def _gff_process(self, gff_files, limit_info, target_lines=None):
"""Process GFF addition, using Disco to parallelize the process.
"""
assert target_lines is None, "Cannot split parallelized jobs"
# make these imports local; only need them when using disco
import simplejson
import disco
# absolute path names unless they are special disco files
full_files = []
for f in gff_files:
if f.split(":")[0] != "disco":
full_files.append(os.path.abspath(f))
else:
full_files.append(f)
results = disco.job(
self._disco_host,
name="gff_reader",
input=full_files,
params=disco.Params(limit_info=limit_info,
jsonify=True,
filter_info=self._examiner._filter_info),
required_modules=["simplejson", "collections", "re"],
map=self._map_fn,
reduce=self._reduce_fn)
processed = dict()
for out_key, out_val in disco.result_iterator(results):
processed[out_key] = simplejson.loads(out_val)
yield processed
def _gff_process(self, gff_files, limit_info, target_lines=None):
"""Process GFF addition, using Disco to parallelize the process.
"""
assert target_lines is None, "Cannot split parallelized jobs"
# make these imports local; only need them when using disco
import simplejson
import disco
# absolute path names unless they are special disco files
full_files = []
for f in gff_files:
if f.split(":")[0] != "disco":
full_files.append(os.path.abspath(f))
else:
full_files.append(f)
results = disco.job(
self._disco_host,
name="gff_reader",
input=full_files,
params=disco.Params(limit_info=limit_info, jsonify=True, filter_info=self._examiner._filter_info),
required_modules=["simplejson", "collections", "re"],
map=self._map_fn,
reduce=self._reduce_fn,
)
processed = dict()
for out_key, out_val in disco.result_iterator(results):
processed[out_key] = simplejson.loads(out_val)
yield processed
def run_disco(limit):
results = disco.job(sys.argv[1], "test_sort",
tserver.makeurl([""] * int(1e3)),
fun_map, reduce = fun_reduce, nr_reduces = 50,
sort = True, mem_sort_limit = limit)
k = len(list(disco.result_iterator(results)))
if k != int(1e5):
raise "not enough results: Got %d, expected %d" % (k, 1e5)
def estimate(input, ys, splitter=' ', host="disco://localhost", map_reader=disco.chain_reader):
ys=dict([(id,1) for id in ys])
results = disco.job(host, name = 'naive_bayes_estimate',
input_files = input,
map_reader = map_reader,
fun_map = estimate_map,
combiner = estimate_combiner,
reduce = estimate_reduce,
params = disco.Params(ys=ys,splitter=splitter),
sort = False, clean = False)
total=0
items={}
classes={}
pairs={}
for key,value in disco.result_iterator(results):
l=key.split(splitter)
value=int(value)
if len(l)==1:
if l[0]=='': total=value
elif ys.has_key(l[0]): classes[l[0]]=value
else: items[l[0]]=value
else:
pairs[key]=value
#counts[key]=[[c,i], [not c, i], [c, not i], [not c, not i]]
counts={}
for i in items:
for y in ys:
key=y+splitter+i
counts[key]=[0,0,0,0]
if pairs.has_key(key): counts[key][0]=pairs[key]
counts[key][1]=items[i]-counts[key][0]
if not classes.has_key(y): counts[key][2]=0
else: counts[key][2]=classes[y]-counts[key][0]
counts[key][3]=total-sum(counts[key][:3])
# add pseudocounts
counts[key]=map(lambda x: x+1, counts[key])
total+=4
import math
loglikelihoods={}
for key,value in counts.iteritems():
log_c=math.log(value[0]+value[2])
l=key.split(splitter)
if not loglikelihoods.has_key(l[0]): loglikelihoods[l[0]]=0.0
loglikelihoods[l[0]]+=math.log(value[2])-log_c
loglikelihoods[key]=math.log(value[0])-math.log(value[2])
return loglikelihoods
def estimate(input, y_id, w=None, learning_rate=1.0, iterations=10, host="disco://localhost", map_reader=disco.chain_reader):
for i in range(iterations):
results = disco.job(host, name = 'perceptron_estimate_' + str(i),
input_files = input,
map_reader = map_reader,
fun_map = estimate_map,
combiner = estimate_combiner,
reduce = estimate_reduce,
params = disco.Params(w = w, learning_rate=learning_rate,y_id=y_id),
sort = False, clean = True)
for key,value in disco.result_iterator(results):
v=map(float,value.split(' '))
if w==None: w=v
else: w=[w[i]+v[i] for i in range(len(w))]
print >>sys.stderr,w
return w
def _disco_process(self, gff_files, limit_info):
"""Process GFF addition, using Disco to parallelize the process.
