def compute_task_metrics(task_name, logits, labels):
if logits.shape[1] == 1:
pred_arr = logits.reshape(-1)
else:
pred_arr = np.argmax(logits, axis=1)
return compute_metrics(
task_name=task_name,
pred_srs=pred_arr,
label_srs=labels,
)
per_epoch_loss += loss.item()
per_epoch_loss = per_epoch_loss / num_batches
return predictions, diff_targets, all_attention_bin, all_attention_hm, per_epoch_loss, all_gene_ids
best_valid_loss = 10000000000
best_valid_avgAUPR = -1
best_valid_avgAUC = -1
best_test_avgAUC = -1
if (args.test_on_saved_model == False):
for epoch in range(0, args.epochs):
print('---------------------------------------- Training ' +
str(epoch + 1) + ' -----------------------------------')
predictions, diff_targets, alpha_train, beta_train, train_loss, _ = train(
Train)
train_avgAUPR, train_avgAUC = evaluate.compute_metrics(
predictions, diff_targets)
predictions, diff_targets, alpha_valid, beta_valid, valid_loss, gene_ids_valid = test(
Valid, "Validation")
valid_avgAUPR, valid_avgAUC = evaluate.compute_metrics(
predictions, diff_targets)
predictions, diff_targets, alpha_test, beta_test, test_loss, gene_ids_test = test(
Test, 'Testing')
test_avgAUPR, test_avgAUC = evaluate.compute_metrics(
predictions, diff_targets)
if (valid_avgAUC >= best_valid_avgAUC):
# save best epoch -- models converge early
best_valid_avgAUC = valid_avgAUC
best_test_avgAUC = test_avgAUC
def run_model(name, context, conf, double_input, use_elmo=False, save_predictions=False, save_model=False):
"""
Runs the given model 'name' for the given 'context' and agreement level 'conf'. If double_input is True, runs the combined model using context comment text. Optionally saves the trained model & its vocabulary, and predictions.
Allowed names: lstm | bilstm | stacked_bilstm | cnn | dense_lstm | dense_bilstm | dense_stacked_bilstm | dense_cnn | nli_cnn | bert | dense_bert
If use_elmo=True, uses ELMo's pre-trained language model for embeddings.
"""
if use_elmo:
token_indexer = ELMoTokenCharactersIndexer() # token indexer is responsible for mapping tokens to integers: this makes sure that the mapping is consistent with what was used in the original ELMo training.
elif name == 'bert':
global bert_token_indexer
bert_token_indexer = PretrainedBertIndexer(pretrained_model=BERT_MODEL, do_lowercase=True)
else:
token_indexer = SingleIdTokenIndexer()
if name == 'bert': # BERT uses a special wordpiece tokenizer
reader = data_reader.UnpalatableDatasetReader(main_input=context, additional_context=double_input,
tokenizer=tokenizer_bert, token_indexers={"tokens": bert_token_indexer},
label_cols=LABEL_COLS)
else:
reader = data_reader.UnpalatableDatasetReader(main_input=context, additional_context=double_input, tokenizer=tokenizer,
token_indexers={"tokens": token_indexer}, label_cols=LABEL_COLS)
map_reply_id_pred_probability = {}; n_epochs = []
f1s, AUROCs, weighted_f1s, precision_s, recall_s, accuracies, AUPRCs = [], [], [], [], [], [], []
for fold_number in range(1,6): # 5-fold cross validation
train_fname = 'train_data_fold_'+str(fold_number)+'_OneHot.csv'
val_fname = 'val_data_fold_'+str(fold_number)+'_OneHot.csv'
test_fname = 'test_data_fold_'+str(fold_number)+'_OneHot.csv'
train_dataset = reader.read(file_path=DATA_ROOT / conf / train_fname)
validation_dataset = reader.read(file_path=DATA_ROOT / conf / val_fname)
test_dataset = reader.read(file_path=DATA_ROOT / conf / test_fname)
print("\n#####################################################\n", double_input, context, conf, name, len(train_dataset), len(validation_dataset), len(test_dataset))
# Train model:
if name == 'lstm':
model, vocab, ep = train.train_lstm(train_dataset, validation_dataset, BATCH_SIZE, dense_vector=False,
num_layers=1, bidirectional=False, use_elmo=use_elmo, double_input=double_input)
elif name == 'dense_lstm':
model, vocab, ep = train.train_lstm(train_dataset, validation_dataset, BATCH_SIZE, dense_vector=True,
col_name=context, num_layers=1, bidirectional=False, use_elmo=use_elmo,
double_input=double_input)
elif name == 'bilstm':
model, vocab, ep = train.train_lstm(train_dataset, validation_dataset, BATCH_SIZE, dense_vector=False,
num_layers=1, bidirectional=True, use_elmo=use_elmo, double_input=double_input)
elif name == 'dense_bilstm':
model, vocab, ep = train.train_lstm(train_dataset, validation_dataset, BATCH_SIZE, dense_vector=True,
col_name=context, num_layers=1, bidirectional=True, use_elmo=use_elmo,
double_input=double_input)
elif name == 'stacked_bilstm':
model, vocab, ep = train.train_lstm(train_dataset, validation_dataset, BATCH_SIZE, dense_vector=False,
num_layers=2, bidirectional=True, use_elmo=use_elmo, double_input=double_input)
elif name == 'dense_stacked_bilstm':
model, vocab, ep = train.train_lstm(train_dataset, validation_dataset, BATCH_SIZE, dense_vector=True,
col_name=context, num_layers=2, bidirectional=True, use_elmo=use_elmo,
double_input=double_input)
elif name == 'cnn':
if context == 'reply_text': filter_sizes = (2,3) # kernels can not be bigger than the shortest sentence
else: filter_sizes = (2,)
model, vocab, ep = train.train_cnn(train_dataset, validation_dataset, BATCH_SIZE, dense_vector=False,
num_filters=100, filter_sizes=filter_sizes, use_elmo=use_elmo,
double_input=double_input)
elif name == 'dense_cnn':
if context == 'reply_text': filter_sizes = (2,3) # kernels can not be bigger than the shortest sentence
else: filter_sizes = (2,)
model, vocab, ep = train.train_cnn(train_dataset, validation_dataset, BATCH_SIZE, dense_vector=True,
col_name=context, num_filters=100, filter_sizes=filter_sizes, use_elmo=use_elmo,
double_input=double_input)
elif name == 'nli_cnn':
if double_input == False:
print("Error: NLI-inspired architecture only accepts double-input.")
return [None]*9
filter_sizes = (2,3)
model, vocab, ep = train.train_nli(train_dataset, validation_dataset, BATCH_SIZE, use_elmo=use_elmo,
num_filters=100, filter_sizes=filter_sizes)
elif name == 'bert':
model, vocab, ep = train.train_bert(train_dataset, validation_dataset, BATCH_SIZE, pretrained_model=BERT_MODEL,
dense_vector=False, double_input=double_input)
elif name == 'dense_bert':
model, vocab, ep = train.train_bert(train_dataset, validation_dataset, BATCH_SIZE, pretrained_model=BERT_MODEL,
dense_vector=True, col_name=context, double_input=double_input)
else:
sys.exit("'name' not valid")
n_epochs.append(ep) # keep track of number of actual training epochs for each fold
# Predict and evaluate model on test set:
preds = evaluate.make_predictions(model, vocab, test_dataset, BATCH_SIZE, use_gpu=False) # NOTE: preds is of shape (number of samples, 2) - the columns represent the probabilities for the two classes in order ['yes_unp', 'not_unp']
f1, auroc, w_f1, precision, recall, acc, auprc = evaluate.compute_metrics(preds, test_dataset)
if save_predictions: # save predictions for error analysis
replyid_pred = evaluate.map_id_prediction(preds, test_dataset)
if set(replyid_pred.keys()).intersection(set(map_reply_id_pred_probability.keys())) != set(): # sanity check
sys.exit("Error: There is overlap in Test IDs across folds.")
