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 train_combined_wdspeck_svm(org_list_file, signal="tss", data_path="SRA-seq"):
"""
train a global classifier for multiple organisms
@args org_list_file: organism name in a text file
@type org_list_file: str
@args signal: genomic signal type (default: tss)
@type signal: str
@args data_path: file path for training data points
@type data_path: str
"""
t0 = time.time()
## getting multiple oragnisms
ORG_LIST = get_orgdb(org_list_file)
## loading data
train_examples = []
train_labels = []
for ORG_NAME in ORG_LIST:
local_data_path = "%s/%s/set_1" % (data_path, ORG_NAME) ## FIXME common data path
data = load_examples_from_fasta(signal, ORG_NAME, local_data_path)
assert len(data["examples"]) == len(data["labels"])
## split the data
train_examples.extend(data["examples"])
train_labels.extend(data["labels"])
## set parameters TODO
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_model_%s" % (signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print ("saving the model in file %s" % fname)
time_taken = time.time() - t0
print ("time taken for the experiment: ", time_taken)
return fname
def train_combined_wdspeck_svm(org_list_file, signal="tss", data_path="SRA-seq"):
"""
train a global classifier for multiple organisms
@args org_list_file: organism name in a text file
@type org_list_file: str
@args signal: genomic signal type (default: tss)
@type signal: str
@args data_path: file path for training data points
@type data_path: str
"""
t0 = time.time()
## getting multiple oragnisms
ORG_LIST = get_orgdb(org_list_file)
## loading data
train_examples = []
train_labels = []
for ORG_NAME in ORG_LIST:
local_data_path = "%s/%s/set_1" % (data_path, ORG_NAME) ## FIXME common data path
data = load_examples_from_fasta(signal, ORG_NAME, local_data_path)
assert(len(data["examples"]) == len(data["labels"]))
## split the data
train_examples.extend(data["examples"])
train_labels.extend(data["labels"])
## set parameters TODO
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_model_%s" % (signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print("saving the model in file %s" % fname)
time_taken = time.time() - t0
print("time taken for the experiment: ", time_taken)
return fname
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 train_wdspeck_svm(org_code, signal="tss", data_path="SRA-rnaseq"):
"""
train SVM based on the examples from different sources
@args org_code: organism name (ex: A_thaliana)
@type org_code: str
@args signal: genomic signal type (default: tss)
@type signal: str
@args data_path: file path for training data points
@type data_path: str
"""
t0 = time.time()
## loading data
data = load_examples_from_fasta(signal, org_code, data_path)
assert len(data["examples"]) == len(data["labels"])
## split the data
train_examples = data["examples"]
train_labels = data["labels"]
## set parameters TODO
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_%s_model_%s" % (org_code, signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print ("saving the model in file %s" % fname)
time_taken = time.time() - t0
print ("time taken for the experiment: ", time_taken)
return fname
def train_wdspeck_svm(org_code, signal="tss", data_path="SRA-rnaseq"):
"""
train SVM based on the examples from different sources
@args org_code: organism name (ex: A_thaliana)
@type org_code: str
@args signal: genomic signal type (default: tss)
@type signal: str
@args data_path: file path for training data points
@type data_path: str
"""
t0 = time.time()
## loading data
data = load_examples_from_fasta(signal, org_code, data_path)
assert(len(data["examples"]) == len(data["labels"]))
## split the data
train_examples = data["examples"]
train_labels = data["labels"]
## set parameters TODO
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_%s_model_%s" % (org_code, signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print("saving the model in file %s" % fname)
time_taken = time.time() - t0
print("time taken for the experiment: ", time_taken)
return fname
def train_shifted_wdk_svm(org_code, signal="tss", data_path="SRA-rnaseq"):
"""
train SVM based on the examples from different sources
"""
import time
t0 = time.time()
## loading data
data = load_examples_from_fasta(signal, org_code, data_path)
assert len(data["examples"]) == len(data["labels"])
## split the data
train_examples = data["examples"]
train_labels = data["labels"]
## set parameters
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_%s_model_%s" % (org_code, signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print ("saving the model in file %s" % fname)
time_taken = time.time() - t0
print ("time taken for the experiment: ", time_taken)
return fname
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)
