def classify_batch(self, abstracts):
"""
Only batch-mode classify is supported due to the underlying operations.
Single-item classification is expected to be slow.
@param abstracts a mapping from docid to a list of lines
"""
self.__unpack()
ts = {}
for filename, lines in abstracts.iteritems():
for i, line in enumerate(lines):
docid = "{0}-{1}".format(filename, i + 1)
ts[docid] = line
try:
handle, store_path = tempfile.mkstemp(dir=self.tempdir)
os.close(handle)
logger.debug("temporary store at {}".format(store_path))
with Timer() as feat_timer:
induce(ts, store_path, self.model.features, self.model.spaces)
logger.info("computing features took {0:.2f}s".format(feat_timer.elapsed))
store = Store(store_path, "r")
with Timer() as cl_timer:
L0_preds = []
for feat, cl in zip(self.model.features, self.model.L0_cl):
fm = store.get_FeatureMap("NewDocuments", feat)
# We need to trim the fv as the feature space may have grown when we tokenized more documents.
# Hydrat's design is such that new features are appended to the end of a feature space, so
# we can safely truncate the feature map.
train_feat_count = cl.metadata["train_feat_count"]
assert train_feat_count <= fm.raw.shape[1]
L0_preds.append(cl(fm.raw[:, :train_feat_count]))
L0_preds = sp.csr_matrix(np.hstack(L0_preds))
L1_preds = self.model.L1_cl(L0_preds)
logger.info(
"classification took {0:.2f}s ({1:.2f} inst/s)".format(
cl_timer.elapsed, cl_timer.rate(L0_preds.shape[0])
)
)
cl_space = store.get_Space("ebmcat")
instance_ids = store.get_Space("NewDocuments")
finally:
logger.debug("unlinking {}".format(store_path))
os.unlink(store_path)
return PIBOSOOutput(instance_ids, cl_space, L1_preds)
for feat, cl in zip(features, L0_cl):
fm = store.get_FeatureMap('NewDocuments', feat)
# We need to trim the fv as the feature space may have grown when we tokenized more documents.
# Hydrat's design is such that new features are appended to the end of a feature space, so
# we can safely truncate the feature map.
train_feat_count = cl.metadata['train_feat_count']
assert(train_feat_count <= fm.raw.shape[1])
fv = fm.raw[:,:train_feat_count]
with Timer() as cl_timer:
pred = cl(fv)
print >>sys.stderr, "== L1 feat for {0} took {1:.2f}s ({2:.2f} inst/s) ==".format(feat, cl_timer.elapsed, cl_timer.rate(fv.shape[0]))
L0_preds.append(pred)
L0_preds = sp.csr_matrix(np.hstack(L0_preds))
with Timer() as cl_timer:
L1_preds = L1_cl(L0_preds)
print >>sys.stderr, "== L1 classify took {0:.2f}s ({1:.2f} inst/s)==".format(cl_timer.elapsed, cl_timer.rate(L0_preds.shape[0]))
print >>sys.stderr, "== classification took {0:.2f}s ({1:.2f} inst/s)==".format(overall_timer.elapsed, overall_timer.rate(L0_preds.shape[0]))
cl_space = store.get_Space('ebmcat')
instance_ids = store.get_Space('NewDocuments')
with open(args.output,'w') as f:
writer = csv.writer(f)
for inst_id, cl_id in zip(instance_ids, L1_preds.argmax(axis=1)):
cl_name = cl_space[cl_id]
writer.writerow((inst_id, cl_name))
parser.add_argument('feat_store', help='existing hydrat Store generated by features.py')
parser.add_argument("output", help="produce output in PATH", metavar="PATH")
args = parser.parse_args()
class_space = 'ebmcat'
try:
features = features.feature_sets[args.feats]
except KeyError:
parser.error("unknown feature group: {0}".format(args.feats))
l = repeat.RepeatLearner(liblinear.liblinearL(svm_type=0, output_probability=True))
store = Store(args.feat_store, 'r') # TODO: Do we want this read-only?
for feature in features:
spaces[feature] = store.get_Space(feature)
spaces['ebmcat'] = store.get_Space('ebmcat')
proxy = DataProxy(ALTA2012Full(), store=store)
proxy.class_space = class_space
L0_cl = []
L1_fv = []
L1_gs = None
for feat in features:
proxy.feature_spaces = feat
proxy.split_name = 'crossvalidation'
with Timer() as L0_timer:
L0_cl.append( l(proxy.featuremap.raw, proxy.classmap.raw) )
print >>sys.stderr, "== training L0 for {0} took {1:.2f}s ==".format(feat, L0_timer.elapsed)
metavar="PATH")
args = parser.parse_args()
class_space = 'ebmcat'
try:
features = features.feature_sets[args.feats]
except KeyError:
parser.error("unknown feature group: {0}".format(args.feats))
l = repeat.RepeatLearner(
liblinear.liblinearL(svm_type=0, output_probability=True))
store = Store(args.feat_store, 'r') # TODO: Do we want this read-only?
for feature in features:
spaces[feature] = store.get_Space(feature)
spaces['ebmcat'] = store.get_Space('ebmcat')
proxy = DataProxy(ALTA2012Full(), store=store)
proxy.class_space = class_space
L0_cl = []
L1_fv = []
L1_gs = None
for feat in features:
proxy.feature_spaces = feat
proxy.split_name = 'crossvalidation'
with Timer() as L0_timer:
L0_cl.append(l(proxy.featuremap.raw, proxy.classmap.raw))
print >> sys.stderr, "== training L0 for {0} took {1:.2f}s ==".format(
# We need to trim the fv as the feature space may have grown when we tokenized more documents.
# Hydrat's design is such that new features are appended to the end of a feature space, so
# we can safely truncate the feature map.
train_feat_count = cl.metadata['train_feat_count']
assert (train_feat_count <= fm.raw.shape[1])
fv = fm.raw[:, :train_feat_count]
with Timer() as cl_timer:
pred = cl(fv)
print >> sys.stderr, "== L1 feat for {0} took {1:.2f}s ({2:.2f} inst/s) ==".format(
feat, cl_timer.elapsed, cl_timer.rate(fv.shape[0]))
L0_preds.append(pred)
L0_preds = sp.csr_matrix(np.hstack(L0_preds))
with Timer() as cl_timer:
L1_preds = L1_cl(L0_preds)
print >> sys.stderr, "== L1 classify took {0:.2f}s ({1:.2f} inst/s)==".format(
cl_timer.elapsed, cl_timer.rate(L0_preds.shape[0]))
print >> sys.stderr, "== classification took {0:.2f}s ({1:.2f} inst/s)==".format(
overall_timer.elapsed, overall_timer.rate(L0_preds.shape[0]))
cl_space = store.get_Space('ebmcat')
instance_ids = store.get_Space('NewDocuments')
with open(args.output, 'w') as f:
writer = csv.writer(f)
for inst_id, cl_id in zip(instance_ids, L1_preds.argmax(axis=1)):
cl_name = cl_space[cl_id]
writer.writerow((inst_id, cl_name))
