From d3c264321f0a5e2d3d1611e754d357fe14e92233 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Sun, 10 Dec 2023 20:10:46 +0100 Subject: [PATCH 01/49] Adding Bayesian to models, predictors and train. --- recover/train.py | 509 ++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 506 insertions(+), 3 deletions(-) diff --git a/recover/train.py b/recover/train.py index 6b398ad..3de6991 100644 --- a/recover/train.py +++ b/recover/train.py @@ -11,6 +11,8 @@ import importlib from scipy import stats from scipy.stats import spearmanr +import torchbnn as bnn #adding a Bayesian NN library +import tempfile ######################################################################################################################## @@ -32,7 +34,6 @@ def train_epoch(data, loader, model, optim): optim.zero_grad() out = model.forward(data, drug_drug_batch) - # Save all predictions and targets all_mean_preds.extend(out.mean(dim=1).tolist()) all_targets.extend(drug_drug_batch[2].tolist()) @@ -56,6 +57,64 @@ def train_epoch(data, loader, model, optim): return summary_dict +#training epoch for BNN using KL divergence for loss + +def train_epoch_bayesian(data, loader, model, optim): + + model.train() + + epoch_loss = 0 + num_batches = len(loader) + + #initialization of KL loss + kl_loss = bnn.BKLLoss(reduction='mean', last_layer_only=False) + + + all_mean_preds = [] + all_targets = [] + + for _, drug_drug_batch in enumerate(loader): + optim.zero_grad() + + out = model.forward(data, drug_drug_batch) + + # Save all predictions and targets + all_mean_preds.extend(out.mean(dim=1).tolist()) + all_targets.extend(drug_drug_batch[2].tolist()) + + #MSE is computed as in the original paper and is dependant on data + loss_mse = model.loss(out, drug_drug_batch) + kl_weight = 0.01 + + #KL loss depends on the entire model + kl = kl_loss(model) + + #final loss is calculated + loss = loss_mse + kl_weight*kl + + loss.backward() + optim.step() + + epoch_loss += loss.item() + + + epoch_comb_r_squared = stats.linregress(all_mean_preds, all_targets).rvalue**2 + preds = np.array(all_mean_preds) + targets = np.array(all_targets) + epoch_mse = np.mean(np.square(preds - targets)) + + + summary_dict = { + "loss_mean": epoch_loss / num_batches, + "comb_r_squared": epoch_comb_r_squared, + "kl_loss": epoch_mse+kl_weight*kl.item() + } + + print("Training", summary_dict) + + return summary_dict + + def eval_epoch(data, loader, model): model.eval() @@ -87,13 +146,58 @@ def eval_epoch(data, loader, model): "spearman": epoch_spear } - print("Testing", summary_dict, '\n') + print("Validation", summary_dict, '\n') all_out = torch.cat(all_out) return summary_dict, all_out +#testing epoch for realizations od the trained model +def test_epoch(data, loader, model): + drugs = [] + model.eval() + + epoch_loss = 0 + num_batches = len(loader) + + all_out = [] + all_mean_preds = [] + all_targets = [] + + with torch.no_grad(): + for _, drug_drug_batch in enumerate(loader): + out = model.forward(data, drug_drug_batch) + + drug_batch = drug_drug_batch[0].tolist() + #condatinate all batches to get the entire dataset + drugs = drugs + drug_batch + + # Save all predictions and targets + all_out.append(out) + all_mean_preds.extend(out.mean(dim=1).tolist()) + all_targets.extend(drug_drug_batch[2].tolist()) + + loss = model.loss(out, drug_drug_batch) + epoch_loss += loss.item() + + epoch_comb_r_squared = stats.linregress(all_mean_preds, all_targets).rvalue**2 + epoch_spear = spearmanr(all_targets, all_mean_preds).correlation + + summary_dict = { + "loss_mean": epoch_loss / num_batches, + "comb_r_squared": epoch_comb_r_squared, + "spearman": epoch_spear, + } + + print("Testing", summary_dict, '\n') + + all_out = torch.cat(all_out) + + #returning drugs and predictions + return summary_dict, all_out, drugs, all_mean_preds + + ######################################################################################################################## # Basic trainer ######################################################################################################################## @@ -213,7 +317,174 @@ def step(self): def save_checkpoint(self, checkpoint_dir): checkpoint_path = os.path.join(checkpoint_dir, "model.pth") torch.save(self.model.state_dict(), checkpoint_path) - return checkpoint_path + return checkpoint_dir + + def load_checkpoint(self, checkpoint_path): + self.model.load_state_dict(torch.load(checkpoint_path)) + + +######################################################################################################################## +# Bayesian basic trainer +######################################################################################################################## + + +#BasicTrainer modified for Bayesian +class BayesianBasicTrainer(tune.Trainable): + def setup(self, config): + print("Initializing regular training pipeline") + + self.batch_size = config["batch_size"] + device_type = "cuda" if torch.cuda.is_available() else "cpu" + self.device = torch.device(device_type) + self.training_it = 0 + + # Initialize dataset + dataset = config["dataset"]( + fp_bits=config["fp_bits"], + fp_radius=config["fp_radius"], + cell_line=config["cell_line"], + study_name=config["study_name"], + in_house_data=config["in_house_data"], + rounds_to_include=config["rounds_to_include"], + ) + + self.data = dataset.data.to(self.device) + + # If a score is the target, we store it in the ddi_edge_response attribute of the data object + if "target" in config.keys(): + possible_target_dicts = { + "bliss_max": self.data.ddi_edge_bliss_max, + "bliss_av": self.data.ddi_edge_bliss_av, + "css_av": self.data.ddi_edge_css_av, + } + self.data.ddi_edge_response = possible_target_dicts[config["target"]] + + torch.manual_seed(config["seed"]) + np.random.seed(config["seed"]) + + + # Perform train/valid/test split. Test split is fixed regardless of the user defined seed + self.train_idxs, self.val_idxs, self.test_idxs = dataset.random_split(config) + + # Train loader + train_ddi_dataset = get_tensor_dataset(self.data, self.train_idxs) + + self.train_loader = DataLoader( + train_ddi_dataset, + batch_size=config["batch_size"] + ) + + # Valid loader + valid_ddi_dataset = get_tensor_dataset(self.data, self.val_idxs) + + self.valid_loader = DataLoader( + valid_ddi_dataset, + batch_size=config["batch_size"] + ) + + # Added a Test loader since we have train/validation/test split + test_ddi_dataset = get_tensor_dataset(self.data, self.test_idxs) + + self.test_loader = DataLoader( + test_ddi_dataset, + batch_size=config["batch_size"] + ) + + + # Initialize model + self.model = config["model"](self.data, config) + + # Initialize model with weights from file + load_model_weights = config.get("load_model_weights", False) + if load_model_weights: + model_weights_file = config.get("model_weights_file") + model_weights = torch.load(model_weights_file, map_location="cpu") + self.model.load_state_dict(model_weights) + print("pretrained weights loaded") + else: + print("model initialized randomly") + + self.model = self.model.to(self.device) + print(self.model) + + # Initialize optimizer + self.optim = torch.optim.Adam( + self.model.parameters(), + lr=config["lr"], + weight_decay=config["weight_decay"], + ) + + self.train_epoch = config["train_epoch"] + self.eval_epoch = config["eval_epoch"] + + #test empch included + self.test_epoch = config["test_epoch"] + + self.patience = 0 + self.max_eval_r_squared = -1 + + #saving the stopping points so we know when the training has stoped and the model can be tested + self.patience_stop = config["stop"]["patience"] + self.iterations_stop = config["stop"]["training_iteration"] + + def step(self): + + train_metrics = self.train_epoch( + self.data, + self.train_loader, + self.model, + self.optim, + ) + + eval_metrics, _ = self.eval_epoch(self.data, self.valid_loader, self.model) + train_metrics = [("train/" + k, v) for k, v in train_metrics.items()] + eval_metrics = [("eval/" + k, v) for k, v in eval_metrics.items()] + metrics = dict(train_metrics + eval_metrics) + + metrics["training_iteration"] = self.training_it + self.training_it += 1 + + # Compute patience + if metrics['eval/comb_r_squared'] > self.max_eval_r_squared: + self.patience = 0 + self.max_eval_r_squared = metrics['eval/comb_r_squared'] + else: + self.patience += 1 + + metrics['patience'] = self.patience + metrics['all_space_explored'] = 0 + + #if training is finished + if((self.patience >= self.patience_stop) | (self.training_it > self.iterations_stop)): + results = [] + r_squared = [] + spearman = [] + for i in range(10): #define the number of realizations + #calling the test_epoch to obtain the results + test_metrics, _, drugs, result = self.test_epoch(self.data, self.test_loader, self.model) + #saving results and metrics from each realization in order to compute uncertainty + results.append(result) + r_squared.append(test_metrics['comb_r_squared']) + spearman.append(test_metrics['spearman']) + test_metrics = [(f"test {i}/" + k, v) for k, v in test_metrics.items()] + metrics.update(dict(test_metrics)) + + mean_values = [sum(col) / len(results) for col in zip(*results)] + std_dev_values = [((sum((x - mean) ** 2 for x in col) / len(results)) ** 0.5) for col, mean in zip(zip(*results), mean_values)] + metrics['mean_r_squared'] = sum(r_squared) / len(r_squared) + metrics['std_r_squared'] = np.std(r_squared) + metrics['mean_spearman'] = sum(spearman) / len(spearman) + metrics['std_spearman'] = np.std(spearman) + #metrics['std_dev'] = list(std_dev_values) + #metrics['means'] = list(mean_values) + #metrics['drugs'] = list(drugs) + + return metrics + + def save_checkpoint(self, checkpoint_dir): + checkpoint_path = os.path.join(checkpoint_dir, "model.pth") + torch.save(self.model.state_dict(), checkpoint_path) + return checkpoint_dir def load_checkpoint(self, checkpoint_path): self.model.load_state_dict(torch.load(checkpoint_path)) @@ -421,6 +692,238 @@ def update_loaders(self, seen_idxs, unseen_idxs): ) return seen_loader, unseen_loader + + +######################################################################################################################## +# Bayesian Active learning Trainer +######################################################################################################################## + + + +class BayesianActiveTrainer(BayesianBasicTrainer): + """ + Trainer class to perform active learning. Retrains models from scratch after each query. Uses early stopping + """ + + def setup(self, config): + print("Initializing active training pipeline") + super(BayesianActiveTrainer, self).setup(config) + + self.acquire_n_at_a_time = config["acquire_n_at_a_time"] + self.acquisition = config["acquisition"](config) + self.n_epoch_between_queries = config["n_epoch_between_queries"] + + # randomly acquire data at the beginning + self.seen_idxs = self.train_idxs[:config["n_initial"]] + self.unseen_idxs = self.train_idxs[config["n_initial"]:] + self.immediate_regrets = torch.empty(0) + + # Initialize variable that saves the last query + self.last_query_idxs = self.seen_idxs + + # Initialize dataloaders + self.seen_loader, self.unseen_loader = self.update_loaders(self.seen_idxs, self.unseen_idxs) + + # Get the set of top 1% most synergistic combinations + one_perc = int(0.01 * len(self.unseen_idxs)) + scores = self.data.ddi_edge_response[self.unseen_idxs] + self.best_score = scores.max() + self.top_one_perc = set(self.unseen_idxs[torch.argsort(scores, descending=True)[:one_perc]].numpy()) + self.count = 0 + + def step(self): + # Check whether we have explored everything + if len(self.unseen_loader) == 0: + print("All space has been explored") + return {"all_space_explored": 1, "training_iteration": self.training_it} + + # Train on seen examples + if len(self.seen_idxs) > 0: + seen_metrics = self.train_between_queries() + else: + seen_metrics = {} + + # Evaluate on valid set + + + eval_metrics, _ = self.eval_epoch(self.data, self.valid_loader, self.model) + + + #Instead of using EnsembleModel to get a approximation of mean and standard deviation and obtain the score, we use the properties of BNN and achieve this using different