def mapper_single_snp_2K_given_chrom(test_chr):
logging.info("Working on chr={0}".format(test_chr))
test_snps_chrom = test_snps[:,test_snps.pos[:,0]==test_chr]
G_for_chrom = _K_per_chrom(G, test_chr, G.iid).snpreader
chrom_index = chrom_list.index(test_chr)
best_sid = chrom_index_to_best_sid[chrom_index]
K1 = G_for_chrom[:,G_for_chrom.sid_to_index(best_sid)]
result = single_snp(test_snps=test_snps_chrom, K0=G_for_chrom, K1=K1, pheno=pheno,
covar=covar, leave_out_one_chrom=False,
GB_goal=GB_goal, force_full_rank=force_full_rank, force_low_rank=force_low_rank,mixing=mixing,h2=h2)
return result
def mapper_single_snp_2K_given_chrom(test_chr):
logging.info("Working on chr={0}".format(test_chr))
test_snps_chrom = test_snps[:,test_snps.pos[:,0]==test_chr]
G_for_chrom = _K_per_chrom(G, test_chr, G.iid).snpreader
chrom_index = chrom_list.index(test_chr)
best_sid = chrom_index_to_best_sid[chrom_index]
K1 = G_for_chrom[:,G_for_chrom.sid_to_index(best_sid)]
result = single_snp(test_snps=test_snps_chrom, K0=G_for_chrom, K1=K1, pheno=pheno,
covar=covar, leave_out_one_chrom=False,
GB_goal=GB_goal, force_full_rank=force_full_rank, force_low_rank=force_low_rank,mixing=mixing,h2=h2)
return result
def mapper_find_best_given_chrom(test_chr):
G_for_chrom = _K_per_chrom(G, test_chr, G.iid).snpreader
def mapper_gather_lots(i_fold_and_pair):
i_fold, (train_idx, test_idx) = i_fold_and_pair
logging.info("Working on GWAS_1K and k search, chrom={0}, i_fold={1}".format(test_chr, i_fold))
G_train = G_for_chrom[train_idx,:]
#Precompute whole x whole standardized on train
from fastlmm.association.single_snp import _internal_determine_block_size, _block_size_from_GB_goal
min_count = _internal_determine_block_size(G_for_chrom, None, None, force_full_rank, force_low_rank)
block_size = _block_size_from_GB_goal(GB_goal, G_for_chrom.iid_count, min_count)
K_whole_unittrain = _SnpWholeWithTrain(whole=G_for_chrom,train_idx=train_idx, standardizer=Unit(), block_size=block_size).read()
assert np.array_equal(K_whole_unittrain.iid,G_for_chrom.iid),"real assert"
K_train = K_whole_unittrain[train_idx]
single_snp_result = single_snp(test_snps=G_train, K0=K_train, pheno=pheno, #iid intersection means when can give the whole covariate and pheno
covar=covar, leave_out_one_chrom=False,
GB_goal=GB_goal, force_full_rank=force_full_rank, force_low_rank=force_low_rank, mixing=mixing, h2=h2)
is_all = (i_fold == n_folds) if n_folds > 1 else True
k_list_in = [0] + [int(k) for k in k_list if 0 < k and k < len(single_snp_result)]
if is_all:
top_snps = list(single_snp_result.SNP[:max_k])
else:
top_snps = None
if i_fold == n_folds:
k_index_to_nLL = None
else:
k_index_to_nLL = []
for k in k_list_in:
top_k = G_for_chrom[:,G_for_chrom.sid_to_index(single_snp_result.SNP[:k])]
logging.info("Working on chr={0}, i_fold={1}, and K_{2}".format(test_chr,i_fold,k))
top_k_train = top_k[train_idx,:] if k > 0 else None
fastlmm = FastLMM(force_full_rank=force_full_rank, force_low_rank=force_low_rank,GB_goal=GB_goal)
fastlmm.fit(K0_train=K_train, K1_train=top_k_train, X=covar, y=pheno,mixing=mixing,h2=h2) #iid intersection means when can give the whole covariate and pheno
top_k_test = top_k[test_idx,:] if k > 0 else None
K0_whole_test = K_whole_unittrain[:,test_idx]
nLL = fastlmm.score(K0_whole_test=K0_whole_test,K1_whole_test=top_k_test,X=covar,y=pheno) #iid intersection means when can give the whole covariate and pheno
k_index_to_nLL.append(nLL)
if i_fold > 0:
k_list_in = None
return k_list_in, top_snps, k_index_to_nLL
def reducer_find_best(top_snps_and_k_index_to_nLL_sequence):
#Starts fold_index+all -> k_index -> nll
#Need: k_index -> sum(fold_index -> nll)
k_index_to_sum_nll = None
top_snps_all = None
k_list_in_all = None
for i_fold, (k_list_in, top_snps, k_index_to_nLL) in enumerate(top_snps_and_k_index_to_nLL_sequence):
if k_list_in is not None:
assert k_list_in_all is None, "real assert"
k_list_in_all = k_list_in
k_index_to_sum_nll = np.zeros(len(k_list_in))
if top_snps is not None:
assert top_snps_all is None, "real assert"
top_snps_all = top_snps
if k_index_to_nLL is not None:
assert i_fold < n_folds or n_folds == 1, "real assert"
for k_index, nLL in enumerate(k_index_to_nLL):
k_index_to_sum_nll[k_index] += nLL
#find best # top_snps
best_k = k_list_in_all[np.argmin(k_index_to_sum_nll)]
logging.info("For chrom={0}, best_k={1}".format(test_chr,best_k))
if do_plot: _nll_plot(k_list_in_all, k_index_to_sum_nll)
#Return the top snps from all
result = top_snps_all[:best_k]
return result
i_fold_index_to_top_snps_and_k_index_to_nLL = map_reduce(
_kfold(G_for_chrom.iid_count, n_folds, seed, end_with_all=True),
mapper=mapper_gather_lots,
reducer=reducer_find_best)
return i_fold_index_to_top_snps_and_k_index_to_nLL
def mapper_find_best_given_chrom(test_chr):
G_for_chrom = _K_per_chrom(G, test_chr, G.iid).snpreader
def mapper_gather_lots(i_fold_and_pair):
i_fold, (train_idx, test_idx) = i_fold_and_pair
logging.info(
"Working on GWAS_1K and k search, chrom={0}, i_fold={1}".
