def _read_kernel(train, standardizer, block_size=None, order='A', dtype=np.float64, force_python_only=False, view_ok=False, return_trained=False):
'''
The method creates a kernel for the in-memory SNP data. It handles these cases
* No standardization is needed & everything is in memory OR uses the FROM-DISK method
'''
from pysnptools.pstreader import PstReader
#Just do a 'python' dot, if no standardization is needed and everything is the right type
if isinstance(standardizer,Identity) and train.val.dtype == dtype:
ts = time.time()
#is_worth_logging = train.val.shape[0] * train.val.shape[1] * test.val.shape[0] > 1e9
#if is_worth_logging: logging.info(" _read_kernel about to multiply train{0} x test{1}".format(train.val.shape,test.val.shape))
if order == 'F': #numpy's 'dot' always returns 'C' order
K = (train.val.dot(train.val.T)).T
else:
K = train.val.dot(train.val.T)
assert PstReader._array_properties_are_ok(K,order,dtype), "internal error: K is not of the expected order or dtype"
#if is_worth_logging: logging.info(" _read_kernel took %.2f seconds" % (time.time()-ts))
if return_trained:
return K, standardizer
else:
return K
else: #Do things the more general SnpReader way.
return SnpReader._read_kernel(train, standardizer, block_size=block_size, order=order, dtype=dtype, force_python_only=force_python_only,view_ok=view_ok, return_trained=return_trained)
def _read_kernel(train, standardizer, block_size=None, order='A', dtype=np.float64, force_python_only=False, view_ok=False, return_trained=False):
'''
The method creates a kernel for the in-memory SNP data. It handles these cases
* No standardization is needed & everything is in memory OR uses the FROM-DISK method
'''
from pysnptools.pstreader import PstReader
#Just do a 'python' dot, if no standardization is needed and everything is the right type
if isinstance(standardizer,Identity) and train.val.dtype == dtype:
ts = time.time()
#is_worth_logging = train.val.shape[0] * train.val.shape[1] * test.val.shape[0] > 1e9
#if is_worth_logging: logging.info(" _read_kernel about to multiply train{0} x test{1}".format(train.val.shape,test.val.shape))
if order == 'F': #numpy's 'dot' always returns 'C' order
K = (train.val.dot(train.val.T)).T
else:
K = train.val.dot(train.val.T)
assert PstReader._array_properties_are_ok(K,order,dtype), "internal error: K is not of the expected order or dtype"
#if is_worth_logging: logging.info(" _read_kernel took %.2f seconds" % (time.time()-ts))
if return_trained:
return K, standardizer
else:
return K
else: #Do things the more general SnpReader way.
return SnpReader._read_kernel(train, standardizer, block_size=block_size, order=order, dtype=dtype, force_python_only=force_python_only,view_ok=view_ok, return_trained=return_trained)
def test_respect_inputs(self):
np.random.seed(0)
for dtype_start, decimal_start in [(np.float32, 5), (np.float64, 10)]:
for order_start in ['F', 'C', 'A']:
for sid_count in [20, 2]:
snpdataX = SnpData(
iid=[["0", "0"], ["1", "1"], ["2", "2"]],
sid=[str(i) for i in range(sid_count)],
val=np.array(np.random.randint(3, size=[3, sid_count]),
dtype=dtype_start,
order=order_start))
for stdx in [
stdizer.Beta(1, 25),
stdizer.Identity(),
stdizer.Unit()
]:
for snpreader0 in [snpdataX, snpdataX[:, 1:]]:
snpreader1 = snpreader0[1:, :]
refdata0, trained_standardizer = snpreader0.read(
).standardize(stdx,
return_trained=True,
force_python_only=True)
refval0 = refdata0.val.dot(refdata0.val.T)
refdata1 = snpreader1.read().standardize(
trained_standardizer, force_python_only=True
) #LATER why aren't these used?
refval1 = refdata0.val.dot(
refdata1.val.T) #LATER why aren't these used?
for dtype_goal, decimal_goal in [(np.float32, 5),
(np.float64, 10)]:
for order_goal in ['F', 'C', 'A']:
k = snpreader0.read_kernel(
standardizer=stdx,
block_size=1,
order=order_goal,
dtype=dtype_goal)
PstReader._array_properties_are_ok(
k.val, order_goal, dtype_goal)
np.testing.assert_array_almost_equal(
refval0,
k.val,
decimal=min(decimal_start,
decimal_goal))
def test_respect_inputs(self):
np.random.seed(0)
for dtype_start,decimal_start in [(np.float32,5),(np.float64,10)]:
for order_start in ['F','C','A']:
for snp_count in [20,2]:
snpdataX = SnpData(iid=[["0","0"],["1","1"],["2","2"]],sid=[str(i) for i in range(snp_count)],val=np.array(np.random.randint(3,size=[3,snp_count]),dtype=dtype_start,order=order_start))
for stdx in [stdizer.Beta(1,25),stdizer.Identity(),stdizer.Unit()]:
for snpreader0 in [snpdataX,snpdataX[:,1:]]:
snpreader1 = snpreader0[1:,:]
refdata0, trained_standardizer = snpreader0.read().standardize(stdx,return_trained=True,force_python_only=True)
refval0 = refdata0.val.dot(refdata0.val.T)
refdata1 = snpreader1.read().standardize(trained_standardizer,force_python_only=True)
refval1 = refdata0.val.dot(refdata1.val.T)
for dtype_goal,decimal_goal in [(np.float32,5),(np.float64,10)]:
for order_goal in ['F','C','A']:
k = snpreader0.read_kernel(standardizer=stdx,block_size=1,order=order_goal,dtype=dtype_goal)
PstReader._array_properties_are_ok(k.val,order_goal,dtype_goal)
np.testing.assert_array_almost_equal(refval0,k.val, decimal=min(decimal_start,decimal_goal))