def test_sub_matrix(self):
import pysnptools.util as pstutil
np.random.seed(0) # set seed so that results are deterministic
matrix = np.random.rand(12, 7) # create a 12 x 7 ndarray
submatrix = pstutil.sub_matrix(matrix, [0, 2, 11],
[6, 5, 4, 3, 2, 1, 0])
assert matrix[2, 0] == submatrix[
1, 6] #The row # 2 is now #1, the column #0 is now #6.
np.random.seed(0) # set seed so that results are deterministic
matrix = np.random.rand(12, 7, 3) # create a 12 x 7 ndarray
submatrix = pstutil.sub_matrix(matrix, [0, 2, 11],
[6, 5, 4, 3, 2, 1, 0])
assert matrix[2, 0, 1] == submatrix[
1, 6, 1] #The row # 2 is now #1, the column #0 is now #6.
def _apply_sparray_or_slice_to_val(self, val, row_indexer_or_none, col_indexer_or_none, order, dtype, force_python_only):
if (PstReader._is_all_slice(row_indexer_or_none) and PstReader._is_all_slice(col_indexer_or_none) and not force_python_only and
(order == 'A' or (order == 'F' and val.flags['F_CONTIGUOUS']) or (order == 'C' and val.flags['C_CONTIGUOUS'])) and
(dtype is None or val.dtype == dtype)):
return val, True
row_indexer = PstReader._make_sparray_or_slice(row_indexer_or_none)
col_indexer = PstReader._make_sparray_or_slice(col_indexer_or_none)
if not force_python_only:
row_index = PstReader._make_sparray_from_sparray_or_slice(self.row_count, row_indexer)
col_index = PstReader._make_sparray_from_sparray_or_slice(self.col_count, col_indexer)
sub_val = pstutil.sub_matrix(val, row_index, col_index, order=order, dtype=dtype)
return sub_val, False
if PstReader._is_all_slice(row_indexer) or PstReader._is_all_slice(col_indexer):
sub_val = val[row_indexer, col_indexer] #!!is this faster than the C++?
else:
row_index = PstReader._make_sparray_from_sparray_or_slice(self.row_count, row_indexer)
col_index = PstReader._make_sparray_from_sparray_or_slice(self.col_count, col_indexer)
#See http://stackoverflow.com/questions/21349133/numpy-array-integer-indexing-in-more-than-one-dimension
sub_val = val[row_index.reshape(-1,1), col_index]
assert len(sub_val.shape)==2, "Expect result of subsetting to be 2 dimensional"
if not PstReader._array_properties_are_ok(sub_val, order, dtype):
if order is None:
order = "K"
if dtype is None:
dtype = sub_val.dtype
sub_val = sub_val.astype(dtype, order, copy=True)
shares_memory = np.may_share_memory(val, sub_val)
assert(PstReader._array_properties_are_ok(sub_val, order, dtype))
return sub_val, shares_memory
def _apply_sparray_or_slice_to_val(self, val, row_indexer_or_none,
col_indexer_or_none, order, dtype,
force_python_only, num_threads):
dtype = np.dtype(dtype)
if (PstReader._is_all_slice(row_indexer_or_none)
and PstReader._is_all_slice(col_indexer_or_none) and
(order == 'A' or (order == 'F' and val.flags['F_CONTIGUOUS']) or
(order == 'C' and val.flags['C_CONTIGUOUS']))
and (dtype is None or val.dtype == dtype)):
return val, True
row_indexer = PstReader._make_sparray_or_slice(row_indexer_or_none)
col_indexer = PstReader._make_sparray_or_slice(col_indexer_or_none)
if not force_python_only:
row_index = PstReader._make_sparray_from_sparray_or_slice(
self.row_count, row_indexer)
col_index = PstReader._make_sparray_from_sparray_or_slice(
self.col_count, col_indexer)
sub_val = pstutil.sub_matrix(val,
row_index,
col_index,
order=order,
dtype=dtype,
num_threads=num_threads)
return sub_val, False
if PstReader._is_all_slice(row_indexer) or PstReader._is_all_slice(
col_indexer):
sub_val = val[row_indexer,
col_indexer] #!!is this faster than the C++?
