def calculate_pred_score(svm_file, org, example_type="pos", signal="tss", data_path="SRA-rnaseq"):
"""
calculate svm prediction score around the true signal site
"""
local = False ## switch between local and compute cluster
## cluster compute options
cluster_resource = {'pvmem':'8gb', 'pmem':'8gb', 'mem':'8gb', 'vmem':'8gb','ppn':'1', 'nodes':'1', 'walltime':'24:00:00'}
#cluster_resource = {'mem':'6000', 'nodes':'1', 'walltime':'08:00'}
num_seq_ex = 10 ## number of sequences are in single job
center_offset = 500 ## nearby regions FIXME
args_req_list = data_process_depot(svm_file, org, example_type, signal, data_path, num_seq_ex)
intm_ret = pg.pg_map(predict_site_region, args_req_list, param=cluster_resource, local=local, maxNumThreads=1, mem="8gb")
print("Done with calculating the score for center region of example sequences")
pred_out_val = reduce_pred_score(intm_ret)
print("Done with collecting scores from different workers")
## save the scores
fname = "%s_%s_ex_pred_score_%s" % (signal, example_type, uuid.uuid1())
compressed_pickle.save(fname, pred_out_val)
print("saving the scores in file %s" % fname)
def setup_splits(signal, method_name, method, param, num_folds, test_size,
random_state):
"""
splitting the example data into train/test/validation group
"""
data = data_loader.load_all(signal)
sizes = dict((org, len(data[org]["labels"])) for org in data.keys())
# set up splitting strategy
kf = MultitaskShuffleSplitThreeWay(sizes,
n_iter=num_folds,
indices=True,
test_size=test_size * 2,
random_state=random_state)
param_grid = list(ParameterGrid(param))
argument_list = []
for fold_idx, (train_idx, dev_idx, test_idx) in enumerate(kf):
for grid_idx, grid_point in enumerate(param_grid):
arg = [
signal, method, fold_idx, train_idx, dev_idx, test_idx,
grid_idx, grid_point
]
argument_list.append(arg)
local = False
max_num_threads = 2
if method_name in ['union', 'individual']:
param = {
'vmem': '4gb',
'pvmem': '4gb',
'pmem': '4gb',
'mem': '4gb',
'ppn': '1',
'nodes': '1',
'walltime': '2:00:00'
}
intermediate_ret = pg.pg_map(compute_core,
argument_list,
param=param,
local=local,
maxNumThreads=1,
mem="4gb")
#import ipdb
#ipdb.set_trace()
print "DONE with computation"
flat_intermediate = list(chain.from_iterable(intermediate_ret))
perf_dev, perf_test = reduce_result(flat_intermediate)
print "DONE reducing"
return perf_dev, perf_test
def calculate_pred_score(svm_file,
org,
example_type="pos",
signal="tss",
data_path="SRA-rnaseq"):
"""
calculate svm prediction score around the true signal site
"""
local = False ## switch between local and compute cluster
## cluster compute options
cluster_resource = {
'pvmem': '8gb',
'pmem': '8gb',
'mem': '8gb',
'vmem': '8gb',
'ppn': '1',
'nodes': '1',
'walltime': '24:00:00'
}
#cluster_resource = {'mem':'6000', 'nodes':'1', 'walltime':'08:00'}
num_seq_ex = 10 ## number of sequences are in single job
center_offset = 500 ## nearby regions FIXME
args_req_list = data_process_depot(svm_file, org, example_type, signal,
data_path, num_seq_ex)
intm_ret = pg.pg_map(predict_site_region,
args_req_list,
param=cluster_resource,
local=local,
maxNumThreads=1,
mem="8gb")
print(
"Done with calculating the score for center region of example sequences"
)
pred_out_val = reduce_pred_score(intm_ret)
print("Done with collecting scores from different workers")
## save the scores
fname = "%s_%s_ex_pred_score_%s" % (signal, example_type, uuid.uuid1())
compressed_pickle.save(fname, pred_out_val)
print("saving the scores in file %s" % fname)
def shift_signal_position(svm_file, org, example_type="pos", signal="tss", data_path="SRA-rnaseq"):
"""
manually look at the position around the original position
"""
local = False ## switch between local and compute cluster
## cluster compute options
cluster_resource = {'pvmem':'4gb', 'pmem':'4gb', 'mem':'4gb', 'vmem':'4gb','ppn':'1', 'nodes':'1', 'walltime':'24:00:00'}
num_seq_ex = 2 ## number of sequences are in a single job
args_req_list = data_process_depot(svm_file, org, example_type, signal, data_path, num_seq_ex)
## job dispatching
intm_ret = pg.pg_map(recenter_examples, args_req_list, param=cluster_resource, local=local, maxNumThreads=1, mem="4gb")
print("Done with trimming example sequences")
fixed_example_seq = reduce_modified_seq(intm_ret)
print("Done with collecting the trimmed examples")