map_reply_id_pred_probability.update(replyid_pred)
if save_model: # save the model weights and vocabulary
with open('./tmp/'+name+'_model_conf_'+conf.split('-')[1]+'_fold_'+str(fold_number)+'.th', 'wb') as f:
torch.save(model.state_dict(), f)
vocab.save_to_files("./tmp/"+name+"_vocabulary_"+conf.split('-')[1]+"_fold_"+str(fold_number))
print("\nFold #{} | F1 = {} | AUROC = {} | AUPRC = {}".format(fold_number, f1, auroc, auprc))
f1s.append(f1); AUROCs.append(auroc); weighted_f1s.append(w_f1); precision_s.append(precision);
recall_s.append(recall); accuracies.append(acc); AUPRCs.append(auprc)
mean_f1 = np.array(f1s).mean(); mean_auroc = np.array(AUROCs).mean(); mean_weighted_f1 = np.array(weighted_f1s).mean();
mean_precision = np.array(precision_s).mean(); mean_recall = np.array(recall_s).mean(); mean_accuracy = np.array(accuracies).mean(); mean_auprc = np.array(AUPRCs).mean()
print("Total predictions: {} | Save Predictions: {}".format(len(map_reply_id_pred_probability), save_predictions))
return mean_f1, mean_auroc, mean_weighted_f1, mean_precision, mean_recall, mean_accuracy, mean_auprc, map_reply_id_pred_probability, n_epochs
diff_predictions[start:end] = batch_diff_predictions.data.cpu()
per_epoch_loss += loss.item()
per_epoch_loss = per_epoch_loss / num_batches
return diff_predictions, diff_targets, all_attention_bin, all_attention_hm, per_epoch_loss, all_gene_ids
best_valid_loss = 10000000000
best_valid_MSE = 100000
best_valid_R2 = -1
if (args.test_on_saved_model == False):
for epoch in range(0, args.epochs):
print('=---------------------------------------- Training ' +
str(epoch + 1) + ' -----------------------------------=')
diff_predictions, diff_targets, alpha_train, beta_train, train_loss, _ = train(
Train)
train_MSE, train_R2 = evaluate.compute_metrics(diff_predictions,
diff_targets)
diff_predictions, diff_targets, alpha_valid, beta_valid, valid_loss, gene_ids_valid = test(
Valid)
valid_MSE, valid_R2 = evaluate.compute_metrics(diff_predictions,
diff_targets)
if (valid_R2 >= best_valid_R2):
# save best epoch -- models converge early
best_valid_R2 = valid_R2
torch.save(model, model_dir + "/" + model_name + '_R2_model.pt')
print("Epoch:", epoch)
print("train R2:", train_R2)
print("valid R2:", valid_R2)
print("best valid R2:", best_valid_R2)
def run_pipeline(ignition_file, persist_all, load_all_fresh):
"""
An adhoc pipeline created to mirror the standard ML pipeline and work
with citations data.
Parameters:
===========
ignition_file: string
name of the yaml file for which you want to run an experiment
persist_all: boolean
T if you want to persist all data for future use
load_all_fresh: boolean
T if you want to avoid any persisted data and load new data from scrath
Returns:
========
None
"""
model_parts = {}
##### 1. LOAD ENVIRONMENT DATA #####
# load local paths
local_paths_env = load_local_paths('local_paths.yaml')
print('Local paths loaded.')
# load ignition file
ignition = load_config(local_paths_env['ignition_path'] + ignition_file)
print('Ignition loaded.')
# id used for persisting
hash_id = create_hash_id(str(ignition['id']))
print('Hash id created.')
# create hyperparameter combinations (for k-folding)
hyperparameters = expand_grid(ignition['hyperparameters'])
# load environment file
psql_env = load_psql_env(pgpass_path=local_paths_env['pgpass_path'])
print('PSQL environment file loaded.')
# Initiate PSQL Connection
connection = SQLConn(psql_env)
connection.open()
##### 2. LOAD TRAIN AND TEST DATA #####
if check_persisted(local_paths_env['store_train_data'], f'{hash_id}_x',
load_all_fresh):
print("Found data")
# data loaded before: load from file
X_train = load(local_paths_env['store_train_data'], f'{hash_id}_x')
X_test = load(local_paths_env['store_test_data'], f'{hash_id}_x')
y_train = load(local_paths_env['store_train_data'], f'{hash_id}_y')
y_test = load(local_paths_env['store_test_data'], f'{hash_id}_y')
print('Loaded data from file.')
else:
print("Data not found in storage - load from database")
# data not loaded: pull from database and create features
X_train, X_test, y_train, y_test = sample(
ignition, connection, local_paths_env['store_features'])
print(f"X_train shape: {X_train.shape}")
print(f"X_test shape: {X_test.shape}")
print(f"y_train shape: {y_train.shape}")
print(f"y_test shape: {y_test.shape}")
# add fold index column to data
X_train, y_train = k_fold(X_train, y_train, ignition['k_folds'],
ignition['k_folds_seed'])
# save data to file for future use
save(X_train, local_paths_env['store_train_data'], f'{hash_id}_x',
persist_all)
save(X_test, local_paths_env['store_test_data'], f'{hash_id}_x',
persist_all)
save(y_train, local_paths_env['store_train_data'], f'{hash_id}_y',
persist_all)
save(y_test, local_paths_env['store_test_data'], f'{hash_id}_y',
persist_all)
print('Data loading completed.')
##### 3. K-FOLDING #####
# loop over folds
for fold in tqdm(range(ignition['k_folds']), desc='Folds'):
# get fold id hash (for persisting)
fold_id = create_hash_id(str(ignition['id']) + str(fold))
# get fold data
fold_X_train = X_train[X_train['k'] != fold]
fold_X_test = X_train[X_train['k'] == fold]
fold_y_train = y_train[y_train['k'] != fold]
fold_y_test = y_train[y_train['k'] == fold]
# store fold features, if any
fold_features = {}
##### 4. LOOP OVER HYPERPARAMETERS: TRAIN CLASSIFIER #####
for hyperparam in tqdm(hyperparameters, desc='Hyperparameters'):
# create hyperparam unique id and hyperparam-fold unique id
hyperparam_id = create_hash_id(
str(ignition['id']) + str(hyperparam))
hyperparam_fold_id = create_hash_id(
str(ignition['id']) + str(hyperparam) + str(fold))
# if not check_val_in_db(connection, ignition['results_table_name'],
# 'results', 'hash_id', hyperparam_fold_id, len(ignition['recalls'])):
# create classifier of specified type and with specified target
classifier = select_classifier(ignition["model_type"],
fold_id,
ignition["target"],
ignition["classes"],
fold_features,
hyperparameters=hyperparam,
seed=ignition['seed'],
env=local_paths_env,
load_fresh=load_all_fresh)
#print('Classifier created.')
# train classifier
classifier.train(fold_X_train, fold_y_train)
##### 5. TEST CLASSIFIER #####
# generate predictions from classifier
y_probs = classifier.predict(fold_X_test)
##### 6. EVALUATION #####
for recall in tqdm(ignition['recalls'], desc='Evaluations'):
# compute evaluation metrics
all_metrics = compute_metrics(
metric_names=ignition['metrics'],
y_true=fold_y_test.drop(columns=['k']),
y_pred=y_probs,
k=recall)
# store results in database
unique_id = create_hash_id(
str(ignition['id']) + str(hyperparam) + str(fold) +
str(recall))
results_to_db(metrics=all_metrics,
table_name=ignition['results_table_name'],
ignition_id=ignition['id'],
hash_id=hyperparam_fold_id,
algorithm=ignition['model_type'],
hyperparameters=hyperparam,
fold=str(fold),
recall=recall,
unique_id=unique_id,
connection=connection)
connection.close()
print(f"Done running pipeline for ignition id: {ignition['id']}!")
def main(args):
torch.manual_seed(1)
model_name = ''
model_name += (args.cell_type) + ('_')
model_name += args.model_type
args.bidirectional = not args.unidirectional
print('the model name: ', model_name)
args.data_root += ''
args.save_root += ''
args.dataset = args.cell_type
args.data_root = os.path.join(args.data_root)
print('loading data from: ', args.data_root)
args.save_root = os.path.join(args.save_root, args.dataset)
print('saving results in from: ', args.save_root)
if args.model_root is None:
model_dir = os.path.join(args.save_root, model_name)
else:
args.model_root = os.path.join(args.model_root, args.dataset)
model_dir = os.path.join(args.model_root, model_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
attentionmapfile = model_dir + '/' + args.attentionfilename
orig_attentionmapfile = model_dir + '/' + 'orig_' + args.attentionfilename
print('==>processing data')
Train, Valid, Test = data.load_data(args)
print('==>building model')
model = Model.att_chrome(args)
if torch.cuda.device_count() > 0:
torch.cuda.manual_seed_all(1)
dtype = torch.cuda.FloatTensor
# cuda.set_device(args.gpuid)
model.type(dtype)
print('Using GPU ' + str(args.gpuid))
else:
dtype = torch.FloatTensor
#print(model)
if (args.test_on_saved_model == False):
print("==>initializing a new model")
for p in model.parameters():
p.data.uniform_(-0.1, 0.1)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
