def main():
""" From command line, all parsing are handled here """
parser = argparse.ArgumentParser(
description="Uncertainty quantification in neural networks.")
parser.add_argument("-meg",
action="store_true",
help="Train with MEGNet. [default: False]",
default=False)
parser.add_argument("-cnn",
action="store_true",
help="Train with CNN (Not implemented).",
default=False)
parser.add_argument("-noactive",
action="store_true",
help="Don't do active learning [default: False]",
default=False)
parser.add_argument(
"-cycle",
help="Number of structures to sample and maximum number of times\
to sample separated by spaces for active learning. [default: 20 5]",
nargs=2,
type=int)
parser.add_argument(
"-samp",
help="Type of sampling for active learning. Use random or\
entropy [No default]",
type=str)
parser.add_argument(
"-data",
help="Input dataset(s). Multiple datasets can be passed, one\
per optical property of interest. [No default]",
type=str,
nargs="+")
parser.add_argument(
"-key",
help="API key for data download and the optical properties of\
interest, separated by spaces. For MEGNet users only. [eg. Key band_gap\
formation_energy_per_atom e_above_hull]",
type=str,
nargs="+")
parser.add_argument(
"-frac",
help="Fraction of data for training and fraction of the training set\
for validation separated by spaces. [default: 0.3 0.7]",
nargs=2,
type=float)
parser.add_argument(
"-nsplit",
help="Number of training set splits for k-fold cross-validation.\
[default: 1 i.e no cross-validation]",
type=int)
parser.add_argument("-bond",
help="MEGNet feature bond. [default: 10]",
type=int)
parser.add_argument("-g",
help="MEGNet feature global. [default: 2]",
type=int)
parser.add_argument("-c",
"--cutoff",
help="MEGNet radial cutoff. [default: 5]",
type=int)
parser.add_argument("-w",
"--width",
help="MEGNet gaussian width. [default: 0.5]",
type=float)
parser.add_argument(
"-include",
action="store_true",
help="Include zero optical property values in the MEGNet training\
and/or Gaussian process analysis. [default: False]",
default=False)
parser.add_argument(
"-epochs",
help="Epochs. [default: 0 ie. Perform no training with MEGNet or CNN]",
type=int)
parser.add_argument(
"-batch",
help="Batch size for training with MEGNet or CNN. [default: 256]",
type=int)
parser.add_argument(
"-prev",
action="store_true",
help="Use a pre-trained MEGNet model during training with MEGNet.\
[default: False]",
default=False)
parser.add_argument(
"-l",
help="MEGNet fitted model layer to analyse. [default: readout_0 i.e 32\
dense layer]",
type=str)
parser.add_argument("-ltype",
help="Display the layers in a fitted MEGNet model.",
type=str)
parser.add_argument(
"-p",
"--perp",
help="Perplexity value to use in dimension reduction with tSNE.\
[default: 50]",
type=float)
parser.add_argument(
"-niters",
help="Number of iterations for optimisation in tSNE. [default: 1000]",
type=int)
parser.add_argument(
"-ndims",
help="Dimensions of embedded space in tSNE. [default: 2]",
type=int)
parser.add_argument(
"-opt",
help="Optimizer for optimizing GP hyperparameters. [default: adam]",
type=str)
parser.add_argument("-rate",
help="Adam optimizer Learning rate. [default: 0.01]",
type=float)
parser.add_argument("-amp",
help="Amplitude of the GP kernel. [default: 1]",
type=float)
parser.add_argument("-length",
help="The length scale of the GP kernel. [default: 1]",
type=float)
parser.add_argument(
"-maxiters",
help="Maximum iterations for optimising GP hyperparameters. For\
k-fold cross-validation, maxiters for training per fold and\
maxiters for training the train-test split.\nIf no k-fold\
cross-validation, a single input is required\
[default: 0 0 i.e no MEGNet/CNN and GP training]",
nargs="+",
type=int)
args = parser.parse_args()
cycle = args.cycle or Params().cycle
fraction = args.frac or Params().frac
nsplit = args.nsplit or Params().nsplit
ZeroVals = args.include or Params().include
bond = args.bond or Params().bond
nfeat_global = args.g or Params().g
cutoff = args.cutoff or Params().cutoff
width = args.width or Params().width
epochs = args.epochs or Params().epochs
batch = args.batch or Params().batch
prev = args.prev or Params().prev
layer = args.l or Params().l
perp = args.perp or Params().perp
niters = args.niters or Params().niters
ndims = args.ndims or Params().ndims
rate = args.rate or Params().rate
amp = args.amp or Params().amp
length_scale = args.length or Params().length
opt = args.opt or Params().opt
maxiters = args.maxiters or Params().maxiters
# Display layers in a pre-fitted MEGNet model
if args.ltype:
from aux.get_info import show_layers
show_layers(args.ltype)
sys.exit()
# Train with MEGNet or CNN
if (args.meg or args.cnn) == False:
sys.exit("Set -meg or -cnn!")
