def cross_validate(x, y, peak_names, output_file_path):
kf = KFold(n_splits=10, shuffle=True)
pred_all = []
corr_all = []
peak_order = []
for train_index, test_index in kf.split(x):
train_data, eval_data = x[train_index, :, :], x[test_index, :, :]
train_labels, eval_labels = y[train_index, :], y[test_index, :]
train_names, eval_name = peak_names[train_index], peak_names[
test_index]
# Data loader
train_dataset = torch.utils.data.TensorDataset(
torch.from_numpy(train_data), torch.from_numpy(train_labels))
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=False)
eval_dataset = torch.utils.data.TensorDataset(
torch.from_numpy(eval_data), torch.from_numpy(eval_labels))
eval_loader = torch.utils.data.DataLoader(dataset=eval_dataset,
batch_size=batch_size,
shuffle=False)
# create model
model = aitac.ConvNet(num_classes, num_filters).to(device)
# Loss and optimizer
criterion = aitac.pearson_loss
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# train model
model, best_loss = aitac.train_model(train_loader, eval_loader, model,
device, criterion, optimizer,
num_epochs, output_file_path)
# Predict on test set
predictions, max_activations, max_act_index = aitac.test_model(
eval_loader, model, device)
# plot the correlations histogram
correlations = plot_utils.plot_cors(eval_labels, predictions,
output_file_path)
pred_all.append(predictions)
corr_all.append(correlations)
peak_order.append(eval_name)
pred_all = np.vstack(pred_all)
corr_all = np.hstack(corr_all)
peak_order = np.hstack(peak_order)
return pred_all, corr_all, peak_order
shuffle=False)
valid_dataset = torch.utils.data.TensorDataset(torch.from_numpy(valid_data),
torch.from_numpy(valid_labels))
valid_loader = torch.utils.data.DataLoader(dataset=valid_dataset,
batch_size=batch_size,
shuffle=False)
eval_dataset = torch.utils.data.TensorDataset(torch.from_numpy(eval_data),
torch.from_numpy(eval_labels))
eval_loader = torch.utils.data.DataLoader(dataset=eval_dataset,
batch_size=batch_size,
shuffle=False)
# create model
model = aitac.ConvNet(num_classes, num_filters).to(device)
# weights from model with 300 filters
checkpoint = torch.load("../models/" + original_model + ".ckpt")
#indices of filters in original model
filters = np.loadtxt('../data/filter_set99_index.txt')
#load new weights into model
checkpoint2 = model.state_dict()
#copy original model weights into new model
for i, (layer_name, layer_weights) in enumerate(checkpoint.items()):
# for all first layer weights take subset
if i < 2: