本文整理汇总了Python中pysnptools.snpreader.Bed.read_kernel方法的典型用法代码示例。如果您正苦于以下问题:Python Bed.read_kernel方法的具体用法?Python Bed.read_kernel怎么用?Python Bed.read_kernel使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pysnptools.snpreader.Bed
的用法示例。
在下文中一共展示了Bed.read_kernel方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_some_std
# 需要导入模块: from pysnptools.snpreader import Bed [as 别名]
# 或者: from pysnptools.snpreader.Bed import read_kernel [as 别名]
def test_some_std(self):
k0 = self.snpdata.read_kernel(standardizer=Unit()).val
from pysnptools.kernelreader import SnpKernel
k1 = self.snpdata.read_kernel(standardizer=Unit())
np.testing.assert_array_almost_equal(k0, k1.val, decimal=10)
from pysnptools.snpreader import SnpData
snpdata2 = SnpData(iid=self.snpdata.iid,sid=self.snpdata.sid,pos=self.snpdata.pos,val=np.array(self.snpdata.val))
s = str(snpdata2)
snpdata2.standardize()
s = str(snpdata2)
snpreader = Bed(self.currentFolder + "/examples/toydata",count_A1=False)
k2 = snpreader.read_kernel(standardizer=Unit(),block_size=500).val
np.testing.assert_array_almost_equal(k0, k2, decimal=10)
from pysnptools.standardizer.identity import Identity
from pysnptools.standardizer.diag_K_to_N import DiagKtoN
for dtype in [sp.float64,sp.float32]:
for std in [Unit(),Beta(1,25),Identity(),DiagKtoN()]:
s = str(std)
np.random.seed(0)
x = np.array(np.random.randint(3,size=[60,100]),dtype=dtype)
x2 = x[:,::2]
x2b = np.array(x2)
#LATER what's this about? It doesn't do non-contiguous?
#assert not x2.flags['C_CONTIGUOUS'] and not x2.flags['F_CONTIGUOUS'] #set up to test non contiguous
#assert x2b.flags['C_CONTIGUOUS'] or x2b.flags['F_CONTIGUOUS'] #set up to test non contiguous
#a,b = std.standardize(x2b),std.standardize(x2)
#np.testing.assert_array_almost_equal(a,b)
logging.info("done")
示例2: test_npz
# 需要导入模块: from pysnptools.snpreader import Bed [as 别名]
# 或者: from pysnptools.snpreader.Bed import read_kernel [as 别名]
def test_npz(self):
logging.info("in test_npz")
snpreader = Bed(self.currentFolder + "/../examples/toydata",count_A1=False)
kerneldata1 = snpreader.read_kernel(standardizer=stdizer.Unit())
s = str(kerneldata1)
output = "tempdir/kernelreader/toydata.kernel.npz"
create_directory_if_necessary(output)
KernelNpz.write(output,kerneldata1)
kernelreader2 = KernelNpz(output)
kerneldata2 = kernelreader2.read()
np.testing.assert_array_almost_equal(kerneldata1.val, kerneldata2.val, decimal=10)
logging.info("done with test")
示例3: test_subset
# 需要导入模块: from pysnptools.snpreader import Bed [as 别名]
# 或者: from pysnptools.snpreader.Bed import read_kernel [as 别名]
def test_subset(self):
logging.info("in test_subset")
snpreader = Bed(self.currentFolder + "/../examples/toydata",count_A1=False)
snpkernel = SnpKernel(snpreader,stdizer.Unit())
krsub = snpkernel[::2,::2]
kerneldata1 = krsub.read()
expected = snpreader.read_kernel(stdizer.Unit())[::2].read()
np.testing.assert_array_almost_equal(kerneldata1.val, expected.val, decimal=10)
krsub2 = snpkernel[::2]
kerneldata2 = krsub2.read()
np.testing.assert_array_almost_equal(kerneldata2.val, expected.val, decimal=10)
logging.info("done with test")
示例4: TestFastLMM
# 需要导入模块: from pysnptools.snpreader import Bed [as 别名]
# 或者: from pysnptools.snpreader.Bed import read_kernel [as 别名]
#.........这里部分代码省略.........
self.compare_files(predicted_pheno,"lr2a_"+first_name)
self.compare_files(covar2,"lr2a.cov_"+first_name)
#Predict with model (test on test)
predicted_pheno, covar = fastlmm.predict(K0_whole_test=K0_whole_test, X=covariate_test,count_A1=False) #test on train
output_file = self.file_name("lr2b_"+name)
Dat.write(output_file,predicted_pheno)
covar2 = SnpData(iid=covar.row,sid=covar.col[:,1],val=covar.val) #kludge to write kernel to text format
output_file = self.file_name("lr2b.cov_"+name)
Dat.write(output_file,covar2)
yerr = np.sqrt(np.diag(covar.val))
predicted = predicted_pheno.val
if do_plot:
pylab.plot(covariate_test.val, pheno_test.val,"g.",covariate_test.val, predicted,"r.")
pylab.xlim([-1, 10])
pylab.errorbar(covariate_test.val, predicted,yerr,linestyle='None')
pylab.suptitle(name+": test on test: test X to true target (green) and prediction (red)")
pylab.show()
## Plot y and predicted y (test on train)
#pylab.plot(pheno_test.val,predicted_pheno.val,".")
#pylab.suptitle(name+": test on test: true target to prediction")
#pylab.show()
self.compare_files(predicted_pheno,"lr2b_"+first_name)
self.compare_files(covar2,"lr2b.cov_"+first_name)
def test_str2(self):
logging.info("TestLmmTrain test_str2")
#Standardize train and test together
whole_kernel = self.snpreader_whole.read_kernel(Unit())
train_idx = np.r_[10:self.snpreader_whole.iid_count] # iids 10 and on
test_idx = np.r_[0:10] # the first 10 iids
covariate_train = self.covariate_whole[train_idx,:]
pheno_train = self.pheno_whole[train_idx,:]
K0_train_filename = self.tempout_dir + "/model_str2.kernel.npz"
pstutil.create_directory_if_necessary(K0_train_filename)
from pysnptools.kernelreader import KernelNpz
KernelNpz.write(K0_train_filename,whole_kernel[train_idx].read(order='A',view_ok=True))
fastlmm1 = FastLMM(GB_goal=2).fit(K0_train=K0_train_filename, X=covariate_train, y=pheno_train)
filename = self.tempout_dir + "/model_str2.flm.p"
pstutil.create_directory_if_necessary(filename)
joblib.dump(fastlmm1, filename)
fastlmm2 = joblib.load(filename)
# predict on test set
G0_test = self.snpreader_whole[test_idx,:]
covariate_test = self.covariate_whole[test_idx,:]
predicted_pheno, covar = fastlmm2.predict(K0_whole_test=whole_kernel[:,test_idx].read(order='A',view_ok=True), X=covariate_test,count_A1=False)
output_file = self.file_name("str2")
Dat.write(output_file,predicted_pheno)
#pheno_actual = self.pheno_whole[test_idx,:].read().val[:,0]
#pylab.plot(pheno_actual, predicted_pheno.val,".")
#pylab.show()