def trial_init(recdr, logr):
logr.log('Initializing new trial...', 'standard')
b = DataGenerator()
b.set_baseline_response_prob(baseline)
b.add_random_user_attrs(num_user_atts, min_user_att_levels,
max_user_att_levels)
b.add_random_inter_attrs(num_msg_atts, min_msg_att_levels,
max_msg_att_levels)
templates = b.set_random_propensities(
num_propensity_groups, min_group_user_atts, max_group_user_atts,
min_group_msg_atts, max_group_msg_atts, min_group_pos_prob,
max_group_pos_prob)
# -> Returns: a pair (user templates, interaction templates)
logr.log('Generating data...', 'standard')
messages = b.gen_random_inters(num_test_messages)
rows = ut.unzip(b.gen_crossprod_rows(b.unique_users(), messages))
logr.log('Number of rows: ' + str(len(rows)), 'standard')
# Split data into train, calibration, and test.
train, calibrate, test = ut.split_data(rows, 0.5, 0.25, 0.25)
calibration_users = map(lambda (u, m, r): u, calibrate)
test_users = map(lambda (u, m, r): u, test)
controls = su.build_std_control_solvers(calibrate, b, messages, 15)
treatments = su.build_std_knn_optims(train, calibrate, b, recorder, 1, 15)
solvers = controls + treatments
return (train, test_users, b, solvers)
def trial_init(recdr, logr):
logr.log('Initializing new trial...', 'standard')
b = DataGenerator()
b.set_baseline_response_prob(baseline)
b.add_random_user_attrs(num_user_atts, min_user_att_levels, max_user_att_levels)
b.add_random_inter_attrs(num_msg_atts, min_msg_att_levels, max_msg_att_levels)
templates = b.set_random_propensities(num_propensity_groups,
min_group_user_atts, max_group_user_atts,
min_group_msg_atts, max_group_msg_atts,
min_group_pos_prob, max_group_pos_prob)
# -> Returns: a pair (user templates, interaction templates)
logr.log('Generating data...', 'standard')
messages = b.gen_random_inters(num_test_messages)
users = b.gen_random_users(num_users)
#rows = ut.unzip(b.gen_crossprod_rows(b.unique_users(), messages))
rows = ut.unzip(b.gen_random_rows_from(users, messages))
logr.log('Number of rows: ' + str(len(rows)), 'standard')
# Split data into train, calibration, and test.
train, calibrate, test = ut.split_data(rows, 0.5, 0.25, 0.25)
calibration_users = map(lambda (u, m, r): u, calibrate)
test_users = map(lambda (u, m, r): u, test)
controls = su.build_std_control_solvers(calibrate, b, messages, 15)
treatments = su.build_std_knn_optims(train, calibrate, b, recorder, 1, 15)
solvers = controls + treatments
return (train, test_users, b, solvers)
def trial_init(recdr, logr): # Split data into train, calibration, and test. train, calibrate, test = ut.split_data(rows, 0.5, 0.25, 0.25) calibration_users = map(lambda (u, m, r): u, calibrate) test_users = map(lambda (u, m, r): u, test) controls = su.build_std_control_solvers(calibrate, b, 100, 15) treatments = su.build_std_knn_optims(train, calibrate, b, recorder, 1, 15) solvers = controls + treatments return (train, test_users, b, solvers)
def trial_init(recdr, logr):
# Split data into train, calibration, and test.
train, calibrate, test = ut.split_data(rows, 0.5, 0.25, 0.25)
calibration_users = map(lambda (u, m, r): u, calibrate)
test_users = map(lambda (u, m, r): u, test)
controls = su.build_std_control_solvers(calibrate, b, 100, 15)
treatments = su.build_std_knn_optims(train, calibrate, b, recorder, 1, 15)
solvers = controls + treatments
return (train, test_users, b, solvers)
mt5 = {'IA_2':'L_4', 'IA_4':'L_3'}
b.set_user_inter_propensity(ut1, mt1, 0.5)
b.set_user_inter_propensity(ut2, mt2, 0.5)
b.set_user_inter_propensity(ut3, mt3, 0.5)
b.set_user_inter_propensity(ut4, mt4, 0.99)
b.set_user_inter_propensity(ut5, mt5, 0.5)
rows = []
rows += ut.unzip(b.gen_random_rows_from_template(ut1, mt1, 100))
rows += ut.unzip(b.gen_random_rows_from_template(ut2, mt2, 100))
rows += ut.unzip(b.gen_random_rows_from_template(ut3, mt3, 100))
rows += ut.unzip(b.gen_random_rows_from_template(ut4, mt4, 100))
rows += ut.unzip(b.gen_random_rows_from_template(ut5, mt5, 100))
rows += ut.unzip(b.gen_random_rows(2000))
log = su.BasicLogger()
recorder = su.ScenarioRecorder()
# Split data into train, calibration, and test.
train, calibrate, test = ut.split_data(rows, 0.5, 0.25, 0.25)
calibration_users = map(lambda (u, m, r): u, calibrate)
test_users = map(lambda (u, m, r): u, test)
controls = su.build_std_control_solvers(calibrate, b, 100, 15)
treatments = su.build_std_knn_optims(train, calibrate, b, recorder, 1, 15)
solvers = controls + treatments
su.execute_trial(train, test_users, b, solvers, recorder, logger = log)
mt5 = {'IA_2': 'L_4', 'IA_4': 'L_3'}
b.set_user_inter_propensity(ut1, mt1, 0.5)
b.set_user_inter_propensity(ut2, mt2, 0.5)
b.set_user_inter_propensity(ut3, mt3, 0.5)
b.set_user_inter_propensity(ut4, mt4, 0.99)
b.set_user_inter_propensity(ut5, mt5, 0.5)
rows = []
rows += ut.unzip(b.gen_random_rows_from_template(ut1, mt1, 100))
rows += ut.unzip(b.gen_random_rows_from_template(ut2, mt2, 100))
rows += ut.unzip(b.gen_random_rows_from_template(ut3, mt3, 100))
rows += ut.unzip(b.gen_random_rows_from_template(ut4, mt4, 100))
rows += ut.unzip(b.gen_random_rows_from_template(ut5, mt5, 100))
rows += ut.unzip(b.gen_random_rows(2000))
log = su.BasicLogger()
recorder = su.ScenarioRecorder()
# Split data into train, calibration, and test.
train, calibrate, test = ut.split_data(rows, 0.5, 0.25, 0.25)
calibration_users = map(lambda (u, m, r): u, calibrate)
test_users = map(lambda (u, m, r): u, test)
controls = su.build_std_control_solvers(calibrate, b, 100, 15)
treatments = su.build_std_knn_optims(train, calibrate, b, recorder, 1, 15)
solvers = controls + treatments
su.execute_trial(train, test_users, b, solvers, recorder, logger=log)