# Read the Data Format as input
if len(sys.argv) != 3:
print "usage: python ranked_list_generation.py <EXPERIMENT_NAME> <REGION (e.g. chicago)>"
exit(1)
EXPERIMENT_NAME = sys.argv[1]
REGION = sys.argv[2]
DATA_DIR = "data"
PARTITIONED_DATA_DIR = path.join(DATA_DIR, "partitioned_data", REGION)
EXPERIMENT_DATA_DIR = path.join(DATA_DIR, "experiments", EXPERIMENT_NAME)
RECOMMENDATION_DATA_DIR = path.join(EXPERIMENT_DATA_DIR, REGION,
"recommendations")
# Reading the Experiment Attributes
EXPERIMENT_ATTS = read_experiment_atts(EXPERIMENT_DATA_DIR)
# Iterate over the EXPERIMENT partitions
for part in EXPERIMENT_ATTS['partitions']:
print "Partition %s" % part
# Define the DB partition dir and the RESULT partition dir
db_partition_dir = path.join(PARTITIONED_DATA_DIR,
"partition_%s" % part)
result_partition_dir = path.join(RECOMMENDATION_DATA_DIR,
"partition_%s" % part)
# Define the partition ranks directory
partition_rank_dir = path.join(result_partition_dir, "ranks")
print "\tReading the mapping files..."
# Read the Data Format as input
if len(sys.argv) != 3:
print "usage: python ranked_list_generation.py <EXPERIMENT_NAME> <REGION (e.g. chicago)>"
exit(1)
EXPERIMENT_NAME = sys.argv[1]
REGION = sys.argv[2]
DATA_DIR = "data"
PARTITIONED_DATA_DIR = path.join(DATA_DIR, "partitioned_data", REGION)
EXPERIMENT_DATA_DIR = path.join(DATA_DIR, "experiments", EXPERIMENT_NAME)
RECOMMENDATION_DATA_DIR = path.join(EXPERIMENT_DATA_DIR, REGION, "recommendations")
# Reading the Experiment Attributes
EXPERIMENT_ATTS = read_experiment_atts(EXPERIMENT_DATA_DIR)
# Iterate over the EXPERIMENT partitions
for part in EXPERIMENT_ATTS['partitions']:
print "Partition %s" % part
# Define the DB partition dir and the RESULT partition dir
db_partition_dir = path.join(PARTITIONED_DATA_DIR, "partition_%s" % part)
result_partition_dir = path.join(RECOMMENDATION_DATA_DIR, "partition_%s" % part)
# Define the partition ranks directory
partition_rank_dir = path.join(result_partition_dir, "ranks")
print "\tReading the mapping files..."
# Read the mapping user_id files
map_event_ids = read_map_event_ids(db_partition_dir)
REGION = ARGS.region
ALGORITHMS = ARGS.algorithms
MAX_PARALLEL = ARGS.max_parallel
DATA_DIR = "data"
PARTITIONED_REGION_DATA_DIR = os.path.join(DATA_DIR, "partitioned_data", REGION)
EXPERIMENT_DIR = os.path.join(DATA_DIR, "experiments", EXPERIMENT_NAME)
REC_REGION_DATA_DIR = os.path.join(EXPERIMENT_DIR, REGION, "recommendations")
REC_RESULT_DIR_NAME = "hybrid_models"
LOGGER.info("HYBRID Algorithms")
LOGGER.info(ALGORITHMS)
# Read the experiment attributes
PARTITIONS = read_experiment_atts(EXPERIMENT_DIR)["partitions"]
DICT_ALG_RANKFILE = get_dict_alg_files(REGION)
ENSEMBLE_LIST = sorted(DICT_ALG_RANKFILE.keys())
if MAX_PARALLEL > 1:
# Define the Multiprocessing Pool (with size equals to CPU_COUNT -1)
EXPERIMENT_POOL = multiprocessing.Pool(MAX_PARALLEL)
# Starts the multiple processes
EXPERIMENT_POOL.map(create_models_and_recommend,
get_models_to_experiment(PARTITIONS, ALGORITHMS,
PARTITIONED_REGION_DATA_DIR, REC_REGION_DATA_DIR, REC_RESULT_DIR_NAME))
else:
for experiment_data in get_models_to_experiment(PARTITIONS, ALGORITHMS,
PARTITIONED_REGION_DATA_DIR, REC_REGION_DATA_DIR, REC_RESULT_DIR_NAME):
EXPERIMENT_NAME = ARGS.experiment_name
REGION = ARGS.region
ALGORITHMS = ARGS.algorithms
PARALLEL_EXECUTION = not ARGS.not_parallel
DATA_DIR = "data"
PARTITIONED_REGION_DATA_DIR = path.join(DATA_DIR, "partitioned_data",
REGION)
EXPERIMENT_DIR = path.join(DATA_DIR, "experiments", EXPERIMENT_NAME)
EXPERIMENT_REGION_DATA_DIR = path.join(EXPERIMENT_DIR, REGION)
LOGGER.info("Content-Based Algorithms")
LOGGER.info(ALGORITHMS)
# Read the experiment attributes
PARTITIONS = read_experiment_atts(EXPERIMENT_DIR)["partitions"]
if PARALLEL_EXECUTION:
# Define the Multiprocessing Pool (with size equals to CPU_COUNT -1)
EXPERIMENT_POOL = multiprocessing.Pool(multiprocessing.cpu_count() - 1)
# Starts the multiple processes
EXPERIMENT_POOL.map(
create_models_and_recommend,
get_models_to_experiment(PARTITIONS, ALGORITHMS,
PARTITIONED_REGION_DATA_DIR,
EXPERIMENT_REGION_DATA_DIR))
else:
for experiment_data in get_models_to_experiment(
PARTITIONS, ALGORITHMS, PARTITIONED_REGION_DATA_DIR,
EXPERIMENT_REGION_DATA_DIR):
create_models_and_recommend(experiment_data)