if args.data or (args.data and args.key):
from aux.get_info import load_data
properties = load_data(args.data)
elif args.key:
from aux.get_info import download
properties = download(args.key)
else:
sys.exit("No input data provided. Use -data or -key option!")
for prop in properties:
if args.meg:
print("MEGNet training requested ...")
(model, activations_input_full, Xfull, yfull, Xpool, ypool, Xtest,
ytest, Xtrain, ytrain, Xval,
yval) = megnet_input(prop, ZeroVals, bond, nfeat_global, cutoff,
width, fraction)
if not args.noactive:
print("\nActive learning requested ...")
import subprocess
from train.MEGNetTrain import training
from aux.activations import latent
from aux.pool_sampling import selection_fn
if fraction[1] >= 1:
sys.exit("The second parameter to -frac must be less than 1!")
if len(maxiters) > 1:
sys.exit("-maxiters must have length 1!")
else:
maxiters = maxiters[0]
query = cycle[0]
max_query = cycle[1]
training_data = []
mae_val_entropy = []
mae_gp_entropy = []
mae_val_random = []
mae_gp_random = []
print("Number of cycle(s): ", max_query)
print("Number of samples per cycle: ", query)
i = 0
while i < max_query + 1:
print("\nQuery number ", i)
if args.meg:
print("Training MEGNet on the pool ...")
if i == 0:
training.active(i, prop, prev, model, args.samp, batch,
5, Xpool, ypool, Xtest, ytest)
training_data.append(len(ytrain))
print(
"\nObtaining latent points for the full dataset ..."
)
tsne_train, tsne_val, tsne_test = latent.active(
i, prop, perp, layer, args.samp,
activations_input_full, Xfull, Xpool, Xtest,
Xtrain, Xval, ndims, niters)
np.save('tsne_train.npy', tsne_train)
np.save('tsne_test.npy', tsne_test)
np.save('tsne_val.npy', tsne_val)
np.save('y_train.npy', ytrain)
np.save('y_test.npy', ytest)
np.save('y_val.npy', yval)
print("\nGP Training on the DFT %s ..." % prop)
ampo = amp
leno = length_scale
gprm_dft, dft_variance, mae_val, mae_gp, amp, length_scale = adam.active(
tsne_train, tsne_val, tsne_test, ytrain, yval, ytest,
maxiters, amp, length_scale, rate)
amp = ampo
length_scale = leno
if args.samp == "entropy":
mae_val_entropy.append(mae_val)
mae_gp_entropy.append(mae_gp)
EntropySelection = selection_fn.EntropySelection
tsne_pool, ypool, tsne_train, ytrain, tsne_test, ytest \
= EntropySelection(i, tsne_train, ytrain, tsne_test, ytest,
tsne_val, yval, dft_variance, query, max_query)
elif args.samp == "random":
mae_val_random.append(mae_val)
mae_gp_random.append(mae_gp)
RandomSelection = selection_fn.RandomSelection
tsne_pool, ypool, tsne_train, ytrain, tsne_test, ytest \
= RandomSelection(i, tsne_train, ytrain, tsne_test, ytest,
tsne_val, yval, dft_variance, query, max_query)
else:
sys.exit("Sampling type not recognised")
if i == max_query:
subprocess.call(["rm", "-r", "callback"])
i += 1
gaussian_centers = np.linspace(0, cutoff, bond)
distance_converter = GaussianDistance(gaussian_centers, width)
graph_converter = CrystalGraph(bond_converter=distance_converter)
for s in Xactive:
activations.append(StructureGraph.get_input(graph_converter, s))
# Obtain latent points
tsne_active = latent.active(
i, prop, perp, layer, 'entropy', activations, Xactive, Xpool, ypool,
Xtest, val_frac, ndims, niters)
# Split the data
tsne_pool = tsne_active[:len(ypool)]
tsne_unlab = tsne_active[len(ypool):]
cut = int(len(tsne_pool)*split_pool)
tsne_train = tsne_pool[:cut]
ytrain = ypool[:cut]
tsne_val = tsne_pool[cut:]
yval = ypool[cut:]
# Train the GP
amp = ampo
length_scale = length_scaleo
gprm_dft, dft_variance, mae_val, mae_gp, amp, length_scale = adam.active(
tsne_active, tsne_train, tsne_val, tsne_unlab, yactive, ytrain, yval, yunlab,