"""
# make these imports local; only need them when using disco
import simplejson
import disco
# absolute path names unless they are special disco files
full_files = [(os.path.abspath(f) if f.split(":")[0] != "disco" else f)
for f in gff_files]
results = disco.job(self._disco_host, name="gff_reader",
input=full_files,
params=disco.Params(limit_info=limit_info, jsonify=True,
filter_info=self._filter_info),
required_modules=["simplejson", "collections", "re"],
map=self._map_fn, reduce=self._reduce_fn)
processed = dict()
for out_key, out_val in disco.result_iterator(results):
processed[out_key] = simplejson.loads(out_val)
return processed
def estimate(input, y_ids, w=None, learning_rate=1.0, iterations=10, host="disco://localhost", map_reader=disco.chain_reader):
y_ids=dict([(y,1) for y in y_ids])
for i in range(iterations):
results = disco.job(host, name = 'widrow_hoff_estimate_' + str(i),
input_files = input,
map_reader = map_reader,
fun_map = estimate_map,
combiner = estimate_combiner,
reduce = estimate_reduce,
params = disco.Params(w = w, learning_rate=learning_rate, y_ids=y_ids),
sort = False, clean = True)
if w==None: w={}
for k,v in disco.result_iterator(results):
k=int(k)
v=map(float, v.split(' '))
if not w.has_key(k): w[k]=v
else: w[k]=[w[k][i]+v[i] for i in range(len(v))]
print >>sys.stderr, w
return w
def _disco_process(self, gff_files, limit_info):
"""Process GFF addition, using Disco to parallelize the process.
"""
# make these imports local; only need them when using disco
import simplejson
import disco
# absolute path names unless they are special disco files
full_files = [(os.path.abspath(f) if f.split(":")[0] != "disco" else f)
for f in gff_files]
results = disco.job(
self._disco_host,
name="gff_reader",
input=full_files,
params=disco.Params(limit_info=limit_info,
jsonify=True,
filter_info=self._filter_info),
required_modules=["simplejson", "collections", "re"],
map=self._map_fn,
reduce=self._reduce_fn)
processed = dict()
for out_key, out_val in disco.result_iterator(results):
processed[out_key] = simplejson.loads(out_val)
return processed
def estimate(input, centers, k, iterations=10, host="disco://localhost", map_reader=disco.chain_reader, nr_reduces=None):
if centers!=None: k=len(centers)
if nr_reduces==None: nr_reduces=k
results=None
if centers==None:
results = disco.job(host, name = 'kmeans_init',
input_files = input,
map_reader = map_reader,
fun_map = init_map,
combiner = estimate_combiner,
reduce = estimate_reduce,
nr_reduces = nr_reduces,
params = disco.Params(k=k),
sort = False, clean = True)
for i in range(iterations):
if results!=None:
centers=[None]*k
counts=[None]*k
for key,value in disco.result_iterator(results):
x=map(float,value.split(' '))
centers[int(key)]=x[:-1]
counts[int(key)]=x[-1]
results = disco.job(host, name = 'kmeans_iterate_'+str(i),
input_files = input,
map_reader = map_reader,
fun_map = estimate_map,
combiner = estimate_combiner,
reduce = estimate_reduce,
nr_reduces = nr_reduces,
params = disco.Params(centers=centers,dist=d2),
sort = False, clean = True)
return centers
def estimate(input, y_id, host="disco://localhost", map_reader=disco.chain_reader):
results = disco.job(host, name = 'naive_linear_regression_estimate',
