def test_old(self):
do_plot = False
from fastlmm.feature_selection.feature_selection_two_kernel import FeatureSelectionInSample
from pysnptools.util import intersect_apply
logging.info("TestSingleSnpAllPlusSelect test_old")
bed_fn = self.pythonpath + "/tests/datasets/synth/all.bed"
pheno_fn = self.pythonpath + "/tests/datasets/synth/pheno_10_causals.txt"
cov_fn = self.pythonpath + "/tests/datasets/synth/cov.txt"
#load data
###################################################################
snp_reader = Bed(bed_fn, count_A1=False)
pheno = Pheno(pheno_fn)
cov = Pheno(cov_fn)
# intersect sample ids
snp_reader, pheno, cov = intersect_apply([snp_reader, pheno, cov])
# read in snps
# partition snps on chr5 vs rest
test_chr = 5
G0 = snp_reader[:, snp_reader.pos[:, 0] != test_chr].read(
order='C').standardize()
test_snps = snp_reader[:, snp_reader.pos[:, 0] == test_chr].read(
order='C').standardize()
y = pheno.read().val[:, 0]
y -= y.mean()
y /= y.std()
# load covariates
X_cov = cov.read().val
X_cov.flags.writeable = False
# invoke feature selection to learn which SNPs to use to build G1
logging.info(
"running feature selection conditioned on background kernel")
# partition data into the first 50 SNPs on chr1 and all but chr1
select = FeatureSelectionInSample(max_log_k=7,
n_folds=7,
order_by_lmm=True,
measure="ll",
random_state=42)
best_k, feat_idx, best_mix, best_delta = select.run_select(G0.val,
G0.val,
y,
cov=X_cov)
# plot out of sample error
if do_plot: select.plot_results(measure="ll")
# select.plot_results(measure="mse")
# print results
logging.info("best_k:{0}".format(best_k))
logging.info("best_mix:{0}".format(best_mix))
logging.info("best_delta:{0}".format(best_delta))
###############################
# use selected SNPs to build G1
logging.info(feat_idx)
G1 = G0[:, feat_idx]
output_file_name = self.file_name("old")
results_df = single_snp(test_snps,
pheno,
G0=G0,
G1=G1,
mixing=best_mix,
h2=None,
leave_out_one_chrom=False,
output_file_name=output_file_name,
count_A1=False)
logging.info("results:")
logging.info("#" * 40)
logging.info(results_df.head())
self.compare_files(results_df, "old")
def test_old(self):
do_plot = False
from fastlmm.feature_selection.feature_selection_two_kernel import FeatureSelectionInSample
from pysnptools.util import intersect_apply
logging.info("TestSingleSnpAllPlusSelect test_old")
bed_fn = self.pythonpath + "/tests/datasets/synth/all.bed"
pheno_fn = self.pythonpath + "/tests/datasets/synth/pheno_10_causals.txt"
cov_fn = self.pythonpath + "/tests/datasets/synth/cov.txt"
#load data
###################################################################
snp_reader = Bed(bed_fn)
pheno = Pheno(pheno_fn)
cov = Pheno(cov_fn)
# intersect sample ids
snp_reader, pheno, cov = intersect_apply([snp_reader, pheno, cov])
# read in snps
# partition snps on chr5 vs rest
test_chr = 5
G0 = snp_reader[:,snp_reader.pos[:,0] != test_chr].read(order='C').standardize()
test_snps = snp_reader[:,snp_reader.pos[:,0] == test_chr].read(order='C').standardize()
y = pheno.read().val[:,0]
y -= y.mean()
y /= y.std()
# load covariates
X_cov = cov.read().val
X_cov.flags.writeable = False
# invoke feature selection to learn which SNPs to use to build G1
logging.info("running feature selection conditioned on background kernel")
# partition data into the first 50 SNPs on chr1 and all but chr1
select = FeatureSelectionInSample(max_log_k=7, n_folds=7, order_by_lmm=True, measure="ll", random_state=42)
best_k, feat_idx, best_mix, best_delta = select.run_select(G0.val, G0.val, y, cov=X_cov)
# plot out of sample error
if do_plot: select.plot_results(measure="ll")
# select.plot_results(measure="mse")
# print results
logging.info("best_k:{0}".format(best_k))
logging.info("best_mix:{0}".format(best_mix))
logging.info("best_delta:{0}".format(best_delta))
###############################
# use selected SNPs to build G1
logging.info(feat_idx)
G1 = G0[:,feat_idx]
output_file_name = self.file_name("old")
results_df = single_snp(test_snps, pheno, G0=G0, G1=G1, mixing=best_mix, h2=None,leave_out_one_chrom=False,output_file_name=output_file_name)
logging.info("results:")
logging.info("#"*40)
logging.info(results_df.head())
self.compare_files(results_df,"old")
