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/README.md b/README.md index d42df10..51b82e2 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,20 @@ -# 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") +## 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 -**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 +28,3 @@ 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. diff --git a/docs/images/ProjectInfographics.png b/docs/images/ProjectInfographics.png new file mode 100644 index 0000000..60576e3 Binary files /dev/null and b/docs/images/ProjectInfographics.png differ 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, "train/kl_loss": 100.00393831773002, "eval/loss_mean": 83.35131853798958, "eval/comb_r_squared": 0.23105831834981294, "eval/spearman": 0.2641797569843928, "training_iteration": 0, "patience": 0, "all_space_explored": 0, "done": false, "trial_id": "633eb_00000", "date": 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"time_total_s": 97.29054427146912, "pid": 202882, "hostname": "uan01", "node_ip": "130.237.230.196", "config": {"use_tune": true, "num_epoch_without_tune": 500, "seed": 2, "lr": 0.0001, "weight_decay": 0.01, "batch_size": 128, "train_epoch": "", "eval_epoch": "", "test_epoch": "", "predictor": "", "predictor_layers": [2048, 128, 64, 1], "merge_n_layers_before_the_end": 2, "allow_neg_eigval": true, "stop": {"training_iteration": 1000, "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} 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)}") diff --git a/recover/acquisition/acquisition.py b/recover/acquisition/acquisition.py index f19076e..b25df38 100644 --- a/recover/acquisition/acquisition.py +++ b/recover/acquisition/acquisition.py @@ -1,4 +1,7 @@ import torch +import numpy as np +from scipy.special import erf +from scipy.stats import norm ######################################################################################################################## # Abstract Acquisition @@ -18,16 +21,41 @@ 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 + """ + 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 + ######################################################################################################################## # 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_current_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): @@ -35,6 +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): diff --git a/recover/config/active_learning_UCB_bayesian.py b/recover/config/active_learning_UCB_bayesian.py new file mode 100644 index 0000000..2102a8e --- /dev/null +++ b/recover/config/active_learning_UCB_bayesian.py @@ -0,0 +1,112 @@ +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, ExpectedImprovementAcquisition +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": 1000, '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, ExpectedImprovementAcquisition]), + "patience_max": 8, + "kappa": 1, + "kappa_decrease_factor": 1, + "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 +} + +######################################################################################################################## +# 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, +} diff --git a/recover/config/cell_line_transfer_bayesian.py b/recover/config/cell_line_transfer_bayesian.py new file mode 100644 index 0000000..b30f845 --- /dev/null +++ b/recover/config/cell_line_transfer_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 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 + "realizations": 10 #define the number of realizations +} + +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, +} 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..8c90490 --- /dev/null +++ b/recover/config/cell_line_transfer_no_permut_invariance_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 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 + "realizations": 10 #define the number of realizations +} + +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, +} 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..eaef2ee --- /dev/null +++ b/recover/config/cell_line_transfer_shuffled_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 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 + "realizations": 10 #define the number of realizations +} + +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, +} diff --git a/recover/config/model_evaluation_bayesian.py b/recover/config/model_evaluation_bayesian.py new file mode 100644 index 0000000..aa60dff --- /dev/null +++ b/recover/config/model_evaluation_bayesian.py @@ -0,0 +1,88 @@ +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 + "realizations": 10 #define the number of realizations +} + +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, +} 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..fd92b19 --- /dev/null +++ b/recover/config/model_evaluation_multi_cell_line_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, 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 + "realizations": 10 #define the number of realizations +} + +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, +} 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..a85dacf --- /dev/null +++ b/recover/config/model_evaluation_multi_cell_line_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, 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": 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 = { + "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": 1}, + "scheduler": None, + "search_alg": None, +} 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..d745e27 --- /dev/null +++ b/recover/config/model_evaluation_multi_cell_line_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 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 + "realizations": 10 #define the number of realizations +} + +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, +} 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..6e7f854 --- /dev/null +++ b/recover/config/model_evaluation_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 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 + "realizations": 10 #define the number of realizations +} + +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, +} diff --git a/recover/config/model_evaluation_shuffled_bayesian.py b/recover/config/model_evaluation_shuffled_bayesian.py new file mode 100644 index 0000000..2441282 --- /dev/null +++ b/recover/config/model_evaluation_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 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 + "realizations": 10 #define the number of realizations +} + +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, +} 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..2be7086 --- /dev/null +++ b/recover/config/one_hidden_drug_split_bayesian.py @@ -0,0 +1,87 @@ +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 + "realizations": 10 #define the number of realizations +} + +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": True, + "checkpoint_freq": 1, + "resources_per_trial": {"cpu": 32, "gpu": 2}, + "scheduler": None, + "search_alg": None, +} 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..c56e85c --- /dev/null +++ b/recover/config/one_hidden_drug_split_no_permut_invariance_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, 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 + "realizations": 10 #define the number of realizations +} + +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, +} 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..99bd70f --- /dev/null +++ b/recover/config/one_hidden_drug_split_shuffled_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, 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 + "realizations": 10 #define the number of realizations +} + +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, +} 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..e7b7edb 100644 --- a/recover/models/predictors.py +++ b/recover/models/predictors.py @@ -2,6 +2,8 @@ import numpy as np import torch.nn as nn from torch.nn import Parameter +import torchbnn as bnn + ######################################################################################################################## # Modules @@ -15,7 +17,16 @@ 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): + 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): @@ -104,8 +115,6 @@ def forward(self, input): ######################################################################################################################## # Bilinear MLP ######################################################################################################################## - - class BilinearMLPPredictor(torch.nn.Module): def __init__(self, data, config, predictor_layers): @@ -210,7 +219,125 @@ 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 +######################################################################################################################## + + +class BayesianBilinearMLPPredictor(torch.nn.Module): + 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 @@ -255,6 +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 # ######################################################################################################################## @@ -366,6 +537,96 @@ 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)] ######################################################################################################################## @@ -409,6 +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])] ######################################################################################################################## @@ -551,6 +855,63 @@ 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 shuffled models +######################################################################################################################## + + +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) + + +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])] + + + ######################################################################################################################## # Partially shuffled models diff --git a/recover/train.py b/recover/train.py index 6b398ad..58c2037 100644 --- a/recover/train.py +++ b/recover/train.py @@ -11,13 +11,15 @@ import importlib from scipy import stats from scipy.stats import spearmanr +import torchbnn as bnn +import tempfile ######################################################################################################################## # Epoch loops ######################################################################################################################## - - + + def train_epoch(data, loader, model, optim): model.train() @@ -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()) @@ -54,6 +55,67 @@ 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): + + 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 = [] + batch = 1 + + 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 = 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) + + #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): @@ -74,7 +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() @@ -87,11 +149,56 @@ 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 ######################################################################################################################## @@ -213,7 +320,175 @@ 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"] + self.realizations = config["realizations"] #get the number of realizations + + 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(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 + 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)) @@ -272,8 +547,241 @@ 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 + 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 + + +######################################################################################################################## +# 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"] + self.realizations = config["realizations"] #get the number of realizations + + # 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(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']) + 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()] @@ -415,7 +923,7 @@ 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"), ) @@ -431,11 +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) 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