def __init__(self, filename, labeler=None):
self.filename = filename
instance_dict = load_evidence_file(filename)
num_docs = len(instance_dict)
doc_names = sorted(instance_dict.keys())
feature_ids = sorted(set(chain(*[each.iterkeys() for each in instance_dict.values()])))
vocab_size = len(feature_ids)
# Create a map of feature_id => dense feature index
feature_index = {k:i for i, k in enumerate(feature_ids)}
doc_matrix = np.zeros((num_docs, vocab_size))
doc_labels = []
# For each document, convert sparse features to dense L2-normalized feature vector and write it into the
# document matrix
for i, name in enumerate(doc_names):
sparse_features = instance_dict[name]
doc_data = np.zeros(vocab_size)
for id, count in sparse_features.iteritems():
doc_data[feature_index[id]] = count
doc_data = l2_normalize(doc_data)
doc_labels[i] = labeler(name) if labeler else None
doc_matrix[i, :] = doc_data
self.num_docs = num_docs
self.raw_labels = doc_labels
self.class_names = sorted(set(doc_labels))
self.doc_matrix = doc_matrix
self.dim = len(feature_index)
def __init__(self, filename, labeler=None):
self.filename = filename
instance_dict = load_evidence_file(filename)
num_docs = len(instance_dict)
doc_names = sorted(instance_dict.keys())
feature_ids = sorted(
set(chain(*[each.iterkeys() for each in instance_dict.values()])))
vocab_size = len(feature_ids)
# Create a map of feature_id => dense feature index
feature_index = {k: i for i, k in enumerate(feature_ids)}
doc_matrix = np.zeros((num_docs, vocab_size))
doc_labels = []
# For each document, convert sparse features to dense L2-normalized feature vector and write it into the
# document matrix
for i, name in enumerate(doc_names):
sparse_features = instance_dict[name]
doc_data = np.zeros(vocab_size)
for id, count in sparse_features.iteritems():
doc_data[feature_index[id]] = count
doc_data = l2_normalize(doc_data)
doc_labels[i] = labeler(name) if labeler else None
doc_matrix[i, :] = doc_data
self.num_docs = num_docs
self.raw_labels = doc_labels
self.class_names = sorted(set(doc_labels))
self.doc_matrix = doc_matrix
self.dim = len(feature_index)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = ArgumentParser()
parser.add_argument('input_file',
type=str,
help='Input file in evidence format')
parser.add_argument('output_file',
type=str,
help='Path to destination corpus file')
parser.add_argument('--labeler', type=str, help='Labeler to apply')
options = parser.parse_args(argv[1:])
labeler = None
if options.labeler is None:
log.warning('no labeler provided')
elif options.labeler not in labelers.registry:
labeler_names = ', '.join(sorted(labelers.registry.keys()))
parser.error('Invalid labeler "%s"; available options are %s' %
(options.labeler, labeler_names))
else:
labeler = labelers.registry[options.labeler]
instance_dict = load_evidence_file(options.input_file)
num_docs = len(instance_dict)
feature_ids = sorted(
set(chain(*[each.iterkeys() for each in instance_dict.values()])))
vocab_size = len(feature_ids)
log.info('Read %d docs (vocabulary size %d) from %s' %
(num_docs, vocab_size, options.input_file))
log.info('Writing L2-normalized corpus to %s' % options.output_file)
writer = CorpusWriter(options.output_file,
data_series='sam',
dim=vocab_size)
# Create a map of feature_id => dense feature index
feature_index = {k: i for i, k in enumerate(feature_ids)}
# For each document, convert sparse features to dense L2-normalized feature vector and write it to the corpus
for name, sparse_features in instance_dict.iteritems():
doc_data = np.zeros((vocab_size, 1))
for id, count in sparse_features.iteritems():
doc_data[feature_index[id]] = count
doc_data = l2_normalize(doc_data)
doc_label = labeler(name) if labeler else None
writer.write_doc(doc_data, name=name, label=doc_label)
writer.close()
wordlist_path = options.output_file + '.wordlist'
log.info('Writing wordlist to %s' % wordlist_path)
with open(wordlist_path, 'w') as f:
f.writelines([s + '\n' for s in feature_ids])
def main(argv=None):
if argv is None:
argv = sys.argv
parser = ArgumentParser()
parser.add_argument('input_file', type=str, help='Input file in evidence format')
parser.add_argument('output_file', type=str, help='Path to destination corpus file')
parser.add_argument('--labeler', type=str, help='Labeler to apply')
options = parser.parse_args(argv[1:])
labeler = None
if options.labeler is None:
log.warning('no labeler provided')
elif options.labeler not in labelers.registry:
labeler_names = ', '.join(sorted(labelers.registry.keys()))
parser.error('Invalid labeler "%s"; available options are %s' % (options.labeler, labeler_names))
else:
labeler = labelers.registry[options.labeler]
instance_dict = load_evidence_file(options.input_file)
num_docs = len(instance_dict)
feature_ids = sorted(set(chain(*[each.iterkeys() for each in instance_dict.values()])))
vocab_size = len(feature_ids)
log.info('Read %d docs (vocabulary size %d) from %s' % (num_docs, vocab_size, options.input_file))
log.info('Writing L2-normalized corpus to %s' % options.output_file)
writer = CorpusWriter(options.output_file, data_series='sam', dim=vocab_size)
# Create a map of feature_id => dense feature index
feature_index = {k:i for i, k in enumerate(feature_ids)}
# For each document, convert sparse features to dense L2-normalized feature vector and write it to the corpus
for name, sparse_features in instance_dict.iteritems():
doc_data = np.zeros((vocab_size, 1))
for id, count in sparse_features.iteritems():
doc_data[feature_index[id]] = count
doc_data = l2_normalize(doc_data)
doc_label = labeler(name) if labeler else None
writer.write_doc(doc_data, name=name, label=doc_label)
writer.close()
wordlist_path = options.output_file + '.wordlist'
log.info('Writing wordlist to %s' % wordlist_path)
with open(wordlist_path, 'w') as f:
f.writelines([s + '\n' for s in feature_ids])
