
for side, side_name in zip(side_names, ('left', 'right')):
for clade, clade_name in clade_name_pairs:
if clade & side:
print >> out, 'the', side_name, 'side intersects', clade_name
print >> out
# prepare to do the secondary splits
left_indices, right_indices = eigensplit
full_label_sets = [set([i]) for i in range(len(ordered_names))]
# get a secondary split
for index_selection, index_complement in ((left_indices, right_indices), (right_indices, left_indices)):
L_s1 = SchurAlgebra.mmerge(L, index_complement)
next_label_sets = SchurAlgebra.vmerge(full_label_sets, index_complement)
v = BuildTreeTopology.laplacian_to_fiedler(L_s1)
left_subindices, right_subindices = BuildTreeTopology.eigenvector_to_split(v)
left_sublabels = set()
for i in left_subindices:
left_sublabels.update(next_label_sets[i])
right_sublabels = set()
for i in right_subindices:
right_sublabels.update(next_label_sets[i])
left_subnames = set(ordered_names[i] for i in left_sublabels)
right_subnames = set(ordered_names[i] for i in right_sublabels)
print >> out, 'clade intersections with a subsplit:'
for clade, clade_name in clade_name_pairs:
if clade & left_subnames:
print >> out, 'the left side intersects', clade_name
for clade, clade_name in clade_name_pairs:
if clade & right_subnames:
print >> out, 'the right side intersects', clade_name
print >> out
# show debug info
print >> out, 'archaea names:'
print >> out, '\n'.join(x for x in sorted(archaea_names))
print >> out
print >> out, 'bacteria names:'
print >> out, '\n'.join(x for x in sorted(bacteria_names))
print >> out
print >> out, 'eukaryota names:'
print >> out, '\n'.join(x for x in sorted(eukaryota_names))
print >> out
# return the response
response_text = out.getvalue().strip()
return [('Content-Type', 'text/plain')], response_text
def get_standard_response(fs):
"""
@param fs: a FieldStorage object containing the cgi arguments
@return: a (response_headers, response_text) pair
"""
# begin the response
out = StringIO()
# show a summary of the original data
print >> out, 'data summary before removing branches with zero length:'
print >> out, len(archaea_names), 'archaea names in the original tree'
print >> out, len(bacteria_names), 'bacteria names in the original tree'
print >> out, len(eukaryota_names), 'eukaryota names in the original tree'
print >> out, len(all_names), 'total names in the original tree'
print >> out
# get the pruned full tree
pruned_full_tree = get_pruned_tree(full_tree)
ordered_names = list(node.get_name()
for node in pruned_full_tree.gen_tips())
# show a summary of the processed data
print >> out, 'data summary after removing branches with zero length:'
print >> out, len(
ordered_names), 'total names in the processed non-degenerate tree'
print >> out
# draw the pruned full tree
print >> out, 'this is the tree that represents all clades but for which redundant nodes have been pruned:'
formatted_tree_string = NewickIO.get_narrow_newick_string(
pruned_full_tree, 120)
print >> out, formatted_tree_string
print >> out
# split the distance matrix
D = np.array(pruned_full_tree.get_distance_matrix(ordered_names))
L = Euclid.edm_to_laplacian(D)
v = BuildTreeTopology.laplacian_to_fiedler(L)
eigensplit = BuildTreeTopology.eigenvector_to_split(v)
# report the eigendecomposition
print >> out, get_eigendecomposition_report(D)
# report the clade intersections of sides of the split
side_names = [set(ordered_names[i] for i in side) for side in eigensplit]
clade_name_pairs = ((archaea_names, 'archaea'),
(bacteria_names, 'bacteria'), (eukaryota_names,
'eukaryota'))
print >> out, 'clade intersections with each side of the split:'
for side, side_name in zip(side_names, ('left', 'right')):
for clade, clade_name in clade_name_pairs:
if clade & side:
print >> out, 'the', side_name, 'side intersects', clade_name
print >> out
# prepare to do the secondary splits
left_indices, right_indices = eigensplit
full_label_sets = [set([i]) for i in range(len(ordered_names))]
# get a secondary split
for index_selection, index_complement in ((left_indices, right_indices),
(right_indices, left_indices)):
L_s1 = SchurAlgebra.mmerge(L, index_complement)
next_label_sets = SchurAlgebra.vmerge(full_label_sets,
index_complement)
v = BuildTreeTopology.laplacian_to_fiedler(L_s1)
left_subindices, right_subindices = BuildTreeTopology.eigenvector_to_split(
v)
left_sublabels = set()
for i in left_subindices:
left_sublabels.update(next_label_sets[i])
right_sublabels = set()
for i in right_subindices:
right_sublabels.update(next_label_sets[i])
left_subnames = set(ordered_names[i] for i in left_sublabels)
right_subnames = set(ordered_names[i] for i in right_sublabels)
print >> out, 'clade intersections with a subsplit:'
for clade, clade_name in clade_name_pairs:
if clade & left_subnames:
print >> out, 'the left side intersects', clade_name
for clade, clade_name in clade_name_pairs:
if clade & right_subnames:
print >> out, 'the right side intersects', clade_name
print >> out
# show debug info
print >> out, 'archaea names:'
print >> out, '\n'.join(x for x in sorted(archaea_names))
print >> out
print >> out, 'bacteria names:'
print >> out, '\n'.join(x for x in sorted(bacteria_names))
print >> out
print >> out, 'eukaryota names:'
print >> out, '\n'.join(x for x in sorted(eukaryota_names))
print >> out
# return the response
response_text = out.getvalue().strip()
return [('Content-Type', 'text/plain')], response_text
def get_verbose_summary(self):
"""
@return: a multiline string
"""
# begin the response
out = StringIO()
# show the number of taxa in various domains
print >> out, self._get_name_summary()
print >> out
# show the pruned full tree
formatted_tree_string = NewickIO.get_narrow_newick_string(
self.pruned_tree, 120)
print >> out, 'this is the tree that represents all clades but for which redundant nodes have been pruned:'
print >> out, formatted_tree_string
print >> out
# split the distance matrix
D = np.array(self.pruned_tree.get_distance_matrix(self.pruned_names))
L = Euclid.edm_to_laplacian(D)
v = BuildTreeTopology.laplacian_to_fiedler(L)
eigensplit = BuildTreeTopology.eigenvector_to_split(v)
# report the eigendecomposition
print >> out, get_eigendecomposition_report(D)
print >> out
# report the clade intersections of sides of the split
side_names = [
set(self.pruned_names[i] for i in side) for side in eigensplit
]
print >> out, 'domains represented by each side of the primary split:'
print >> out, 'the left side has:\t', ', '.join(
self._get_domains(side_names[0]))
print >> out, 'the right side has:\t', ', '.join(
self._get_domains(side_names[1]))
print >> out
# prepare to do the secondary splits
left_indices, right_indices = eigensplit
full_label_sets = [set([i]) for i in range(len(self.pruned_names))]
# do the secondary splits
for index_selection, index_complement in ((left_indices,
right_indices),
(right_indices,
left_indices)):
L_secondary = SchurAlgebra.mmerge(L, index_complement)
next_label_sets = SchurAlgebra.vmerge(full_label_sets,
index_complement)
v = BuildTreeTopology.laplacian_to_fiedler(L_secondary)
left_subindices, right_subindices = BuildTreeTopology.eigenvector_to_split(
v)
left_sublabels = set()
for i in left_subindices:
left_sublabels.update(next_label_sets[i])
right_sublabels = set()
for i in right_subindices:
right_sublabels.update(next_label_sets[i])
left_subnames = set(self.pruned_names[i] for i in left_sublabels)
right_subnames = set(self.pruned_names[i] for i in right_sublabels)
print >> out, 'domains represented by a subsplit:'
print >> out, 'the left side has:\t', ', '.join(
self._get_domains(left_subnames))
print >> out, 'the right side has:\t', ', '.join(
self._get_domains(right_subnames))
print >> out
# return the multiline string
return out.getvalue().strip()
def _do_analysis(self, use_generalized_nj):
"""
Do some splits of the tree.