maxiters, amp, length_scale, rate)
# Select the next round of structures
EntropySelection = selection_fn.EntropySelection
#mae_val_entropy.append(mae_val)
#mae_gp_entropy.append(mae_gp)
Xpool, ypool, Xunlab, yunlab = EntropySelection(i, Xpool, ypool, Xunlab, yunlab,
dft_variance, query, max_query)
tsne_train = np.load(data_directory + 'tsne_train.npy')
tsne_test = np.load(data_directory + 'tsne_test.npy')
tsne_val = np.load(data_directory + 'tsne_val.npy')
ytrain = np.load(data_directory + 'y_train.npy')
ytest = np.load(data_directory + 'y_test.npy')
yval = np.load(data_directory + 'y_val.npy')
for i in range(queries):
#
print('Query ', i)
print('Sample from y test for consistency', len(ytest), ytest[0])
# Train the GP
amp = ampo
length_scale = length_scaleo
gprm_dft, dft_variance, mae_val, mae_gp, amp, length_scale = adam.active(
tsne_train, tsne_val, tsne_test, ytrain, yval, ytest, maxiters, amp,
length_scale, rate)
# Select the next round of structures
if samp == "entropy":
EntropySelection = selection_fn.EntropySelection
tsne_pool, ypool, tsne_train, ytrain, tsne_test, ytest \
= EntropySelection(i, tsne_train, ytrain, tsne_test, ytest,
tsne_val, yval, dft_variance, query, max_query)
elif samp == "random":
RandomSelection = selection_fn.RandomSelection
tsne_pool, ypool, tsne_train, ytrain, tsne_test, ytest \
= RandomSelection(i, tsne_train, ytrain, tsne_test, ytest,
tsne_val, yval, dft_variance, query, max_query)
def main():
""" From command line, all parsing are handled here """
parser = argparse.ArgumentParser(
description="Uncertainty quantification in neural networks.")
parser.add_argument(
"-checkdata",
action="store_true",
help="Check number of entries in the dataset. [default: False]",
default=False)
parser.add_argument("-ltype",
help="Display the layers in a fitted MEGNet model.",
type=str)
parser.add_argument("-nomeg",
action="store_true",
help="Do not train with MEGNet. [default: False]",
default=False)
parser.add_argument("-noactive",
action="store_true",
help="Don't do active learning [default: False]",
default=False)
parser.add_argument(
"-samp",
help="Type of sampling for active learning. Use random or\
entropy [default: entropy]",
type=str)
parser.add_argument(
"-cycle",
help="Number of structures to sample and maximum number of times\
to sample separated by spaces for active learning. [default: 1 5]",
nargs=2,
type=int)
parser.add_argument(
"-repeat",
action="store_true",
help=
"MEGNet train and pre-process activations in each active learning cycle\
[default: False]",
default=False)
parser.add_argument("-q",
"--quan",
help="Quantity of data for norepeat active learning\
[default: 1000]",
type=int)
parser.add_argument(
"-stop",
help="Minimum fraction of test set required for active learning\
[default: 0.1]",
type=float)
parser.add_argument(
"-data",
help="Input dataset(s). Multiple datasets can be passed, one\
per optical property of interest. [No default]",
type=str,
nargs="+")
parser.add_argument(
"-key",
help="API key for data download and the optical properties of\
interest, separated by spaces. For MEGNet users only. [eg. Key band_gap\
formation_energy_per_atom e_above_hull]",
type=str,
nargs="+")
parser.add_argument(
"-frac",
help="Fraction of data for training and testing separated by spaces\
for train-test split and k-fold cross-validation. Fraction of data for\
training, and fraction of training data for validation in repeat active\
learning. For norepeat active learning, single input as the fraction of\
the training data for validation. [default: 0.3]",
nargs="+",
type=float)
parser.add_argument(
"-include",
action="store_true",
