def run(self, name, datafiles, goldnet_file):
import numpy
os.chdir(os.environ["gene_path"])
datastore = ReadData(datafiles[0], "steadystate")
for file in datafiles[1:]:
datastore.combine(ReadData(file, "steadystate"))
datastore.normalize()
settings = {}
settings = ReadConfig(settings)
# TODO: CHANGE ME
settings["global"]["working_dir"] = os.getcwd() + '/'
# Setup job manager
print "Starting new job manager"
jobman = JobManager(settings)
# Make GENIE3 jobs
genie3 = GENIE3()
genie3.setup(datastore, settings, name)
print "Queuing job..."
jobman.queueJob(genie3)
print jobman.queue
print "Running queue..."
jobman.runQueue()
jobman.waitToClear()
print "Queue finished"
job = jobman.finished[0]
print job.alg.gene_list
print job.alg.read_output(settings)
jobnet = job.alg.network
print "PREDICTED NETWORK:"
print job.alg.network.network
print jobnet.original_network
if goldnet_file != None:
goldnet = Network()
goldnet.read_goldstd(goldnet_file)
print "GOLD NETWORK:"
print goldnet.network
print jobnet.analyzeMotifs(goldnet).ToString()
print jobnet.calculateAccuracy(goldnet)
return jobnet.original_network
def run(self, ts_file, name=None, delta_t=30):
os.chdir(os.environ["gene_path"])
print "Reading in knockout data"
timeseries_storage = ReadData(ts_file, "timeseries")
settings = {}
settings = ReadConfig(settings)
# TODO: CHANGE ME
settings["global"]["working_dir"] = os.getcwd() + "/"
# Setup job manager
print "Starting new job manager"
jobman = JobManager(settings)
# Make Banjo jobs
banjojob = Banjo()
if delta_t != None:
settings["global"]["time_series_delta_t"] = int(delta_t)
else:
settings["global"]["time_series_delta_t"] = 30
if name != None:
banjojob.setup(timeseries_storage, settings, name)
else:
banjojob.setup(timeseries_storage, settings)
print "Queuing job..."
jobman.queueJob(banjojob)
print jobman.queue
print "Running queue..."
jobman.runQueue()
jobman.waitToClear()
print "Queue finished"
job = jobman.finished[0]
print job.alg.gene_list
print job.alg.read_output(settings)
jobnet = job.alg.network
print "PREDICTED NETWORK:"
# print job.alg.network.network
# print jobnet.original_network
return jobnet.original_network
def run(self, kofile, tsfile, wtfile, datafiles, name, goldnet_file, normalize=False):
os.chdir(os.environ["gene_path"])
knockout_storage = ReadData(kofile, "knockout")
print "Reading in knockout data"
wildtype_storage = ReadData(wtfile, "steadystate")
if datafiles == []:
other_storage = None
else:
other_storage = ReadData(datafiles[0], "steadystate")
for file in datafiles[1:]:
other_storage.combine(ReadData(file, "steadystate"))
timeseries_storage = None
if tsfile != None:
timeseries_storage = ReadData(tsfile, "timeseries")
#for ts in timeseries_storage:
#ts.normalize()
#if normalize:
#knockout_storage.normalize()
#wildtype_storage.normalize()
#other_storage.normalize()
settings = {}
settings = ReadConfig(settings)
# TODO: CHANGE ME
settings["global"]["working_dir"] = os.getcwd() + '/'
# Setup job manager
print "Starting new job manager"
jobman = JobManager(settings)
# Make inferelator jobs
inferelatorjob = inferelator()
inferelatorjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, other_storage, name)
print "Queuing job..."
jobman.queueJob(inferelatorjob)
print jobman.queue
print "Running queue..."
