From a5ad04aa20428f29e1e4a027c3f03b6c7e94e66c Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Thu, 19 Oct 2023 14:59:02 +0200 Subject: [PATCH 01/59] add relevant attributes to the Trainer and improve their logic --- deeprank2/trainer.py | 61 ++++++++++++++++++++++++++++++++------------ 1 file changed, 45 insertions(+), 16 deletions(-) diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py index 3e9f14933..2ab4edc52 100644 --- a/deeprank2/trainer.py +++ b/deeprank2/trainer.py @@ -3,6 +3,7 @@ from time import time from typing import List, Optional, Tuple, Union +import dill import h5py import numpy as np import torch @@ -119,7 +120,10 @@ def __init__( # pylint: disable=too-many-arguments # noqa: MC0001 _log.info(f"CUDA device name is {torch.cuda.get_device_name(0)}.") _log.info(f"Number of GPUs set to {self.ngpu}.") - if pretrained_model is None: + self.pretrained_model_path = pretrained_model + self.model_load_state_dict = None + + if self.pretrained_model_path is None: if self.dataset_train is None: raise ValueError("No training data specified. Training data is required if there is no pretrained model.") if self.neuralnet is None: @@ -166,10 +170,7 @@ def __init__( # pylint: disable=too-many-arguments # noqa: MC0001 raise ValueError("No neural network class found. Please add it to complete loading the pretrained model.") if self.dataset_test is None: raise ValueError("No dataset_test found. Please add it to evaluate the pretrained model.") - if self.target is None: - raise ValueError("No target set. Make sure the pretrained model explicitly defines the target to train against.") - self.pretrained_model_path = pretrained_model self.classes_to_index = self.dataset_test.classes_to_index self._load_params() @@ -223,17 +224,25 @@ def _init_from_dataset(self, dataset: Union[GraphDataset, GridDataset]): self.node_features = dataset.node_features self.edge_features = dataset.edge_features self.features = None + self.features_transform = dataset.features_transform + self.means = dataset.means + self.devs = dataset.devs elif isinstance(dataset, GridDataset): self.clustering_method = None self.node_features = None self.edge_features = None self.features = dataset.features + self.features_transform = None + self.means = None + self.devs = None else: raise TypeError(type(dataset)) self.target = dataset.target + self.target_transform = dataset.target_transform self.task = dataset.task + self.classes = dataset.classes def _load_model(self): """Loads the neural network model.""" @@ -549,6 +558,9 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc else: self.valid_loader = None _log.info("No validation set provided\n") + _log.warning( + "Training data will be used both for learning and model selection, which may lead to overfitting." + + "\nIt is usually preferable to use a validation set during the training phase.") # Assign weights to each class if self.task == targets.CLASSIF and self.class_weights: @@ -622,9 +634,6 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc # if no validation set, save the best performing model on the training set if best_model: if min(train_losses) == loss_: - _log.warning( - "Training data is used both for learning and model selection, which will to overfitting." + - "\n\tIt is preferable to use an independent training and validation data sets.") checkpoint_model = self._save_model() self.epoch_saved_model = epoch _log.info(f'Best model saved at epoch # {self.epoch_saved_model}.') @@ -637,7 +646,7 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc # Now that the training loop is over, save the model if filename: - torch.save(checkpoint_model, filename) + torch.save(checkpoint_model, filename, pickle_module = dill) self.opt_loaded_state_dict = checkpoint_model["optimizer_state"] self.model_load_state_dict = checkpoint_model["model_state"] self.optimizer.load_state_dict(self.opt_loaded_state_dict) @@ -822,6 +831,13 @@ def test( num_workers (int, optional): How many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. Defaults to 0. """ + if (not self.pretrained_model_path) and (not self.model_load_state_dict): + raise ValueError( + """ + No pretrained model provided and no training performed. + Please provide a pretrained model or train the model before testing.\n + """) + self.batch_size_test = batch_size if self.dataset_test is not None: @@ -848,28 +864,36 @@ def _load_params(self): Loads the parameters of a pretrained model """ - state = torch.load(self.pretrained_model_path) + if torch.cuda.is_available(): + state = torch.load(self.pretrained_model_path, pickle_module = dill) + else: + state = torch.load(self.pretrained_model_path, pickle_module = dill, map_location=torch.device('cpu')) + self.model_load_state_dict = state["model_state"] + self.optimizer = state["optimizer"] + self.opt_loaded_state_dict = state["optimizer_state"] + self.lossfunction = state["lossfunction"] self.target = state["target"] + self.target_transform = state["target_transform"] + self.task = state["task"] + self.classes = state["classes"] + self.class_weights = state["class_weights"] self.batch_size_train = state["batch_size_train"] self.batch_size_test = state["batch_size_test"] self.val_size = state["val_size"] self.test_size = state["test_size"] self.lr = state["lr"] self.weight_decay = state["weight_decay"] + self.epoch_saved_model = state["epoch_saved_model"] self.subset = state["subset"] - self.class_weights = state["class_weights"] - self.task = state["task"] - self.classes = state["classes"] self.shuffle = state["shuffle"] - self.optimizer = state["optimizer"] - self.opt_loaded_state_dict = state["optimizer_state"] - self.lossfunction = state["lossfunction"] - self.model_load_state_dict = state["model_state"] self.clustering_method = state["clustering_method"] self.node_features = state["node_features"] self.edge_features = state["edge_features"] self.features = state["features"] + self.features_transform = state["features_transform"] + self.means = state["means"] + self.devs = state["devs"] self.cuda = state["cuda"] self.ngpu = state["ngpu"] @@ -886,6 +910,7 @@ def _save_model(self): "optimizer_state": self.optimizer.state_dict(), "lossfunction": self.lossfunction, "target": self.target, + "target_transform": self.target_transform, "task": self.task, "classes": self.classes, "class_weights": self.class_weights, @@ -895,12 +920,16 @@ def _save_model(self): "test_size": self.test_size, "lr": self.lr, "weight_decay": self.weight_decay, + "epoch_saved_model": self.epoch_saved_model, "subset": self.subset, "shuffle": self.shuffle, "clustering_method": self.clustering_method, "node_features": self.node_features, "edge_features": self.edge_features, "features": self.features, + "features_transform": self.features_transform, + "means": self.means, + "devs": self.devs, "cuda": self.cuda, "ngpu": self.ngpu } From a094e2fc9663661ba0d0aabb91b573fc0890544e Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Thu, 19 Oct 2023 15:01:35 +0200 Subject: [PATCH 02/59] add tests for testing when no test is provided and when no mmodel is loaded/trained --- tests/test_trainer.py | 53 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 49 insertions(+), 4 deletions(-) diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 94fa1afcc..033f8a934 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -9,7 +9,11 @@ import h5py import pytest import torch + from deeprank2.dataset import GraphDataset, GridDataset +from deeprank2.domain import edgestorage as Efeat +from deeprank2.domain import nodestorage as Nfeat +from deeprank2.domain import targetstorage as targets from deeprank2.neuralnets.cnn.model3d import CnnClassification, CnnRegression from deeprank2.neuralnets.gnn.foutnet import FoutNet from deeprank2.neuralnets.gnn.ginet import GINet @@ -19,10 +23,6 @@ from deeprank2.utils.exporters import (HDF5OutputExporter, ScatterPlotExporter, TensorboardBinaryClassificationExporter) -from deeprank2.domain import edgestorage as Efeat -from deeprank2.domain import nodestorage as Nfeat -from deeprank2.domain import targetstorage as targets - _log = logging.getLogger(__name__) default_features = [Nfeat.RESTYPE, Nfeat.POLARITY, Nfeat.BSA, Nfeat.RESDEPTH, Nfeat.HSE, Nfeat.INFOCONTENT, Nfeat.PSSM] @@ -383,6 +383,31 @@ def test_incompatible_pretrained_no_Net(self): pretrained_model = self.save_path ) + def test_no_training_no_pretrained(self): + dataset_train = GraphDataset( + hdf5_path = "tests/data/hdf5/test.hdf5", + clustering_method = "mcl", + target = targets.BINARY, + ) + dataset_val = GraphDataset( + hdf5_path = "tests/data/hdf5/test.hdf5", + train = False, + dataset_train = dataset_train + ) + dataset_test = GraphDataset( + hdf5_path = "tests/data/hdf5/test.hdf5", + train = False, + dataset_train = dataset_train + ) + trainer = Trainer( + neuralnet = GINet, + dataset_train = dataset_train, + dataset_val = dataset_val, + dataset_test = dataset_test + ) + with pytest.raises(ValueError): + trainer.test() + def test_no_valid_provided(self): dataset = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", @@ -397,6 +422,26 @@ def test_no_valid_provided(self): assert len(trainer.train_loader) == int(0.75 * len(dataset)) assert len(trainer.valid_loader) == int(0.25 * len(dataset)) + def test_no_test_provided(self): + dataset_train = GraphDataset( + hdf5_path = "tests/data/hdf5/test.hdf5", + clustering_method = "mcl", + target = targets.BINARY, + ) + dataset_val = GraphDataset( + hdf5_path = "tests/data/hdf5/test.hdf5", + train = False, + dataset_train = dataset_train + ) + trainer = Trainer( + neuralnet = GINet, + dataset_train = dataset_train, + dataset_val = dataset_val, + ) + trainer.train(batch_size = 1, best_model=False, filename=None) + with pytest.raises(ValueError): + trainer.test() + def test_no_valid_full_train(self): dataset = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", From a6209cba5943690d711a427e2a8043ec05896d4a Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Fri, 20 Oct 2023 14:25:17 +0200 Subject: [PATCH 03/59] fix test_optim --- tests/test_trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 033f8a934..4ebbca2e5 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -483,7 +483,7 @@ def test_optim(self): dataset_test=dataset, pretrained_model=self.save_path) - assert isinstance(trainer_pretrained.optimizer, optimizer) + assert str(type(trainer_pretrained.optimizer)) == "" assert trainer_pretrained.lr == lr assert trainer_pretrained.weight_decay == weight_decay From d5625134d6040a245d7246f09102e98a127b94ee Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Fri, 20 Oct 2023 15:48:46 +0200 Subject: [PATCH 04/59] change dataset_train to train_data and update docs, for later functionality refactor --- deeprank2/dataset.py | 103 +++++++++++++++++++++---------------------- 1 file changed, 51 insertions(+), 52 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index a915606b2..5d7dc7021 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -30,6 +30,8 @@ class DeeprankDataset(Dataset): def __init__(self, # pylint: disable=too-many-arguments hdf5_path: Union[str, List[str]], subset: Optional[List[str]], + train: bool, + train_data: Optional[Union[str, GridDataset, GraphDataset]], target: Optional[str], task: Optional[str], classes: Optional[Union[List[str], List[int], List[float]]], @@ -54,11 +56,11 @@ def __init__(self, # pylint: disable=too-many-arguments else: raise TypeError(f"hdf5_path: unexpected type: {type(hdf5_path)}") - self.use_tqdm = use_tqdm - + self.train = train + self.train_data = train_data self.target = target self.subset = subset - + self.use_tqdm = use_tqdm self.target_filter = target_filter if check_integrity: @@ -412,7 +414,7 @@ def __init__( # pylint: disable=too-many-arguments hdf5_path: Union[str, list], subset: Optional[List[str]] = None, train: bool = True, - dataset_train: Optional[GridDataset] = None, + train_data: Optional[Union[str, GridDataset]] = None, features: Optional[Union[List[str], str]] = "all", target: Optional[str] = None, target_transform: Optional[bool] = False, @@ -429,16 +431,16 @@ def __init__( # pylint: disable=too-many-arguments hdf5_path (Union[str,list]): Path to .HDF5 file(s). For multiple .HDF5 files, insert the paths in a List. Defaults to None. subset (Optional[List[str]], optional): List of keys from .HDF5 file to include. Defaults to None (meaning include all). train (bool, optional): Boolean flag to determine if the instance represents the training set. - If False, a dataset_train of the same class must be provided as well. - The latter will be used to scale the validation/testing set according to its features values and to match the datasets' parameters. + It needs to be set to False only if the instance represents the validation or testing set. Defaults to True. - dataset_train (class:`GridDataset`, optional): If `train` is True, assign here the training set. - If `train` is False and `dataset_train` is assigned, - the parameters `features`, `target`, `traget_transform`, `task`, and `classes` will be inherited from `dataset_train`. + train_data (Optional[Union[str, class:`GridDataset`]], optional)): data to inherit information from the training dataset or the pre-trained model. + It is considered only if `train` is False. In the latter case, `train_data` needs to be a dataset of the same class or + the path of a DeepRank2 pre-trained model. If set, the parameters `features`, `target`, `traget_transform`, `task`, and `classes` + will be inherited from `train_data`. Defaults to None. features (Optional[Union[List[str], str]], optional): Consider all pre-computed features ("all") or some defined node features (provide a list, example: ["res_type", "polarity", "bsa"]). The complete list can be found in `deeprank2.domain.gridstorage`. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to "all". target (Optional[str], optional): Default options are irmsd, lrmsd, fnat, binary, capri_class, and dockq. It can also be a custom-defined target given to the Query class as input (see: `deeprank2.query`); in this case, @@ -446,11 +448,11 @@ def __init__( # pylint: disable=too-many-arguments Only numerical target variables are supported, not categorical. If the latter is your case, please convert the categorical classes into numerical class indices before defining the :class:`GraphDataset` instance. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to None. target_transform (Optional[bool], optional): Apply a log and then a sigmoid transformation to the target (for regression only). This puts the target value between 0 and 1, and can result in a more uniform target distribution and speed up the optimization. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to False. target_filter (Optional[Dict[str, str]], optional): Dictionary of type [target: cond] to filter the molecules. Note that the you can filter on a different target than the one selected as the dataset target. @@ -459,10 +461,10 @@ def __init__( # pylint: disable=too-many-arguments ['irmsd', 'lrmsd', 'fnat', 'binary', 'capri_class', or 'dockq'], otherwise this setting is ignored. Automatically set to 'classif' if the target is 'binary' or 'capri_classes'. Automatically set to 'regress' if the target is 'irmsd', 'lrmsd', 'fnat', or 'dockq'. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to None. classes (Optional[Union[List[str], List[int], List[float]], optional]): Define the dataset target classes in classification mode. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to None. tqdm (Optional[bool], optional): Show progress bar. Defaults to True. @@ -471,31 +473,30 @@ def __init__( # pylint: disable=too-many-arguments check_integrity (bool, optional): Whether to check the integrity of the hdf5 files. Defaults to True. """ - super().__init__(hdf5_path, subset, target, task, classes, tqdm, root, target_filter, check_integrity) + super().__init__(hdf5_path, subset, train, train_data, target, task, classes, tqdm, root, target_filter, check_integrity) self.default_vars = { k: v.default for k, v in inspect.signature(self.__init__).parameters.items() if v.default is not inspect.Parameter.empty } - self.train = train - self.dataset_train = dataset_train + self.features = features self.target_transform = target_transform self._check_features() if not train: - if not isinstance(dataset_train, GridDataset): - raise TypeError(f"""The train dataset provided is type: {type(dataset_train)} + if not isinstance(train_data, GridDataset): + raise TypeError(f"""The train dataset provided is type: {type(train_data)} Please provide a valid training GridDataset.""") #check inherited parameter with the ones in the training set inherited_params = ["features", "target", "target_transform", "task", "classes"] - self._check_inherited_params(inherited_params, dataset_train) + self._check_inherited_params(inherited_params, train_data) - elif train and dataset_train: - _log.warning("""dataset_train has been set but train flag was set to True. - dataset_train will be ignored since the current dataset will be considered as training set.""") + elif train and train_data: + _log.warning("""`train_data` has been set but train flag was set to True. + `train_data` will be ignored since the current dataset will be considered as training set.""") self.features_dict = {} self.features_dict[gridstorage.MAPPED_FEATURES] = self.features @@ -622,7 +623,7 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local hdf5_path: Union[str, List[str]], subset: Optional[List[str]] = None, train: bool = True, - dataset_train: Optional[GraphDataset] = None, + train_data: Optional[Union[str, GraphDataset]] = None, node_features: Optional[Union[List[str], str]] = "all", edge_features: Optional[Union[List[str], str]] = "all", features_transform: Optional[dict] = None, @@ -644,23 +645,23 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local subset (Optional[List[str]], optional): List of keys from .HDF5 file to include. Defaults to None (meaning include all). train (bool, optional): Boolean flag to determine if the instance represents the training set. - If False, a dataset_train of the same class must be provided as well. - The latter will be used to scale the validation/testing set according to its features values and to match the datasets' parameters. + It needs to be set to False only if the instance represents the validation or testing set. Defaults to True. - dataset_train (class:`GraphDataset`, optional): If `train` is True, assign here the training set. - If `train` is False and `dataset_train` is assigned, - the parameters `node_features`, `edge_features`, `features_transform`, `target`, - `target_transform`, `task` and `classes` will be inherited from `dataset_train`. + train_data (Optional[Union[str, class:`GraphDataset`]], optional)): data to inherit information from the training dataset or the pre-trained model. + It is considered only if `train` is False. In the latter case, `train_data` needs to be a dataset of the same class or + the path of a DeepRank2 pre-trained model. If set, the parameters `node_features`, `edge_features`, `features_transform`, `target`, `target_transform`, + `task`, and `classes` will be inherited from `train_data`. If standardization was performed in the training dataset/step, also the attributes + `means` and `devs` will be inherited from `train_data`, and they will be used to scale the validation/testing set. Defaults to None. node_features (Optional[Union[List[str], str], optional): Consider all pre-computed node features ("all") or some defined node features (provide a list, example: ["res_type", "polarity", "bsa"]). The complete list can be found in `deeprank2.domain.nodestorage`. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to "all". edge_features (Optional[Union[List[str], str], optional): Consider all pre-computed edge features ("all") or some defined edge features (provide a list, example: ["dist", "coulomb"]). The complete list can be found in `deeprank2.domain.edgestorage`. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to "all". features_transform (Optional[dict], optional): Dictionary to indicate the transformations to apply to each feature in the dictionary, being the transformations lambda functions and/or standardization. @@ -668,7 +669,7 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local An `all` key can be set in the dictionary for indicating to apply the same `standardize` and `transform` to all the features. Example: `features_transform = {'all': {'transform': lambda t:np.log(t+1), 'standardize': True}}`. If both `all` and feature name/s are present, the latter have the priority over what indicated in `all`. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to None. clustering_method (Optional[str], optional): "mcl" for Markov cluster algorithm (see https://micans.org/mcl/), or "louvain" for Louvain method (see https://en.wikipedia.org/wiki/Louvain_method). @@ -685,11 +686,11 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local Only numerical target variables are supported, not categorical. If the latter is your case, please convert the categorical classes into numerical class indices before defining the :class:`GraphDataset` instance. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to None. target_transform (Optional[bool], optional): Apply a log and then a sigmoid transformation to the target (for regression only). This puts the target value between 0 and 1, and can result in a more uniform target distribution and speed up the optimization. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to False. target_filter (Optional[Dict[str, str]], optional): Dictionary of type [target: cond] to filter the molecules. Note that the you can filter on a different target than the one selected as the dataset target. @@ -698,10 +699,10 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local ['irmsd', 'lrmsd', 'fnat', 'binary', 'capri_class', or 'dockq'], otherwise this setting is ignored. Automatically set to 'classif' if the target is 'binary' or 'capri_classes'. Automatically set to 'regress' if the target is 'irmsd', 'lrmsd', 'fnat', or 'dockq'. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to None. classes (Optional[Union[List[str], List[int], List[float]]], optional): Define the dataset target classes in classification mode. - Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned. + Value will be ignored and inherited from `train_data` if `train` is set as False and `train_data` is assigned. Defaults to None. tqdm (Optional[bool], optional): Show progress bar. Defaults to True. root (Optional[str], optional): Root directory where the dataset should be saved. Defaults to "./". @@ -709,15 +710,13 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local Defaults to True. """ - super().__init__(hdf5_path, subset, target, task, classes, tqdm, root, target_filter, check_integrity) + super().__init__(hdf5_path, subset, train, train_data, target, task, classes, tqdm, root, target_filter, check_integrity) self.default_vars = { k: v.default for k, v in inspect.signature(self.__init__).parameters.items() if v.default is not inspect.Parameter.empty } - self.train = train - self.dataset_train = dataset_train self.node_features = node_features self.edge_features = edge_features self.clustering_method = clustering_method @@ -726,17 +725,17 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local self._check_features() if not train: - if not isinstance(dataset_train, GraphDataset): - raise TypeError(f"""The train dataset provided is type: {type(dataset_train)} + if not isinstance(train_data, GraphDataset): + raise TypeError(f"""The train dataset provided is type: {type(train_data)} Please provide a valid training GraphDataset.""") #check inherited parameter with the ones in the training set inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] - self._check_inherited_params(inherited_params, dataset_train) + self._check_inherited_params(inherited_params, train_data) - elif train and dataset_train: - _log.warning("""dataset_train has been set but train flag was set to True. - dataset_train will be ignored since the current dataset will be considered as training set.""") + elif train and train_data: + _log.warning("""`train_data` has been set but train flag was set to True. + `train_data` will be ignored since the current dataset will be considered as training set.""") self.features_dict = {} self.features_dict[Nfeat.NODE] = self.node_features @@ -757,12 +756,12 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local self.hdf5_to_pandas() self._compute_mean_std() elif standardize and (not train): - if (dataset_train.means is None) or (dataset_train.devs is None): - if dataset_train.df is None: - dataset_train.hdf5_to_pandas() - dataset_train._compute_mean_std() - self.means = dataset_train.means - self.devs = dataset_train.devs + if (train_data.means is None) or (train_data.devs is None): + if train_data.df is None: + train_data.hdf5_to_pandas() + train_data._compute_mean_std() + self.means = train_data.means + self.devs = train_data.devs def get(self, idx: int) -> Data: """Gets one graph item from its unique index. From fa45c605ef9e495fa8919bea9883ebceddbdb7f1 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Fri, 20 Oct 2023 15:49:59 +0200 Subject: [PATCH 05/59] change dataset_train to train_data in all relevant scripts --- README.md | 8 ++++---- deeprank2/trainer.py | 8 ++++---- docs/getstarted.md | 12 ++++++------ tests/test_dataset.py | 20 ++++++++++---------- tests/test_integration.py | 8 ++++---- tests/test_set_lossfunction.py | 6 +++--- tests/test_trainer.py | 22 +++++++++++----------- tutorials/training.ipynb | 8 ++++---- 8 files changed, 46 insertions(+), 46 deletions(-) diff --git a/README.md b/README.md index 9d8e1ce0f..9ac066d99 100644 --- a/README.md +++ b/README.md @@ -216,13 +216,13 @@ dataset_val = GraphDataset( hdf5_path = hdf5_paths, subset = valid_ids, train = False, - dataset_train = dataset_train + train_data = dataset_train ) dataset_test = GraphDataset( hdf5_path = hdf5_paths, subset = test_ids, train = False, - dataset_train = dataset_train + train_data = dataset_train ) ``` @@ -250,13 +250,13 @@ dataset_val = GridDataset( hdf5_path = hdf5_paths, subset = valid_ids, train = False, - dataset_train = dataset_train, + train_data = dataset_train, ) dataset_test = GridDataset( hdf5_path = hdf5_paths, subset = test_ids, train = False, - dataset_train = dataset_train, + train_data = dataset_train, ) ``` diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py index 2ab4edc52..299468f0f 100644 --- a/deeprank2/trainer.py +++ b/deeprank2/trainer.py @@ -278,10 +278,10 @@ def _check_dataset_value(self, dataset_train, dataset_check, type_dataset): if dataset_check.train is not False: raise ValueError(f"""{type_dataset} dataset has train parameter {dataset_check.train} Make sure to set it as False""") - # Check dataset_train parameter in valid/test is equivalent to train which passed to Trainer. - if dataset_check.dataset_train != dataset_train: - raise ValueError(f"""{type_dataset} dataset has different dataset_train parameter compared to the one given in Trainer. - Make sure to assign equivalent dataset_train in Trainer""") + # Check train_data parameter in valid/test is equivalent to train which passed to Trainer. + if dataset_check.train_data != dataset_train: + raise ValueError(f"""{type_dataset} dataset has different train_data parameter compared to the one given in Trainer. + Make sure to assign equivalent train_data in Trainer""") def _load_pretrained_model(self): """ diff --git a/docs/getstarted.md b/docs/getstarted.md index 12fe532b4..155d14ef4 100644 --- a/docs/getstarted.md +++ b/docs/getstarted.md @@ -189,13 +189,13 @@ dataset_val = GraphDataset( hdf5_path = hdf5_paths, subset = valid_ids, train = False, - dataset_train = dataset_train + train_data = dataset_train ) dataset_test = GraphDataset( hdf5_path = hdf5_paths, subset = test_ids, train = False, - dataset_train = dataset_train + train_data = dataset_train ) ``` @@ -266,13 +266,13 @@ dataset_val = GraphDataset( hdf5_path = hdf5_paths, subset = valid_ids, train = False, - dataset_train = dataset_train # dataset_train means and stds will be used + train_data = dataset_train # dataset_train means and stds will be used ) dataset_test = GraphDataset( hdf5_path = hdf5_paths, subset = test_ids, train = False, - dataset_train = dataset_train # dataset_train means and stds will be used + train_data = dataset_train # dataset_train means and stds will be used ) ``` @@ -300,13 +300,13 @@ dataset_val = GridDataset( hdf5_path = hdf5_paths, subset = valid_ids, train = False, - dataset_train = dataset_train + train_data = dataset_train ) dataset_test = GridDataset( hdf5_path = hdf5_paths, subset = test_ids, train = False, - dataset_train = dataset_train + train_data = dataset_train ) ``` diff --git a/tests/test_dataset.py b/tests/test_dataset.py index fbc6b02bd..81b60aaef 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -8,9 +8,9 @@ import h5py import numpy as np import pytest -from deeprank2.dataset import GraphDataset, GridDataset, save_hdf5_keys from torch_geometric.loader import DataLoader +from deeprank2.dataset import GraphDataset, GridDataset, save_hdf5_keys from deeprank2.domain import edgestorage as Efeat from deeprank2.domain import nodestorage as Nfeat from deeprank2.domain import targetstorage as targets @@ -220,7 +220,7 @@ def test_inherit_info_training_griddataset(self): dataset_test = GridDataset( hdf5_path = self.hdf5_path, train = False, - dataset_train = dataset_train + train_data = dataset_train ) # features, features_dict, target, target_transform, task, and classes @@ -231,7 +231,7 @@ def test_inherit_info_training_griddataset(self): dataset_test = GridDataset( hdf5_path = self.hdf5_path, train = False, - dataset_train = dataset_train, + train_data = dataset_train, features = [Efeat.DISTANCE, Efeat.COVALENT, Efeat.SAMECHAIN], target = targets.IRMSD, target_transform = True, @@ -467,7 +467,7 @@ def test_logic_train_graphdataset(self):# noqa: MC0001, pylint: disable=too-many hdf5_path = hdf5_path, target = 'binary', train = False, - dataset_train = dataset_train + train_data = dataset_train ) # mean and devs should be None assert dataset_train.means == dataset_test.means @@ -494,7 +494,7 @@ def test_logic_train_graphdataset(self):# noqa: MC0001, pylint: disable=too-many GraphDataset( hdf5_path = hdf5_path, train = False, - dataset_train = dataset_train, + train_data = dataset_train, target = 'binary', ) @@ -885,7 +885,7 @@ def test_features_transform_logic_graphdataset(self): dataset_test = GraphDataset( hdf5_path = hdf5_path, train = False, - dataset_train = dataset_train, + train_data = dataset_train, target = 'binary' ) @@ -900,7 +900,7 @@ def test_features_transform_logic_graphdataset(self): dataset_test = GraphDataset( hdf5_path = hdf5_path, train = False, - dataset_train = dataset_train, + train_data = dataset_train, features_transform = other_feature_transform, target = 'binary' ) @@ -943,7 +943,7 @@ def test_inherit_info_training_graphdataset(self): dataset_test = GraphDataset( hdf5_path = hdf5_path, train = False, - dataset_train = dataset_train, + train_data = dataset_train, ) # node_features, edge_features, features_dict, feature_transform, target, target_transform, task, and classes @@ -954,7 +954,7 @@ def test_inherit_info_training_graphdataset(self): dataset_test = GraphDataset( hdf5_path = hdf5_path, train = False, - dataset_train = dataset_train, + train_data = dataset_train, node_features = "all", edge_features = "all", features_transform = None, @@ -980,7 +980,7 @@ def test_incompatible_dataset_train_type(self): GridDataset( hdf5_path = "tests/data/hdf5/1ATN_ppi.hdf5", train = False, - dataset_train = dataset_train + train_data = dataset_train ) diff --git a/tests/test_integration.py b/tests/test_integration.py index d8a37a58c..2d09e0d73 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -82,13 +82,13 @@ def test_cnn(): # pylint: disable=too-many-locals dataset_val = GridDataset( hdf5_path = hdf5_paths, train = False, - dataset_train = dataset_train, + train_data = dataset_train, ) dataset_test = GridDataset( hdf5_path = hdf5_paths, train = False, - dataset_train = dataset_train, + train_data = dataset_train, ) output_exporters = [HDF5OutputExporter(output_directory)] @@ -165,14 +165,14 @@ def test_gnn(): # pylint: disable=too-many-locals dataset_val = GraphDataset( hdf5_path = hdf5_paths, train = False, - dataset_train = dataset_train, + train_data = dataset_train, clustering_method = "mcl" ) dataset_test = GraphDataset( hdf5_path = hdf5_paths, train = False, - dataset_train = dataset_train, + train_data = dataset_train, clustering_method = "mcl" ) diff --git a/tests/test_set_lossfunction.py b/tests/test_set_lossfunction.py index 91d099a14..1ad42a732 100644 --- a/tests/test_set_lossfunction.py +++ b/tests/test_set_lossfunction.py @@ -4,13 +4,13 @@ import warnings import pytest -from deeprank2.dataset import GraphDataset -from deeprank2.neuralnets.gnn.naive_gnn import NaiveNetwork -from deeprank2.trainer import Trainer from torch import nn +from deeprank2.dataset import GraphDataset from deeprank2.domain import losstypes as losses from deeprank2.domain import targetstorage as targets +from deeprank2.neuralnets.gnn.naive_gnn import NaiveNetwork +from deeprank2.trainer import Trainer hdf5_path = 'tests/data/hdf5/test.hdf5' diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 4ebbca2e5..5a3e7f7ca 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -57,7 +57,7 @@ def _model_base_test( # pylint: disable=too-many-arguments, too-many-locals dataset_val = GraphDataset( hdf5_path = val_hdf5_path, train = False, - dataset_train = dataset_train, + train_data = dataset_train, clustering_method = clustering_method, ) else: @@ -67,7 +67,7 @@ def _model_base_test( # pylint: disable=too-many-arguments, too-many-locals dataset_test = GraphDataset( hdf5_path = test_hdf5_path, train = False, - dataset_train = dataset_train, + train_data = dataset_train, clustering_method = clustering_method, ) else: @@ -392,12 +392,12 @@ def test_no_training_no_pretrained(self): dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = False, - dataset_train = dataset_train + train_data = dataset_train ) dataset_test = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = False, - dataset_train = dataset_train + train_data = dataset_train ) trainer = Trainer( neuralnet = GINet, @@ -431,7 +431,7 @@ def test_no_test_provided(self): dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = False, - dataset_train = dataset_train + train_data = dataset_train ) trainer = Trainer( neuralnet = GINet, @@ -551,12 +551,12 @@ def test_dataset_equivalence_no_pretrained(self): dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = True, - dataset_train = dataset_train + train_data = dataset_train ) dataset_test = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = True, - dataset_train = dataset_train + train_data = dataset_train ) with pytest.raises(ValueError): Trainer( @@ -582,12 +582,12 @@ def test_dataset_equivalence_no_pretrained(self): dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = False, - dataset_train = dataset_train + train_data = dataset_train ) dataset_test = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = False, - dataset_train = dataset_train + train_data = dataset_train ) with pytest.raises(ValueError): Trainer( @@ -672,7 +672,7 @@ def test_invalid_cuda_ngpus(self): dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = False, - dataset_train = dataset_train + train_data = dataset_train ) with pytest.raises(ValueError): @@ -691,7 +691,7 @@ def test_invalid_no_cuda_available(self): dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = False, - dataset_train = dataset_train + train_data = dataset_train ) with pytest.raises(ValueError): diff --git a/tutorials/training.ipynb b/tutorials/training.ipynb index f93108a62..ebb3003b2 100644 --- a/tutorials/training.ipynb +++ b/tutorials/training.ipynb @@ -263,14 +263,14 @@ " hdf5_path = input_data_path,\n", " subset = list(df_valid.entry), # selects only data points with ids in df_valid.entry\n", " train = False,\n", - " dataset_train = dataset_train\n", + " train_data = dataset_train\n", ")\n", "print('\\nLoading test data...')\n", "dataset_test = GraphDataset(\n", " hdf5_path = input_data_path,\n", " subset = list(df_test.entry), # selects only data points with ids in df_test.entry\n", " train = False,\n", - " dataset_train = dataset_train\n", + " train_data = dataset_train\n", ")" ] }, @@ -553,14 +553,14 @@ " hdf5_path = input_data_path,\n", " subset = list(df_valid.entry), # selects only data points with ids in df_valid.entry\n", " train = False,\n", - " dataset_train = dataset_train\n", + " train_data = dataset_train\n", ")\n", "print('\\nLoading test data...')