#optimizer = optim.SGD(model.parameters(), lr = args.lr, momentum=args.momentum)
def pgd_attack(model,
inputs_1,
batch_diff_targets,
eps=1.0,
alpha=1.0,
iters=2):
ori_inputs_1 = inputs_1.data
if args.pgd_mask:
if args.pgd_mask == 'fg':
pgd_mask = (ori_inputs_1 > args.pgd_mask_threshold).to(
torch.float32)
elif args.pgd_mask == 'bg':
pgd_mask = (ori_inputs_1 <= args.pgd_mask_threshold).to(
torch.float32)
#print(ori_images.min(), ori_images.max())
for i in range(iters):
inputs_1.requires_grad = True
if args.input_threshold:
inputs = inputs_1 * (inputs_1 > args.input_threshold).to(
torch.float32)
else:
inputs = inputs_1
if i == 0:
batch_predictions_orig, batch_beta_orig, batch_alpha_orig = model(
inputs.type(dtype))
batch_predictions, batch_beta, batch_alpha = model(
inputs.type(dtype))
if i < iters - 1:
model.zero_grad()
loss = F.binary_cross_entropy_with_logits(
batch_predictions,
batch_diff_targets.cuda(),
reduction='mean')
loss.backward()
grad_values = inputs_1.grad
adv_inputs_1 = inputs_1 + alpha * grad_values.sign()
eta = torch.clamp(adv_inputs_1 - ori_inputs_1,
min=-eps,
max=eps)
if args.pgd_mask:
eta = eta * pgd_mask
inputs_1 = torch.clamp(ori_inputs_1 + eta,
min=ori_inputs_1.min(),
max=ori_inputs_1.max()).detach_()
ori_inputs_1_flat = ori_inputs_1.detach().cpu().numpy().flatten()
inputs_1_flat = inputs_1.detach().cpu().numpy().flatten()
grad_values_flat = grad_values.detach().cpu().numpy().flatten()
corr = pearsonr(inputs_1.detach().cpu().numpy().flatten(),
ori_inputs_1.cpu().numpy().flatten())[0]
return batch_predictions, corr, inputs_1_flat, grad_values_flat, ori_inputs_1_flat, batch_beta, batch_alpha, batch_predictions_orig, batch_beta_orig, batch_alpha_orig
def train(TrainData):
model.train()
# initialize attention
diff_targets = torch.zeros(TrainData.dataset.__len__(), 1)
predictions = torch.zeros(diff_targets.size(0), 1)
all_attention_bin = torch.zeros(TrainData.dataset.__len__(),
(args.n_hms * args.n_bins))
all_attention_hm = torch.zeros(TrainData.dataset.__len__(), args.n_hms)
num_batches = int(
math.ceil(TrainData.dataset.__len__() / float(args.batch_size)))
all_gene_ids = [None] * TrainData.dataset.__len__()
per_epoch_loss = 0
print('Training')
for idx, Sample in enumerate(TrainData):
start, end = (idx * args.batch_size), min(
(idx * args.batch_size) + args.batch_size,
TrainData.dataset.__len__())
inputs_1 = Sample['input']
batch_diff_targets = Sample['label'].unsqueeze(1).float()
optimizer.zero_grad()
if args.pgd:
batch_predictions, corr, adv_inputs, grad_values, ori_inputs, batch_beta, batch_alpha, batch_predictions_orig, batch_beta_orig, batch_alpha_orig = pgd_attack(
model,
inputs_1.type(dtype),
batch_diff_targets,
eps=1.0,
alpha=1.0,
iters=args.pgd_steps)
else:
batch_predictions, batch_beta, batch_alpha = model(
inputs_1.type(dtype))
loss = F.binary_cross_entropy_with_logits(batch_predictions.cpu(),
batch_diff_targets,
reduction='mean')
per_epoch_loss += loss.item()
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
optimizer.step()
diff_targets[start:end, 0] = batch_diff_targets[:, 0]
all_gene_ids[start:end] = Sample['geneID']
batch_predictions = torch.sigmoid(batch_predictions)
predictions[start:end] = batch_predictions.data.cpu()
all_attention_bin[start:end] = batch_alpha.data
all_attention_hm[start:end] = batch_beta.data
per_epoch_loss = per_epoch_loss / num_batches
return predictions, diff_targets, all_attention_bin, all_attention_hm, per_epoch_loss
def test(ValidData):
if args.pgd:
model.train()
else:
model.eval()
diff_targets = torch.zeros(ValidData.dataset.__len__(), 1)
predictions = torch.zeros(diff_targets.size(0), 1)
predictions_orig = torch.zeros(diff_targets.size(0), 1)
all_attention_bin = torch.zeros(ValidData.dataset.__len__(),
(args.n_hms * args.n_bins))
all_attention_hm = torch.zeros(ValidData.dataset.__len__(), args.n_hms)
all_attention_bin_orig = torch.zeros(ValidData.dataset.__len__(),
(args.n_hms * args.n_bins))
all_attention_hm_orig = torch.zeros(ValidData.dataset.__len__(),
args.n_hms)
num_batches = int(
math.ceil(ValidData.dataset.__len__() / float(args.batch_size)))
all_gene_ids = [None] * ValidData.dataset.__len__()
per_epoch_loss = 0
per_epoch_corr = 0
all_adv_inputs = []
all_grad_values = []
all_ori_inputs = []
for idx, Sample in enumerate(ValidData):
start, end = (idx * args.batch_size), min(
(idx * args.batch_size) + args.batch_size,
ValidData.dataset.__len__())
optimizer.zero_grad()
inputs_1 = Sample['input']
batch_diff_targets = Sample['label'].unsqueeze(1).float()
if args.pgd:
batch_predictions, corr, adv_inputs, grad_values, ori_inputs, batch_beta, batch_alpha, batch_predictions_orig, batch_beta_orig, batch_alpha_orig = pgd_attack(
model,
inputs_1.type(dtype),
batch_diff_targets,
eps=1.0,
alpha=1.0,
iters=args.pgd_steps)
all_adv_inputs = all_adv_inputs + list(adv_inputs)
all_grad_values = all_grad_values + list(grad_values)
all_ori_inputs = all_ori_inputs + list(ori_inputs)
per_epoch_corr += corr
else:
if args.input_threshold:
inputs_1 = inputs_1 * (inputs_1 > args.input_threshold).to(
torch.float32)
batch_predictions, batch_beta, batch_alpha = model(
inputs_1.type(dtype))
batch_predictions_orig = batch_predictions
#batch_predictions = model(inputs_1.type(dtype))
loss = F.binary_cross_entropy_with_logits(batch_predictions.cpu(),
batch_diff_targets,
reduction='mean')
all_attention_bin[start:end] = batch_alpha.data
all_attention_hm[start:end] = batch_beta.data
all_attention_bin_orig[start:end] = batch_alpha_orig.data
all_attention_hm_orig[start:end] = batch_beta_orig.data
diff_targets[start:end, 0] = batch_diff_targets[:, 0]
all_gene_ids[start:end] = Sample['geneID']
batch_predictions = torch.sigmoid(batch_predictions)
batch_predictions_orig = torch.sigmoid(batch_predictions_orig)
predictions[start:end] = batch_predictions.data.cpu()
predictions_orig[start:end] = batch_predictions_orig.data.cpu()
per_epoch_loss += loss.item()
per_epoch_loss = per_epoch_loss / num_batches
per_epoch_corr = per_epoch_corr / num_batches
return predictions, diff_targets, all_attention_bin, all_attention_hm, per_epoch_loss, all_gene_ids, per_epoch_corr, np.array(
all_adv_inputs), np.array(all_grad_values), np.array(
all_ori_inputs
), all_attention_bin_orig, all_attention_hm_orig, predictions_orig
best_valid_loss = 10000000000
best_valid_avgAUPR = -1
best_valid_avgAUC = -1
best_test_avgAUC = -1
if (args.test_on_saved_model == False):
for epoch in range(0, args.epochs):
print('---------------------------------------- Training ' +
str(epoch + 1) + ' -----------------------------------')
predictions, diff_targets, all_attention_bin, all_attention_hm, per_epoch_loss = train(
Train)
train_avgAUPR, train_avgAUC = evaluate.compute_metrics(
predictions, diff_targets)
predictions, diff_targets, alpha_valid, beta_valid, valid_loss, gene_ids_valid, test_corr, adv_inputs_valid, grad_values_valid, ori_inputs_valid, _, _, _ = test(
Valid)
valid_avgAUPR, valid_avgAUC = evaluate.compute_metrics(
predictions, diff_targets)
predictions, diff_targets, alpha_test, beta_test, test_loss, gene_ids_test, test_corr, adv_inputs_test, grad_values_test, ori_inputs_test, _, _, _ = test(
Test)
