def experiment_future_program(exp_name, previous_data_files, future_data_file, \
min_occ_user, min_occ_prog, method_list, top_k):
'''
experiment entrance for future programs.
Top-k precision.
Parameters
----------
exp_name: a human-readable experiment name.
method_list: a list of recommendation models
Returns
----------
'''
# define mcpl_log style.
mcpl_log = lambda msg: Logger.Log(msg, Logger.MSG_CATEGORY_EXP)
exp_id = exp_name + '_mu' + str(min_occ_user) + '_mp' + str(min_occ_prog)
mcpl_log('Experimental ID: ' + exp_id)
reader = DailyWatchTimeReader()
tr_data = reader.read_file_with_minval(previous_data_files, min_occ_user,
min_occ_prog)
te_data = reader.read_file_with_minval(future_data_file, min_occ_user,
min_occ_prog)
mcpl_log('Normalization data ...')
tr_data.normalize_row()
# there is no need to normalize train data because we evaluate the hits.
result = {}
for method in method_list:
# do for each method
mcpl_log('Method: ' + method.unique_str())
method_result = experiment_unit_future_program(exp_id, method, tr_data,
te_data, top_k)
result[method.unique_str()] = method_result
mcpl_log('Experiment Done [' + exp_id + ']')
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 = 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
This is a testing pipeline for KDD_2014 algorithm. Created on Jan 30, 2014 @author: Shiyu C. ([email protected]) ''' from rs.data.daily_watchtime import DailyWatchTimeReader from rs.algorithms.recommendation.TriUHV import TriUHV from rs.data.recdata import FeedbackData if __name__ == '__main__': filename = "../../../datasample/agg_duid_pid_watchtime_genre/20131209_100000" # load data. reader = DailyWatchTimeReader() feedback_data = reader.read_file_with_minval(filename, 1, 1) feedback_data.normalize_row() # build model with 3 latent factors. r = 5 # the L_2 norm regularizer lamb = 0.2 # the stopping delta value delta = 0.01 # the maximium iteration number maxiter = 500 # TriUHV_model = TriUHV(r,lamb,delta,maxiter, verbose = True); # TriUHV_model.train(feedback_data); '''
# turn off screen display.
Logger.GetInstance().display_level = 10;
mcpl_log('Data file: ' + filename);
mcpl_log('ROVI daily file: ' + rovi_daily_file);
# build ROVI daily mapping
mcpl_log('Building ROVI daily mapping');
rovi_mapping = {};
with open(rovi_daily_file) as csvfile:
rovi_reader = csv.reader(csvfile, delimiter = '\t', quotechar = '|')
for row in rovi_reader:
rovi_mapping[row[3]] = row[7];
# load data from file and transform into a sparse matrix.
reader = DailyWatchTimeReader();
fbdata = reader.read_file_with_minval(filename, 5, 5);
mat = coo_matrix((fbdata.data_val, (fbdata.data_row, fbdata.data_col)), \
shape = (fbdata.num_row, fbdata.num_col));
# memo: if we do multiple days, we might use coo_matrix summation, but we need
# to align the program and user.
# we have a mapping from program id to row.
program_mapping = fbdata.col_mapping;
# from which we build a reverse mapping from row id to program
# the reverse mapping allows us to find program ID from matrix position.
program_inv_mapping = {y: x for x, y in program_mapping.items()};
# check the consistency.
if not (len(program_mapping) == len(program_inv_mapping)):
raise ValueError('Mapping inverse error!');
def experiment_rand_split(exp_name, daily_data_file, min_occ_user, min_occ_prog, \
method_list, training_prec, total_iteration):
'''
Parameters
----------
exp_name: a human-readable experiment name.
method_list: a list of matrix completion models
Returns
----------
'''
# define mcpl_log style.
mcpl_log = lambda msg: Logger.Log(msg, Logger.MSG_CATEGORY_EXP);
#mcpl_log('Data ID: ' + hash(daily_data_file));
# here we use a regular hash.
exp_id = exp_name + '_data' +str(hash(daily_data_file)) + '_mu' + str(min_occ_user) + '_mp' + str(min_occ_prog) \
+ '_trprec' + str(training_prec) + '_toiter' + str(total_iteration);
mcpl_log('Experiment ID: ' + exp_id);
# save experiment splitting as resources.
reader = DailyWatchTimeReader();
data = reader.read_file_with_minval(daily_data_file, min_occ_user, min_occ_prog);
# we normalize here before splitting.
mcpl_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;
mcpl_log('Method: '+ method.unique_str() + ' Iteration: '+ str(iteration));
# data split of the current iteration.
split_resource_str = 'exp' + exp_id + '_split_iter' + str(iteration);
split_dir = exp_id + '/split';
split = URM.LoadResource(URM.RTYPE_RESULT, split_resource_str, split_dir);
if not split:
split = ds.split(data.num_row, training_prec);
URM.SaveResource(URM.RTYPE_RESULT, split_resource_str, split, split_dir);
[split_tr, split_te] = split;
data_tr = data.subdata_row(split_tr);
data_te = data.subdata_row(split_te);
iter_result = experiment_unit_rand_split(exp_id, method, data_tr, data_te, iteration);
perf_vect.append(iter_result);
result[method.unique_str()] = perf_vect;
mcpl_log('Experiment Done [' + exp_id + ']');
return result;
''' This is a testing pipeline for KDD_2014 algorithm. Created on Jan 30, 2014 @author: Shiyu C. ([email protected]) ''' import numpy as np; from rs.data.daily_watchtime import DailyWatchTimeReader from rs.algorithms.recommendation.HierLat import HierLat if __name__ == '__main__': # load data. reader = DailyWatchTimeReader(); #filename = "../../../datasample/agg_duid_pid_watchtime_genre/20131209_100000"; #feedback_data = reader.read_file_with_minval(filename, 1, 1); filename = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131209/part-r-00000"; #feedback_data = reader.read_file_with_minval(filename, 25, 300); feedback_data = reader.read_file_with_minval(filename, 35, 300); print feedback_data; print 'Maximum Genre.' print np.max(feedback_data.meta['pggr_gr']) + 1; print 'Normalizing data.'
@author: jiayu.zhou
'''
from rs.data.daily_watchtime import DailyWatchTimeReader
if __name__ == '__main__':
#daily_data_file = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/test_comb/test";
#daily_data_file_p1 = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/test_comb/test1";
#daily_data_file_p2 = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/test_comb/test2";
daily_data_file = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131210/part";
daily_data_file_p1 = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131210/part-1";
daily_data_file_p2 = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131210/part-2";
min_occ_user = 0;
min_occ_prog = 0;
reader = DailyWatchTimeReader();
#[occur_duid1, occur_pid1, cnt_duid1, cnt_pid1] = reader.read_file_info(daily_data_file);
#[occur_duid2, occur_pid2, cnt_duid2, cnt_pid2] = reader.read_file_info([daily_data_file_p1, daily_data_file_p2]);
data1 = reader.read_file_with_minval(daily_data_file, min_occ_user, min_occ_prog);
print data1;
#print data1.get_sparse_matrix().todense();
data2 = reader.read_file_with_minval([daily_data_file_p1, daily_data_file_p2], \
min_occ_user, min_occ_prog);
print data2;
#print data2.get_sparse_matrix().todense();
'''
Test the functionality of the share_row_data function in the rs.data.recdata
Created on Feb 4, 2014
@author: jiayu.zhou
'''
from rs.data.daily_watchtime import DailyWatchTimeReader
from rs.data.recdata import share_row_data;
if __name__ == '__main__':
reader = DailyWatchTimeReader();
filename1 = "../../datasample/agg_duid_pid_watchtime_genre/toy_small_day1";
filename2 = "../../datasample/agg_duid_pid_watchtime_genre/toy_small_day2";
fb_data1 = reader.read_file_with_minval(filename1, 0, 0);
fb_data2 = reader.read_file_with_minval(filename2, 0, 0);
print 'Matrix 1'
print fb_data1.row_mapping;
print fb_data1.col_mapping;
print fb_data1.get_sparse_matrix().todense();
print 'Matrix 2'
print fb_data2.row_mapping;
print fb_data2.col_mapping;
print fb_data2.get_sparse_matrix().todense();
