def main(options):
"""Run the recommender system on a sequence of topics.
"""
description = "System using LDA, Kmeans and Solr to optimize diversification and exploitation of different topics"
parser = argparse.ArgumentParser(description=description)
parser.add_argument("--overwrite", action="store_true")
args = yakonfig.parse_args(parser, [yakonfig])
logging.basicConfig(level=logging.DEBUG)
config = yakonfig.get_global_config("harness")
batch_size = config.get("batch_size", 5)
run_file_path = config["run_file_path"]
if os.path.exists(run_file_path):
if args.overwrite:
os.remove(run_file_path)
else:
os.remove(run_file_path)
# sys.exit('%r already exists' % run_file_path)
kvl_config = {"storage_type": "local", "namespace": "test", "app_name": "test"}
kvl = kvlayer.client(kvl_config)
method, feedback_options, poids, id_config = options[0], options[1], options[2], options[3]
print method, poids
system = SearchSystem([], method, feedback_options, poids)
print args.config
args.config = "config" + str(id_config) + ".yaml"
print args.config
ambassador = HarnessAmbassadorCLI(system, args.config, batch_size)
ambassador.run()
def main():
'''Run the random recommender system on a sequence of topics.
'''
description = (
'A baseline recommender system that uses the truth data to'
' create output that has perfect recall and would also have'
' perfect precision if you ignore subtopic diversity/novelty.'
' This generates output directly from the truth data and'
' randomly shuffles the truth data per topic, so that'
' the ordering of passages does not attempt to optimize any'
' particular quality metric.')
parser = argparse.ArgumentParser(description=description)
parser.add_argument('--overwrite', action='store_true')
args = yakonfig.parse_args(parser, [yakonfig])
logging.basicConfig(level=logging.DEBUG)
config = yakonfig.get_global_config('harness')
batch_size = config.get('batch_size', 5)
run_file_path = config['run_file_path']
if os.path.exists(run_file_path):
if args.overwrite:
os.remove(run_file_path)
else:
sys.exit('%r already exists' % run_file_path)
kvl_config = {
'storage_type': 'local',
'namespace': 'test',
'app_name': 'test'
}
kvl = kvlayer.client(kvl_config)
label_store = LabelStore(kvl)
parse_truth_data(label_store, config['truth_data_path'])
# Set up the system
doc_store = make_doc_store(label_store)
system = RandomSystem(doc_store)
ambassador = HarnessAmbassadorCLI(system, args.config, batch_size)
ambassador.run()
def main():
'''Run the random recommender system on a sequence of topics.
'''
description = ('A baseline recommender system that uses the truth data to'
' create output that has perfect recall and would also have'
' perfect precision if you ignore subtopic diversity/novelty.'
' This generates output directly from the truth data and'
' randomly shuffles the truth data per topic, so that'
' the ordering of passages does not attempt to optimize any'
' particular quality metric.')
parser = argparse.ArgumentParser(description=description)
parser.add_argument('--overwrite', action='store_true')
args = yakonfig.parse_args(parser, [yakonfig])
logging.basicConfig(level=logging.DEBUG)
config = yakonfig.get_global_config('harness')
batch_size = config.get('batch_size', 5)
run_file_path = config['run_file_path']
if os.path.exists(run_file_path):
if args.overwrite:
os.remove(run_file_path)
else:
sys.exit('%r already exists' % run_file_path)
kvl_config = {'storage_type': 'local',
'namespace': 'test',
'app_name': 'test'}
kvl = kvlayer.client(kvl_config)
label_store = LabelStore(kvl)
parse_truth_data(label_store, config['truth_data_path'])
# Set up the system
doc_store = make_doc_store(label_store)
system = RandomSystem(doc_store)
ambassador = HarnessAmbassadorCLI(system, args.config, batch_size)
ambassador.run()