else:
row_index = PstReader._make_sparray_from_sparray_or_slice(
self.row_count, row_indexer)
col_index = PstReader._make_sparray_from_sparray_or_slice(
self.col_count, col_indexer)
#See http://stackoverflow.com/questions/21349133/numpy-array-integer-indexing-in-more-than-one-dimension
sub_val = val[row_index.reshape(-1, 1), col_index]
assert len(sub_val.shape) in {
2, 3
}, "Expect result of subsetting to be 2 or 3 dimensional"
if not PstReader._array_properties_are_ok(sub_val, order, dtype):
if order is None:
order = "K"
if dtype is None:
dtype = sub_val.dtype
sub_val = sub_val.astype(dtype, order, copy=True)
shares_memory = np.may_share_memory(val, sub_val)
assert (PstReader._array_properties_are_ok(sub_val, order, dtype))
return sub_val, shares_memory
def _read(self, row_index_or_none, col_index_or_none, order, dtype,
force_python_only, view_ok, num_threads):
self._run_once()
import pysnptools.util as pstutil
if order == 'A':
order = 'F'
dtype = np.dtype(dtype)
row_index_count = len(
row_index_or_none
) if row_index_or_none is not None else self._iid_count # turn to a count of the index positions e.g. all of them
col_index = col_index_or_none if col_index_or_none is not None else np.arange(
self._sid_count
) # turn to an array of index positions, e.g. 0,1,200,2200,10
batch_index = col_index // self._block_size #find the batch index of each index position, e.g. 0,0,0,2,0
val = np.empty((row_index_count, len(col_index), 3),
order=order,
dtype=dtype) #allocate memory for result
list_batch_index = list(set(batch_index))
for ii, i in enumerate(
list_batch_index
): #for each distinct batch index, generate dists #!!!fix up snpgen this way, too with ii
i = int(
i) # convert np.uint64 to python int to avoid uint*int->float
#LATER logging.info("working on distgen batch {0} of {1}".format(ii,len(list_batch_index))) #!!!why does this produce messages like 'working on distgen batch 8 of 2'?
start = i * self._block_size #e.g. 0 (then 2000)
stop = start + self._block_size #e.g. 1000, then 3000
batch_val = self._get_val(start, stop,
dtype) # generate whole batch
a = (
batch_index == i
) #e.g. [True,True,True,False,True], then [False,False,False,True,False]
b = col_index[a] - start #e.g. 0,1,200,10, then 200
val[:,
a, :] = batch_val[:,
b, :] if row_index_or_none is None else pstutil.sub_matrix(
batch_val,
row_index_or_none,
b,
num_threads=num_threads)
return val
def _read(self, row_index_or_none, col_index_or_none, order, dtype,
force_python_only, view_ok):
self._run_once()
import pysnptools.util as pstutil
dtype = np.dtype(dtype)
if order == 'A':
order = 'F'
row_index_count = len(
row_index_or_none
) if row_index_or_none is not None else self._iid_count # turn to a count of the index positions e.g. all of them
col_index = col_index_or_none if col_index_or_none is not None else np.arange(
self._sid_count
) # turn to an array of index positions, e.g. 0,1,200,2200,10
batch_index = col_index // self._block_size #find the batch index of each index position, e.g. 0,0,0,2,0
val = np.empty((row_index_count, len(col_index)),
order=order,
dtype=dtype) #allocate memory for result
list_batch_index = list(set(batch_index))
with log_in_place("working on snpgen batch", logging.INFO) as updater:
for ii, i in enumerate(
list_batch_index
): #for each distinct batch index, generate snps
updater("{0} of {1}".format(ii, len(list_batch_index)))
start = i * self._block_size #e.g. 0 (then 2000)
stop = start + self._block_size #e.g. 1000, then 3000
batch_val = self._get_val2(start,
stop,
order=order,
dtype=dtype) # generate whole batch
a = (
batch_index == i
) #e.g. [True,True,True,False,True], then [False,False,False,True,False]
b = col_index[a] - start #e.g. 0,1,200,10, then 200
val[:,
a] = batch_val[:,
b] if row_index_or_none is None else pstutil.sub_matrix(
batch_val, row_index_or_none, b)
return val
def work_item2(pheno, G_kernel, spatial_coor, spatial_iid, alpha, alpha_power,
xxx_todo_changeme, xxx_todo_changeme1, xxx_todo_changeme2,
just_testing, do_uncorr, do_gxe2, a2):
#########################################
# Load GPS info from filename if that's the way it is given
########################################
(jackknife_index, jackknife_count, jackknife_seed) = xxx_todo_changeme
(permute_plus_index, permute_plus_count,
permute_plus_seed) = xxx_todo_changeme1
(permute_times_index, permute_times_count,
permute_times_seed) = xxx_todo_changeme2
if isinstance(spatial_coor, str):
assert spatial_iid is None, "if spatial_coor is a str, then spatial_iid should be None"
gps_table = pd.read_csv(spatial_coor, delimiter=" ").dropna()
spatial_iid = np.array([(v, v) for v in gps_table["id"].values])
spatial_coor = gps_table[["south_new", "east_new"]].values
#########################################
# Remove any missing values from pheno
########################################
assert pheno.sid_count == 1, "Expect only one pheno in work_item"
pheno = pheno.read()
pheno = pheno[pheno.val[:, 0] == pheno.
val[:, 0], :] #Excludes NaN because NaN is not equal to NaN
#########################################
# Environment: Turn spatial info info a KernelData
#########################################
spatial_val = spatial_similarity(spatial_coor, alpha, power=alpha_power)
E_kernel = KernelData(iid=spatial_iid, val=spatial_val)
#########################################
# Intersect, apply the jackknife or permutation, and then (because we now know the iids) standardize appropriately
#########################################
from pysnptools.util import intersect_apply
G_kernel, E_kernel, pheno = intersect_apply([G_kernel, E_kernel, pheno])
if jackknife_index >= 0:
assert jackknife_count <= G_kernel.iid_count, "expect the number of groups to be less than the number of iids"
assert jackknife_index < jackknife_count, "expect the jackknife index to be less than the count"
m_fold = model_selection.KFold(n_splits=jackknife_count,
shuffle=True,
random_state=jackknife_seed %
4294967295).split(
list(range(G_kernel.iid_count)))
iid_index, _ = _nth(m_fold, jackknife_index)
pheno = pheno[iid_index, :]
G_kernel = G_kernel[iid_index]
E_kernel = E_kernel[iid_index]
if permute_plus_index >= 0:
#We shuffle the val, but not the iid, because that would cancel out.
#Integrate the permute_plus_index into the random.
np.random.seed((permute_plus_seed + permute_plus_index) % 4294967295)
new_index = np.arange(G_kernel.iid_count)
np.random.shuffle(new_index)
E_kernel_temp = E_kernel[new_index].read()
E_kernel = KernelData(