input_files = input,
map_reader = map_reader,
fun_map = estimate_map,
combiner = estimate_combiner,
params=disco.Params(y_id=y_id),
sort = False, clean = False)
c=0
y=0.0
l=None
x=None
x2=None
xy=None
for key,value in disco.result_iterator(results):
v=map(float,value.split(' '))
if l==None:
l=(len(v)-2)/3
x=[0.0]*l
x2=[0.0]*l
xy=[0.0]*l
c+=v[-1]
y+=v[-2]
for i in range(l):
x[i]+=v[i]
x2[i]+=v[l+i]
xy[i]+=v[2*l+i]
b = [ (c*xy[i] - x[i]*y)/(c*x2[i]+x[i]*x[i]) for i in range(l) ]
a = [ (y-b[i]*x[i])/c for i in range(l) ]
return zip(*(a,b))
return 0
def data_gen(path):
return "\n".join([path[1:]] * 10)
def fun_map(e, params):
return [(e, params.f1(int(e), params.x))]
def fun_reduce(iter, out, params):
for k, v in iter:
out.add(k, params.f2(int(v)))
tserver.run_server(data_gen)
inputs = range(10)
job = Disco(sys.argv[1]).new_job(name = "test_params",
input = tserver.makeurl(inputs),
map = fun_map,
params = Params(x = 5, f1 = fun1, f2 = fun2),
reduce = fun_reduce,
nr_reduces = 1,
sort = False)
for x, y in result_iterator(job.wait()):
if fun2(int(x) + 5) != int(y):
raise "Invalid result: %s and %s" % (x, y)
job.purge()
print "ok"
out.add("red_" + k, "red_" + v)
tserver.run_server(data_gen)
inputs = ["ape", "cat", "dog"]
params = {"test1": "1,2,3",\
"one two three": "dim\ndam\n",\
"dummy": "value"}
job = Disco(sys.argv[1]).new_job(
name = "test_external",
input = tserver.makeurl(inputs),
map = external(["ext_test"]),
reduce = fun_reduce,
ext_params = params,
nr_reduces = 1,
sort = False)
results = sorted([(v, k) for k, v in result_iterator(job.wait())])
for i, e in enumerate(results):
v, k = e
if k != "red_dkey" or v != "red_test_%s" % inputs[i / 3]:
raise Exception("Invalid answer: %s, %s" % (k, v))
if len(results) != 9:
raise Exception("Wrong number of results: %u vs. 9" % len(results))
job.purge()
print "ok"
if x > 10:
return 1
else:
return 0
def data_gen(path):
return "\n".join([path[1:]] * 10)
def fun_map(e, params):
return [(e, params.f1(int(e), params.x))]
def fun_reduce(iter, out, params):
for k, v in iter:
out.add(k, params.f2(int(v)))
tserver.run_server(data_gen)
inputs = range(10)
results = disco.job(sys.argv[1], "test_params", tserver.makeurl(inputs),
fun_map,
params = disco.Params(x = 5, f1 = fun1, f2 = fun2),
reduce = fun_reduce,
nr_reduces = 1,
sort = False)
for x, y in disco.result_iterator(results):
if fun2(int(x) + 5) != int(y):
raise "Invalid result: %s and %s" % (x, y)
print "ok"
def fun_map(e, params):
k = str(int(math.ceil(float(e))) ** 2)
return [(md5.new(k).hexdigest(), "")]
tserver.run_server(data_gen)
disco = Disco(sys.argv[1])
inputs = [1, 485, 3245]
job = disco.new_job(name = "test_reqmodules",
nr_reduces = 1,
input = tserver.makeurl(inputs),
map = fun_map,
required_modules = ["math", "md5"],
sort = False)
res = list(result_iterator(job.wait()))
if len(res) != len(inputs):
raise Exception("Too few results: Got: %d Should be %d" %
(len(res), len(inputs)))
cor = map(lambda x: md5.new(str(int(math.ceil(x)) ** 2)).hexdigest(), inputs)
for k, v in res:
if k not in cor:
raise Exception("Invalid answer: %s" % k)
cor.remove(k)
job.purge()
print "ok"
ANS = "1028380578493512611198383005758052057919386757620401"\
"58350002406688858214958513887550465113168573010369619140625"