write_fasta_rec(fixed_example_seq, signal, example_type)
print("Done with writing examples in fasta format")
def shift_signal_position(svm_file,
org,
example_type="pos",
signal="tss",
data_path="SRA-rnaseq"):
"""
manually look at the position around the original position
"""
local = False ## switch between local and compute cluster
## cluster compute options
cluster_resource = {
'pvmem': '4gb',
'pmem': '4gb',
'mem': '4gb',
'vmem': '4gb',
'ppn': '1',
'nodes': '1',
'walltime': '24:00:00'
}
num_seq_ex = 2 ## number of sequences are in a single job
args_req_list = data_process_depot(svm_file, org, example_type, signal,
data_path, num_seq_ex)
## job dispatching
intm_ret = pg.pg_map(recenter_examples,
args_req_list,
param=cluster_resource,
local=local,
maxNumThreads=1,
mem="4gb")
print("Done with trimming example sequences")
fixed_example_seq = reduce_modified_seq(intm_ret)
print("Done with collecting the trimmed examples")
write_fasta_rec(fixed_example_seq, signal, example_type)
print("Done with writing examples in fasta format")
def setup_splits(signal, method_name, method, param, num_folds, test_size, random_state):
"""
splitting the example data into train/test/validation group
"""
data = data_loader.load_all(signal)
sizes = dict((org, len(data[org]["labels"])) for org in data.keys())
# set up splitting strategy
kf = MultitaskShuffleSplitThreeWay(sizes, n_iter=num_folds, indices=True, test_size=test_size*2, random_state=random_state)
param_grid = list(ParameterGrid(param))
argument_list = []
for fold_idx, (train_idx, dev_idx, test_idx) in enumerate(kf):
for grid_idx, grid_point in enumerate(param_grid):
arg = [signal, method, fold_idx, train_idx, dev_idx, test_idx, grid_idx, grid_point]
argument_list.append(arg)
local = False
max_num_threads = 2
if method_name in ['union', 'individual']:
param = {'vmem':'4gb', 'pvmem':'4gb', 'pmem':'4gb', 'mem':'4gb', 'ppn':'1', 'nodes':'1', 'walltime':'2:00:00'}
intermediate_ret = pg.pg_map(compute_core, argument_list, param=param, local=local, maxNumThreads=1, mem="4gb")
#import ipdb
#ipdb.set_trace()
print "DONE with computation"
flat_intermediate = list(chain.from_iterable(intermediate_ret))
perf_dev, perf_test = reduce_result(flat_intermediate)
print "DONE reducing"
return perf_dev, perf_test
def manual_pos_shift(svm_file, org, signal="tss", data_path="SRA-rnaseq"):
"""
manually look at the position around the original position
"""
## loading data
data = load_examples_from_fasta(signal, org, data_path)
assert len(data["examples"]) == len(data["labels"])
## unpack the model
import bz2
import cPickle
fh = bz2.BZ2File(svm_file, "rb")
model = cPickle.load(fh)
fh.close()
## getting the model information
center_pos = model.param["center_pos"]
center_offset = model.param["center_offset"]
print ("model - center pos: %i, center reg: %i" % (center_pos, center_offset))
start_scan = center_pos - center_offset
stop_scan = center_pos + center_offset
cnt = 0
data_set = []
argument_list = []
label_type = -1 ## label_type will be +1/-1
## get the individual examples to recenter the signal position manually
for idx, single_example in enumerate(data["examples"]):
datum = [single_example]
label_info = data["labels"][idx]
if label_info != label_type:
cnt += 1
if cnt % 10 == 0: ## packing 10 seq to one job
data_set.append(datum)
arg = [start_scan, stop_scan, model, data_set]
argument_list.append(arg)
data_set = []
else:
data_set.append(datum)
local = False
cluster_resource = {
"pvmem": "4gb",
"pmem": "4gb",
"mem": "4gb",
"vmem": "4gb",
"ppn": "1",
"nodes": "1",
"walltime": "4:00:00",
}
task_type = 0 # 1 recenter seq, 0 predict score
if task_type:
intm_ret = pg.pg_map(
predict_and_recenter, argument_list, param=cluster_resource, local=local, maxNumThreads=2, mem="4gb"
)
print "Done with computation"
fixed_example_seq = reduce_modified_seq(intm_ret)
print "Done reducing the results"
write_fasta_rec(fixed_example_seq, signal)
else:
intm_ret = pg.pg_map(
predict_around_region, argument_list, param=cluster_resource, local=local, maxNumThreads=2, mem="4gb"
)
print "Done with computation"
pred_out_val = reduce_pred_score(intm_ret)
print "Done reducing the results"
## save the scores
fname = "%s_pred_score_%s" % (signal, uuid.uuid1())
compressed_pickle.save(fname, pred_out_val)
print ("saving the score in file %s" % fname)
