def fun(x):
print("on %s print %g" % (MPI.COMM_WORLD.rank, x))
seed = uuid.uuid4()
run_number = str(seed)
print('run_number: ' + run_number)
#try:
# run_number = sys.argv[1]
#except:
# "requires an input argument"
# raise
L1orL2 = 'L1'
TRAINING_ERROR = 'gaussian'
hidden_layer1 = 50
hidden_layer2 = 50
hidden_layers = (hidden_layer1, hidden_layer2)
setup_list = np.repeat([('CO','low','GCN'),('CO','high','binding_type'),('CO',1,'GCN')\
,('CO','high','combine_hollow_sites'),('CO','high','GCN')\
,('NO','low','GCN'),('NO','high','binding_type')\
,('NO','high','combine_hollow_sites')\
,('NO',1,'binding_type'),('NO',1,'combine_hollow_sites'),('NO',1,'GCN')\
,('C2H4','low','binding_type'),('C2H4','low','GCN')\
,('CO','low','binding_type'),('CO','low','combine_hollow_sites')\
,('CO',1,'binding_type'),('CO',1,'combine_hollow_sites')\
,('NO','low','binding_type'),('NO','low','combine_hollow_sites')],5,axis=0).tolist()
which_setup = setup_list[x]
ADSORBATE = which_setup[0]
COVERAGE = which_setup[1]
try:
COVERAGE = float(COVERAGE)
except:
COVERAGE = COVERAGE
TARGET = which_setup[2]
epsilon = 10**-12
alpha = 10**-3
learning_rate = 0.0004
if COVERAGE == 'high' and TARGET in [
'binding_type', 'combine_hollow_sites'
]:
NUM_TRAIN = 500
training_sets = 100
batch_size = 5
epochs = 20
else:
NUM_TRAIN = 5000
training_sets = 200
batch_size = 50
epochs = 10
print('batch_size: ' + str(batch_size))
print('learning_rate: ' + str(learning_rate))
print('epsilon: ' + str(epsilon))
print('alpha: ' + str(alpha))
print('NUM_TRAIN: ' + str(NUM_TRAIN))
print('training_sets: ' + str(training_sets))
print('epochs: ' + str(epochs))
print('hidden layers: ' + str(hidden_layers))
#batch_size: 2297
#learning_rate = 0.06651629336077657
#epsilon = 0.02872678693613251
#alpha = 0.001931666535492889
#NUM_TRAIN = 100000
#epochs=2
#training_sets = 2
if ADSORBATE == 'CO':
INCLUDED_BINDING_TYPES = [1, 2, 3, 4]
MAX_COVERAGES = [1, 0.7, 0.2, 0.2]
BINDING_TYPE_FOR_GCN = [1]
HIGH_FREQUENCY = 2200
ENERGY_POINTS = 500
GCN_ALL = False
elif ADSORBATE == 'NO':
INCLUDED_BINDING_TYPES = [1, 2, 3, 4]
MAX_COVERAGES = [1, 1, 1, 1]
BINDING_TYPE_FOR_GCN = [1]
HIGH_FREQUENCY = 2000
ENERGY_POINTS = 450
GCN_ALL = False
elif ADSORBATE == 'C2H4':
INCLUDED_BINDING_TYPES = [1, 2]
MAX_COVERAGES = [1, 1]
BINDING_TYPE_FOR_GCN = [2]
HIGH_FREQUENCY = 2000
ENERGY_POINTS = 450
if TARGET == 'GCN':
GCN_ALL = True
else:
GCN_ALL = False
print('ADSORBATE: ' + ADSORBATE)
print('TARGET: ' + str(TARGET))
print('COVERAGE: ' + str(COVERAGE))
print('GCN_ALL: ' + str(GCN_ALL))
work_dir = os.getcwd()
cross_validation_path = os.path.join(
work_dir,
'cross_validation_' + ADSORBATE + '_' + TARGET + '_' + str(COVERAGE))
print(cross_validation_path)