\n", "dataset_test = GridDataset(\n", " hdf5_path = input_data_path,\n", " subset = list(df_test.entry), # selects only data points with ids in df_test.entry\n", " train = False,\n", - " dataset_train = dataset_train \n", + " train_data = dataset_train \n", ")" ] }, From fe18d3d390897d9f4f53e6c8082e747bd4a7e1ef Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Fri, 20 Oct 2023 16:09:32 +0200 Subject: [PATCH 06/59] improve logic for handling both a pre-trained model and a dataset_train when train is False in dataset.py --- deeprank2/dataset.py | 42 ++++++++++++++++++++++-------------------- 1 file changed, 22 insertions(+), 20 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index 5d7dc7021..48757ef67 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -486,13 +486,15 @@ def __init__( # pylint: disable=too-many-arguments self._check_features() if not train: - if not isinstance(train_data, GridDataset): - raise TypeError(f"""The train dataset provided is type: {type(train_data)} - Please provide a valid training GridDataset.""") - - #check inherited parameter with the ones in the training set - inherited_params = ["features", "target", "target_transform", "task", "classes"] - self._check_inherited_params(inherited_params, train_data) + if isinstance(train_data, str): + pass + elif isinstance(train_data, GridDataset): + #check inherited parameter with the ones in the training set + inherited_params = ["features", "target", "target_transform", "task", "classes"] + self._check_inherited_params(inherited_params, train_data) + else: + raise TypeError(f"""The train data provided is type: {type(train_data)} + Please provide a valid training GridDataset or the path to a valid DeepRank2 pre-trained model.""") elif train and train_data: _log.warning("""`train_data` has been set but train flag was set to True. @@ -725,13 +727,17 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local self._check_features() if not train: - if not isinstance(train_data, GraphDataset): - raise TypeError(f"""The train dataset provided is type: {type(train_data)} - Please provide a valid training GraphDataset.""") - - #check inherited parameter with the ones in the training set - inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] - self._check_inherited_params(inherited_params, train_data) + if isinstance(train_data, str): + pass + elif isinstance(train_data, GraphDataset): + #check inherited parameter with the ones in the training set + inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] + self._check_inherited_params(inherited_params, train_data) + train_means = train_data.means + train_devs = train_data.devs + else: + raise TypeError(f"""The train data provided is type: {type(train_data)} + Please provide a valid training GraphDataset or the path to a valid DeepRank2 pre-trained model.""") elif train and train_data: _log.warning("""`train_data` has been set but train flag was set to True. @@ -756,12 +762,8 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local self.hdf5_to_pandas() self._compute_mean_std() elif standardize and (not train): - if (train_data.means is None) or (train_data.devs is None): - if train_data.df is None: - train_data.hdf5_to_pandas() - train_data._compute_mean_std() - self.means = train_data.means - self.devs = train_data.devs + self.means = train_means + self.devs = train_devs def get(self, idx: int) -> Data: """Gets one graph item from its unique index. From f9b82dedfd47e68503d31db184ee21b97d955baf Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Sat, 21 Oct 2023 18:14:49 +0200 Subject: [PATCH 07/59] add logic for handling the pre-trained model as input in DeeprankDataset classes --- deeprank2/dataset.py | 35 ++++++++++++++++++++++++++++++----- deeprank2/trainer.py | 4 ++++ 2 files changed, 34 insertions(+), 5 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index 48757ef67..0cf5a818b 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -3,12 +3,14 @@ import inspect import logging import os +import pickle import re import sys import warnings from ast import literal_eval from typing import Dict, List, Optional, Tuple, Union +import dill import h5py import matplotlib.pyplot as plt import numpy as np @@ -487,7 +489,18 @@ def __init__( # pylint: disable=too-many-arguments if not train: if isinstance(train_data, str): - pass + try: + if torch.cuda.is_available(): + state = torch.load(train_data, pickle_module = dill) + else: + state = torch.load(train_data, pickle_module = dill, map_location=torch.device('cpu')) + if state["data_type"] is not GridDataset: + raise TypeError (f"""The pre-trained model has been trained with data of type {state["data_type"]}, but you are trying + to define a GridDataset-class validation/testing dataset. Please provide a valid DeepRank2 + model trained with GridDataset-class type data, or define the dataset using the appropriate class.""") + except pickle.UnpicklingError as e: + raise ValueError("""The path provided to `train_data` is not a valid DeepRank2 pre-trained model. + Please provide a valid path to a DeepRank2 pre-trained model.""") from e elif isinstance(train_data, GridDataset): #check inherited parameter with the ones in the training set inherited_params = ["features", "target", "target_transform", "task", "classes"] @@ -651,9 +664,10 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local Defaults to True. train_data (Optional[Union[str, class:`GraphDataset`]], optional)): data to inherit information from the training dataset or the pre-trained model. It is considered only if `train` is False. In the latter case, `train_data` needs to be a dataset of the same class or - the path of a DeepRank2 pre-trained model. If set, the parameters `node_features`, `edge_features`, `features_transform`, `target`, `target_transform`, - `task`, and `classes` will be inherited from `train_data`. If standardization was performed in the training dataset/step, also the attributes - `means` and `devs` will be inherited from `train_data`, and they will be used to scale the validation/testing set. + the path of a DeepRank2 pre-trained model. If set, the parameters `node_features`, `edge_features`, `features_transform`, + `target`, `target_transform`, `task`, and `classes` will be inherited from `train_data`. If standardization was performed in the + training dataset/step, also the attributes `means` and `devs` will be inherited from `train_data`, and they will be used to scale + the validation/testing set. Defaults to None. node_features (Optional[Union[List[str], str], optional): Consider all pre-computed node features ("all") or some defined node features (provide a list, example: ["res_type", "polarity", "bsa"]). @@ -728,7 +742,18 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local if not train: if isinstance(train_data, str): - pass + try: + if torch.cuda.is_available(): + state = torch.load(train_data, pickle_module = dill) + else: + state = torch.load(train_data, pickle_module = dill, map_location=torch.device('cpu')) + if state["data_type"] is not GraphDataset: + raise TypeError (f"""The pre-trained model has been trained with data of type {state["data_type"]}, but you are trying + to define a GraphDataset-class validation/testing dataset. Please provide a valid DeepRank2 + model trained with GraphDataset-class type data, or define the dataset using the appropriate class.""") + except pickle.UnpicklingError as e: + raise ValueError("""The path provided to `train_data` is not a valid DeepRank2 pre-trained model. + Please provide a valid path to a DeepRank2 pre-trained model.""") from e elif isinstance(train_data, GraphDataset): #check inherited parameter with the ones in the training set inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py index 299468f0f..536015029 100644 --- a/deeprank2/trainer.py +++ b/deeprank2/trainer.py @@ -65,6 +65,7 @@ def __init__( # pylint: disable=too-many-arguments # noqa: MC0001 over the epochs. If None, defaults to :class:`HDF5OutputExporter`, which saves all the results in an .HDF5 file stored in ./output directory. Defaults to None. """ + self.data_type = None self.batch_size_train = None self.batch_size_test = None self.shuffle = None @@ -534,6 +535,7 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc filename (str, optional): Name of the file where to save the selected model. If not None, the model is saved to `filename`. If None, the model is not saved. Defaults to 'model.pth.tar'. """ + self.data_type = type(self.dataset_train) self.batch_size_train = batch_size self.shuffle = shuffle @@ -869,6 +871,7 @@ def _load_params(self): else: state = torch.load(self.pretrained_model_path, pickle_module = dill, map_location=torch.device('cpu')) + self.data_type = state["data_type"] self.model_load_state_dict = state["model_state"] self.optimizer = state["optimizer"] self.opt_loaded_state_dict = state["optimizer_state"] @@ -905,6 +908,7 @@ def _save_model(self): filename (str, optional): Name of the file. Defaults to None. """ state = { + "data_type": self.data_type, "model_state": self.model.state_dict(), "optimizer": self.optimizer, "optimizer_state": self.optimizer.state_dict(), From c21fe2bf669b44deb273d7624bb0aca62ab49c3a Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Sat, 21 Oct 2023 18:16:04 +0200 Subject: [PATCH 08/59] add tests for catching uncorrect pre-trained models --- tests/test_dataset.py | 40 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 81b60aaef..f6b2117e7 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -925,7 +925,7 @@ def test_invalid_value_features_transform(self): warnings.filterwarnings('ignore', r'divide by zero encountered in divide') _compute_features_with_get(hdf5_path, transf_dataset) - def test_inherit_info_training_graphdataset(self): + def test_inherit_info_train_graphdataset(self): hdf5_path = "tests/data/hdf5/train.hdf5" feature_transform = {'all': {'transform': None, 'standardize': True}} @@ -983,6 +983,44 @@ def test_incompatible_dataset_train_type(self): train_data = dataset_train ) + def test_invalid_pretrained_model_path(self): + + hdf5_graph = "tests/data/hdf5/train.hdf5" + with self.assertRaises(ValueError): + GraphDataset( + hdf5_path = hdf5_graph, + train = False, + train_data = hdf5_graph + ) + + hdf5_grid = "tests/data/hdf5/grid_data.hdf5" + with self.assertRaises(ValueError): + GridDataset( + hdf5_path = hdf5_grid, + train = False, + train_data = hdf5_grid + ) + + def test_invalid_pretrained_model_data_type(self): + + hdf5_graph = "tests/data/hdf5/test.hdf5" + pretrained_grid_model = "tests/data/testing_grid_model.pth.tar" + with self.assertRaises(TypeError): + GraphDataset( + hdf5_path = hdf5_graph, + train = False, + train_data = pretrained_grid_model + ) + + hdf5_grid = "tests/data/hdf5/grid_data.hdf5" + pretrained_graph_model = "tests/data/testing_graph_model.pth.tar" + with self.assertRaises(TypeError): + GridDataset( + hdf5_path = hdf5_grid, + train = False, + train_data = pretrained_graph_model + ) + if __name__ == "__main__": unittest.main() From fc5f6afb1dc100018fa5a570fddee89accce5034 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Sat, 21 Oct 2023 18:40:14 +0200 Subject: [PATCH 09/59] add folder for pretrained models in tests --- .../pretrained/testing_graph_model.pth.tar | Bin 0 -> 208117 bytes .../data/pretrained/testing_grid_model.pth.tar | Bin 0 -> 416771 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/data/pretrained/testing_graph_model.pth.tar create mode 100644 tests/data/pretrained/testing_grid_model.pth.tar diff --git a/tests/data/pretrained/testing_graph_model.pth.tar b/tests/data/pretrained/testing_graph_model.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..d0b2aaa74ec83d821c17dccc0fb97a498d4ff0bd GIT binary patch literal 208117 zcmeEv34B|{wZ0_FwxT$W<2WG+A&6au#97z_1VVrSCIJ*E1`=f1k}ciXlJC)VLWtA? 