iid=E_kernel.iid,
val=E_kernel_temp.val,
name="permutation {0}".format(permute_plus_index))
pheno = pheno.read().standardize() # defaults to Unit standardize
G_kernel = G_kernel.read().standardize(
) # defaults to DiagKtoN standardize
E_kernel = E_kernel.read().standardize(
) # defaults to DiagKtoN standardize
#########################################
# find h2uncoor, the best mixing weight of pure random noise and G_kernel
#########################################
if not do_uncorr:
h2uncorr, nLLuncorr = np.nan, np.nan
else:
logging.info("Find best h2 for G_kernel")
lmmg = LMM()
lmmg.setK(K0=G_kernel.val)
lmmg.setX(np.ones([G_kernel.iid_count, 1])) # just a bias column
lmmg.sety(pheno.val[:, 0])
if not just_testing:
resg = lmmg.findH2()
h2uncorr, nLLuncorr = resg["h2"], resg["nLL"]
else:
h2uncorr, nLLuncorr = 0, 0
logging.info("just G: h2uncorr: {0}, nLLuncorr: {1}".format(
h2uncorr, nLLuncorr))
#########################################
# Find a2, the best mixing for G_kernel and E_kernel
#########################################
if a2 is None:
logging.info("Find best mixing for G_kernel and E_kernel")
lmm1 = LMM()
lmm1.setK(K0=G_kernel.val, K1=E_kernel.val, a2=0.5)
lmm1.setX(np.ones([G_kernel.iid_count, 1])) # just a bias column
lmm1.sety(pheno.val[:, 0])
if not just_testing:
res1 = lmm1.findA2()
h2, a2, nLLcorr = res1["h2"], res1["a2"], res1["nLL"]
h2corr = h2 * (1 - a2)
e2 = h2 * a2
h2corr_raw = h2
else:
h2corr, e2, a2, nLLcorr, h2corr_raw = 0, 0, .5, 0, 0
logging.info(
"G plus E mixture: h2corr: {0}, e2: {1}, a2: {2}, nLLcorr: {3} (h2corr_raw:{4})"
.format(h2corr, e2, a2, nLLcorr, h2corr_raw))
else:
h2corr, e2, nLLcorr, h2corr_raw = np.nan, np.nan, np.nan, np.nan
#########################################
# Find a2_gxe2, the best mixing for G+E_kernel and the GxE kernel
#########################################
if not do_gxe2:
gxe2, a2_gxe2, nLL_gxe2 = np.nan, np.nan, np.nan
else:
#Create the G+E kernel by mixing according to a2
val = (1 - a2) * G_kernel.val + a2 * E_kernel.val
GplusE_kernel = KernelData(iid=G_kernel.iid,
val=val,
name="{0} G + {1} E".format(1 - a2, a2))
#Don't need to standardize GplusE_kernel because it's the weighted combination of standardized kernels
# Create GxE Kernel and then find the best mixing of it and GplusE
logging.info("Find best mixing for GxE and GplusE_kernel")
val = G_kernel.val * E_kernel.val
if permute_times_index >= 0:
#We shuffle the val, but not the iid, because doing both would cancel out
np.random.seed(
(permute_times_seed + permute_times_index) % 4294967295)
new_index = np.arange(G_kernel.iid_count)
np.random.shuffle(new_index)
val = pstutil.sub_matrix(val, new_index, new_index)
GxE_kernel = KernelData(
iid=G_kernel.iid, val=val, name="GxE"
) # recall that Python '*' is just element-wise multiplication
GxE_kernel = GxE_kernel.standardize()
lmm2 = LMM()
lmm2.setK(K0=GplusE_kernel.val, K1=GxE_kernel.val, a2=0.5)
lmm2.setX(np.ones([G_kernel.iid_count, 1])) # just a bias column
lmm2.sety(pheno.val[:, 0])
if not just_testing:
res2 = lmm2.findA2()
gxe2, a2_gxe2, nLL_gxe2 = res2["h2"], res2["a2"], res2["nLL"]
gxe2 *= a2_gxe2
else:
gxe2, a2_gxe2, nLL_gxe2 = 0, .5, 0
logging.info(
"G+E plus GxE mixture: gxe2: {0}, a2_gxe2: {1}, nLL_gxe2: {2}".
