def do_analysis(self):
'''A greedy algorithm for heuristic partitioning searches'''
log.info("Performing greedy analysis")
partnum = len(self.cfg.partitions)
scheme_count = submodels.count_greedy_schemes(partnum)
subset_count = submodels.count_greedy_subsets(partnum)
self.cfg.progress.begin(scheme_count, subset_count)
# Start with the most partitioned scheme
start_description = range(len(self.cfg.partitions))
start_scheme = scheme.create_scheme(
self.cfg, "start_scheme", start_description)
log.info("Analysing starting scheme (scheme %s)" % start_scheme.name)
self.analyse_scheme(start_scheme)
step = 1
cur_s = 2
# Now we try out all lumpings of the current scheme, to see if we can
# find a better one and if we do, we just keep going
while True:
log.info("***Greedy algorithm step %d***" % step)
# Get a list of all possible lumpings of the best_scheme
lumpings = algorithm.lumpings(start_description)
# Save the current best score we have in results
old_best_score = self.results.best_score
for lumped_description in lumpings:
lumped_scheme = scheme.create_scheme(self.cfg, cur_s, lumped_description)
cur_s += 1
# This is just checking to see if a scheme is any good, if it
# is, we remember and write it later
self.analyse_scheme(lumped_scheme)
# Did out best score change (It ONLY gets better -- see in
# results.py)
if self.results.best_score == old_best_score:
# It didn't, so we're done
break
# Let's look further. We use the description from our best scheme
# (which will be the one that just changed in the last lumpings
# iteration)
start_description = self.results.best_result.scheme.description
# Rename and record the best scheme for this step
self.results.best_scheme.name = "step_%d" % step
self.cfg.reporter.write_scheme_summary(
self.results.best_scheme, self.results.best_result)
# If it's the scheme with everything equal, quit
if len(set(start_description)) == 1:
break
# Go do the next round...
step += 1
log.info("Greedy algorithm finished after %d steps" % step)
log.info("Highest scoring scheme is scheme %s, with %s score of %.3f" %
(self.results.best_scheme.name, self.cfg.model_selection,
self.results.best_score))
txt = "Best scheme according to Greedy algorithm, analysed with %s" % self.cfg.model_selection
self.cfg.reporter.write_best_scheme(txt, self.results)
def do_analysis(self):
'''A greedy algorithm for heuristic partitioning searches'''
partnum = len(the_config.user_subsets)
scheme_count = submodels.count_greedy_schemes(partnum)
subset_count = submodels.count_greedy_subsets(partnum)
the_config.progress.begin(scheme_count, subset_count)
# Start with the most partitioned scheme, and record it.
with logtools.indented(log, "*** Analysing starting scheme ***"):
the_config.progress.begin(scheme_count, partnum)
start_scheme = scheme.create_scheme(
the_config, "start_scheme", range(partnum))
start_result = self.analyse_scheme(start_scheme)
start_score = start_result.score
if not the_config.quick:
the_config.reporter.write_scheme_summary(
self.results.best_scheme, self.results.best_result)
subsets = [s for s in start_scheme.subsets]
step = 1
while len(set(start_scheme.subsets)) > 1:
with logtools.indented(log, "***Greedy algorithm step %d***" % step):
name_prefix = "step_%d" % (step)
# get distances between subsets
max_schemes = comb(len(start_scheme.subsets), 2)
# this is a fake distance matrix, so that the greedy algorithm
# can use all the tricks of the relaxed clustering algorithm
dim = len(subsets)
d_matrix = np.zeros((((dim*dim)-dim))/2)
d_matrix[:] = np.inf
if step == 1:
# Now initialise a change in info score matrix to inf
c_matrix = np.empty(d_matrix.shape)