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to the tests --- tests/test_dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 6267a568b..7b269f3f5 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -256,7 +256,7 @@ def test_inherit_info_pretrained_model_griddataset(self): # features, target, target_transform, task, and classes # in the test should be inherited from the pre-trained model - inherited_params = ["features", "target", "target_transform", "task", "classes"] + inherited_params = ["features", "target", "target_transform", "task", "classes", "classes_to_index"] data = torch.load(pretrained_model, pickle_module = dill, map_location=torch.device('cpu')) dataset_test_vars = vars(dataset_test) @@ -1017,7 +1017,7 @@ def test_inherit_info_pretrained_model_graphdataset(self): # node_features, edge_features, feature_transform, target, target_transform, task, and classes # in the test should be inherited from the pre-trained model - inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] + inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes", "classes_to_index"] data = torch.load(pretrained_model, pickle_module = dill, map_location=torch.device('cpu')) dataset_test_vars = vars(dataset_test) From 8e2496c2fe3ba118f11e1fa81e82cddfbef1361c Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 15:21:57 +0200 Subject: [PATCH 16/59] save features_transform's lambdas as strings and load them as functions (much more reliable) --- deeprank2/dataset.py | 10 ++++++++-- deeprank2/trainer.py | 19 ++++++++++++++++++- 2 files changed, 26 insertions(+), 3 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index f076c87c1..82f392712 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -509,7 +509,7 @@ def __init__( # pylint: disable=too-many-arguments raise TypeError(f"""The train data provided is type: {type(train_data)} Please provide a valid training GridDataset or the path to a valid DeepRank2 pre-trained model.""") - #check inherited parameter with the ones in the training set + #match parameters with the ones in the training set inherited_params = ["features", "target", "target_transform", "task", "classes", "classes_to_index"] self._check_inherited_params(inherited_params, data) @@ -758,6 +758,12 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local model trained with GraphDataset-class type data, or define the dataset using the appropriate class.""") train_means = data["means"] train_devs = data["devs"] + # convert strings in 'transform' key to lambda functions + if data["features_transform"]: + for _, key in data["features_transform"].items(): + if key['transform'] is None: + continue + key['transform'] = eval(key['transform']) except pickle.UnpicklingError as e: raise ValueError("""The path provided to `train_data` is not a valid DeepRank2 pre-trained model. Please provide a valid path to a DeepRank2 pre-trained model.""") from e @@ -769,7 +775,7 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local raise TypeError(f"""The train data provided is type: {type(train_data)} Please provide a valid training GraphDataset or the path to a valid DeepRank2 pre-trained model.""") - #check inherited parameter with the ones in the training set + #match parameters with the ones in the training set inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes", "classes_to_index"] self._check_inherited_params(inherited_params, data) diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py index ed7fdfb6a..d305962c6 100644 --- a/deeprank2/trainer.py +++ b/deeprank2/trainer.py @@ -1,5 +1,7 @@ import copy +import inspect import logging +import re from time import time from typing import List, Optional, Tuple, Union @@ -164,8 +166,10 @@ def __init__( # pylint: disable=too-many-arguments # noqa: MC0001 else: if self.dataset_train is not None: + self.dataset_train = None _log.warning("Pretrained model loaded: dataset_train will be ignored.") if self.dataset_val is not None: + self.dataset_val = None _log.warning("Pretrained model loaded: dataset_val will be ignored.") if self.neuralnet is None: raise ValueError("No neural network class found. Please add it to complete loading the pretrained model.") @@ -535,6 +539,9 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc filename (str, optional): Name of the file where to save the selected model. If not None, the model is saved to `filename`. If None, the model is not saved. Defaults to 'model.pth.tar'. """ + if self.dataset_train is None: + raise ValueError("No training dataset provided.") + self.data_type = type(self.dataset_train) self.batch_size_train = batch_size self.shuffle = shuffle @@ -908,6 +915,16 @@ def _save_model(self): Args: filename (str, optional): Name of the file. Defaults to None. """ + features_transform_to_save = copy.deepcopy(self.features_transform) + # prepare transform dictionary for being saved + if features_transform_to_save: + for _, key in features_transform_to_save.items(): + if key['transform'] is None: + continue + str_expr = inspect.getsource(key['transform']) + match = re.search(r'\'transform\':.*(lambda.*).*,.*\'standardize\'.*', str_expr).group(1) + key['transform'] = match + state = { "data_type": self.data_type, "model_state": self.model.state_dict(), @@ -933,7 +950,7 @@ def _save_model(self): "node_features": self.node_features, "edge_features": self.edge_features, "features": self.features, - "features_transform": self.features_transform, + "features_transform": features_transform_to_save, "means": self.means, "devs": self.devs, "cuda": self.cuda, From 11f826e89e9d8b0938fb8d285570b90faa9e3eee Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 15:22:17 +0200 Subject: [PATCH 17/59] update pre-trained models --- 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test_invalid_no_cuda_available was skipped') _log.info('CUDA is available; test_invalid_no_cuda_available was skipped') + def test_train_method_no_train(self): + + # Graphs data + test_data_graph = "tests/data/hdf5/test.hdf5" + pretrained_model_graph = "tests/data/pretrained/testing_graph_model.pth.tar" + + dataset_test = GraphDataset( + hdf5_path = test_data_graph, + train = False, + train_data = pretrained_model_graph + ) + trainer = Trainer( + neuralnet = NaiveNetwork, + dataset_test = dataset_test, + pretrained_model = pretrained_model_graph + ) + + with pytest.raises(ValueError): + trainer.train() + + # Grids data + test_data_grid = "tests/data/hdf5/1ATN_ppi.hdf5" + pretrained_model_grid = "tests/data/pretrained/testing_grid_model.pth.tar" + + dataset_test = GridDataset( + hdf5_path = test_data_grid, + train = False, + train_data = pretrained_model_grid + ) + trainer = Trainer( + neuralnet = CnnClassification, + dataset_test = dataset_test, + pretrained_model = pretrained_model_grid + ) + + with pytest.raises(ValueError): + trainer.train() + + def test_test_method_pretrained_model_on_dataset_with_target(self): + + # Graphs data + test_data_graph = "tests/data/hdf5/test.hdf5" + pretrained_model_graph = "tests/data/pretrained/testing_graph_model.pth.tar" + + dataset_test = GraphDataset( + hdf5_path = test_data_graph, + train = False, + train_data = pretrained_model_graph + ) + + trainer = Trainer( + neuralnet = NaiveNetwork, + dataset_test = dataset_test, + pretrained_model = pretrained_model_graph, + output_exporters = [HDF5OutputExporter("./")] + ) + + trainer.test() + + output = pd.read_hdf("output_exporter.hdf5", key="testing") + assert len(output) == len(dataset_test) + + # Grids data + test_data_grid = "tests/data/hdf5/1ATN_ppi.hdf5" + pretrained_model_grid = "tests/data/pretrained/testing_grid_model.pth.tar" + + dataset_test = GridDataset( + hdf5_path = test_data_grid, + train = False, + train_data = pretrained_model_grid + ) + + trainer = Trainer( + neuralnet = CnnClassification, + dataset_test = dataset_test, + pretrained_model = pretrained_model_grid, + output_exporters = [HDF5OutputExporter("./")] + ) + + trainer.test() + + output = pd.read_hdf("output_exporter.hdf5", key="testing") + assert len(output) == len(dataset_test) if __name__ == "__main__": From f8e6c5708204753182896bc4be4f4d189b0ba4e3 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 15:25:13 +0200 Subject: [PATCH 19/59] fix test_dataset.py for the newly defined features_transform in the state dict --- tests/test_dataset.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 7b269f3f5..25dabe2b1 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -1019,6 +1019,11 @@ def test_inherit_info_pretrained_model_graphdataset(self): # in the test should be inherited from the pre-trained model inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes", "classes_to_index"] data = torch.load(pretrained_model, pickle_module = dill, map_location=torch.device('cpu')) + if data["features_transform"]: + for _, key in data["features_transform"].items(): + if key['transform'] is None: + continue + key['transform'] = eval(key['transform']) dataset_test_vars = vars(dataset_test) for param in inherited_params: From b8e2348491506c51a28b0af36318f9d26d558ff8 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 15:38:01 +0200 Subject: [PATCH 20/59] remove dill usage since we're not saving lambda functions anymore (dill not needed) --- deeprank2/dataset.py | 9 ++++----- deeprank2/trainer.py | 7 +++---- tests/test_dataset.py | 5 ++--- 3 files changed, 9 insertions(+), 12 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index 82f392712..0a4c3a78a 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -10,7 +10,6 @@ from ast import literal_eval from typing import Dict, List, Optional, Tuple, Union -import dill import h5py import matplotlib.pyplot as plt import numpy as np @@ -493,9 +492,9 @@ def __init__( # pylint: disable=too-many-arguments if isinstance(train_data, str): try: if torch.cuda.is_available(): - data = torch.load(train_data, pickle_module = dill) + data = torch.load(train_data) else: - data = torch.load(train_data, pickle_module = dill, map_location=torch.device('cpu')) + data = torch.load(train_data, map_location=torch.device('cpu')) if data["data_type"] is not GridDataset: raise TypeError (f"""The pre-trained model has been trained with data of type {data["data_type"]}, but you are trying to define a GridDataset-class validation/testing dataset. Please provide a valid DeepRank2 @@ -749,9 +748,9 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local if isinstance(train_data, str): try: if torch.cuda.is_available(): - data = torch.load(train_data, pickle_module = dill) + data = torch.load(train_data) else: - data = torch.load(train_data, pickle_module = dill, map_location=torch.device('cpu')) + data = torch.load(train_data, map_location=torch.device('cpu')) if data["data_type"] is not GraphDataset: raise TypeError (f"""The pre-trained model has been trained with data of type {data["data_type"]}, but you are trying to define a GraphDataset-class validation/testing dataset. Please provide a valid DeepRank2 diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py index d305962c6..d1bd2b58b 100644 --- a/deeprank2/trainer.py +++ b/deeprank2/trainer.py @@ -5,7 +5,6 @@ from time import time from typing import List, Optional, Tuple, Union -import dill import h5py import numpy as np import torch @@ -655,7 +654,7 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc # Now that the training loop is over, save the model if filename: - torch.save(checkpoint_model, filename, pickle_module = dill) + torch.save(checkpoint_model, filename) self.opt_loaded_state_dict = checkpoint_model["optimizer_state"] self.model_load_state_dict = checkpoint_model["model_state"] self.optimizer.load_state_dict(self.opt_loaded_state_dict) @@ -874,9 +873,9 @@ def _load_params(self): """ if torch.cuda.is_available(): - state = torch.load(self.pretrained_model_path, pickle_module = dill) + state = torch.load(self.pretrained_model_path) else: - state = torch.load(self.pretrained_model_path, pickle_module = dill, map_location=torch.device('cpu')) + state = torch.load(self.pretrained_model_path, map_location=torch.device('cpu')) self.data_type = state["data_type"] self.model_load_state_dict = state["model_state"] diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 25dabe2b1..f957242f9 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -5,7 +5,6 @@ from tempfile import mkdtemp from typing import List, Union -import dill import h5py import numpy as np import pytest @@ -257,7 +256,7 @@ def test_inherit_info_pretrained_model_griddataset(self): # features, target, target_transform, task, and classes # in the test should be inherited from the pre-trained model inherited_params = ["features", "target", "target_transform", "task", "classes", "classes_to_index"] - data = torch.load(pretrained_model, pickle_module = dill, map_location=torch.device('cpu')) + data = torch.load(pretrained_model, map_location=torch.device('cpu')) dataset_test_vars = vars(dataset_test) for param in inherited_params: @@ -1018,7 +1017,7 @@ def test_inherit_info_pretrained_model_graphdataset(self): # node_features, edge_features, feature_transform, target, target_transform, task, and classes # in the test should be inherited from the pre-trained model inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes", "classes_to_index"] - data = torch.load(pretrained_model, pickle_module = dill, map_location=torch.device('cpu')) + data = torch.load(pretrained_model, map_location=torch.device('cpu')) if data["features_transform"]: for _, key in data["features_transform"].items(): if key['transform'] is None: From 060e6bf572fbd4931c7f96825ceb9da65e39ba7b Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 16:21:06 +0200 Subject: [PATCH 21/59] improve initialization order in the Trainer class --- deeprank2/trainer.py | 38 +++++++++++++++----------------------- 1 file changed, 15 insertions(+), 23 deletions(-) diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py index d1bd2b58b..2afab715e 100644 --- a/deeprank2/trainer.py +++ b/deeprank2/trainer.py @@ -66,14 +66,8 @@ def __init__( # pylint: disable=too-many-arguments # noqa: MC0001 over the epochs. If None, defaults to :class:`HDF5OutputExporter`, which saves all the results in an .HDF5 file stored in ./output directory. Defaults to None. """ - self.data_type = None - self.batch_size_train = None - self.batch_size_test = None - self.shuffle = None - - self._init_output_exporters(output_exporters) - self.neuralnet = neuralnet + self.pretrained_model_path = pretrained_model self._init_datasets(dataset_train, dataset_val, dataset_test, val_size, test_size) @@ -122,7 +116,13 @@ def __init__( # pylint: disable=too-many-arguments # noqa: MC0001 _log.info(f"CUDA device name is {torch.cuda.get_device_name(0)}.") _log.info(f"Number of GPUs set to {self.ngpu}.") - self.pretrained_model_path = pretrained_model + self._init_output_exporters(output_exporters) + + # other attributes not set in init + self.data_type = None + self.batch_size_train = None + self.batch_size_test = None + self.shuffle = None self.model_load_state_dict = None if self.pretrained_model_path is None: @@ -131,8 +131,7 @@ def __init__( # pylint: disable=too-many-arguments # noqa: MC0001 if self.neuralnet is None: raise ValueError("No neural network specified. Specifying a model framework is required if there is no pretrained model.") - self.classes = self.dataset_train.classes - self.classes_to_index = self.dataset_train.classes_to_index + self._init_from_dataset(self.dataset_train) self.optimizer = None self.class_weights = class_weights self.subset = self.dataset_train.subset @@ -164,19 +163,17 @@ def __init__( # pylint: disable=too-many-arguments # noqa: MC0001 "Please set clustering_method to 'mcl', 'louvain' or None. Default to 'mcl' \n\t") else: + if self.neuralnet is None: + raise ValueError("No neural network class found. Please add it to complete loading the pretrained model.") + if self.dataset_test is None: + raise ValueError("No dataset_test found. Please add it to evaluate the pretrained model.") if self.dataset_train is not None: self.dataset_train = None _log.warning("Pretrained model loaded: dataset_train will be ignored.") if self.dataset_val is not None: self.dataset_val = None _log.warning("Pretrained model loaded: dataset_val will be ignored.") - if self.neuralnet is None: - raise ValueError("No neural network class found. Please add it to complete loading the pretrained model.") - if self.dataset_test is None: - raise ValueError("No dataset_test found. Please add it to evaluate the pretrained model.") - - self.classes_to_index = self.dataset_test.classes_to_index - + self._init_from_dataset(self.dataset_test) self._load_params() self._load_pretrained_model() @@ -215,12 +212,6 @@ def _init_datasets(self, # pylint: disable=too-many-arguments else: _log.warning("Validation dataset was provided to Trainer; val_size parameter is ignored.") - # Copy settings from the dataset that we will use. - if self.dataset_train is not None: - self._init_from_dataset(self.dataset_train) - else: - self._init_from_dataset(self.dataset_test) - def _init_from_dataset(self, dataset: Union[GraphDataset, GridDataset]): if isinstance(dataset, GraphDataset): @@ -247,6 +238,7 @@ def _init_from_dataset(self, dataset: Union[GraphDataset, GridDataset]): self.target_transform = dataset.target_transform self.task = dataset.task self.classes = dataset.classes + self.classes_to_index = dataset.classes_to_index def _load_model(self): """Loads the neural network model.""" From 1d3c0f5ac3ff6ad05aff65b2de187a1970f5434a Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Tue, 24 Oct 2023 11:08:18 +0200 Subject: [PATCH 22/59] fix datasets for cases in which there is a target attribute but no target values are present in the hdf5 file/s --- deeprank2/dataset.py | 34 +++++++++++++++++++++++++--------- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index 0a4c3a78a..5e2cc4fc7 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -614,22 +614,36 @@ def load_one_grid(self, hdf5_path: str, entry_name: str) -> Data: """ feature_data = [] - target_value = None with h5py.File(hdf5_path, 'r') as hdf5_file: - entry_group = hdf5_file[entry_name] + grp = hdf5_file[entry_name] - mapped_features_group = entry_group[gridstorage.MAPPED_FEATURES] + mapped_features_group = grp[gridstorage.MAPPED_FEATURES] for feature_name in self.features: if feature_name[0] != '_': # ignore metafeatures feature_data.append(mapped_features_group[feature_name][:]) + x=torch.tensor(np.expand_dims(np.array(feature_data), axis=0), dtype=torch.float) - target_value = entry_group[targets.VALUES][self.target][()] + # target + if self.target is None: + y = None + else: + if targets.VALUES in grp and self.target in grp[targets.VALUES]: + y = torch.tensor([grp[targets.VALUES][self.target][()]], dtype=torch.float) - # Wrap up the data in this object, for the collate_fn to handle it properly: - data = Data(x=torch.tensor(np.expand_dims(np.array(feature_data), axis=0), dtype=torch.float), - y=torch.tensor([target_value], dtype=torch.float)) + if self.task == targets.REGRESS and self.target_transform is True: + y = torch.sigmoid(torch.log(y)) + elif self.task is not targets.REGRESS and self.target_transform is True: + raise ValueError(f"Task is set to {self.task}. Please set it to regress to transform the target with a sigmoid.") + else: + y = None + possible_targets = grp[targets.VALUES].keys() + if self.train: + raise ValueError(f"Target {self.target} missing in entry {entry_name} in file {hdf5_path}, possible targets are {possible_targets}." + + "\n Use the query class to add more target values to input data.") + # Wrap up the data in this object, for the collate_fn to handle it properly: + data = Data(x=x, y=y) data.entry_names = entry_name return data @@ -948,9 +962,11 @@ def load_one_graph(self, fname: str, entry_name: str) -> Data: # pylint: disabl raise ValueError(f"Task is set to {self.task}. Please set it to regress to transform the target with a sigmoid.") else: + y = None possible_targets = grp[targets.VALUES].keys() - raise ValueError(f"Target {self.target} missing in entry {entry_name} in file {fname}, possible targets are {possible_targets}." + - "\n Use the query class to add more target values to input data.") + if self.train: + raise ValueError(f"Target {self.target} missing in entry {entry_name} in file {fname}, possible targets are {possible_targets}." + + "\n Use the query class to add more target values to input data.") # positions pos = torch.tensor(grp[f"{Nfeat.NODE}/{Nfeat.POSITION}/"][()], dtype=torch.float).contiguous() From 4d588e12d8049b7275dfb9c8d935928e1994b555 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Tue, 24 Oct 2023 11:09:29 +0200 Subject: [PATCH 23/59] fix Trainer _eval method for cases in which there is a target attribute but no target values are present in the hdf5 file/s --- deeprank2/trainer.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py index 2afab715e..0a9a92892 100644 --- a/deeprank2/trainer.py +++ b/deeprank2/trainer.py @@ -748,6 +748,9 @@ def _eval( # pylint: disable=too-many-locals loss_ = loss_func(pred, y) count_predictions += pred.shape[0] sum_of_losses += loss_.detach().item() * pred.shape[0] + else: + target_vals = ['None'] * pred.shape[0] + eval_loss = 'None' # Get the outputs for export # Remember that non-linear activation is automatically applied in CrossEntropyLoss @@ -764,7 +767,7 @@ def _eval( # pylint: disable=too-many-locals if count_predictions > 0: eval_loss = sum_of_losses / count_predictions else: - eval_loss = 0.0 + eval_loss = 'None' self._output_exporters.process( pass_name, epoch_number, entry_names, outputs, target_vals, eval_loss) From 147a16a526038402f50b6f81e7eb1504a2feee43 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Tue, 24 Oct 2023 15:38:38 +0200 Subject: [PATCH 24/59] add logic for checking the target settings in the init, and fix _filter_targets --- deeprank2/dataset.py | 43 +++++++++++++++++++++++++++++++++++-------- 1 file changed, 35 insertions(+), 8 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index 5e2cc4fc7..6a806bd1f 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -225,10 +225,11 @@ def _filter_targets(self, entry_group: h5py.Group) -> bool: if isinstance(target_condition, str): operation = target_condition + target_value = entry_group[targets.VALUES][target_name][()] for operator_string in [">", "<", "==", "<=", ">=", "!="]: - operation = operation.replace(operator_string, "target_value" + operator_string) + operation = operation.replace(operator_string, f"{target_value}" + operator_string) - if not literal_eval(operation): + if not eval(operation): return False elif target_condition is not None: @@ -512,9 +513,22 @@ def __init__( # pylint: disable=too-many-arguments inherited_params = ["features", "target", "target_transform", "task", "classes", "classes_to_index"] self._check_inherited_params(inherited_params, data) - elif train and train_data: - _log.warning("""`train_data` has been set but train flag was set to True. - `train_data` will be ignored since the current dataset will be considered as training set.""") + else: + if train_data: + _log.warning("""`train_data` has been set but train flag was set to True. + `train_data` will be ignored since the current dataset will be considered as training set.""") + + try: + fname, mol = self.index_entries[0] + except IndexError: + raise IndexError("No entries found in the dataset. Please check the dataset parameters.") + with h5py.File(fname, 'r') as f5: + grp = f5[mol] + possible_targets = grp[targets.VALUES].keys() + if self.target is None: + raise ValueError(f"Please set the target during training dataset definition; targets present in the file/s are {possible_targets}.") + if self.target not in possible_targets: + raise ValueError(f"Target {self.target} not present in the file/s; targets present in the file/s are {possible_targets}.") self.features_dict = {} self.features_dict[gridstorage.MAPPED_FEATURES] = self.features @@ -792,9 +806,22 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes", "classes_to_index"] self._check_inherited_params(inherited_params, data) - elif train and train_data: - _log.warning("""`train_data` has been set but train flag was set to True. - `train_data` will be ignored since the current dataset will be considered as training set.""") + else: + if train_data: + _log.warning("""`train_data` has been set but train flag was set to True. + `train_data` will be ignored since the current dataset will be considered as training set.""") + + try: + fname, mol = self.index_entries[0] + except IndexError: + raise IndexError("No entries found in the dataset. Please check the dataset parameters.") + with h5py.File(fname, 'r') as f5: + grp = f5[mol] + possible_targets = grp[targets.VALUES].keys() + if self.target is None: + raise ValueError(f"Please set the target during training dataset definition; targets present in the file/s are {possible_targets}.") + if self.target not in possible_targets: + raise ValueError(f"Target {self.target} not present in the file/s; targets present in the file/s are {possible_targets}.") self.features_dict = {} self.features_dict[Nfeat.NODE] = self.node_features From 08c90e0084e3ffdf91f7f56036bf186f8194a4e9 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Tue, 24 Oct 2023 15:40:02 +0200 Subject: [PATCH 25/59] add tests for cases with no target and improve target's filter tests --- tests/test_dataset.py | 107 ++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 103 insertions(+), 4 deletions(-) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index f957242f9..2c0f1d51c 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -279,15 +279,76 @@ def test_inherit_info_pretrained_model_griddataset(self): for param in inherited_params: assert dataset_test_vars[param] == data[param] + def test_no_target_dataset_griddataset(self): + hdf5_no_target = "tests/data/hdf5/test_no_target.hdf5" + hdf5_target = "tests/data/hdf5/1ATN_ppi.hdf5" + pretrained_model = "tests/data/pretrained/testing_grid_model.pth.tar" + + dataset = GridDataset( + hdf5_path = hdf5_no_target, + train = False, + train_data = pretrained_model + ) + + assert dataset.target is not None + assert dataset.get(0).y is