num_user = 10000
num_prog = 3000
total_iteration = 2
iteration = 1
# iteration out of total_iteration.
leave_k_out = 20
lafactor = 5
method = HierLat(latent_factor=lafactor)
hash_file_str = str(hash(tuple(daily_data_file)))
reader = DailyWatchTimeReader()
feedback_data = reader.read_file_with_minval(daily_data_file, min_occ_user,
min_occ_prog, num_user,
num_prog)
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)
result_resource_str = 'exp' + exp_id + \
'_method' + method.unique_str() + \
'_iter' + str(iteration)
sub_folder = exp_id + '/models/' + method.unique_str()
# use a sub folder to store the experiment resource.
min_occ_prog = 1000;
num_user = 10000;
num_prog = 3000;
total_iteration = 2;
iteration = 1; # iteration out of total_iteration.
leave_k_out = 20;
lafactor = 5;
method = HierLat(latent_factor=lafactor);
hash_file_str = str(hash(tuple(daily_data_file)));
reader = DailyWatchTimeReader();
feedback_data = reader.read_file_with_minval(daily_data_file, min_occ_user, min_occ_prog, num_user, num_prog);
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);
result_resource_str = 'exp' + exp_id + \
'_method' + method.unique_str() + \
'_iter' + str(iteration);
sub_folder = exp_id + '/models/' + method.unique_str(); # use a sub folder to store the experiment resource.
trained_model = URM.LoadResource(URM.RTYPE_RESULT, result_resource_str, sub_folder);
[method] = trained_model;
#@UnusedImport
from rs.data.daily_watchtime import DailyWatchTimeReader
if __name__ == '__main__':
filename = "../../datasample/agg_duid_pid_watchtime_genre/20131209_100000"
#filename = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131209/part-r-00000";
if len(sys.argv) == 1:
print 'Use default sample data.'
else:
filename = sys.argv[1]
print 'processing file', filename
reader = DailyWatchTimeReader()
#reader.readFile(filename);
#[mapping_duid, mapping_pid, row, col, data, pggr_pg, pggr_gr] = \
# reader.read_file_with_minval(filename, 5, 5);
dataStruct = reader.read_file_with_minval(filename, 1, 1)
print dataStruct
#print len(pggr_pg);
#print len(pggr_gr);
#print len(set(pggr_gr));
#[occur_duid, occur_pid, cnt_duid, cnt_pid] = reader.readFileInfo(filename);
#print cnt_duid;
#print cnt_pid;
#histplot(occur_duid.values());
#histplot(occur_pid.values());
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;
'''
Test the functionality of the share_row_data function in the rs.data.recdata
Created on Feb 4, 2014
@author: jiayu.zhou
'''
from rs.data.daily_watchtime import DailyWatchTimeReader
from rs.data.recdata import share_row_data
if __name__ == '__main__':
reader = DailyWatchTimeReader()
filename1 = "../../datasample/agg_duid_pid_watchtime_genre/toy_small_day1"
filename2 = "../../datasample/agg_duid_pid_watchtime_genre/toy_small_day2"
fb_data1 = reader.read_file_with_minval(filename1, 0, 0)
fb_data2 = reader.read_file_with_minval(filename2, 0, 0)
print 'Matrix 1'
print fb_data1.row_mapping
print fb_data1.col_mapping
print fb_data1.get_sparse_matrix().todense()
print 'Matrix 2'
print fb_data2.row_mapping
print fb_data2.col_mapping
print fb_data2.get_sparse_matrix().todense()