c_matrix[:] = np.inf
c_matrix = spatial.distance.squareform(c_matrix)
# 1. pick top N subset pairs from distance matrix
cutoff = max_schemes # this defines the greedy algorithm: we look at all schemes
closest_pairs = neighbour.get_N_closest_subsets(
subsets, the_config, cutoff, d_matrix)
# 2. analyse subsets in top N that have not yet been analysed
pairs_todo = neighbour.get_pairs_todo(closest_pairs, c_matrix, subsets)
if len(pairs_todo)>0:
log.info("Analysing %d new subset pairs" % len(pairs_todo))
new_subs = []
sub_tuples = []
for pair in pairs_todo:
new_sub = subset_ops.merge_subsets(pair)
new_subs.append(new_sub)
sub_tuples.append((new_sub, pair))
the_config.progress.begin(scheme_count, len(new_subs))
self.analyse_list_of_subsets(new_subs)
# 3. for all K new subsets, update improvement matrix and find best pair
log.info("Finding the best partitioning scheme")
diffs = []
scheme_name = "step_%d" %(step)
for t in sub_tuples:
pair_merged = t[0]
pair = t[1]
new_scheme = neighbour.make_clustered_scheme(
start_scheme, scheme_name, pair, pair_merged, the_config)
r = self.analyse_scheme(new_scheme)
diff = r.score - start_score
diffs.append(diff)
c_matrix = neighbour.update_c_matrix(c_matrix, sub_tuples, subsets, diffs)
# 4. Find the best pair of subsets, and build a scheme based on that
# note that this matrix includes diagonals, which will all be zero
# since this is equivalent to comparing a scheme to itself.
# so we need to be careful to only proceed if we have a negative change
# which indicates an improvement in the score
best_change = np.amin(c_matrix)
log.debug("Biggest improvement in info score: %s", str(best_change))
if best_change>=0:
log.info("Found no schemes that improve the score, stopping")
break
best_pair = neighbour.get_best_pair(c_matrix, best_change, subsets)
best_merged = subset_ops.merge_subsets(best_pair)
best_scheme = neighbour.make_clustered_scheme(
start_scheme, scheme_name, best_pair, best_merged, the_config)
best_result = self.analyse_scheme(best_scheme)
# the best change can get updated a fraction at this point
# because calaculting the info score on the whole alignment
# is a little different from doing it on the one subset
best_change = self.results.best_score - start_score
log.info("Best scheme combines subsets: '%s' and '%s'" %(best_pair[0].name, best_pair[1].name))
log.info("The best scheme improves the %s score by %.2f to %.1f",
the_config.model_selection,
np.abs(best_change),
self.results.best_score)
start_scheme = best_scheme
start_score = best_result.score
log.debug("Best pair: %s", str([s.name for s in best_pair]))
log.debug("Merged into: %s", str([best_merged.name]))
# 5. reset_c_matrix and the subset list
c_matrix = neighbour.reset_c_matrix(c_matrix, list(best_pair), [best_merged], subsets)
# we updated the subset list in a special way, which matches how we update the c matrix:
subsets = neighbour.reset_subsets(subsets, list(best_pair), [best_merged])
if not the_config.quick:
the_config.reporter.write_scheme_summary(
best_scheme, best_result)
step += 1
log.info("Greedy algorithm finished after %d steps" % step)
log.info("Best scoring scheme is scheme %s, with %s score of %.3f"
% (self.results.best_scheme.name, the_config.model_selection,
self.results.best_score))
the_config.reporter.write_best_scheme(self.results)
def do_analysis(self):
'''A greedy algorithm for heuristic partitioning searches'''
partnum = len(the_config.user_subsets)
scheme_count = submodels.count_greedy_schemes(partnum)
subset_count = submodels.count_greedy_subsets(partnum)
the_config.progress.begin(scheme_count, subset_count)