realizations of the same trained model. The code is exactly the same as the testing part in BayesianBasicTrainer. The results array is in the end transposed and converted to a tensor to be in the same format as the output from the EnsembleModel. + results = [] + r_squared = [] + spearman = [] + for i in range(10): + unseen_metrics, _, drugs, result = self.test_epoch(self.data, self.unseen_loader, self.model) + results.append(result) + r_squared.append(unseen_metrics['comb_r_squared']) + spearman.append(unseen_metrics['spearman']) + #unseen_metrics = [(f"eval {i}/" + k, v) for k, v in unseen_metrics.items()] + + mean_values = [sum(col) / len(results) for col in zip(*results)] + std_dev_values = [((sum((x - mean) ** 2 for x in col) / len(results)) ** 0.5) for col, mean in zip(zip(*results), mean_values)] + unseen_metrics['mean_r_squared'] = sum(r_squared) / len(r_squared) + unseen_metrics['std_r_squared'] = np.std(r_squared) + unseen_metrics['std_spearman'] = np.std(spearman) + unseen_metrics['mean_spearman'] = sum(spearman) / len(spearman) + #unseen_metrics['std_dev'] = list(std_dev_values) + #unseen_metrics['means'] = list(mean_values) + #unseen_metrics['drugs'] = list(drugs) + unseen_metrics.update(dict(unseen_metrics)) + + #transposing the results and converting to tensor to make it compatible with original code + unseen_preds = list(map(list, zip(*results))) + unseen_preds = torch.tensor(unseen_preds) + + active_scores = self.acquisition.get_scores(unseen_preds) + active_scores = torch.tensor(active_scores) + + # Build summary + seen_metrics = [("seen/" + k, v) for k, v in seen_metrics.items()] + unseen_metrics = [("unseen/" + k, v) for k, v in unseen_metrics.items()] + eval_metrics = [("eval/" + k, v) for k, v in eval_metrics.items()] + + metrics = dict( + seen_metrics + + unseen_metrics + + eval_metrics + ) + + # Acquire new data + print("query data") + query = self.unseen_idxs[torch.argsort(active_scores, descending=True)[:self.acquire_n_at_a_time]] + + # Get the best score among unseen examples + self.best_score = self.data.ddi_edge_response[self.unseen_idxs].max().detach().cpu() + # remove the query from the unseen examples + self.unseen_idxs = self.unseen_idxs[torch.argsort(active_scores, descending=True)[self.acquire_n_at_a_time:]] + + # Add the query to the seen examples + self.seen_idxs = torch.cat((self.seen_idxs, query)) + metrics["seen_idxs"] = self.data.ddi_edge_idx[:, self.seen_idxs].detach().cpu().tolist() + metrics["seen_idxs_in_dataset"] = self.seen_idxs.detach().cpu().tolist() + + # Compute proportion of top 1% synergistic drugs which have been discovered + query_set = set(query.detach().numpy()) + self.count += len(query_set & self.top_one_perc) + metrics["top"] = self.count / len(self.top_one_perc) + + query_ground_truth = self.data.ddi_edge_response[query].detach().cpu() + + query_pred_syn = unseen_preds[torch.argsort(active_scores, descending=True)[:self.acquire_n_at_a_time]] + query_pred_syn = query_pred_syn.detach().cpu() + + metrics["query_pred_syn_mean"] = query_pred_syn.mean().item() + metrics["query_true_syn_mean"] = query_ground_truth.mean().item() + + # Diversity metric + metrics["n_unique_drugs_in_query"] = len(self.data.ddi_edge_idx[:, query].unique()) + + # Get the quantiles of the distribution of true synergy in the query + for q in np.arange(0, 1.1, 0.1): + metrics["query_pred_syn_quantile_" + str(q)] = np.quantile(query_pred_syn, q) + metrics["query_true_syn_quantile_" + str(q)] = np.quantile(query_ground_truth, q) + + query_immediate_regret = torch.abs(self.best_score - query_ground_truth) + self.immediate_regrets = torch.cat((self.immediate_regrets, query_immediate_regret)) + + metrics["med_immediate_regret"] = self.immediate_regrets.median().item() + metrics["log10_med_immediate_regret"] = np.log10(metrics["med_immediate_regret"]) + metrics["min_immediate_regret"] = self.immediate_regrets.min().item() + metrics["log10_min_immediate_regret"] = np.log10(metrics["min_immediate_regret"]) + + # Update the dataloaders + self.seen_loader, self.unseen_loader = self.update_loaders(self.seen_idxs, self.unseen_idxs) + + metrics["training_iteration"] = self.training_it + metrics["all_space_explored"] = 0 + self.training_it += 1 + + return metrics + + def train_between_queries(self): + # Create the train and early_stop loaders for this iteration + iter_dataset = self.seen_loader.dataset + train_length = int(0.8 * len(iter_dataset)) + early_stop_length = len(iter_dataset) - train_length + + train_dataset, early_stop_dataset = random_split(iter_dataset, [train_length, early_stop_length]) + + train_loader = DataLoader( + train_dataset, + batch_size=self.batch_size, + pin_memory=(self.device == "cpu"), + shuffle=len(train_dataset) > 0, + ) + + early_stop_loader = DataLoader( + early_stop_dataset, + batch_size=self.batch_size, + pin_memory=(self.device == "cpu"), + shuffle=len(early_stop_dataset) > 0, + ) + + # Reinitialize model before training + self.model = self.config["model"](self.data, self.config).to(self.device) + + # Initialize model with weights from file + load_model_weights = self.config.get("load_model_weights", False) + if load_model_weights: + model_weights_file = self.config.get("model_weights_file") + model_weights = torch.load(model_weights_file, map_location="cpu") + self.model.load_state_dict(model_weights) + print("pretrained weights loaded") + else: + print("model initialized randomly") + + # Reinitialize optimizer + self.optim = torch.optim.Adam(self.model.parameters(), lr=self.config["lr"], + weight_decay=self.config["weight_decay"]) + + best_eval_r2 = float("-inf") + patience_max = self.config["patience_max"] + patience = 0 + + for _ in range(self.n_epoch_between_queries): + # Perform several training epochs. Save only metrics from the last epoch + train_metrics = self.train_epoch(self.data, train_loader, self.model, self.optim) + + early_stop_metrics, _ = self.eval_epoch(self.data, early_stop_loader, self.model) + + if early_stop_metrics["comb_r_squared"] > best_eval_r2: + best_eval_r2 = early_stop_metrics["comb_r_squared"] + print("best early stop r2", best_eval_r2) + patience = 0 + else: + patience += 1 + + if patience > patience_max: + break + + return train_metrics + + def update_loaders(self, seen_idxs, unseen_idxs): + # Seen loader + seen_ddi_dataset = get_tensor_dataset(self.data, seen_idxs) + + seen_loader = DataLoader( + seen_ddi_dataset, + batch_size=self.batch_size, + pin_memory=(self.device == "cpu"), + shuffle=len(seen_idxs) > 0, + ) + + # Unseen loader + unseen_ddi_dataset = get_tensor_dataset(self.data, unseen_idxs) + + unseen_loader = DataLoader( + unseen_ddi_dataset, + batch_size=self.batch_size, + shuffle=False, + pin_memory=(self.device == "cpu"), + ) + + return seen_loader, unseen_loader ######################################################################################################################## From 56798e79bf3b6afcf5520cc7f890357842fde731 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Sun, 10 Dec 2023 20:13:26 +0100 Subject: [PATCH 02/49] Adding Bayesian to models, predictors and train. --- recover/models/models.py | 6 +- recover/models/predictors.py | 127 +++++++++++++++++++++++++++++++++++ 2 files changed, 130 insertions(+), 3 deletions(-) diff --git a/recover/models/models.py b/recover/models/models.py index 0f393f6..a1b3862 100644 --- a/recover/models/models.py +++ b/recover/models/models.py @@ -1,4 +1,5 @@ import torch +import torchbnn as bnn ######################################################################################################################## @@ -15,7 +16,7 @@ def __init__(self, data, config): self.device = torch.device(device_type) self.criterion = torch.nn.MSELoss() - + # Compute dimension of input for predictor predictor_layers = [data.x_drugs.shape[1]] + config["predictor_layers"] @@ -39,10 +40,9 @@ def loss(self, output, drug_drug_batch): comb = output ground_truth_scores = drug_drug_batch[2][:, None] loss = self.criterion(comb, ground_truth_scores) - + return loss - ######################################################################################################################## # Model wrappers: Ensemble and Predictive Uncertainty ######################################################################################################################## diff --git a/recover/models/predictors.py b/recover/models/predictors.py index db62b44..2375a96 100644 --- a/recover/models/predictors.py +++ b/recover/models/predictors.py @@ -2,6 +2,7 @@ import numpy as np import torch.nn as nn from torch.nn import Parameter +import torchbnn as bnn ######################################################################################################################## # Modules @@ -17,6 +18,14 @@ def forward(self, input): return [super().forward(x), cell_line] +#added a Bayesian Linear Module +class BayesianLinearModule(bnn.BayesLinear): + def __init__(self, prior_mu, prior_sigma, in_features, out_features): + super(BayesianLinearModule, self).__init__(prior_mu, prior_sigma, in_features, out_features) + + def forward(self, input): + x, cell_line = input[0], input[1] + return [super().forward(x), cell_line] class ReLUModule(nn.ReLU): def __init__(self): super(ReLUModule, self).__init__() @@ -212,6 +221,124 @@ def linear_layer(self, i, dim_i, dim_i_plus_1): return [LinearModule(dim_i, dim_i_plus_1)] +######################################################################################################################## +# Bayesian Bilinear MLP +######################################################################################################################## + + +class BayesianBilinearMLPPredictor(torch.nn.Module): #BAYESIAN ADD ON + def __init__(self, data, config, predictor_layers): + + super(BayesianBilinearMLPPredictor, self).__init__() + + self.num_cell_lines = len(data.cell_line_to_idx_dict.keys()) + device_type = "cuda" if torch.cuda.is_available() else "cpu" + self.device = torch.device(device_type) + + self.layer_dims = predictor_layers + + self.merge_n_layers_before_the_end = config["merge_n_layers_before_the_end"] + self.merge_dim = self.layer_dims[-self.merge_n_layers_before_the_end - 1] + + assert 0 < self.merge_n_layers_before_the_end < len(predictor_layers) + + layers_before_merge = [] + layers_after_merge = [] + + # Build early Bayesian layers (before addition of the two embeddings) + for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end): + layers_before_merge = self.add_bayesian_layer( + layers_before_merge, + i, + 0, + 0.005, + self.layer_dims[i], + self.layer_dims[i + 1] + ) + + # Build last Bayesian layers (after addition of the two embeddings) + for i in range( + len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end, + len(self.layer_dims) - 1, + ): + + layers_after_merge = self.add_bayesian_layer( + layers_after_merge, + i, + 0, + 0.005, + self.layer_dims[i], + self.layer_dims[i + 1] + ) + + self.before_merge_mlp = nn.Sequential(*layers_before_merge) + self.after_merge_mlp = nn.Sequential(*layers_after_merge) + + # Number of bilinear transformations == the dimension of the layer at which the merge is performed + # Initialize weights close to identity + self.bilinear_weights = Parameter( + 1 / 100 * torch.randn((self.merge_dim, self.merge_dim, self.merge_dim)) + + torch.cat([torch.eye(self.merge_dim)[None, :, :]] * self.merge_dim, dim=0) + ) + self.bilinear_offsets = Parameter(1 / 100 * torch.randn((self.merge_dim))) + + self.allow_neg_eigval = config["allow_neg_eigval"] + if self.allow_neg_eigval: + self.bilinear_diag = Parameter(1 / 100 * torch.randn((self.merge_dim, self.merge_dim)) + 1) + + def forward(self, data, drug_drug_batch): + h_drug_1, h_drug_2, cell_lines = self.get_batch(data, drug_drug_batch) + + # Apply before merge MLP + h_1 = self.before_merge_mlp([h_drug_1, cell_lines])[0] + h_2 = self.before_merge_mlp([h_drug_2, cell_lines])[0] + + # compute = h_1.T . W.T.W . h_2 + h_1 = self.bilinear_weights.matmul(h_1.T).T + h_2 = self.bilinear_weights.matmul(h_2.T).T + + if self.allow_neg_eigval: + # Multiply by diagonal matrix to allow for negative eigenvalues + h_2 *= self.bilinear_diag + + # "Transpose" h_1 + h_1 = h_1.permute(0, 2, 1) + + # Multiplication + h_1_scal_h_2 = (h_1 * h_2).sum(1) + + # Add offset + h_1_scal_h_2 += self.bilinear_offsets + + comb = self.after_merge_mlp([h_1_scal_h_2, cell_lines])[0] + + return comb + + def get_batch(self, data, drug_drug_batch): + + drug_1s = drug_drug_batch[0][:, 0] # Edge-tail drugs in the batch + drug_2s = drug_drug_batch[0][:, 1] # Edge-head drugs in the batch + cell_lines = drug_drug_batch[1] # Cell line of all examples in the batch + + h_drug_1 = data.x_drugs[drug_1s] + h_drug_2 = data.x_drugs[drug_2s] + + return h_drug_1, h_drug_2, cell_lines + + + #functions for defining and adding a bayesian linear layer using Bayesian Linear Module + def add_bayesian_layer(self, layers, i, mu, sigma, dim_i, dim_i_plus_1): + layers.extend(self.bayesian_linear_layer(i, mu, sigma, dim_i, dim_i_plus_1)) + if i != len(self.layer_dims) - 2: + layers.append(ReLUModule()) + + return layers + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1)] + + + ######################################################################################################################## # Bilinear MLP with Film conditioning ######################################################################################################################## From 6a15c717cb1a725f15576a8b85eec6be425d91d7 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Sun, 10 Dec 2023 20:15:35 +0100 Subject: [PATCH 03/49] Adding Bayesian to active learning --- .../config/active_learning_UCB_bayesian.py | 111 ++++++++++++++++++ 1 file changed, 111 insertions(+) create mode 100644 recover/config/active_learning_UCB_bayesian.py diff --git a/recover/config/active_learning_UCB_bayesian.py b/recover/config/active_learning_UCB_bayesian.py new file mode 100644 index 0000000..8ea0172 --- /dev/null +++ b/recover/config/active_learning_UCB_bayesian.py @@ -0,0 +1,111 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline, EnsembleModel +from recover.models.predictors import BilinearFilmMLPPredictor, \ + BilinearMLPPredictor, BilinearFilmWithFeatMLPPredictor, BayesianBilinearMLPPredictor #, BilinearCellLineInputMLPPredictor +from recover.utils.utils import get_project_root +from recover.acquisition.acquisition import RandomAcquisition, GreedyAcquisition, UCB +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer, BayesianActiveTrainer +import os +from ray import tune + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([1, 2, 3]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, +} + +predictor_config = { + "predictor": BayesianBilinearMLPPredictor, + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1, 'patience': 10} +} + +model_config = { + "model": Baseline, + # Loading pretrained model + "load_model_weights": False, # tune.grid_search([True, False]), + "model_weights_file": "", +} + +""" +List of cell line names: + +['786-0', 'A498', 'A549', 'ACHN', 'BT-549', 'CAKI-1', 'EKVX', 'HCT-15', 'HCT116', 'HOP-62', 'HOP-92', 'HS 578T', 'HT29', + 'IGROV1', 'K-562', 'KM12', 'LOX IMVI', 'MALME-3M', 'MCF7', 'MDA-MB-231', 'MDA-MB-468', 'NCI-H226', 'NCI-H460', + 'NCI-H522', 'NCIH23', 'OVCAR-4', 'OVCAR-5', 'OVCAR-8', 'OVCAR3', 'PC-3', 'RPMI-8226', 'SF-268', 'SF-295', 'SF-539', + 'SK-MEL-2', 'SK-MEL-28', 'SK-MEL-5', 'SK-OV-3', 'SNB-75', 'SR', 'SW-620', 'T-47D', 'U251', 'UACC-257', 'UACC62', + 'UO-31'] +""" + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "cell_line": 'MCF7', # Restrict to a specific cell line + "val_set_prop": 0.1, + "test_set_prop": 0., + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", # either "cell_line_level" or "pair_level" + "cell_lines_in_test": None, # ['MCF7', 'PC-3'], + "target": "bliss_max", + "fp_bits": 1024, + "fp_radius": 2 +} + +active_learning_config = { + "ensemble_size": 10, + "acquisition": tune.grid_search([GreedyAcquisition, UCB, RandomAcquisition]), + "patience_max": 8, + "kappa": 1, + "kappa_decrease_factor": 1, + "n_epoch_between_queries": 500, + "acquire_n_at_a_time": 30, + "n_initial": 30, +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianActiveTrainer, # PUT NUM GPU BACK TO 1 + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + **active_learning_config + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'all_space_explored': 1}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 32, "gpu": 2}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 3778655e129df95a61d78f8d5d09e94bb2db6096 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Sun, 10 Dec 2023 20:17:27 +0100 Subject: [PATCH 04/49] Adding Bayesian to default --- recover/config/model_evaluation_bayesian.py | 87 +++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 recover/config/model_evaluation_bayesian.py diff --git a/recover/config/model_evaluation_bayesian.py b/recover/config/model_evaluation_bayesian.py new file mode 100644 index 0000000..a8a21d9 --- /dev/null +++ b/recover/config/model_evaluation_bayesian.py @@ -0,0 +1,87 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BilinearFilmMLPPredictor, BayesianBilinearMLPPredictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer +import os +from ray import tune +from importlib import import_module +import torchbnn as bnn + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianBilinearMLPPredictor, #BayesinBilinearMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in oreder to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": 'MCF7', # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, # PUT NUM GPU BACK TO 1 + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": True, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 32, "gpu": 2}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From ce8f6922bcf20414e883d0ba69cf0321ad754e69 Mon Sep 17 00:00:00 2001 From: Pavithya M B D Date: Mon, 11 Dec 2023 07:20:47 +0530 Subject: [PATCH 05/49] changes done during setup --- .gitignore | 3 ++- recover/train.py | 2 +- requirements.txt | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index 1e0728c..983550d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ +Reservoir RayLogs __pycache__ *pyc *egg-info -.DS_Store \ No newline at end of file +.DS_Store diff --git a/recover/train.py b/recover/train.py index 6b398ad..3da20e0 100644 --- a/recover/train.py +++ b/recover/train.py @@ -213,7 +213,7 @@ def step(self): def save_checkpoint(self, checkpoint_dir): checkpoint_path = os.path.join(checkpoint_dir, "model.pth") torch.save(self.model.state_dict(), checkpoint_path) - return checkpoint_path + return None def load_checkpoint(self, checkpoint_path): self.model.load_state_dict(torch.load(checkpoint_path)) diff --git a/requirements.txt b/requirements.txt index 88814f8..ac3619b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,6 @@ seaborn==0.11.0 tqdm scikit-learn==0.23.2 umap-learn -numpy +numpy==1.22.4 pandas scipy From 442f24d9cdddd2cdd035e0d07635d30419d3280d Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Tue, 12 Dec 2023 12:54:15 +0100 Subject: [PATCH 06/49] Adding Bayesain to one_hidden_drug.py --- .../config/one_hidden_drug_split_bayesian.py | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 recover/config/one_hidden_drug_split_bayesian.py diff --git a/recover/config/one_hidden_drug_split_bayesian.py b/recover/config/one_hidden_drug_split_bayesian.py new file mode 100644 index 0000000..b7792fe --- /dev/null +++ b/recover/config/one_hidden_drug_split_bayesian.py @@ -0,0 +1,86 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrixOneHiddenDrugSplitTest +from recover.models.models import Baseline +from recover.models.predictors import BilinearFilmMLPPredictor, BayesianBilinearMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module +import torchbnn as bnn #adding a Bayesian Neural Network library + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianBilinearMLPPredictor, #BayesinBilinearMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in oreder to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrixOneHiddenDrugSplitTest, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": 'MCF7', # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, ccc + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": True, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 32, "gpu": 2}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 4bb0e683076baaa69cb50466e8a29a31da9fe96a Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Tue, 12 Dec 2023 13:36:23 +0100 Subject: [PATCH 07/49] Adding Bayesian shuffled predictor --- recover/models/predictors.py | 33 ++++++++++++++++++++++++++------- 1 file changed, 26 insertions(+), 7 deletions(-) diff --git a/recover/models/predictors.py b/recover/models/predictors.py index 2375a96..cfd6327 100644 --- a/recover/models/predictors.py +++ b/recover/models/predictors.py @@ -4,6 +4,7 @@ from torch.nn import Parameter import torchbnn as bnn + ######################################################################################################################## # Modules ######################################################################################################################## @@ -16,8 +17,8 @@ def __init__(self, in_features, out_features, bias=True): def forward(self, input): x, cell_line = input[0], input[1] return [super().forward(x), cell_line] - - + + #added a Bayesian Linear Module class BayesianLinearModule(bnn.BayesLinear): def __init__(self, prior_mu, prior_sigma, in_features, out_features): @@ -26,6 +27,7 @@ def __init__(self, prior_mu, prior_sigma, in_features, out_features): def forward(self, input): x, cell_line = input[0], input[1] return [super().forward(x), cell_line] + class ReLUModule(nn.ReLU): def __init__(self): super(ReLUModule, self).__init__() @@ -113,8 +115,6 @@ def forward(self, input): ######################################################################################################################## # Bilinear MLP ######################################################################################################################## - - class BilinearMLPPredictor(torch.nn.Module): def __init__(self, data, config, predictor_layers): @@ -219,7 +219,7 @@ def add_layer(self, layers, i, dim_i, dim_i_plus_1): def linear_layer(self, i, dim_i, dim_i_plus_1): return [LinearModule(dim_i, dim_i_plus_1)] - + ######################################################################################################################## # Bayesian Bilinear MLP @@ -336,8 +336,8 @@ def add_bayesian_layer(self, layers, i, mu, sigma, dim_i, dim_i_plus_1): def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1)] - - + + ######################################################################################################################## # Bilinear MLP with Film conditioning @@ -678,6 +678,25 @@ def linear_layer(self, i, dim_i, dim_i_plus_1): return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])] +######################################################################################################################## +# Shuffled model Bayesian +######################################################################################################################## + + +class BayesianShuffledBilinearMLPPredictor(BayesianBilinearMLPPredictor): + def __init__(self, data, config, predictor_layers): + + # Shuffle the identities of the drugs + data.x_drugs = data.x_drugs[torch.randperm(data.x_drugs.shape[0])] + + # Shuffle the identities of the cell lines + value_perm = torch.randperm(len(data.cell_line_to_idx_dict)) + data.cell_line_to_idx_dict = {k: value_perm[v].item() for k, v in data.cell_line_to_idx_dict.items()} + + super(BayesianShuffledBilinearMLPPredictor, self).__init__(data, config, predictor_layers) + + + ######################################################################################################################## # Partially shuffled models From fb75d6cb05bd6a0fa457d59d2daadf0f4be2e116 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Tue, 12 Dec 2023 13:37:48 +0100 Subject: [PATCH 08/49] Adding Bayesian to default shuffled --- .../model_evaluation_shuffled_bayesian.