format(test_chr, i_fold))
G_train = G_for_chrom[train_idx, :]
#Precompute whole x whole standardized on train
from fastlmm.association.single_snp import _internal_determine_block_size, _block_size_from_GB_goal
min_count = _internal_determine_block_size(
G_for_chrom, None, None, force_full_rank, force_low_rank)
block_size = _block_size_from_GB_goal(GB_goal,
G_for_chrom.iid_count,
min_count)
K_whole_unittrain = _SnpWholeWithTrain(
whole=G_for_chrom,
train_idx=train_idx,
standardizer=Unit(),
block_size=block_size).read()
assert np.array_equal(K_whole_unittrain.iid,
G_for_chrom.iid), "real assert"
K_train = K_whole_unittrain[train_idx]
single_snp_result = single_snp(
test_snps=G_train,
K0=K_train,
pheno=
pheno, #iid intersection means when can give the whole covariate and pheno
covar=covar,
leave_out_one_chrom=False,
GB_goal=GB_goal,
force_full_rank=force_full_rank,
force_low_rank=force_low_rank,
mixing=mixing,
h2=h2,
count_A1=count_A1)
is_all = (i_fold == n_folds) if n_folds > 1 else True
k_list_in = [0] + [
int(k)
for k in k_list if 0 < k and k < len(single_snp_result)
]
if is_all:
top_snps = list(single_snp_result.SNP[:max_k])
else:
top_snps = None
if i_fold == n_folds:
k_index_to_nLL = None
else:
k_index_to_nLL = []
for k in k_list_in:
top_k = G_for_chrom[:,
G_for_chrom.sid_to_index(
single_snp_result.SNP[:k])]
logging.info(
"Working on chr={0}, i_fold={1}, and K_{2}".format(
test_chr, i_fold, k))
top_k_train = top_k[train_idx, :] if k > 0 else None
fastlmm = FastLMM(force_full_rank=force_full_rank,
force_low_rank=force_low_rank,
GB_goal=GB_goal)
fastlmm.fit(
K0_train=K_train,
K1_train=top_k_train,
X=covar,
y=pheno,
mixing=mixing,
h2raw=h2
) #iid intersection means when can give the whole covariate and pheno
top_k_test = top_k[test_idx, :] if k > 0 else None
K0_whole_test = K_whole_unittrain[:, test_idx]
nLL = fastlmm.score(
K0_whole_test=K0_whole_test,
K1_whole_test=top_k_test,
X=covar,
y=pheno
) #iid intersection means when can give the whole covariate and pheno
k_index_to_nLL.append(nLL)
if i_fold > 0:
k_list_in = None
return k_list_in, top_snps, k_index_to_nLL
def reducer_find_best(top_snps_and_k_index_to_nLL_sequence):
#Starts fold_index+all -> k_index -> nll
#Need: k_index -> sum(fold_index -> nll)
k_index_to_sum_nll = None
top_snps_all = None
k_list_in_all = None
for i_fold, (k_list_in, top_snps, k_index_to_nLL) in enumerate(
top_snps_and_k_index_to_nLL_sequence):
if k_list_in is not None:
assert k_list_in_all is None, "real assert"
k_list_in_all = k_list_in
k_index_to_sum_nll = np.zeros(len(k_list_in))
if top_snps is not None:
assert top_snps_all is None, "real assert"
top_snps_all = top_snps
if k_index_to_nLL is not None:
assert i_fold < n_folds or n_folds == 1, "real assert"
for k_index, nLL in enumerate(k_index_to_nLL):
k_index_to_sum_nll[k_index] += nLL
#find best # top_snps
best_k = k_list_in_all[np.argmin(k_index_to_sum_nll)]
logging.info("For chrom={0}, best_k={1}".format(
test_chr, best_k))
if do_plot: _nll_plot(k_list_in_all, k_index_to_sum_nll)
#Return the top snps from all
result = top_snps_all[:best_k]
return result
i_fold_index_to_top_snps_and_k_index_to_nLL = map_reduce(
_kfold(G_for_chrom.iid_count, n_folds, seed,
end_with_all=True),
mapper=mapper_gather_lots,
reducer=reducer_find_best)
return i_fold_index_to_top_snps_and_k_index_to_nLL