# Start with the most partitioned scheme, and record it.
with logtools.indented(log, "*** Analysing starting scheme ***"):
the_config.progress.begin(scheme_count, partnum)
start_scheme = scheme.create_scheme(the_config, "start_scheme",
range(partnum))
start_result = self.analyse_scheme(start_scheme)
start_score = start_result.score
if not the_config.quick:
the_config.reporter.write_scheme_summary(
self.results.best_scheme, self.results.best_result)
subsets = [s for s in start_scheme.subsets]
step = 1
while len(set(start_scheme.subsets)) > 1:
with logtools.indented(log,
"***Greedy algorithm step %d***" % step):
name_prefix = "step_%d" % (step)
# get distances between subsets
max_schemes = comb(len(start_scheme.subsets), 2)
# this is a fake distance matrix, so that the greedy algorithm
# can use all the tricks of the relaxed clustering algorithm
dim = len(subsets)
d_matrix = np.zeros((((dim * dim) - dim)) / 2)
d_matrix[:] = np.inf
if step == 1:
# Now initialise a change in info score matrix to inf
c_matrix = np.empty(d_matrix.shape)
c_matrix[:] = np.inf
c_matrix = spatial.distance.squareform(c_matrix)
# 1. pick top N subset pairs from distance matrix
cutoff = max_schemes # this defines the greedy algorithm: we look at all schemes
closest_pairs = neighbour.get_N_closest_subsets(
subsets, the_config, cutoff, d_matrix)
# 2. analyse subsets in top N that have not yet been analysed
pairs_todo = neighbour.get_pairs_todo(closest_pairs, c_matrix,
subsets)
if len(pairs_todo) > 0:
log.info("Analysing %d new subset pairs" % len(pairs_todo))
new_subs = []
sub_tuples = []
for pair in pairs_todo:
new_sub = subset_ops.merge_subsets(pair)
new_subs.append(new_sub)
sub_tuples.append((new_sub, pair))
the_config.progress.begin(scheme_count, len(new_subs))
self.analyse_list_of_subsets(new_subs)
# 3. for all K new subsets, update improvement matrix and find best pair
log.info("Finding the best partitioning scheme")
diffs = []
scheme_name = "step_%d" % (step)
for t in sub_tuples:
pair_merged = t[0]
pair = t[1]
new_scheme = neighbour.make_clustered_scheme(
start_scheme, scheme_name, pair, pair_merged,
the_config)
r = self.analyse_scheme(new_scheme)
diff = r.score - start_score
diffs.append(diff)
c_matrix = neighbour.update_c_matrix(
c_matrix, sub_tuples, subsets, diffs)
# 4. Find the best pair of subsets, and build a scheme based on that
# note that this matrix includes diagonals, which will all be zero
# since this is equivalent to comparing a scheme to itself.
# so we need to be careful to only proceed if we have a negative change
# which indicates an improvement in the score
best_change = np.amin(c_matrix)
log.debug("Biggest improvement in info score: %s",
str(best_change))
if best_change >= 0:
log.info(
"Found no schemes that improve the score, stopping")
break
best_pair = neighbour.get_best_pair(c_matrix, best_change,
subsets)
best_merged = subset_ops.merge_subsets(best_pair)
best_scheme = neighbour.make_clustered_scheme(
start_scheme, scheme_name, best_pair, best_merged,
the_config)
best_result = self.analyse_scheme(best_scheme)
# the best change can get updated a fraction at this point
# because calaculting the info score on the whole alignment
# is a little different from doing it on the one subset
best_change = self.results.best_score - start_score
log.info("Best scheme combines subsets: '%s' and '%s'" %
(best_pair[0].name, best_pair[1].name))
log.info(
"The best scheme improves the %s score by %.2f to %.1f",
the_config.model_selection, np.abs(best_change),
self.results.best_score)
start_scheme = best_scheme
start_score = best_result.score
log.debug("Best pair: %s", str([s.name for s in best_pair]))
log.debug("Merged into: %s", str([best_merged.name]))
# 5. reset_c_matrix and the subset list
c_matrix = neighbour.reset_c_matrix(c_matrix, list(best_pair),
[best_merged], subsets)
