def analyze_matrix(M, block_size):
"""
Get results for g(f(M)) and f(g(M)).
Each block is square with block_size rows.
@param M: a matrix
@param block_size: the number of rows in blocks of the partitioned matrix
@return: a string of results
"""
# define the response
out = StringIO()
# get the new matrix using the first composition of functions
M_11 = SchurAlgebra.mmerge(M, set(range(2*block_size)))
M_12 = SchurAlgebra.mschur(
M_11, set(1 + block_size + k for k in range(block_size)))
print >> out, M_12
# get the new matrix using the second composition of functions
M_21 = SchurAlgebra.mschur(
M, set(3*block_size + k for k in range(block_size)))
M_22 = SchurAlgebra.mmerge(M_21, set(range(2*block_size)))
print >> out, M_22
if np.allclose(M_12, M_22):
print >> out, 'the matrices are similar'
else:
print >> out, 'the matrices are different'
return out.getvalue().strip()
def analyze_matrix(M, block_size):
"""
Get results for g(f(M)) and f(g(M)).
Each block is square with block_size rows.
@param M: a matrix
@param block_size: the number of rows in blocks of the partitioned matrix
@return: a string of results
"""
# define the response
out = StringIO()
# get the new matrix using the first composition of functions
M_11 = SchurAlgebra.mmerge(M, set(range(2 * block_size)))
M_12 = SchurAlgebra.mschur(
M_11, set(1 + block_size + k for k in range(block_size)))
print >> out, M_12
# get the new matrix using the second composition of functions
M_21 = SchurAlgebra.mschur(
M, set(3 * block_size + k for k in range(block_size)))
M_22 = SchurAlgebra.mmerge(M_21, set(range(2 * block_size)))
print >> out, M_22
if np.allclose(M_12, M_22):
print >> out, 'the matrices are similar'
else:
print >> out, 'the matrices are different'
return out.getvalue().strip()
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 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 update_using_laplacian(D, index_set):
"""
Update the distance matrix by summing rows and columns of the removed indices.
@param D: the distance matrix
@param index_set: the set of indices that will be removed from the updated distance matrix
@return: an updated distance matrix
"""
L = Euclid.edm_to_laplacian(D)
L_small = SchurAlgebra.mmerge(L, index_set)
D_small = Euclid.laplacian_to_edm(L_small)
return D_small
def update_using_laplacian(D, index_set):
"""
Update the distance matrix by summing rows and columns of the removed indices.
@param D: the distance matrix
@param index_set: the set of indices that will be removed from the updated distance matrix
@return: an updated distance matrix
"""
L = Euclid.edm_to_laplacian(D)
L_small = SchurAlgebra.mmerge(L, index_set)
D_small = Euclid.laplacian_to_edm(L_small)
return D_small
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 test_commutativity(self):
"""
Schur complementation and merging can be done in either order.
"""
reciprocal_adjacency_big = np.array([
[0, 0, 0, 0, 0, 0, 0, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 9, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 3, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 2],
[0, 2, 9, 0, 0, 0, 0, 4, 0, 0],
[2, 0, 0, 0, 0, 0, 4, 0, 0, 1],
[0, 0, 0, 1, 3, 0, 0, 0, 0, 7],
[0, 0, 0, 0, 0, 2, 0, 1, 7, 0]], dtype=float)
A_big = self.nonzero_reciprocal(reciprocal_adjacency_big)
L_big = adjacency_to_laplacian(A_big)
# define the pruned branch length
p = 101.0 / 39.0
reciprocal_adjacency_small = np.array([
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 2, 0],
[0, 0, 0, 0, 9, 0],
[0, 0, 0, 0, 0, p],
[0, 2, 9, 0, 0, 4],
[2, 0, 0, p, 4, 0]])
A_small = self.nonzero_reciprocal(reciprocal_adjacency_small)
L_small = adjacency_to_laplacian(A_small)
# get the small matrix in terms of the big matrix by schur complementation followed by merging
reconstructed_small_a = SchurAlgebra.mmerge(SchurAlgebra.mschur(L_big, set([8, 9])), set([3, 4, 5]))
self.assertTrue(np.allclose(L_small, reconstructed_small_a))
# get the small matrix in terms of the big matrix by merging followed by schur complementation
reconstructed_small_b = SchurAlgebra.mschur(SchurAlgebra.mmerge(L_big, set([3, 4, 5])), set([6, 7]))
self.assertTrue(np.allclose(L_small, reconstructed_small_b))
# get the laplacian associated with a 4x4 distance matrix in multiple ways
first_result = SchurAlgebra.mmerge(SchurAlgebra.mschur(L_big, set([6, 7, 8, 9])), set([3, 4, 5]))
second_result = SchurAlgebra.mschur(L_small, set([4, 5]))
self.assertTrue(np.allclose(first_result, second_result))
def get_response_content(fs):
# read the matrix
D = fs.matrix
