def experiment_leave_k_out(exp_name, daily_data_file, min_occ_user, min_occ_prog, \
method_list, leave_k_out, total_iteration, top_n, binary = False):
'''
Parameters
----------
@param exp_name: the experiment name (prefix)
@param daily_datafile:
@param min_occ_user:
@param method_list:
@param leave_k_out: leave k out for each user. The k must be strict less than
min_occ_user
@param binary: if this is set to true then the binary data is used (non-zero set to 1).
Returns
----------
@return out
'''
if leave_k_out >= min_occ_user:
raise ValueError(
'The k in the leave k out should be strictly less than min_occ_user.'
)
# define lko_log style.
lko_log = lambda msg: Logger.Log(msg, Logger.MSG_CATEGORY_EXP)
# construct exp_id
if binary:
exp_id = 'lko_bi_' + exp_name + '_data' +str(hash(daily_data_file)) + '_mu' + str(min_occ_user) + '_mp' + str(min_occ_prog) \
+ '_k' + str(leave_k_out) + '_toiter' + str(total_iteration)
else:
exp_id = 'lko_' + exp_name + '_data' +str(hash(daily_data_file)) + '_mu' + str(min_occ_user) + '_mp' + str(min_occ_prog) \
+ '_k' + str(leave_k_out) + '_toiter' + str(total_iteration)
lko_log('Experiment ID: ' + exp_id)
# load data.
lko_log('Read data...')
reader = DailyWatchTimeReader()
data = reader.read_file_with_minval(daily_data_file, min_occ_user,
min_occ_prog)
lko_log('Data loaded: ' + str(data))
if binary:
lko_log('Binarizing data...')
data.binarize()
else:
# normalize
lko_log('Normalizing data...')
data.normalize_row()
result = {}
for method in method_list:
# do for each method
perf_vect = []
for iteration in range(total_iteration):
# do for each iteration for each method.
lko_log('Method: ' + method.unique_str() + ' Iteration: ' +
str(iteration))
# data split of the current iteration.
split_resource_str = 'exp' + exp_id + '_lvidx_iter' + str(
iteration)
split_dir = exp_id + '/lv_idx'
leave_k_out_idx = URM.LoadResource(URM.RTYPE_RESULT,
split_resource_str, split_dir)
if not leave_k_out_idx:
# randomly generate k items from each row/user.
leave_k_out_idx = ds.leave_k_out(data, leave_k_out)
URM.SaveResource(URM.RTYPE_RESULT, split_resource_str,
leave_k_out_idx, split_dir)
# split the k items as a separate.
[data_left, data_tr] = data.leave_k_out(leave_k_out_idx)
iter_result = experiment_unit_leave_k_out(exp_id, method, \
data_tr, data_left, iteration, top_n)
perf_vect.append(iter_result)
result[method.unique_str()] = perf_vect
return result
def experiment_leave_k_out(exp_name, daily_data_file, min_occ_user, min_occ_prog, \
method_list, leave_k_out, total_iteration, top_n, binary = False):
'''
Parameters
----------
@param exp_name: the experiment name (prefix)
@param daily_datafile:
@param min_occ_user:
@param method_list:
@param leave_k_out: leave k out for each user. The k must be strict less than
min_occ_user
@param binary: if this is set to true then the binary data is used (non-zero set to 1).
Returns
----------
@return out
'''
if leave_k_out >= min_occ_user:
raise ValueError('The k in the leave k out should be strictly less than min_occ_user.');
# define lko_log style.
lko_log = lambda msg: Logger.Log(msg, Logger.MSG_CATEGORY_EXP);
# construct exp_id
if binary:
exp_id = 'lko_bi_' + exp_name + '_data' +str(hash(daily_data_file)) + '_mu' + str(min_occ_user) + '_mp' + str(min_occ_prog) \
+ '_k' + str(leave_k_out) + '_toiter' + str(total_iteration);
else:
exp_id = 'lko_' + exp_name + '_data' +str(hash(daily_data_file)) + '_mu' + str(min_occ_user) + '_mp' + str(min_occ_prog) \
+ '_k' + str(leave_k_out) + '_toiter' + str(total_iteration);
lko_log('Experiment ID: ' + exp_id);
# load data.
lko_log('Read data...');
reader = UtilityDataReader();
data = reader.read_file_with_minval(daily_data_file, min_occ_user, min_occ_prog);
lko_log('Data loaded: ' + str(data));
if binary:
lko_log('Binarizing data...');
data.binarize();
else:
# normalize
lko_log('Normalizing data...');
data.normalize_row();
result = {};
for method in method_list:
# do for each method
perf_vect = [];
for iteration in range(total_iteration):
# do for each iteration for each method.
lko_log('Method: '+ method.unique_str() + ' Iteration: '+ str(iteration));
# data split of the current iteration.
split_resource_str = 'exp' + exp_id + '_lvidx_iter' + str(iteration);
split_dir = exp_id + '/lv_idx';
leave_k_out_idx = URM.LoadResource(URM.RTYPE_RESULT, split_resource_str, split_dir);
if not leave_k_out_idx:
# randomly generate k items from each row/user.
leave_k_out_idx = ds.leave_k_out(data, leave_k_out);
URM.SaveResource(URM.RTYPE_RESULT, split_resource_str, leave_k_out_idx, split_dir);
