def get_model(data_dir,
mat_prop,
classification=False,
batch_size=None,
transfer=None,
verbose=True):
# Get the TorchedCrabNet architecture loaded
model = Model(CrabNet(compute_device=compute_device).to(compute_device),
model_name=f'{mat_prop}',
verbose=verbose)
# Train network starting at pretrained weights
if transfer is not None:
model.load_network(f'{transfer}.pth')
model.model_name = f'{mat_prop}'
# Apply BCEWithLogitsLoss to model output if binary classification is True
if classification:
model.classification = True
# Get the datafiles you will learn from
train_data = f'{data_dir}/{mat_prop}/train.csv'
try:
val_data = f'{data_dir}/{mat_prop}/val.csv'
except:
print('Please ensure you have train (train.csv) and validation data',
f'(val.csv) in folder "data/materials_data/{mat_prop}"')
# Load the train and validation data before fitting the network
data_size = pd.read_csv(train_data).shape[0]
batch_size = 2**round(np.log2(data_size) - 4)
if batch_size < 2**7:
batch_size = 2**7
if batch_size > 2**12:
batch_size = 2**12
model.load_data(train_data, batch_size=batch_size, train=True)
print(f'training with batchsize {model.batch_size} '
f'(2**{np.log2(model.batch_size):0.3f})')
model.load_data(val_data, batch_size=batch_size)
# Set the number of epochs, decide if you want a loss curve to be plotted
model.fit(epochs=40, losscurve=False)
# Save the network (saved as f"{model_name}.pth")
model.save_network()
return model
def get_model(mat_prop,
i,
classification=False,
batch_size=None,
transfer=None,
verbose=True):
# Get the TorchedCrabNet architecture loaded
model = Model(CrabNet(compute_device=compute_device).to(compute_device),
model_name=f'{mat_prop}{i}',
verbose=verbose)
# Train network starting at pretrained weights
if transfer is not None:
model.load_network(f'{transfer}.pth')
model.model_name = f'{mat_prop}'
# Apply BCEWithLogitsLoss to model output if binary classification is True
if classification:
model.classification = True
# Get the datafiles you will learn from
train_data = rf'data\matbench_cv\{mat_prop}\train{i}.csv'
val_data = rf'data\matbench_cv\{mat_prop}\val{i}.csv'
# Load the train and validation data before fitting the network
data_size = pd.read_csv(train_data).shape[0]
batch_size = 2**round(np.log2(data_size) - 4)
if batch_size < 2**7:
batch_size = 2**7
if batch_size > 2**12:
batch_size = 2**12
# batch_size = 2**7
model.load_data(train_data, batch_size=batch_size, train=True)
print(f'training with batchsize {model.batch_size} '
f'(2**{np.log2(model.batch_size):0.3f})')
model.load_data(val_data, batch_size=batch_size)
# Set the number of epochs, decide if you want a loss curve to be plotted
model.fit(epochs=300, losscurve=False)
# Save the network (saved as f"{model_name}.pth")
model.save_network()
return model
def model(mat_prop, classification_list, simple=False):
# Get the TorchedCrabNet architecture loaded
model = Model(CrabNet(compute_device=compute_device).to(compute_device),
model_name=f'{mat_prop}')
if True:
model.load_network(f'{mat_prop}.pth')
model.model_name = f'{mat_prop}'
if mat_prop in classification_list:
model.classification = True
dataset = rf'{data_dir}\{mat_prop}\train.csv'
model.load_data(dataset,
batch_size=2**7) # data is reloaded to model.data_loader
model.model.eval()
model.model.avg = False
simple_tracker = {i: [] for i in range(119)}
element_tracker = {i: [] for i in range(119)}
composition_tracker = {}
with torch.no_grad():
for i, data in enumerate(tqdm(model.data_loader)):
X, y, formula = data
src, frac = X.squeeze(-1).chunk(2, dim=1)
src = src.to(compute_device, dtype=torch.long, non_blocking=True)
frac = frac.to(compute_device, dtype=data_type, non_blocking=True)
y = y.to(compute_device, dtype=data_type, non_blocking=True)
output = model.model.forward(src, frac)
mask = (src == 0).unsqueeze(-1).repeat(1, 1, 1)