test_avgAUPR, test_avgAUC = evaluate.compute_metrics(
predictions, diff_targets)
if (valid_avgAUC >= best_valid_avgAUC):
# save best epoch -- models converge early
best_valid_avgAUC = valid_avgAUC
best_test_avgAUC = test_avgAUC
torch.save(model.cpu().state_dict(),
model_dir + "/" + model_name + '_avgAUC_model.pt')
model.type(dtype)
print("Epoch:", epoch)
print("train avgAUC:", train_avgAUC)
print("valid avgAUC:", valid_avgAUC)
print("test avgAUC:", test_avgAUC)
print("best valid avgAUC:", best_valid_avgAUC)
print("best test avgAUC:", best_test_avgAUC)
print("\nFinished training")
print("Best validation avgAUC:", best_valid_avgAUC)
print("Best test avgAUC:", best_test_avgAUC)
if (args.save_attention_maps):
attentionfile = open(attentionmapfile, 'w')
attentionfilewriter = csv.writer(attentionfile)
beta_test = beta_test.numpy()
for i in range(len(gene_ids_test)):
gene_attention = []
gene_attention.append(gene_ids_test[i])
for e in beta_test[i, :]:
gene_attention.append(str(e))
attentionfilewriter.writerow(gene_attention)
attentionfile.close()
return best_test_avgAUC, test_corr
else:
if args.kipoi_model:
model.load_state_dict(
kipoi.get_model("AttentiveChrome/{}".format(
args.cell_type)).model.state_dict())
elif args.test_on_train != None:
model.load_state_dict(
torch.load(args.test_on_train + "/" + args.cell_type + '/' +
model_name + '/' + model_name + '_avgAUC_model.pt'))
else:
model.load_state_dict(
torch.load(model_dir + "/" + model_name + '_avgAUC_model.pt'))
predictions, diff_targets, alpha_test, beta_test, test_loss, gene_ids_test, test_corr, adv_inputs_test, grad_values_test, ori_inputs_test, alpha_test_orig, beta_test_orig, predictions_orig = test(
Test)
test_avgAUPR, test_avgAUC = evaluate.compute_metrics(
predictions, diff_targets)
print("test avgAUC:", test_avgAUC)
print("test corr:", test_corr)
if (args.save_attention_maps):
attentionfile = open(attentionmapfile, 'w')
attentionfilewriter = csv.writer(attentionfile)
beta_test = beta_test.numpy()
for i in range(len(gene_ids_test)):
gene_attention = []
gene_attention.append(gene_ids_test[i])
for e in beta_test[i, :]:
gene_attention.append(str(e))
attentionfilewriter.writerow(gene_attention)
attentionfile.close()
if (args.save_adv_inputs):
attentionfile = open(orig_attentionmapfile, 'w')
attentionfilewriter = csv.writer(attentionfile)
beta_test_orig = beta_test_orig.numpy()
for i in range(len(gene_ids_test)):
gene_attention = []
gene_attention.append(gene_ids_test[i])
for e in beta_test_orig[i, :]:
gene_attention.append(str(e))
attentionfilewriter.writerow(gene_attention)
attentionfile.close()
if (args.save_adv_inputs):
return test_avgAUC, test_corr, gene_ids_test, adv_inputs_test, grad_values_test, ori_inputs_test, alpha_test, alpha_test_orig, predictions, predictions_orig, diff_targets, beta_test, beta_test_orig
return test_avgAUC, test_corr
def run_model(name, use_elmo=False, save_predictions=False, save_model=False):
"""
Trains the given deep learning model on train set, and evaluates on test set.
Parameters
----------
name: str
name of the deep learning model to be run: lstm | bilstm | stacked_bilstm | cnn | bert
use_elmo: bool
use ELMo embeddings if True | GloVe embeddings if False
save_predictions: bool
If True, stores and returns the predicted probabilities mapped to sentence ID
save_model: bool
If True, saves the trained model along with its vocabulary
Returns
-------
F1-score, Precision, Recall, Accuracy, Area Under Precision-Recall Curve on the test set; dictionary mapping predictions to ID, and number of training epochs for each fold.
