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run.py
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run.py
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'''
Created on Dec 9, 2015
@author: donghyun
'''
import argparse
import sys
from data_manager import Data_Factory
parser = argparse.ArgumentParser()
# Option for pre-processing data
parser.add_argument("-c", "--do_preprocess", type=bool,
help="True or False to preprocess raw data for ConvMF (default = False)", default=False)
parser.add_argument("-r", "--raw_rating_data_path", type=str,
help="Path to raw rating data. data format - user id::item id::rating")
parser.add_argument("-i", "--raw_item_document_data_path", type=str,
help="Path to raw item document data. item document consists of multiple text. data format - item id::text1|text2...")
parser.add_argument("-m", "--min_rating", type=int,
help="Users who have less than \"min_rating\" ratings will be removed (default = 1)", default=1)
parser.add_argument("-l", "--max_length_document", type=int,
help="Maximum length of document of each item (default = 300)", default=300)
parser.add_argument("-f", "--max_df", type=float,
help="Threshold to ignore terms that have a document frequency higher than the given value (default = 0.5)", default=0.5)
parser.add_argument("-s", "--vocab_size", type=int,
help="Size of vocabulary (default = 8000)", default=8000)
parser.add_argument("-t", "--split_ratio", type=float,
help="Ratio: 1-ratio, ratio/2 and ratio/2 of the entire dataset (R) will be training, valid and test set, respectively (default = 0.2)", default=0.2)
# Option for pre-processing data and running ConvMF
parser.add_argument("-d", "--data_path", type=str,
help="Path to training, valid and test data sets")
parser.add_argument("-a", "--aux_path", type=str, help="Path to R, D_all sets")
# Option for running ConvMF
parser.add_argument("-o", "--res_dir", type=str,
help="Path to ConvMF's result")
parser.add_argument("-e", "--emb_dim", type=int,
help="Size of latent dimension for word vectors (default: 200)", default=200)
parser.add_argument("-p", "--pretrain_w2v", type=str,
help="Path to pretrain word embedding model to initialize word vectors")
parser.add_argument("-g", "--give_item_weight", type=bool,
help="True or False to give item weight of ConvMF (default = False)", default=True)
parser.add_argument("-k", "--dimension", type=int,
help="Size of latent dimension for users and items (default: 50)", default=50)
parser.add_argument("-u", "--lambda_u", type=float,
help="Value of user regularizer")
parser.add_argument("-v", "--lambda_v", type=float,
help="Value of item regularizer")
parser.add_argument("-n", "--max_iter", type=int,
help="Value of max iteration (default: 200)", default=200)
parser.add_argument("-w", "--num_kernel_per_ws", type=int,
help="Number of kernels per window size for CNN module (default: 100)", default=100)
args = parser.parse_args()
do_preprocess = args.do_preprocess
data_path = args.data_path
aux_path = args.aux_path
if data_path is None:
sys.exit("Argument missing - data_path is required")
if aux_path is None:
sys.exit("Argument missing - aux_path is required")
data_factory = Data_Factory()
if do_preprocess:
path_rating = args.raw_rating_data_path
path_itemtext = args.raw_item_document_data_path
min_rating = args.min_rating
max_length = args.max_length_document
max_df = args.max_df
vocab_size = args.vocab_size
split_ratio = args.split_ratio
print "=================================Preprocess Option Setting================================="
print "\tsaving preprocessed aux path - %s" % aux_path
print "\tsaving preprocessed data path - %s" % data_path
print "\trating data path - %s" % path_rating
print "\tdocument data path - %s" % path_itemtext
print "\tmin_rating: %d\n\tmax_length_document: %d\n\tmax_df: %.1f\n\tvocab_size: %d\n\tsplit_ratio: %.1f" \
% (min_rating, max_length, max_df, vocab_size, split_ratio)
print "==========================================================================================="
R, D_all = data_factory.preprocess(
path_rating, path_itemtext, min_rating, max_length, max_df, vocab_size)
data_factory.save(aux_path, R, D_all)
data_factory.generate_train_valid_test_file_from_R(
data_path, R, split_ratio)
else:
res_dir = args.res_dir
emb_dim = args.emb_dim
pretrain_w2v = args.pretrain_w2v
dimension = args.dimension
lambda_u = args.lambda_u
lambda_v = args.lambda_v
max_iter = args.max_iter
num_kernel_per_ws = args.num_kernel_per_ws
give_item_weight = args.give_item_weight
if res_dir is None:
sys.exit("Argument missing - res_dir is required")
if lambda_u is None:
sys.exit("Argument missing - lambda_u is required")
if lambda_v is None:
sys.exit("Argument missing - lambda_v is required")
print "===================================ConvMF Option Setting==================================="
print "\taux path - %s" % aux_path
print "\tdata path - %s" % data_path
print "\tresult path - %s" % res_dir
print "\tpretrained w2v data path - %s" % pretrain_w2v
print "\tdimension: %d\n\tlambda_u: %.4f\n\tlambda_v: %.4f\n\tmax_iter: %d\n\tnum_kernel_per_ws: %d" \
% (dimension, lambda_u, lambda_v, max_iter, num_kernel_per_ws)
print "==========================================================================================="
R, D_all = data_factory.load(aux_path)
CNN_X = D_all['X_sequence']
vocab_size = len(D_all['X_vocab']) + 1
from models import ConvMF
if pretrain_w2v is None:
init_W = None
else:
init_W = data_factory.read_pretrained_word2vec(
pretrain_w2v, D_all['X_vocab'], emb_dim)
train_user = data_factory.read_rating(data_path + '/train_user.dat')
train_item = data_factory.read_rating(data_path + '/train_item.dat')
valid_user = data_factory.read_rating(data_path + '/valid_user.dat')
test_user = data_factory.read_rating(data_path + '/test_user.dat')
ConvMF(max_iter=max_iter, res_dir=res_dir,
lambda_u=lambda_u, lambda_v=lambda_v, dimension=dimension, vocab_size=vocab_size, init_W=init_W, give_item_weight=give_item_weight, CNN_X=CNN_X, emb_dim=emb_dim, num_kernel_per_ws=num_kernel_per_ws,
train_user=train_user, train_item=train_item, valid_user=valid_user, test_user=test_user, R=R)