jobman.runQueue()
jobman.waitToClear()
print "Queue finished"
job = jobman.finished[0]
#print job.alg.gene_list
#print job.alg.read_output(settings)
jobnet = job.alg.network
#print "PREDICTED NETWORK:"
#print job.alg.network.network
print jobnet.original_network
if goldnet_file != None:
goldnet = Network()
goldnet.read_goldstd(goldnet_file)
#print "GOLD NETWORK:"
#print goldnet.network
#print jobnet.analyzeMotifs(goldnet).ToString()
print jobnet.calculateAccuracy(goldnet)
import AnalyzeResults
tprs, fprs, rocs = AnalyzeResults.GenerateMultiROC(jobman.finished, goldnet )
ps, rs, precs = AnalyzeResults.GenerateMultiPR(jobman.finished, goldnet)
print "Area Under ROC"
print rocs
print "Area Under PR"
print precs
return jobnet.original_network
#for i, exp in enumerate(pert_baseline.experiments):
## For each experiment, replace the value with the diff between base
## and dex data
#pert = pert_data.experiments[i]
#for gene1 in pert_baseline.gene_list:
#baseval = exp.ratios[gene1]
#pertval = pert.ratios[gene1]
#pert.ratios[gene1] = pertval-baseval
#print gene1, baseval, pertval, pert.ratios[gene1]
genie3job = GENIE3()
genie3job.setup(ko_pert_data["combined"], settings, "Genie3_KO_Mult")
jobman.queueJob(genie3job)
both_genie3 = genie3job
genie3job = GENIE3()
genie3job.setup(pert_data["multifactorial_data"], settings, "Genie3_Mult_Only")
jobman.queueJob(genie3job)
pert_genie3 = genie3job
genie3job = GENIE3()
genie3job.setup(pert_data["knockout_data"], settings, "Genie3_KO_Only")
jobman.queueJob(genie3job)
ko_genie3 = genie3job
mczjob = MCZ()
mczjob.setup(ko_pert_data["knockout_data"], ko_pert_data["ss_data"], settings, None, ko_pert_data["multifactorial_data"], "MCZ-KO_Mult")
jobman.queueJob(mczjob)
settings["global"]["experiment_name"] + "-" + t + "/" os.mkdir(settings["global"]["output_dir"]) # Read in the gold standard network # Read in the gold standard network goldnet = Network() #goldnet.read_goldstd(settings["global"]["large_network_goldnet_file"]) ko_file, kd_file, ts_file, wt_file, mf_file, goldnet = get_example_data_files(sys.argv[1], settings) # Read data into program # Where the format is "FILENAME" "DATATYPE" knockout_storage = ReadData(ko_file[0], "knockout") knockdown_storage = ReadData(kd_file[0], "knockdown") timeseries_storage = ReadData(ts_file[0], "timeseries") wildtype_storage = ReadData(wt_file[0], "wildtype") # Setup job manager jobman = JobManager(settings) clusterjob = Cmonkey() clusterjob.setup(knockout_storage, settings) jobman.queueJob(clusterjob) jobman.runQueue() jobman.waitToClear()
ts_file = settings["global"]["dream4100_network_timeseries_file"].split()
wt_file = settings["global"]["dream4100_network_wildtype_file"].split()
# Read data into program
# Where the format is "FILENAME" "DATATYPE"
knockout_storage = ReadData(ko_file[0], "knockout")
knockdown_storage = ReadData(kd_file[0], "knockdown")
timeseries_storage = ReadData(ts_file[0], "timeseries")
wildtype_storage = ReadData(wt_file[0], "wildtype")
# Setup job manager
jobman = JobManager(settings)
# Make NIR jobs
min_restk = max(len(knockout_storage.gene_list) / 5, 3)
max_restk = len(knockout_storage.gene_list) / 2 + 1
rest_list = list(set([3,5,20,21] + [i for i in range(min_restk, max_restk)]))
rest_list = [3,5,10,15,12,20,21]
for i in rest_list:
nirjob = NIR()
nirjob.setup(knockout_storage, settings, "NIR_K="+str(i), 5, i)
jobman.queueJob(nirjob)
print jobman.queue
jobman.runQueue()
jobman.waitToClear()
SaveResults(jobman.finished, goldnet, settings, "Overall", 4)
# Read data into program # Where the format is "FILENAME" "DATATYPE" mf_storage = ReadData(mf_file[0], "multifactorial") knockout_storage = ReadData(ko_file[0], "knockout") knockdown_storage = ReadData(kd_file[0], "knockdown") wildtype_storage = ReadData(wt_file[0], "wildtype") timeseries_storage = ReadData(ts_file[0], "timeseries") gene_list = knockout_storage.gene_list # Setup job manager jobman = JobManager(settings) # MCZ mczjob = MCZ() mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "MCZ") jobman.queueJob(mczjob) # CLR clrjob = CLR() clrjob.setup(knockout_storage, settings, "CLR", "plos", 6) jobman.queueJob(clrjob) # GENIE3 mf_storage.combine(knockout_storage) mf_storage.combine(wildtype_storage) mf_storage.combine(knockdown_storage) genie3job = GENIE3() genie3job.setup(mf_storage, settings, "GENIE3") jobman.queueJob(genie3job) ## TLCLR
goldnet.read_goldstd("algorithms/genenetweaver/InSilicoSize10-Ecoli1_goldstandard.tsv")
# Read data into program
# Where the format is "FILENAME" "DATATYPE"
knockout_storage = ReadData(ko_file, "knockout")
knockdown_storage = ReadData(kd_file, "knockdown")
timeseries_storage = ReadData(ts_file, "timeseries")
wildtype_storage = ReadData(wt_file, "wildtype")
# Setup job manager
jobman = JobManager(settings)
# Make MCZ job
mczjob = MCZ()
mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "MCZ")
jobman.queueJob(mczjob)
print jobman.queue
jobman.runQueue()
jobman.waitToClear()
tprs, fprs, rocs = GenerateMultiROC(
jobman.finished, goldnet, False, settings["global"]["output_dir"] + "/OverallROC.pdf"
)
ps, rs, precs = GenerateMultiPR(jobman.finished, goldnet, False, settings["global"]["output_dir"] + "/OverallPR.pdf")
SaveResults(jobman.finished, goldnet, settings)
# Gather networks
ko_file, kd_file, ts_file, wt_file, mf_file, goldnet = get_example_data_files(sys.argv[1], settings)
# Read data into program
# Where the format is "FILENAME" "DATATYPE"
mf_storage = ReadData(mf_file[0], "multifactorial")
knockout_storage = ReadData(ko_file[0], "knockout")
knockdown_storage = ReadData(kd_file[0], "knockdown")
wildtype_storage = ReadData(wt_file[0], "wildtype")
timeseries_storage = ReadData(ts_file[0], "timeseries")
gene_list = knockout_storage.gene_list
votejob = MCZ()
votejob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "SimAnnealing")
jobman = JobManager(settings)
jobman.queueJob(votejob)
votejob = jobman.queue[0]
jobman.runQueue()
jobman.waitToClear("VotingJob")
# Send to voting algorithm
dream410 = ["dream410","dream410_2","dream410_3","dream410_4","dream410_5"]
#dream410 = ["dream410","dream410_2"]
dream4100 = ["dream4100","dream4100_2","dream4100_3","dream4100_4","dream4100_5"]
if sys.argv[1] == "dream410":
networks = dream410
elif sys.argv[1] == "dream4100":
networks = dream4100
else:
networks = [sys.argv[1]]
results = []
