def evaluate_free_param(f_integration, f_bench, bin_size, median_y_n,
f_ens_red):
# define log
logger = logging.getLogger('evaluation free parameter')
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
logger.addHandler(ch)
# determine the number of free parameter to evaluate
f = open(f_integration, 'r')
head = f.readline()
nb_d = len(head.rstrip().split('\t')) - 2
logger.debug("number of free parameter", nb_d)
# gene pair in bench
sem_sim = data.read_sem_sim(f_bench)
# comparison phenotypic bench vs integrated datase
for d in range(1, nb_d + 1):
# keep only gene pairs with phenotypic semantic similarity score
f_out1 = f_integration + "_d" + str(d)
data.report_pair_with_sem_sim(f_integration, f_out1, sem_sim, d)
# sort gene pair by value
f_out2 = f_integration + "_d" + str(d) + ".ord"
list_arg = "python $AP_PLN_HOME/src/scripts_python/sort_gene_pairs_by_value/sort_pair_value.py %s %s" % (
f_out1, f_out2)
proc = subprocess.Popen(list_arg, stdout=subprocess.PIPE, shell=True)
(out, err) = proc.communicate()
[logger.debug(val) for val in out.split("\n")]
if err is not None: logger.error(err)
os.system("rm %s" % f_out1)
# scale dataset
f_out3 = f_integration + "_d" + str(d) + ".ord.scale"
list_arg = "python $AP_PLN_HOME/src/scripts_python/scale_dataset/scale_dataset.py %s %s" % (
f_out2, f_out3)
proc = subprocess.Popen(list_arg, stdout=subprocess.PIPE, shell=True)
(out, err) = proc.communicate()
[logger.debug(val) for val in out.split("\n")]
if err is not None: logger.error(err)
os.system("rm %s" % f_out2)
# gene pair value vs benchmark
f_out4 = f_integration + "_d" + str(d) + "_bench"
list_arg = "python $AP_PLN_HOME/src/scripts_python/bench_versus_dataset/eval_with_scale.py %s %s %s %s %s %s" % (
f_ens_red, f_bench, f_out3, f_out4, bin_size, median_y_n)
proc = subprocess.Popen(list_arg, stdout=subprocess.PIPE, shell=True)
(out, err) = proc.communicate()
[logger.debug(val) for val in out.split("\n")]
if err is not None: logger.error(err)
def evaluate_free_param(f_integration,f_bench,bin_size,median_y_n,f_ens_red):
# define log
logger = logging.getLogger('evaluation free parameter')
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
logger.addHandler(ch)
# determine the number of free parameter to evaluate
f=open(f_integration,'r')
head=f.readline()
nb_d=len(head.rstrip().split('\t'))-2
logger.debug("number of free parameter",nb_d)
# gene pair in bench
sem_sim=data.read_sem_sim(f_bench)
# comparison phenotypic bench vs integrated datase
for d in range(1,nb_d+1):
# keep only gene pairs with phenotypic semantic similarity score
f_out1=f_integration+"_d"+str(d)
data.report_pair_with_sem_sim(f_integration,f_out1,sem_sim,d)
# sort gene pair by value
f_out2=f_integration+"_d"+str(d)+".ord"
list_arg="python $AP_PLN_HOME/src/scripts_python/sort_gene_pairs_by_value/sort_pair_value.py %s %s" % (f_out1,f_out2)
proc = subprocess.Popen(list_arg, stdout=subprocess.PIPE, shell=True)
(out, err) = proc.communicate()
[logger.debug(val) for val in out.split("\n")]
if err is not None: logger.error(err)
os.system("rm %s" % f_out1)
# scale dataset
f_out3=f_integration+"_d"+str(d)+".ord.scale"
list_arg="python $AP_PLN_HOME/src/scripts_python/scale_dataset/scale_dataset.py %s %s" % (f_out2,f_out3)
proc = subprocess.Popen(list_arg, stdout=subprocess.PIPE, shell=True)
(out, err) = proc.communicate()
[logger.debug(val) for val in out.split("\n")]
if err is not None: logger.error(err)
os.system("rm %s" % f_out2)
# gene pair value vs benchmark
f_out4=f_integration+"_d"+str(d)+"_bench"
list_arg="python $AP_PLN_HOME/src/scripts_python/bench_versus_dataset/eval_with_scale.py %s %s %s %s %s %s" % (f_ens_red,f_bench,f_out3,f_out4,bin_size,median_y_n)
proc = subprocess.Popen(list_arg, stdout=subprocess.PIPE, shell=True)
(out, err) = proc.communicate()
[logger.debug(val) for val in out.split("\n")]
if err is not None: logger.error(err)
# determine the number of free parameter to evaluate
f=open(f_integration,'r')
head=f.readline()
nb_d=len(head.rstrip().split('\t'))-2
print "number of free parameter",nb_d
# gene pair in bench
sem_sim=data.read_sem_sim(f_bench)
# comparison phenotypic bench vs integrated datase
for d in range(1,nb_d+1):
# keep only gene pairs with phenotypic semantic similarity score
f_out1=f_integration+"_d"+str(d)
data.report_pair_with_sem_sim(f_integration,f_out1,sem_sim,d)
# sort gene pair by value
f_out2=f_integration+"_d"+str(d)+".ord"
os.system("python ../sort_gene_pairs_by_value/sort_pair_value.py %s %s" % (f_out1,f_out2))
os.system("rm %s" % f_out1)
# scale dataset
f_out3=f_integration+"_d"+str(d)+".ord.scale"
os.system("python ../scale_dataset/scale_dataset.py %s %s" % (f_out2,f_out3))
os.system("rm %s" % f_out2)
# gene pair value vs benchmark
f_out4=f_integration+"_d"+str(d)+"_bench"
print(os.system("python ../bench_versus_dataset/eval_with_scale.py %s %s %s %s %s %s" % (f_ens_red,f_bench,f_out3,f_out4,bin_size,median_y_n)))
os.system("python ../bench_versus_dataset/eval_with_scale.py %s %s %s %s %s %s" % (f_ens_red,f_bench,f_out3,f_out4,bin_size,median_y_n))
# determine the number of free parameter to evaluate
f = open(f_integration, 'r')
head = f.readline()
nb_d = len(head.rstrip().split('\t')) - 2
print "number of free parameter", nb_d
# gene pair in bench
sem_sim = data.read_sem_sim(f_bench)
# comparison phenotypic bench vs integrated datase
for d in range(1, nb_d + 1):
# keep only gene pairs with phenotypic semantic similarity score
f_out1 = f_integration + "_d" + str(d)
data.report_pair_with_sem_sim(f_integration, f_out1, sem_sim, d)
# sort gene pair by value
f_out2 = f_integration + "_d" + str(d) + ".ord"
os.system("python ../sort_gene_pairs_by_value/sort_pair_value.py %s %s" %
(f_out1, f_out2))
os.system("rm %s" % f_out1)
# scale dataset
f_out3 = f_integration + "_d" + str(d) + ".ord.scale"
os.system("python ../scale_dataset/scale_dataset.py %s %s" %
(f_out2, f_out3))
os.system("rm %s" % f_out2)
# gene pair value vs benchmark
f_out4 = f_integration + "_d" + str(d) + "_bench"
