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 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 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 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 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 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
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')
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):