# split the k items as a separate.
[data_left, data_tr] = data.leave_k_out(leave_k_out_idx);
iter_result = experiment_unit_leave_k_out(exp_id, method, \
data_tr, data_left, iteration, top_n);
perf_vect.append(iter_result);
result[method.unique_str()] = perf_vect;
return result;
lo_data = csr_matrix( (data,indices,indptr), shape=(3, 5)).tocoo();
fb_data = FeedbackData(lo_data.row.tolist(), lo_data.col.tolist(), lo_data.data.tolist(), 3, 5,
np.array([]), np.array([]), np.array([]));
print 'Original data:'
print fb_data.get_sparse_matrix().todense();
# leave_k_out_idx = {};
# leave_k_out_idx [0] = set([4]);
# leave_k_out_idx [1] = set([0, 3]);
# leave_k_out_idx [2] = set([2]);
# generate leave k indices.
leave_k_out_idx = ds.leave_k_out(fb_data, 2);
print 'leave_k_indices'
print leave_k_out_idx;
[lo_data, tr_data] = fb_data.leave_k_out(leave_k_out_idx);
print 'Leave k out:'
print lo_data.get_sparse_matrix().todense();
print 'Remaining:'
print tr_data.get_sparse_matrix().todense();
def experiment_leave_k_out_map(exp_name, daily_data_file,\
min_occ_user, min_occ_prog, num_user, num_prog,\
method_list, leave_k_out, total_iteration, max_rank, binary = False):
'''
Parameters
----------
@param exp_name: the experiment name (prefix)
@param daily_datafile: a list of files.
@param min_occ_user: cold start user criteria
@param min_occ_prog: cold start user criteria
@param num_user: the number of users selected in the experiment.
@param num_prog: the number of programs selected in the experiment.
@param method_list:
@param leave_k_out: leave k out for each user. The k must be strict less than
min_occ_user
@param binary: if this is set to true then the binary data is used (non-zero set to 1).
Returns
----------
@return out
'''
print 'Leave k out: k = ', str(leave_k_out);
print 'Min_occ_user: '******'Min_occ_prog: ', str(min_occ_prog);
if leave_k_out >= min_occ_user:
raise ValueError('The k in the leave k out [' + str(leave_k_out)
+'] should be strictly less than min_occ_user [' + str(min_occ_user) +'].');
# define lko_log style.
lko_log = lambda msg: Logger.Log(msg, Logger.MSG_CATEGORY_EXP);
if isinstance(daily_data_file, list):
hash_file_str = str(hash(tuple(daily_data_file)));
else:
hash_file_str = str(hash(daily_data_file));
# construct exp_id
if binary:
exp_id = 'lko_bi_' + exp_name + '_data' + hash_file_str\
+ '_mu' + str(min_occ_user) + '_mp' + str(min_occ_prog) \
+ '_nu' + str(num_user) + '_np' + str(num_prog) \
+ '_k' + str(leave_k_out) + '_toiter' + str(total_iteration);
else:
exp_id = 'lko_' + exp_name + '_data' + hash_file_str\
+ '_mu' + str(min_occ_user) + '_mp' + str(min_occ_prog) \
+ '_nu' + str(num_user) + '_np' + str(num_prog) \
+ '_k' + str(leave_k_out) + '_toiter' + str(total_iteration);
lko_log('Experiment ID: ' + exp_id);
# load data.
lko_log('Read data...');
reader = DailyWatchTimeReader();
data = reader.read_file_with_minval(daily_data_file, min_occ_user, min_occ_prog, num_user, num_prog);
lko_log('Data loaded: ' + str(data));
if binary:
lko_log('Binarizing data...');
data.binarize();
else:
# normalize
lko_log('Normalizing data...');
data.normalize_row();
result = {};
for method in method_list:
# do for each method
perf_vect = [];
for iteration in range(total_iteration):
# do for each iteration for each method.
lko_log('Method: '+ method.unique_str() + ' Iteration: '+ str(iteration));
# data split of the current iteration.
split_resource_str = 'exp' + exp_id + '_lvidx_iter' + str(iteration);
split_dir = exp_id + '/lv_idx';
leave_k_out_idx = URM.LoadResource(URM.RTYPE_RESULT, split_resource_str, split_dir);
if not leave_k_out_idx:
# randomly generate k items from each row/user.
leave_k_out_idx = ds.leave_k_out(data, leave_k_out);
URM.SaveResource(URM.RTYPE_RESULT, split_resource_str, leave_k_out_idx, split_dir);
# split the k items as a separate.
[data_left, data_tr] = data.leave_k_out(leave_k_out_idx);
iter_result = experiment_unit_leave_k_out_map(exp_id, method, \
data_tr, data_left, iteration, max_rank);
perf_vect.append(iter_result);
result[method.unique_str()] = perf_vect;
return result;
indices = np.array([0, 2, 4, 0, 2, 3, 1, 2, 4])
indptr = np.array([0, 3, 6, 9])
lo_data = csr_matrix((data, indices, indptr), shape=(3, 5)).tocoo()
fb_data = FeedbackData(lo_data.row.tolist(), lo_data.col.tolist(),
lo_data.data.tolist(), 3, 5, np.array([]),
np.array([]), np.array([]))
print 'Original data:'
print fb_data.get_sparse_matrix().todense()
# leave_k_out_idx = {};
# leave_k_out_idx [0] = set([4]);
# leave_k_out_idx [1] = set([0, 3]);
# leave_k_out_idx [2] = set([2]);
# generate leave k indices.
leave_k_out_idx = ds.leave_k_out(fb_data, 2)
print 'leave_k_indices'
print leave_k_out_idx
[lo_data, tr_data] = fb_data.leave_k_out(leave_k_out_idx)
print 'Leave k out:'
print lo_data.get_sparse_matrix().todense()
print 'Remaining:'
print tr_data.get_sparse_matrix().todense()