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 recover/config/model_evaluation_shuffled_bayesian.py diff --git a/recover/config/model_evaluation_shuffled_bayesian.py b/recover/config/model_evaluation_shuffled_bayesian.py new file mode 100644 index 0000000..78be788 --- /dev/null +++ b/recover/config/model_evaluation_shuffled_bayesian.py @@ -0,0 +1,85 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BilinearFilmMLPPredictor, BayesianShuffledBilinearMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianShuffledBilinearMLPPredictor, #BayesianShuffledBilinearMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in oreder to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": 'MCF7', # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 0, + "resources_per_trial": {"cpu": 8, "gpu": 0}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 9a0df5ef2acefb9e831ceb8e9e0d9bba0b9a8c54 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Tue, 12 Dec 2023 14:00:48 +0100 Subject: [PATCH 09/49] Adding Bayesian to one_hidden_shuffled.py --- ...one_hidden_drug_split_shuffled_bayesian.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 recover/config/one_hidden_drug_split_shuffled_bayesian.py diff --git a/recover/config/one_hidden_drug_split_shuffled_bayesian.py b/recover/config/one_hidden_drug_split_shuffled_bayesian.py new file mode 100644 index 0000000..e654440 --- /dev/null +++ b/recover/config/one_hidden_drug_split_shuffled_bayesian.py @@ -0,0 +1,85 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrixOneHiddenDrugSplitTest +from recover.models.models import Baseline +from recover.models.predictors import BilinearFilmMLPPredictor, BayesianBilinearMLPPredictor, BayesianShuffledBilinearMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianShuffledBilinearMLPPredictor, #BayesianShuffledBilinearMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in oreder to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrixOneHiddenDrugSplitTest, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": 'MCF7', # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 32, "gpu": 2}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 6bad5c94520f38d89507659ad610cee5313fafb0 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Tue, 12 Dec 2023 18:01:57 +0100 Subject: [PATCH 10/49] Added Bayesian no permutation invariance MLP predictor --- recover/models/predictors.py | 92 +++++++++++++++++++++++++++++++++++- 1 file changed, 91 insertions(+), 1 deletion(-) diff --git a/recover/models/predictors.py b/recover/models/predictors.py index cfd6327..becad60 100644 --- a/recover/models/predictors.py +++ b/recover/models/predictors.py @@ -226,7 +226,7 @@ def linear_layer(self, i, dim_i, dim_i_plus_1): ######################################################################################################################## -class BayesianBilinearMLPPredictor(torch.nn.Module): #BAYESIAN ADD ON +class BayesianBilinearMLPPredictor(torch.nn.Module): def __init__(self, data, config, predictor_layers): super(BayesianBilinearMLPPredictor, self).__init__() @@ -493,6 +493,96 @@ def add_layer(self, layers, i, dim_i, dim_i_plus_1): def linear_layer(self, i, dim_i, dim_i_plus_1): return [LinearModule(dim_i, dim_i_plus_1)] + + +######################################################################################################################## +# Bayesian no permutation invariance MLP +######################################################################################################################## + + +class BayesianMLPPredictor(torch.nn.Module): + def __init__(self, data, config, predictor_layers): + + super(BayesianMLPPredictor, self).__init__() + + self.num_cell_lines = len(data.cell_line_to_idx_dict.keys()) + device_type = "cuda" if torch.cuda.is_available() else "cpu" + self.device = torch.device(device_type) + + self.layer_dims = predictor_layers + + self.merge_n_layers_before_the_end = config["merge_n_layers_before_the_end"] + self.merge_dim = self.layer_dims[-self.merge_n_layers_before_the_end - 1] + + assert 0 < self.merge_n_layers_before_the_end < len(predictor_layers) + + layers_before_merge = [] + layers_after_merge = [] + + # Build early Bayesian layers (before addition of the two embeddings) + for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end): + layers_before_merge = self.add_bayesian_layer( + layers_before_merge, + i, + 0, + 0.005, + self.layer_dims[i], + self.layer_dims[i + 1] + ) + + # We will concatenate the two single drug embeddings so the input of the after_merge_mlp is twice its usual dim + self.layer_dims[- 1 - self.merge_n_layers_before_the_end] *= 2 + + # Build last Bayesian layers (after addition of the two embeddings) + for i in range( + len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end, + len(self.layer_dims) - 1, + ): + + layers_after_merge = self.add_bayesian_layer( + layers_after_merge, + i, + 0, + 0.005, + self.layer_dims[i], + self.layer_dims[i + 1] + ) + + self.before_merge_mlp = nn.Sequential(*layers_before_merge) + self.after_merge_mlp = nn.Sequential(*layers_after_merge) + + def forward(self, data, drug_drug_batch): + h_drug_1, h_drug_2, cell_lines = self.get_batch(data, drug_drug_batch) + + # Apply before merge MLP + h_1 = self.before_merge_mlp([h_drug_1, cell_lines])[0] + h_2 = self.before_merge_mlp([h_drug_2, cell_lines])[0] + + comb = self.after_merge_mlp([torch.cat((h_1, h_2), dim=1), cell_lines])[0] + + return comb + + def get_batch(self, data, drug_drug_batch): + + drug_1s = drug_drug_batch[0][:, 0] # Edge-tail drugs in the batch + drug_2s = drug_drug_batch[0][:, 1] # Edge-head drugs in the batch + cell_lines = drug_drug_batch[1] # Cell line of all examples in the batch + + h_drug_1 = data.x_drugs[drug_1s] + h_drug_2 = data.x_drugs[drug_2s] + + return h_drug_1, h_drug_2, cell_lines + + #functions for defining and adding a bayesian linear layer using Bayesian Linear Module + def add_bayesian_layer(self, layers, i, mu, sigma, dim_i, dim_i_plus_1): + layers.extend(self.bayesian_linear_layer(i, mu, sigma, dim_i, dim_i_plus_1)) + if i != len(self.layer_dims) - 2: + layers.append(ReLUModule()) + + return layers + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1)] ######################################################################################################################## From 76b898c2f44db88c702ae83b62ccd77e277051f9 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Tue, 12 Dec 2023 18:03:01 +0100 Subject: [PATCH 11/49] Added Bayesian to model_evaluation_no_permut_invariance.py --- ...valuation_no_permut_invariance_bayesian.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 recover/config/model_evaluation_no_permut_invariance_bayesian.py diff --git a/recover/config/model_evaluation_no_permut_invariance_bayesian.py b/recover/config/model_evaluation_no_permut_invariance_bayesian.py new file mode 100644 index 0000000..58ba810 --- /dev/null +++ b/recover/config/model_evaluation_no_permut_invariance_bayesian.py @@ -0,0 +1,85 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BilinearFilmMLPPredictor, BayesianBilinearMLPPredictor, BayesianMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianMLPPredictor, #BayesianMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": 'MCF7', # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 16, "gpu": 1}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From ec2115dfe4c8ca271b27f3949dc917c6e43c8468 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Tue, 12 Dec 2023 18:33:11 +0100 Subject: [PATCH 12/49] Added Bayesian to one_hidden_drug_split_no_permut_invariance.py --- ...rug_split_no_permut_invariance_bayesian.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 recover/config/one_hidden_drug_split_no_permut_invariance_bayesian.py diff --git a/recover/config/one_hidden_drug_split_no_permut_invariance_bayesian.py b/recover/config/one_hidden_drug_split_no_permut_invariance_bayesian.py new file mode 100644 index 0000000..9c11449 --- /dev/null +++ b/recover/config/one_hidden_drug_split_no_permut_invariance_bayesian.py @@ -0,0 +1,85 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrixOneHiddenDrugSplitTest +from recover.models.models import Baseline +from recover.models.predictors import BilinearFilmMLPPredictor, BayesianBilinearMLPPredictor, BayesianMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianMLPPredictor, #BayesianMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrixOneHiddenDrugSplitTest, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": 'MCF7', # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 32, "gpu": 2}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From d60069c0822534454e7657834451a9bffa344e03 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Wed, 13 Dec 2023 13:00:16 +0100 Subject: [PATCH 13/49] Added Bayesian to BilinearFilmMLPPredictor --- recover/models/predictors.py | 44 ++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/recover/models/predictors.py b/recover/models/predictors.py index becad60..9841456 100644 --- a/recover/models/predictors.py +++ b/recover/models/predictors.py @@ -382,6 +382,50 @@ def linear_layer(self, i, dim_i, dim_i_plus_1): self.layer_dims[i + 1])] +######################################################################################################################## +# Bayesian bilinear MLP with Film conditioning +######################################################################################################################## + + +class BayesianBilinearFilmMLPPredictor(BayesianBilinearMLPPredictor): + def __init__(self, data, config, predictor_layers): + super(BayesianBilinearFilmMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])] + + +class BayesianBilinearFilmWithFeatMLPPredictor(BayesianBilinearMLPPredictor): + def __init__(self, data, config, predictor_layers): + self. cl_features_dim = data.cell_line_features.shape[1] + super(BayesianBilinearFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])] + + def get_batch(self, data, drug_drug_batch): + + drug_1s = drug_drug_batch[0][:, 0] # Edge-tail drugs in the batch + drug_2s = drug_drug_batch[0][:, 1] # Edge-head drugs in the batch + cell_lines = drug_drug_batch[1] # Cell line of all examples in the batch + batch_cl_features = data.cell_line_features[cell_lines] + + h_drug_1 = data.x_drugs[drug_1s] + h_drug_2 = data.x_drugs[drug_2s] + + return h_drug_1, h_drug_2, batch_cl_features + + +class BayesianBilinearLinFilmWithFeatMLPPredictor(BilinearFilmWithFeatMLPPredictor): + def __init__(self, data, config, predictor_layers): + super(BayesianBilinearLinFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, + self.layer_dims[i + 1])] + + + # ######################################################################################################################## # # Bilinear MLP with Cell line features as input # ######################################################################################################################## From f26befa8d310b925ad3d7799bc08e07f1c264e42 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Wed, 13 Dec 2023 13:02:13 +0100 Subject: [PATCH 14/49] Added Bayesian to model_evaluation_multi_cell_line.py --- ...del_evaluation_multi_cell_line_bayesian.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 recover/config/model_evaluation_multi_cell_line_bayesian.py diff --git a/recover/config/model_evaluation_multi_cell_line_bayesian.py b/recover/config/model_evaluation_multi_cell_line_bayesian.py new file mode 100644 index 0000000..a38cc6a --- /dev/null +++ b/recover/config/model_evaluation_multi_cell_line_bayesian.py @@ -0,0 +1,85 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BayesianBilinearFilmMLPPredictor, BayesianBilinearMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianBilinearFilmMLPPredictor, #BayesianBilinearFilmMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": None, # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 0, + "resources_per_trial": {"cpu": 16, "gpu": 0}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 1a9a3c08233d80da4cfcc1ec79bc20ab2688a65b Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Wed, 13 Dec 2023 14:33:21 +0100 Subject: [PATCH 15/49] Added Bayesian to model_evaluation_multi_cell_line_shuffled.py --- ...ation_multi_cell_line_shuffled_bayesian.