format(gxe2, a2_gxe2, nLL_gxe2))
#########################################
# Return results
#########################################
ret = {
"h2uncorr": h2uncorr,
"nLLuncorr": nLLuncorr,
"h2corr": h2corr,
"h2corr_raw": h2corr_raw,
"e2": e2,
"a2": a2,
"nLLcorr": nLLcorr,
"gxe2": gxe2,
"a2_gxe2": a2_gxe2,
"nLL_gxe2": nLL_gxe2,
"alpha": alpha,
"alpha_power": alpha_power,
"phen": np.array(pheno.sid, dtype='str')[0],
"jackknife_index": jackknife_index,
"jackknife_count": jackknife_count,
"jackknife_seed": jackknife_seed,
"permute_plus_index": permute_plus_index,
"permute_plus_count": permute_plus_count,
"permute_plus_seed": permute_plus_seed,
"permute_times_index": permute_times_index,
"permute_times_count": permute_times_count,
"permute_times_seed": permute_times_seed
}
logging.info("run_line: {0}".format(ret))
return ret
GplusE_kernel = KernelData(iid=G_kernel.iid,
val=val,
name="{0} G + {1} E".format(1 - a2, a2))
#Don't need to standardize GplusE_kernel because it's the weighted combination of standardized kernels
# Create GxE Kernel and then find the best mixing of it and GplusE
logging.info("Find best mixing for GxE and GplusE_kernel")
val = G_kernel.val * E_kernel.val
if permute_times_index >= 0:
#We shuffle the val, but not the iid, because doing both would cancel out
np.random.seed(
(permute_times_seed + permute_times_index) % 4294967295)
new_index = np.arange(G_kernel.iid_count)
np.random.shuffle(new_index)
val = pstutil.sub_matrix(val, new_index, new_index)
GxE_kernel = KernelData(
iid=G_kernel.iid, val=val, name="GxE"
) # recall that Python '*' is just element-wise multiplication
GxE_kernel = GxE_kernel.standardize()
lmm2 = LMM()
lmm2.setK(K0=GplusE_kernel.val, K1=GxE_kernel.val, a2=0.5)
lmm2.setX(np.ones([G_kernel.iid_count, 1])) # just a bias column
lmm2.sety(pheno.val[:, 0])
if not just_testing:
res2 = lmm2.findA2()
gxe2, a2_gxe2, nLL_gxe2 = res2["h2"], res2["a2"], res2["nLL"]
gxe2 *= a2_gxe2
else:
def work_item(arg_tuple):
(
pheno,
G_kernel,
spatial_coor,
spatial_iid,
alpha,
alpha_power, # The main inputs
(jackknife_index, jackknife_count,
jackknife_seed), # Jackknifing and permutations inputs
(permute_plus_index, permute_plus_count, permute_plus_seed),
(permute_times_index, permute_times_count, permute_times_seed),
just_testing,
do_uncorr,
do_gxe2,
a2) = arg_tuple # Shortcutting work
#########################################
# Remove any missing values from pheno
#########################################
pheno = pheno.read()
pheno = pheno[pheno.val[:, 0] == pheno.
val[:, 0], :] #Excludes NaN because NaN is not equal to NaN
#########################################
# Environment: Turn spatial info info a KernelData
#########################################
spatial_val = spatial_similarity(spatial_coor, alpha, power=alpha_power)
E_kernel = KernelData(iid=spatial_iid, val=spatial_val)
#########################################
# Intersect, apply the jackknife or permutation, and then (because we now know the iids) standardize appropriately
#########################################
from pysnptools.util import intersect_apply
G_kernel, E_kernel, pheno = intersect_apply([G_kernel, E_kernel, pheno])
if jackknife_index >= 0:
assert jackknife_count <= G_kernel.iid_count, "expect the number of groups to be less than the number of iids"
assert jackknife_index < jackknife_count, "expect the jackknife index to be less than the count"
m_fold = cross_validation.KFold(n=G_kernel.iid_count,
n_folds=jackknife_count,
shuffle=True,
random_state=jackknife_seed %
4294967295)
iid_index, _ = _nth(m_fold, jackknife_index)
pheno = pheno[iid_index, :]
G_kernel = G_kernel[iid_index]
E_kernel = E_kernel[iid_index]
if permute_plus_index >= 0:
#We shuffle the val, but not the iid, because that would cancel out.