help="Include zero optical property values in the MEGNet training\
and/or Gaussian process analysis. [default: False]",
default=False)
parser.add_argument(
"-nsplit",
help="Number of training set splits for k-fold cross-validation.\
[default: 1 i.e no cross-validation]",
type=int)
parser.add_argument(
"-epochs",
help="Epochs. [default: 0 ie. Perform no training with MEGNet]",
type=int)
parser.add_argument(
"-batch",
help="Batch size for training with MEGNet or CNN. [default: 256]",
type=int)
parser.add_argument("-bond",
help="MEGNet feature bond. [default: 10]",
type=int)
parser.add_argument("-nfeat",
help="MEGNet feature global. [default: 2]",
type=int)
parser.add_argument("-cutoff",
"--cutoff",
help="MEGNet radial cutoff. [default: 5]",
type=int)
parser.add_argument("-width",
"--width",
help="MEGNet gaussian width. [default: 0.5]",
type=float)
parser.add_argument(
"-prev",
action="store_true",
help="Use a pre-trained MEGNet model during training with MEGNet.\
[default: False]",
default=False)
parser.add_argument(
"-layer",
help="MEGNet fitted model layer to analyse. [default: readout_0 i.e 32\
dense layer]",
type=str)
parser.add_argument(
"-ndims",
help="Dimensions of embedded space. 0 => Do not preprocess activations\
, 1 => scale activations to 0, 1 range, 2 or 3 => Reduce dimensions of\
activations with tSNE. [default: 0]",
type=int)
parser.add_argument(
"-p",
"--perp",
help="Perplexity value to use in dimension reduction with tSNE.\
[default: 150]",
type=float)
parser.add_argument(
"-niters",
help="Number of iterations for optimisation in tSNE. [default: 1000]",
type=int)
parser.add_argument("-rate",
help="Adam optimizer Learning rate. [default: 0.01]",
type=float)
parser.add_argument("-amp",
help="Amplitude of the GP kernel. [default: 1.0]",
type=float)
parser.add_argument(
"-length",
help="The length scale of the GP kernel. [default: 1.0]",
type=float)
parser.add_argument(
"-maxiters",
help="Maximum iterations for optimising GP hyperparameters. For\
k-fold cross-validation, two inputs are required - one for training\
per fold and the other for training using train-test split.\
\nFor active learning and train-test split, a single input\
is required. [default: 0 i.e no GP training]",
nargs="+",
type=int)
args = parser.parse_args()
samp = args.samp or Params().samp
cycle = args.cycle or Params().cycle
quan = args.quan or Params().quan
stop = args.stop or Params().stop
fraction = args.frac or Params().frac
nsplit = args.nsplit or Params().nsplit
epochs = args.epochs or Params().epochs
batch = args.batch or Params().batch
bond = args.bond or Params().bond
nfeat_global = args.nfeat or Params().nfeat
cutoff = args.cutoff or Params().cutoff
width = args.width or Params().width
layer = args.layer or Params().layer
ndims = args.ndims or Params().ndims
perp = args.perp or Params().perp
niters = args.niters or Params().niters
rate = args.rate or Params().rate
amp = args.amp or Params().amp
length_scale = args.length or Params().length
maxiters = args.maxiters or Params().maxiters
# Display layers in a pre-fitted MEGNet model
if args.ltype:
from aux.get_info import show_layers
show_layers(args.ltype)
sys.exit()
if args.include:
logging.info("Include zero optical property values ...")
else:
logging.info("Exclude zero optical property values ...")
if args.nomeg:
logging.info("No MEGNet training requested ...")
sys.exit("No other network implemented!")
else:
if epochs > 0:
logging.info("MEGNet training requested...")
if args.prev:
logging.info(
"Use a pre-trained MEGNet model in MEGNet training ...")
else:
logging.info(
"Do not use a pre-trained MEGNet model in MEGNet training ..."