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 recover/config/model_evaluation_multi_cell_line_shuffled_bayesian.py diff --git a/recover/config/model_evaluation_multi_cell_line_shuffled_bayesian.py b/recover/config/model_evaluation_multi_cell_line_shuffled_bayesian.py new file mode 100644 index 0000000..f15b980 --- /dev/null +++ b/recover/config/model_evaluation_multi_cell_line_shuffled_bayesian.py @@ -0,0 +1,85 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BayesianShuffledBilinearFilmMLPPredictor, BayesianBilinearMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianShuffledBilinearFilmMLPPredictor, #BayesianShuffledBilinearFilmMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": None, # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 16, "gpu": 1}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 21f36969ca18b640dc3c3ebeb9f20686f699361e Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Wed, 13 Dec 2023 15:09:33 +0100 Subject: [PATCH 16/49] Added Bayesian to model_evaluation_multi_cell_line_no_permut_invariance.py --- ...cell_line_no_permut_invariance_bayesian.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 recover/config/model_evaluation_multi_cell_line_no_permut_invariance_bayesian.py diff --git a/recover/config/model_evaluation_multi_cell_line_no_permut_invariance_bayesian.py b/recover/config/model_evaluation_multi_cell_line_no_permut_invariance_bayesian.py new file mode 100644 index 0000000..32b19f8 --- /dev/null +++ b/recover/config/model_evaluation_multi_cell_line_no_permut_invariance_bayesian.py @@ -0,0 +1,85 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BayesianBilinearFilmMLPPredictor, BayesianBilinearMLPPredictor, BayesianFilmMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianFilmMLPPredictor, #BayesianFilmMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 5, 'patience': 10} #in order to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": False, + "split_valid_train": "pair_level", + "cell_line": None, # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 5, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 0, + "resources_per_trial": {"cpu": 16, "gpu": 1}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 144dc5e08061290982e4cb8b54f0f97f4c60b088 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Wed, 13 Dec 2023 16:02:12 +0100 Subject: [PATCH 17/49] Added Bayesian to cell_line_transfer.py --- recover/config/cell_line_transfer_bayesian.py | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 recover/config/cell_line_transfer_bayesian.py diff --git a/recover/config/cell_line_transfer_bayesian.py b/recover/config/cell_line_transfer_bayesian.py new file mode 100644 index 0000000..67d9a9c --- /dev/null +++ b/recover/config/cell_line_transfer_bayesian.py @@ -0,0 +1,86 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BayesianBilinearFilmMLPPredictor, BayesianBilinearLinFilmWithFeatMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianBilinearLinFilmWithFeatMLPPredictor, + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": True, + "cell_lines_in_test": ['MCF7'], + "split_valid_train": "cell_line_level", + "cell_line": None, # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 8, "gpu": 0}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From f4e69ea1475e271b931e6af2ed9606eb1906a58b Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Wed, 13 Dec 2023 16:21:04 +0100 Subject: [PATCH 18/49] Added Bayesian to cell_line_transfer_shuffled.py --- .../cell_line_transfer_shuffled_bayesian.py | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 recover/config/cell_line_transfer_shuffled_bayesian.py diff --git a/recover/config/cell_line_transfer_shuffled_bayesian.py b/recover/config/cell_line_transfer_shuffled_bayesian.py new file mode 100644 index 0000000..be91087 --- /dev/null +++ b/recover/config/cell_line_transfer_shuffled_bayesian.py @@ -0,0 +1,86 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BayesianBilinearFilmMLPPredictor, BayesianShuffledBilinearLinFilmWithFeatMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianShuffledBilinearLinFilmWithFeatMLPPredictor, + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": True, + "cell_lines_in_test": ['MCF7'], + "split_valid_train": "cell_line_level", + "cell_line": None, # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 0, + "resources_per_trial": {"cpu": 32, "gpu": 1}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 355a60c3a18a032ba7c4e58dd61ce6d5a5f1fcec Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Wed, 13 Dec 2023 16:33:11 +0100 Subject: [PATCH 19/49] Added Bayesian to cell_line_transfer_no_permut_invariance.py --- ..._transfer_no_permut_invariance_bayesian.py | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 recover/config/cell_line_transfer_no_permut_invariance_bayesian.py diff --git a/recover/config/cell_line_transfer_no_permut_invariance_bayesian.py b/recover/config/cell_line_transfer_no_permut_invariance_bayesian.py new file mode 100644 index 0000000..3b00d40 --- /dev/null +++ b/recover/config/cell_line_transfer_no_permut_invariance_bayesian.py @@ -0,0 +1,86 @@ +from recover.datasets.drugcomb_matrix_data import DrugCombMatrix +from recover.models.models import Baseline +from recover.models.predictors import BayesianBilinearFilmMLPPredictor, BayesianLinFilmWithFeatMLPPredictor #adding Bayesian predictor +from recover.utils.utils import get_project_root +from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer #adding Bayesian training and trainer and including a tesing epoch +import os +from ray import tune +from importlib import import_module + +######################################################################################################################## +# Configuration +######################################################################################################################## + + +pipeline_config = { + "use_tune": True, + "num_epoch_without_tune": 500, # Used only if "use_tune" == False + "seed": tune.grid_search([2, 3, 4]), + # Optimizer config + "lr": 1e-4, + "weight_decay": 1e-2, + "batch_size": 128, + # Train epoch and eval_epoch to use + "train_epoch": train_epoch_bayesian, #updated train function, where KL divergence is used + "eval_epoch": eval_epoch, + "test_epoch": test_epoch, #added a Bayesian test epoch, used for differebt realizations +} + +predictor_config = { + "predictor": BayesianLinFilmWithFeatMLPPredictor, #BayesianLinFilmWithFeatMLPPredictor + "predictor_layers": + [ + 2048, + 128, + 64, + 1, + ], + "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers + "allow_neg_eigval": True, + "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments +} + +model_config = { + "model": Baseline, + "load_model_weights": False, +} + +dataset_config = { + "dataset": DrugCombMatrix, + "study_name": 'ALMANAC', + "in_house_data": 'without', + "rounds_to_include": [], + "val_set_prop": 0.2, + "test_set_prop": 0.1, + "test_on_unseen_cell_line": True, + "cell_lines_in_test": ['MCF7'], + "split_valid_train": "cell_line_level", + "cell_line": None, # 'PC-3', + "target": "bliss_max", # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]), + "fp_bits": 1024, + "fp_radius": 2 +} + +######################################################################################################################## +# Configuration that will be loaded +######################################################################################################################## + +configuration = { + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer + "trainer_config": { + **pipeline_config, + **predictor_config, + **model_config, + **dataset_config, + }, + "summaries_dir": os.path.join(get_project_root(), "RayLogs"), + "memory": 1800, + "stop": {"training_iteration": 1000, 'patience': 10}, + "checkpoint_score_attr": 'eval/comb_r_squared', + "keep_checkpoints_num": 1, + "checkpoint_at_end": False, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 6, "gpu": 1}, + "scheduler": None, + "search_alg": None, +} \ No newline at end of file From 93d399e8de737f483702a6eb8df4d65918eb5878 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Wed, 13 Dec 2023 16:35:36 +0100 Subject: [PATCH 20/49] Added Bayesian to more predictors --- recover/models/predictors.py | 83 +++++++++++++++++++++++++++++++++++- 1 file changed, 82 insertions(+), 1 deletion(-) diff --git a/recover/models/predictors.py b/recover/models/predictors.py index 9841456..e7b7edb 100644 --- a/recover/models/predictors.py +++ b/recover/models/predictors.py @@ -670,6 +670,49 @@ def __init__(self, data, config, predictor_layers): def linear_layer(self, i, dim_i, dim_i_plus_1): return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])] + + +######################################################################################################################## +# Bayesian no permutation invariance MLP with Film conditioning +######################################################################################################################## + + +class BayesianFilmMLPPredictor(BayesianMLPPredictor): + def __init__(self, data, config, predictor_layers): + super(BayesianFilmMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])] + + +class BayesianFilmWithFeatMLPPredictor(BayesianMLPPredictor): + def __init__(self, data, config, predictor_layers): + self. cl_features_dim = data.cell_line_features.shape[1] + super(BayesianFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])] + + def get_batch(self, data, drug_drug_batch): + + drug_1s = drug_drug_batch[0][:, 0] # Edge-tail drugs in the batch + drug_2s = drug_drug_batch[0][:, 1] # Edge-head drugs in the batch + cell_lines = drug_drug_batch[1] # Cell line of all examples in the batch + batch_cl_features = data.cell_line_features[cell_lines] + + h_drug_1 = data.x_drugs[drug_1s] + h_drug_2 = data.x_drugs[drug_2s] + + return h_drug_1, h_drug_2, batch_cl_features + + +class BayesianLinFilmWithFeatMLPPredictor(FilmWithFeatMLPPredictor): + def __init__(self, data, config, predictor_layers): + super(BayesianLinFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, + self.layer_dims[i + 1])] ######################################################################################################################## @@ -813,7 +856,7 @@ def linear_layer(self, i, dim_i, dim_i_plus_1): self.layer_dims[i + 1])] ######################################################################################################################## -# Shuffled model Bayesian +# Bayesian shuffled models ######################################################################################################################## @@ -828,6 +871,44 @@ def __init__(self, data, config, predictor_layers): data.cell_line_to_idx_dict = {k: value_perm[v].item() for k, v in data.cell_line_to_idx_dict.items()} super(BayesianShuffledBilinearMLPPredictor, self).__init__(data, config, predictor_layers) + + +class BayesianShuffledBilinearFilmMLPPredictor(BayesianShuffledBilinearMLPPredictor): + def __init__(self, data, config, predictor_layers): + super(BayesianShuffledBilinearFilmMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])] + + +class BayesianShuffledBilinearFilmWithFeatMLPPredictor(BayesianShuffledBilinearMLPPredictor): + def __init__(self, data, config, predictor_layers): + self. cl_features_dim = data.cell_line_features.shape[1] + super(BayesianShuffledBilinearFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])] + + def get_batch(self, data, drug_drug_batch): + + drug_1s = drug_drug_batch[0][:, 0] # Edge-tail drugs in the batch + drug_2s = drug_drug_batch[0][:, 1] # Edge-head drugs in the batch + cell_lines = drug_drug_batch[1] # Cell line of all examples in the batch + batch_cl_features = data.cell_line_features[cell_lines] + + h_drug_1 = data.x_drugs[drug_1s] + h_drug_2 = data.x_drugs[drug_2s] + + return h_drug_1, h_drug_2, batch_cl_features + + +class BayesianShuffledBilinearLinFilmWithFeatMLPPredictor(ShuffledBilinearFilmWithFeatMLPPredictor): + def __init__(self, data, config, predictor_layers): + super(BayesianShuffledBilinearLinFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers) + + def bayesian_linear_layer(self, i, mu, sigma, dim_i, dim_i_plus_1): + return [BayesianLinearModule(mu, sigma, dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, + self.layer_dims[i + 1])] From 3b15f1258c3dc8b77bd7de98430e75233722ae21 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Thu, 14 Dec 2023 12:37:22 +0100 Subject: [PATCH 21/49] Added optimal kl weight --- recover/train.