@param use_generalized_nj: True if we use an old method of outgrouping
"""
# define the distance matrix
D = np.array(self.pruned_tree.get_distance_matrix(self.pruned_names))
# get the primary split of the criterion matrix
L = Euclid.edm_to_laplacian(D)
v = BuildTreeTopology.laplacian_to_fiedler(L)
eigensplit = BuildTreeTopology.eigenvector_to_split(v)
# assert that the first split cleanly separates the bacteria from the rest
left_indices, right_indices = eigensplit
left_domains = self._get_domains(
[self.pruned_names[x] for x in left_indices])
right_domains = self._get_domains(
[self.pruned_names[x] for x in right_indices])
if ('bacteria' in left_domains) and ('bacteria' in right_domains):
raise HandlingError('bacteria were not defined by the first split')
# now we have enough info to define the first supplementary csv file
self.first_split_object = SupplementarySpreadsheetObject(
self.pruned_names, L, v)
# define the bacteria indices vs the non-bacteria indices for the second split
if 'bacteria' in left_domains:
bacteria_indices = left_indices
non_bacteria_indices = right_indices
elif 'bacteria' in right_domains:
bacteria_indices = right_indices
non_bacteria_indices = left_indices
# get the secondary split of interest
if use_generalized_nj:
D_secondary = BuildTreeTopology.update_generalized_nj(
D, bacteria_indices)
L_secondary = Euclid.edm_to_laplacian(D_secondary)
else:
L_secondary = SchurAlgebra.mmerge(L, bacteria_indices)
full_label_sets = [set([i]) for i in range(len(self.pruned_names))]
next_label_sets = SchurAlgebra.vmerge(full_label_sets,
bacteria_indices)
v_secondary = BuildTreeTopology.laplacian_to_fiedler(L_secondary)
eigensplit_secondary = BuildTreeTopology.eigenvector_to_split(
v_secondary)
left_subindices, right_subindices = eigensplit_secondary
pruned_names_secondary = []
for label_set in next_label_sets:
if len(label_set) == 1:
label = list(label_set)[0]
pruned_names_secondary.append(self.pruned_names[label])
else:
pruned_names_secondary.append('all-bacteria')
# assert that the second split cleanly separates the eukaryota from the rest
left_subdomains = self._get_domains(
[pruned_names_secondary[x] for x in left_subindices])
right_subdomains = self._get_domains(
[pruned_names_secondary[x] for x in right_subindices])
if ('eukaryota' in left_subdomains) and ('eukaryota'
in right_subdomains):
raise HandlingError(
'eukaryota were not defined by the second split')
# now we have enough info to define the second supplementary csv file
self.second_split_object = SupplementarySpreadsheetObject(
pruned_names_secondary, L_secondary, v_secondary)
def process(ntaxa, nseconds, seqlen, nsamples, branch_length_sampler, use_pbar):
"""
@param ntaxa: the number of taxa per tree
@param nseconds: stop after this many seconds
@param seqlen: use this sequence length
@param nsamples: stop after this many samples per sequence length
@param branch_length_sampler: this function samples branch lengths independently
@param use_pbar: True iff a progress bar should be used
@return: a multi-line string of the contents of an R table
"""
# initialize the global rejection counts
nrejected_zero = 0
nrejected_inf = 0
nrejected_fail = 0
naccepted = 0
# Initialize the accumulation matrix.
# The rows specify the size of the smaller side of the initial split.
# The columns specify the compatibility status of the split.
nsmall_sizes = (ntaxa / 2) + 1
accum = np.zeros((nsmall_sizes, 2), dtype=np.int)
# Repeatedly analyze samples.
# We might have to stop early if we run out of time or if ctrl-c is pressed.
# If we have to stop early, then show the results of the progress so far.
termination_reason = 'no reason for termination was given'
start_time = time.time()
pbar = Progress.Bar(nsamples) if use_pbar else None
try:
for sample_index in range(nsamples):
# keep trying to get an accepted sample
while True:
# check the time
if nseconds and time.time() - start_time > nseconds:
raise TimeoutError()
# first sample a tree and get its set of informative splits
tree = TreeSampler.sample_agglomerated_tree(ntaxa)
true_splits = tree.get_nontrivial_splits()
# sample the branch lengths
for branch in tree.get_branches():
branch.length = branch_length_sampler()
# Attempt to sample a distance matrix.
# If the sample was rejected then note the reason and go back to the drawing board.
try:
D = sample_distance_matrix(tree, seqlen)
except InfiniteDistanceError as e:
nrejected_inf += 1
continue
except ZeroDistanceError as e:
nrejected_zero += 1
continue
# Attempt to estimate the primary split of the tree from the distance matrix.
# If there was a technical failure then note it and go back to the drawing board.
# Otherwise note the compatibility and balance of the split.
try:
eigensplit = BuildTreeTopology.split_using_eigenvector(D)
small_size = min(len(side) for side in eigensplit)
if eigensplit in true_splits:
compatibility = 1
else:
compatibility = 0
except BuildTreeTopology.DegenerateSplitException, e:
small_size = 0
compatibility = 1
except BuildTreeTopology.InvalidSpectralSplitException, e:
nrejected_fail += 1
continue
def process(ntaxa, nseconds, seqlen, nsamples, branch_length_sampler,
use_pbar):
"""
@param ntaxa: the number of taxa per tree
@param nseconds: stop after this many seconds
@param seqlen: use this sequence length
@param nsamples: stop after this many samples per sequence length
@param branch_length_sampler: this function samples branch lengths independently
@param use_pbar: True iff a progress bar should be used
@return: a multi-line string of the contents of an R table
"""
# initialize the global rejection counts
nrejected_zero = 0
nrejected_inf = 0
nrejected_fail = 0
naccepted = 0
# Initialize the accumulation matrix.
# The rows specify the size of the smaller side of the initial split.
# The columns specify the compatibility status of the split.
nsmall_sizes = (ntaxa / 2) + 1
accum = np.zeros((nsmall_sizes, 2), dtype=np.int)
# Repeatedly analyze samples.
# We might have to stop early if we run out of time or if ctrl-c is pressed.
# If we have to stop early, then show the results of the progress so far.
termination_reason = 'no reason for termination was given'
start_time = time.time()
pbar = Progress.Bar(nsamples) if use_pbar else None
try:
for sample_index in range(nsamples):
# keep trying to get an accepted sample
while True:
# check the time
if nseconds and time.time() - start_time > nseconds:
raise TimeoutError()
# first sample a tree and get its set of informative splits
tree = TreeSampler.sample_agglomerated_tree(ntaxa)
true_splits = tree.get_nontrivial_splits()
# sample the branch lengths
for branch in tree.get_branches():
branch.length = branch_length_sampler()
# Attempt to sample a distance matrix.
# If the sample was rejected then note the reason and go back to the drawing board.
try:
D = sample_distance_matrix(tree, seqlen)
except InfiniteDistanceError as e:
nrejected_inf += 1
continue
except ZeroDistanceError as e:
nrejected_zero += 1
continue
# Attempt to estimate the primary split of the tree from the distance matrix.
# If there was a technical failure then note it and go back to the drawing board.
# Otherwise note the compatibility and balance of the split.
try:
eigensplit = BuildTreeTopology.split_using_eigenvector(D)
small_size = min(len(side) for side in eigensplit)
if eigensplit in true_splits:
compatibility = 1
else:
compatibility = 0
except BuildTreeTopology.DegenerateSplitException, e:
small_size = 0
compatibility = 1
except BuildTreeTopology.InvalidSpectralSplitException, e:
nrejected_fail += 1
continue
def get_response_content(fs):
out = StringIO()
# try to make some graphs
unconnected_count = 0
invalid_split_count = 0
valid_split_count = 0
for graph_index in range(fs.ngraphs):
G = erdos_renyi(fs.nvertices, fs.pedge)
if is_connected(G):
# add interesting edge weights
add_exponential_weights(G)
# turn the adjacency matrix into a laplacian matrix
L = Euclid.adjacency_to_laplacian(G)
for v in range(fs.nvertices):
small_index_to_big_index = {}
for i_small, i_big in enumerate(
[i for i in range(fs.nvertices) if i != v]):
small_index_to_big_index[i_small] = i_big
# take the schur complement with respect to the given vertex
L_reduced = get_single_element_schur_complement(L, v)
assert len(L_reduced) == len(L) - 1
# get the loadings of the vertices of the reduced graph
if fs.fiedler_cut:
Y_reduced = BuildTreeTopology.laplacian_to_fiedler(
L_reduced)
elif fs.random_cut:
Y_reduced = get_random_vector(L_reduced)
assert len(Y_reduced) == len(L_reduced)
# expand the fiedler vector with positive and negative valuations for the removed vertex
found_valid_split = False
for augmented_loading in (-1.0, 1.0):
# get the augmented split vector for this assignment of the removed vertex
Y_full = [0] * len(G)
for i_reduced, loading in enumerate(Y_reduced):
i_big = small_index_to_big_index[i_reduced]
Y_full[i_big] = loading
Y_full[v] = augmented_loading
assert len(Y_full) == len(G)
# get the two graphs defined by the split
subgraph_a, subgraph_b = list(gen_subgraphs(G, Y_full))
# if the subgraphs are both connected then the split is valid
if is_connected(subgraph_a) and is_connected(subgraph_b):
found_valid_split = True
# if a valid split was not found then show the matrix
if found_valid_split:
valid_split_count += 1
else:
print >> out, 'Found a matrix that was split incompatibly by a cut of its schur complement!'