#Integrate the permute_plus_index into the random.
np.random.seed((permute_plus_seed + permute_plus_index) % 4294967295)
new_index = np.arange(G_kernel.iid_count)
np.random.shuffle(new_index)
E_kernel_temp = E_kernel[new_index].read()
E_kernel = KernelData(
iid=E_kernel.iid,
val=E_kernel_temp.val,
parent_string="permutation {0}".format(permute_plus_index))
pheno = pheno.read().standardize() # defaults to Unit standardize
G_kernel = G_kernel.read().standardize(
) # defaults to DiagKtoN standardize
E_kernel = E_kernel.read().standardize(
) # defaults to DiagKtoN standardize
#########################################
# find h2uncoor, the best mixing weight of pure random noise and G_kernel
#########################################
if not do_uncorr:
h2uncorr, nLLuncorr = np.nan, np.nan
else:
logging.info("Find best h2 for G_kernel")
lmmg = LMM()
lmmg.setK(K0=G_kernel.val)
lmmg.setX(np.ones([G_kernel.iid_count, 1])) # just a bias column
lmmg.sety(pheno.val[:, 0])
if not just_testing:
resg = lmmg.findH2()
h2uncorr, nLLuncorr = resg["h2"], resg["nLL"]
else:
h2uncorr, nLLuncorr = 0, 0
logging.info("just G: h2uncorr: {0}, nLLuncorr: {1}".format(
h2uncorr, nLLuncorr))
#########################################
# Find a2, the best mixing for G_kernel and E_kernel
#########################################
if a2 is None:
logging.info("Find best mixing for G_kernel and E_kernel")
lmm1 = LMM()
lmm1.setK(K0=G_kernel.val, K1=E_kernel.val, a2=0.5)
lmm1.setX(np.ones([G_kernel.iid_count, 1])) # just a bias column
lmm1.sety(pheno.val[:, 0])
if not just_testing:
res1 = lmm1.findA2()
h2, a2, nLLcorr = res1["h2"], res1["a2"], res1["nLL"]
h2corr = h2 * (1 - a2)
e2 = h2 * a2
else:
h2corr, e2, a2, nLLcorr = 0, 0, .5, 0
logging.info(
"G plus E mixture: h2corr: {0}, e2: {1}, a2: {2}, nLLcorr: {3}".
format(h2corr, e2, a2, nLLcorr))
else:
h2corr, e2, nLLcorr = np.nan, np.nan, np.nan
#########################################
# Find a2_gxe2, the best mixing for G+E_kernel and the GxE kernel
#########################################
if not do_gxe2:
gxe2, a2_gxe2, nLL_gxe2 = np.nan, np.nan, np.nan
else:
#Create the G+E kernel by mixing according to a2
val = (1 - a2) * G_kernel.val + a2 * E_kernel.val
GplusE_kernel = KernelData(iid=G_kernel.iid,
val=val,
parent_string="{0} G + {1} E".format(
1 - a2, a2))
#Don't need to standardize GplusE_kernel because it's the weighted combination of standardized kernels
# Create GxE Kernel and then find the best mixing of it and GplusE
logging.info("Find best mixing for GxE and GplusE_kernel")
val = G_kernel.val * E_kernel.val
if permute_times_index >= 0:
#We shuffle the val, but not the iid, because doing both would cancel out
np.random.seed(
(permute_times_seed + permute_times_index) % 4294967295)
new_index = np.arange(G_kernel.iid_count)
np.random.shuffle(new_index)
val = pstutil.sub_matrix(val, new_index, new_index)
GxE_kernel = KernelData(
iid=G_kernel.iid, val=val, parent_string="GxE"
) # recall that Python '*' is just element-wise multiplication
GxE_kernel = GxE_kernel.standardize()
lmm2 = LMM()
lmm2.setK(K0=GplusE_kernel.val, K1=GxE_kernel.val, a2=0.5)
lmm2.setX(np.ones([G_kernel.iid_count, 1])) # just a bias column
lmm2.sety(pheno.val[:, 0])
if not just_testing:
res2 = lmm2.findA2()
gxe2, a2_gxe2, nLL_gxe2 = res2["h2"], res2["a2"], res2["nLL"]
gxe2 *= a2_gxe2
else:
gxe2, a2_gxe2, nLL_gxe2 = 0, .5, 0
logging.info(
"G+E plus GxE mixture: gxe2: {0}, a2_gxe2: {1}, nLL_gxe2: {2}".