None + + # no target set, training mode + with self.assertRaises(ValueError): + dataset = GridDataset( + hdf5_path = hdf5_no_target, + train = True + ) + + # target set, but not present in the file + with self.assertRaises(ValueError): + dataset = GridDataset( + hdf5_path = hdf5_target, + train = True, + target = 'CAPRI' + ) + + def test_filter_griddataset(self): + + # filtering out all values + with self.assertRaises(IndexError): + GridDataset( + hdf5_path=self.hdf5_path, + subset=None, + target=targets.IRMSD, + target_filter={targets.IRMSD: "<10"} + ) + # filter our some values + dataset = GridDataset( + hdf5_path=self.hdf5_path, + subset=None, + target=targets.IRMSD, + target_filter={targets.IRMSD: ">15"} + ) + assert len(dataset) == 3 + def test_filter_graphdataset(self): - GraphDataset( + + # filtering out all values + with self.assertRaises(IndexError): + GraphDataset( + hdf5_path=self.hdf5_path, + subset=None, + node_features=node_feats, + edge_features=[Efeat.DISTANCE], + target=targets.IRMSD, + target_filter={targets.IRMSD: "<10"} + ) + # filter our some values + dataset = GraphDataset( hdf5_path=self.hdf5_path, subset=None, node_features=node_feats, edge_features=[Efeat.DISTANCE], target=targets.IRMSD, - target_filter={targets.IRMSD: "<10"} + target_filter={targets.IRMSD: ">15"} ) + assert len(dataset) == 3 def test_multi_file_graphdataset(self): dataset = GraphDataset( @@ -338,12 +399,21 @@ def test_subset_graphdataset(self): n = 10 subset = hdf5_keys[:n] - dataset = GraphDataset( + dataset_train = GraphDataset( hdf5_path = "tests/data/hdf5/train.hdf5", subset = subset, + target = targets.BINARY ) - assert n == len(dataset) + dataset_test = GraphDataset( + hdf5_path = "tests/data/hdf5/train.hdf5", + subset = subset, + train = False, + train_data = dataset_train + ) + + assert n == len(dataset_train) + assert n == len(dataset_test) hdf5.close() @@ -1057,6 +1127,35 @@ def test_inherit_info_pretrained_model_graphdataset(self): else: assert dataset_test_vars[param] == data[param] + def test_no_target_dataset_graphdataset(self): + hdf5_no_target = "tests/data/hdf5/test_no_target.hdf5" + hdf5_target = "tests/data/hdf5/test.hdf5" + pretrained_model = "tests/data/pretrained/testing_graph_model.pth.tar" + + dataset = GraphDataset( + hdf5_path = hdf5_no_target, + train = False, + train_data = pretrained_model + ) + + assert dataset.target is not None + assert dataset.get(0).y is None + + # no target set, training mode + with self.assertRaises(ValueError): + dataset = GraphDataset( + hdf5_path = hdf5_no_target, + train = True + ) + + # target set, but not present in the file + with self.assertRaises(ValueError): + dataset = GraphDataset( + hdf5_path = hdf5_target, + train = True, + target = 'CAPRI' + ) + def test_incompatible_dataset_train_type(self): dataset_train = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", From 3b5d7462b7dbc654ed381d51601be64433bd5049 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Tue, 24 Oct 2023 15:40:42 +0200 Subject: [PATCH 26/59] fix tests according to the new target's checks --- tests/test_query.py | 16 +++++++++------- tests/test_trainer.py | 14 +++++++++----- 2 files changed, 18 insertions(+), 12 deletions(-) diff --git a/tests/test_query.py b/tests/test_query.py index 8c99d91ee..e7d9e30c1 100644 --- a/tests/test_query.py +++ b/tests/test_query.py @@ -5,21 +5,21 @@ import h5py import numpy as np import pytest + from deeprank2.dataset import GraphDataset, GridDataset +from deeprank2.domain import edgestorage as Efeat +from deeprank2.domain import nodestorage as Nfeat +from deeprank2.domain import targetstorage as targets from deeprank2.domain.aminoacidlist import (alanine, arginine, asparagine, cysteine, glutamate, glycine, leucine, lysine, phenylalanine) +from deeprank2.features import components, conservation, contact, surfacearea from deeprank2.query import (ProteinProteinInterfaceAtomicQuery, ProteinProteinInterfaceResidueQuery, QueryCollection, SingleResidueVariantAtomicQuery, SingleResidueVariantResidueQuery) from deeprank2.utils.grid import GridSettings, MapMethod -from deeprank2.domain import edgestorage as Efeat -from deeprank2.domain import nodestorage as Nfeat -from deeprank2.domain import targetstorage as targets -from deeprank2.features import components, conservation, contact, surfacearea - def _check_graph_makes_sense(g, node_feature_names, edge_feature_names): @@ -39,6 +39,7 @@ def _check_graph_makes_sense(g, node_feature_names, edge_feature_names): os.close(f) try: + g.targets[targets.BINARY] = 0 g.write_to_hdf5(tmp_path) with h5py.File(tmp_path, "r") as f5: @@ -68,8 +69,9 @@ def _check_graph_makes_sense(g, node_feature_names, edge_feature_names): count_edges_hdf5 = entry_group[f"{Efeat.EDGE}/{Efeat.INDEX}"].shape[0] - dataset = GraphDataset(hdf5_path=tmp_path) + dataset = GraphDataset(hdf5_path=tmp_path, target=targets.BINARY) torch_data_entry = dataset[0] + assert torch_data_entry is not None # expecting twice as many edges, because torch is directional @@ -358,7 +360,7 @@ def test_augmentation(): assert len(entry_names) == expected_entry_count, f"Found {len(entry_names)} entries, expected {expected_entry_count}" - dataset = GridDataset(hdf5_path) + dataset = GridDataset(hdf5_path, target = 'binary') assert len(dataset) == expected_entry_count, f"Found {len(dataset)} data points, expected {expected_entry_count}" finally: diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 629d409cc..b561900a7 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -552,12 +552,14 @@ def test_dataset_equivalence_no_pretrained(self): dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = True, - train_data = dataset_train + train_data = dataset_train, + target = targets.BINARY ) dataset_test = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", train = True, - train_data = dataset_train + train_data = dataset_train, + target = targets.BINARY ) with pytest.raises(ValueError): Trainer( @@ -638,7 +640,7 @@ def test_trainsize(self): for t in test_cases: dataset_train, dataset_val =_divide_dataset( - dataset = GraphDataset(hdf5_path = hdf5), + dataset = GraphDataset(hdf5_path = hdf5, target = targets.BINARY), splitsize = t, ) assert len(dataset_train) == n_train @@ -668,7 +670,8 @@ def test_invalid_trainsize(self): def test_invalid_cuda_ngpus(self): dataset_train = GraphDataset( - hdf5_path = "tests/data/hdf5/test.hdf5" + hdf5_path = "tests/data/hdf5/test.hdf5", + target = targets.BINARY ) dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", @@ -687,7 +690,8 @@ def test_invalid_cuda_ngpus(self): def test_invalid_no_cuda_available(self): if not torch.cuda.is_available(): dataset_train = GraphDataset( - hdf5_path = "tests/data/hdf5/test.hdf5" + hdf5_path = "tests/data/hdf5/test.hdf5", + target = targets.BINARY ) dataset_val = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", From a5fd52454730e498503b76d08f664ef37aaf6ec9 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Tue, 24 Oct 2023 15:41:39 +0200 Subject: [PATCH 27/59] add hdf5 file with no target --- tests/data/hdf5/test_no_target.hdf5 | Bin 0 -> 4183298 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/data/hdf5/test_no_target.hdf5 diff --git a/tests/data/hdf5/test_no_target.hdf5 b/tests/data/hdf5/test_no_target.hdf5 new file mode 100644 index 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Z{&Ox~&mjIx_U{v831f-=>;Jj-{{VxCk@o-q literal 0 HcmV?d00001 From 0a068b591e6663cea42b0117118d9bcbdd996886 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Sat, 21 Oct 2023 18:43:50 +0200 Subject: [PATCH 10/59] update data paths in test_dataset.py --- tests/test_dataset.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index f6b2117e7..e05bc56fb 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -985,7 +985,7 @@ def test_incompatible_dataset_train_type(self): def test_invalid_pretrained_model_path(self): - hdf5_graph = "tests/data/hdf5/train.hdf5" + hdf5_graph = "tests/data/hdf5/test.hdf5" with self.assertRaises(ValueError): GraphDataset( hdf5_path = hdf5_graph, @@ -993,7 +993,7 @@ def test_invalid_pretrained_model_path(self): train_data = hdf5_graph ) - hdf5_grid = "tests/data/hdf5/grid_data.hdf5" + hdf5_grid = "tests/data/hdf5/1ATN_ppi.hdf5" with self.assertRaises(ValueError): GridDataset( hdf5_path = hdf5_grid, @@ -1004,7 +1004,7 @@ def test_invalid_pretrained_model_path(self): def test_invalid_pretrained_model_data_type(self): hdf5_graph = "tests/data/hdf5/test.hdf5" - pretrained_grid_model = "tests/data/testing_grid_model.pth.tar" + pretrained_grid_model = "tests/data/pretrained/testing_grid_model.pth.tar" with self.assertRaises(TypeError): GraphDataset( hdf5_path = hdf5_graph, @@ -1012,8 +1012,8 @@ def test_invalid_pretrained_model_data_type(self): train_data = pretrained_grid_model ) - hdf5_grid = "tests/data/hdf5/grid_data.hdf5" - pretrained_graph_model = "tests/data/testing_graph_model.pth.tar" + hdf5_grid = "tests/data/hdf5/1ATN_ppi.hdf5" + pretrained_graph_model = "tests/data/pretrained/testing_graph_model.pth.tar" with self.assertRaises(TypeError): GridDataset( hdf5_path = hdf5_grid, From bcc5138b1e7ab757861b1b3b568c194eae656026 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 09:10:46 +0200 Subject: [PATCH 11/59] implement inheritance in dataset from a pre-trained model --- deeprank2/dataset.py | 50 +++++++++++++++++++++++++------------------- 1 file changed, 29 insertions(+), 21 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index 0cf5a818b..5c091e6ef 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -137,25 +137,27 @@ def _check_task_and_classes(self, task: str, classes: Optional[str] = None): def _check_inherited_params( self, inherited_params: List[str], - dataset_train: Union[GraphDataset, GridDataset], + data: Union[dict, GraphDataset, GridDataset], ): - """"Check if the parameters for validation and/or testing are the same as in the training set. + """"Check if the parameters for validation and/or testing are the same as in the pre-trained model or training set provided. Args: inherited_params (List[str]): List of parameters that need to be checked for inheritance. - dataset_train (Union[class:`GraphDataset`, class:`GridDataset`]): The parameters in `inherited_param` will be inherited from `dataset_train`. + data (Union[dict, class:`GraphDataset`, class:`GridDataset`]): The parameters in `inherited_param` will be inherited + from the information contained in `data`. """ self_vars = vars(self) - dataset_train_vars = vars(dataset_train) + if not isinstance(data, dict): + data = vars(data) for param in inherited_params: - if (self_vars[param] != dataset_train_vars[param]): + if (self_vars[param] != data[param]): if (self_vars[param] != self.default_vars[param]): _log.warning(f"The {param} parameter set here is: {self_vars[param]}, " + - f"which is not equivalent to the one in the training phase: {dataset_train_vars[param]}./n" + + f"which is not equivalent to the one in the training phase: {data[param]}./n" + f"Overwriting {param} parameter with the one used in the training phase.") - setattr(self, param, dataset_train_vars[param]) + setattr(self, param, data[param]) def _create_index_entries(self): """Creates the indexing of each molecule in the dataset. @@ -491,24 +493,26 @@ def __init__( # pylint: disable=too-many-arguments if isinstance(train_data, str): try: if torch.cuda.is_available(): - state = torch.load(train_data, pickle_module = dill) + data = torch.load(train_data, pickle_module = dill) else: - state = torch.load(train_data, pickle_module = dill, map_location=torch.device('cpu')) - if state["data_type"] is not GridDataset: - raise TypeError (f"""The pre-trained model has been trained with data of type {state["data_type"]}, but you are trying + data = torch.load(train_data, pickle_module = dill, map_location=torch.device('cpu')) + if data["data_type"] is not GridDataset: + raise TypeError (f"""The pre-trained model has been trained with data of type {data["data_type"]}, but you are trying to define a GridDataset-class validation/testing dataset. Please provide a valid DeepRank2 model trained with GridDataset-class type data, or define the dataset using the appropriate class.""") except pickle.UnpicklingError as e: raise ValueError("""The path provided to `train_data` is not a valid DeepRank2 pre-trained model. Please provide a valid path to a DeepRank2 pre-trained model.""") from e elif isinstance(train_data, GridDataset): - #check inherited parameter with the ones in the training set - inherited_params = ["features", "target", "target_transform", "task", "classes"] - self._check_inherited_params(inherited_params, train_data) + data = train_data else: raise TypeError(f"""The train data provided is type: {type(train_data)} Please provide a valid training GridDataset or the path to a valid DeepRank2 pre-trained model.""") + #check inherited parameter with the ones in the training set + inherited_params = ["features", "target", "target_transform", "task", "classes"] + self._check_inherited_params(inherited_params, data) + elif train and train_data: _log.warning("""`train_data` has been set but train flag was set to True. `train_data` will be ignored since the current dataset will be considered as training set.""") @@ -744,26 +748,30 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local if isinstance(train_data, str): try: if torch.cuda.is_available(): - state = torch.load(train_data, pickle_module = dill) + data = torch.load(train_data, pickle_module = dill) else: - state = torch.load(train_data, pickle_module = dill, map_location=torch.device('cpu')) - if state["data_type"] is not GraphDataset: - raise TypeError (f"""The pre-trained model has been trained with data of type {state["data_type"]}, but you are trying + data = torch.load(train_data, pickle_module = dill, map_location=torch.device('cpu')) + if data["data_type"] is not GraphDataset: + raise TypeError (f"""The pre-trained model has been trained with data of type {data["data_type"]}, but you are trying to define a GraphDataset-class validation/testing dataset. Please provide a valid DeepRank2 model trained with GraphDataset-class type data, or define the dataset using the appropriate class.""") + train_means = data["means"] + train_devs = data["devs"] except pickle.UnpicklingError as e: raise ValueError("""The path provided to `train_data` is not a valid DeepRank2 pre-trained model. Please provide a valid path to a DeepRank2 pre-trained model.""") from e elif isinstance(train_data, GraphDataset): - #check inherited parameter with the ones in the training set - inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] - self._check_inherited_params(inherited_params, train_data) train_means = train_data.means train_devs = train_data.devs + data = train_data else: raise TypeError(f"""The train data provided is type: {type(train_data)} Please provide a valid training GraphDataset or the path to a valid DeepRank2 pre-trained model.""") + #check inherited parameter with the ones in the training set + inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] + self._check_inherited_params(inherited_params, data) + elif train and train_data: _log.warning("""`train_data` has been set but train flag was set to True. `train_data` will be ignored since the current dataset will be considered as training set.""") From 5280ecea828ad00d21868c139dc7d38fdd595063 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 09:13:02 +0200 Subject: [PATCH 12/59] add tests for inheritance from pre-trained model --- tests/test_dataset.py | 97 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 91 insertions(+), 6 deletions(-) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index e05bc56fb..6267a568b 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -5,9 +5,11 @@ from tempfile import mkdtemp from typing import List, Union +import dill import h5py import numpy as np import pytest +import torch from torch_geometric.loader import DataLoader from deeprank2.dataset import GraphDataset, GridDataset, save_hdf5_keys @@ -206,7 +208,7 @@ def test_classification_griddataset(self): # 1 entry with class value assert dataset[0].y.shape == (1,) - def test_inherit_info_training_griddataset(self): + def test_inherit_info_dataset_train_griddataset(self): dataset_train = GridDataset( hdf5_path = self.hdf5_path, @@ -223,9 +225,9 @@ def test_inherit_info_training_griddataset(self): train_data = dataset_train ) - # features, features_dict, target, target_transform, task, and classes + # features, target, target_transform, task, and classes # in the test should be inherited from the train - inherited_param = ["features", "features_dict", "target", "target_transform", "task", "classes"] + inherited_param = ["features", "target", "target_transform", "task", "classes"] _check_inherited_params(inherited_param, dataset_train, dataset_test) dataset_test = GridDataset( @@ -243,6 +245,41 @@ def test_inherit_info_training_griddataset(self): # in the test should be inherited from the train _check_inherited_params(inherited_param, dataset_train, dataset_test) + def test_inherit_info_pretrained_model_griddataset(self): + + pretrained_model = "tests/data/pretrained/testing_grid_model.pth.tar" + dataset_test = GridDataset( + hdf5_path = self.hdf5_path, + train = False, + train_data = pretrained_model + ) + + # features, target, target_transform, task, and classes + # in the test should be inherited from the pre-trained model + inherited_params = ["features", "target", "target_transform", "task", "classes"] + data = torch.load(pretrained_model, pickle_module = dill, map_location=torch.device('cpu')) + + dataset_test_vars = vars(dataset_test) + for param in inherited_params: + assert dataset_test_vars[param] == data[param] + + dataset_test = GridDataset( + hdf5_path = self.hdf5_path, + train = False, + train_data = pretrained_model, + features = [Efeat.DISTANCE, Efeat.COVALENT, Efeat.SAMECHAIN], + target = targets.IRMSD, + target_transform = True, + task = targets.REGRESS, + classes = None + ) + + # features, target, target_transform, task, and classes + # in the test should be inherited from the pre-trained model + dataset_test_vars = vars(dataset_test) + for param in inherited_params: + assert dataset_test_vars[param] == data[param] + def test_filter_graphdataset(self): GraphDataset( hdf5_path=self.hdf5_path, @@ -925,7 +962,7 @@ def test_invalid_value_features_transform(self): warnings.filterwarnings('ignore', r'divide by zero encountered in divide') _compute_features_with_get(hdf5_path, transf_dataset) - def test_inherit_info_train_graphdataset(self): + def test_inherit_info_dataset_train_graphdataset(self): hdf5_path = "tests/data/hdf5/train.hdf5" feature_transform = {'all': {'transform': None, 'standardize': True}} @@ -946,9 +983,9 @@ def test_inherit_info_train_graphdataset(self): train_data = dataset_train, ) - # node_features, edge_features, features_dict, feature_transform, target, target_transform, task, and classes + # node_features, edge_features, feature_transform, target, target_transform, task, and classes # in the test should be inherited from the train - inherited_param = ["node_features", "edge_features", "features_dict", "features_transform", "target", "target_transform", "task", "classes"] + inherited_param = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] _check_inherited_params(inherited_param, dataset_train, dataset_test) dataset_test = GraphDataset( @@ -968,6 +1005,54 @@ def test_inherit_info_train_graphdataset(self): # in the test should be inherited from the train _check_inherited_params(inherited_param, dataset_train, dataset_test) + def test_inherit_info_pretrained_model_graphdataset(self): + + hdf5_path = "tests/data/hdf5/test.hdf5" + pretrained_model = "tests/data/pretrained/testing_graph_model.pth.tar" + dataset_test = GraphDataset( + hdf5_path = hdf5_path, + train = False, + train_data = pretrained_model + ) + + # node_features, edge_features, feature_transform, target, target_transform, task, and classes + # in the test should be inherited from the pre-trained model + inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] + data = torch.load(pretrained_model, pickle_module = dill, map_location=torch.device('cpu')) + + dataset_test_vars = vars(dataset_test) + for param in inherited_params: + if param == 'features_transform': + for item, key in data[param].items(): + assert key['transform'].__code__.co_code == dataset_test_vars[param][item]['transform'].__code__.co_code + assert key['standardize'] == dataset_test_vars[param][item]['standardize'] + else: + assert dataset_test_vars[param] == data[param] + + dataset_test = GraphDataset( + hdf5_path = hdf5_path, + train = False, + train_data = pretrained_model, + node_features = "all", + edge_features = "all", + features_transform = None, + target = 'BA', + target_transform = True, + task = "regress", + classes = None + ) + + # node_features, edge_features, feature_transform, target, target_transform, task, and classes + # in the test should be inherited from the pre-trained model + dataset_test_vars = vars(dataset_test) + for param in inherited_params: + if param == 'features_transform': + for item, key in data[param].items(): + assert key['transform'].__code__.co_code == dataset_test_vars[param][item]['transform'].__code__.co_code + assert key['standardize'] == dataset_test_vars[param][item]['standardize'] + else: + assert dataset_test_vars[param] == data[param] + def test_incompatible_dataset_train_type(self): dataset_train = GraphDataset( hdf5_path = "tests/data/hdf5/test.hdf5", From 7fcc03382a344d7a645725114de6a5353710e5f7 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 10:59:59 +0200 Subject: [PATCH 13/59] add classes_to_index as inherited param and to the pre-trained model --- deeprank2/dataset.py | 7 ++++--- deeprank2/trainer.py | 2 ++ 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/deeprank2/dataset.py b/deeprank2/dataset.py index 5c091e6ef..f076c87c1 100644 --- a/deeprank2/dataset.py +++ b/deeprank2/dataset.py @@ -484,7 +484,7 @@ def __init__( # pylint: disable=too-many-arguments for k, v in inspect.signature(self.__init__).parameters.items() if v.default is not inspect.Parameter.empty } - + self.default_vars["classes_to_index"] = None self.features = features self.target_transform = target_transform self._check_features() @@ -510,7 +510,7 @@ def __init__( # pylint: disable=too-many-arguments Please provide a valid training GridDataset or the path to a valid DeepRank2 pre-trained model.""") #check inherited parameter with the ones in the training set - inherited_params = ["features", "target", "target_transform", "task", "classes"] + inherited_params = ["features", "target", "target_transform", "task", "classes", "classes_to_index"] self._check_inherited_params(inherited_params, data) elif train and train_data: @@ -737,6 +737,7 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local for k, v in inspect.signature(self.__init__).parameters.items() if v.default is not inspect.Parameter.empty } + self.default_vars["classes_to_index"] = None self.node_features = node_features self.edge_features = edge_features self.clustering_method = clustering_method @@ -769,7 +770,7 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local Please provide a valid training GraphDataset or the path to a valid DeepRank2 pre-trained model.""") #check inherited parameter with the ones in the training set - inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes"] + inherited_params = ["node_features", "edge_features", "features_transform", "target", "target_transform", "task", "classes", "classes_to_index"] self._check_inherited_params(inherited_params, data) elif train and train_data: diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py index 536015029..ed7fdfb6a 100644 --- a/deeprank2/trainer.py +++ b/deeprank2/trainer.py @@ -880,6 +880,7 @@ def _load_params(self): self.target_transform = state["target_transform"] self.task = state["task"] self.classes = state["classes"] + self.classes_to_index = state["classes_to_index"] self.class_weights = state["class_weights"] self.batch_size_train = state["batch_size_train"] self.batch_size_test = state["batch_size_test"] @@ -917,6 +918,7 @@ def _save_model(self): "target_transform": self.target_transform, "task": self.task, "classes": self.classes, + "classes_to_index": self.classes_to_index, "class_weights": self.class_weights, "batch_size_train": self.batch_size_train, "batch_size_test": self.batch_size_test, From cc3d79a606753218ac91717ee452793a066856f5 Mon Sep 17 00:00:00 2001 From: gcroci2 Date: Mon, 23 Oct 2023 11:00:22 +0200 Subject: [PATCH 14/59] add classes_to_index to the tests' models --- .../pretrained/testing_graph_model.pth.tar | Bin 208117 -> 210293 bytes .../pretrained/testing_grid_model.pth.tar | Bin 416771 -> 418883 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/tests/data/pretrained/testing_graph_model.pth.tar 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