def timing_triple_cloud():
execfile('picloud_venture_credentials.py')
exp_params = experiment.exp_param_defaults({})
exp_params['intermediate_iter'] = 1
exp_params['max_initial_run_time'] = 30
exp_params['max_burn_time'] = 30
exp_params['max_sample_time'] = 30
exp_params['n_samples'] = 25
print experiment.exp_params_to_str(exp_params)
data = scipy.io.loadmat("../data/irm_synth/irm_synth_20.mat", squeeze_me=True)
observed = list(zip(data['train_i'].flat, data['train_j'].flat, data['train_v'].flat))
missing = list(zip(data['test_i'].flat, data['test_j'].flat, data['test_v'].flat))
data = {'observations' : observed, 'missing' : missing}
model = models.product_IRM
model_params = {'D' : 1, 'alpha' : 1, 'symmetric' : True}
# Timing run
print 'Timing'
job_id = cloud.call(experiment.network_cv_timing_run, data, model, exp_params, model_params, _max_runtime=5, _env=cloud_environment)
time_per_mh_iter = cloud.result(job_id)['time_per_mh_iter']
# Live run
print 'Live'
exp_params['intermediate_iter'] = max(1, int(round(0.9 * exp_params['max_sample_time'] / (exp_params['n_samples'] * time_per_mh_iter))))
job_id = cloud.call(experiment.network_cv_single_run, data, model, exp_params, model_params, _max_runtime=5, _env=cloud_environment)
cloud.join(job_id)
print cloud.result(job_id)
def timing_run_local():
exp_params = experiment.exp_param_defaults({})
exp_params['intermediate_iter'] = 1
exp_params['max_initial_run_time'] = 30
print experiment.exp_params_to_str(exp_params)
data = scipy.io.loadmat("../data/irm_synth/irm_synth_20.mat", squeeze_me=True)
observed = list(zip(data['train_i'].flat, data['train_j'].flat, data['train_v'].flat))
missing = list(zip(data['test_i'].flat, data['test_j'].flat, data['test_v'].flat))
data = {'observations' : observed, 'missing' : missing}
model = models.product_IRM
model_params = {'D' : 1, 'alpha' : 1, 'symmetric' : True}
print experiment.network_cv_timing_run(data, model, exp_params, model_params)
def fold(unused=None):
execfile('picloud_venture_credentials.py')
data_file = '../data/irm_synth/irm_synth_20.mat'
data_dir = '../data/irm_synth/'
model = models.product_IRM
model_params = {'D' : 1, 'alpha' : 1, 'symmetric' : True}
exp_params = experiment.exp_param_defaults({})
exp_params['intermediate_iter'] = 1
exp_params['max_initial_run_time'] = 20
exp_params['max_burn_time'] = 10
exp_params['max_sample_time'] = 20
exp_params['n_samples'] = 25
exp_params['n_restarts'] = 3
print experiment.exp_params_to_str(exp_params)
print experiment.network_cv_fold(data_file, data_dir, model, exp_params, model_params)
def timing_run_cloud():
execfile('picloud_venture_credentials.py')
exp_params = experiment.exp_param_defaults({})
exp_params['intermediate_iter'] = 1
exp_params['max_initial_run_time'] = 30
print experiment.exp_params_to_str(exp_params)
data = scipy.io.loadmat("../data/irm_synth/irm_synth_20.mat", squeeze_me=True)
observed = list(zip(data['train_i'].flat, data['train_j'].flat, data['train_v'].flat))
missing = list(zip(data['test_i'].flat, data['test_j'].flat, data['test_v'].flat))
data = {'observations' : observed, 'missing' : missing}
model = models.product_IRM
model_params = {'D' : 1, 'alpha' : 1, 'symmetric' : True}
job_id = cloud.call(experiment.network_cv_timing_run, data, model, exp_params, model_params, _max_runtime=5, _env=cloud_environment)
cloud.join(job_id)
print cloud.result(job_id)
def timing_triple_local():
exp_params = experiment.exp_param_defaults({})
exp_params['intermediate_iter'] = 1
exp_params['max_initial_run_time'] = 30
exp_params['max_burn_time'] = 30
exp_params['max_sample_time'] = 30
exp_params['n_samples'] = 25
print experiment.exp_params_to_str(exp_params)
data = scipy.io.loadmat("../data/irm_synth/irm_synth_20.mat", squeeze_me=True)
observed = list(zip(data['train_i'].flat, data['train_j'].flat, data['train_v'].flat))
missing = list(zip(data['test_i'].flat, data['test_j'].flat, data['test_v'].flat))
data = {'observations' : observed, 'missing' : missing}
model = models.product_IRM
model_params = {'D' : 1, 'alpha' : 1, 'symmetric' : True}
# Timing run
time_per_mh_iter = experiment.network_cv_timing_run(data, model, exp_params, model_params)['time_per_mh_iter']
# Live run
exp_params['intermediate_iter'] = max(1, int(round(0.9 * exp_params['max_sample_time'] / (exp_params['n_samples'] * time_per_mh_iter))))
print experiment.network_cv_single_run(data, model, exp_params, model_params)