"""
# token_indexer maps tokens to integers; using special built-in indexers for ELMo and BERT to ensure mapping is consistent with the original models
if use_elmo:
token_indexer = ELMoTokenCharactersIndexer()
elif name == 'bert':
global bert_token_indexer
bert_token_indexer = PretrainedBertIndexer(pretrained_model=BERT_MODEL,
do_lowercase=True)
else:
token_indexer = SingleIdTokenIndexer()
if name == 'bert': # BERT uses a special wordpiece tokenizer
reader = data_reader.GeneralizationDatasetReader(
tokenizer=tokenizer_bert,
token_indexers={"tokens": bert_token_indexer},
label_cols=LABEL_COLS)
else:
reader = data_reader.GeneralizationDatasetReader(
tokenizer=tokenizer,
token_indexers={"tokens": token_indexer},
label_cols=LABEL_COLS)
map_id_pred_probability = {} # used if save_predictions is True
f1s, precision_s, recall_s, accuracies, AUPRCs, n_epochs = [], [], [], [], [], []
for fold_number in range(1, 4): # 3-fold cross validation
train_fname = 'train_data_fold_' + str(fold_number) + '.csv'
val_fname = 'val_data_fold_' + str(fold_number) + '.csv'
test_fname = 'test_data_fold_' + str(fold_number) + '.csv'
train_dataset = reader.read(file_path=DATA_ROOT / train_fname)
validation_dataset = reader.read(file_path=DATA_ROOT / val_fname)
test_dataset = reader.read(file_path=DATA_ROOT / test_fname)
# print("\n##################################\n", name, len(train_dataset), len(validation_dataset), len(test_dataset))
# Train the model:
if name == 'lstm':
model, vocab, ep = train.train_lstm(train_dataset,
validation_dataset,
BATCH_SIZE,
num_layers=1,
bidirectional=False,
use_elmo=use_elmo)
elif name == 'bilstm':
model, vocab, ep = train.train_lstm(train_dataset,
validation_dataset,
BATCH_SIZE,
num_layers=1,
bidirectional=True,
use_elmo=use_elmo)
elif name == 'stacked_bilstm':
model, vocab, ep = train.train_lstm(train_dataset,
validation_dataset,
BATCH_SIZE,
num_layers=2,
bidirectional=True,
use_elmo=use_elmo)
elif name == 'cnn':
model, vocab, ep = train.train_cnn(train_dataset,
validation_dataset,
BATCH_SIZE,
num_filters=100,
filter_sizes=(2, 3, 4, 5),
use_elmo=use_elmo)
elif name == 'bert':
model, vocab, ep = train.train_bert(train_dataset,
validation_dataset,
BATCH_SIZE,
pretrained_model=BERT_MODEL)
else:
sys.exit("'name' not valid")
n_epochs.append(
ep) # keep track of number of actual training epochs for each fold
# Predict and evaluate the model on test set:
preds = evaluate.make_predictions(
model, vocab, test_dataset, BATCH_SIZE
) # Note that 'preds' is of the shape (number of samples, 2) - the columns represent the probabilities for the two classes ['generalization', 'neutral']
f1, precision, recall, acc, auprc = evaluate.compute_metrics(
preds, test_dataset)
if save_predictions:
id_pred = evaluate.map_id_prediction(preds, test_dataset)
if set(id_pred.keys()).intersection(
set(map_id_pred_probability.keys())) != set(
): # sanity check
sys.exit(
"Error: There is overlap in test set IDs across folds.")
map_id_pred_probability.update(id_pred)
if save_model: # save the model weights and vocabulary
with open(
'./tmp/' + name + '_model' + '_fold_' + str(fold_number) +
'.th', 'wb') as f:
torch.save(model.state_dict(), f)
vocab.save_to_files("./tmp/" + name + "_vocabulary_fold_" +
str(fold_number))
print("\nFold #{} | F1 = {}".format(fold_number, f1))
f1s.append(f1)
precision_s.append(precision)
recall_s.append(recall)
accuracies.append(acc)
AUPRCs.append(auprc)
mean_f1 = np.array(f1s).mean()
mean_precision = np.array(precision_s).mean()
mean_recall = np.array(recall_s).mean()
mean_accuracy = np.array(accuracies).mean()
mean_auprc = np.array(AUPRCs).mean()
print("Total # predictions: {} | Saving Predictions = {}".format(
len(map_id_pred_probability), save_predictions))
return mean_f1, mean_precision, mean_recall, mean_accuracy, mean_auprc, map_id_pred_probability, n_epochs