)
if args.noactive:
logging.info("No active learning requested ...")
assert len(fraction) == 2, "-frac requires two inputs!"
assert (fraction[0] + fraction[1]) == 1., "The sum of -frac must be 1!"
if not (0 < (fraction[0] and fraction[1]) < 1):
logging.error("-frac must be of the form 0 < parameter < 1!")
sys.exit()
if nsplit == 1:
logging.info("Train-test split approach requested ...")
assert len(maxiters) == 1, "-maxiters must have length 1!"
maxiters = maxiters[0]
else:
print("%s-fold cross-validation requested ..." % nsplit)
assert len(maxiters) == 2, "-maxiters must have length 2!"
else:
logging.info("Perform active learning ...")
assert stop < 1., "Stop argument should be less than 1!"
assert nsplit == 1, "Active learning with k-fold cross validation not supported!"
assert len(maxiters) == 1, "-maxiters must have length 1!"
maxiters = maxiters[0]
if samp not in ("entropy", "random"):
logging.error("Sampling type not recognised!")
sys.exit()
if args.repeat:
logging.info(
"MEGNet train and perform activation analysis per cycle of active learning ..."
)
assert len(fraction) == 2, "-frac requires two inputs!"
assert (fraction[0] +
fraction[1]) < 1., "The sum of -frac must be less than 1!"
if not (0 < (fraction[0] and fraction[1]) < 1):
logging.error("-frac must be of the form 0 < parameter < 1!")
sys.exit()
else:
logging.info(
"MEGNet train and perform activation analysis ONCE during the active learning ..."
)
assert len(
fraction
) == 1, "-frac requires a single input as the validation fraction!"
assert fraction[
0] < 1., "-frac must be less than 1!" #fraction is a list and we need to pass a single input
if not quan:
logging.error(
"Provide quantity of data to use with -q or --quan!")
sys.exit()
# Get data for processing
if args.data or (args.data and args.key):
from aux.get_info import load_data
properties = load_data(args.data)
elif args.key:
from aux.get_info import download
properties = download(args.key)
else:
logging.error("No input data provided. Use -data or -key option!")
sys.exit()
# Check number of entries in dataset
if args.checkdata:
from aux.get_info import ReadData
for prop in properties:
for dat in args.data:
ReadData(dat, args.include)
sys.exit()
for prop in properties:
if args.noactive:
if not args.nomeg:
model, activations_input_full, Xfull, yfull, Xpool, ypool, Xtest, ytest =\
megnet_input(prop, args.include, bond, nfeat_global, cutoff, width, fraction)
if nsplit == 1:
#*****************************
# TRAIN-TEST SPLIT APPROACH
#*****************************
datadir = "train_test_split/%s_results" % prop
if not args.nomeg and epochs > 0:
logging.info("Training MEGNet on the pool ...")
training.train_test_split(datadir, prop, args.prev, model,
batch, epochs, Xpool, ypool,
Xtest, ytest)
logging.info(
"Obtaining latent points for the full dataset ...")
latent_pool, latent_test = latent.train_test_split(
datadir, prop, layer, activations_input_full, Xpool, ytest,
perp, ndims, niters)
logging.info("Gaussian Process initiated ...")
OptLoss, OptAmp, OptLength, Optmae, Optmse, Optsae, gp_mean, gp_stddev, R =\
adam.train_test_split(datadir, prop, latent_pool, latent_test, ypool, ytest,
maxiters, amp, length_scale, rate)
logging.info(
"Saving optimised hyperparameters and GP posterior plots ..."