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/recover/train.py b/recover/train.py index 3de6991..95aff49 100644 --- a/recover/train.py +++ b/recover/train.py @@ -72,6 +72,7 @@ def train_epoch_bayesian(data, loader, model, optim): all_mean_preds = [] all_targets = [] + batch = 1 for _, drug_drug_batch in enumerate(loader): optim.zero_grad() @@ -84,7 +85,9 @@ def train_epoch_bayesian(data, loader, model, optim): #MSE is computed as in the original paper and is dependant on data loss_mse = model.loss(out, drug_drug_batch) - kl_weight = 0.01 + + kl_weight = pow(2, num_batches-batch)/(pow(2, num_batches)-1) #kl weight based on the paper "Weight Uncertainty in Neural Networks" + batch += 1 #KL loss depends on the entire model kl = kl_loss(model) From ddaa0ddf310d53f55dcc071a183e5dbd0217cdd3 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Thu, 14 Dec 2023 19:31:07 +0100 Subject: [PATCH 22/49] Added Expected Improvement --- recover/acquisition/acquisition.py | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/recover/acquisition/acquisition.py b/recover/acquisition/acquisition.py index f19076e..a933c50 100644 --- a/recover/acquisition/acquisition.py +++ b/recover/acquisition/acquisition.py @@ -1,4 +1,6 @@ import torch +import numpy as np +from scipy.special import erf ######################################################################################################################## # Abstract Acquisition @@ -18,16 +20,37 @@ def get_scores(self, output): raise NotImplementedError def get_mean_and_std(self, output): + output = torch.tensor(output) mean = output.mean(dim=1) std = output.std(dim=1) - return mean, std + def get_best(self, output): + best, _ = output.max(dim=1) + + return best + ######################################################################################################################## # Acquisition functions ######################################################################################################################## +class ExpectedImprovementAcquisition(AbstractAcquisition): + def __init__(self, config): + super().__init__(config) + + def get_scores(self, output): + mean, std = self.get_mean_and_std(output) + best = self.get_best(output) + epsilon = 1e-6 + + z = (mean-best-epsilon)/(std+epsilon) + phi = np.exp(-0.5*(z**2))/np.sqrt(2*np.pi) + Phi = 0.5*(1+erf(z/np.sqrt(2))) + scores = (mean-best)*Phi+std*phi + + return scores.to("cpu") + class RandomAcquisition(AbstractAcquisition): def __init__(self, config): From 7b605bd110a195e2545ffda1352a326e414e8c11 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic Date: Thu, 14 Dec 2023 19:32:03 +0100 Subject: [PATCH 23/49] Updated acquisition functions --- recover/config/active_learning_UCB_bayesian.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/recover/config/active_learning_UCB_bayesian.py b/recover/config/active_learning_UCB_bayesian.py index 8ea0172..7806673 100644 --- a/recover/config/active_learning_UCB_bayesian.py +++ b/recover/config/active_learning_UCB_bayesian.py @@ -3,7 +3,7 @@ from recover.models.predictors import BilinearFilmMLPPredictor, \ BilinearMLPPredictor, BilinearFilmWithFeatMLPPredictor, BayesianBilinearMLPPredictor #, BilinearCellLineInputMLPPredictor from recover.utils.utils import get_project_root -from recover.acquisition.acquisition import RandomAcquisition, GreedyAcquisition, UCB +from recover.acquisition.acquisition import RandomAcquisition, GreedyAcquisition, UCB, ExpectedImprovementAcquisition from recover.train import train_epoch_bayesian, eval_epoch, test_epoch, BayesianBasicTrainer, BayesianActiveTrainer import os from ray import tune @@ -38,7 +38,7 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1, 'patience': 10} + "stop": {"training_iteration": 1000, 'patience': 10} } model_config = { @@ -76,7 +76,7 @@ active_learning_config = { "ensemble_size": 10, - "acquisition": tune.grid_search([GreedyAcquisition, UCB, RandomAcquisition]), + "acquisition": tune.grid_search([GreedyAcquisition, UCB, RandomAcquisition, ExpectedImprovementAcquisition]), "patience_max": 8, "kappa": 1, "kappa_decrease_factor": 1, From afb21056aa7fb1e02f68df2f5539c90d9bb7acf1 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Thu, 14 Dec 2023 19:43:14 +0100 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zZlN2%b<6A5`_&PF8JDfBewxv5BBO`t|KYDhB0o;&p8#T&a# znlK@SC3)Q`r{>HGlPRC#vh%0LyKWd}Fk$8DuUS9;^u2sBKfgeUhbLKc>+boboA3Y3 zIOF#7*(VKge;oUd`)QlHVwh+A_Bs($8+$H_&91%fb|#=*?xabX+}S$mmHF4Msd0Y# z>C5nf_46G1isk%2J_qr$@u3p#w#1>k)c?Z+{Qvlk?aGbQwtoN6|C6@3NVA!9jZYXl G1phB%03(M0 literal 0 HcmV?d00001 From f9699f03b06e48fddd758e6a92bbcc9fad7e7ef5 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Thu, 14 Dec 2023 19:44:09 +0100 Subject: [PATCH 25/49] Rename Project Infographics.png to ProjectInfographics.png --- ...ect Infographics.png => ProjectInfographics.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/images/{Project Infographics.png => ProjectInfographics.png} (100%) diff --git a/docs/images/Project Infographics.png b/docs/images/ProjectInfographics.png similarity index 100% rename from docs/images/Project Infographics.png rename to docs/images/ProjectInfographics.png From 24faaf5ce3d3e04b0ae00bf8528cc84b2f2a5282 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Thu, 14 Dec 2023 19:44:32 +0100 Subject: [PATCH 26/49] Update README.md --- README.md | 30 ++++++++++-------------------- 1 file changed, 10 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index d42df10..f923cf7 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,19 @@ -# RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds *in vitro* +# Machine Learning Driven Candidate Compound Generation for Drug Repurposing +Based on RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds *in vitro* [![DOI](https://zenodo.org/badge/320327566.svg)](https://zenodo.org/badge/latestdoi/320327566) -RECOVER is a platform that can guide wet lab experiments to quickly discover synergistic drug combinations active -against a cancer cell line, requiring substantially less screening than an exhaustive evaluation -([preprint](https://arxiv.org/abs/2202.04202)). +This repository is an implementation of RECOVER, a platform that can guide wet lab experiments to quickly discover synergistic drug combinations, +([preprint](https://arxiv.org/abs/2202.04202)), howerver instead of using an ensemble model to get Synergy predictions with uncertainty, we used multiple realization of a Bayesian Neural Network model. +Since the weights are drawn from a distribution, they differ for every run of a trained model and hence give different results. The goal was to get a more precise uncertainty and achieve i quicker since the model doesn't have to be trained multiple times. - -![Overview](docs/images/overview.png "Overview") +

## Environment setup -**Requirements**: Anaconda (https://www.anaconda.com/) and Git LFS (https://git-lfs.github.com/). Please make sure -both are installed on the system prior to running installation. - -**Installation**: enter the command `source install.sh` and follow the instructions. This will create a conda -environment named **recover** and install all the required packages including the -[reservoir](https://github.com/RECOVERcoalition/Reservoir) package that stores the primary data acquisition scripts. - -In case you have any issue with the installation procedure of the *reservoir*, you can access and download all files directly from this [google drive](https://drive.google.com/drive/folders/1MYeDoAi0-qnhSJTvs68r861iMOdoqYki?usp=share_link). +**Requirements and Installation**: +For all the requirements and installation steps check th orginal RECOVER repository (https://github.com/RECOVERcoalition/Recover.git). ## Running the pipeline @@ -31,9 +27,3 @@ For example, to run the pipeline with configuration from the file `model_evaluation.py`, run `python train.py --config model_evaluation`. Log files will automatically be created to save the results of the experiments. - -## Note - -This Recover repository is based on research funded by (or in part by) the Bill & Melinda Gates Foundation. The -findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies -of the Bill & Melinda Gates Foundation. From 71302c3bac866c5db4091ece1374c03477d77613 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 14:53:22 +0100 Subject: [PATCH 27/49] Added probability of improvement --- recover/acquisition/acquisition.py | 27 +++++++++++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/recover/acquisition/acquisition.py b/recover/acquisition/acquisition.py index a933c50..b25df38 100644 --- a/recover/acquisition/acquisition.py +++ b/recover/acquisition/acquisition.py @@ -1,6 +1,7 @@ import torch import numpy as np from scipy.special import erf +from scipy.stats import norm ######################################################################################################################## # Abstract Acquisition @@ -25,9 +26,13 @@ def get_mean_and_std(self, output): std = output.std(dim=1) return mean, std - def get_best(self, output): + """ + The max synergy is considered as current best since we don't have access to ground truth + """ + def get_current_best(self, output): best, _ = output.max(dim=1) + return best @@ -41,7 +46,7 @@ def __init__(self, config): def get_scores(self, output): mean, std = self.get_mean_and_std(output) - best = self.get_best(output) + best = self.get_current_best(output) epsilon = 1e-6 z = (mean-best-epsilon)/(std+epsilon) @@ -58,6 +63,24 @@ def __init__(self, config): def get_scores(self, output): return torch.randn(output.shape[0]) + + +class ProbabilityOfImprovementAcquisition(AbstractAcquisition): + """ + Probability of Improvement Aquisition Function + + """ + def __init__(self, config): + super().__init__(config) + + def get_scores(self, output): + mean, std = self.get_mean_and_std(output) + current_best = self.get_current_best(output) + + z = (mean - current_best) / std + prob_of_improvement_scores = norm.cdf(z) + + return torch.tensor(prob_of_improvement_scores).to("cpu") class UCB(AbstractAcquisition): From 020e1e69ea1d97f905b694e79b77b2c87851a573 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:11:09 +0100 Subject: [PATCH 28/49] Added realizations --- recover/train.py | 38 ++++++++++++++++++++------------------ 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/recover/train.py b/recover/train.py index 95aff49..58c2037 100644 --- a/recover/train.py +++ b/recover/train.py @@ -11,15 +11,15 @@ import importlib from scipy import stats from scipy.stats import spearmanr -import torchbnn as bnn #adding a Bayesian NN library +import torchbnn as bnn import tempfile ######################################################################################################################## # Epoch loops ######################################################################################################################## - - + + def train_epoch(data, loader, model, optim): model.train() @@ -55,8 +55,8 @@ def train_epoch(data, loader, model, optim): print("Training", summary_dict) return summary_dict - - + + #training epoch for BNN using KL divergence for loss def train_epoch_bayesian(data, loader, model, optim): @@ -136,7 +136,7 @@ def eval_epoch(data, loader, model): all_out.append(out) all_mean_preds.extend(out.mean(dim=1).tolist()) all_targets.extend(drug_drug_batch[2].tolist()) - + loss = model.loss(out, drug_drug_batch) epoch_loss += loss.item() @@ -150,11 +150,11 @@ def eval_epoch(data, loader, model): } print("Validation", summary_dict, '\n') - + all_out = torch.cat(all_out) return summary_dict, all_out - + #testing epoch for realizations od the trained model def