# turn off screen display.
Logger.GetInstance().display_level = 10
mcpl_log('Data file: ' + filename)
mcpl_log('ROVI daily file: ' + rovi_daily_file)
# build ROVI daily mapping
mcpl_log('Building ROVI daily mapping')
rovi_mapping = {}
with open(rovi_daily_file) as csvfile:
rovi_reader = csv.reader(csvfile, delimiter='\t', quotechar='|')
for row in rovi_reader:
rovi_mapping[row[3]] = row[7]
# load data from file and transform into a sparse matrix.
reader = DailyWatchTimeReader()
fbdata = reader.read_file_with_minval(filename, 5, 5)
mat = coo_matrix((fbdata.data_val, (fbdata.data_row, fbdata.data_col)), \
shape = (fbdata.num_row, fbdata.num_col))
# memo: if we do multiple days, we might use coo_matrix summation, but we need
# to align the program and user.
# we have a mapping from program id to row.
program_mapping = fbdata.col_mapping
# from which we build a reverse mapping from row id to program
# the reverse mapping allows us to find program ID from matrix position.
program_inv_mapping = {y: x
for x, y in program_mapping.items()}
# check the consistency.
if not (len(program_mapping) == len(program_inv_mapping)):
"""
Created on Jan 31, 2014
@author: jiayu.zhou
"""
from rs.data.daily_watchtime import DailyWatchTimeReader
if __name__ == "__main__":
filename = "../../datasample/agg_duid_pid_watchtime_genre/20131209_100000"
reader = DailyWatchTimeReader()
dataStruct = reader.read_file_with_minval(filename, 7, 1)
print dataStruct
print dataStruct.row_mapping
print dataStruct.get_sparse_matrix().todense()
print "-----------------"
print ">>>subsample 3 rows"
[subdata_row, subidx] = dataStruct.subsample_row(3)
print subidx
print subdata_row.get_sparse_matrix().todense()
print subdata_row.row_mapping
print "-----------------"
print ">>>subsample 50% rows"
''' This is a testing pipeline for KDD_2014 algorithm. Created Feb 7, 2014 @author: Shiyu C. ([email protected]) ''' from rs.data.daily_watchtime import DailyWatchTimeReader from rs.algorithms.recommendation.CF_ONMTF import CF_ONMTF if __name__ == '__main__': filename = "../../../datasample/agg_duid_pid_watchtime_genre/20131209_100000"; # load data. reader = DailyWatchTimeReader(); feedback_data = reader.read_file_with_minval(filename, 1, 1); feedback_data.normalize_row(); # build model with 3 latent factors. r = 5; # the L_2 norm regularizer lamb = 0.2; # the stopping delta value delta = 0.01; # the maximium iteration number maxiter = 500; CF_ONMTF_model = CF_ONMTF(r,lamb,delta,maxiter, verbose = True); CF_ONMTF_model.train(feedback_data); '''
'''
Created on Feb 13, 2014
@author: jiayu.zhou
'''
import scipy.io as sio
from rs.data.daily_watchtime import DailyWatchTimeReader
if __name__ == '__main__':
daily_data_file = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131209/part-r-00000"
reader = DailyWatchTimeReader()
data = reader.read_file_with_minval(daily_data_file, 1, 1)
data_mat = data.get_sparse_matrix()
### directory save sparse matrix data structure to Matlab.
#sio.savemat("/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131209.mat", {'data': data_mat})
###
sio.savemat("/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131209_sparse.mat",
{'data': data_mat.data, 'i': data_mat.row, 'j': data_mat.col, \
'm': data_mat.shape[0], 'n': data_mat.shape[1]})
print 'Done'
'''
Created on Feb 13, 2014
@author: jiayu.zhou
'''
import scipy.io as sio;
from rs.data.daily_watchtime import DailyWatchTimeReader
if __name__ == '__main__':
daily_data_file = "/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131209/part-r-00000";
reader = DailyWatchTimeReader();
data = reader.read_file_with_minval(daily_data_file, 1, 1);
data_mat = data.get_sparse_matrix();
### directory save sparse matrix data structure to Matlab.
#sio.savemat("/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131209.mat", {'data': data_mat})
###
sio.savemat("/Users/jiayu.zhou/Data/duid-program-watchTime-genre/20131209_sparse.mat",
{'data': data_mat.data, 'i': data_mat.row, 'j': data_mat.col, \
'm': data_mat.shape[0], 'n': data_mat.shape[1]});
print 'Done';