)
plot.train_test_split(datadir, prop, layer, maxiters, rate,
OptLoss, OptAmp, OptLength, ytest,
gp_mean, gp_stddev, None, None, Optmae,
Optmse, Optsae, R)
elif nsplit > 1:
#***************************
# K-FOLD CROSS VALIDATION
#***************************
from sklearn.model_selection import KFold
OptAmp_fold = np.array([])
OptLength_fold = np.array([])
Optmae_val_fold = np.array([])
Optmse_val_fold = np.array([])
mae_test_fold = np.array([])
kf = KFold(n_splits=nsplit, shuffle=True, random_state=0)
for fold, (train_idx, val_idx) in enumerate(kf.split(Xpool)):
datadir = "k_fold/%s_results/0%s_fold" % (prop, fold)
Xtrain, Xval = Xpool[train_idx], Xpool[val_idx]
ytrain, yval = ypool[train_idx], ypool[val_idx]
if not args.nomeg and epochs > 0:
print("\nTraining MEGNet on fold %s training set ..." %
fold)
training.k_fold(datadir, fold, prop, args.prev, model,
batch, epochs, Xtrain, ytrain, Xval,
yval)
logging.info(
"Obtaining latent points for the full dataset ...")
latent_train, latent_val, latent_test = latent.k_fold(
datadir, fold, prop, layer, activations_input_full,
train_idx, val_idx, Xpool, perp, ndims, niters)
logging.info("Gaussian Process initiated ...")
amp, length_scale, Optmae_val, Optmse_val, mae_test = adam.k_fold(
datadir, prop, latent_train, latent_val, latent_test,
ytrain, yval, ytest, maxiters[0], amp, length_scale,
rate)
OptAmp_fold = np.append(OptAmp_fold, amp)
OptLength_fold = np.append(OptLength_fold, length_scale)
Optmae_val_fold = np.append(Optmae_val_fold, Optmae_val)
Optmse_val_fold = np.append(Optmse_val_fold, Optmse_val)
mae_test_fold = np.append(mae_test_fold, mae_test)
if all(Optmae_val_fold):
print(
"\nCross-validation statistics: MAE = %.4f, MSE = %.4f"
% (Optmae_val_fold.mean(), Optmse_val_fold.mean()))
logging.info("Cross-validation complete!")
print("")
# Choose the best fitted model for the train-test split training
logging.info("Training MEGNet on the pool ...")
if args.prev:
prev = "k_fold/%s_results/0%s_fold/model-best-new-%s.h5" % (
prop, np.argmin(Optmae_val_fold), prop)
print("The selected best fitted model: %s" % prev)
args.prev = prev
datadir = "k_fold/%s_results" % prop
if not args.nomeg and epochs > 0:
training.train_test_split(datadir, prop, args.prev, model,
batch, epochs, Xpool, ypool,
Xtest, ytest)
logging.info(
"Obtaining latent points for the full dataset ...")
latent_pool, latent_test = latent.train_test_split(
datadir, prop, layer, activations_input_full, Xpool, ytest,
perp, ndims, niters)
logging.info("Gaussian Process initiated ...")
OptLoss, OptAmp, OptLength, Optmae, Optmse, Optsae, gp_mean, gp_stddev, R =\
adam.train_test_split(datadir, prop, latent_pool, latent_test, ypool, ytest,
maxiters[1], amp, length_scale, rate)
logging.info(
"Saving optimised hyperparameters and GP posterior plots ..."
)
plot.train_test_split(datadir, prop, layer, maxiters[1], rate,
OptLoss, OptAmp, OptLength, ytest,
gp_mean, gp_stddev, Optmae_val_fold,
mae_test_fold, Optmae, Optmse, Optsae, R)
else:
import subprocess
from aux.pool_sampling import selection_fn
EntropySelection = selection_fn.EntropySelection
RandomSelection = selection_fn.RandomSelection
query = cycle[0]
max_query = cycle[1]
print("Number of cycle(s): ", max_query)
print("Number of samples to move per cycle: ", query)
if args.repeat:
#********************************************
# ACTIVE LEARNING WITH CYCLES OF NETWORK
# TRAINING AND ACTIVATION EXTRACTION ANALYSIS
#********************************************
training_data = np.array([])
Optmae_val_cycle = np.array([])
mae_test_cycle = np.array([])
mse_test_cycle = np.array([])
sae_test_cycle = np.array([])
if not args.nomeg:
(model, activations_input_full, Xfull, yfull, Xpool, ypool,
Xtest, ytest, Xtrain, ytrain, Xval,
yval) = megnet_input(prop, args.include, bond,
nfeat_global, cutoff, width,
fraction)
# Ensure there is adequate data in test set before proceeding
assert (query * max_query) < int(stop * len(ytest)),\
"Test set size should be at least %s%% the dataset after active learning. Reduce stop and/or cycle parameters!" %stop
for i in range(max_query + 1):
print("\nQuery number ", i)
datadir = "active_learn/repeat/%s_results/%s/0%s_model" % (
prop, samp, i)
if not args.nomeg and epochs > 0:
logging.info("Training MEGNet on the pool ...")