# read the ordered labels
ordered_labels = Util.get_stripped_lines(StringIO(fs.labels))
if not ordered_labels:
raise HandlingError('no ordered taxa were provided')
if len(ordered_labels) != len(set(ordered_labels)):
raise HandlingError('the ordered taxa should be unique')
# get the label selection and its complement
min_selected_labels = 2
min_unselected_labels = 1
selected_labels = set(Util.get_stripped_lines(StringIO(fs.selection)))
if len(selected_labels) < min_selected_labels:
raise HandlingError('at least %d taxa should be selected to be grouped' % min_selected_labels)
# get the set of labels in the complement
unselected_labels = set(ordered_labels) - selected_labels
if len(unselected_labels) < min_unselected_labels:
raise HandlingError('at least %d taxa should remain outside the selected group' % min_unselected_labels)
# assert that no bizarre labels were selected
weird_labels = selected_labels - set(ordered_labels)
if weird_labels:
raise HandlingError('some selected taxa are invalid: ' + str(weird_labels))
# assert that the size of the distance matrix is compatible with the number of ordered labels
if len(D) != len(ordered_labels):
raise HandlingError('the number of listed taxa does not match the number of rows in the distance matrix')
# get the set of selected indices and its complement
n = len(D)
index_selection = set(i for i, label in enumerate(ordered_labels) if label in selected_labels)
index_complement = set(range(n)) - index_selection
# begin the response
out = StringIO()
# get the ordered list of sets of indices to merge
merged_indices = SchurAlgebra.vmerge([set([x]) for x in range(n)], index_selection)
# calculate the new distance matrix
L = Euclid.edm_to_laplacian(D)
L_merged = SchurAlgebra.mmerge(L, index_selection)
D_merged = Euclid.laplacian_to_edm(L_merged)
# print the output distance matrix and the labels of its rows
print >> out, 'new distance matrix:'
print >> out, MatrixUtil.m_to_string(D_merged)
print >> out
print >> out, 'new taxon labels:'
for merged_index_set in merged_indices:
if len(merged_index_set) == 1:
print >> out, ordered_labels[merged_index_set.pop()]
else:
print >> out, '{' + ', '.join(selected_labels) + '}'
# write the response
return out.getvalue()
def update_generalized_nj(D, index_set):
"""
Create a new distance matrix according to a neighbor-joining-like criterion.
Do this according to the explanation in our tree reconstruction manuscript.
The length of the branch defined by the split is divided evenly
between the two successor distance matrices.
@param D: the distance matrix
@param index_set: the subset of indices that will be removed from the updated distance matrix
@return: a new distance matrix
"""
n = len(D)
A = set(range(n)) - set(index_set)
B = set(index_set)
nA = len(A)
nB = len(B)
if nA < 2 or nB < 2:
raise ValueError(
'expected each side of the split to have at least two elements')
# The split of the indices into A and B defines a single internal branch.
# The average distance from A to the branch is alpha.
# The average distance from B to the branch is beta.
# The length of the branch is gamma.
# The expected distance from i to a taxon in the other group is R[i].
R = {}
R.update((i, sum(D[i, b] for b in B) / float(nB)) for i in A)
R.update((j, sum(D[a, j] for a in A) / float(nA)) for j in B)
gamma_plus_beta = 0.5 * min(R[i] + R[j] - D[i, j]
for i, j in itertools.combinations(A, 2))
alpha_plus_gamma = 0.5 * min(R[i] + R[j] - D[i, j]
for i, j in itertools.combinations(B, 2))
alpha_plus_gamma_plus_beta = sum(
D[i, j] for i, j in itertools.product(A, B)) / float(nA * nB)
gamma = alpha_plus_gamma + gamma_plus_beta - alpha_plus_gamma_plus_beta
beta = gamma_plus_beta - gamma
# Initialize the new distance matrix.
D_out = SchurAlgebra.mmerge(D, index_set)
# Find the index of D_out that corresponds to the outgroup.
outgroup_index = sum(1 for a in A if a < min(B))
D_out[outgroup_index, outgroup_index] = 0
# Adjust one of the rows and columns to reflect distances to the outgroup.
label_sets = SchurAlgebra.vmerge([set([i]) for i in range(n)], index_set)
for i, labels in enumerate(label_sets):
if i != outgroup_index:
a = iterutils.get_only(labels)
d = R[a] - beta - 0.5 * gamma
D_out[i, outgroup_index] = D_out[outgroup_index, i] = d
return D_out
def update_generalized_nj(D, index_set):
"""
Create a new distance matrix according to a neighbor-joining-like criterion.
Do this according to the explanation in our tree reconstruction manuscript.
The length of the branch defined by the split is divided evenly
between the two successor distance matrices.