# we updated the subset list in a special way, which matches how we update the c matrix:
subsets = neighbour.reset_subsets(subsets, list(best_pair),
[best_merged])
if not the_config.quick:
the_config.reporter.write_scheme_summary(
best_scheme, best_result)
step += 1
log.info("Greedy algorithm finished after %d steps" % step)
log.info("Best scoring scheme is scheme %s, with %s score of %.3f" %
(self.results.best_scheme.name, the_config.model_selection,
self.results.best_score))
the_config.reporter.write_best_scheme(self.results)
def do_analysis(self):
"""A greedy algorithm for heuristic partitioning searches"""
log.info("Performing greedy analysis")
models = self.cfg.models
model_selection = self.cfg.model_selection
partnum = len(self.cfg.partitions)
self.total_scheme_num = submodels.count_greedy_schemes(partnum)
log.info("This will result in a maximum of %s schemes being created", self.total_scheme_num)
self.total_subset_num = submodels.count_greedy_subsets(partnum)
log.info(
"PartitionFinder will have to analyse a maximum of %d subsets of sites to complete this analysis"
% (self.total_subset_num)
)
if self.total_subset_num > 10000:
log.warning("%d is a lot of subsets, this might take a long time to analyse", self.total_subset_num)
log.warning("Perhaps consider using a different search scheme instead (see Manual)")
# clear any schemes that are currently loaded
# TODO Not sure we need this...
self.cfg.schemes.clear_schemes()
# start with the most partitioned scheme
start_description = range(len(self.cfg.partitions))
start_scheme = scheme.create_scheme(self.cfg, 1, start_description)
log.info("Analysing starting scheme (scheme %s)" % start_scheme.name)
result = self.analyse_scheme(start_scheme, models)
def get_score(my_result):
# TODO: this is bad. Should use self.cfg.model_selection, or write
# a new model_selection for scheme.py
if model_selection == "aic":
score = my_result.aic
elif model_selection == "aicc":
score = my_result.aicc
elif model_selection == "bic":
score = my_result.bic
else:
log.error("Unrecognised model_selection variable '%s', please check" % (score))
raise AnalysisError
return score
best_result = result
best_score = get_score(result)
step = 1
cur_s = 2
# now we try out all lumpings of the current scheme, to see if we can find a better one
# and if we do, we just keep going
while True:
log.info("***Greedy algorithm step %d***" % step)
# get a list of all possible lumpings of the best_scheme
lumpings = algorithm.lumpings(start_description)
# we reset the counters as we go, for better user information
self.total_scheme_num = len(lumpings)
self.schemes_analysed = 0
best_lumping_score = None
for lumped_description in lumpings:
lumped_scheme = scheme.create_scheme(self.cfg, cur_s, lumped_description)
cur_s += 1
result = self.analyse_scheme(lumped_scheme, models)
new_score = get_score(result)
if best_lumping_score == None or new_score < best_lumping_score:
best_lumping_score = new_score
best_lumping_result = result
best_lumping_scheme = lumped_scheme
best_lumping_desc = lumped_description
if best_lumping_score < best_score:
best_scheme = best_lumping_scheme
best_score = best_lumping_score
best_result = best_lumping_result
start_description = best_lumping_desc
if len(set(best_lumping_desc)) == 1: # then it's the scheme with everything equal, so quit
break
step += 1
else:
break
log.info("Greedy algorithm finished after %d steps" % step)
log.info(
"Highest scoring scheme is scheme %s, with %s score of %.3f"
% (best_result.scheme.name, model_selection, best_score)
)
self.best_result = best_result
def do_analysis(self):
'''A greedy algorithm for heuristic partitioning searches'''
log.info("Performing greedy analysis")
partnum = len(self.cfg.partitions)
scheme_count = submodels.count_greedy_schemes(partnum)
subset_count = submodels.count_greedy_subsets(partnum)