training.active(datadir, i, prop, args.prev, model,
samp, batch, epochs, Xpool, ypool,
Xtest, ytest)
logging.info(
"Obtaining latent points for the full dataset ...")
latent_train, latent_val, latent_test = latent.active(
datadir, prop, layer, samp, activations_input_full,
Xfull, Xtest, ytest, Xtrain, Xval, perp, ndims, niters)
logging.info("Gaussian Process initiated ...")
(OptLoss, OptAmp, OptLength, amp, length_scale, gp_mean, gp_stddev,
gp_variance, Optmae_val, mae_test, mse_test, sae_test, R) =\
adam.active(datadir, prop, latent_train, latent_val, latent_test,
ytrain, yval, ytest, maxiters, amp, length_scale, rate)
# Save some parameters for plotting purposes.
training_data = np.append(training_data, len(ytrain))
Optmae_val_cycle = np.append(Optmae_val_cycle, Optmae_val)
mae_test_cycle = np.append(mae_test_cycle, mae_test)
mse_test_cycle = np.append(mse_test_cycle, mse_test)
sae_test_cycle = np.append(sae_test_cycle, sae_test)
logging.info(
"Saving optimised hyperparameters and GP posterior plots ..."
)
plot.active(datadir, prop, layer, maxiters, rate, OptLoss,
OptAmp, OptLength, samp, query, training_data,
ytest, gp_mean, gp_stddev, Optmae_val_cycle,
mae_test_cycle, mae_test, mse_test, sae_test,
R)
# Sample using variance on the predictions
if i < max_query:
if samp == "entropy":
if i == 0:
logging.info(
"Entropy sampling for active learning enabled ..."
)
Xpool, ypool, Xtrain, ytrain, Xtest, ytest = EntropySelection(
i, Xtrain, ytrain, Xtest, ytest, Xval, yval,
gp_variance, query, max_query)
elif samp == "random":
if i == 0:
logging.info(
"Random sampling for active learning enabled ..."
)
Xpool, ypool, Xtrain, ytrain, Xtest, ytest = RandomSelection(
i, Xtrain, ytrain, Xtest, ytest, Xval, yval,
gp_variance, query, max_query)
elif i == max_query:
if os.path.isdir("callback/"):
subprocess.call(["rm", "-r", "callback"])
else:
import matplotlib
matplotlib.use("agg")
import matplotlib.pyplot as plt
#************************************
# ACTIVE LEARNING WITHOUT CYCLES OF
# NETWORK TRAINING AND tSNE ANALYSIS
#*************************************
val_frac = fraction[0]
training_data = np.array([])
Optmae_val_cycle = np.array([])
mae_test_cycle = np.array([])
mse_test_cycle = np.array([])
sae_test_cycle = np.array([])
samp_idx = np.array([])
if not args.nomeg:
model, activations_input_full, Xfull, yfull =\
megnet_input(prop, args.include, bond, nfeat_global, cutoff, width,
fraction, quan)
datadir = "active_learn/norepeat/%s_results/%s_model" % (prop,
quan)
if not os.path.isdir(datadir):
os.makedirs(datadir)
Xpool = Xfull[:quan]
ypool = yfull[:quan]
Xtest = Xfull[quan:]
ytest = yfull[quan:]
# Ensure there is adequate data in test set before proceeding
assert (query * max_query) < int(stop * len(ytest)),\
"Test set size should be at least %s%% the dataset after active learning. Reduce stop and/or cycle parameters!" %stop
val_boundary = int(len(Xpool) * val_frac)
Xtrain = Xpool[:-val_boundary]
ytrain = ypool[:-val_boundary]
Xval = Xpool[-val_boundary:]
yval = ypool[-val_boundary:]
print("Requested validation set: %s%% of pool" %
(val_frac * 100))
print("Training set:", ytrain.shape)
print("Validation set:", yval.shape)
print("Test set:", ytest.shape)
logging.info("Saving the data to file ...")