test_epoch(data, loader, model): @@ -199,7 +199,7 @@ def test_epoch(data, loader, model): #returning drugs and predictions return summary_dict, all_out, drugs, all_mean_preds - + ######################################################################################################################## # Basic trainer @@ -325,7 +325,7 @@ def save_checkpoint(self, checkpoint_dir): def load_checkpoint(self, checkpoint_path): self.model.load_state_dict(torch.load(checkpoint_path)) - + ######################################################################################################################## # Bayesian basic trainer ######################################################################################################################## @@ -429,6 +429,7 @@ def setup(self, config): #saving the stopping points so we know when the training has stoped and the model can be tested self.patience_stop = config["stop"]["patience"] self.iterations_stop = config["stop"]["training_iteration"] + self.realizations = config["realizations"] #get the number of realizations def step(self): @@ -462,7 +463,7 @@ def step(self): results = [] r_squared = [] spearman = [] - for i in range(10): #define the number of realizations + for i in range(self.realizations): #calling the test_epoch to obtain the results test_metrics, _, drugs, result = self.test_epoch(self.data, self.test_loader, self.model) #saving results and metrics from each realization in order to compute uncertainty @@ -546,7 +547,7 @@ def step(self): # Score unseen examples unseen_metrics, unseen_preds = self.eval_epoch(self.data, self.unseen_loader, self.model) - + active_scores = self.acquisition.get_scores(unseen_preds) # Build summary @@ -563,7 +564,7 @@ def step(self): # Acquire new data print("query data") query = self.unseen_idxs[torch.argsort(active_scores, descending=True)[:self.acquire_n_at_a_time]] - + # Get the best score among unseen examples self.best_score = self.data.ddi_edge_response[self.unseen_idxs].max().detach().cpu() # remove the query from the unseen examples @@ -689,13 +690,13 @@ def update_loaders(self, seen_idxs, unseen_idxs): unseen_loader = DataLoader( unseen_ddi_dataset, - batch_size=self.batch_size, + batch_size=self.batch_size, shuffle=False, pin_memory=(self.device == "cpu"), ) return seen_loader, unseen_loader - + ######################################################################################################################## # Bayesian Active learning Trainer @@ -715,6 +716,7 @@ def setup(self, config): self.acquire_n_at_a_time = config["acquire_n_at_a_time"] self.acquisition = config["acquisition"](config) self.n_epoch_between_queries = config["n_epoch_between_queries"] + self.realizations = config["realizations"] #get the number of realizations # randomly acquire data at the beginning self.seen_idxs = self.train_idxs[:config["n_initial"]] @@ -756,7 +758,7 @@ def step(self): results = [] r_squared = [] spearman = [] - for i in range(10): + for i in range(self.realizations): unseen_metrics, _, drugs, result = self.test_epoch(self.data, self.unseen_loader, self.model) results.append(result) r_squared.append(unseen_metrics['comb_r_squared']) @@ -937,11 +939,11 @@ def update_loaders(self, seen_idxs, unseen_idxs): def train(configuration): if configuration["trainer_config"]["use_tune"]: ########################################### - # Use tune + # Use tune ########################################### ray.init(num_cpus=configuration["resources_per_trial"]["cpu"], - num_gpus=configuration["resources_per_trial"]["gpu"]) + num_gpus=configuration["resources_per_trial"]["gpu"]) time_to_sleep = 5 print("Sleeping for %d seconds" % time_to_sleep) From 4249a31e28fbdecd4ec6f21f9cda11b5f9bbc84d Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:12:03 +0100 Subject: [PATCH 29/49] Added realizations to config --- recover/config/active_learning_UCB_bayesian.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/recover/config/active_learning_UCB_bayesian.py b/recover/config/active_learning_UCB_bayesian.py index 7806673..2102a8e 100644 --- a/recover/config/active_learning_UCB_bayesian.py +++ b/recover/config/active_learning_UCB_bayesian.py @@ -83,6 +83,7 @@ "n_epoch_between_queries": 500, "acquire_n_at_a_time": 30, "n_initial": 30, + "realizations": 10, #define the number of realizations instead of Ensemble Model } ######################################################################################################################## @@ -108,4 +109,4 @@ "resources_per_trial": {"cpu": 32, "gpu": 2}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From 61e31780ac12c50f04d2e9d1ccbcdba2e2dc0481 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:13:58 +0100 Subject: [PATCH 30/49] Added realizations to config --- .../cell_line_transfer_no_permut_invariance_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/cell_line_transfer_no_permut_invariance_bayesian.py b/recover/config/cell_line_transfer_no_permut_invariance_bayesian.py index 3b00d40..8c90490 100644 --- a/recover/config/cell_line_transfer_no_permut_invariance_bayesian.py +++ b/recover/config/cell_line_transfer_no_permut_invariance_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in order to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -83,4 +84,4 @@ "resources_per_trial": {"cpu": 6, "gpu": 1}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From b622c8601c39a0e80bd037d83d7a75ab10ed6aec Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:14:53 +0100 Subject: [PATCH 31/49] Added realizations to config --- recover/config/cell_line_transfer_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/cell_line_transfer_bayesian.py b/recover/config/cell_line_transfer_bayesian.py index 67d9a9c..b30f845 100644 --- a/recover/config/cell_line_transfer_bayesian.py +++ b/recover/config/cell_line_transfer_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in order to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -83,4 +84,4 @@ "resources_per_trial": {"cpu": 8, "gpu": 0}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From 5a2c84cace5bdb3adb04e76e5d01458f11ba6b5b Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:15:33 +0100 Subject: [PATCH 32/49] Added realizations to config --- recover/config/cell_line_transfer_shuffled_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/cell_line_transfer_shuffled_bayesian.py b/recover/config/cell_line_transfer_shuffled_bayesian.py index be91087..eaef2ee 100644 --- a/recover/config/cell_line_transfer_shuffled_bayesian.py +++ b/recover/config/cell_line_transfer_shuffled_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in order to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -83,4 +84,4 @@ "resources_per_trial": {"cpu": 32, "gpu": 1}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From 3eb83b6aff8a9f17bffd119a539acb39f0a40fe1 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:16:21 +0100 Subject: [PATCH 33/49] Added realizations to config --- recover/config/model_evaluation_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/model_evaluation_bayesian.py b/recover/config/model_evaluation_bayesian.py index a8a21d9..aa60dff 100644 --- a/recover/config/model_evaluation_bayesian.py +++ b/recover/config/model_evaluation_bayesian.py @@ -38,7 +38,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in oreder to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in oreder to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -84,4 +85,4 @@ "resources_per_trial": {"cpu": 32, "gpu": 2}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From 4322b32167c086c24fe0820e8a872aa615b1f2d8 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:17:01 +0100 Subject: [PATCH 34/49] Added realizations to config --- recover/config/model_evaluation_multi_cell_line_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/model_evaluation_multi_cell_line_bayesian.py b/recover/config/model_evaluation_multi_cell_line_bayesian.py index a38cc6a..fd92b19 100644 --- a/recover/config/model_evaluation_multi_cell_line_bayesian.py +++ b/recover/config/model_evaluation_multi_cell_line_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in order to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -82,4 +83,4 @@ "resources_per_trial": {"cpu": 16, "gpu": 0}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From a22b67fc8b7f95ae4576bae74c0d2b0c9a027d0f Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:17:58 +0100 Subject: [PATCH 35/49] Added realizations to config --- ...uation_multi_cell_line_no_permut_invariance_bayesian.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/recover/config/model_evaluation_multi_cell_line_no_permut_invariance_bayesian.py b/recover/config/model_evaluation_multi_cell_line_no_permut_invariance_bayesian.py index 32b19f8..a85dacf 100644 --- a/recover/config/model_evaluation_multi_cell_line_no_permut_invariance_bayesian.py +++ b/recover/config/model_evaluation_multi_cell_line_no_permut_invariance_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 5, 'patience': 10} #in order to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in order to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -74,7 +75,7 @@ }, "summaries_dir": os.path.join(get_project_root(), "RayLogs"), "memory": 1800, - "stop": {"training_iteration": 5, 'patience': 10}, + "stop": {"training_iteration": 1000, 'patience': 10}, "checkpoint_score_attr": 'eval/comb_r_squared', "keep_checkpoints_num": 1, "checkpoint_at_end": False, @@ -82,4 +83,4 @@ "resources_per_trial": {"cpu": 16, "gpu": 1}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From 664c05486757b129b0a7e2a8b73bb3c3f2d7b118 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:18:38 +0100 Subject: [PATCH 36/49] Added realizations to config --- .../model_evaluation_multi_cell_line_shuffled_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/model_evaluation_multi_cell_line_shuffled_bayesian.py b/recover/config/model_evaluation_multi_cell_line_shuffled_bayesian.py index f15b980..d745e27 100644 --- a/recover/config/model_evaluation_multi_cell_line_shuffled_bayesian.py +++ b/recover/config/model_evaluation_multi_cell_line_shuffled_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in order to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -82,4 +83,4 @@ "resources_per_trial": {"cpu": 16, "gpu": 1}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From 9cefae404eca28c826f2411a84e8eceab12e801e Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:19:23 +0100 Subject: [PATCH 37/49] Added realizations to config --- .../config/model_evaluation_no_permut_invariance_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/model_evaluation_no_permut_invariance_bayesian.py b/recover/config/model_evaluation_no_permut_invariance_bayesian.py index 58ba810..6e7f854 100644 --- a/recover/config/model_evaluation_no_permut_invariance_bayesian.py +++ b/recover/config/model_evaluation_no_permut_invariance_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in order to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -82,4 +83,4 @@ "resources_per_trial": {"cpu": 16, "gpu": 1}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From d56a934e97fdee3fa9980bb5911c3a469d9b865e Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:20:11 +0100 Subject: [PATCH 38/49] Added realizations to config --- recover/config/model_evaluation_shuffled_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/model_evaluation_shuffled_bayesian.py b/recover/config/model_evaluation_shuffled_bayesian.py index 78be788..2441282 100644 --- a/recover/config/model_evaluation_shuffled_bayesian.py +++ b/recover/config/model_evaluation_shuffled_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in oreder to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in oreder to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -82,4 +83,4 @@ "resources_per_trial": {"cpu": 8, "gpu": 0}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From 18abf160a24cfc71f2e493ab817fffad6df561af Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:21:08 +0100 Subject: [PATCH 39/49] Added realizations to config --- recover/config/one_hidden_drug_split_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/one_hidden_drug_split_bayesian.py b/recover/config/one_hidden_drug_split_bayesian.py index b7792fe..eae078d 100644 --- a/recover/config/one_hidden_drug_split_bayesian.py +++ b/recover/config/one_hidden_drug_split_bayesian.py @@ -38,7 +38,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in oreder to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10, #in oreder to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -83,4 +84,4 @@ "resources_per_trial": {"cpu": 32, "gpu": 2}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From d72149e1446c4e621bec4e83552bb7dd51d1b474 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:23:22 +0100 Subject: [PATCH 40/49] Added realizations to config --- .../one_hidden_drug_split_no_permut_invariance_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/one_hidden_drug_split_no_permut_invariance_bayesian.py b/recover/config/one_hidden_drug_split_no_permut_invariance_bayesian.py index 9c11449..c56e85c 100644 --- a/recover/config/one_hidden_drug_split_no_permut_invariance_bayesian.py +++ b/recover/config/one_hidden_drug_split_no_permut_invariance_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in order to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in order to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -82,4 +83,4 @@ "resources_per_trial": {"cpu": 32, "gpu": 2}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From 8b2a927a4d3136637932b7d35dc6b590a8d2add3 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 15:23:52 +0100 Subject: [PATCH 41/49] Added realizations to config --- recover/config/one_hidden_drug_split_shuffled_bayesian.