#knockout_storage = ReadData(ko_file[0], "knockout") knockout_storage = None #knockdown_storage = ReadData(kd_file[0], "knockdown") knockdown_storage = None timeseries_storage = ReadData(ts_file[0], "timeseries") wildtype_storage = ReadData(wt_file[0], "wildtype") # Setup job manager jobman = JobManager(settings) # Make BANJO jobs tlclrjob = TLCLR() tlclrjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "TLCLR_All_Data") jobman.queueJob(tlclrjob) tlclrjob = TLCLR() tlclrjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, None, "TLCLR_No_KD") jobman.queueJob(tlclrjob) #tlclrjob = TLCLR() #tlclrjob.setup(None, wildtype_storage, settings, timeseries_storage, knockdown_storage, "TLCLR_No_KO") #jobman.queueJob(tlclrjob) #tlclrjob = TLCLR() #tlclrjob.setup(None, wildtype_storage, settings, timeseries_storage, None, "TLCLR_No_KO_or_KD") #jobman.queueJob(tlclrjob) print jobman.queue
# Read data into program # Where the format is "FILENAME" "DATATYPE" knockout_storage = ReadData(ko_file[0], "knockout") knockdown_storage = ReadData(kd_file[0], "knockdown") timeseries_storage = ReadData(ts_file[0], "timeseries") wildtype_storage = ReadData(wt_file[0], "wildtype") mf_storage = ReadData(mf_file[0], "multifactorial") # Setup job manager jobman = JobManager(settings) # Make BANJO jobs mczjob = MCZ() mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "MCZ_Alone") jobman.queueJob(mczjob) clrjob = CLR() clrjob.setup(knockout_storage, settings, "clr_" + t + "_Bins-" + str(6), "plos", 6) jobman.queueJob(clrjob) #cojob = ConvexOptimization() #cojob.setup(knockout_storage, settings, "ConvOpt_T-Plos",None, None, 0.04) #jobman.queueJob(cojob) mf_storage.combine(knockout_storage) mf_storage.combine(wildtype_storage) mf_storage.combine(knockdown_storage) genie3job = GENIE3() genie3job.setup(mf_storage, settings, "MF_KO_WT_KD") jobman.queueJob(genie3job)
os.mkdir(settings["global"]["output_dir"]) # Get config file for Banjo settings = ReadConfig(settings, "./config/default_values/banjo.cfg") #settings = ReadConfig(settings, settings["banjo"]["config"]) # Setup job manager jobman = JobManager(settings) # Make BANJO jobs settings["banjo"]["discretization_policy"] = "q4" settings["banjo"]["max_time"] = "1" bjob = Banjo() bjob.setup(timeseries_storage, settings, "banjo_" + settings["banjo"]["discretization_policy"] ) jobman.queueJob(bjob) settings["banjo"]["discretization_policy"] = "q3" bjob = Banjo() bjob.setup(timeseries_storage, settings, "banjo_" + settings["banjo"]["discretization_policy"] ) jobman.queueJob(bjob) settings["banjo"]["discretization_policy"] = "q2" bjob = Banjo() bjob.setup(timeseries_storage, settings, "banjo_" + settings["banjo"]["discretization_policy"] ) jobman.queueJob(bjob) settings["banjo"]["discretization_policy"] = "q5" bjob = Banjo() bjob.setup(timeseries_storage, settings, "banjo_" + settings["banjo"]["discretization_policy"] ) jobman.queueJob(bjob)
# Make BANJO jobs
#mczjob = MCZ()
#mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "mcz-test-run-1")
#jobman.queueJob(mczjob)
#print jobman.queue
#jobman.runQueue()
#jobman.waitToClear()
accs = []
precs = []
cojob = ConvexOptimization()
cojob.setup(knockout_storage, settings, "ConvOpt_Baseline")
jobman.queueJob(cojob)
#accs.append("MCZ:")
#for job in jobman.finished:
##threshnet = job.alg.network.copy()
##threshnet.network = threshnet.apply_threshold(0)
##accs.append((job.alg.name, threshnet.calculateAccuracy(goldnet)))
##pre, rec, area = GeneratePR(job.alg.network, goldnet, True, False, job.alg.name)