np.save("%s/ytrain.npy" % datadir, ytrain)
np.save("%s/yval.npy" % datadir, yval)
print("\nProcessing %s samples ..." % quan)
# MEGNet train and tSNE analyse or scale features once
if not args.nomeg and epochs > 0:
training.train_test_split(datadir, prop, args.prev, model,
batch, epochs, Xpool, ypool,
Xtest, ytest)
logging.info(
"Obtaining latent points for the full dataset ...")
latent.active(datadir, prop, layer, samp,
activations_input_full, Xfull, Xtest, ytest,
Xtrain, Xval, perp, ndims, niters)
logging.info("Loading the latent points ...")
latent_train = np.load("%s/latent_train.npy" % datadir)
latent_test = np.load("%s/latent_test.npy" % datadir)
latent_val = np.load("%s/latent_val.npy" % datadir)
# Lets create a new data directory and dump GP results into it
datadir = datadir + "/" + samp + "/%s_samples" % query
if not os.path.isdir(datadir):
os.makedirs(datadir)
for i in range(max_query + 1):
print("\nQuery number ", i)
# Run the Gaussian Process
# GP train only at query 0 for the best hyperparameters
# required for the subsequent queries
if i == 0:
(OptLoss, OptAmp, OptLength, amp, length_scale, gp_mean, gp_stddev,
gp_variance, Optmae_val, mae_test, mse_test, sae_test, R) =\
adam.active(datadir, prop, latent_train, latent_val, latent_test,
ytrain, yval, ytest, maxiters, amp, length_scale, rate)
else:
maxiters = 0
(OptLoss, OptAmp, OptLength, Amp, Length_Scale, gp_mean, gp_stddev,
gp_variance, Optmae_val, mae_test, mse_test, sae_test, R) =\
adam.active(datadir, prop, latent_train, latent_val, latent_test,
ytrain, yval, ytest, maxiters, amp, length_scale, rate)
# Set the new hyperparameters to those from query 0
Amp = amp
Length_Scale = length_scale
# Dump some parameters to an array for plotting purposes.
training_data = np.append(training_data, len(ytrain))
mae_test_cycle = np.append(mae_test_cycle, mae_test)
mse_test_cycle = np.append(mse_test_cycle, mse_test)
sae_test_cycle = np.append(sae_test_cycle, sae_test)
if maxiters > 0:
Optmae_val_cycle = np.append(Optmae_val_cycle,
Optmae_val)
if i < max_query:
if samp == "entropy":
if i == 0:
logging.info(
"Entropy sampling for active learning enabled ..."
)
idx, latent_pool, ypool, latent_train, ytrain, latent_test, ytest =\
EntropySelection(i, latent_train, ytrain, latent_test, ytest,
latent_val, yval, gp_variance, query, max_query)
elif samp == "random":
if i == 0:
logging.info(
"Random sampling for active learning enabled ..."
)
idx, latent_pool, ypool, latent_train, ytrain, latent_test, ytest =\
RandomSelection(i, latent_train, ytrain, latent_test, ytest,
latent_val, yval, gp_variance, query, max_query)
samp_idx = np.append(samp_idx, idx)
logging.info("Writing the results to file ...")
np.save("%s/training_data_for_plotting.npy" % datadir,
training_data)
np.save("%s/gp_mae.npy" % datadir, mae_test_cycle)
np.save("%s/gp_mse.npy" % datadir, mse_test_cycle)
np.save("%s/gp_sae.npy" % datadir, sae_test_cycle)
np.save("%s/samp_indices.npy" % datadir, samp_idx)
np.save("%s/Xtest.npy" % datadir, np.delete(Xtest, samp_idx))
if maxiters > 0:
np.save("%s/val_mae.npy" % datadir, Optmae_val_cycle)
logging.info("Saving plots ...")
plot.norepeat(datadir, prop, layer, samp, query, maxiters)