prediction, uncertainty, prob = output.chunk(3, dim=-1)
prediction = prediction * torch.sigmoid(prob)
uncertainty = torch.exp(uncertainty) * model.scaler.std
prediction = model.scaler.unscale(prediction)
prediction = prediction * ~mask
uncertainty = uncertainty * ~mask
if model.classification:
prediction = torch.sigmoid(prediction)
for i in range(src.shape[0]):
if any(prediction[i].cpu().numpy().ravel() < 0):
composition_tracker[formula[i]] = [
src[i].cpu().numpy(), frac[i].cpu().numpy(),
y[i].cpu().numpy(), prediction[i].cpu().numpy(),
uncertainty[i].cpu().numpy()
]
for j in range(src.shape[1]):
element_tracker[int(src[i][j])].append(
float(prediction[i][j]))
simple_tracker[int(src[i][j])].append(float(y[i]))
def elem_view(element_tracker, plot=True):
property_tracker = {}
x_max = max([y[1] for y in model.data_loader.dataset])
x_min = min([y[1] for y in model.data_loader.dataset])
x_range = x_max - x_min
x_min_buffer = 0.1 * x_range
x_max_buffer = 0.1 * x_range
for key in element_tracker.keys():
data = element_tracker[key]
if len(data) > 10:
sum_prop = sum(data)
mean_prop = sum_prop / len(data)
prop = mean_prop
property_tracker[all_symbols[key]] = prop
if plot:
plt.figure(figsize=(4, 4))
hist_kws = {
'edgecolor': 'k',
'linewidth': 2,
'alpha': 1,
'facecolor': '#A1D884'
}
ax = sns.distplot(
data,
label=f'{all_symbols[key]}, n={len(data)}',
kde=False,
bins=np.arange(0, 500, 25),
hist_kws=hist_kws,
kde_kws={
'color': 'k',
'linewidth': 2
})
ax.axes.yaxis.set_visible(False)
plt.legend()
plt.xlim(x_min - x_min_buffer, x_max + x_max_buffer)
plt.xlabel('Bulk Modulus Contribution (GPa)')
plt.tick_params(axis='both', which='both', direction='in')
save_dir = f'figures/contributions/{mat_prop}/'
os.makedirs(save_dir, exist_ok=True)
plt.savefig(f'{save_dir}{all_symbols[key]}.png',
dpi=300,
bbox_inches='tight')
plt.show()
return property_tracker
if simple:
property_tracker = elem_view(simple_tracker, plot=True)
else:
property_tracker = elem_view(element_tracker, plot=True)
return property_tracker
color = [
'#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33',
'#a65628', '#f781bf'
]
classification_list = []
num = ''
mat_prop = 'aflow__Egap'
# Get the TorchedCrabNet architecture loaded
model = Model(CrabNet().to(compute_device), model_name=f'{mat_prop}')
if True:
model.load_network(f'{mat_prop}{num}.pth')
model.model_name = f'{mat_prop}{num}'
if mat_prop in classification_list:
model.classification = True
mat_prop = 'aflow__Egap'
test_data = rf'data\benchmark_data\{mat_prop}\train.csv'
# test_data = rf'data\matbench_cv\{mat_prop}\train{num}.csv'
model.load_data(test_data,
batch_size=2**0) # data is reloaded to model.data_loader
len_dataset = len(model.data_loader.dataset)
n_atoms = int(len(model.data_loader.dataset[0][0]) / 2)
act = np.zeros(len_dataset)
pred = np.zeros(len_dataset)
'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk', 'Cf', 'Es', 'Fm', 'Md',
'No', 'Lr', 'Rf', 'Db', 'Sg', 'Bh', 'Hs', 'Mt', 'Ds', 'Rg',
'Cn', 'Nh', 'Fl', 'Mc', 'Lv', 'Ts', 'Og']
color = ['#e41a1c','#377eb8','#4daf4a','#984ea3','#ff7f00','#ffff33','#a65628','#f781bf']
classification_list = []
mat_prop = 'OQMD_Bandgap'
simple=False
# Get the TorchedCrabNet architecture loaded
model = Model(CrabNet().to(compute_device), model_name=f'{mat_prop}')
if True:
model.load_network(f'{mat_prop}.pth')
model.model_name = f'{mat_prop}'
if mat_prop in classification_list:
model.classification = True
mat_prop = 'OQMD_Bandgap'
test_data = rf'data\benchmark_data\{mat_prop}\val.csv'
model.load_data(test_data, batch_size=2**0) # data is reloaded to model.data_loader
len_dataset = len(model.data_loader.dataset)
n_atoms = int(len(model.data_loader.dataset[0][0])/2)
act = np.zeros(len_dataset)
pred = np.zeros(len_dataset)
uncert = np.zeros(len_dataset)
formulae = np.empty(len_dataset, dtype=list)