def data_gen(path):
return "\n".join([path[1:]] * 10)
def fun_map(e, params):
return [('=' + e, e)]
def fun_reduce(iter, out, params):
s = 1
for k, v in iter:
if k != "=" + v:
raise Exception("Corrupted key")
s *= int(v)
out.add("result", s)
tserver.run_server(data_gen)
inputs = [3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
results = disco.job(sys.argv[1], "test_simple", tserver.makeurl(inputs),
fun_map,
reduce = fun_reduce,
nr_reduces = 1,
sort = False)
if list(disco.result_iterator(results)) != [("result", ANS)]:
raise Exception("Invalid answer")
print "ok"
def fun_reduce(iter, out, params):
for k, v in iter:
out.add("red_" + k, "red_" + v)
tserver.run_server(data_gen)
inputs = ["ape", "cat", "dog"]
params = {"test1": "1,2,3",\
"one two three": "dim\ndam\n",\
"dummy": "value"}
results = Disco(sys.argv[1]).new_job(
name = "test_external",
input = tserver.makeurl(inputs),
map = external(["ext_test"]),
reduce = fun_reduce,
ext_params = params,
nr_reduces = 1,
sort = False).wait()
results = sorted([(v, k) for k, v in result_iterator(results)])
for i, e in enumerate(results):
v, k = e
if k != "red_dkey" or v != "red_test_%s" % inputs[i / 3]:
raise Exception("Invalid answer: %s, %s" % (k, v))
if len(results) != 9:
raise Exception("Wrong number of results: %u vs. 9" % len(results))
print "ok"
for k, v in iter:
if k in s:
s[k] += int(v)
else:
s[k] = int(v)
for k, v in s.iteritems():
out.add(k, v)
tserver.run_server(data_gen)
results = disco.job(sys.argv[1], "test_50k", tserver.makeurl([""] * int(5e4)),
fun_map, reduce = fun_reduce, nr_reduces = 300,
sort = False)
ANS = {"gutta": int(5e6), "cavat": int(1e7), "capidem": int(5e6)}
i = 0
for key, value in disco.result_iterator(results):
i += 1
if ANS[key] == int(value):
print "Correct: %s %s" % (key, value)
else:
raise "Results don't match"
if i != 3:
raise "Too few results"
disco.Disco(sys.argv[1]).purge(disco.util.jobname(results[0]))
print "ok"
tserver.run_server(data_gen)
disco = Disco(sys.argv[1])
num = sum(x['max_workers'] for x in disco.nodeinfo()['available'])
print >> sys.stderr, num, "slots available"
inputs = tserver.makeurl(range(num * 10))
random.shuffle(inputs)
jobs = []
for i in range(5):
jobs.append(disco.new_job(name = "test_async_%d" % i,
input = inputs[i * (num * 2):(i + 1) * (num * 2)],
map = fun_map, reduce = fun_reduce, nr_reduces = 11,
sort = False))
time.sleep(1)
all = dict(("[%s]" % i, 0) for i in range(num * 10))
for job in jobs:
results = job.wait()
print "Job", job, "done"
for k, v in result_iterator(results):
all[k] += 1
job.clean()
for v in all.values():
if v != 10:
raise "Invalid results: %s" % all
print "ok"
if x != "value:" + e:
raise "Invalid value for key %s: %s" % (e, x)
return [("good", "")]
tserver.run_server(data_gen)
inputs = list(string.ascii_lowercase)
disco = Disco(sys.argv[1])
job1 = disco.new_job(name = "test_oob1",
input = tserver.makeurl(inputs),
map = fun_map,
reduce = fun_reduce,
nr_reduces = 10)
res = list(result_iterator(job1.wait()))
if [("all", "ok")] * 10 != res:
raise "Invalid result: %s" % res
keys = ["reduce:%d" % i for i in range(10)] + inputs
lst = job1.oob_list()
if len(lst) != len(keys):
raise "Invalid number of OOB keys: got %d, expected %d" %\