##precs.append((job.alg.name, area))
##for i in range(8, 10):
##for i in [15,20,25,30,35,5,3,1,2,50]:
#num_edge_list = [x for x in range(21)]
##num_edge_list += [ 25, 30, 45, 50, 55, 60, 65, 70 ]
#num_edge_list = [70, 80, 50, 10]
#for i in num_edge_list:
if sys.argv[1] == "dream4100":
goldnet.read_goldstd(settings["global"]["dream4100_network_goldnet_file"])
#Get a list of the knockout files
ko_file = settings["global"]["dream4100_network_knockout_file"].split()
kd_file = settings["global"]["dream4100_network_knockdown_file"].split()
ts_file = settings["global"]["dream4100_network_timeseries_file"].split()
wt_file = settings["global"]["dream4100_network_wildtype_file"].split()
# Read data into program
# Where the format is "FILENAME" "DATATYPE"
knockout_storage = ReadData(ko_file[0], "knockout")
knockdown_storage = ReadData(kd_file[0], "knockdown")
timeseries_storage = ReadData(ts_file[0], "timeseries")
wildtype_storage = ReadData(wt_file[0], "wildtype")
# Setup job manager
jobman = JobManager(settings)
# Make clr jobs
for t in ['normal', 'rayleigh', 'beta', 'plos', 'kde']:
for n in range(5,15):
clrjob = CLR()
clrjob.setup(knockout_storage, settings, "clr_" + t + "_Bins-" + str(n), t, n)
jobman.queueJob(clrjob)
print jobman.queue
jobman.runQueue()
jobman.waitToClear()
accs, precs, rocs = SaveResults(jobman.finished, goldnet, settings, "Overall", 4)
grid = Generate_Grid("dfg4grn", None, settings, ["eta_z", "lambda_w", "tau"], 5).test_list
jobman = JobManager(settings)
dfg = DFG4GRN()
settings["dfg4grn"]["eta_z"] = 0.1
settings["dfg4grn"]["lambda_w"] = 0.01
settings["dfg4grn"]["tau"] = 3.5
dfg.setup(
timeseries_storage,
TFList(timeseries_storage[0].gene_list),
settings,
"EtaZ-{0}_LamdaW-{1}_Tau-{2}".format(0.1, 0.01, 3.5),
20,
)
jobman.queueJob(dfg)
dfg = DFG4GRN()
settings["dfg4grn"]["eta_z"] = 0.01
settings["dfg4grn"]["lambda_w"] = 0.001
settings["dfg4grn"]["tau"] = 3
dfg.setup(
timeseries_storage,
TFList(timeseries_storage[0].gene_list),
settings,
"EtaZ-{0}_LamdaW-{1}_Tau-{2}".format(0.01, 0.001, 3),
20,
)
jobman.queueJob(dfg)
for i, p in enumerate(grid):
settings["dfg4grn"]["eta_z"] = p[0]
# Read data into program # Where the format is "FILENAME" "DATATYPE" knockout_storage = ReadData(ko_file[0], "knockout") knockdown_storage = ReadData(kd_file[0], "knockdown") timeseries_storage = ReadData(ts_file[0], "timeseries") wildtype_storage = ReadData(wt_file[0], "wildtype") # Setup job manager jobman = JobManager(settings) # Make BANJO jobs infjob = InferelatorPipeline() infjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "InferelatorPipeline") jobman.queueJob(infjob) print jobman.queue jobman.runQueue() jobman.waitToClear() accs = [] precs = [] #dfg = DFG4GRN() #dfg.setup(timeseries_storage, TFList(timeseries_storage[0].gene_list), settings, "DFG4GRN_Baseline", 20) #jobman.queueJob(dfg) #import pickle
ts_storage = [kno3_1, kno3_2, kno3_3, kno3_4]
#for s in ts_storage:
#s.normalize()
# Setup job manager
jobman = JobManager(settings)
# Make BANJO jobs
#mczjob = MCZ()
#mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "MCZ")
#jobman.queueJob(mczjob)
clr_cnlojob = CLR()
clr_cnlojob.setup(cnlo_storage, settings, "clr_cnlo")
jobman.queueJob(clr_cnlojob)
genie3_cnlojob = GENIE3()
genie3_cnlojob.setup(cnlo_storage, settings, "genie3_cnlo")
