def main(train_file, user_sideinformation_file, test_file, normalize):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
if normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
W = sslim_train(A, B)
recommendations = slim_recommender(A, W)
return compute_precision(recommendations, test_file)
def main(train_file, user_sideinformation_file, test_file, normalize):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
if normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
W = sslim_train(A, B)
save_matrix(W, 'sslim_oracle_wmatrix.tsv')
recommendations = slim_recommender(A, W)
precisions = compute_precision_as_an_oracle(recommendations, test_file)
return precisions
def main(train_file, user_sideinformation_file, test_file, normalize):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
if normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
W = sslim_train(A, B)
save_matrix(W, 'sslim_oracle_wmatrix.tsv')
recommendations = slim_recommender(A, W)
precisions = compute_precision_as_an_oracle(recommendations, test_file)
return precisions
def main(train_file, user_sideinformation_file, test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
A, B = make_compatible(A, B)
"""
from util import mm2csr
mm2csr(A, '/tmp/train.mat')
mm2csr(useritem_featureitem, '/tmp/train_feature.mat')
C = tsv_to_matrix(test_file)
mm2csr(C, '/tmp/test.mat')
"""
W = sslim_train(A, B)
recommendations = slim_recommender(A, W)
compute_precision(recommendations, test_file)
def main(train_file, user_sideinformation_file, test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
A, B = make_compatible(A, B)
"""
from util import mm2csr
mm2csr(A, '/tmp/train.mat')
mm2csr(useritem_featureitem, '/tmp/train_feature.mat')
C = tsv_to_matrix(test_file)
mm2csr(C, '/tmp/test.mat')
"""
W = sslim_train(A, B)
recommendations = slim_recommender(A, W)
compute_precision(recommendations, test_file)
train_file = args.train
user_sideinformation_file = args.side_information
output = args.output
test_file = args.test
beta = args.beta or 0.011
normalize = args.normalize
fold = args.fold or 0
# Loading matrices
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
if normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1]**2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# We create a work function to fit each one of the columns of our W matrix,
# because in SLIM each column is independent we can use make this work in
# parallel
def work(params, W=shared_array):
from_j = params[0]
to_j = params[1]
M = params[2]
model = params[3]
counter = 0
import datetime
from util import parse_args
import simplejson as json
print '>>> Start: %s' % datetime.datetime.now()
args = parse_args(side_information=True, beta=True)
# Loading matrices
A = tsv_to_matrix(args.train)
B = tsv_to_matrix(args.side_information)
if args.normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1]**2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# We create a work function to fit each one of the columns of our W matrix,
# because in SLIM each column is independent we can use make this work in
# parallel
def work(params, W=shared_array):
from_j = params[0]
to_j = params[1]
M = params[2]
model = params[3]