@param D: the distance matrix
@param index_set: the subset of indices that will be removed from the updated distance matrix
@return: a new distance matrix
"""
n = len(D)
A = set(range(n)) - set(index_set)
B = set(index_set)
nA = len(A)
nB = len(B)
if nA < 2 or nB < 2:
raise ValueError("expected each side of the split to have at least two elements")
# The split of the indices into A and B defines a single internal branch.
# The average distance from A to the branch is alpha.
# The average distance from B to the branch is beta.
# The length of the branch is gamma.
# The expected distance from i to a taxon in the other group is R[i].
R = {}
R.update((i, sum(D[i, b] for b in B) / float(nB)) for i in A)
R.update((j, sum(D[a, j] for a in A) / float(nA)) for j in B)
gamma_plus_beta = 0.5 * min(R[i] + R[j] - D[i, j] for i, j in itertools.combinations(A, 2))
alpha_plus_gamma = 0.5 * min(R[i] + R[j] - D[i, j] for i, j in itertools.combinations(B, 2))
alpha_plus_gamma_plus_beta = sum(D[i, j] for i, j in itertools.product(A, B)) / float(nA * nB)
gamma = alpha_plus_gamma + gamma_plus_beta - alpha_plus_gamma_plus_beta
beta = gamma_plus_beta - gamma
# Initialize the new distance matrix.
D_out = SchurAlgebra.mmerge(D, index_set)
# Find the index of D_out that corresponds to the outgroup.
outgroup_index = sum(1 for a in A if a < min(B))
D_out[outgroup_index, outgroup_index] = 0
# Adjust one of the rows and columns to reflect distances to the outgroup.
label_sets = SchurAlgebra.vmerge([set([i]) for i in range(n)], index_set)
for i, labels in enumerate(label_sets):
if i != outgroup_index:
a = iterutils.get_only(labels)
d = R[a] - beta - 0.5 * gamma
D_out[i, outgroup_index] = D_out[outgroup_index, i] = d
return D_out
def process(npoints, nseconds):
"""
@param npoints: attempt to form each counterexample from this many points
@param nseconds: allow this many seconds to run
@return: a multi-line string that summarizes the results
"""
start_time = time.time()
best_result = None
nchecked = 0
while time.time() - start_time < nseconds:
# look for a counterexample
points = sample_points(npoints)
D = points_to_edm(points)
L = Euclid.edm_to_laplacian(D)
L_small = SchurAlgebra.mmerge(L, set([0, 1]))
w = np.linalg.eigvalsh(L_small)
D_small = Euclid.laplacian_to_edm(L_small)
result = Counterexample(points, D, w, D_small)
# see if the counterexample is interesting
if best_result is None:
best_result = result
elif min(result.L_eigenvalues) < min(best_result.L_eigenvalues):
best_result = result
nchecked += 1
out = StringIO()
print >> out, 'checked', nchecked, 'matrices each formed from', npoints, 'points'
print >> out
print >> out, 'eigenvalues of the induced matrix with lowest eigenvalue:'
for value in reversed(sorted(best_result.L_eigenvalues)):
print >> out, value
print >> out
print >> out, 'corresponding induced distance matrix:'
print >> out, MatrixUtil.m_to_string(best_result.D_small)
print >> out
print >> out, 'the original distance matrix corresponding to this matrix:'
print >> out, MatrixUtil.m_to_string(best_result.D)
print >> out
print >> out, 'the points that formed the original distance matrix:'
for point in best_result.points:
print >> out, '\t'.join(str(x) for x in point)
return out.getvalue().strip()
def process(npoints, nseconds):
"""
@param npoints: attempt to form each counterexample from this many points
@param nseconds: allow this many seconds to run
@return: a multi-line string that summarizes the results
"""
start_time = time.time()
best_result = None
nchecked = 0
while time.time() - start_time < nseconds:
# look for a counterexample
points = sample_points(npoints)
D = points_to_edm(points)
L = Euclid.edm_to_laplacian(D)
L_small = SchurAlgebra.mmerge(L, set([0, 1]))
w = np.linalg.eigvalsh(L_small)
D_small = Euclid.laplacian_to_edm(L_small)
result = Counterexample(points, D, w, D_small)
# see if the counterexample is interesting
if best_result is None:
best_result = result
elif min(result.L_eigenvalues) < min(best_result.L_eigenvalues):
best_result = result
nchecked += 1
out = StringIO()
print >> out, 'checked', nchecked, 'matrices each formed from', npoints, 'points'
print >> out
print >> out, 'eigenvalues of the induced matrix with lowest eigenvalue:'
for value in reversed(sorted(best_result.L_eigenvalues)):
print >> out, value
print >> out
print >> out, 'corresponding induced distance matrix:'
print >> out, MatrixUtil.m_to_string(best_result.D_small)
print >> out
print >> out, 'the original distance matrix corresponding to this matrix:'
print >> out, MatrixUtil.m_to_string(best_result.D)
print >> out
print >> out, 'the points that formed the original distance matrix:'
for point in best_result.points:
print >> out, '\t'.join(str(x) for x in point)
return out.getvalue().strip()
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 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
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