CV_class = CROSS_VALIDATION(ADSORBATE=ADSORBATE,INCLUDED_BINDING_TYPES=INCLUDED_BINDING_TYPES\
,cross_validation_path=cross_validation_path, VERBOSE=False)
CV_SPLITS = 3
CV_class.generate_test_cv_indices(CV_SPLITS=CV_SPLITS, BINDING_TYPE_FOR_GCN=BINDING_TYPE_FOR_GCN\
, test_fraction=0.25, random_state=x, read_file=False, write_file=False)
properties_dictionary = {'batch_size':batch_size, 'learning_rate_init':learning_rate\
, 'epsilon':epsilon,'hidden_layer_sizes':hidden_layers,'regularization':L1orL2\
,'alpha':alpha, 'epochs_per_training_set':epochs,'training_sets':training_sets,'loss': 'wasserstein_loss'}
CV_class.set_model_parameters(TARGET=TARGET, COVERAGE=COVERAGE\
, MAX_COVERAGES = MAX_COVERAGES, NN_PROPERTIES=properties_dictionary\
, NUM_TRAIN=NUM_TRAIN, NUM_VAL=10000, NUM_TEST=10000\
, MIN_GCN_PER_LABEL=12, NUM_GCN_LABELS=10, GCN_ALL = GCN_ALL, TRAINING_ERROR = TRAINING_ERROR\
, LOW_FREQUENCY=200, HIGH_FREQUENCY=HIGH_FREQUENCY, ENERGY_POINTS=ENERGY_POINTS)
CV_RESULTS_FILE = ADSORBATE + '_' + TARGET + '_' + str(
COVERAGE) + '_' + run_number
CV_class.run_CV_multiprocess(write_file=True,
CV_RESULTS_FILE=CV_RESULTS_FILE,
num_procs=CV_SPLITS + 1)
HIGH_FREQUENCY = 3350
ENERGY_POINTS = 750
GCN_ALL = True
print('ADSORBATE: ' + ADSORBATE)
print('TARGET: ' + TARGET)
print('COVERAGE: ' + str(COVERAGE))
print('TRAINING_ERROR: ' + str(TRAINING_ERROR))
print('GCN_ALL: ' + str(GCN_ALL))
work_dir = os.getcwd()
cross_validation_path = os.path.join(
work_dir, 'cross_validation_' + ADSORBATE + '_' +
TARGET + '_' + str(COVERAGE))
print(cross_validation_path)
CV_class = CROSS_VALIDATION(ADSORBATE,INCLUDED_BINDING_TYPES\
,cross_validation_path, VERBOSE=True)
CV_SPLITS = 3
CV_class.generate_test_cv_indices(CV_SPLITS, BINDING_TYPE_FOR_GCN\
, test_fraction=0.25, random_state=0, read_file=False, write_file=False)
properties_dictionary = {'batch_size':batch_size, 'learning_rate_init':learning_rate\
, 'epsilon':epsilon,'hidden_layer_sizes':hidden_layers,'regularization':'L2'\
,'alpha':alpha, 'epochs_per_training_set':epochs,'training_sets':training_sets,'loss': 'wasserstein_loss'}
CV_class.set_model_parameters(TARGET=TARGET, COVERAGE=COVERAGE\
, MAX_COVERAGES = MAX_COVERAGES, NN_PROPERTIES=properties_dictionary\
, NUM_TRAIN=NUM_TRAIN, NUM_VAL=100, NUM_TEST=100\
, MIN_GCN_PER_LABEL=12, NUM_GCN_LABELS=10, GCN_ALL = GCN_ALL, TRAINING_ERROR = TRAINING_ERROR\
, LOW_FREQUENCY=200, HIGH_FREQUENCY=HIGH_FREQUENCY, ENERGY_POINTS=ENERGY_POINTS)
CV_RESULTS_FILE = ADSORBATE + '_' + TARGET + '_' + str(
COVERAGE) + '_' + run_number
#CV_class.run_CV_multiprocess(write_file=True, CV_RESULTS_FILE = CV_RESULTS_FILE, num_procs=CV_SPLITS+1)
#CV_class.run_CV(write_file=True, CV_RESULTS_FILE = CV_RESULTS_FILE)
