def _get_dataset_fits(data, dataset, fitter, k_of_n, n_correlation_iterations, correlations_k_of_n, allow_new_computation):
def arg_mapper(gr,f_proxy):
g,r = gr
series = dataset.get_one_series(g,r)
return f_proxy(series,fitter)
# sharding is done by gene, so plots.plot_and_save_all_genes can work on a shard
# this also requires that the list of all genes be taken from the whole data
# and not from each dataset. Otherwise we can get a mismatch between the genes
# in the shard for different datasets.
dataset_fits = job_splitting.compute(
name = 'fits',
f = _compute_fit,
arg_mapper = arg_mapper,
all_keys = list(product(dataset.gene_names,dataset.region_names)),
all_sharding_keys = data.gene_names,
f_sharding_key = lambda gr: gr[0],
k_of_n = k_of_n,
base_filename = fit_results_relative_path(dataset,fitter),
allow_new_computation = allow_new_computation,
)
if n_correlation_iterations > 0:
# The problem is that if we're using a shard for the basic fits we won't have theta for all genes in a region
# which is necessary for computing correlations in that region.
assert k_of_n is None, "Can't perform correlation computations when sharding is enabled at the basic fit level"
_add_dataset_correlation_fits(dataset, fitter, dataset_fits, n_correlation_iterations, correlations_k_of_n, allow_new_computation)
if cfg.verbosity > 0:
print 'Adding fit scores... ',
_add_scores(dataset, dataset_fits)
if cfg.verbosity > 0:
print 'done!'
return dataset_fits
def _add_dataset_correlation_fits(dataset, fitter, ds_fits, n_iterations,
k_of_n, allow_new_computation):
def arg_mapper(key, f_proxy):
ir, loo_point = key
r = dataset.region_names[ir]
series = dataset.get_several_series(dataset.gene_names, r)
basic_theta = [ds_fits[(g, r)].theta for g in dataset.gene_names]
return f_proxy(series, fitter, basic_theta, loo_point, n_iterations)
all_keys = []
for ir, r in enumerate(dataset.region_names):
all_keys.append((ir, None))
series = dataset.get_several_series(dataset.gene_names, r)
for iy, g in enumerate(dataset.gene_names):
for ix in xrange(len(series.ages)):
loo_point = (ix, iy)
all_keys.append((ir, loo_point))
def f_sharding_key(
key): # keep all x points in the same shard for same r,iy
r, loo_point = key
if loo_point is None:
return (r, None)
else:
ix, iy = loo_point
return (r, iy)
dct_results = job_splitting.compute(
name='fits-correlations',
f=_compute_fit_with_correlations,
arg_mapper=arg_mapper,
all_keys=all_keys,
f_sharding_key=f_sharding_key,
k_of_n=k_of_n,
base_filename=fit_results_relative_path(dataset, fitter) +
'-correlations-{}'.format(n_iterations),
allow_new_computation=allow_new_computation,
)
_add_dataset_correlation_fits_from_results_dictionary(
dataset, ds_fits, dct_results)
def _get_dataset_fits(data, dataset, fitter, k_of_n, n_correlation_iterations,
correlations_k_of_n, allow_new_computation):
def arg_mapper(gr, f_proxy):
g, r = gr
series = dataset.get_one_series(g, r)
return f_proxy(series, fitter)
# sharding is done by gene, so plots.plot_and_save_all_genes can work on a shard
# this also requires that the list of all genes be taken from the whole data
# and not from each dataset. Otherwise we can get a mismatch between the genes
# in the shard for different datasets.
dataset_fits = job_splitting.compute(
name='fits',
f=_compute_fit,
arg_mapper=arg_mapper,
all_keys=list(product(dataset.gene_names, dataset.region_names)),
all_sharding_keys=data.gene_names,
f_sharding_key=lambda gr: gr[0],
k_of_n=k_of_n,
base_filename=fit_results_relative_path(dataset, fitter),
allow_new_computation=allow_new_computation,
)
if n_correlation_iterations > 0:
# The problem is that if we're using a shard for the basic fits we won't have theta for all genes in a region
# which is necessary for computing correlations in that region.
assert k_of_n is None, "Can't perform correlation computations when sharding is enabled at the basic fit level"
_add_dataset_correlation_fits(dataset, fitter, dataset_fits,
n_correlation_iterations,
correlations_k_of_n,
allow_new_computation)
if cfg.verbosity > 0:
print 'Adding fit scores... ',
_add_scores(dataset, dataset_fits)
if cfg.verbosity > 0:
print 'done!'
return dataset_fits
def _add_dataset_correlation_fits(dataset, fitter, ds_fits, n_iterations, k_of_n, allow_new_computation):
def arg_mapper(key, f_proxy):
ir, loo_point = key
r = dataset.region_names[ir]
series = dataset.get_several_series(dataset.gene_names,r)
basic_theta = [ds_fits[(g,r)].theta for g in dataset.gene_names]
return f_proxy(series, fitter, basic_theta, loo_point, n_iterations)
all_keys = []
for ir,r in enumerate(dataset.region_names):
all_keys.append((ir,None))
series = dataset.get_several_series(dataset.gene_names,r)
for iy,g in enumerate(dataset.gene_names):
for ix in xrange(len(series.ages)):
loo_point = (ix,iy)
all_keys.append((ir,loo_point))
def f_sharding_key(key): # keep all x points in the same shard for same r,iy
r, loo_point = key
if loo_point is None:
return (r,None)
else:
ix,iy = loo_point
return (r,iy)
dct_results = job_splitting.compute(
name = 'fits-correlations',
f = _compute_fit_with_correlations,
arg_mapper = arg_mapper,
all_keys = all_keys,
f_sharding_key = f_sharding_key,
k_of_n = k_of_n,
base_filename = fit_results_relative_path(dataset,fitter) + '-correlations-{}'.format(n_iterations),
allow_new_computation = allow_new_computation,
)
_add_dataset_correlation_fits_from_results_dictionary(dataset, ds_fits, dct_results)