print >> out, 'matrix:'
print >> out, MatrixUtil.m_to_string(G)
print >> out, 'index that was removed:', v
invalid_split_count += 1
else:
unconnected_count += 1
# show the number of connected and of unconnected graphs
print >> out, 'this many random graphs were connected:', fs.ngraphs - unconnected_count
print >> out, 'this many random graphs were not connected:', unconnected_count
print >> out, 'this many splits were valid:', valid_split_count
print >> out, 'this many splits were invalid:', invalid_split_count
# return the result
return out.getvalue()
class TreeSearch:
"""
This is a virtual base class.
"""
def __init__(self):
# boolean requirements defined by the user
self.informative_children = None
self.force_difference = None
self.informative_full_split = None
self.invalid_dendrogram = None
# search options defined by the subclass
self.tree = None
self.desired_primary_split = None
self.id_to_index = None
# initialize the counts that are tracked for bookkeeping
self.aug_split_collision_count = 0
self.aug_split_degenerate_count = 0
self.error_primary_split_count = 0
self.invalid_primary_split_count = 0
self.degenerate_primary_split_count = 0
self.undesired_primary_split_count = 0
self.desired_primary_split_count = 0
self.uninformative_child_count = 0
self.informative_child_count = 0
self.valid_dendrogram_count = 0
self.success_count = 0
def is_initialized(self):
required_data = [
self.informative_children,
self.force_difference,
self.informative_full_split,
self.invalid_dendrogram,
self.tree,
self.desired_primary_split,
self.id_to_index]
return not (None in required_data)
def get_result_text(self):
"""
@return: a multi-line string of text
"""
out = StringIO()
if self.force_difference or self.informative_full_split:
print >> out, 'full graph split stats:'
print >> out, self.aug_split_collision_count,
print >> out, 'full graph splits collided with the desired primary split'
print >> out, self.aug_split_degenerate_count,
print >> out, 'full graph splits were degenerate'
print >> out
print >> out, 'primary split stats:'
print >> out, self.error_primary_split_count,
print >> out, 'errors in finding the primary split (should be 0)'
print >> out, self.invalid_primary_split_count,
print >> out, 'invalid primary splits (should be 0)'
print >> out, self.degenerate_primary_split_count,
print >> out, 'degenerate primary splits'
print >> out, self.undesired_primary_split_count,
print >> out, 'primary splits were not the target split'
print >> out, self.desired_primary_split_count,
print >> out, 'primary splits were the target split'
print >> out
if self.informative_children:
print >> out, 'secondary split stats:'
print >> out, self.uninformative_child_count,
print >> out, 'samples had at least one uninformative child tree'
print >> out, self.informative_child_count,
print>> out, 'samples had two informative child trees'
print >> out
if self.invalid_dendrogram:
print >> out, 'naive dendrogram stats:'
print >> out, self.valid_dendrogram_count,
print >> out, 'naive dendrograms were valid'
print >> out
return out.getvalue().strip()
def do_search(self, nseconds, sampling_function):
"""
@param nseconds: allowed search time or None
@param sampling_function: a function that samples a branch length
@return: True if a tree was found that met the criteria
"""
if not self.is_initialized():
raise RuntimeError('the search was not sufficiently initialized')
true_splits = self.tree.get_nontrivial_splits()
start_time = time.time()
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
return False
# assign new sampled branch lengths
for branch in self.tree.get_branches():
branch.length = sampling_function()
# get the distance matrix so we can use a library function to get the split
D = np.array(self.tree.get_distance_matrix())
ntips = len(D)
# get the Laplacian matrix of the full tree and the corresponding Fiedler split of the leaves
if self.force_difference or self.informative_full_split:
A_aug = np.array(self.tree.get_weighted_adjacency_matrix(self.id_to_index))
L_aug = Euclid.adjacency_to_laplacian(A_aug)
v_aug = BuildTreeTopology.laplacian_to_fiedler(L_aug)
left_aug, right_aug = BuildTreeTopology.eigenvector_to_split(v_aug)
left = [x for x in left_aug if x in range(ntips)]
right = [x for x in right_aug if x in range(ntips)]
leaf_eigensplit_aug = BuildTreeTopology.make_split(left, right)
if self.force_difference:
if leaf_eigensplit_aug == self.desired_primary_split:
self.aug_split_collision_count += 1
continue
if self.informative_full_split:
if min(len(s) for s in leaf_eigensplit_aug) < 2:
self.aug_split_degenerate_count += 1
continue
# get the eigensplit
try:
eigensplit = BuildTreeTopology.split_using_eigenvector(D)
except BuildTreeTopology.DegenerateSplitException, e:
self.degenerate_primary_split_count += 1
continue
except BuildTreeTopology.InvalidSpectralSplitException, e:
self.error_primary_split_count += 1
continue
if eigensplit not in true_splits:
raise RuntimeError('INVALID SPLIT:' + tree.get_newick_string())
if eigensplit != self.desired_primary_split:
self.undesired_primary_split_count += 1
continue
self.desired_primary_split_count += 1
# check the splits of the two child trees
degenerate_subsplit_count = 0
L = Euclid.edm_to_laplacian(D)
for side in eigensplit:
L_child = SchurAlgebra.mmerge(L, side)
v = BuildTreeTopology.laplacian_to_fiedler(L_child)
child_eigensplit = BuildTreeTopology.eigenvector_to_split(v)
if min(len(s) for s in child_eigensplit) < 2:
degenerate_subsplit_count += 1
if degenerate_subsplit_count:
self.uninformative_child_count += 1
else:
self.informative_child_count += 1
if self.informative_children:
if degenerate_subsplit_count:
continue
# check the dendrogram
if self.invalid_dendrogram:
labels = range(len(D))
hierarchy = Dendrogram.get_hierarchy(D, Dendrogram.spectral_split, labels)
dendrogram_splits = set(Dendrogram.hierarchy_to_nontrivial_splits(hierarchy))
if dendrogram_splits == true_splits:
self.valid_dendrogram_count += 1
continue
# the tree has met all of the requirements
return True
try:
D = sample_distance_matrix(tree, sequence_length)
except InfiniteDistanceError as e:
return incr_attribute(attribute_array, 'nsamples.rejected.inf')
except ZeroDistanceError as e:
return incr_attribute(attribute_array, 'nsamples.rejected.zero')
except BuildTreeTopology.InvalidSpectralSplitException, e:
return incr_attribute(attribute_array, 'nsamples.rejected.fail')
# see if the top down reconstruction was successful
try:
splitter = BuildTreeTopology.split_using_eigenvector_with_nj_fallback
if nj_like:
updater = BuildTreeTopology.update_generalized_nj
else:
updater = BuildTreeTopology.update_using_laplacian
all_spectral_splits = BuildTreeTopology.get_splits(D, splitter, updater)
top_down_success = (all_spectral_splits == true_splits)
except BuildTreeTopology.InvalidSpectralSplitException, e:
return incr_attribute(attribute_array, 'nsamples.rejected.fail')
# at this point the sample is accepted
incr_attribute(attribute_array, 'nsamples.accepted')
# determine whether or not the distance matrix is Atteson with respect to the tree
if BuildTreeTopology.is_atteson(tree, D):
incr_attribute(attribute_array, 'nsamples.accepted.atteson')
# see if the bottom up reconstruction was successful
nj_splits = BuildTreeTopology.get_splits(D, BuildTreeTopology.split_nj, BuildTreeTopology.update_nj)
nj_success = (nj_splits == true_splits)
# note the joint results of the two reconstruction methods
if top_down_success and nj_success:
incr_attribute(attribute_array, 'nsuccesses.both')
elif (not top_down_success) and (not nj_success):