jobman.queueJob(genie3_cnlojob)
clr_no3job = CLR()
clr_no3job.setup(no3_storage, settings, "clr_no3")
jobman.queueJob(clr_no3job)
genie3_no3job = GENIE3()
genie3_no3job.setup(no3_storage, settings, "genie3_no3")
jobman.queueJob(genie3_no3job)
clr_cnlo_no3job = CLR()
clr_cnlo_no3job.setup(cnlo_no3_storage, settings, "clr_cnlo_no3")
permtp = 1
meth = "BBSR"
inf = Inferelator2(settings)
settings["inferelator2"]["num_cores"] = 12
settings["inferelator2"]["num_bootstraps"] = 50
#settings["inferelator2"]["num_bootstraps"] = 1
settings["inferelator2"]["permtp"] = permtp
settings["inferelator2"]["permfp"] = permfp
settings["inferelator2"]["nCv"] = 8
settings["inferelator2"]["perctp"] = perctp
settings["inferelator2"]["percfp"] = percfp
settings["inferelator2"]["method"] = meth
inf.setup(None, wildtypes[exp_name], settings, ts_storage, None, "Inferelator2-TSAlone-{0}-{1}".format(meth, exp_name), None)
jobman.queueJob(inf)
meth = "BBSR"
inf = Inferelator2(settings)
settings["inferelator2"]["num_cores"] = 12
settings["inferelator2"]["num_bootstraps"] = 50
#settings["inferelator2"]["num_bootstraps"] = 1
settings["inferelator2"]["permtp"] = permtp
settings["inferelator2"]["permfp"] = permfp
settings["inferelator2"]["nCv"] = 8
settings["inferelator2"]["perctp"] = perctp
settings["inferelator2"]["percfp"] = percfp
settings["inferelator2"]["method"] = meth
inf.setup(None, wildtypes[exp_name], settings, ts_storage, knockdowns[exp_name], "Inferelator2-KD-TS-{0}-{1}".format(meth, exp_name), None)
jobman.queueJob(inf)
def get_network_results(name, settings, cache):
print "STARTING", name
if name in cache.keys():
print "CACHE HIT"
return cache[name]
ko_file, kd_file, ts_file, wt_file, mf_file, goldnet = get_example_data_files(name, settings)
# Create date string to append to output_dir
t = datetime.now().strftime("%Y-%m-%d_%H.%M.%S")
settings["global"]["output_dir"] = settings["global"]["output_dir_save"] + "/" + \
settings["global"]["experiment_name"] + "-" + t + "-" + name + "/"
os.mkdir(settings["global"]["output_dir"])
# Get a list of the multifactorial files
# Read data into program
# Where the format is "FILENAME" "DATATYPE"
mf_storage = ReadData(mf_file[0], "multifactorial")
knockout_storage = ReadData(ko_file[0], "knockout")
knockdown_storage = ReadData(kd_file[0], "knockdown")
wildtype_storage = ReadData(wt_file[0], "wildtype")
timeseries_storage = ReadData(ts_file[0], "timeseries")
gene_list = knockout_storage.gene_list
# Setup job manager
jobman = JobManager(settings)
# MCZ
mczjob = MCZ()
mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "MCZ")
jobman.queueJob(mczjob)
# CLR
clrjob = CLR()
clrjob.setup(knockout_storage, settings, "CLR", "plos", 6)
jobman.queueJob(clrjob)
# GENIE3
mf_storage.combine(knockout_storage)
mf_storage.combine(wildtype_storage)
mf_storage.combine(knockdown_storage)
genie3job = GENIE3()
genie3job.setup(mf_storage, settings, "GENIE3")
jobman.queueJob(genie3job)
## TLCLR
tlclrjob = TLCLR()
tlclrjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "TLCLR")
jobman.queueJob(tlclrjob)
#if sys.argv[1] != "dream4100":
#cojob = ConvexOptimization()
#cojob.setup(knockout_storage, settings, "ConvOpt_T-"+ str(0.01),None, None, 0.01)
#jobman.queueJob(cojob)
### DFG4GRN
dfg = DFG4GRN()
settings["dfg4grn"]["eta_z"] = 0.01