format(gxe2, a2_gxe2, nLL_gxe2))
#########################################
# Return results
#########################################
ret = {
"h2uncorr": h2uncorr,
"nLLuncorr": nLLuncorr,
"h2corr": h2corr,
"e2": e2,
"a2": a2,
"nLLcorr": nLLcorr,
"gxe2": gxe2,
"a2_gxe2": a2_gxe2,
"nLL_gxe2": nLL_gxe2,
"alpha": alpha,
"alpha_power": alpha_power,
"phen": pheno.sid[0],
"jackknife_index": jackknife_index,
"jackknife_count": jackknife_count,
"jackknife_seed": jackknife_seed,
"permute_plus_index": permute_plus_index,
"permute_plus_count": permute_plus_count,
"permute_plus_seed": permute_plus_seed,
"permute_times_index": permute_times_index,
"permute_times_count": permute_times_count,
"permute_times_seed": permute_times_seed
}
logging.info("run_line: {0}".format(ret))
return ret
def work_item(arg_tuple):
(pheno, G_kernel, spatial_coor, spatial_iid, alpha,alpha_power, # The main inputs
(jackknife_index, jackknife_count, jackknife_seed), # Jackknifing and permutations inputs
(permute_plus_index, permute_plus_count, permute_plus_seed),
(permute_times_index, permute_times_count, permute_times_seed),
just_testing, do_uncorr, do_gxe2, a2) = arg_tuple # Shortcutting work
#########################################
# Remove any missing values from pheno
#########################################
pheno = pheno.read()
pheno = pheno[pheno.val[:,0]==pheno.val[:,0],:] #Excludes NaN because NaN is not equal to NaN
#########################################
# Environment: Turn spatial info info a KernelData
#########################################
spatial_val = spatial_similarity(spatial_coor, alpha, power=alpha_power)
E_kernel = KernelData(iid=spatial_iid,val=spatial_val)
#########################################
# Intersect, apply the jackknife or permutation, and then (because we now know the iids) standardize appropriately
#########################################
from pysnptools.util import intersect_apply
G_kernel, E_kernel, pheno = intersect_apply([G_kernel, E_kernel, pheno])
if jackknife_index >= 0:
assert jackknife_count <= G_kernel.iid_count, "expect the number of groups to be less than the number of iids"
assert jackknife_index < jackknife_count, "expect the jackknife index to be less than the count"
m_fold = cross_validation.KFold(n=G_kernel.iid_count, n_folds=jackknife_count, shuffle=True, random_state=jackknife_seed%4294967295)
iid_index,_ = _nth(m_fold, jackknife_index)
pheno = pheno[iid_index,:]
G_kernel = G_kernel[iid_index]
E_kernel = E_kernel[iid_index]
if permute_plus_index >= 0:
#We shuffle the val, but not the iid, because that would cancel out.
#Integrate the permute_plus_index into the random.