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recover/config/one_hidden_drug_split_shuffled_bayesian.py b/recover/config/one_hidden_drug_split_shuffled_bayesian.py index e654440..99bd70f 100644 --- a/recover/config/one_hidden_drug_split_shuffled_bayesian.py +++ b/recover/config/one_hidden_drug_split_shuffled_bayesian.py @@ -37,7 +37,8 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10} #in oreder to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in oreder to check when the training in over, we parse these arguments + "realizations": 10 #define the number of realizations } model_config = { @@ -82,4 +83,4 @@ "resources_per_trial": {"cpu": 32, "gpu": 2}, "scheduler": None, "search_alg": None, -} \ No newline at end of file +} From c6589f5afc4a41a27dc4fa172fcce08f682e842c Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Fri, 15 Dec 2023 17:34:00 +0100 Subject: [PATCH 42/49] Update README.md --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index f923cf7..51b82e2 100644 --- a/README.md +++ b/README.md @@ -6,9 +6,10 @@ This repository is an implementation of RECOVER, a platform that can guide wet l ([preprint](https://arxiv.org/abs/2202.04202)), howerver instead of using an ensemble model to get Synergy predictions with uncertainty, we used multiple realization of a Bayesian Neural Network model. Since the weights are drawn from a distribution, they differ for every run of a trained model and hence give different results. The goal was to get a more precise uncertainty and achieve i quicker since the model doesn't have to be trained multiple times. -
- Overview -
+## Bayesian Before and After Merge +This branch is refering to a model using Bayesian modeling in both single drug MLP and Combination MLP. The predictors with all Bayesian layers are added in `Recover/recover/models/predictors.py`. The `train.py` was updated with a train_epoch_bayesian function that trains the model using KL loss and test_epoch for testing of the model. In Bayesian Basic Trainer test_epoch is used to test the trained model and easily get the mean and the standard deviation of synergy predictions. +In Bayesian Active Trainer, realizations of the trained model are used instead of the Ensemble Model to get the acqusition function scores and rank the drug combinations. Probability of Improvement and Expected Improvement acquistion functions were added to `Recover/recover/acquisition/acquisition.py` since we are now working with Bayesian Optimization. +Config files are also updated to be use BNNs. In this branch there are separate bayesian config files, while in the **master** use of bayesian layers feature was added to existing config files. ## Environment setup From 567bd1e17fdb3c673050b7df1cf5bee190d42e20 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Wed, 27 Dec 2023 09:23:18 +0100 Subject: [PATCH 43/49] Fixed typos --- recover/config/one_hidden_drug_split_bayesian.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recover/config/one_hidden_drug_split_bayesian.py b/recover/config/one_hidden_drug_split_bayesian.py index eae078d..2be7086 100644 --- a/recover/config/one_hidden_drug_split_bayesian.py +++ b/recover/config/one_hidden_drug_split_bayesian.py @@ -38,7 +38,7 @@ ], "merge_n_layers_before_the_end": 2, # Computation on the sum of the two drug embeddings for the last n layers "allow_neg_eigval": True, - "stop": {"training_iteration": 1000, 'patience': 10, #in oreder to check when the training in over, we parse these arguments + "stop": {"training_iteration": 1000, 'patience': 10}, #in oreder to check when the training in over, we parse these arguments "realizations": 10 #define the number of realizations } @@ -67,7 +67,7 @@ ######################################################################################################################## configuration = { - "trainer": BayesianBasicTrainer, ccc + "trainer": BayesianBasicTrainer, #Adding Bayesian trainer "trainer_config": { **pipeline_config, **predictor_config, From 443cd46ccd8b9945444c7ec91c2a66418dd4b5f4 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Mon, 8 Jan 2024 11:49:50 +0100 Subject: [PATCH 44/49] Create data --- experiments/data | 1 + 1 file changed, 1 insertion(+) create mode 100644 experiments/data diff --git a/experiments/data b/experiments/data new file mode 100644 index 0000000..9c558e3 --- /dev/null +++ b/experiments/data @@ -0,0 +1 @@ +. From 9e7a666256bf26b6ce69d0d290b93f2b2b9614e0 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Mon, 8 Jan 2024 11:51:23 +0100 Subject: [PATCH 45/49] Delete experiments/data --- experiments/data | 1 - 1 file changed, 1 deletion(-) delete mode 100644 experiments/data diff --git a/experiments/data b/experiments/data deleted file mode 100644 index 9c558e3..0000000 --- a/experiments/data +++ /dev/null @@ -1 +0,0 @@ -. From 920bd7729ac258f29084bbe518cce050644d2d1b Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Mon, 8 Jan 2024 11:53:35 +0100 Subject: [PATCH 46/49] File for getting stats from json files --- experiments/stats.py | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 experiments/stats.py diff --git a/experiments/stats.py b/experiments/stats.py new file mode 100644 index 0000000..e3b96bb --- /dev/null +++ b/experiments/stats.py @@ -0,0 +1,39 @@ +import json +import numpy as np + +# Specify the path to your JSON file +json_file_path = "result.json" + +# Load data from the JSON file +with open(json_file_path, "r") as json_file: + # Wrap the content in square brackets to form a JSON array + json_data = "[" + json_file.read().replace("}\n{", "},\n{") + "]" + + # Parse the JSON array + data = json.loads(json_data) + +# Extract relevant values +spearman_values = [entry["eval/spearman"] for entry in data] +rsquared_values = [entry["eval/comb_r_squared"] for entry in data] + +# Calculate mean and standard deviation +mean_spearman = np.mean(spearman_values) +std_dev_spearman = np.std(spearman_values) + +mean_rsquared = np.mean(rsquared_values) +std_dev_rsquared = np.std(rsquared_values) + +# Print the results +print(f"Mean eval/rsquared: {round(mean_rsquared, 3)}, Standard Deviation: {round(std_dev_rsquared, 3)}") +print(f"Mean eval/spearman: {round(mean_spearman, 3)}, Standard Deviation: {round(std_dev_spearman, 3)}") + +last_entry = data[-1] +mean_r_squared_last = last_entry["mean_r_squared"] +std_r_squared_last = last_entry["std_r_squared"] + +mean_spearman_last = last_entry["mean_spearman"] +std_spearman_last = last_entry["std_spearman"] + +# Print values for the last entry +print(f"Mean test/rsquared: {round(mean_r_squared_last, 3)}, Standard Deviation: {round(std_r_squared_last, 3)}") +print(f"Mean test/spearman: {round(mean_spearman_last, 3)}, Standard Deviation: {round(std_spearman_last, 3)}") From 21ea6081f6332eca2fc0aa24e926dcf66d1f6ec3 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Mon, 8 Jan 2024 12:11:03 +0100 Subject: [PATCH 47/49] Create he --- experiments/data/he | 1 + 1 file changed, 1 insertion(+) create mode 100644 experiments/data/he diff --git a/experiments/data/he b/experiments/data/he new file mode 100644 index 0000000..31fc20f --- /dev/null +++ b/experiments/data/he @@ -0,0 +1 @@ +he From 3fe2debe0878f92a44b1842246e8091abaa0f112 Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Mon, 8 Jan 2024 12:12:10 +0100 Subject: [PATCH 48/49] Adding results for different configs --- .../cell_line_transfer_bayesian-result.json | 46 ++++++++ ..._no_permut_invariance_bayesian-result.json | 23 ++++ ...ine_transfer_shuffled_bayesian-result.json | 36 +++++++ .../model_evaluation_bayesian-result.json | 49 +++++++++ ...ation_multi_cell_line_bayesian-result.json | 29 +++++ ..._no_permut_invariance_bayesian-result.json | 32 ++++++ ...ti_cell_line_shuffled_bayesian-result.json | 27 +++++ ..._no_permut_invariance_bayesian-result.json | 83 ++++++++++++++ ...l_evaluation_shuffled_bayesian-result.json | 72 +++++++++++++ ...one_hidden_drug_split_bayesian-result.json | 25 +++++ ..._no_permut_invariance_bayesian-result.json | 102 ++++++++++++++++++ ...n_drug_split_shuffled_bayesian-result.json | 36 +++++++ 12 files changed, 560 insertions(+) create mode 100644 experiments/data/cell_line_transfer_bayesian-result.json create mode 100644 experiments/data/cell_line_transfer_no_permut_invariance_bayesian-result.json create mode 100644 experiments/data/cell_line_transfer_shuffled_bayesian-result.json create mode 100644 experiments/data/model_evaluation_bayesian-result.json create mode 100644 experiments/data/model_evaluation_multi_cell_line_bayesian-result.json create mode 100644 experiments/data/model_evaluation_multi_cell_line_no_permut_invariance_bayesian-result.json create mode 100644 experiments/data/model_evaluation_multi_cell_line_shuffled_bayesian-result.json create mode 100644 experiments/data/model_evaluation_no_permut_invariance_bayesian-result.json create mode 100644 experiments/data/model_evaluation_shuffled_bayesian-result.json create mode 100644 experiments/data/one_hidden_drug_split_bayesian-result.json create mode 100644 experiments/data/one_hidden_drug_split_no_permut_invariance_bayesian-result.json create mode 100644 experiments/data/one_hidden_drug_split_shuffled_bayesian-result.json diff --git a/experiments/data/cell_line_transfer_bayesian-result.json b/experiments/data/cell_line_transfer_bayesian-result.json new file mode 100644 index 0000000..9a943eb --- /dev/null +++ b/experiments/data/cell_line_transfer_bayesian-result.json @@ -0,0 +1,46 @@ +{"train/loss_mean": 100.01108189925407, "train/comb_r_squared": 0.20868373419101738, 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"patience": 10}, "realizations": 10, "model": "", "load_model_weights": false, "dataset": "", "study_name": "ALMANAC", "in_house_data": "without", "rounds_to_include": [], "val_set_prop": 0.2, "test_set_prop": 0.1, "test_on_unseen_cell_line": false, "split_valid_train": "pair_level", "cell_line": "MCF7", "target": "bliss_max", "fp_bits": 1024, "fp_radius": 2}, "time_since_restore": 97.29054427146912, "iterations_since_restore": 36} From 99b207a0dbab53635aff3d470d416a2bf2c5521c Mon Sep 17 00:00:00 2001 From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com> Date: Mon, 8 Jan 2024 12:12:32 +0100 Subject: [PATCH 49/49] Delete experiments/data/he --- experiments/data/he | 1 - 1 file changed, 1 deletion(-) delete mode 100644 experiments/data/he diff --git a/experiments/data/he b/experiments/data/he deleted file mode 100644 index 31fc20f..0000000 --- a/experiments/data/he +++ /dev/null @@ -1 +0,0 @@ -he