self.cfg.progress.begin(scheme_count, subset_count)
# Start with the most partitioned scheme
start_description = range(len(self.cfg.partitions))
start_scheme = scheme.create_scheme(self.cfg, "start_scheme",
start_description)
log.info("Analysing starting scheme (scheme %s)" % start_scheme.name)
self.analyse_scheme(start_scheme)
step = 1
cur_s = 2
# Now we try out all lumpings of the current scheme, to see if we can
# find a better one and if we do, we just keep going
while True:
log.info("***Greedy algorithm step %d***" % step)
# Get a list of all possible lumpings of the best_scheme
lumpings = algorithm.lumpings(start_description)
# Save the current best score we have in results
old_best_score = self.results.best_score
for lumped_description in lumpings:
lumped_scheme = scheme.create_scheme(self.cfg, cur_s,
lumped_description)
cur_s += 1
# This is just checking to see if a scheme is any good, if it
# is, we remember and write it later
self.analyse_scheme(lumped_scheme)
# Did out best score change (It ONLY gets better -- see in
# results.py)
if self.results.best_score == old_best_score:
# It didn't, so we're done
break
# Let's look further. We use the description from our best scheme
# (which will be the one that just changed in the last lumpings
# iteration)
start_description = self.results.best_result.scheme.description
# Rename and record the best scheme for this step
self.results.best_scheme.name = "step_%d" % step
self.cfg.reporter.write_scheme_summary(self.results.best_scheme,
self.results.best_result)
# If it's the scheme with everything equal, quit
if len(set(start_description)) == 1:
break
# Go do the next round...
step += 1
log.info("Greedy algorithm finished after %d steps" % step)
log.info("Highest scoring scheme is scheme %s, with %s score of %.3f" %
(self.results.best_scheme.name, self.cfg.model_selection,
self.results.best_score))
self.cfg.reporter.write_best_scheme(self.results)
def do_analysis(self):
# Copied and pasted from greedy analysis
partnum = len(self.cfg.user_subsets)
scheme_count = submodels.count_greedy_schemes(partnum)
subset_count = submodels.count_greedy_subsets(partnum)
self.cfg.progress.begin(scheme_count, subset_count)
# Start with the most partitioned scheme
start_description = range(partnum)
start_scheme = scheme.create_scheme(
self.cfg, "start_scheme", start_description)
log.info("Analysing starting scheme (scheme %s)" % start_scheme.name)
old_score = self.analyse_scheme(start_scheme)
# Get first scheme
best_scheme = start_scheme
subset_index = 0
all_subsets = list(best_scheme.subsets)
processor = self.cfg.processor
alignment_path = self.filtered_alignment_path
tree_path = processor.make_tree_path(alignment_path)
while subset_index < len(all_subsets):
current_subset = all_subsets[subset_index]
split_subsets = kmeans.kmeans_split_subset(self.cfg, self.alignment, current_subset, tree_path)
if split_subsets == 1:
subset_index += 1
else:
# Take a copy
updated_subsets = all_subsets[:]
# Replace the current one with the split one
# Google "slice assignments"
# This list is the key to avoiding recursion. It expands to contain
# all of the split subsets by replacing them with the split ones
updated_subsets[subset_index:subset_index+1] = split_subsets
test_scheme = scheme.Scheme(self.cfg, "Current Scheme", updated_subsets)
try:
best_result = self.analyse_scheme(best_scheme)
new_result = self.analyse_scheme(test_scheme)
log.info("Current best score is: " + str(best_result))
log.info("Current new score is: " + str(new_result))
if new_result.score < best_result.score:
log.info("New score " + str(subset_index) + " is better and will be set to best score")
best_scheme = test_scheme
# Change this to the one with split subsets in it. Note that
# the subset_index now points a NEW subset, one that was split
all_subsets = updated_subsets
else:
# Move to the next subset in the all_subsets list
subset_index += 1
# In PhyML or RAxML, it is likely because of no alignment patterns,
# catch that and move to the next subset without splitting.
except PhylogenyProgramError:
log.info("Phylogeny program generated an error so this subset was not split, see error above")
subset_index += 1
# Now start the Greedy Analysis: need to figure out how to make it go through more
# than one scheme...
start_scheme = best_scheme
partnum = len(start_scheme.subsets)
scheme_count = submodels.count_greedy_schemes(partnum)
subset_count = submodels.count_greedy_subsets(partnum)
self.cfg.progress.begin(scheme_count, subset_count)
start_description = range(partnum)
step = 1
cur_s = 2
# Now we try out all lumpings of the current scheme, to see if we can
# find a better one and if we do, we just keep going
while True:
log.info("***Greedy algorithm step %d***" % step)
old_best_score = self.results.best_score
# Get an iterable of all possible pairs of subsets in best_scheme
lumped_subsets = itertools.combinations(start_scheme.subsets, 2)
for subset_grouping in lumped_subsets:
scheme_name = cur_s
lumped_scheme = neighbour.make_clustered_scheme(
start_scheme, scheme_name, subset_grouping, self.cfg)
new_result = self.analyse_scheme(lumped_scheme)
log.debug("Difference in %s: %.1f",
self.cfg.model_selection,
(new_result.score-old_best_score))
cur_s += 1
if self.results.best_score != old_best_score:
log.info("Analysed all schemes for this step. The best "
"scheme changed the %s score by %.1f units.",
self.cfg.model_selection,
(self.results.best_score - old_best_score))
self.results.best_scheme.name = "step_%d" % step
self.cfg.reporter.write_scheme_summary(
self.results.best_scheme, self.results.best_result)
# Now we find out which is the best lumping we know of for this step
start_scheme = self.results.best_scheme
else:
log.info("Analysed all schemes for this step and found no schemes "
"that improve the score, stopping")
break
# We're done if it's the scheme with everything together
if len(set(lumped_scheme.subsets)) == 1:
break
step += 1
log.info("Greedy algorithm finished after %d steps" % step)
log.info("Best scoring scheme is scheme %s, with %s score of %.3f"
% (self.results.best_scheme.name, self.cfg.model_selection, self.results.best_score))
self.cfg.reporter.write_best_scheme(self.results)
def do_analysis(self):
# Copied and pasted from greedy analysis
partnum = len(self.cfg.user_subsets)
scheme_count = submodels.count_greedy_schemes(partnum)
subset_count = submodels.count_greedy_subsets(partnum)
self.cfg.progress.begin(scheme_count, subset_count)
# Start with the most partitioned scheme
start_description = range(partnum)
start_scheme = scheme.create_scheme(
self.cfg, "start_scheme", start_description)
log.info("Analysing starting scheme (scheme %s)" % start_scheme.name)
old_score = self.analyse_scheme(start_scheme)
# Get first scheme
best_scheme = start_scheme
subset_index = 0
processor = self.cfg.processor
alignment_path = self.filtered_alignment_path
tree_path = processor.make_tree_path(alignment_path)
split_subsets = []
for a_subset in start_scheme:
how_many = kmeans.kmeans_wrapper(self.cfg, self.alignment,
a_subset, tree_path)
split_subsets += how_many
split_scheme = scheme.Scheme(self.cfg, "split_scheme", split_subsets)
best_result = self.analyse_scheme(best_scheme)
split_score = self.analyse_scheme(split_scheme)
if split_score.score < best_result.score:
best_scheme = split_scheme
log.info("Initial splits generated superior scheme")
all_subsets = list(best_scheme.subsets)
fabricated_subsets =[]
step = 1
while subset_index < len(all_subsets):
log.info("Best scheme has %s score of %.2f and %d subset(s)"
%(self.cfg.model_selection.upper(), best_result.score, len(best_scheme.subsets)))