np.random.seed((permute_plus_seed + permute_plus_index)%4294967295)
new_index = np.arange(G_kernel.iid_count)
np.random.shuffle(new_index)
E_kernel_temp = E_kernel[new_index].read()
E_kernel = KernelData(iid=E_kernel.iid,val=E_kernel_temp.val,name="permutation {0}".format(permute_plus_index))
pheno = pheno.read().standardize() # defaults to Unit standardize
G_kernel = G_kernel.read().standardize() # defaults to DiagKtoN standardize
E_kernel = E_kernel.read().standardize() # defaults to DiagKtoN standardize
#########################################
# find h2uncoor, the best mixing weight of pure random noise and G_kernel
#########################################
if not do_uncorr:
h2uncorr, nLLuncorr = np.nan,np.nan
else:
logging.info("Find best h2 for G_kernel")
lmmg = LMM()
lmmg.setK(K0=G_kernel.val)
lmmg.setX(np.ones([G_kernel.iid_count,1])) # just a bias column
lmmg.sety(pheno.val[:,0])
if not just_testing:
resg = lmmg.findH2()
h2uncorr, nLLuncorr = resg["h2"], resg["nLL"]
else:
h2uncorr, nLLuncorr = 0,0
logging.info("just G: h2uncorr: {0}, nLLuncorr: {1}".format(h2uncorr,nLLuncorr))
#########################################
# Find a2, the best mixing for G_kernel and E_kernel
#########################################
if a2 is None:
logging.info("Find best mixing for G_kernel and E_kernel")
lmm1 = LMM()
lmm1.setK(K0=G_kernel.val, K1=E_kernel.val, a2=0.5)
lmm1.setX(np.ones([G_kernel.iid_count,1])) # just a bias column
lmm1.sety(pheno.val[:,0])
if not just_testing:
res1 = lmm1.findA2()
h2, a2, nLLcorr = res1["h2"], res1["a2"], res1["nLL"]
h2corr = h2 * (1-a2)
e2 = h2 * a2
else:
h2corr, e2, a2, nLLcorr = 0,0,.5,0
logging.info("G plus E mixture: h2corr: {0}, e2: {1}, a2: {2}, nLLcorr: {3}".format(h2corr,e2,a2,nLLcorr))
else:
h2corr, e2, nLLcorr = np.nan, np.nan, np.nan
#########################################
# Find a2_gxe2, the best mixing for G+E_kernel and the GxE kernel
#########################################
if not do_gxe2:
gxe2, a2_gxe2, nLL_gxe2 = np.nan, np.nan, np.nan
else:
#Create the G+E kernel by mixing according to a2
val=(1-a2)*G_kernel.val + a2*E_kernel.val
GplusE_kernel = KernelData(iid=G_kernel.iid, val=val,name="{0} G + {1} E".format(1-a2,a2))
#Don't need to standardize GplusE_kernel because it's the weighted combination of standardized kernels
# Create GxE Kernel and then find the best mixing of it and GplusE
logging.info("Find best mixing for GxE and GplusE_kernel")
val=G_kernel.val * E_kernel.val
if permute_times_index >= 0:
#We shuffle the val, but not the iid, because doing both would cancel out
np.random.seed((permute_times_seed + permute_times_index)%4294967295)
new_index = np.arange(G_kernel.iid_count)
np.random.shuffle(new_index)
val = pstutil.sub_matrix(val, new_index, new_index)
GxE_kernel = KernelData(iid=G_kernel.iid, val=val,name="GxE") # recall that Python '*' is just element-wise multiplication
GxE_kernel = GxE_kernel.standardize()
lmm2 = LMM()
lmm2.setK(K0=GplusE_kernel.val, K1=GxE_kernel.val, a2=0.5)
lmm2.setX(np.ones([G_kernel.iid_count,1])) # just a bias column
lmm2.sety(pheno.val[:,0])
if not just_testing:
res2 = lmm2.findA2()
gxe2, a2_gxe2, nLL_gxe2 = res2["h2"], res2["a2"], res2["nLL"]
gxe2 *= a2_gxe2
else:
gxe2, a2_gxe2, nLL_gxe2 = 0,.5,0
logging.info("G+E plus GxE mixture: gxe2: {0}, a2_gxe2: {1}, nLL_gxe2: {2}".format(gxe2, a2_gxe2, nLL_gxe2))
#########################################
# Return results
#########################################
ret = {"h2uncorr": h2uncorr, "nLLuncorr": nLLuncorr, "h2corr": h2corr, "e2":e2, "a2": a2, "nLLcorr": nLLcorr,
"gxe2": gxe2, "a2_gxe2": a2_gxe2, "nLL_gxe2": nLL_gxe2, "alpha": alpha, "alpha_power":alpha_power, "phen": pheno.sid[0],
"jackknife_index": jackknife_index, "jackknife_count":jackknife_count, "jackknife_seed":jackknife_seed,
"permute_plus_index": permute_plus_index, "permute_plus_count":permute_plus_count, "permute_plus_seed":permute_plus_seed,
"permute_times_index": permute_times_index, "permute_times_count":permute_times_count, "permute_times_seed":permute_times_seed
}
logging.info("run_line: {0}".format(ret))
return ret