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 25 11:46:31 2017
@author: lansford
"""
from __future__ import absolute_import, division, print_function
from jl_spectra_2_structure.cross_validation import CROSS_VALIDATION
CV_SPLITS = 3
#write data for CO
CV_class = CROSS_VALIDATION(ADSORBATE='CO',
INCLUDED_BINDING_TYPES=[1, 2, 3, 4])
CV_class.generate_test_cv_indices(CV_SPLITS=CV_SPLITS, BINDING_TYPE_FOR_GCN=[1]\
, test_fraction=0.25, random_state=0, read_file=False, write_file=True)
#Write data for NO
CV_class = CROSS_VALIDATION(ADSORBATE='NO',
INCLUDED_BINDING_TYPES=[1, 2, 3, 4])
CV_class.generate_test_cv_indices(CV_SPLITS=CV_SPLITS, BINDING_TYPE_FOR_GCN=[1]\
, test_fraction=0.25, random_state=0, read_file=False, write_file=True)
#write data for C2H4
CV_class = CROSS_VALIDATION(ADSORBATE='C2H4', INCLUDED_BINDING_TYPES=[1, 2])
CV_class.generate_test_cv_indices(CV_SPLITS=CV_SPLITS, BINDING_TYPE_FOR_GCN=[2]\
, test_fraction=0.25, random_state=0, read_file=False, write_file=True)
def fun(x):
print("on %s print %g" % (MPI.COMM_WORLD.rank, x))
seed = uuid.uuid4()
run_number = str(seed)
print('run_number: ' + run_number)
#try:
# run_number = sys.argv[1]
#except:
# "requires an input argument"
# raise
L1orL2 = np.random.RandomState().choice(['L1', 'L2'])
TRAINING_ERROR = np.random.RandomState().choice(['gaussian', 0.003, None])
hidden_layer1 = np.random.RandomState().randint(50, 151)
hidden_layer2 = np.random.RandomState().randint(50, 151)
hidden_layer_3 = np.random.RandomState().randint(50, 151)
num_layers = np.random.RandomState().randint(2, 3 + 1)
if num_layers == 2:
hidden_layers = (hidden_layer1, hidden_layer2)
elif num_layers == 3:
hidden_layers = (hidden_layer1, hidden_layer2, hidden_layer_3)
random_numbers = np.random.RandomState().random_sample(7)
setup_list = [('CO','low','binding_type'),('CO','low','combine_hollow_sites')\
,('CO','low','GCN'),('CO','high','binding_type')\
,('CO','high','combine_hollow_sites'),('CO','high','GCN')\
,('CO',1,'binding_type'),('CO',1,'combine_hollow_sites'),('CO',1,'GCN')\
,('NO','low','binding_type'),('NO','low','combine_hollow_sites')\
,('NO','low','GCN'),('NO','high','binding_type')\
,('NO','high','combine_hollow_sites')\
,('NO',1,'binding_type'),('NO',1,'combine_hollow_sites'),('NO',1,'GCN')\
,('C2H4','low','binding_type'),('C2H4','low','GCN',False),('C2H4','low','GCN',True)]
which_setup = setup_list[np.random.RandomState().choice(
np.arange(len(setup_list)))]
batch_size = int(10**(2 * random_numbers[0] + 1))
learning_rate = 10**(random_numbers[1] - 4)
epsilon = 10**(4 * random_numbers[2] - 14)
alpha = 10**(5 * random_numbers[3] - 6)
NUM_TRAIN = int(5 * 10**(random_numbers[4] + 3))
training_sets = int(10**(random_numbers[5] + 2))
epochs = 5
