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_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()
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()