settings["dfg4grn"]["lambda_w"] = 0.001
settings["dfg4grn"]["tau"] = 3
dfg.setup(timeseries_storage, TFList(timeseries_storage[0].gene_list), settings, "DFG", 20)
jobman.queueJob(dfg)
### Inferelator
### NIR
nirjob = NIR()
nirjob.setup(knockout_storage, settings, "NIR", 5, 5)
jobman.queueJob(nirjob)
#### TDARACNE
settings = ReadConfig(settings, "./config/default_values/tdaracne.cfg")
bjob = tdaracne()
settings["tdaracne"]["num_bins"] = 4
bjob.setup(timeseries_storage, settings, "TDARACNE")
jobman.queueJob(bjob)
print jobman.queue
jobman.runQueue()
jobman.waitToClear(name)
SaveResults(jobman.finished, goldnet, settings, name)
cache[name] = jobman.finished[:]
return cache[name]
os.mkdir(settings["global"]["output_dir"])
knockdown_filenames = settings["global"]["small_network_knockdown_file"].split()
knockdown_storage = ReadData(knockdown_filenames[0], "knockdown")
from nirest import *
settings = ReadConfig(settings, "./config/default_values/nirest.cfg")
settings = ReadConfig(settings, settings["nirest"]["config"])
jobman = JobManager(settings)
nirrun = NIRest()
nirrun.setup(knockdown_storage, settings, "nirest-test")
jobman.queueJob(nirrun)
print jobman.queue
jobman.runQueue()
jobman.waitToClear()
accs = []
for job in jobman.finished:
print job.alg.gene_list
print job.alg.read_output(settings)
print job.alg.network.calculateAccuracy(goldnet)
report = job.alg.network.analyzeMotifs(goldnet)
print report.ToString()
genie3nets = {}
for i in range(20):
for name in data.keys():
ts_storage = data[name]
settings["global"]["time_series_delta_t"] = (1008.0 / (len(ts_storage[0].experiments)-1))
combined = ReadData(exp_data_directory + '/' + name + '/' + timeseries_filename, "timeseries")[0]
for ts in timeseries_as_steady_state[name][1:11]:
combined.combine(ts)
#combined.combine(knockouts[name])
combined.combine(multifactorials[name])
genie3job = GENIE3()
genie3job.setup(combined, settings, "Genie3_TimeSeries_{0}_{1}".format(name, i))
jobman.queueJob(genie3job)
genie3nets[name] = genie3job
genie3job.goldnet = goldnets[name]
jobman.runQueue()
jobman.waitToClear()
for job in jobman.finished:
job.alg.network.normalize()
#tprs, fprs, rocs = GenerateMultiROC(jobman.finished, goldnet, False, settings["global"]["output_dir"] + "/OverallROC.pdf")
#ps, rs, precs = GenerateMultiPR(jobman.finished, goldnet, False, settings["global"]["output_dir"] + "/OverallPR.pdf")
#for job in jobman.finished:
print len(ts_storage[1].experiments)
# for s in ts_storage:
# s.normalize()
# Setup job manager
jobman = JobManager(settings)
# Train on 15
dfg15 = DFG4GRN()
settings["dfg4grn"]["eta_z"] = 0.1
settings["dfg4grn"]["lambda_w"] = 0.1
settings["dfg4grn"]["tau"] = 3
settings["dfg4grn"]["delta_t"] = "3 3 3 3 3"
dfg15.setup(ts_storage, TFList(tfs), settings, "DFG-15_LambdaW-{0}".format(0.1, d), 20, None, None, None, False)
jobman.queueJob(dfg15)
settings["global"]["time_series_delta_t"] = "3 3 3 3 3 5"
# Train on 20
dfg20 = DFG4GRN()
settings["dfg4grn"]["eta_z"] = 0.1
settings["dfg4grn"]["lambda_w"] = 0.1
settings["dfg4grn"]["tau"] = 3
settings["dfg4grn"]["delta_t"] = "3 3 3 3 3 5"
dfg20.setup(ts_storage_20, TFList(tfs), settings, "DFG-20_LambdaW-{0}".format(0.1, d), 20, None, None, None, False)
jobman.queueJob(dfg20)
jobman.runQueue()
jobman.waitToClear()