log.info("***Kmeans algorithm step %d***" % step)
step += 1
current_subset = all_subsets[subset_index]
log.info("Analysing subset of %d sites", len(current_subset.columns))
# First check if the subset is large enough to split, if it isn't,
# move to the next subset
if len(current_subset.columns) == 1:
log.info("This subset cannot be split further")
subset_index += 1
continue
if current_subset.fabricated:
log.info("This subset cannot be split further because %s cannot analyse it",
self.cfg.phylogeny_program)
subset_index += 1
fabricated_subsets.append(current_subset)
continue
split_subsets = kmeans.kmeans_split_subset(self.cfg,
self.alignment, current_subset, tree_path)
# kmeans_split_subset will return a 1 and flag the subset as
# fabricated if for some reason it raises a PhylogenyProgramError,
# this it to catch those fabricated subsets
if split_subsets == 1:
subset_index += 1
fabricated_subsets.append(current_subset)
continue
for each_subset in split_subsets:
log.info("Subset resulting from split is %d sites long", len(each_subset.columns))
# Take a copy
updated_subsets = all_subsets[:]
# Replace the current one with the split one
# Google "slice assignments"
# This list is the key to avoiding recursion. It expands to contain
# all of the split subsets by replacing them with the split ones
updated_subsets[subset_index:subset_index+1] = split_subsets
test_scheme = scheme.Scheme(self.cfg, str(step-1),
updated_subsets)
new_result = self.analyse_scheme(test_scheme)
if new_result.score < best_result.score:
best_scheme = test_scheme
best_result = new_result
# Change this to the one with split subsets in it. Note that
# the subset_index now points a NEW subset, one that was split
all_subsets = updated_subsets
# record each scheme that's an improvement
self.cfg.reporter.write_scheme_summary(
self.results.best_scheme, self.results.best_result)
if len(split_subsets)==2:
log.info("Splitting subset into %d:%d sites improved the %s score"
%(len(split_subsets[0].columns),
len(split_subsets[1].columns),
self.cfg.model_selection))
for s in split_subsets:
m = [x%3 for x in s.columns]
l = float(len(s.columns))
props = [(float(m.count(1))/l), (float(m.count(2))/l), (float(m.count(0))/l)]
log.info("%d subset has 1st, 2nd, 3rd props: %s" %(len(s.columns), str(props)))
else:
log.info("Splitting this subset did not improve the %s score",
self.cfg.model_selection.upper())
# Move to the next subset in the all_subsets list
subset_index += 1
log.info("Best scheme has %s score of %.2f and %d subset(s)"
%(self.cfg.model_selection.upper(), best_result.score, len(best_scheme.subsets)))
if fabricated_subsets:
log.info("Finalising partitioning scheme")
log.info("This involves cleaning up small subsets which %s "
"can't analyse", self.cfg.phylogeny_program)
# Now join the fabricated subsets back up with other subsets
while fabricated_subsets:
log.info("***Kmeans algorithm step %d***" % step)
step += 1
# Take the first subset in the list (to be "popped" off later)
s = fabricated_subsets[0]
centroid = s.centroid
best_match = None
# Take a list copy of the best scheme
scheme_list = list(best_scheme)
scheme_list.remove(s)
# Loop through the subsets in the best scheme and find the one
# with the nearest centroid
for sub in scheme_list:
centroid_array = [sub.centroid, centroid]
# euclid_dist = abs(sub.centroid[0] - centroid[0])
warnings.simplefilter('ignore', DeprecationWarning)
euclid_dist = spatial.distance.pdist(centroid_array)
if euclid_dist < best_match or best_match == None:
best_match = euclid_dist
closest_sub = sub
# Now merge those subsets
merged_sub = subset_ops.merge_fabricated_subsets([s, closest_sub])
# Remove the offending subset from the fabricated subset list
fabricated_subsets.pop(0)
