def subsample_row(self, sel_row_num):
'''
randomly sub-sample the data of a given number of users. This will modify
the row/col/val.
The method first constructs a coo sparse matrix and then convert to csr matrix
for row slicing. And then the csr matrix is converted back.
Parameters
----------
@param sel_row_num: the number of selected rows.
Returns
----------
@return out: a list of two components [sample_data, selidx]
sample_data:
sel_idx:
'''
# sampling a set of rows
sel_idx = ds.sample_num(self.num_row, sel_row_num);
# construct data set using the selected rows
sample_data = self.subdata_row(sel_idx);
return [sample_data, sel_idx];
def subsample_row(self, sel_row_num):
'''
randomly sub-sample the data of a given number of users. This will modify
the row/col/val.
The method first constructs a coo sparse matrix and then convert to csr matrix
for row slicing. And then the csr matrix is converted back.
Parameters
----------
@param sel_row_num: the number of selected rows.
Returns
----------
@return out: a list of two components [sample_data, selidx]
sample_data:
sel_idx:
'''
# sampling a set of rows
sel_idx = ds.sample_num(self.num_row, sel_row_num)
# construct data set using the selected rows
sample_data = self.subdata_row(sel_idx)
return [sample_data, sel_idx]
def experiment_coldstart_map(exp_name, daily_data_file,\
min_occ_user, min_occ_prog, num_user, num_prog,\
method_list, blind_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 blind_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 'Blind k out: k = ', str(blind_k_out);
print 'Min_occ_user: '******'Min_occ_prog: ', str(min_occ_prog);
if blind_k_out >= min_occ_user:
raise ValueError('The k in the leave k out [' + str(blind_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 = 'cst_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(blind_k_out) + '_toiter' + str(total_iteration);
else:
exp_id = 'cst_' + 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(blind_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 + '_blind_idx_iter' + str(iteration);
split_dir = exp_id + '/blind_idx';
blind_out_idx = URM.LoadResource(URM.RTYPE_RESULT, split_resource_str, split_dir);
if not blind_out_idx:
# randomly generate k items to blind out.
blind_out_idx = ds.sample_num(data.num_col, blind_k_out);
URM.SaveResource(URM.RTYPE_RESULT, split_resource_str, blind_out_idx, split_dir);
lko_log('Blind index done.');
# split the k items as a separate.
[data_tr, data_left] = data.blind_k_out(blind_out_idx);
lko_log('Start index');
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;
