def add_result(self, cfg, model, result):
"""
We get the result class from raxml or phyml. We need to transform this
into a numpy record, and then store it locally, and in the database
"""
K = float(cfg.processor.models.get_num_params(model))
n = float(len(self.column_set))
lnL = float(result.lnl)
aic = get_aic(lnL, K)
bic = get_bic(lnL, K, n)
aicc = get_aicc(lnL, K, n)
result.subset_id = self.subset_id
result.model_id = model
result.params = K
result.aic = aic
result.aicc = aicc
result.bic = bic
# Now assign the data record in the result into the current model
# result and save it to the database
self.result_array[self.result_current] = result._data
cfg.database.save_result(self, self.result_current)
self.result_current += 1
log.debug(
"Added model to subset. Model: %s, params: %d, sites:%d, lnL:%.2f, site_rate %f"
% (model, K, n, lnL, result.site_rate))
def add_result(self, cfg, model, result):
"""
We get the result class from raxml or phyml. We need to transform this
into a numpy record, and then store it locally, and in the database
"""
K = float(cfg.processor.models.get_num_params(model))
n = float(len(self.column_set))
lnL = float(result.lnl)
aic = get_aic(lnL, K)
bic = get_bic(lnL, K, n)
aicc = get_aicc(lnL, K, n)
result.subset_id = self.subset_id
result.model_id = model
result.params = K
result.aic = aic
result.aicc = aicc
result.bic = bic
# Now assign the data record in the result into the current model
# result and save it to the database
self.result_array[self.result_current] = result._data
cfg.database.save_result(self, self.result_current)
self.result_current += 1
log.debug("Added model to subset. Model: %s, params: %d, sites:%d, lnL:%.2f, site_rate %f"
% (model, K, n, lnL, result.site_rate))
def subset_list_score(list_of_subsets, the_config, alignment):
"""Takes a list of subsets and return the aic, aicc, or bic score"""
lnL, sum_k, subs_len = subset_list_stats(list_of_subsets, the_config, alignment)
if the_config.model_selection == 'aic':
return get_aic(lnL, sum_k)
elif the_config.model_selection == 'aicc':
return get_aicc(lnL, sum_k, subs_len)
elif the_config.model_selection == 'bic':
return get_bic(lnL, sum_k, subs_len)
def subset_list_score(list_of_subsets, the_config, alignment):
"""Takes a list of subsets and return the aic, aicc, or bic score"""
lnL, sum_k, subs_len = subset_list_stats(list_of_subsets, the_config,
alignment)
if the_config.model_selection == 'aic':
return get_aic(lnL, sum_k)
elif the_config.model_selection == 'aicc':
return get_aicc(lnL, sum_k, subs_len)
elif the_config.model_selection == 'bic':
return get_bic(lnL, sum_k, subs_len)
def __init__(self, sch, nseq, branchlengths, model_selection):
self.scheme_name = sch.name
self.scheme = sch
self.model_selection = model_selection
# Calculate AIC, BIC, AICc for each scheme.
# How you do this depends on whether brlens are linked or not.
self.nsubs = len(sch.subsets) # number of subsets
sum_subset_k = sum([
s.best_params for s in sch
]) # sum of number of parameters in the best model of each subset
log.debug("""Calculating number of parameters in scheme.
Total parameters from subset models: %d""" % (sum_subset_k))
if branchlengths == 'linked': # linked brlens - only one extra parameter per subset
self.sum_k = sum_subset_k + (self.nsubs - 1) + (
(2 * nseq) - 3) # number of parameters in a scheme
log.debug("Total parameters from brlens: %d" % ((2 * nseq) - 3))
log.debug("Parameters from subset multipliers: %d" %
(self.nsubs - 1))
elif branchlengths == 'unlinked': # unlinked brlens - every subset has its own set of brlens
self.sum_k = sum_subset_k + (self.nsubs * (
(2 * nseq) - 3)) # number of parameters in a scheme
log.debug("Total parameters from brlens: %d" % ((2 * nseq) - 3) *
self.nsubs)
else:
# WTF?
log.error("Unknown option for branchlengths: %s", branchlengths)
raise PartitionFinderError
log.debug("Grand total parameters: %d" % (self.sum_k))
self.lnl = sum([s.best_lnl for s in sch])
self.nsites = sum([len(s.column_set) for s in sch])
K = float(self.sum_k)
n = float(self.nsites)
lnL = float(self.lnl)
log.debug("n: %d\tK: %d\tlnL: %d" % (n, K, lnL))
self.aic = get_aic(lnL, K)
self.bic = get_bic(lnL, K, n)
self.aicc = get_aicc(lnL, K, n)
def __init__(self, sch, nseq, branchlengths, model_selection):
self.scheme_name = sch.name
self.scheme = sch
self.model_selection = model_selection
# Calculate AIC, BIC, AICc for each scheme.
# How you do this depends on whether brlens are linked or not.
self.nsubs = len(sch.subsets) # number of subsets
sum_subset_k = sum([s.best_params for s in sch]) # sum of number of parameters in the best model of each subset
log.debug("""Calculating number of parameters in scheme.
Total parameters from subset models: %d""" % (sum_subset_k))
if branchlengths == 'linked': # linked brlens - only one extra parameter per subset
self.sum_k = sum_subset_k + (self.nsubs - 1) + (
(2 * nseq) - 3) # number of parameters in a scheme
log.debug("Total parameters from brlens: %d" %
((2 * nseq) - 3))
log.debug("Parameters from subset multipliers: %d" %
(self.nsubs - 1))
elif branchlengths == 'unlinked': # unlinked brlens - every subset has its own set of brlens
self.sum_k = sum_subset_k + (self.nsubs * (
(2 * nseq) - 3)) # number of parameters in a scheme
log.debug("Total parameters from brlens: %d" % ((
2 * nseq) - 3) * self.nsubs)
else:
# WTF?
log.error("Unknown option for branchlengths: %s", branchlengths)
raise PartitionFinderError
log.debug("Grand total parameters: %d" % (self.sum_k))
self.lnl = sum([s.best_lnl for s in sch])
self.nsites = sum([len(s.column_set) for s in sch])
K = float(self.sum_k)
n = float(self.nsites)
lnL = float(self.lnl)
log.debug("n: %d\tK: %d\tlnL: %d" % (n, K, lnL))
self.aic = get_aic(lnL, K)
self.bic = get_bic(lnL, K, n)
self.aicc = get_aicc(lnL, K, n)