if which_setup[1] == 'high' and which_setup[2] in [
'binding_type', 'combine_hollow_sites'
]:
NUM_TRAIN = 500
batch_size = 5
training_sets = 100
print('batch_size: ' + str(batch_size))
print('learning_rate: ' + str(learning_rate))
print('epsilon: ' + str(epsilon))
print('alpha: ' + str(alpha))
print('NUM_TRAIN: ' + str(NUM_TRAIN))
print('training_sets: ' + str(training_sets))
print('epochs: ' + str(epochs))
print('hidden layers: ' + str(hidden_layers))
#batch_size: 2297
#learning_rate = 0.06651629336077657
#epsilon = 0.02872678693613251
#alpha = 0.001931666535492889
#NUM_TRAIN = 100000
#epochs=2
#training_sets = 2
ADSORBATE = which_setup[0]
if ADSORBATE == 'CO':
INCLUDED_BINDING_TYPES = [1, 2, 3, 4]
MAX_COVERAGES = [1, 0.7, 0.2, 0.2]
BINDING_TYPE_FOR_GCN = [1]
HIGH_FREQUENCY = 2200
ENERGY_POINTS = 500
GCN_ALL = False
elif ADSORBATE == 'NO':
INCLUDED_BINDING_TYPES = [1, 2, 3, 4]
MAX_COVERAGES = [1, 1, 1, 1]
BINDING_TYPE_FOR_GCN = [1]
HIGH_FREQUENCY = 2000
ENERGY_POINTS = 450
GCN_ALL = False
elif ADSORBATE == 'C2H4':
INCLUDED_BINDING_TYPES = [1, 2]
MAX_COVERAGES = [1, 1]
BINDING_TYPE_FOR_GCN = [2]
HIGH_FREQUENCY = 2000
ENERGY_POINTS = 450
if which_setup[2] == 'GCN':
GCN_ALL = which_setup[3]
else:
GCN_ALL = False
COVERAGE = which_setup[1]
TARGET = which_setup[2]
print('ADSORBATE: ' + ADSORBATE)
print('TARGET: ' + str(TARGET))
print('COVERAGE: ' + str(COVERAGE))
print('GCN_ALL: ' + str(GCN_ALL))
work_dir = os.getcwd()
cross_validation_path = os.path.join(
work_dir,
'cross_validation_' + ADSORBATE + '_' + TARGET + '_' + str(COVERAGE))
print(cross_validation_path)
CV_class = CROSS_VALIDATION(ADSORBATE=ADSORBATE,INCLUDED_BINDING_TYPES=INCLUDED_BINDING_TYPES\
,cross_validation_path=cross_validation_path, VERBOSE=True)
CV_SPLITS = 3
CV_class.generate_test_cv_indices(CV_SPLITS=CV_SPLITS, BINDING_TYPE_FOR_GCN=BINDING_TYPE_FOR_GCN\
, test_fraction=0.25, random_state=0, read_file=True, write_file=False)
properties_dictionary = {'batch_size':batch_size, 'learning_rate_init':learning_rate\
, 'epsilon':epsilon,'hidden_layer_sizes':hidden_layers,'regularization':L1orL2\
,'alpha':alpha, 'epochs_per_training_set':epochs,'training_sets':training_sets,'loss': 'wasserstein_loss'}
CV_class.set_model_parameters(TARGET=TARGET, COVERAGE=COVERAGE\
, MAX_COVERAGES = MAX_COVERAGES, NN_PROPERTIES=properties_dictionary\
, NUM_TRAIN=NUM_TRAIN, NUM_VAL=10000, NUM_TEST=10000\
, MIN_GCN_PER_LABEL=12, NUM_GCN_LABELS=10, GCN_ALL = GCN_ALL, TRAINING_ERROR = TRAINING_ERROR\
, LOW_FREQUENCY=200, HIGH_FREQUENCY=HIGH_FREQUENCY, ENERGY_POINTS=ENERGY_POINTS)
CV_RESULTS_FILE = ADSORBATE + '_' + TARGET + '_' + str(
COVERAGE) + '_' + run_number
CV_class.run_CV_multiprocess(write_file=True,
CV_RESULTS_FILE=CV_RESULTS_FILE,
num_procs=CV_SPLITS + 1)