(len(lst), len(keys))
for key in job1.oob_list():
if key not in keys:
raise "Invalid key: %s" % key
x = job1.oob_get(key)
if x != "value:" + key:
tserver.run_server(data_gen)
inputs = ["01/11/1965", "14/03/1983", "12/12/2002"]
job = Disco(sys.argv[1]).new_job(name = "test_objectrw",
input = tserver.makeurl(inputs),
map = fun_map,
map_writer = func.object_writer,
reduce = fun_reduce,
reduce_reader = func.object_reader,
reduce_writer = func.object_writer,
required_modules = ["math", "datetime", "time"],
nr_reduces = 1,
sort = False)
i = 0
for k, v in result_iterator(job.wait(), reader = func.object_reader):
if k["PI2"] != math.pi:
raise "Invalid key: %s" % k
if v.strftime("%d/%m/%Y") not in inputs:
raise "Invalid value: %s" % v
i += 1
if i != 30:
raise "Wrong number of results, got %d, expected 30" % i
job.purge()
print "ok"
params={"suffix": "0"},
).wait()
i = 1
while i < 10:
nresults = disco.new_job(
name="test_chain_%d" % i,
input=results,
map=fun_map,
reduce=fun_reduce,
nr_reduces=4,
map_reader=chain_reader,
sort=False,
clean=True,
params={"suffix": str(i)},
).wait()
disco.purge(jobname(results[0]))
results = nresults
i += 1
for key, value in result_iterator(results):
if key[:5] not in ani or key[5:] != "0-1-2-3-4-5-6-7-8-9-":
raise "Corrupted key: %s" % key
if value != "9":
raise "Corrupted value: %s" % value
disco.purge(jobname(results[0]))
print "ok"
s = 1
for k, v in iter:
if k != "=" + v:
raise Exception("Corrupted key")
s *= int(v)
out.add("result", s)
tserver.run_server(data_gen)
inputs = [3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
job = Disco(sys.argv[1]).new_job(
name = "test_writers",
input = tserver.makeurl(inputs),
map = fun_map,
map_writer = fun_map_writer,
reduce = fun_reduce,
reduce_reader = fun_reduce_reader,
reduce_writer = fun_reduce_writer,
nr_reduces = 1,
sort = False)
res = list(result_iterator(job.wait(), reader = result_reader))
if res != [ANS]:
raise Exception("Invalid answer: %s" % res)
job.purge()
print "ok"
inputs = []
for i in range(N):
a = [i] * 10
b = range(i, i + 10)
inputs += ["%d:%d" % x for x in zip(a, b)]
results[str(i)] = str(sum(b))
disco = Disco(sys.argv[1])
job = disco.new_job(\
name = "test_chunked",
input = tserver.makeurl(inputs),
map = fun_map,
partition = fun_partition,
reduce = fun_reduce,
chunked = False,
nr_reduces = N,
sort = False)
for k, v in result_iterator(job.wait()):
if results[k] != v:
raise "Invalid result, got %s, expected %s" %\
(v, results[k])
del results[k]
if results:
raise "Not enough results"
job.purge()
print "ok"
out.add(k, v)
tserver.run_server(data_gen)
job = Disco(sys.argv[1]).new_job(\
name = "test_profile",\
input = tserver.makeurl([""] * int(100)),\
map = really_unique_function_name,\
reduce = fun_reduce,\
nr_reduces = 30,\
sort = False,\
profile = True)
ANS = {"gutta": int(1e4), "cavat": int(2e4), "capidem": int(1e4)}
i = 0
for key, value in result_iterator(job.wait()):
i += 1
if ANS[key] == int(value):
print "Correct: %s %s" % (key, value)
else:
raise "Results don't match (%s): Got %d expected %d" %\
(key, int(value), ANS[key])
if i != 3:
raise "Too few results"
buf = cStringIO.StringIO()
sys.stdout = buf
job.profile_stats().print_stats()
sys.stdout = sys.__stdout__
#stats = job.profile_stats()
