def streaming_vw_createcache_modular(fname):
# First creates a binary cache from an ascii data file.
# and then trains using the StreamingVwCacheFile as input
# Open the input file as a StreamingVwFile
input_file = StreamingVwFile(fname)
# Default file name will be vw_cache.dat.cache
input_file.set_write_to_cache(True)
# Tell VW that the file is in SVMLight format
# Supported types are T_DENSE, T_SVMLIGHT and T_VW
input_file.set_parser_type(T_SVMLIGHT)
# Create a StreamingVwFeatures object, `True' indicating the examples are labelled
features = StreamingVwFeatures(input_file, True, 1024)
# Create a VW object from the features
vw = VowpalWabbit(features)
vw.set_no_training(True)
# Train (in this case does nothing but run over all examples)
vw.train()
#Finally Train using the generated cache file
# Open the input cache file as a StreamingVwCacheFile
input_file = StreamingVwCacheFile("vw_cache.dat.cache")
# The rest is exactly as for normal input
features = StreamingVwFeatures(input_file, True, 1024)
vw = VowpalWabbit(features)
vw.train()
def streaming_vw_modular(dummy):
"""Runs the VW algorithm on a toy dataset in SVMLight format."""
# Open the input file as a StreamingVwFile
input_file = StreamingVwFile("../data/fm_train_sparsereal.dat")
# Tell VW that the file is in SVMLight format
# Supported types are T_DENSE, T_SVMLIGHT and T_VW
input_file.set_parser_type(T_SVMLIGHT)
# Create a StreamingVwFeatures object, `True' indicating the examples are labelled
features = StreamingVwFeatures(input_file, True, 1024)
# Create a VW object from the features
vw = VowpalWabbit(features)
# Train
vw.train()