# If the closest subset happens to be "fabricated" as well, take
# it out of the fabricated_subsets list
if closest_sub in fabricated_subsets:
fabricated_subsets.remove(closest_sub)
# Get rid of the two subsets that were merged from the best_scheme
scheme_list.remove(closest_sub)
# Now add the new subset to the scheme and see if the new subset
# can be analyzed
scheme_list.append(merged_sub)
merged_scheme = scheme.Scheme(self.cfg, str(step-1), scheme_list)
merged_result = self.analyse_scheme(merged_scheme)
# If it can be analyzed, move the algorithm forward, if it can't
# be analyzed add it to the list of fabricated_subsets
for new_subs in merged_scheme:
if new_subs.fabricated and new_subs not in fabricated_subsets:
fabricated_subsets.append(new_subs)
best_scheme = merged_scheme
best_result = merged_result
# Since the AIC will likely be better before we dealt with the
# fabricated subsets, we need to set the best scheme and best result
# to those from the last merged_scheme. TODO: add a variable to scheme
# to take care of this problem so that the best AND analysable scheme
# is the one that gets automatically flagged as the best scheme
self.results.best_scheme = best_scheme
self.results.best_result = best_result
self.cfg.reporter.write_scheme_summary(
self.results.best_scheme, self.results.best_result)
log.info("** Kmeans algorithm finished after %d steps **" % (step - 1))
log.info("Best scoring scheme is scheme %s, with %s score of %.3f"
% (self.results.best_scheme.name, self.cfg.model_selection, self.results.best_score))
self.cfg.reporter.write_best_scheme(self.results)
def do_analysis(self):
'''A greedy algorithm for heuristic partitioning searches'''
log.info("Performing greedy analysis")
partnum = len(self.cfg.user_subsets)
scheme_count = submodels.count_greedy_schemes(partnum)
subset_count = submodels.count_greedy_subsets(partnum)
self.cfg.progress.begin(scheme_count, subset_count)
# Start with the most partitioned scheme
start_description = range(partnum)
start_scheme = scheme.create_scheme(
self.cfg, "start_scheme", start_description)
log.info("Analysing starting scheme (scheme %s)" % start_scheme.name)
self.analyse_scheme(start_scheme)
step = 1
cur_s = 2
# Now we try out all lumpings of the current scheme, to see if we can
# find a better one and if we do, we just keep going
while True:
log.info("***Greedy algorithm step %d***" % step)
old_best_score = self.results.best_score
# Get an iterable of all possible pairs of subsets in best_scheme
lumped_subsets = itertools.combinations(start_scheme.subsets, 2)
for subset_grouping in lumped_subsets:
scheme_name = cur_s
lumped_scheme = neighbour.make_clustered_scheme(
start_scheme, scheme_name, subset_grouping, self.cfg)
new_result = self.analyse_scheme(lumped_scheme)
log.debug("Difference in %s: %.1f",
self.cfg.model_selection,
(new_result.score-old_best_score))
cur_s += 1
if self.results.best_score != old_best_score:
log.info("Analysed all schemes for this step. The best "
"scheme changed the %s score by %.1f units.",
self.cfg.model_selection,
(self.results.best_score - old_best_score))
self.results.best_scheme.name = "step_%d" % step
self.cfg.reporter.write_scheme_summary(
self.results.best_scheme, self.results.best_result)
# Now we find out which is the best lumping we know of for this step
start_scheme = self.results.best_scheme
else:
log.info("Analysed all schemes for this step and found no schemes "
"that improve the score, stopping")
break
# We're done if it's the scheme with everything together
if len(set(lumped_scheme.subsets)) == 1:
break
step += 1
log.info("Greedy algorithm finished after %d steps" % step)
log.info("Best scoring scheme is scheme %s, with %s score of %.3f"
% (self.results.best_scheme.name, self.cfg.model_selection, self.results.best_score))
self.cfg.reporter.write_best_scheme(self.results)
